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import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def get_swin_config(swin_name):
config = SwinConfig()
name_split = swin_name.split("_")
model_size = name_split[1]
img_size = int(name_split[4])
window_size = int(name_split[3][-1])
if model_size == "tiny":
embed_dim = 96
depths = (2, 2, 6, 2)
num_heads = (3, 6, 12, 24)
elif model_size == "small":
embed_dim = 96
depths = (2, 2, 18, 2)
num_heads = (3, 6, 12, 24)
elif model_size == "base":
embed_dim = 128
depths = (2, 2, 18, 2)
num_heads = (4, 8, 16, 32)
else:
embed_dim = 192
depths = (2, 2, 18, 2)
num_heads = (6, 12, 24, 48)
if "in22k" in swin_name:
num_classes = 21841
else:
num_classes = 1000
repo_id = "huggingface/label-files"
filename = "imagenet-1k-id2label.json"
id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
id2label = {int(k): v for k, v in id2label.items()}
config.id2label = id2label
config.label2id = {v: k for k, v in id2label.items()}
config.image_size = img_size
config.num_labels = num_classes
config.embed_dim = embed_dim
config.depths = depths
config.num_heads = num_heads
config.window_size = window_size
return config
def rename_key(name):
if "patch_embed.proj" in name:
name = name.replace("patch_embed.proj", "embeddings.patch_embeddings.projection")
if "patch_embed.norm" in name:
name = name.replace("patch_embed.norm", "embeddings.norm")
if "layers" in name:
name = "encoder." + name
if "attn.proj" in name:
name = name.replace("attn.proj", "attention.output.dense")
if "attn" in name:
name = name.replace("attn", "attention.self")
if "norm1" in name:
name = name.replace("norm1", "layernorm_before")
if "norm2" in name:
name = name.replace("norm2", "layernorm_after")
if "mlp.fc1" in name:
name = name.replace("mlp.fc1", "intermediate.dense")
if "mlp.fc2" in name:
name = name.replace("mlp.fc2", "output.dense")
if name == "norm.weight":
name = "layernorm.weight"
if name == "norm.bias":
name = "layernorm.bias"
if "head" in name:
name = name.replace("head", "classifier")
else:
name = "swin." + name
return name
def convert_state_dict(orig_state_dict, model):
for key in orig_state_dict.copy().keys():
val = orig_state_dict.pop(key)
if "mask" in key:
continue
elif "qkv" in key:
key_split = key.split(".")
layer_num = int(key_split[1])
block_num = int(key_split[3])
dim = model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
orig_state_dict[
f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.query.weight"
] = val[:dim, :]
orig_state_dict[f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.key.weight"] = val[
dim : dim * 2, :
]
orig_state_dict[
f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.value.weight"
] = val[-dim:, :]
else:
orig_state_dict[f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.query.bias"] = val[
:dim
]
orig_state_dict[f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.key.bias"] = val[
dim : dim * 2
]
orig_state_dict[f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.value.bias"] = val[
-dim:
]
else:
orig_state_dict[rename_key(key)] = val
return orig_state_dict
def convert_swin_checkpoint(swin_name, pytorch_dump_folder_path):
timm_model = timm.create_model(swin_name, pretrained=True)
timm_model.eval()
config = get_swin_config(swin_name)
model = SwinForImageClassification(config)
model.eval()
new_state_dict = convert_state_dict(timm_model.state_dict(), model)
model.load_state_dict(new_state_dict)
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image_processor = AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_", "-")))
image = Image.open(requests.get(url, stream=True).raw)
inputs = image_processor(images=image, return_tensors="pt")
timm_outs = timm_model(inputs["pixel_values"])
hf_outs = model(**inputs).logits
assert torch.allclose(timm_outs, hf_outs, atol=1e-3)
print(f"Saving model {swin_name} to {pytorch_dump_folder_path}")
model.save_pretrained(pytorch_dump_folder_path)
print(f"Saving image processor to {pytorch_dump_folder_path}")
image_processor.save_pretrained(pytorch_dump_folder_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--swin_name",
default="swin_tiny_patch4_window7_224",
type=str,
help="Name of the Swin timm model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
args = parser.parse_args()
convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)
| transformers/src/transformers/models/swin/convert_swin_timm_to_pytorch.py/0 | {
"file_path": "transformers/src/transformers/models/swin/convert_swin_timm_to_pytorch.py",
"repo_id": "transformers",
"token_count": 2718
} | 340 |
# coding=utf-8
# Copyright 2022 SwitchTransformers Authors and HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch SwitchTransformers model."""
import copy
import math
import warnings
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss
from ...activations import ACT2FN
from ...modeling_outputs import (
MoEModelOutput,
MoEModelOutputWithPastAndCrossAttentions,
Seq2SeqMoEModelOutput,
Seq2SeqMoEOutput,
)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import ALL_LAYERNORM_LAYERS, find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import (
DUMMY_INPUTS,
DUMMY_MASK,
add_start_docstrings,
add_start_docstrings_to_model_forward,
is_torch_fx_proxy,
logging,
replace_return_docstrings,
)
from .configuration_switch_transformers import SwitchTransformersConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "SwitchTransformersConfig"
_CHECKPOINT_FOR_DOC = "google/switch-base-8"
####################################################
# This dict contains ids and associated url
# for the pretrained weights provided with the models
####################################################
SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST = [
"google/switch-base-8",
"google/switch-base-16",
"google/switch-base-32",
"google/switch-base-64",
"google/switch-base-128",
"google/switch-base-256",
"google/switch-large-128",
"google/switch-xxl-128",
"google/switch-c-2048",
# See all SwitchTransformers models at https://huggingface.co/models?filter=switch_transformers
]
def router_z_loss_func(router_logits: torch.Tensor) -> float:
r"""
Compute the router z-loss implemented in PyTorch.
The router z-loss was introduced in [Designing Effective Sparse Expert Models](https://arxiv.org/abs/2202.08906).
It encourages router logits to remain small in an effort to improve stability.
Args:
router_logits (`float`):
Input logits of shape [batch_size, sequence_length, num_experts]
Returns:
Scalar router z-loss.
"""
num_groups, tokens_per_group, _ = router_logits.shape
log_z = torch.logsumexp(router_logits, dim=-1)
z_loss = log_z**2
return torch.sum(z_loss) / (num_groups * tokens_per_group)
def load_balancing_loss_func(router_probs: torch.Tensor, expert_indices: torch.Tensor) -> float:
r"""
Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.
See Switch Transformer (https://arxiv.org/abs/2101.03961) for more details. This function implements the loss
function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
experts is too unbalanced.
Args:
router_probs (`torch.Tensor`):
Probability assigned to each expert per token. Shape: [batch_size, seqeunce_length, num_experts].
expert_indices (`torch.Tensor`):
Indices tensor of shape [batch_size, seqeunce_length] identifying the selected expert for a given token.
Returns:
The auxiliary loss.
"""
num_experts = router_probs.shape[-1]
# cast the expert indices to int64, otherwise one-hot encoding will fail
if expert_indices.dtype != torch.int64:
expert_indices = expert_indices.to(torch.int64)
if len(expert_indices.shape) == 2:
expert_indices = expert_indices.unsqueeze(2)
expert_mask = torch.nn.functional.one_hot(expert_indices, num_experts)
# For a given token, determine if it was routed to a given expert.
expert_mask = torch.max(expert_mask, axis=-2).values
# cast to float32 otherwise mean will fail
expert_mask = expert_mask.to(torch.float32)
tokens_per_group_and_expert = torch.mean(expert_mask, axis=-2)
router_prob_per_group_and_expert = torch.mean(router_probs, axis=-2)
return torch.mean(tokens_per_group_and_expert * router_prob_per_group_and_expert) * (num_experts**2)
class SwitchTransformersTop1Router(nn.Module):
"""
Router using tokens choose top-1 experts assignment.
This router uses the same mechanism as in Switch Transformer (https://arxiv.org/abs/2101.03961) and V-MoE
(https://arxiv.org/abs/2106.05974): tokens choose their top experts. Items are sorted by router_probs and then
routed to their choice of expert until the expert's expert_capacity is reached. **There is no guarantee that each
token is processed by an expert**, or that each expert receives at least one token.
"""
def __init__(self, config: SwitchTransformersConfig):
super().__init__()
self.num_experts = config.num_experts
self.expert_capacity = config.expert_capacity
self.classifier = nn.Linear(config.hidden_size, self.num_experts, bias=config.router_bias)
self.jitter_noise = config.router_jitter_noise
self.ignore_padding_tokens = config.router_ignore_padding_tokens
self.dtype = getattr(torch, config.router_dtype)
def _compute_router_probabilities(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
r"""
Computes router probabilities from input hidden states.
Args:
hidden_states (`torch.Tensor`):
(batch_size, sequence_length, hidden_dim) from which router probabilities are computed.
Returns:
router_probabilities (`torch.Tensor`):
Tensor of shape (batch_size, sequence_length, num_experts) corresponding to the probabilities for each
token and expert. Used for routing tokens to experts.
router_logits (`torch.Tensor`):
Logits tensor of shape (batch_size, sequence_length, num_experts) corresponding to raw router logits.
This is used later for computing router z-loss.
"""
# float32 is used to ensure stability. See the discussion of "selective precision" in
# https://arxiv.org/abs/2101.03961.
# We also store the previous dtype to cast back the output to the previous dtype
self.input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(self.dtype)
if self.training and self.jitter_noise > 0:
# Multiply the token inputs by the uniform distribution - adding some noise
hidden_states *= torch.empty_like(hidden_states).uniform_(1.0 - self.jitter_noise, 1.0 + self.jitter_noise)
# Shape: [num_groups, tokens_per_group, num_experts]
self._cast_classifier()
router_logits = self.classifier(hidden_states)
# Apply Softmax and cast back to the original `dtype`
router_probabilities = nn.functional.softmax(router_logits, dim=-1, dtype=self.dtype).to(self.input_dtype)
return router_probabilities, router_logits
def _cast_classifier(self):
r"""
`bitsandbytes` `Linear8bitLt` layers does not support manual casting Therefore we need to check if they are an
instance of the `Linear8bitLt` class by checking special attributes.
"""
if not (hasattr(self.classifier, "SCB") or hasattr(self.classifier, "CB")):
self.classifier = self.classifier.to(self.dtype)
def forward(self, hidden_states: torch.Tensor) -> Tuple:
r"""
Generic forward function for every Router class. Each Router expects to have the same input hidden states
(`hidden_states`) corresponding to the hidden states for each token, the `expert_capacity` corresponding to the
number of tokens the Router will send to each expert, some Routers can send up to few tokens to each expert.
Each Router works as the following: it expects the hidden states for each token, gets the `router_probs` and
`router_logits` from the `router_weights`. This will assign for each token, the raw probability to be assigned
to an expert. Then each Router class will have to define its own `_compute_routing_instructions`.
Args:
hidden_states (`torch.Tensor`) :
[num_groups, tokens_per_group, hidden_dim] inputs to send to experts.
Returns:
Tuple[`torch.Tensor`, `torch.Tensor`, `torch.Tensor`] Tuple containing the expert index, the router probs
and the router logits. The router probabilities and logits are required to compute the loss.
"""
router_probs, router_logits = self._compute_router_probabilities(hidden_states)
expert_index = torch.argmax(router_probs, dim=-1)
expert_index = torch.nn.functional.one_hot(expert_index, num_classes=self.num_experts)
# Mask tokens outside expert capacity. Sum over each sequence
token_priority = torch.cumsum(expert_index, dim=-2)
# mask if the token routed to to the expert will overflow
expert_capacity_mask = token_priority <= self.expert_capacity
expert_index = expert_index * expert_capacity_mask
router_probs = torch.max(router_probs, dim=-1).values.unsqueeze(-1)
return expert_index, router_probs, router_logits
# Copied from transformers.models.t5.modeling_t5.T5LayerNorm with T5->SwitchTransformers
class SwitchTransformersLayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
Construct a layernorm module in the SwitchTransformers style. No bias and no subtraction of mean.
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
# SwitchTransformers uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus varience is calculated
# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
# half-precision inputs is done in fp32
variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
# convert into half-precision if necessary
if self.weight.dtype in [torch.float16, torch.bfloat16]:
hidden_states = hidden_states.to(self.weight.dtype)
return self.weight * hidden_states
ALL_LAYERNORM_LAYERS.append(SwitchTransformersLayerNorm)
# Copied from transformers.models.t5.modeling_t5.T5DenseActDense with T5->SwitchTransformers
class SwitchTransformersDenseActDense(nn.Module):
def __init__(self, config: SwitchTransformersConfig):
super().__init__()
self.wi = nn.Linear(config.d_model, config.d_ff, bias=False)
self.wo = nn.Linear(config.d_ff, config.d_model, bias=False)
self.dropout = nn.Dropout(config.dropout_rate)
self.act = ACT2FN[config.dense_act_fn]
def forward(self, hidden_states):
hidden_states = self.wi(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.dropout(hidden_states)
if (
isinstance(self.wo.weight, torch.Tensor)
and hidden_states.dtype != self.wo.weight.dtype
and self.wo.weight.dtype != torch.int8
):
hidden_states = hidden_states.to(self.wo.weight.dtype)
hidden_states = self.wo(hidden_states)
return hidden_states
class SwitchTransformersSparseMLP(nn.Module):
r"""
Implementation of the Switch Transformers Sparse MLP module.
"""
def __init__(self, config: SwitchTransformersConfig, expert_class: nn.Module = SwitchTransformersDenseActDense):
super().__init__()
# Step 1: Get the correct router according to its class
self.router = SwitchTransformersTop1Router(config)
# Step 2: Get the experts
self.experts = nn.ModuleDict()
for idx in range(config.num_experts):
self.experts[f"expert_{idx}"] = expert_class(config)
def forward(self, hidden_states):
r"""
Hold on, this will be slightly tricky to understand In the correct order, a MoE layer does the following:
1- Gets the `router_mask` from the router. The shape of the mask is `(batch_size, sequence_length, num_expert)`
and corresponds to the argmax of the `router_probs`. The probabilities are needed in the computation of the
hidden states : they are broadcasted to the hidden states values (can be interpreted as a scaling factor).
2- Dispatch the tokens to its associated experts. We do a classic for loop over the experts and assign for each
expert the corresponding hidden states.
"""
# Step 1: Get the router_mask from the router as wel as the probabilities
router_mask, router_probs, router_logits = self.router(hidden_states)
expert_index = torch.argmax(router_mask, dim=-1)
# The routers introduced might not always map all the tokens, to a router, which means that some hidden states
# can be unchanged from one layer to another. That is why the hidden states are cloned before updating only the seleced ones.
next_states = hidden_states.clone()
for idx, expert in enumerate(self.experts.values()):
token_indices = router_mask[:, :, idx].bool()
next_states[token_indices] = expert(hidden_states[token_indices]).to(next_states.dtype)
hidden_states = router_probs * next_states
return hidden_states, (router_logits, expert_index)
class SwitchTransformersLayerFF(nn.Module):
r"""
Switch Transformers Feed Forward layer module. This is a wrapper around the Mixture of Experts module.
Parameters:
config : ([`SwitchTransformersConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
is_sparse (`bool`):
Whether the MLP layer is a `Sparse` layer (contains a Mixture of Experts) or not
"""
def __init__(self, config: SwitchTransformersConfig, is_sparse=False):
super().__init__()
self.is_sparse = is_sparse
# Check if it is a sparse layer, if not then it is a dense layer
if not self.is_sparse:
self.mlp = SwitchTransformersDenseActDense(config)
else:
self.mlp = SwitchTransformersSparseMLP(config)
self.layer_norm = SwitchTransformersLayerNorm(config.d_model, eps=config.layer_norm_epsilon)
self.dropout = nn.Dropout(config.dropout_rate)
def forward(self, hidden_states, output_router_logits):
forwarded_states = self.layer_norm(hidden_states)
forwarded_states = self.mlp(forwarded_states)
if isinstance(forwarded_states, tuple):
forwarded_states, router_tuple = forwarded_states
else:
router_tuple = None
output = hidden_states + self.dropout(forwarded_states)
if output_router_logits and router_tuple is not None:
output = (output, router_tuple)
return output
# Copied from transformers.models.t5.modeling_t5.T5Attention with T5->SwitchTransformers
class SwitchTransformersAttention(nn.Module):
def __init__(self, config: SwitchTransformersConfig, has_relative_attention_bias=False):
super().__init__()
self.is_decoder = config.is_decoder
self.has_relative_attention_bias = has_relative_attention_bias
self.relative_attention_num_buckets = config.relative_attention_num_buckets
self.relative_attention_max_distance = config.relative_attention_max_distance
self.d_model = config.d_model
self.key_value_proj_dim = config.d_kv
self.n_heads = config.num_heads
self.dropout = config.dropout_rate
self.inner_dim = self.n_heads * self.key_value_proj_dim
# Mesh TensorFlow initialization to avoid scaling before softmax
self.q = nn.Linear(self.d_model, self.inner_dim, bias=False)
self.k = nn.Linear(self.d_model, self.inner_dim, bias=False)
self.v = nn.Linear(self.d_model, self.inner_dim, bias=False)
self.o = nn.Linear(self.inner_dim, self.d_model, bias=False)
if self.has_relative_attention_bias:
self.relative_attention_bias = nn.Embedding(self.relative_attention_num_buckets, self.n_heads)
self.pruned_heads = set()
self.gradient_checkpointing = False
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.n_heads, self.key_value_proj_dim, self.pruned_heads
)
# Prune linear layers
self.q = prune_linear_layer(self.q, index)
self.k = prune_linear_layer(self.k, index)
self.v = prune_linear_layer(self.v, index)
self.o = prune_linear_layer(self.o, index, dim=1)
# Update hyper params
self.n_heads = self.n_heads - len(heads)
self.inner_dim = self.key_value_proj_dim * self.n_heads
self.pruned_heads = self.pruned_heads.union(heads)
@staticmethod
def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
"""
Adapted from Mesh Tensorflow:
https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
Translate relative position to a bucket number for relative attention. The relative position is defined as
memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for
small absolute relative_position and larger buckets for larger absolute relative_positions. All relative
positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.
This should allow for more graceful generalization to longer sequences than the model has been trained on
Args:
relative_position: an int32 Tensor
bidirectional: a boolean - whether the attention is bidirectional
num_buckets: an integer
max_distance: an integer
Returns:
a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
"""
relative_buckets = 0
if bidirectional:
num_buckets //= 2
relative_buckets += (relative_position > 0).to(torch.long) * num_buckets
relative_position = torch.abs(relative_position)
else:
relative_position = -torch.min(relative_position, torch.zeros_like(relative_position))
# now relative_position is in the range [0, inf)
# half of the buckets are for exact increments in positions
max_exact = num_buckets // 2
is_small = relative_position < max_exact
# The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
relative_position_if_large = max_exact + (
torch.log(relative_position.float() / max_exact)
/ math.log(max_distance / max_exact)
* (num_buckets - max_exact)
).to(torch.long)
relative_position_if_large = torch.min(
relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1)
)
relative_buckets += torch.where(is_small, relative_position, relative_position_if_large)
return relative_buckets
def compute_bias(self, query_length, key_length, device=None):
"""Compute binned relative position bias"""
if device is None:
device = self.relative_attention_bias.weight.device
context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None]
memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :]
relative_position = memory_position - context_position # shape (query_length, key_length)
relative_position_bucket = self._relative_position_bucket(
relative_position, # shape (query_length, key_length)
bidirectional=(not self.is_decoder),
num_buckets=self.relative_attention_num_buckets,
max_distance=self.relative_attention_max_distance,
)
values = self.relative_attention_bias(relative_position_bucket) # shape (query_length, key_length, num_heads)
values = values.permute([2, 0, 1]).unsqueeze(0) # shape (1, num_heads, query_length, key_length)
return values
def forward(
self,
hidden_states,
mask=None,
key_value_states=None,
position_bias=None,
past_key_value=None,
layer_head_mask=None,
query_length=None,
use_cache=False,
output_attentions=False,
):
"""
Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states).
"""
# Input is (batch_size, seq_length, dim)
# Mask is (batch_size, key_length) (non-causal) or (batch_size, key_length, key_length)
# past_key_value[0] is (batch_size, n_heads, q_len - 1, dim_per_head)
batch_size, seq_length = hidden_states.shape[:2]
real_seq_length = seq_length
if past_key_value is not None:
if len(past_key_value) != 2:
raise ValueError(
f"past_key_value should have 2 past states: keys and values. Got { len(past_key_value)} past states"
)
real_seq_length += past_key_value[0].shape[2] if query_length is None else query_length
key_length = real_seq_length if key_value_states is None else key_value_states.shape[1]
def shape(states):
"""projection"""
return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
def unshape(states):
"""reshape"""
return states.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim)
def project(hidden_states, proj_layer, key_value_states, past_key_value):
"""projects hidden states correctly to key/query states"""
if key_value_states is None:
# self-attn
# (batch_size, n_heads, seq_length, dim_per_head)
hidden_states = shape(proj_layer(hidden_states))
elif past_key_value is None:
# cross-attn
# (batch_size, n_heads, seq_length, dim_per_head)
hidden_states = shape(proj_layer(key_value_states))
if past_key_value is not None:
if key_value_states is None:
# self-attn
# (batch_size, n_heads, key_length, dim_per_head)
hidden_states = torch.cat([past_key_value, hidden_states], dim=2)
elif past_key_value.shape[2] != key_value_states.shape[1]:
# checking that the `sequence_length` of the `past_key_value` is the same as
# the provided `key_value_states` to support prefix tuning
# cross-attn
# (batch_size, n_heads, seq_length, dim_per_head)
hidden_states = shape(proj_layer(key_value_states))
else:
# cross-attn
hidden_states = past_key_value
return hidden_states
# get query states
query_states = shape(self.q(hidden_states)) # (batch_size, n_heads, seq_length, dim_per_head)
# get key/value states
key_states = project(
hidden_states, self.k, key_value_states, past_key_value[0] if past_key_value is not None else None
)
value_states = project(
hidden_states, self.v, key_value_states, past_key_value[1] if past_key_value is not None else None
)
# compute scores
scores = torch.matmul(
query_states, key_states.transpose(3, 2)
) # equivalent of torch.einsum("bnqd,bnkd->bnqk", query_states, key_states), compatible with onnx op>9
if position_bias is None:
if not self.has_relative_attention_bias:
position_bias = torch.zeros(
(1, self.n_heads, real_seq_length, key_length), device=scores.device, dtype=scores.dtype
)
if self.gradient_checkpointing and self.training:
position_bias.requires_grad = True
else:
position_bias = self.compute_bias(real_seq_length, key_length, device=scores.device)
# if key and values are already calculated
# we want only the last query position bias
if past_key_value is not None:
position_bias = position_bias[:, :, -hidden_states.size(1) :, :]
if mask is not None:
position_bias = position_bias + mask # (batch_size, n_heads, seq_length, key_length)
if self.pruned_heads:
mask = torch.ones(position_bias.shape[1])
mask[list(self.pruned_heads)] = 0
position_bias_masked = position_bias[:, mask.bool()]
else:
position_bias_masked = position_bias
scores += position_bias_masked
attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(
scores
) # (batch_size, n_heads, seq_length, key_length)
attn_weights = nn.functional.dropout(
attn_weights, p=self.dropout, training=self.training
) # (batch_size, n_heads, seq_length, key_length)
# Mask heads if we want to
if layer_head_mask is not None:
attn_weights = attn_weights * layer_head_mask
attn_output = unshape(torch.matmul(attn_weights, value_states)) # (batch_size, seq_length, dim)
attn_output = self.o(attn_output)
present_key_value_state = (key_states, value_states) if (self.is_decoder and use_cache) else None
outputs = (attn_output,) + (present_key_value_state,) + (position_bias,)
if output_attentions:
outputs = outputs + (attn_weights,)
return outputs
# Copied from transformers.models.t5.modeling_t5.T5LayerSelfAttention with T5->SwitchTransformers
class SwitchTransformersLayerSelfAttention(nn.Module):
def __init__(self, config, has_relative_attention_bias=False):
super().__init__()
self.SelfAttention = SwitchTransformersAttention(
config, has_relative_attention_bias=has_relative_attention_bias
)
self.layer_norm = SwitchTransformersLayerNorm(config.d_model, eps=config.layer_norm_epsilon)
self.dropout = nn.Dropout(config.dropout_rate)
def forward(
self,
hidden_states,
attention_mask=None,
position_bias=None,
layer_head_mask=None,
past_key_value=None,
use_cache=False,
output_attentions=False,
):
normed_hidden_states = self.layer_norm(hidden_states)
attention_output = self.SelfAttention(
normed_hidden_states,
mask=attention_mask,
position_bias=position_bias,
layer_head_mask=layer_head_mask,
past_key_value=past_key_value,
use_cache=use_cache,
output_attentions=output_attentions,
)
hidden_states = hidden_states + self.dropout(attention_output[0])
outputs = (hidden_states,) + attention_output[1:] # add attentions if we output them
return outputs
# Copied from transformers.models.t5.modeling_t5.T5LayerCrossAttention with T5->SwitchTransformers
class SwitchTransformersLayerCrossAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.EncDecAttention = SwitchTransformersAttention(config, has_relative_attention_bias=False)
self.layer_norm = SwitchTransformersLayerNorm(config.d_model, eps=config.layer_norm_epsilon)
self.dropout = nn.Dropout(config.dropout_rate)
def forward(
self,
hidden_states,
key_value_states,
attention_mask=None,
position_bias=None,
layer_head_mask=None,
past_key_value=None,
use_cache=False,
query_length=None,
output_attentions=False,
):
normed_hidden_states = self.layer_norm(hidden_states)
attention_output = self.EncDecAttention(
normed_hidden_states,
mask=attention_mask,
key_value_states=key_value_states,
position_bias=position_bias,
layer_head_mask=layer_head_mask,
past_key_value=past_key_value,
use_cache=use_cache,
query_length=query_length,
output_attentions=output_attentions,
)
layer_output = hidden_states + self.dropout(attention_output[0])
outputs = (layer_output,) + attention_output[1:] # add attentions if we output them
return outputs
class SwitchTransformersBlock(nn.Module):
def __init__(self, config, has_relative_attention_bias=False, is_sparse=False):
super().__init__()
self.is_decoder = config.is_decoder
self.is_sparse = is_sparse
self.layer = nn.ModuleList()
self.layer.append(
SwitchTransformersLayerSelfAttention(config, has_relative_attention_bias=has_relative_attention_bias)
)
if self.is_decoder:
self.layer.append(SwitchTransformersLayerCrossAttention(config))
self.layer.append(SwitchTransformersLayerFF(config, is_sparse=self.is_sparse))
def forward(
self,
hidden_states,
attention_mask=None,
position_bias=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
encoder_decoder_position_bias=None,
layer_head_mask=None,
cross_attn_layer_head_mask=None,
past_key_value=None,
use_cache=False,
output_attentions=False,
output_router_logits=True,
return_dict=True,
):
if past_key_value is not None:
if not self.is_decoder:
logger.warning("`past_key_values` is passed to the encoder. Please make sure this is intended.")
expected_num_past_key_values = 2 if encoder_hidden_states is None else 4
if len(past_key_value) != expected_num_past_key_values:
raise ValueError(
f"There should be {expected_num_past_key_values} past states. "
f"{'2 (past / key) for cross attention. ' if expected_num_past_key_values == 4 else ''}"
f"Got {len(past_key_value)} past key / value states"
)
self_attn_past_key_value = past_key_value[:2]
cross_attn_past_key_value = past_key_value[2:]
else:
self_attn_past_key_value, cross_attn_past_key_value = None, None
self_attention_outputs = self.layer[0](
hidden_states,
attention_mask=attention_mask,
position_bias=position_bias,
layer_head_mask=layer_head_mask,
past_key_value=self_attn_past_key_value,
use_cache=use_cache,
output_attentions=output_attentions,
)
hidden_states, present_key_value_state = self_attention_outputs[:2]
attention_outputs = self_attention_outputs[2:] # Keep self-attention outputs and relative position weights
# clamp inf values to enable fp16 training
if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any():
clamp_value = torch.finfo(hidden_states.dtype).max - 1000
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
do_cross_attention = self.is_decoder and encoder_hidden_states is not None
if do_cross_attention:
# the actual query length is unknown for cross attention
# if using past key value states. Need to inject it here
if present_key_value_state is not None:
query_length = present_key_value_state[0].shape[2]
else:
query_length = None
cross_attention_outputs = self.layer[1](
hidden_states,
key_value_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
position_bias=encoder_decoder_position_bias,
layer_head_mask=cross_attn_layer_head_mask,
past_key_value=cross_attn_past_key_value,
query_length=query_length,
use_cache=use_cache,
output_attentions=output_attentions,
)
hidden_states = cross_attention_outputs[0]
# clamp inf values to enable fp16 training
if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any():
clamp_value = torch.finfo(hidden_states.dtype).max - 1000
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
# Combine self attn and cross attn key value states
if present_key_value_state is not None:
present_key_value_state = present_key_value_state + cross_attention_outputs[1]
# Keep cross-attention outputs and relative position weights
attention_outputs = attention_outputs + cross_attention_outputs[2:]
# Apply Feed Forward layer
hidden_states = self.layer[-1](hidden_states, output_router_logits)
if isinstance(hidden_states, tuple):
hidden_states, router_tuple = hidden_states
else:
router_tuple = (torch.zeros((1,), device=hidden_states.device, dtype=torch.int64),)
# clamp inf values to enable fp16 training
if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any():
clamp_value = torch.finfo(hidden_states.dtype).max - 1000
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
outputs = (hidden_states,)
if use_cache:
outputs = outputs + (present_key_value_state,) + attention_outputs + (router_tuple,)
else:
outputs = outputs + attention_outputs + (router_tuple,)
return outputs # hidden-states, present_key_value_states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights), (router_tuple)
class SwitchTransformersPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = SwitchTransformersConfig
base_model_prefix = "switch_transformers"
supports_gradient_checkpointing = True
_no_split_modules = ["SwitchTransformersBlock"]
@property
def dummy_inputs(self):
input_ids = torch.tensor(DUMMY_INPUTS)
input_mask = torch.tensor(DUMMY_MASK)
dummy_inputs = {
"decoder_input_ids": input_ids,
"input_ids": input_ids,
"decoder_attention_mask": input_mask,
}
return dummy_inputs
def _init_weights(self, module):
"""Initialize the weights"""
factor = self.config.initializer_factor # Used for testing weights initialization
if isinstance(module, SwitchTransformersLayerNorm):
module.weight.data.fill_(factor * 1.0)
elif isinstance(
module,
(SwitchTransformersModel, SwitchTransformersForConditionalGeneration, SwitchTransformersEncoderModel),
):
# Mesh TensorFlow embeddings initialization
# See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L1624
module.shared.weight.data.normal_(mean=0.0, std=factor * 1.0)
if hasattr(module, "lm_head") and not self.config.tie_word_embeddings:
module.lm_head.weight.data.normal_(mean=0.0, std=factor * 1.0)
elif isinstance(module, SwitchTransformersDenseActDense):
# Mesh TensorFlow FF initialization
# See https://github.com/tensorflow/mesh/blob/master/mesh_tensorflow/transformer/transformer_layers.py#L56
# and https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L89
module.wi.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
if hasattr(module.wi, "bias") and module.wi.bias is not None:
module.wi.bias.data.zero_()
module.wo.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_ff) ** -0.5))
if hasattr(module.wo, "bias") and module.wo.bias is not None:
module.wo.bias.data.zero_()
elif isinstance(module, SwitchTransformersAttention):
# Mesh TensorFlow attention initialization to avoid scaling before softmax
# See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/attention.py#L136
d_model = self.config.d_model
key_value_proj_dim = self.config.d_kv
n_heads = self.config.num_heads
module.q.weight.data.normal_(mean=0.0, std=factor * ((d_model * key_value_proj_dim) ** -0.5))
module.k.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5))
module.v.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5))
module.o.weight.data.normal_(mean=0.0, std=factor * ((n_heads * key_value_proj_dim) ** -0.5))
if module.has_relative_attention_bias:
module.relative_attention_bias.weight.data.normal_(mean=0.0, std=factor * ((d_model) ** -0.5))
elif isinstance(module, SwitchTransformersSparseMLP):
# Mesh TensorFlow attention initialization to avoid scaling before softmax
# See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/attention.py#L136
d_model = self.config.d_model
key_value_proj_dim = self.config.d_kv
n_heads = self.config.num_heads
module.router.classifier.weight.data.normal_(mean=0.0, std=factor * 1)
for idx in range(self.config.num_experts):
module.experts[f"expert_{idx}"].wi.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5))
module.experts[f"expert_{idx}"].wo.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5))
def _shift_right(self, input_ids):
decoder_start_token_id = self.config.decoder_start_token_id
pad_token_id = self.config.pad_token_id
if decoder_start_token_id is None:
raise ValueError(
"self.model.config.decoder_start_token_id has to be defined. In SwitchTransformers it is usually set"
" to the pad_token_id. See SwitchTransformers docs for more information"
)
# shift inputs to the right
if is_torch_fx_proxy(input_ids):
# Item assignment is not supported natively for proxies.
shifted_input_ids = torch.full(input_ids.shape[:-1] + (1,), decoder_start_token_id)
shifted_input_ids = torch.cat([shifted_input_ids, input_ids[..., :-1]], dim=-1)
else:
shifted_input_ids = input_ids.new_zeros(input_ids.shape)
shifted_input_ids[..., 1:] = input_ids[..., :-1].clone()
shifted_input_ids[..., 0] = decoder_start_token_id
if pad_token_id is None:
raise ValueError("self.model.config.pad_token_id has to be defined.")
# replace possible -100 values in labels by `pad_token_id`
shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
return shifted_input_ids
class SwitchTransformersStack(SwitchTransformersPreTrainedModel):
def __init__(self, config, embed_tokens=None):
super().__init__(config)
self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model)
if embed_tokens is not None:
self.embed_tokens.weight = embed_tokens.weight
self.is_decoder = config.is_decoder
sparse_step = config.decoder_sparse_step if self.is_decoder else config.encoder_sparse_step
config.num_layers = config.num_decoder_layers if self.is_decoder else config.num_layers
self.block = nn.ModuleList()
for i in range(config.num_layers):
is_sparse = (i % sparse_step == 1 or sparse_step == 1) if sparse_step > 0 else False
self.block.append(
SwitchTransformersBlock(config, has_relative_attention_bias=bool(i == 0), is_sparse=is_sparse)
)
self.final_layer_norm = SwitchTransformersLayerNorm(config.d_model, eps=config.layer_norm_epsilon)
self.dropout = nn.Dropout(config.dropout_rate)
# Initialize weights and apply final processing
self.post_init()
self.device_map = None
self.gradient_checkpointing = False
def get_input_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, new_embeddings):
self.embed_tokens = new_embeddings
def forward(
self,
input_ids=None,
attention_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
inputs_embeds=None,
head_mask=None,
cross_attn_head_mask=None,
past_key_values=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
output_router_logits=True,
return_dict=None,
):
use_cache = use_cache if use_cache is not None else self.config.use_cache
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and inputs_embeds is not None:
err_msg_prefix = "decoder_" if self.is_decoder else ""
raise ValueError(
f"You cannot specify both {err_msg_prefix}input_ids and {err_msg_prefix}inputs_embeds at the same time"
)
elif input_ids is not None:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
err_msg_prefix = "decoder_" if self.is_decoder else ""
raise ValueError(f"You have to specify either {err_msg_prefix}input_ids or {err_msg_prefix}inputs_embeds")
if inputs_embeds is None:
if self.embed_tokens is None:
raise ValueError("You have to initialize the model with valid token embeddings")
inputs_embeds = self.embed_tokens(input_ids)
batch_size, seq_length = input_shape
# required mask seq length can be calculated via length of past
mask_seq_length = past_key_values[0][0].shape[2] + seq_length if past_key_values is not None else seq_length
if use_cache is True:
if not self.is_decoder:
raise ValueError(f"`use_cache` can only be set to `True` if {self} is used as a decoder")
if attention_mask is None:
attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device)
if self.is_decoder and encoder_attention_mask is None and encoder_hidden_states is not None:
encoder_seq_length = encoder_hidden_states.shape[1]
encoder_attention_mask = torch.ones(
batch_size, encoder_seq_length, device=inputs_embeds.device, dtype=torch.long
)
# initialize past_key_values with `None` if past does not exist
if past_key_values is None:
past_key_values = [None] * len(self.block)
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape)
# If a 2D or 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.is_decoder and encoder_hidden_states is not None:
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=inputs_embeds.device)
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
else:
encoder_extended_attention_mask = None
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
# Prepare head mask if needed
head_mask = self.get_head_mask(head_mask, self.config.num_layers)
cross_attn_head_mask = self.get_head_mask(cross_attn_head_mask, self.config.num_layers)
present_key_value_states = () if use_cache else None
all_hidden_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
all_router_probs = () if output_router_logits else None
all_cross_attentions = () if (output_attentions and self.is_decoder) else None
position_bias = None
encoder_decoder_position_bias = None
hidden_states = self.dropout(inputs_embeds)
for i, (layer_module, past_key_value) in enumerate(zip(self.block, past_key_values)):
layer_head_mask = head_mask[i]
cross_attn_layer_head_mask = cross_attn_head_mask[i]
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer_module.forward,
hidden_states,
extended_attention_mask,
position_bias,
encoder_hidden_states,
encoder_extended_attention_mask,
encoder_decoder_position_bias,
layer_head_mask,
cross_attn_layer_head_mask,
None, # past_key_value is always None with gradient checkpointing
use_cache,
output_attentions,
)
else:
layer_outputs = layer_module(
hidden_states,
attention_mask=extended_attention_mask,
position_bias=position_bias,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_extended_attention_mask,
encoder_decoder_position_bias=encoder_decoder_position_bias,
layer_head_mask=layer_head_mask,
cross_attn_layer_head_mask=cross_attn_layer_head_mask,
past_key_value=past_key_value,
use_cache=use_cache,
output_attentions=output_attentions,
output_router_logits=output_router_logits,
)
router_probs = layer_outputs[-1]
layer_outputs = layer_outputs[:-1]
# layer_outputs is a tuple with:
# hidden-states, key-value-states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights)
if use_cache is False:
layer_outputs = layer_outputs[:1] + (None,) + layer_outputs[1:]
hidden_states, present_key_value_state = layer_outputs[:2]
# We share the position biases between the layers - the first layer store them
# layer_outputs = hidden-states, key-value-states (self-attention position bias), (self-attention weights),
# (cross-attention position bias), (cross-attention weights)
position_bias = layer_outputs[2]
if self.is_decoder and encoder_hidden_states is not None:
encoder_decoder_position_bias = layer_outputs[4 if output_attentions else 3]
# append next layer key value states
if use_cache:
present_key_value_states = present_key_value_states + (present_key_value_state,)
if output_attentions:
all_attentions = all_attentions + (layer_outputs[3],)
if self.is_decoder:
all_cross_attentions = all_cross_attentions + (layer_outputs[5],)
if output_router_logits:
all_router_probs = all_router_probs + (router_probs,)
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.dropout(hidden_states)
# Add last layer
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(
v
for v in [
hidden_states,
present_key_value_states,
all_hidden_states,
all_attentions,
all_cross_attentions,
all_router_probs,
]
if v is not None
)
return MoEModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=present_key_value_states,
hidden_states=all_hidden_states,
attentions=all_attentions,
cross_attentions=all_cross_attentions,
router_probs=all_router_probs,
)
SWITCH_TRANSFORMERS_START_DOCSTRING = r"""
The SWITCH_TRANSFORMERS model was proposed in [Switch Transformers: Scaling to Trillion Parameter Models with
Simple and Efficient Sparsity](https://arxiv.org/abs/2101.03961) by [William
Fedus](https://arxiv.org/search/cs?searchtype=author&query=Fedus%2C+W), [Barret
Zoph](https://arxiv.org/search/cs?searchtype=author&query=Zoph%2C+B), and [Noam
Shazeer](https://arxiv.org/search/cs?searchtype=author&query=Shazeer%2C+N). It's an encoder-decoder T5-like model
with sparse Feed Forward that stands for Mixture of Experts (MoE) architecture.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`SwitchTransformersConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
SWITCH_TRANSFORMERS_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. SWITCH_TRANSFORMERS is a model with relative position
embeddings so you should be able to pad the inputs on both the right and the left.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for detail.
[What are input IDs?](../glossary#input-ids)
To know more on how to prepare `input_ids` for pretraining take a look a [SWITCH_TRANSFORMERS
Training](./switch_transformers#training).
attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are decoder input IDs?](../glossary#decoder-input-ids)
SWITCH_TRANSFORMERS uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If
`past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
`past_key_values`).
To know more on how to prepare `decoder_input_ids` for pretraining take a look at [SWITCH_TRANSFORMERS
Training](./switch_transformers#training).
decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
be used by default.
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules in the encoder. Mask values selected in `[0,
1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
decoder_head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules in the decoder. Mask values selected in `[0,
1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`torch.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in
`[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
Tuple consists of (`last_hidden_state`, `optional`: *hidden_states*, `optional`: *attentions*)
`last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)` is a sequence of hidden states at
the output of the last layer of the encoder. Used in the cross-attention of the decoder.
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be
input (see `past_key_values`). This is useful if you want more control over how to convert
`decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value
of `inputs_embeds`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
output_router_logits (`bool`, *optional*):
Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
should not be returned during inference.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
SWITCH_TRANSFORMERS_ENCODER_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. SWITCH_TRANSFORMERS is a model with relative position
embeddings so you should be able to pad the inputs on both the right and the left.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for detail.
To know more on how to prepare `input_ids` for pretraining take a look a [SWITCH_TRANSFORMERS
Training](./switch_transformers#training).
attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
output_router_logits (`bool`, *optional*):
Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
should not be returned during inference.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
# Warning message for FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
__HEAD_MASK_WARNING_MSG = """
The input argument `head_mask` was split into two arguments `head_mask` and `decoder_head_mask`. Currently,
`decoder_head_mask` is set to copy `head_mask`, but this feature is deprecated and will be removed in future versions.
If you do not want to use any `decoder_head_mask` now, please set `decoder_head_mask = torch.ones(num_layers,
num_heads)`.
"""
@add_start_docstrings(
"The bare SWITCH_TRANSFORMERS Model transformer outputting raw hidden-states without any specific head on top.",
SWITCH_TRANSFORMERS_START_DOCSTRING,
)
class SwitchTransformersModel(SwitchTransformersPreTrainedModel):
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
def __init__(self, config: SwitchTransformersConfig):
super().__init__(config)
self.shared = nn.Embedding(config.vocab_size, config.d_model)
encoder_config = copy.deepcopy(config)
encoder_config.is_decoder = False
encoder_config.use_cache = False
encoder_config.is_encoder_decoder = False
self.encoder = SwitchTransformersStack(encoder_config, self.shared)
decoder_config = copy.deepcopy(config)
decoder_config.is_decoder = True
decoder_config.is_encoder_decoder = False
self.decoder = SwitchTransformersStack(decoder_config, self.shared)
# Initialize weights and apply final processing
self.post_init()
# Model parallel
self.device_map = None
def get_input_embeddings(self):
return self.shared
def set_input_embeddings(self, new_embeddings):
self.shared = new_embeddings
self.encoder.set_input_embeddings(new_embeddings)
self.decoder.set_input_embeddings(new_embeddings)
def _tie_weights(self):
if self.config.tie_word_embeddings:
self._tie_or_clone_weights(self.encoder.embed_tokens, self.shared)
self._tie_or_clone_weights(self.decoder.embed_tokens, self.shared)
def get_encoder(self):
return self.encoder
def get_decoder(self):
return self.decoder
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
@add_start_docstrings_to_model_forward(SWITCH_TRANSFORMERS_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqMoEModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.BoolTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
decoder_head_mask: Optional[torch.FloatTensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
inputs_embeds: Optional[torch.Tensor] = None,
decoder_inputs_embeds: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_router_logits: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.FloatTensor], Seq2SeqMoEModelOutput]:
r"""
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, SwitchTransformersModel
>>> tokenizer = AutoTokenizer.from_pretrained("google/switch-base-8")
>>> model = SwitchTransformersModel.from_pretrained("google/switch-base-8")
>>> input_ids = tokenizer(
... "Studies have been shown that owning a dog is good for you", return_tensors="pt"
... ).input_ids # Batch size 1
>>> decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids # Batch size 1
>>> # preprocess: Prepend decoder_input_ids with start token which is pad token for SwitchTransformersModel.
>>> # This is not needed for torch's SwitchTransformersForConditionalGeneration as it does this internally using labels arg.
>>> decoder_input_ids = model._shift_right(decoder_input_ids)
>>> # forward pass
>>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
>>> last_hidden_states = outputs.last_hidden_state
```"""
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
if head_mask is not None and decoder_head_mask is None:
if self.config.num_layers == self.config.num_decoder_layers:
warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning)
decoder_head_mask = head_mask
if (
output_router_logits
and self.config.num_sparse_encoder_layers == 0
and self.config.num_sparse_encoder_layers == 0
):
raise ValueError(
"You asked to return `output_router_logits` but the transformer in dense, and does "
" not contain any sparse MLP Layers. Set `output_router_logits = False` and restart"
)
# Encode if needed (training, first prediction pass)
if encoder_outputs is None:
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
output_router_logits=output_router_logits,
return_dict=return_dict,
)
elif return_dict and not isinstance(encoder_outputs, MoEModelOutput):
encoder_outputs = MoEModelOutput(
last_hidden_state=encoder_outputs[0],
hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
router_probs=encoder_outputs[3] if len(encoder_outputs) > 3 else None,
)
hidden_states = encoder_outputs[0]
# Decode
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
inputs_embeds=decoder_inputs_embeds,
past_key_values=past_key_values,
encoder_hidden_states=hidden_states,
encoder_attention_mask=attention_mask,
head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
output_router_logits=output_router_logits,
return_dict=return_dict,
)
if not return_dict:
return decoder_outputs + encoder_outputs
return Seq2SeqMoEModelOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
past_key_values=decoder_outputs.past_key_values,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
decoder_router_logits=decoder_outputs.router_probs,
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
encoder_hidden_states=encoder_outputs.hidden_states,
encoder_attentions=encoder_outputs.attentions,
encoder_router_logits=encoder_outputs.router_probs,
)
@add_start_docstrings(
"""SWITCH_TRANSFORMERS Model with a `language modeling` head on top.""", SWITCH_TRANSFORMERS_START_DOCSTRING
)
class SwitchTransformersForConditionalGeneration(SwitchTransformersPreTrainedModel):
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
def __init__(self, config: SwitchTransformersConfig):
super().__init__(config)
self.model_dim = config.d_model
self.shared = nn.Embedding(config.vocab_size, config.d_model)
encoder_config = copy.deepcopy(config)
encoder_config.is_decoder = False
encoder_config.use_cache = False
encoder_config.is_encoder_decoder = False
self.encoder = SwitchTransformersStack(encoder_config, self.shared)
decoder_config = copy.deepcopy(config)
decoder_config.is_decoder = True
decoder_config.is_encoder_decoder = False
decoder_config.num_layers = config.num_decoder_layers
self.decoder = SwitchTransformersStack(decoder_config, self.shared)
self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
self.router_z_loss_coef = config.router_z_loss_coef
self.router_aux_loss_coef = config.router_aux_loss_coef
# Initialize weights and apply final processing
self.post_init()
# Model parallel
self.device_map = None
def get_input_embeddings(self):
return self.shared
def set_input_embeddings(self, new_embeddings):
self.shared = new_embeddings
self.encoder.set_input_embeddings(new_embeddings)
self.decoder.set_input_embeddings(new_embeddings)
def _tie_weights(self):
if self.config.tie_word_embeddings:
self._tie_or_clone_weights(self.encoder.embed_tokens, self.shared)
self._tie_or_clone_weights(self.decoder.embed_tokens, self.shared)
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def get_output_embeddings(self):
return self.lm_head
def get_encoder(self):
return self.encoder
def get_decoder(self):
return self.decoder
@add_start_docstrings_to_model_forward(SWITCH_TRANSFORMERS_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqMoEOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.BoolTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
decoder_head_mask: Optional[torch.FloatTensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[Tuple[Tuple[torch.Tensor]]] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_router_logits: Optional[bool] = True,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.FloatTensor], Seq2SeqMoEOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[-100, 0, ...,
config.vocab_size - 1]`. All labels set to `-100` are ignored (masked), the loss is only computed for
labels in `[0, ..., config.vocab_size]`
Returns:
Examples:
```python
>>> from transformers import AutoTokenizer, SwitchTransformersForConditionalGeneration
>>> tokenizer = AutoTokenizer.from_pretrained("google/switch-base-8")
>>> model = SwitchTransformersForConditionalGeneration.from_pretrained("google/switch-base-8")
>>> # training
>>> input_ids = tokenizer("The <extra_id_0> walks in <extra_id_1> park", return_tensors="pt").input_ids
>>> labels = tokenizer("<extra_id_0> cute dog <extra_id_1> the <extra_id_2>", return_tensors="pt").input_ids
>>> outputs = model(input_ids=input_ids, labels=labels)
>>> loss = outputs.loss
>>> logits = outputs.logits
>>> # inference
>>> input_ids = tokenizer(
... "summarize: studies have shown that owning a dog is good for you", return_tensors="pt"
... ).input_ids # Batch size 1
>>> outputs = model.generate(input_ids)
>>> # . To, let’s say you have a dog. To summarize:
>>> # Since the model has been trained on MLM, this will output gibberish
```"""
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
if head_mask is not None and decoder_head_mask is None:
if self.config.num_layers == self.config.num_decoder_layers:
warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning)
decoder_head_mask = head_mask
# Encode if needed (training, first prediction pass)
if encoder_outputs is None:
# Convert encoder inputs in embeddings if needed
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
output_router_logits=output_router_logits,
return_dict=return_dict,
)
elif return_dict and not isinstance(encoder_outputs, MoEModelOutput):
encoder_outputs = MoEModelOutput(
last_hidden_state=encoder_outputs[0],
hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
router_probs=encoder_outputs[3] if len(encoder_outputs) > 3 else None,
)
hidden_states = encoder_outputs[0]
if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None:
# get decoder inputs from shifting lm labels to the right
decoder_input_ids = self._shift_right(labels)
# Decode
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
inputs_embeds=decoder_inputs_embeds,
past_key_values=past_key_values,
encoder_hidden_states=hidden_states,
encoder_attention_mask=attention_mask,
head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
output_router_logits=output_router_logits,
return_dict=return_dict,
)
sequence_output = decoder_outputs[0]
if self.config.tie_word_embeddings:
# Rescale output before projecting on vocab
# See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
sequence_output = sequence_output * (self.model_dim**-0.5)
lm_logits = self.lm_head(sequence_output)
loss = None
encoder_z_loss = None
encoder_aux_loss = None
decoder_z_loss = None
decoder_aux_loss = None
if output_router_logits:
# Compute the router loss (z_loss + auxiliary loss) for each router in the encoder and decoder
if self.encoder.config.encoder_sparse_step > 1:
encoder_router_logits, encoder_expert_indexes = self._unpack_router_logits(encoder_outputs[-1])
encoder_z_loss = router_z_loss_func(encoder_router_logits)
encoder_router_probs = nn.Softmax(dim=-1)(encoder_router_logits)
encoder_aux_loss = load_balancing_loss_func(encoder_router_probs, encoder_expert_indexes)
else:
encoder_z_loss = 0
encoder_aux_loss = 0
if self.decoder.config.decoder_sparse_step > 1:
decoder_router_logits, decoder_expert_indexes = self._unpack_router_logits(decoder_outputs[-1])
decoder_z_loss = router_z_loss_func(decoder_router_logits)
decoder_router_probs = nn.Softmax(dim=-1)(decoder_router_logits)
decoder_aux_loss = load_balancing_loss_func(decoder_router_probs, decoder_expert_indexes)
else:
decoder_z_loss = 0
decoder_aux_loss = 0
if labels is not None:
loss_fct = CrossEntropyLoss(ignore_index=-100)
# move labels to correct device to enable PP
labels = labels.to(lm_logits.device)
loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))
if output_router_logits:
z_loss = self.router_z_loss_coef * (encoder_z_loss + decoder_z_loss)
aux_loss = self.router_aux_loss_coef * (encoder_aux_loss + decoder_aux_loss)
loss = loss + z_loss + aux_loss
if not return_dict:
output = (lm_logits,)
if output_router_logits:
output += (encoder_z_loss, encoder_aux_loss, decoder_z_loss, decoder_aux_loss)
output += (*decoder_outputs[1:], *encoder_outputs)
return ((loss,) + output) if loss is not None else output
return Seq2SeqMoEOutput(
loss=loss,
logits=lm_logits,
encoder_z_loss=encoder_z_loss,
encoder_aux_loss=encoder_aux_loss,
decoder_z_loss=decoder_z_loss,
decoder_aux_loss=decoder_aux_loss,
past_key_values=decoder_outputs.past_key_values,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
decoder_router_logits=decoder_outputs.router_probs,
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
encoder_hidden_states=encoder_outputs.hidden_states,
encoder_attentions=encoder_outputs.attentions,
encoder_router_logits=encoder_outputs.router_probs,
)
def _unpack_router_logits(self, router_outputs):
total_router_logits = []
total_expert_indexes = []
for router_output in router_outputs:
if len(router_output[0].shape) > 1:
router_logits, expert_indexes = router_output
total_router_logits.append(router_logits)
total_expert_indexes.append(expert_indexes)
return torch.cat(total_router_logits, dim=1), torch.cat(total_expert_indexes, dim=1)
def prepare_inputs_for_generation(
self,
input_ids,
past_key_values=None,
attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
use_cache=None,
encoder_outputs=None,
**kwargs,
):
# cut decoder_input_ids if past_key_values is used
if past_key_values is not None:
past_length = past_key_values[0][0].shape[2]
# Some generation methods already pass only the last input ID
if input_ids.shape[1] > past_length:
remove_prefix_length = past_length
else:
# Default to old behavior: keep only final ID
remove_prefix_length = input_ids.shape[1] - 1
input_ids = input_ids[:, remove_prefix_length:]
return {
"decoder_input_ids": input_ids,
"past_key_values": past_key_values,
"encoder_outputs": encoder_outputs,
"attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
"use_cache": use_cache,
}
def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
return self._shift_right(labels)
def _reorder_cache(self, past_key_values, beam_idx):
# if decoder past is not included in output
# speedy decoding is disabled and no need to reorder
if past_key_values is None:
logger.warning("You might want to consider setting `use_cache=True` to speed up decoding")
return past_key_values
reordered_decoder_past = ()
for layer_past_states in past_key_values:
# get the correct batch idx from layer past batch dim
# batch dim of `past` is at 2nd position
reordered_layer_past_states = ()
for layer_past_state in layer_past_states:
# need to set correct `past` for each of the four key / value states
reordered_layer_past_states = reordered_layer_past_states + (
layer_past_state.index_select(0, beam_idx.to(layer_past_state.device)),
)
if reordered_layer_past_states[0].shape != layer_past_states[0].shape:
raise ValueError(
"expected reordered_layer_past_states to have the same shape than layer_past_states, "
f"but got {reordered_layer_past_states[0].shape} and {layer_past_states[0].shape}"
)
if len(reordered_layer_past_states) != len(layer_past_states):
raise ValueError(
"expected layer_past_states to have the same length as reordered_layer_past_states, "
f"but got {len(layer_past_states)} and {len(reordered_layer_past_states)}"
)
reordered_decoder_past = reordered_decoder_past + (reordered_layer_past_states,)
return reordered_decoder_past
@add_start_docstrings(
"The bare SWITCH_TRANSFORMERS Model transformer outputting encoder's raw hidden-states without any specific head"
" on top.",
SWITCH_TRANSFORMERS_START_DOCSTRING,
)
class SwitchTransformersEncoderModel(SwitchTransformersPreTrainedModel):
_tied_weights_keys = ["encoder.embed_tokens.weight"]
def __init__(self, config: SwitchTransformersConfig):
super().__init__(config)
self.shared = nn.Embedding(config.vocab_size, config.d_model)
encoder_config = copy.deepcopy(config)
encoder_config.use_cache = False
encoder_config.is_encoder_decoder = False
self.encoder = SwitchTransformersStack(encoder_config, self.shared)
# Initialize weights and apply final processing
self.post_init()
# Model parallel
self.device_map = None
def get_input_embeddings(self):
return self.shared
def set_input_embeddings(self, new_embeddings):
self.shared = new_embeddings
self.encoder.set_input_embeddings(new_embeddings)
def _tie_weights(self):
if self.config.tie_word_embeddings:
self._tie_or_clone_weights(self.encoder.embed_tokens, self.shared)
def get_encoder(self):
return self.encoder
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.block[layer].layer[0].SelfAttention.prune_heads(heads)
@add_start_docstrings_to_model_forward(SWITCH_TRANSFORMERS_ENCODER_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=MoEModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_router_logits: Optional[bool] = True,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.FloatTensor], MoEModelOutput]:
r"""
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, SwitchTransformersEncoderModel
>>> tokenizer = AutoTokenizer.from_pretrained("google/switch-base-8")
>>> model = SwitchTransformersEncoderModel.from_pretrained("google/switch-base-8")
>>> input_ids = tokenizer(
... "Studies have been shown that owning a dog is good for you", return_tensors="pt"
... ).input_ids # Batch size 1
>>> outputs = model(input_ids=input_ids)
>>> last_hidden_states = outputs.last_hidden_state
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
output_router_logits=output_router_logits,
return_dict=return_dict,
)
return encoder_outputs
| transformers/src/transformers/models/switch_transformers/modeling_switch_transformers.py/0 | {
"file_path": "transformers/src/transformers/models/switch_transformers/modeling_switch_transformers.py",
"repo_id": "transformers",
"token_count": 37581
} | 341 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig
if is_timm_available():
import timm
if is_torch_available():
from torch import Tensor
class TimmBackbone(PreTrainedModel, BackboneMixin):
"""
Wrapper class for timm models to be used as backbones. This enables using the timm models interchangeably with the
other models in the library keeping the same API.
"""
main_input_name = "pixel_values"
supports_gradient_checkpointing = False
config_class = TimmBackboneConfig
def __init__(self, config, **kwargs):
requires_backends(self, "timm")
super().__init__(config)
self.config = config
if config.backbone is None:
raise ValueError("backbone is not set in the config. Please set it to a timm model name.")
if config.backbone not in timm.list_models():
raise ValueError(f"backbone {config.backbone} is not supported by timm.")
if hasattr(config, "out_features") and config.out_features is not None:
raise ValueError("out_features is not supported by TimmBackbone. Please use out_indices instead.")
pretrained = getattr(config, "use_pretrained_backbone", None)
if pretrained is None:
raise ValueError("use_pretrained_backbone is not set in the config. Please set it to True or False.")
# We just take the final layer by default. This matches the default for the transformers models.
out_indices = config.out_indices if getattr(config, "out_indices", None) is not None else (-1,)
self._backbone = timm.create_model(
config.backbone,
pretrained=pretrained,
# This is currently not possible for transformer architectures.
features_only=config.features_only,
in_chans=config.num_channels,
out_indices=out_indices,
**kwargs,
)
# Converts all `BatchNorm2d` and `SyncBatchNorm` or `BatchNormAct2d` and `SyncBatchNormAct2d` layers of provided module into `FrozenBatchNorm2d` or `FrozenBatchNormAct2d` respectively
if getattr(config, "freeze_batch_norm_2d", False):
self.freeze_batch_norm_2d()
# These are used to control the output of the model when called. If output_hidden_states is True, then
# return_layers is modified to include all layers.
self._return_layers = self._backbone.return_layers
self._all_layers = {layer["module"]: str(i) for i, layer in enumerate(self._backbone.feature_info.info)}
super()._init_backbone(config)
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
requires_backends(cls, ["vision", "timm"])
from ...models.timm_backbone import TimmBackboneConfig
config = kwargs.pop("config", TimmBackboneConfig())
use_timm = kwargs.pop("use_timm_backbone", True)
if not use_timm:
raise ValueError("use_timm_backbone must be True for timm backbones")
num_channels = kwargs.pop("num_channels", config.num_channels)
features_only = kwargs.pop("features_only", config.features_only)
use_pretrained_backbone = kwargs.pop("use_pretrained_backbone", config.use_pretrained_backbone)
out_indices = kwargs.pop("out_indices", config.out_indices)
config = TimmBackboneConfig(
backbone=pretrained_model_name_or_path,
num_channels=num_channels,
features_only=features_only,
use_pretrained_backbone=use_pretrained_backbone,
out_indices=out_indices,
)
return super()._from_config(config, **kwargs)
def freeze_batch_norm_2d(self):
timm.layers.freeze_batch_norm_2d(self._backbone)
def unfreeze_batch_norm_2d(self):
timm.layers.unfreeze_batch_norm_2d(self._backbone)
def _init_weights(self, module):
"""
Empty init weights function to ensure compatibility of the class in the library.
"""
pass
def forward(
self,
pixel_values: torch.FloatTensor,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs,
) -> Union[BackboneOutput, Tuple[Tensor, ...]]:
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
if output_attentions:
raise ValueError("Cannot output attentions for timm backbones at the moment")
if output_hidden_states:
# We modify the return layers to include all the stages of the backbone
self._backbone.return_layers = self._all_layers
hidden_states = self._backbone(pixel_values, **kwargs)
self._backbone.return_layers = self._return_layers
feature_maps = tuple(hidden_states[i] for i in self.out_indices)
else:
feature_maps = self._backbone(pixel_values, **kwargs)
hidden_states = None
feature_maps = tuple(feature_maps)
hidden_states = tuple(hidden_states) if hidden_states is not None else None
if not return_dict:
output = (feature_maps,)
if output_hidden_states:
output = output + (hidden_states,)
return output
return BackboneOutput(feature_maps=feature_maps, hidden_states=hidden_states, attentions=None)
| transformers/src/transformers/models/timm_backbone/modeling_timm_backbone.py/0 | {
"file_path": "transformers/src/transformers/models/timm_backbone/modeling_timm_backbone.py",
"repo_id": "transformers",
"token_count": 2548
} | 342 |
# coding=utf-8
# Copyright 2023 The Intel AIA Team Authors, and HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License=, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing=, software
# distributed under the License is distributed on an "AS IS" BASIS=,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND=, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Processor class for TVP.
"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class TvpProcessor(ProcessorMixin):
r"""
Constructs an TVP processor which wraps a TVP image processor and a Bert tokenizer into a single processor.
[`TvpProcessor`] offers all the functionalities of [`TvpImageProcessor`] and [`BertTokenizerFast`]. See the
[`~TvpProcessor.__call__`] and [`~TvpProcessor.decode`] for more information.
Args:
image_processor ([`TvpImageProcessor`], *optional*):
The image processor is a required input.
tokenizer ([`BertTokenizerFast`], *optional*):
The tokenizer is a required input.
"""
attributes = ["image_processor", "tokenizer"]
image_processor_class = "TvpImageProcessor"
tokenizer_class = ("BertTokenizer", "BertTokenizerFast")
def __init__(self, image_processor=None, tokenizer=None, **kwargs):
if image_processor is None:
raise ValueError("You need to specify an `image_processor`.")
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`.")
super().__init__(image_processor, tokenizer)
def __call__(self, text=None, videos=None, return_tensors=None, **kwargs):
"""
Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
and `kwargs` arguments to BertTokenizerFast's [`~BertTokenizerFast.__call__`] if `text` is not `None` to encode
the text. To prepare the image(s), this method forwards the `videos` and `kwargs` arguments to
TvpImageProcessor's [`~TvpImageProcessor.__call__`] if `videos` is not `None`. Please refer to the doctsring of
the above two methods for more information.
Args:
text (`str`, `List[str]`, `List[List[str]]`):
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
videos (`List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`, `List[List[PIL.Image.Image]]`, `List[List[np.ndarrray]]`,:
`List[List[torch.Tensor]]`): The video or batch of videos to be prepared. Each video should be a list
of frames, which can be either PIL images or NumPy arrays. In case of NumPy arrays/PyTorch tensors,
each frame should be of shape (H, W, C), where H and W are frame height and width, and C is a number of
channels.
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors of a particular framework. Acceptable values are:
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return NumPy `np.ndarray` objects.
- `'jax'`: Return JAX `jnp.ndarray` objects.
Returns:
[`BatchEncoding`]: A [`BatchEncoding`] with the following fields:
- **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
`return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
`None`).
- **pixel_values** -- Pixel values to be fed to a model. Returned when `videos` is not `None`.
"""
max_text_length = kwargs.pop("max_text_length", None)
if text is None and videos is None:
raise ValueError("You have to specify either text or videos. Both cannot be none.")
encoding = {}
if text is not None:
textual_input = self.tokenizer.batch_encode_plus(
text,
truncation=True,
padding="max_length",
max_length=max_text_length,
pad_to_max_length=True,
return_tensors=return_tensors,
return_token_type_ids=False,
**kwargs,
)
encoding.update(textual_input)
if videos is not None:
image_features = self.image_processor(videos, return_tensors=return_tensors, **kwargs)
encoding.update(image_features)
return BatchEncoding(data=encoding, tensor_type=return_tensors)
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to BertTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to BertTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
def post_process_video_grounding(self, logits, video_durations):
"""
Compute the time of the video.
Args:
logits (`torch.Tensor`):
The logits output of TvpForVideoGrounding.
video_durations (`float`):
The video's duration.
Returns:
start (`float`):
The start time of the video.
end (`float`):
The end time of the video.
"""
start, end = (
round(logits.tolist()[0][0] * video_durations, 1),
round(logits.tolist()[0][1] * video_durations, 1),
)
return start, end
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def model_input_names(self):
tokenizer_input_names = self.tokenizer.model_input_names
image_processor_input_names = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
| transformers/src/transformers/models/tvp/processing_tvp.py/0 | {
"file_path": "transformers/src/transformers/models/tvp/processing_tvp.py",
"repo_id": "transformers",
"token_count": 2826
} | 343 |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" VideoMAE model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"MCG-NJU/videomae-base": "https://huggingface.co/MCG-NJU/videomae-base/resolve/main/config.json",
}
class VideoMAEConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`VideoMAEModel`]. It is used to instantiate a
VideoMAE model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the VideoMAE
[MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
patch_size (`int`, *optional*, defaults to 16):
The size (resolution) of each patch.
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
num_frames (`int`, *optional*, defaults to 16):
The number of frames in each video.
tubelet_size (`int`, *optional*, defaults to 2):
The number of tubelets.
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
qkv_bias (`bool`, *optional*, defaults to `True`):
Whether to add a bias to the queries, keys and values.
use_mean_pooling (`bool`, *optional*, defaults to `True`):
Whether to mean pool the final hidden states instead of using the final hidden state of the [CLS] token.
decoder_num_attention_heads (`int`, *optional*, defaults to 6):
Number of attention heads for each attention layer in the decoder.
decoder_hidden_size (`int`, *optional*, defaults to 384):
Dimensionality of the decoder.
decoder_num_hidden_layers (`int`, *optional*, defaults to 4):
Number of hidden layers in the decoder.
decoder_intermediate_size (`int`, *optional*, defaults to 1536):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the decoder.
norm_pix_loss (`bool`, *optional*, defaults to `True`):
Whether to normalize the target patch pixels.
Example:
```python
>>> from transformers import VideoMAEConfig, VideoMAEModel
>>> # Initializing a VideoMAE videomae-base style configuration
>>> configuration = VideoMAEConfig()
>>> # Randomly initializing a model from the configuration
>>> model = VideoMAEModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "videomae"
def __init__(
self,
image_size=224,
patch_size=16,
num_channels=3,
num_frames=16,
tubelet_size=2,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.0,
attention_probs_dropout_prob=0.0,
initializer_range=0.02,
layer_norm_eps=1e-12,
qkv_bias=True,
use_mean_pooling=True,
decoder_num_attention_heads=6,
decoder_hidden_size=384,
decoder_num_hidden_layers=4,
decoder_intermediate_size=1536,
norm_pix_loss=True,
**kwargs,
):
super().__init__(**kwargs)
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.num_frames = num_frames
self.tubelet_size = tubelet_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.qkv_bias = qkv_bias
self.use_mean_pooling = use_mean_pooling
self.decoder_num_attention_heads = decoder_num_attention_heads
self.decoder_hidden_size = decoder_hidden_size
self.decoder_num_hidden_layers = decoder_num_hidden_layers
self.decoder_intermediate_size = decoder_intermediate_size
self.norm_pix_loss = norm_pix_loss
| transformers/src/transformers/models/videomae/configuration_videomae.py/0 | {
"file_path": "transformers/src/transformers/models/videomae/configuration_videomae.py",
"repo_id": "transformers",
"token_count": 2593
} | 344 |
# coding=utf-8
# Copyright 2022 Facebook AI and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" TF 2.0 ViT MAE (masked autoencoder) model."""
from __future__ import annotations
import collections.abc
import math
from copy import deepcopy
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...file_utils import (
ModelOutput,
add_start_docstrings,
add_start_docstrings_to_model_forward,
replace_return_docstrings,
)
from ...modeling_tf_outputs import TFBaseModelOutput
from ...modeling_tf_utils import (
TFModelInputType,
TFPreTrainedModel,
get_initializer,
keras,
keras_serializable,
unpack_inputs,
)
from ...tf_utils import shape_list, stable_softmax
from ...utils import logging
from .configuration_vit_mae import ViTMAEConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "ViTMAEConfig"
_CHECKPOINT_FOR_DOC = "facebook/vit-mae-base"
@dataclass
class TFViTMAEModelOutput(ModelOutput):
"""
Class for TFViTMAEModel's outputs, with potential hidden states and attentions.
Args:
last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
mask (`tf.Tensor` of shape `(batch_size, sequence_length)`):
Tensor indicating which patches are masked (1) and which are not (0).
ids_restore (`tf.Tensor` of shape `(batch_size, sequence_length)`):
Tensor containing the original index of the (shuffled) masked patches.
hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
`(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus
the initial embedding outputs.
attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
"""
last_hidden_state: tf.Tensor = None
mask: tf.Tensor = None
ids_restore: tf.Tensor = None
hidden_states: Tuple[tf.Tensor] | None = None
attentions: Tuple[tf.Tensor] | None = None
@dataclass
class TFViTMAEDecoderOutput(ModelOutput):
"""
Class for TFViTMAEDecoder's outputs, with potential hidden states and attentions.
Args:
logits (`tf.Tensor` of shape `(batch_size, sequence_length, patch_size ** 2 * num_channels)`):
Pixel reconstruction logits.
hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
`(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus
the initial embedding outputs.
attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
"""
logits: tf.Tensor = None
hidden_states: Tuple[tf.Tensor] | None = None
attentions: Tuple[tf.Tensor] | None = None
@dataclass
class TFViTMAEForPreTrainingOutput(ModelOutput):
"""
Class for TFViTMAEForPreTraining's outputs, with potential hidden states and attentions.
Args:
loss (`tf.Tensor` of shape `(1,)`):
Pixel reconstruction loss.
logits (`tf.Tensor` of shape `(batch_size, sequence_length, patch_size ** 2 * num_channels)`):
Pixel reconstruction logits.
mask (`tf.Tensor` of shape `(batch_size, sequence_length)`):
Tensor indicating which patches are masked (1) and which are not (0).
ids_restore (`tf.Tensor` of shape `(batch_size, sequence_length)`):
Tensor containing the original index of the (shuffled) masked patches.
hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
`(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus
the initial embedding outputs.
attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
"""
loss: tf.Tensor | None = None
logits: tf.Tensor = None
mask: tf.Tensor = None
ids_restore: tf.Tensor = None
hidden_states: Tuple[tf.Tensor] | None = None
attentions: Tuple[tf.Tensor] | None = None
def get_2d_sincos_pos_embed(embed_dim, grid_size, add_cls_token=False):
"""
Create 2D sin/cos positional embeddings.
Args:
embed_dim (`int`):
Embedding dimension.
grid_size (`int`):
The grid height and width.
add_cls_token (`bool`, *optional*, defaults to `False`):
Whether or not to add a classification (CLS) token.
Returns:
(`tf.Tensor` of shape (grid_size*grid_size, embed_dim) or (1+grid_size*grid_size, embed_dim): the position
embeddings (with or without classification token)
"""
grid_h = tf.range(grid_size, dtype=tf.float32)
grid_w = tf.range(grid_size, dtype=tf.float32)
grid = tf.meshgrid(grid_w, grid_h) # here w goes first
grid = tf.stack(grid, axis=0)
grid = tf.reshape(grid, [2, 1, grid_size, grid_size])
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
if add_cls_token:
pos_embed = tf.concat([tf.zeros((1, embed_dim)), pos_embed], axis=0)
return pos_embed
def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
if embed_dim % 2 != 0:
raise ValueError("embed_dim must be even")
# use half of dimensions to encode grid_h
emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)
emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)
emb = tf.concat([emb_h, emb_w], axis=1) # (H*W, D)
return emb
def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
"""
embed_dim: output dimension for each position pos: a list of positions to be encoded: size (M,) out: (M, D)
"""
if embed_dim % 2 != 0:
raise ValueError("embed_dim must be even")
omega = tf.range(embed_dim // 2, dtype="float32")
omega /= embed_dim / 2.0
omega = 1.0 / 10000**omega # (D/2,)
pos = tf.reshape(pos, [-1]) # (M,)
out = tf.einsum("m,d->md", pos, omega) # (M, D/2), outer product
# half of the positions get sinusoidal pattern and the rest gets
# cosine pattern and then they are concatenated
emb_sin = tf.sin(out) # (M, D/2)
emb_cos = tf.cos(out) # (M, D/2)
emb = tf.concat([emb_sin, emb_cos], axis=1) # (M, D)
return emb
class TFViTMAEEmbeddings(keras.layers.Layer):
"""
Construct the CLS token, position and patch embeddings.
"""
def __init__(self, config: ViTMAEConfig, **kwargs):
super().__init__(**kwargs)
self.patch_embeddings = TFViTMAEPatchEmbeddings(config, name="patch_embeddings")
self.num_patches = self.patch_embeddings.num_patches
self.config = config
def build(self, input_shape=None):
self.cls_token = self.add_weight(
shape=(1, 1, self.config.hidden_size),
initializer=tf.random_normal_initializer(stddev=self.config.initializer_range),
trainable=True,
name="cls_token",
)
self.position_embeddings = self.add_weight(
shape=(1, self.num_patches + 1, self.config.hidden_size),
initializer="zeros",
trainable=False, # fixed sin-cos embedding
name="position_embeddings",
)
pos_embed = get_2d_sincos_pos_embed(
self.position_embeddings.shape[-1],
int(self.patch_embeddings.num_patches**0.5),
add_cls_token=True,
)[None, ...]
self.position_embeddings.assign(pos_embed)
if self.built:
return
self.built = True
if getattr(self, "patch_embeddings", None) is not None:
with tf.name_scope(self.patch_embeddings.name):
self.patch_embeddings.build(None)
def random_masking(self, sequence: tf.Tensor, noise: tf.Tensor | None = None):
"""
Perform per-sample random masking by per-sample shuffling. Per-sample shuffling is done by argsort random
noise.
Args:
sequence (`tf.Tensor` of shape `(batch_size, sequence_length, dim)`)
noise (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) which is
mainly used for testing purposes to control randomness and maintain the reproducibility
"""
batch_size, seq_length, dim = shape_list(sequence)
len_keep = int(seq_length * (1 - self.config.mask_ratio))
if noise is None:
noise = tf.random.uniform(shape=(batch_size, seq_length), minval=0.0, maxval=1.0) # noise in [0, 1)
# sort noise for each sample
ids_shuffle = tf.argsort(noise, axis=1) # ascend: small is keep, large is remove
ids_restore = tf.argsort(ids_shuffle, axis=1)
# keep the first subset
ids_keep = ids_shuffle[:, :len_keep]
sequence_unmasked = tf.gather(
sequence,
axis=1,
batch_dims=1,
indices=ids_keep,
)
# generate the binary mask: 0 is keep, 1 is remove
# this hack is needed because TF's EagerTensors don't support
# assignment
mask_keep = tf.zeros((batch_size, len_keep))
mask_remove = tf.ones((batch_size, seq_length - len_keep))
mask = tf.concat([mask_keep, mask_remove], axis=-1)
# unshuffle to get the binary mask
mask = tf.gather(mask, axis=1, batch_dims=1, indices=ids_restore)
return sequence_unmasked, mask, ids_restore
def call(self, pixel_values: tf.Tensor, noise: tf.Tensor = None) -> tf.Tensor:
embeddings = self.patch_embeddings(pixel_values)
# add position embeddings w/o cls token
embeddings = embeddings + self.position_embeddings[:, 1:, :]
# masking: length -> length * config.mask_ratio
embeddings, mask, ids_restore = self.random_masking(embeddings, noise)
# append cls token
cls_token = self.cls_token + self.position_embeddings[:, :1, :]
cls_tokens = tf.tile(cls_token, (shape_list(embeddings)[0], 1, 1))
embeddings = tf.concat([cls_tokens, embeddings], axis=1)
return embeddings, mask, ids_restore
class TFViTMAEPatchEmbeddings(keras.layers.Layer):
"""
This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
`hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
Transformer.
"""
def __init__(self, config: ViTMAEConfig, **kwargs):
super().__init__(**kwargs)
image_size, patch_size = config.image_size, config.patch_size
num_channels, hidden_size = config.num_channels, config.hidden_size
image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.image_size = image_size
self.patch_size = patch_size
self.num_patches = num_patches
self.num_channels = num_channels
self.config = config
self.projection = keras.layers.Conv2D(
filters=hidden_size,
kernel_size=patch_size,
strides=patch_size,
padding="valid",
data_format="channels_last",
kernel_initializer="glorot_uniform", # following torch.nn.Linear
bias_initializer="zeros",
name="projection",
)
def call(self, pixel_values: tf.Tensor, training: bool = False) -> tf.Tensor:
batch_size, num_channels, height, width = shape_list(pixel_values)
if tf.executing_eagerly():
if num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the"
" configuration."
)
if height != self.image_size[0] or width != self.image_size[1]:
raise ValueError(
f"Input image size ({height}*{width}) doesn't match model"
f" ({self.image_size[0]}*{self.image_size[1]})."
)
# When running on CPU, `keras.layers.Conv2D` doesn't support `NCHW` format.
# So change the input format from `NCHW` to `NHWC`.
# shape = (batch_size, in_height, in_width, in_channels=num_channels)
pixel_values = tf.transpose(pixel_values, perm=(0, 2, 3, 1))
projection = self.projection(pixel_values)
# Change the 2D spatial dimensions to a single temporal dimension.
# shape = (batch_size, num_patches, out_channels=embed_dim)
num_patches = (width // self.patch_size[1]) * (height // self.patch_size[0])
x = tf.reshape(tensor=projection, shape=(batch_size, num_patches, -1))
return x
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "projection", None) is not None:
with tf.name_scope(self.projection.name):
self.projection.build([None, None, None, self.num_channels])
# Copied from transformers.models.vit.modeling_tf_vit.TFViTSelfAttention with ViT->ViTMAE
class TFViTMAESelfAttention(keras.layers.Layer):
def __init__(self, config: ViTMAEConfig, **kwargs):
super().__init__(**kwargs)
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number "
f"of attention heads ({config.num_attention_heads})"
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.sqrt_att_head_size = math.sqrt(self.attention_head_size)
self.query = keras.layers.Dense(
units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query"
)
self.key = keras.layers.Dense(
units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key"
)
self.value = keras.layers.Dense(
units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value"
)
self.dropout = keras.layers.Dropout(rate=config.attention_probs_dropout_prob)
self.config = config
def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
# Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
tensor = tf.reshape(tensor=tensor, shape=(batch_size, -1, self.num_attention_heads, self.attention_head_size))
# Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, attention_head_size]
return tf.transpose(tensor, perm=[0, 2, 1, 3])
def call(
self,
hidden_states: tf.Tensor,
head_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> Tuple[tf.Tensor]:
batch_size = shape_list(hidden_states)[0]
mixed_query_layer = self.query(inputs=hidden_states)
mixed_key_layer = self.key(inputs=hidden_states)
mixed_value_layer = self.value(inputs=hidden_states)
query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)
key_layer = self.transpose_for_scores(mixed_key_layer, batch_size)
value_layer = self.transpose_for_scores(mixed_value_layer, batch_size)
# Take the dot product between "query" and "key" to get the raw attention scores.
# (batch size, num_heads, seq_len_q, seq_len_k)
attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype)
attention_scores = tf.divide(attention_scores, dk)
# Normalize the attention scores to probabilities.
attention_probs = stable_softmax(logits=attention_scores, axis=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(inputs=attention_probs, training=training)
# Mask heads if we want to
if head_mask is not None:
attention_probs = tf.multiply(attention_probs, head_mask)
attention_output = tf.matmul(attention_probs, value_layer)
attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3])
# (batch_size, seq_len_q, all_head_size)
attention_output = tf.reshape(tensor=attention_output, shape=(batch_size, -1, self.all_head_size))
outputs = (attention_output, attention_probs) if output_attentions else (attention_output,)
return outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "query", None) is not None:
with tf.name_scope(self.query.name):
self.query.build([None, None, self.config.hidden_size])
if getattr(self, "key", None) is not None:
with tf.name_scope(self.key.name):
self.key.build([None, None, self.config.hidden_size])
if getattr(self, "value", None) is not None:
with tf.name_scope(self.value.name):
self.value.build([None, None, self.config.hidden_size])
# Copied from transformers.models.vit.modeling_tf_vit.TFViTSelfOutput with ViT->ViTMAE
class TFViTMAESelfOutput(keras.layers.Layer):
"""
The residual connection is defined in TFViTMAELayer instead of here (as is the case with other models), due to the
layernorm applied before each block.
"""
def __init__(self, config: ViTMAEConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob)
self.config = config
def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
hidden_states = self.dense(inputs=hidden_states)
hidden_states = self.dropout(inputs=hidden_states, training=training)
return hidden_states
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "dense", None) is not None:
with tf.name_scope(self.dense.name):
self.dense.build([None, None, self.config.hidden_size])
# Copied from transformers.models.vit.modeling_tf_vit.TFViTAttention with ViT->ViTMAE
class TFViTMAEAttention(keras.layers.Layer):
def __init__(self, config: ViTMAEConfig, **kwargs):
super().__init__(**kwargs)
self.self_attention = TFViTMAESelfAttention(config, name="attention")
self.dense_output = TFViTMAESelfOutput(config, name="output")
def prune_heads(self, heads):
raise NotImplementedError
def call(
self,
input_tensor: tf.Tensor,
head_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> Tuple[tf.Tensor]:
self_outputs = self.self_attention(
hidden_states=input_tensor, head_mask=head_mask, output_attentions=output_attentions, training=training
)
attention_output = self.dense_output(
hidden_states=self_outputs[0], input_tensor=input_tensor, training=training
)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "self_attention", None) is not None:
with tf.name_scope(self.self_attention.name):
self.self_attention.build(None)
if getattr(self, "dense_output", None) is not None:
with tf.name_scope(self.dense_output.name):
self.dense_output.build(None)
# Copied from transformers.models.vit.modeling_tf_vit.TFViTIntermediate with ViT->ViTMAE
class TFViTMAEIntermediate(keras.layers.Layer):
def __init__(self, config: ViTMAEConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = get_tf_activation(config.hidden_act)
else:
self.intermediate_act_fn = config.hidden_act
self.config = config
def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
hidden_states = self.dense(inputs=hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "dense", None) is not None:
with tf.name_scope(self.dense.name):
self.dense.build([None, None, self.config.hidden_size])
# Copied from transformers.models.vit.modeling_tf_vit.TFViTOutput with ViT->ViTMAE
class TFViTMAEOutput(keras.layers.Layer):
def __init__(self, config: ViTMAEConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob)
self.config = config
def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
hidden_states = self.dense(inputs=hidden_states)
hidden_states = self.dropout(inputs=hidden_states, training=training)
hidden_states = hidden_states + input_tensor
return hidden_states
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "dense", None) is not None:
with tf.name_scope(self.dense.name):
self.dense.build([None, None, self.config.intermediate_size])
# Copied from transformers.models.vit.modeling_tf_vit.TFViTLayer with ViT->ViTMAE
class TFViTMAELayer(keras.layers.Layer):
"""This corresponds to the Block class in the timm implementation."""
def __init__(self, config: ViTMAEConfig, **kwargs):
super().__init__(**kwargs)
self.attention = TFViTMAEAttention(config, name="attention")
self.intermediate = TFViTMAEIntermediate(config, name="intermediate")
self.vit_output = TFViTMAEOutput(config, name="output")
self.layernorm_before = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm_before")
self.layernorm_after = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm_after")
self.config = config
def call(
self,
hidden_states: tf.Tensor,
head_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> Tuple[tf.Tensor]:
attention_outputs = self.attention(
# in ViTMAE, layernorm is applied before self-attention
input_tensor=self.layernorm_before(inputs=hidden_states),
head_mask=head_mask,
output_attentions=output_attentions,
training=training,
)
attention_output = attention_outputs[0]
# first residual connection
hidden_states = attention_output + hidden_states
# in ViTMAE, layernorm is also applied after self-attention
layer_output = self.layernorm_after(inputs=hidden_states)
intermediate_output = self.intermediate(hidden_states=layer_output)
# second residual connection is done here
layer_output = self.vit_output(
hidden_states=intermediate_output, input_tensor=hidden_states, training=training
)
outputs = (layer_output,) + attention_outputs[1:] # add attentions if we output them
return outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "attention", None) is not None:
with tf.name_scope(self.attention.name):
self.attention.build(None)
if getattr(self, "intermediate", None) is not None:
with tf.name_scope(self.intermediate.name):
self.intermediate.build(None)
if getattr(self, "vit_output", None) is not None:
with tf.name_scope(self.vit_output.name):
self.vit_output.build(None)
if getattr(self, "layernorm_before", None) is not None:
with tf.name_scope(self.layernorm_before.name):
self.layernorm_before.build([None, None, self.config.hidden_size])
if getattr(self, "layernorm_after", None) is not None:
with tf.name_scope(self.layernorm_after.name):
self.layernorm_after.build([None, None, self.config.hidden_size])
# Copied from transformers.models.vit.modeling_tf_vit.TFViTEncoder with ViT->ViTMAE
class TFViTMAEEncoder(keras.layers.Layer):
def __init__(self, config: ViTMAEConfig, **kwargs):
super().__init__(**kwargs)
self.layer = [TFViTMAELayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)]
def call(
self,
hidden_states: tf.Tensor,
head_mask: tf.Tensor,
output_attentions: bool,
output_hidden_states: bool,
return_dict: bool,
training: bool = False,
) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]:
all_hidden_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_outputs = layer_module(
hidden_states=hidden_states,
head_mask=head_mask[i],
output_attentions=output_attentions,
training=training,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
# Add last layer
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
return TFBaseModelOutput(
last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "layer", None) is not None:
for layer in self.layer:
with tf.name_scope(layer.name):
layer.build(None)
@keras_serializable
class TFViTMAEMainLayer(keras.layers.Layer):
config_class = ViTMAEConfig
def __init__(self, config: ViTMAEConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embeddings = TFViTMAEEmbeddings(config, name="embeddings")
self.encoder = TFViTMAEEncoder(config, name="encoder")
self.layernorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm")
def get_input_embeddings(self) -> keras.layers.Layer:
return self.embeddings.patch_embeddings
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
raise NotImplementedError
@unpack_inputs
def call(
self,
pixel_values: TFModelInputType | None = None,
noise: tf.Tensor = None,
head_mask: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> Union[TFViTMAEModelOutput, Tuple[tf.Tensor]]:
embedding_output, mask, ids_restore = self.embeddings(
pixel_values=pixel_values, training=training, noise=noise
)
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
if head_mask is not None:
raise NotImplementedError
else:
head_mask = [None] * self.config.num_hidden_layers
encoder_outputs = self.encoder(
embedding_output,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
sequence_output = encoder_outputs[0]
sequence_output = self.layernorm(inputs=sequence_output)
if not return_dict:
return (sequence_output, mask, ids_restore) + encoder_outputs[1:]
return TFViTMAEModelOutput(
last_hidden_state=sequence_output,
mask=mask,
ids_restore=ids_restore,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "embeddings", None) is not None:
with tf.name_scope(self.embeddings.name):
self.embeddings.build(None)
if getattr(self, "encoder", None) is not None:
with tf.name_scope(self.encoder.name):
self.encoder.build(None)
if getattr(self, "layernorm", None) is not None:
with tf.name_scope(self.layernorm.name):
self.layernorm.build([None, None, self.config.hidden_size])
class TFViTMAEPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ViTMAEConfig
base_model_prefix = "vit"
main_input_name = "pixel_values"
VIT_MAE_START_DOCSTRING = r"""
This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.
<Tip>
TensorFlow models and layers in `transformers` accept two formats as input:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
- a single Tensor with `pixel_values` only and nothing else: `model(pixel_values)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`model([pixel_values, attention_mask])` or `model([pixel_values, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
`model({"pixel_values": pixel_values, "token_type_ids": token_type_ids})`
Note that when creating models and layers with
[subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
about any of this, as you can just pass inputs like you would to any other Python function!
</Tip>
Args:
config ([`ViTMAEConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
"""
VIT_MAE_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`ViTImageProcessor.__call__`]
for details.
head_mask (`np.ndarray` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (`bool`, *optional*):
Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. This argument can be used
in eager mode, in graph mode the value will always be set to True.
training (`bool`, *optional*, defaults to `False``):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
@add_start_docstrings(
"The bare ViTMAE Model transformer outputting raw hidden-states without any specific head on top.",
VIT_MAE_START_DOCSTRING,
)
class TFViTMAEModel(TFViTMAEPreTrainedModel):
def __init__(self, config: ViTMAEConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.vit = TFViTMAEMainLayer(config, name="vit")
def get_input_embeddings(self):
return self.vit.get_input_embeddings()
@unpack_inputs
@add_start_docstrings_to_model_forward(VIT_MAE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFViTMAEModelOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
pixel_values: TFModelInputType | None = None,
noise: tf.Tensor = None,
head_mask: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> Union[TFViTMAEModelOutput, Tuple[tf.Tensor]]:
r"""
Returns:
Examples:
```python
>>> from transformers import AutoImageProcessor, TFViTMAEModel
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("facebook/vit-mae-base")
>>> model = TFViTMAEModel.from_pretrained("facebook/vit-mae-base")
>>> inputs = image_processor(images=image, return_tensors="tf")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state
```"""
outputs = self.vit(
pixel_values=pixel_values,
noise=noise,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
return outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "vit", None) is not None:
with tf.name_scope(self.vit.name):
self.vit.build(None)
class TFViTMAEDecoder(keras.layers.Layer):
def __init__(self, config, num_patches, **kwargs):
super().__init__(**kwargs)
self.decoder_embed = keras.layers.Dense(config.decoder_hidden_size, name="decoder_embed")
decoder_config = deepcopy(config)
decoder_config.hidden_size = config.decoder_hidden_size
decoder_config.num_hidden_layers = config.decoder_num_hidden_layers
decoder_config.num_attention_heads = config.decoder_num_attention_heads
decoder_config.intermediate_size = config.decoder_intermediate_size
self.decoder_layers = [
TFViTMAELayer(decoder_config, name=f"decoder_layers.{j}") for j in range(config.decoder_num_hidden_layers)
]
self.decoder_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="decoder_norm")
self.decoder_pred = keras.layers.Dense(
config.patch_size**2 * config.num_channels,
kernel_initializer=get_initializer(config.initializer_range),
name="decoder_pred",
) # encoder to decoder
self.config = config
self.num_patches = num_patches
def build(self, input_shape=None):
self.mask_token = self.add_weight(
shape=(1, 1, self.config.decoder_hidden_size),
initializer=tf.random_normal_initializer(stddev=self.config.initializer_range),
trainable=True,
name="mask_token",
)
self.decoder_pos_embed = self.add_weight(
shape=(1, self.num_patches + 1, self.config.decoder_hidden_size),
initializer="zeros",
trainable=False,
name="decoder_pos_embed",
)
decoder_pos_embed = get_2d_sincos_pos_embed(
self.decoder_pos_embed.shape[-1],
int(self.num_patches**0.5),
add_cls_token=True,
)[None, ...]
self.decoder_pos_embed.assign(decoder_pos_embed)
if self.built:
return
self.built = True
if getattr(self, "decoder_embed", None) is not None:
with tf.name_scope(self.decoder_embed.name):
self.decoder_embed.build([None, None, self.config.hidden_size])
if getattr(self, "decoder_norm", None) is not None:
with tf.name_scope(self.decoder_norm.name):
self.decoder_norm.build([None, None, self.config.decoder_hidden_size])
if getattr(self, "decoder_pred", None) is not None:
with tf.name_scope(self.decoder_pred.name):
self.decoder_pred.build([None, None, self.config.decoder_hidden_size])
if getattr(self, "decoder_layers", None) is not None:
for layer in self.decoder_layers:
with tf.name_scope(layer.name):
layer.build(None)
def call(
self,
hidden_states,
ids_restore,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
):
# embed tokens
x = self.decoder_embed(hidden_states)
# append mask tokens to sequence
mask_tokens = tf.tile(
self.mask_token,
(shape_list(x)[0], shape_list(ids_restore)[1] + 1 - shape_list(x)[1], 1),
)
x_ = tf.concat([x[:, 1:, :], mask_tokens], axis=1) # no cls token
x_ = tf.gather(x_, axis=1, batch_dims=1, indices=ids_restore) # unshuffle
x = tf.concat([x[:, :1, :], x_], axis=1) # append cls token
# add pos embed
hidden_states = x + self.decoder_pos_embed
# apply Transformer layers (blocks)
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
for i, layer_module in enumerate(self.decoder_layers):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_outputs = layer_module(
hidden_states,
head_mask=None,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
hidden_states = self.decoder_norm(hidden_states)
# predictor projection
logits = self.decoder_pred(hidden_states)
# remove cls token
logits = logits[:, 1:, :]
if not return_dict:
return tuple(v for v in [logits, all_hidden_states, all_self_attentions] if v is not None)
return TFViTMAEDecoderOutput(logits=logits, hidden_states=all_hidden_states, attentions=all_self_attentions)
@add_start_docstrings(
"The ViTMAE Model transformer with the decoder on top for self-supervised pre-training.",
VIT_MAE_START_DOCSTRING,
)
class TFViTMAEForPreTraining(TFViTMAEPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.vit = TFViTMAEMainLayer(config, name="vit")
self.decoder = TFViTMAEDecoder(
config,
num_patches=self.vit.embeddings.num_patches,
name="decoder",
)
def get_input_embeddings(self):
return self.vit.get_input_embeddings()
def _prune_heads(self, heads_to_prune):
raise NotImplementedError
def patchify(self, pixel_values):
"""
Args:
pixel_values (`tf.Tensor` of shape `(batch_size, height, width, num_channels)` or `(batch_size, num_channels, height, width)`):
Pixel values.
Returns:
`tf.Tensor` of shape `(batch_size, num_patches, patch_size**2 * num_channels)`:
Patchified pixel values.
"""
patch_size, num_channels = self.config.patch_size, self.config.num_channels
# make sure channels are last
if shape_list(pixel_values)[1] == num_channels:
pixel_values = tf.transpose(pixel_values, perm=(0, 2, 3, 1))
# sanity checks
tf.debugging.assert_equal(
shape_list(pixel_values)[1],
shape_list(pixel_values)[2],
message="Make sure the pixel values have a squared size",
)
tf.debugging.assert_equal(
shape_list(pixel_values)[1] % patch_size,
0,
message="Make sure the pixel values have a size that is divisible by the patch size",
)
tf.debugging.assert_equal(
shape_list(pixel_values)[3],
num_channels,
message=(
"Make sure the number of channels of the pixel values is equal to the one set in the configuration"
),
)
# patchify
batch_size = shape_list(pixel_values)[0]
num_patches_one_direction = shape_list(pixel_values)[2] // patch_size
patchified_pixel_values = tf.reshape(
pixel_values,
(batch_size, num_patches_one_direction, patch_size, num_patches_one_direction, patch_size, num_channels),
)
patchified_pixel_values = tf.einsum("nhpwqc->nhwpqc", patchified_pixel_values)
patchified_pixel_values = tf.reshape(
patchified_pixel_values,
(batch_size, num_patches_one_direction * num_patches_one_direction, patch_size**2 * num_channels),
)
return patchified_pixel_values
def unpatchify(self, patchified_pixel_values):
"""
Args:
patchified_pixel_values (`tf.Tensor` of shape `(batch_size, num_patches, patch_size**2 * num_channels)`:
Patchified pixel values.
Returns:
`tf.Tensor` of shape `(batch_size, height, width, num_channels)`:
Pixel values.
"""
patch_size, num_channels = self.config.patch_size, self.config.num_channels
num_patches_one_direction = int(shape_list(patchified_pixel_values)[1] ** 0.5)
# sanity check
tf.debugging.assert_equal(
num_patches_one_direction * num_patches_one_direction,
shape_list(patchified_pixel_values)[1],
message="Make sure that the number of patches can be squared",
)
# unpatchify
batch_size = shape_list(patchified_pixel_values)[0]
patchified_pixel_values = tf.reshape(
patchified_pixel_values,
(batch_size, num_patches_one_direction, num_patches_one_direction, patch_size, patch_size, num_channels),
)
patchified_pixel_values = tf.einsum("nhwpqc->nhpwqc", patchified_pixel_values)
pixel_values = tf.reshape(
patchified_pixel_values,
(batch_size, num_patches_one_direction * patch_size, num_patches_one_direction * patch_size, num_channels),
)
return pixel_values
def forward_loss(self, pixel_values, pred, mask):
"""
Args:
pixel_values (`tf.Tensor` of shape `(batch_size, height, width, num_channels)`):
Pixel values.
pred (`tf.Tensor` of shape `(batch_size, num_patches, patch_size**2 * num_channels)`:
Predicted pixel values.
mask (`tf.Tensor` of shape `(batch_size, sequence_length)`):
Tensor indicating which patches are masked (1) and which are not (0).
Returns:
`tf.Tensor`: Pixel reconstruction loss.
"""
target = self.patchify(pixel_values)
if self.config.norm_pix_loss:
mean = tf.reduce_mean(target, axis=-1, keepdims=True)
var = tf.math.reduce_variance(target, axis=-1, keepdims=True)
target = (target - mean) / (var + 1.0e-6) ** 0.5
loss = (pred - target) ** 2
loss = tf.reduce_mean(loss, axis=-1) # [batch_size, num_patches], mean loss per patch
loss = tf.reduce_sum(loss * mask) / tf.reduce_sum(mask) # mean loss on removed patches
loss = tf.reshape(loss, (1,))
return loss
@unpack_inputs
@add_start_docstrings_to_model_forward(VIT_MAE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFViTMAEForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
pixel_values: TFModelInputType | None = None,
noise: tf.Tensor = None,
head_mask: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> Union[TFViTMAEForPreTrainingOutput, Tuple[tf.Tensor]]:
r"""
Returns:
Examples:
```python
>>> from transformers import AutoImageProcessor, TFViTMAEForPreTraining
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("facebook/vit-mae-base")
>>> model = TFViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base")
>>> inputs = image_processor(images=image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> loss = outputs.loss
>>> mask = outputs.mask
>>> ids_restore = outputs.ids_restore
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.vit(
pixel_values=pixel_values,
noise=noise,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
latent = outputs.last_hidden_state
ids_restore = outputs.ids_restore
mask = outputs.mask
decoder_outputs = self.decoder(latent, ids_restore) # [batch_size, num_patches, patch_size**2*3]
logits = decoder_outputs.logits
loss = self.forward_loss(pixel_values, logits, mask)
if not return_dict:
output = (logits, mask, ids_restore) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFViTMAEForPreTrainingOutput(
loss=loss,
logits=logits,
mask=mask,
ids_restore=ids_restore,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "vit", None) is not None:
with tf.name_scope(self.vit.name):
self.vit.build(None)
if getattr(self, "decoder", None) is not None:
with tf.name_scope(self.decoder.name):
self.decoder.build(None)
| transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py/0 | {
"file_path": "transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py",
"repo_id": "transformers",
"token_count": 22784
} | 345 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert VITS checkpoint."""
import argparse
import json
import tempfile
import torch
from huggingface_hub import hf_hub_download
from transformers import VitsConfig, VitsModel, VitsTokenizer, logging
logging.set_verbosity_info()
logger = logging.get_logger("transformers.models.vits")
MAPPING_TEXT_ENCODER = {
"enc_p.emb": "text_encoder.embed_tokens",
"enc_p.encoder.attn_layers.*.conv_k": "text_encoder.encoder.layers.*.attention.k_proj",
"enc_p.encoder.attn_layers.*.conv_v": "text_encoder.encoder.layers.*.attention.v_proj",
"enc_p.encoder.attn_layers.*.conv_q": "text_encoder.encoder.layers.*.attention.q_proj",
"enc_p.encoder.attn_layers.*.conv_o": "text_encoder.encoder.layers.*.attention.out_proj",
"enc_p.encoder.attn_layers.*.emb_rel_k": "text_encoder.encoder.layers.*.attention.emb_rel_k",
"enc_p.encoder.attn_layers.*.emb_rel_v": "text_encoder.encoder.layers.*.attention.emb_rel_v",
"enc_p.encoder.norm_layers_1.*.gamma": "text_encoder.encoder.layers.*.layer_norm.weight",
"enc_p.encoder.norm_layers_1.*.beta": "text_encoder.encoder.layers.*.layer_norm.bias",
"enc_p.encoder.ffn_layers.*.conv_1": "text_encoder.encoder.layers.*.feed_forward.conv_1",
"enc_p.encoder.ffn_layers.*.conv_2": "text_encoder.encoder.layers.*.feed_forward.conv_2",
"enc_p.encoder.norm_layers_2.*.gamma": "text_encoder.encoder.layers.*.final_layer_norm.weight",
"enc_p.encoder.norm_layers_2.*.beta": "text_encoder.encoder.layers.*.final_layer_norm.bias",
"enc_p.proj": "text_encoder.project",
}
MAPPING_STOCHASTIC_DURATION_PREDICTOR = {
"dp.pre": "duration_predictor.conv_pre",
"dp.proj": "duration_predictor.conv_proj",
"dp.convs.convs_sep.*": "duration_predictor.conv_dds.convs_dilated.*",
"dp.convs.convs_1x1.*": "duration_predictor.conv_dds.convs_pointwise.*",
"dp.convs.norms_1.*.gamma": "duration_predictor.conv_dds.norms_1.*.weight",
"dp.convs.norms_1.*.beta": "duration_predictor.conv_dds.norms_1.*.bias",
"dp.convs.norms_2.*.gamma": "duration_predictor.conv_dds.norms_2.*.weight",
"dp.convs.norms_2.*.beta": "duration_predictor.conv_dds.norms_2.*.bias",
"dp.flows.0.logs": "duration_predictor.flows.0.log_scale",
"dp.flows.0.m": "duration_predictor.flows.0.translate",
"dp.flows.*.pre": "duration_predictor.flows.*.conv_pre",
"dp.flows.*.proj": "duration_predictor.flows.*.conv_proj",
"dp.flows.*.convs.convs_1x1.0": "duration_predictor.flows.*.conv_dds.convs_pointwise.0",
"dp.flows.*.convs.convs_1x1.1": "duration_predictor.flows.*.conv_dds.convs_pointwise.1",
"dp.flows.*.convs.convs_1x1.2": "duration_predictor.flows.*.conv_dds.convs_pointwise.2",
"dp.flows.*.convs.convs_sep.0": "duration_predictor.flows.*.conv_dds.convs_dilated.0",
"dp.flows.*.convs.convs_sep.1": "duration_predictor.flows.*.conv_dds.convs_dilated.1",
"dp.flows.*.convs.convs_sep.2": "duration_predictor.flows.*.conv_dds.convs_dilated.2",
"dp.flows.*.convs.norms_1.0.gamma": "duration_predictor.flows.*.conv_dds.norms_1.0.weight",
"dp.flows.*.convs.norms_1.0.beta": "duration_predictor.flows.*.conv_dds.norms_1.0.bias",
"dp.flows.*.convs.norms_1.1.gamma": "duration_predictor.flows.*.conv_dds.norms_1.1.weight",
"dp.flows.*.convs.norms_1.1.beta": "duration_predictor.flows.*.conv_dds.norms_1.1.bias",
"dp.flows.*.convs.norms_1.2.gamma": "duration_predictor.flows.*.conv_dds.norms_1.2.weight",
"dp.flows.*.convs.norms_1.2.beta": "duration_predictor.flows.*.conv_dds.norms_1.2.bias",
"dp.flows.*.convs.norms_2.0.gamma": "duration_predictor.flows.*.conv_dds.norms_2.0.weight",
"dp.flows.*.convs.norms_2.0.beta": "duration_predictor.flows.*.conv_dds.norms_2.0.bias",
"dp.flows.*.convs.norms_2.1.gamma": "duration_predictor.flows.*.conv_dds.norms_2.1.weight",
"dp.flows.*.convs.norms_2.1.beta": "duration_predictor.flows.*.conv_dds.norms_2.1.bias",
"dp.flows.*.convs.norms_2.2.gamma": "duration_predictor.flows.*.conv_dds.norms_2.2.weight",
"dp.flows.*.convs.norms_2.2.beta": "duration_predictor.flows.*.conv_dds.norms_2.2.bias",
"dp.post_pre": "duration_predictor.post_conv_pre",
"dp.post_proj": "duration_predictor.post_conv_proj",
"dp.post_convs.convs_sep.*": "duration_predictor.post_conv_dds.convs_dilated.*",
"dp.post_convs.convs_1x1.*": "duration_predictor.post_conv_dds.convs_pointwise.*",
"dp.post_convs.norms_1.*.gamma": "duration_predictor.post_conv_dds.norms_1.*.weight",
"dp.post_convs.norms_1.*.beta": "duration_predictor.post_conv_dds.norms_1.*.bias",
"dp.post_convs.norms_2.*.gamma": "duration_predictor.post_conv_dds.norms_2.*.weight",
"dp.post_convs.norms_2.*.beta": "duration_predictor.post_conv_dds.norms_2.*.bias",
"dp.post_flows.0.logs": "duration_predictor.post_flows.0.log_scale",
"dp.post_flows.0.m": "duration_predictor.post_flows.0.translate",
"dp.post_flows.*.pre": "duration_predictor.post_flows.*.conv_pre",
"dp.post_flows.*.proj": "duration_predictor.post_flows.*.conv_proj",
"dp.post_flows.*.convs.convs_1x1.0": "duration_predictor.post_flows.*.conv_dds.convs_pointwise.0",
"dp.post_flows.*.convs.convs_1x1.1": "duration_predictor.post_flows.*.conv_dds.convs_pointwise.1",
"dp.post_flows.*.convs.convs_1x1.2": "duration_predictor.post_flows.*.conv_dds.convs_pointwise.2",
"dp.post_flows.*.convs.convs_sep.0": "duration_predictor.post_flows.*.conv_dds.convs_dilated.0",
"dp.post_flows.*.convs.convs_sep.1": "duration_predictor.post_flows.*.conv_dds.convs_dilated.1",
"dp.post_flows.*.convs.convs_sep.2": "duration_predictor.post_flows.*.conv_dds.convs_dilated.2",
"dp.post_flows.*.convs.norms_1.0.gamma": "duration_predictor.post_flows.*.conv_dds.norms_1.0.weight",
"dp.post_flows.*.convs.norms_1.0.beta": "duration_predictor.post_flows.*.conv_dds.norms_1.0.bias",
"dp.post_flows.*.convs.norms_1.1.gamma": "duration_predictor.post_flows.*.conv_dds.norms_1.1.weight",
"dp.post_flows.*.convs.norms_1.1.beta": "duration_predictor.post_flows.*.conv_dds.norms_1.1.bias",
"dp.post_flows.*.convs.norms_1.2.gamma": "duration_predictor.post_flows.*.conv_dds.norms_1.2.weight",
"dp.post_flows.*.convs.norms_1.2.beta": "duration_predictor.post_flows.*.conv_dds.norms_1.2.bias",
"dp.post_flows.*.convs.norms_2.0.gamma": "duration_predictor.post_flows.*.conv_dds.norms_2.0.weight",
"dp.post_flows.*.convs.norms_2.0.beta": "duration_predictor.post_flows.*.conv_dds.norms_2.0.bias",
"dp.post_flows.*.convs.norms_2.1.gamma": "duration_predictor.post_flows.*.conv_dds.norms_2.1.weight",
"dp.post_flows.*.convs.norms_2.1.beta": "duration_predictor.post_flows.*.conv_dds.norms_2.1.bias",
"dp.post_flows.*.convs.norms_2.2.gamma": "duration_predictor.post_flows.*.conv_dds.norms_2.2.weight",
"dp.post_flows.*.convs.norms_2.2.beta": "duration_predictor.post_flows.*.conv_dds.norms_2.2.bias",
"dp.cond": "duration_predictor.cond", # num_speakers > 1
}
MAPPING_FLOW = {
"flow.flows.*.pre": "flow.flows.*.conv_pre",
"flow.flows.*.enc.in_layers.0": "flow.flows.*.wavenet.in_layers.0",
"flow.flows.*.enc.in_layers.1": "flow.flows.*.wavenet.in_layers.1",
"flow.flows.*.enc.in_layers.2": "flow.flows.*.wavenet.in_layers.2",
"flow.flows.*.enc.in_layers.3": "flow.flows.*.wavenet.in_layers.3",
"flow.flows.*.enc.res_skip_layers.0": "flow.flows.*.wavenet.res_skip_layers.0",
"flow.flows.*.enc.res_skip_layers.1": "flow.flows.*.wavenet.res_skip_layers.1",
"flow.flows.*.enc.res_skip_layers.2": "flow.flows.*.wavenet.res_skip_layers.2",
"flow.flows.*.enc.res_skip_layers.3": "flow.flows.*.wavenet.res_skip_layers.3",
"flow.flows.*.enc.cond_layer": "flow.flows.*.wavenet.cond_layer", # num_speakers > 1
"flow.flows.*.post": "flow.flows.*.conv_post",
}
MAPPING_GENERATOR = {
"dec.conv_pre": "decoder.conv_pre",
"dec.ups.0": "decoder.upsampler.0",
"dec.ups.1": "decoder.upsampler.1",
"dec.ups.2": "decoder.upsampler.2",
"dec.ups.3": "decoder.upsampler.3",
"dec.resblocks.*.convs1.0": "decoder.resblocks.*.convs1.0",
"dec.resblocks.*.convs1.1": "decoder.resblocks.*.convs1.1",
"dec.resblocks.*.convs1.2": "decoder.resblocks.*.convs1.2",
"dec.resblocks.*.convs2.0": "decoder.resblocks.*.convs2.0",
"dec.resblocks.*.convs2.1": "decoder.resblocks.*.convs2.1",
"dec.resblocks.*.convs2.2": "decoder.resblocks.*.convs2.2",
"dec.conv_post": "decoder.conv_post",
"dec.cond": "decoder.cond", # num_speakers > 1
}
MAPPING_POSTERIOR_ENCODER = {
"enc_q.pre": "posterior_encoder.conv_pre",
"enc_q.enc.in_layers.*": "posterior_encoder.wavenet.in_layers.*",
"enc_q.enc.res_skip_layers.*": "posterior_encoder.wavenet.res_skip_layers.*",
"enc_q.enc.cond_layer": "posterior_encoder.wavenet.cond_layer", # num_speakers > 1
"enc_q.proj": "posterior_encoder.conv_proj",
}
MAPPING = {
**MAPPING_TEXT_ENCODER,
**MAPPING_STOCHASTIC_DURATION_PREDICTOR,
**MAPPING_FLOW,
**MAPPING_GENERATOR,
**MAPPING_POSTERIOR_ENCODER,
"emb_g": "embed_speaker", # num_speakers > 1
}
TOP_LEVEL_KEYS = []
IGNORE_KEYS = []
def set_recursively(hf_pointer, key, value, full_name, weight_type):
for attribute in key.split("."):
hf_pointer = getattr(hf_pointer, attribute)
if weight_type is not None:
hf_shape = getattr(hf_pointer, weight_type).shape
else:
hf_shape = hf_pointer.shape
# strip off the kernel dimension at the end (original weights are Conv1d)
if key.endswith(".k_proj") or key.endswith(".v_proj") or key.endswith(".q_proj") or key.endswith(".out_proj"):
value = value.squeeze(-1)
if hf_shape != value.shape:
raise ValueError(
f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
f" {value.shape} for {full_name}"
)
if weight_type == "weight":
hf_pointer.weight.data = value
elif weight_type == "weight_g":
hf_pointer.weight_g.data = value
elif weight_type == "weight_v":
hf_pointer.weight_v.data = value
elif weight_type == "bias":
hf_pointer.bias.data = value
elif weight_type == "running_mean":
hf_pointer.running_mean.data = value
elif weight_type == "running_var":
hf_pointer.running_var.data = value
elif weight_type == "num_batches_tracked":
hf_pointer.num_batches_tracked.data = value
else:
hf_pointer.data = value
logger.info(f"{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}.")
def should_ignore(name, ignore_keys):
for key in ignore_keys:
if key.endswith(".*"):
if name.startswith(key[:-1]):
return True
elif ".*." in key:
prefix, suffix = key.split(".*.")
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def recursively_load_weights(fairseq_dict, hf_model):
unused_weights = []
for name, value in fairseq_dict.items():
if should_ignore(name, IGNORE_KEYS):
logger.info(f"{name} was ignored")
continue
is_used = False
for key, mapped_key in MAPPING.items():
if key.endswith(".*"):
key = key[:-1]
elif "*" in key:
prefix, suffix = key.split(".*.")
if prefix in name and suffix in name:
key = suffix
if key in name:
is_used = True
if mapped_key.endswith(".*"):
layer_index = name.split(key)[-1].split(".")[0]
mapped_key = mapped_key.replace("*", layer_index)
elif "*" in mapped_key:
layer_index = name.split(key)[0].split(".")[-2]
# remap the layer index since we removed the Flip layers
if "flow.flows" in mapped_key:
layer_index = str(int(layer_index) // 2)
if "duration_predictor.flows" in mapped_key or "duration_predictor.post_flows" in mapped_key:
layer_index = str(int(layer_index) // 2 + 1)
mapped_key = mapped_key.replace("*", layer_index)
if "weight_g" in name:
weight_type = "weight_g"
elif "weight_v" in name:
weight_type = "weight_v"
elif "bias" in name:
weight_type = "bias"
elif "weight" in name:
weight_type = "weight"
elif "running_mean" in name:
weight_type = "running_mean"
elif "running_var" in name:
weight_type = "running_var"
elif "num_batches_tracked" in name:
weight_type = "num_batches_tracked"
else:
weight_type = None
set_recursively(hf_model, mapped_key, value, name, weight_type)
continue
if not is_used:
unused_weights.append(name)
logger.warning(f"Unused weights: {unused_weights}")
@torch.no_grad()
def convert_checkpoint(
pytorch_dump_folder_path,
checkpoint_path=None,
config_path=None,
vocab_path=None,
language=None,
num_speakers=None,
sampling_rate=None,
repo_id=None,
):
"""
Copy/paste/tweak model's weights to transformers design.
"""
if config_path is not None:
config = VitsConfig.from_pretrained(config_path)
else:
config = VitsConfig()
if num_speakers:
config.num_speakers = num_speakers
config.speaker_embedding_size = 256
if sampling_rate:
config.sampling_rate = sampling_rate
if checkpoint_path is None:
logger.info(f"***Converting model: facebook/mms-tts {language}***")
vocab_path = hf_hub_download(
repo_id="facebook/mms-tts",
filename="vocab.txt",
subfolder=f"models/{language}",
)
config_file = hf_hub_download(
repo_id="facebook/mms-tts",
filename="config.json",
subfolder=f"models/{language}",
)
checkpoint_path = hf_hub_download(
repo_id="facebook/mms-tts",
filename="G_100000.pth",
subfolder=f"models/{language}",
)
with open(config_file, "r") as f:
data = f.read()
hps = json.loads(data)
is_uroman = hps["data"]["training_files"].split(".")[-1] == "uroman"
if is_uroman:
logger.warning("For this checkpoint, you should use `uroman` to convert input text before tokenizing it!")
else:
logger.info(f"***Converting model: {checkpoint_path}***")
is_uroman = False
# original VITS checkpoint
if vocab_path is None:
_pad = "_"
_punctuation = ';:,.!?¡¿—…"«»“” '
_letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"
_letters_ipa = "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ"
symbols = _pad + _punctuation + _letters + _letters_ipa
symbol_to_id = {s: i for i, s in enumerate(symbols)}
phonemize = True
else:
# Save vocab as temporary json file
symbols = [line.replace("\n", "") for line in open(vocab_path, encoding="utf-8").readlines()]
symbol_to_id = {s: i for i, s in enumerate(symbols)}
# MMS-TTS does not use a <pad> token, so we set to the token used to space characters
_pad = symbols[0]
phonemize = False
with tempfile.NamedTemporaryFile() as tf:
with open(tf.name, "w", encoding="utf-8") as f:
f.write(json.dumps(symbol_to_id, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
tokenizer = VitsTokenizer(tf.name, language=language, phonemize=phonemize, is_uroman=is_uroman, pad_token=_pad)
config.vocab_size = len(symbols)
model = VitsModel(config)
model.decoder.apply_weight_norm()
orig_checkpoint = torch.load(checkpoint_path, map_location=torch.device("cpu"))
recursively_load_weights(orig_checkpoint["model"], model)
model.decoder.remove_weight_norm()
model.save_pretrained(pytorch_dump_folder_path)
tokenizer.save_pretrained(pytorch_dump_folder_path)
if repo_id:
print("Pushing to the hub...")
tokenizer.push_to_hub(repo_id)
model.push_to_hub(repo_id)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--checkpoint_path", default=None, type=str, help="Local path to original checkpoint")
parser.add_argument("--vocab_path", default=None, type=str, help="Path to vocab.txt")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument("--language", default=None, type=str, help="Tokenizer language (three-letter code)")
parser.add_argument("--num_speakers", default=None, type=int, help="Number of speakers")
parser.add_argument(
"--sampling_rate", default=None, type=int, help="Sampling rate on which the model was trained."
)
parser.add_argument(
"--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
)
parser.add_argument(
"--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
)
args = parser.parse_args()
convert_checkpoint(
args.pytorch_dump_folder_path,
args.checkpoint_path,
args.config_path,
args.vocab_path,
args.language,
args.num_speakers,
args.sampling_rate,
args.push_to_hub,
)
| transformers/src/transformers/models/vits/convert_original_checkpoint.py/0 | {
"file_path": "transformers/src/transformers/models/vits/convert_original_checkpoint.py",
"repo_id": "transformers",
"token_count": 8722
} | 346 |
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Speech processor class for Wav2Vec2
"""
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wav2vec2 import Wav2Vec2FeatureExtractor
from .tokenization_wav2vec2 import Wav2Vec2CTCTokenizer
class Wav2Vec2Processor(ProcessorMixin):
r"""
Constructs a Wav2Vec2 processor which wraps a Wav2Vec2 feature extractor and a Wav2Vec2 CTC tokenizer into a single
processor.
[`Wav2Vec2Processor`] offers all the functionalities of [`Wav2Vec2FeatureExtractor`] and [`PreTrainedTokenizer`].
See the docstring of [`~Wav2Vec2Processor.__call__`] and [`~Wav2Vec2Processor.decode`] for more information.
Args:
feature_extractor (`Wav2Vec2FeatureExtractor`):
An instance of [`Wav2Vec2FeatureExtractor`]. The feature extractor is a required input.
tokenizer ([`PreTrainedTokenizer`]):
An instance of [`PreTrainedTokenizer`]. The tokenizer is a required input.
"""
feature_extractor_class = "Wav2Vec2FeatureExtractor"
tokenizer_class = "AutoTokenizer"
def __init__(self, feature_extractor, tokenizer):
super().__init__(feature_extractor, tokenizer)
self.current_processor = self.feature_extractor
self._in_target_context_manager = False
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
try:
return super().from_pretrained(pretrained_model_name_or_path, **kwargs)
except OSError:
warnings.warn(
f"Loading a tokenizer inside {cls.__name__} from a config that does not"
" include a `tokenizer_class` attribute is deprecated and will be "
"removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"
" attribute to either your `config.json` or `tokenizer_config.json` "
"file to suppress this warning: ",
FutureWarning,
)
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(pretrained_model_name_or_path, **kwargs)
tokenizer = Wav2Vec2CTCTokenizer.from_pretrained(pretrained_model_name_or_path, **kwargs)
return cls(feature_extractor=feature_extractor, tokenizer=tokenizer)
def __call__(self, *args, **kwargs):
"""
When used in normal mode, this method forwards all its arguments to Wav2Vec2FeatureExtractor's
[`~Wav2Vec2FeatureExtractor.__call__`] and returns its output. If used in the context
[`~Wav2Vec2Processor.as_target_processor`] this method forwards all its arguments to PreTrainedTokenizer's
[`~PreTrainedTokenizer.__call__`]. Please refer to the docstring of the above two methods for more information.
"""
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*args, **kwargs)
if "raw_speech" in kwargs:
warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.")
audio = kwargs.pop("raw_speech")
else:
audio = kwargs.pop("audio", None)
sampling_rate = kwargs.pop("sampling_rate", None)
text = kwargs.pop("text", None)
if len(args) > 0:
audio = args[0]
args = args[1:]
if audio is None and text is None:
raise ValueError("You need to specify either an `audio` or `text` input to process.")
if audio is not None:
inputs = self.feature_extractor(audio, *args, sampling_rate=sampling_rate, **kwargs)
if text is not None:
encodings = self.tokenizer(text, **kwargs)
if text is None:
return inputs
elif audio is None:
return encodings
else:
inputs["labels"] = encodings["input_ids"]
return inputs
def pad(self, *args, **kwargs):
"""
When used in normal mode, this method forwards all its arguments to Wav2Vec2FeatureExtractor's
[`~Wav2Vec2FeatureExtractor.pad`] and returns its output. If used in the context
[`~Wav2Vec2Processor.as_target_processor`] this method forwards all its arguments to PreTrainedTokenizer's
[`~PreTrainedTokenizer.pad`]. Please refer to the docstring of the above two methods for more information.
"""
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor.pad(*args, **kwargs)
input_features = kwargs.pop("input_features", None)
labels = kwargs.pop("labels", None)
if len(args) > 0:
input_features = args[0]
args = args[1:]
if input_features is not None:
input_features = self.feature_extractor.pad(input_features, *args, **kwargs)
if labels is not None:
labels = self.tokenizer.pad(labels, **kwargs)
if labels is None:
return input_features
elif input_features is None:
return labels
else:
input_features["labels"] = labels["input_ids"]
return input_features
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer
to the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
@contextmanager
def as_target_processor(self):
"""
Temporarily sets the tokenizer for processing the input. Useful for encoding the labels when fine-tuning
Wav2Vec2.
"""
warnings.warn(
"`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
"labels by using the argument `text` of the regular `__call__` method (either in the same call as "
"your audio inputs, or in a separate call."
)
self._in_target_context_manager = True
self.current_processor = self.tokenizer
yield
self.current_processor = self.feature_extractor
self._in_target_context_manager = False
| transformers/src/transformers/models/wav2vec2/processing_wav2vec2.py/0 | {
"file_path": "transformers/src/transformers/models/wav2vec2/processing_wav2vec2.py",
"repo_id": "transformers",
"token_count": 2839
} | 347 |
# coding=utf-8
# Copyright 2021 The Fairseq Authors, Microsoft Research, and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" WavLM model configuration"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"microsoft/wavlm-base": "https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json",
# See all WavLM models at https://huggingface.co/models?filter=wavlm
}
class WavLMConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`WavLMModel`]. It is used to instantiate an WavLM
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the WavLM
[microsoft/wavlm-base](https://huggingface.co/microsoft/wavlm-base) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 32):
Vocabulary size of the WavLM model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`WavLMModel`]. Vocabulary size of the model. Defines the different tokens
that can be represented by the *inputs_ids* passed to the forward method of [`WavLMModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
activation_dropout (`float`, *optional*, defaults to 0.1):
The dropout ratio for activations inside the fully connected layer.
attention_dropout (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
final_dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for the final projection layer of [`WavLMForCTC`].
layerdrop (`float`, *optional*, defaults to 0.1):
The LayerDrop probability. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more
details.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
feat_extract_norm (`str`, *optional*, defaults to `"group"`):
The norm to be applied to 1D convolutional layers in feature encoder. One of `"group"` for group
normalization of only the first 1D convolutional layer or `"layer"` for layer normalization of all 1D
convolutional layers.
feat_proj_dropout (`float`, *optional*, defaults to 0.0):
The dropout probability for output of the feature encoder.
feat_extract_activation (`str, `optional`, defaults to `"gelu"`):
The non-linear activation function (function or string) in the 1D convolutional layers of the feature
extractor. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported.
conv_dim (`Tuple[int]` or `List[int]`, *optional*, defaults to `(512, 512, 512, 512, 512, 512, 512)`):
A tuple of integers defining the number of input and output channels of each 1D convolutional layer in the
feature encoder. The length of *conv_dim* defines the number of 1D convolutional layers.
conv_stride (`Tuple[int]` or `List[int]`, *optional*, defaults to `(5, 2, 2, 2, 2, 2, 2)`):
A tuple of integers defining the stride of each 1D convolutional layer in the feature encoder. The length
of *conv_stride* defines the number of convolutional layers and has to match the length of *conv_dim*.
conv_kernel (`Tuple[int]` or `List[int]`, *optional*, defaults to `(10, 3, 3, 3, 3, 3, 3)`):
A tuple of integers defining the kernel size of each 1D convolutional layer in the feature encoder. The
length of *conv_kernel* defines the number of convolutional layers and has to match the length of
*conv_dim*.
conv_bias (`bool`, *optional*, defaults to `False`):
Whether the 1D convolutional layers have a bias.
num_conv_pos_embeddings (`int`, *optional*, defaults to 128):
Number of convolutional positional embeddings. Defines the kernel size of 1D convolutional positional
embeddings layer.
num_conv_pos_embedding_groups (`int`, *optional*, defaults to 16):
Number of groups of 1D convolutional positional embeddings layer.
do_stable_layer_norm (`bool`, *optional*, defaults to `False`):
Whether to apply *stable* layer norm architecture of the Transformer encoder. `do_stable_layer_norm is
True` corresponds to applying layer norm before the attention layer, whereas `do_stable_layer_norm is
False` corresponds to applying layer norm after the attention layer.
apply_spec_augment (`bool`, *optional*, defaults to `True`):
Whether to apply *SpecAugment* data augmentation to the outputs of the feature encoder. For reference see
[SpecAugment: A Simple Data Augmentation Method for Automatic Speech
Recognition](https://arxiv.org/abs/1904.08779).
mask_time_prob (`float`, *optional*, defaults to 0.05):
Propability of each feature vector along the time axis to be chosen as the start of the vector span to be
masked. Approximately `mask_time_prob * sequence_length // mask_time_length` feature vectors will be masked
along the time axis. This is only relevant if `apply_spec_augment is True`.
mask_time_length (`int`, *optional*, defaults to 10):
Length of vector span along the time axis.
mask_time_min_masks (`int`, *optional*, defaults to 2),:
The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step,
irrespectively of `mask_feature_prob`. Only relevant if ''mask_time_prob*len(time_axis)/mask_time_length <
mask_time_min_masks''
mask_feature_prob (`float`, *optional*, defaults to 0.0):
Propability of each feature vector along the feature axis to be chosen as the start of the vector span to
be masked. Approximately `mask_time_prob * hidden_size // mask_time_length` feature vectors will be masked
along the time axis. This is only relevant if `apply_spec_augment is True`.
mask_feature_length (`int`, *optional*, defaults to 10):
Length of vector span along the feature axis.
num_codevectors_per_group (`int`, *optional*, defaults to 320):
Number of entries in each quantization codebook (group).
num_codevector_groups (`int`, *optional*, defaults to 2):
Number of codevector groups for product codevector quantization.
contrastive_logits_temperature (`float`, *optional*, defaults to 0.1):
The temperature *kappa* in the contrastive loss.
num_negatives (`int`, *optional*, defaults to 100):
Number of negative samples for the contrastive loss.
codevector_dim (`int`, *optional*, defaults to 256):
Dimensionality of the quantized feature vectors.
proj_codevector_dim (`int`, *optional*, defaults to 256):
Dimensionality of the final projection of both the quantized and the transformer features.
diversity_loss_weight (`int`, *optional*, defaults to 0.1):
The weight of the codebook diversity loss component.
ctc_loss_reduction (`str`, *optional*, defaults to `"mean"`):
Specifies the reduction to apply to the output of `torch.nn.CTCLoss`. Only relevant when training an
instance of [`WavLMForCTC`].
ctc_zero_infinity (`bool`, *optional*, defaults to `False`):
Whether to zero infinite losses and the associated gradients of `torch.nn.CTCLoss`. Infinite losses mainly
occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance
of [`WavLMForCTC`].
use_weighted_layer_sum (`bool`, *optional*, defaults to `False`):
Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an
instance of [`WavLMForSequenceClassification`].
classifier_proj_size (`int`, *optional*, defaults to 256):
Dimensionality of the projection before token mean-pooling for classification.
tdnn_dim (`Tuple[int]` or `List[int]`, *optional*, defaults to `(512, 512, 512, 512, 1500)`):
A tuple of integers defining the number of output channels of each 1D convolutional layer in the *TDNN*
module of the *XVector* model. The length of *tdnn_dim* defines the number of *TDNN* layers.
tdnn_kernel (`Tuple[int]` or `List[int]`, *optional*, defaults to `(5, 3, 3, 1, 1)`):
A tuple of integers defining the kernel size of each 1D convolutional layer in the *TDNN* module of the
*XVector* model. The length of *tdnn_kernel* has to match the length of *tdnn_dim*.
tdnn_dilation (`Tuple[int]` or `List[int]`, *optional*, defaults to `(1, 2, 3, 1, 1)`):
A tuple of integers defining the dilation factor of each 1D convolutional layer in *TDNN* module of the
*XVector* model. The length of *tdnn_dilation* has to match the length of *tdnn_dim*.
xvector_output_dim (`int`, *optional*, defaults to 512):
Dimensionality of the *XVector* embedding vectors.
add_adapter (`bool`, *optional*, defaults to `False`):
Whether a convolutional network should be stacked on top of the Wav2Vec2 Encoder. Can be very useful for
warm-starting Wav2Vec2 for SpeechEncoderDecoder models.
adapter_kernel_size (`int`, *optional*, defaults to 3):
Kernel size of the convolutional layers in the adapter network. Only relevant if `add_adapter is True`.
adapter_stride (`int`, *optional*, defaults to 2):
Stride of the convolutional layers in the adapter network. Only relevant if `add_adapter is True`.
num_adapter_layers (`int`, *optional*, defaults to 3):
Number of convolutional layers that should be used in the adapter network. Only relevant if `add_adapter is
True`.
output_hidden_size (`int`, *optional*):
Dimensionality of the encoder output layer. If not defined, this defaults to *hidden-size*. Only relevant
if `add_adapter is True`.
Example:
```python
```
Example:
```python
>>> from transformers import WavLMConfig, WavLMModel
>>> # Initializing a WavLM facebook/wavlm-base-960h style configuration
>>> configuration = WavLMConfig()
>>> # Initializing a model (with random weights) from the facebook/wavlm-base-960h style configuration
>>> model = WavLMModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "wavlm"
def __init__(
self,
vocab_size=32,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout=0.1,
activation_dropout=0.1,
attention_dropout=0.1,
feat_proj_dropout=0.0,
final_dropout=0.1,
layerdrop=0.1,
initializer_range=0.02,
layer_norm_eps=1e-5,
feat_extract_norm="group",
feat_extract_activation="gelu",
conv_dim=(512, 512, 512, 512, 512, 512, 512),
conv_stride=(5, 2, 2, 2, 2, 2, 2),
conv_kernel=(10, 3, 3, 3, 3, 2, 2),
conv_bias=False,
num_conv_pos_embeddings=128,
num_conv_pos_embedding_groups=16,
num_buckets=320,
max_bucket_distance=800,
do_stable_layer_norm=False,
apply_spec_augment=True,
mask_time_prob=0.05,
mask_time_length=10,
mask_time_min_masks=2,
mask_feature_prob=0.0,
mask_feature_length=10,
num_codevectors_per_group=320,
num_codevector_groups=2,
contrastive_logits_temperature=0.1,
num_negatives=100,
codevector_dim=256,
proj_codevector_dim=256,
diversity_loss_weight=0.1,
ctc_loss_reduction="mean",
ctc_zero_infinity=False,
use_weighted_layer_sum=False,
classifier_proj_size=256,
tdnn_dim=(512, 512, 512, 512, 1500),
tdnn_kernel=(5, 3, 3, 1, 1),
tdnn_dilation=(1, 2, 3, 1, 1),
xvector_output_dim=512,
num_ctc_classes=80,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
add_adapter=False,
adapter_kernel_size=3,
adapter_stride=2,
num_adapter_layers=3,
output_hidden_size=None,
**kwargs,
):
super().__init__(**kwargs, pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id)
self.hidden_size = hidden_size
self.feat_extract_norm = feat_extract_norm
self.feat_extract_activation = feat_extract_activation
self.conv_dim = list(conv_dim)
self.conv_stride = list(conv_stride)
self.conv_kernel = list(conv_kernel)
self.conv_bias = conv_bias
self.num_buckets = num_buckets
self.max_bucket_distance = max_bucket_distance
self.num_conv_pos_embeddings = num_conv_pos_embeddings
self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups
self.num_feat_extract_layers = len(self.conv_dim)
self.num_hidden_layers = num_hidden_layers
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.num_attention_heads = num_attention_heads
self.hidden_dropout = hidden_dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.feat_proj_dropout = feat_proj_dropout
self.final_dropout = final_dropout
self.layerdrop = layerdrop
self.layer_norm_eps = layer_norm_eps
self.initializer_range = initializer_range
self.num_ctc_classes = num_ctc_classes
self.vocab_size = vocab_size
self.do_stable_layer_norm = do_stable_layer_norm
self.use_weighted_layer_sum = use_weighted_layer_sum
self.classifier_proj_size = classifier_proj_size
if (
(len(self.conv_stride) != self.num_feat_extract_layers)
or (len(self.conv_kernel) != self.num_feat_extract_layers)
or (len(self.conv_dim) != self.num_feat_extract_layers)
):
raise ValueError(
"Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
" `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
f" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"
f" `len(config.conv_kernel) = {len(self.conv_kernel)}`."
)
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
self.apply_spec_augment = apply_spec_augment
self.mask_time_prob = mask_time_prob
self.mask_time_length = mask_time_length
self.mask_time_min_masks = mask_time_min_masks
self.mask_feature_prob = mask_feature_prob
self.mask_feature_length = mask_feature_length
# parameters for pretraining with codevector quantized representations
self.num_codevectors_per_group = num_codevectors_per_group
self.num_codevector_groups = num_codevector_groups
self.contrastive_logits_temperature = contrastive_logits_temperature
self.num_negatives = num_negatives
self.codevector_dim = codevector_dim
self.proj_codevector_dim = proj_codevector_dim
self.diversity_loss_weight = diversity_loss_weight
# ctc loss
self.ctc_loss_reduction = ctc_loss_reduction
self.ctc_zero_infinity = ctc_zero_infinity
# adapter
self.add_adapter = add_adapter
self.adapter_kernel_size = adapter_kernel_size
self.adapter_stride = adapter_stride
self.num_adapter_layers = num_adapter_layers
self.output_hidden_size = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
self.classifier_proj_size = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
self.tdnn_dim = list(tdnn_dim)
self.tdnn_kernel = list(tdnn_kernel)
self.tdnn_dilation = list(tdnn_dilation)
self.xvector_output_dim = xvector_output_dim
@property
def inputs_to_logits_ratio(self):
return functools.reduce(operator.mul, self.conv_stride, 1)
| transformers/src/transformers/models/wavlm/configuration_wavlm.py/0 | {
"file_path": "transformers/src/transformers/models/wavlm/configuration_wavlm.py",
"repo_id": "transformers",
"token_count": 7367
} | 348 |
# coding=utf-8
# Copyright 2019-present, Facebook, Inc and the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
TF 2.0 XLM model.
"""
from __future__ import annotations
import itertools
import warnings
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import (
TFBaseModelOutput,
TFMultipleChoiceModelOutput,
TFQuestionAnsweringModelOutput,
TFSequenceClassifierOutput,
TFTokenClassifierOutput,
)
from ...modeling_tf_utils import (
TFModelInputType,
TFMultipleChoiceLoss,
TFPreTrainedModel,
TFQuestionAnsweringLoss,
TFSequenceClassificationLoss,
TFSequenceSummary,
TFSharedEmbeddings,
TFTokenClassificationLoss,
get_initializer,
keras,
keras_serializable,
unpack_inputs,
)
from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
from ...utils import (
MULTIPLE_CHOICE_DUMMY_INPUTS,
ModelOutput,
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
)
from .configuration_xlm import XLMConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "FacebookAI/xlm-mlm-en-2048"
_CONFIG_FOR_DOC = "XLMConfig"
TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST = [
"FacebookAI/xlm-mlm-en-2048",
"FacebookAI/xlm-mlm-ende-1024",
"FacebookAI/xlm-mlm-enfr-1024",
"FacebookAI/xlm-mlm-enro-1024",
"FacebookAI/xlm-mlm-tlm-xnli15-1024",
"FacebookAI/xlm-mlm-xnli15-1024",
"FacebookAI/xlm-clm-enfr-1024",
"FacebookAI/xlm-clm-ende-1024",
"FacebookAI/xlm-mlm-17-1280",
"FacebookAI/xlm-mlm-100-1280",
# See all XLM models at https://huggingface.co/models?filter=xlm
]
def create_sinusoidal_embeddings(n_pos, dim, out):
position_enc = np.array([[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)] for pos in range(n_pos)])
out[:, 0::2] = tf.constant(np.sin(position_enc[:, 0::2]))
out[:, 1::2] = tf.constant(np.cos(position_enc[:, 1::2]))
def get_masks(slen, lengths, causal, padding_mask=None):
"""
Generate hidden states mask, and optionally an attention mask.
"""
bs = shape_list(lengths)[0]
if padding_mask is not None:
mask = padding_mask
else:
# assert lengths.max().item() <= slen
alen = tf.range(slen, dtype=lengths.dtype)
mask = alen < tf.expand_dims(lengths, axis=1)
# attention mask is the same as mask, or triangular inferior attention (causal)
if causal:
attn_mask = tf.less_equal(
tf.tile(tf.reshape(alen, (1, 1, slen)), (bs, slen, 1)), tf.reshape(alen, (1, slen, 1))
)
else:
attn_mask = mask
# sanity check
# assert shape_list(mask) == [bs, slen]
tf.debugging.assert_equal(shape_list(mask), [bs, slen])
if causal:
tf.debugging.assert_equal(shape_list(attn_mask), [bs, slen, slen])
return mask, attn_mask
class TFXLMMultiHeadAttention(keras.layers.Layer):
NEW_ID = itertools.count()
def __init__(self, n_heads, dim, config, **kwargs):
super().__init__(**kwargs)
self.layer_id = next(TFXLMMultiHeadAttention.NEW_ID)
self.dim = dim
self.n_heads = n_heads
self.output_attentions = config.output_attentions
assert self.dim % self.n_heads == 0
self.q_lin = keras.layers.Dense(dim, kernel_initializer=get_initializer(config.init_std), name="q_lin")
self.k_lin = keras.layers.Dense(dim, kernel_initializer=get_initializer(config.init_std), name="k_lin")
self.v_lin = keras.layers.Dense(dim, kernel_initializer=get_initializer(config.init_std), name="v_lin")
self.out_lin = keras.layers.Dense(dim, kernel_initializer=get_initializer(config.init_std), name="out_lin")
self.dropout = keras.layers.Dropout(config.attention_dropout)
self.pruned_heads = set()
self.dim = dim
def prune_heads(self, heads):
raise NotImplementedError
def call(self, input, mask, kv, cache, head_mask, output_attentions, training=False):
"""
Self-attention (if kv is None) or attention over source sentence (provided by kv).
"""
# Input is (bs, qlen, dim)
# Mask is (bs, klen) (non-causal) or (bs, klen, klen)
bs, qlen, dim = shape_list(input)
if kv is None:
klen = qlen if cache is None else cache["slen"] + qlen
else:
klen = shape_list(kv)[1]
# assert dim == self.dim, f'Dimensions do not match: {dim} input vs {self.dim} configured'
dim_per_head = self.dim // self.n_heads
mask_reshape = (bs, 1, qlen, klen) if len(shape_list(mask)) == 3 else (bs, 1, 1, klen)
def shape(x):
"""projection"""
return tf.transpose(tf.reshape(x, (bs, -1, self.n_heads, dim_per_head)), perm=(0, 2, 1, 3))
def unshape(x):
"""compute context"""
return tf.reshape(tf.transpose(x, perm=(0, 2, 1, 3)), (bs, -1, self.n_heads * dim_per_head))
q = shape(self.q_lin(input)) # (bs, n_heads, qlen, dim_per_head)
if kv is None:
k = shape(self.k_lin(input)) # (bs, n_heads, qlen, dim_per_head)
v = shape(self.v_lin(input)) # (bs, n_heads, qlen, dim_per_head)
elif cache is None or self.layer_id not in cache:
k = v = kv
k = shape(self.k_lin(k)) # (bs, n_heads, qlen, dim_per_head)
v = shape(self.v_lin(v)) # (bs, n_heads, qlen, dim_per_head)
if cache is not None:
if self.layer_id in cache:
if kv is None:
k_, v_ = cache[self.layer_id]
k = tf.concat([k_, k], axis=2) # (bs, n_heads, klen, dim_per_head)
v = tf.concat([v_, v], axis=2) # (bs, n_heads, klen, dim_per_head)
else:
k, v = cache[self.layer_id]
cache[self.layer_id] = (k, v)
f_dim_per_head = tf.cast(dim_per_head, dtype=q.dtype)
q = tf.multiply(q, tf.math.rsqrt(f_dim_per_head)) # (bs, n_heads, qlen, dim_per_head)
k = tf.cast(k, dtype=q.dtype)
scores = tf.matmul(q, k, transpose_b=True) # (bs, n_heads, qlen, klen)
mask = tf.reshape(mask, mask_reshape) # (bs, n_heads, qlen, klen)
# scores.masked_fill_(mask, -float('inf')) # (bs, n_heads, qlen, klen)
mask = tf.cast(mask, dtype=scores.dtype)
scores = scores - 1e30 * (1.0 - mask)
weights = stable_softmax(scores, axis=-1) # (bs, n_heads, qlen, klen)
weights = self.dropout(weights, training=training) # (bs, n_heads, qlen, klen)
# Mask heads if we want to
if head_mask is not None:
weights = weights * head_mask
context = tf.matmul(weights, v) # (bs, n_heads, qlen, dim_per_head)
context = unshape(context) # (bs, qlen, dim)
outputs = (self.out_lin(context),)
if output_attentions:
outputs = outputs + (weights,)
return outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "q_lin", None) is not None:
with tf.name_scope(self.q_lin.name):
self.q_lin.build([None, None, self.dim])
if getattr(self, "k_lin", None) is not None:
with tf.name_scope(self.k_lin.name):
self.k_lin.build([None, None, self.dim])
if getattr(self, "v_lin", None) is not None:
with tf.name_scope(self.v_lin.name):
self.v_lin.build([None, None, self.dim])
if getattr(self, "out_lin", None) is not None:
with tf.name_scope(self.out_lin.name):
self.out_lin.build([None, None, self.dim])
class TFXLMTransformerFFN(keras.layers.Layer):
def __init__(self, in_dim, dim_hidden, out_dim, config, **kwargs):
super().__init__(**kwargs)
self.lin1 = keras.layers.Dense(dim_hidden, kernel_initializer=get_initializer(config.init_std), name="lin1")
self.lin2 = keras.layers.Dense(out_dim, kernel_initializer=get_initializer(config.init_std), name="lin2")
self.act = get_tf_activation("gelu") if config.gelu_activation else get_tf_activation("relu")
self.dropout = keras.layers.Dropout(config.dropout)
self.in_dim = in_dim
self.dim_hidden = dim_hidden
def call(self, input, training=False):
x = self.lin1(input)
x = self.act(x)
x = self.lin2(x)
x = self.dropout(x, training=training)
return x
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "lin1", None) is not None:
with tf.name_scope(self.lin1.name):
self.lin1.build([None, None, self.in_dim])
if getattr(self, "lin2", None) is not None:
with tf.name_scope(self.lin2.name):
self.lin2.build([None, None, self.dim_hidden])
@keras_serializable
class TFXLMMainLayer(keras.layers.Layer):
config_class = XLMConfig
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.output_hidden_states = config.output_hidden_states
self.output_attentions = config.output_attentions
self.return_dict = config.use_return_dict
# encoder / decoder, output layer
self.is_encoder = config.is_encoder
self.is_decoder = not config.is_encoder
if self.is_decoder:
raise NotImplementedError("Currently XLM can only be used as an encoder")
# self.with_output = with_output
self.causal = config.causal
# dictionary / languages
self.n_langs = config.n_langs
self.use_lang_emb = config.use_lang_emb
self.n_words = config.n_words
self.eos_index = config.eos_index
self.pad_index = config.pad_index
# self.dico = dico
# self.id2lang = config.id2lang
# self.lang2id = config.lang2id
# assert len(self.dico) == self.n_words
# assert len(self.id2lang) == len(self.lang2id) == self.n_langs
# model parameters
self.dim = config.emb_dim # 512 by default
self.hidden_dim = self.dim * 4 # 2048 by default
self.n_heads = config.n_heads # 8 by default
self.n_layers = config.n_layers
self.max_position_embeddings = config.max_position_embeddings
self.embed_init_std = config.embed_init_std
if self.dim % self.n_heads != 0:
raise ValueError("transformer dim must be a multiple of n_heads")
# embeddings
self.dropout = keras.layers.Dropout(config.dropout)
self.attention_dropout = keras.layers.Dropout(config.attention_dropout)
if config.sinusoidal_embeddings:
raise NotImplementedError
# create_sinusoidal_embeddings(config.max_position_embeddings, self.dim, out=self.position_embeddings.weight)
self.embeddings = TFSharedEmbeddings(
self.n_words, self.dim, initializer_range=config.embed_init_std, name="embeddings"
) # padding_idx=self.pad_index)
self.layer_norm_emb = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm_emb")
# transformer layers
self.attentions = []
self.layer_norm1 = []
self.ffns = []
self.layer_norm2 = []
# if self.is_decoder:
# self.layer_norm15 = []
# self.encoder_attn = []
for i in range(self.n_layers):
self.attentions.append(
TFXLMMultiHeadAttention(self.n_heads, self.dim, config=config, name=f"attentions_._{i}")
)
self.layer_norm1.append(
keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name=f"layer_norm1_._{i}")
)
# if self.is_decoder:
# self.layer_norm15.append(nn.LayerNorm(self.dim, eps=config.layer_norm_eps))
# self.encoder_attn.append(MultiHeadAttention(self.n_heads, self.dim, dropout=self.attention_dropout))
self.ffns.append(
TFXLMTransformerFFN(self.dim, self.hidden_dim, self.dim, config=config, name=f"ffns_._{i}")
)
self.layer_norm2.append(
keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name=f"layer_norm2_._{i}")
)
if hasattr(config, "pruned_heads"):
pruned_heads = config.pruned_heads.copy().items()
config.pruned_heads = {}
for layer, heads in pruned_heads:
if self.attentions[int(layer)].n_heads == config.n_heads:
self.prune_heads({int(layer): list(map(int, heads))})
def build(self, input_shape=None):
if self.built:
return
self.built = True
with tf.name_scope("position_embeddings"):
self.position_embeddings = self.add_weight(
name="embeddings",
shape=[self.max_position_embeddings, self.dim],
initializer=get_initializer(self.embed_init_std),
)
if self.n_langs > 1 and self.use_lang_emb:
with tf.name_scope("lang_embeddings"):
self.lang_embeddings = self.add_weight(
name="embeddings",
shape=[self.n_langs, self.dim],
initializer=get_initializer(self.embed_init_std),
)
if getattr(self, "embeddings", None) is not None:
with tf.name_scope(self.embeddings.name):
self.embeddings.build(None)
if getattr(self, "layer_norm_emb", None) is not None:
with tf.name_scope(self.layer_norm_emb.name):
self.layer_norm_emb.build([None, None, self.dim])
for layer in self.attentions:
with tf.name_scope(layer.name):
layer.build(None)
for layer in self.layer_norm1:
with tf.name_scope(layer.name):
layer.build([None, None, self.dim])
for layer in self.ffns:
with tf.name_scope(layer.name):
layer.build(None)
for layer in self.layer_norm2:
with tf.name_scope(layer.name):
layer.build([None, None, self.dim])
def get_input_embeddings(self):
return self.embeddings
def set_input_embeddings(self, value):
self.embeddings.weight = value
self.embeddings.vocab_size = shape_list(value)[0]
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
raise NotImplementedError
@unpack_inputs
def call(
self,
input_ids=None,
attention_mask=None,
langs=None,
token_type_ids=None,
position_ids=None,
lengths=None,
cache=None,
head_mask=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]:
# removed: src_enc=None, src_len=None
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
bs, slen = shape_list(input_ids)
elif inputs_embeds is not None:
bs, slen = shape_list(inputs_embeds)[:2]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if lengths is None:
if input_ids is not None:
lengths = tf.reduce_sum(
tf.cast(tf.not_equal(input_ids, self.pad_index), dtype=input_ids.dtype), axis=1
)
else:
lengths = tf.convert_to_tensor([slen] * bs)
# mask = input_ids != self.pad_index
# check inputs
# assert shape_list(lengths)[0] == bs
(
tf.debugging.assert_equal(shape_list(lengths)[0], bs),
f"Expected batch size {shape_list(lengths)[0]} and received batch size {bs} mismatched",
)
# assert lengths.max().item() <= slen
# input_ids = input_ids.transpose(0, 1) # batch size as dimension 0
# assert (src_enc is None) == (src_len is None)
# if src_enc is not None:
# assert self.is_decoder
# assert src_enc.size(0) == bs
# generate masks
mask, attn_mask = get_masks(slen, lengths, self.causal, padding_mask=attention_mask)
# if self.is_decoder and src_enc is not None:
# src_mask = torch.arange(src_len.max(), dtype=torch.long, device=lengths.device) < src_len[:, None]
# position_ids
if position_ids is None:
position_ids = tf.expand_dims(tf.range(slen), axis=0)
position_ids = tf.tile(position_ids, (bs, 1))
# assert shape_list(position_ids) == [bs, slen] # (slen, bs)
(
tf.debugging.assert_equal(shape_list(position_ids), [bs, slen]),
f"Position id shape {shape_list(position_ids)} and input shape {[bs, slen]} mismatched",
)
# position_ids = position_ids.transpose(0, 1)
# langs
if langs is not None:
# assert shape_list(langs) == [bs, slen] # (slen, bs)
(
tf.debugging.assert_equal(shape_list(langs), [bs, slen]),
f"Lang shape {shape_list(langs)} and input shape {[bs, slen]} mismatched",
)
# langs = langs.transpose(0, 1)
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x qlen x klen]
if head_mask is not None:
raise NotImplementedError
else:
head_mask = [None] * self.n_layers
# do not recompute cached elements
if cache is not None and input_ids is not None:
_slen = slen - cache["slen"]
input_ids = input_ids[:, -_slen:]
position_ids = position_ids[:, -_slen:]
if langs is not None:
langs = langs[:, -_slen:]
mask = mask[:, -_slen:]
attn_mask = attn_mask[:, -_slen:]
# embeddings
if inputs_embeds is None:
check_embeddings_within_bounds(input_ids, self.embeddings.vocab_size)
inputs_embeds = self.embeddings(input_ids)
tensor = inputs_embeds + tf.gather(self.position_embeddings, position_ids)
if langs is not None and self.use_lang_emb and self.n_langs > 1:
tensor = tensor + tf.gather(self.lang_embeddings, langs)
if token_type_ids is not None:
tensor = tensor + self.embeddings(token_type_ids)
tensor = self.layer_norm_emb(tensor)
tensor = self.dropout(tensor, training=training)
mask = tf.cast(mask, dtype=tensor.dtype)
tensor = tensor * tf.expand_dims(mask, axis=-1)
# transformer layers
hidden_states = () if output_hidden_states else None
attentions = () if output_attentions else None
for i in range(self.n_layers):
if output_hidden_states:
hidden_states = hidden_states + (tensor,)
# self attention
attn_outputs = self.attentions[i](
tensor,
attn_mask,
None,
cache,
head_mask[i],
output_attentions,
training=training,
)
attn = attn_outputs[0]
if output_attentions:
attentions = attentions + (attn_outputs[1],)
attn = self.dropout(attn, training=training)
tensor = tensor + attn
tensor = self.layer_norm1[i](tensor)
# encoder attention (for decoder only)
# if self.is_decoder and src_enc is not None:
# attn = self.encoder_attn[i](tensor, src_mask, kv=src_enc, cache=cache)
# attn = nn.functional.dropout(attn, p=self.dropout, training=self.training)
# tensor = tensor + attn
# tensor = self.layer_norm15[i](tensor)
# FFN
tensor = tensor + self.ffns[i](tensor)
tensor = self.layer_norm2[i](tensor)
tensor = tensor * tf.expand_dims(mask, axis=-1)
# Add last hidden state
if output_hidden_states:
hidden_states = hidden_states + (tensor,)
# update cache length
if cache is not None:
cache["slen"] += tensor.size(1)
# move back sequence length to dimension 0
# tensor = tensor.transpose(0, 1)
if not return_dict:
return tuple(v for v in [tensor, hidden_states, attentions] if v is not None)
return TFBaseModelOutput(last_hidden_state=tensor, hidden_states=hidden_states, attentions=attentions)
class TFXLMPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = XLMConfig
base_model_prefix = "transformer"
@property
def dummy_inputs(self):
# Sometimes XLM has language embeddings so don't forget to build them as well if needed
inputs_list = tf.constant([[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]], dtype=tf.int32)
attns_list = tf.constant([[1, 1, 0, 0, 1], [1, 1, 1, 0, 0], [1, 0, 0, 1, 1]], dtype=tf.int32)
if self.config.use_lang_emb and self.config.n_langs > 1:
return {
"input_ids": inputs_list,
"attention_mask": attns_list,
"langs": tf.constant([[1, 1, 0, 0, 1], [1, 1, 1, 0, 0], [1, 0, 0, 1, 1]], dtype=tf.int32),
}
else:
return {"input_ids": inputs_list, "attention_mask": attns_list}
# Remove when XLMWithLMHead computes loss like other LM models
@dataclass
class TFXLMWithLMHeadModelOutput(ModelOutput):
"""
Base class for [`TFXLMWithLMHeadModel`] outputs.
Args:
logits (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
logits: tf.Tensor = None
hidden_states: Tuple[tf.Tensor, ...] | None = None
attentions: Tuple[tf.Tensor, ...] | None = None
XLM_START_DOCSTRING = r"""
This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.
<Tip>
TensorFlow models and layers in `transformers` accept two formats as input:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
- a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
`model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
Note that when creating models and layers with
[subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
about any of this, as you can just pass inputs like you would to any other Python function!
</Tip>
Parameters:
config ([`XLMConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
XLM_INPUTS_DOCSTRING = r"""
Args:
input_ids (`Numpy array` or `tf.Tensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
[`PreTrainedTokenizer.encode`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
langs (`tf.Tensor` or `Numpy array` of shape `({0})`, *optional*):
A parallel sequence of tokens to be used to indicate the language of each token in the input. Indices are
languages ids which can be obtained from the language names by using two conversion mappings provided in
the configuration of the model (only provided for multilingual models). More precisely, the *language name
to language id* mapping is in `model.config.lang2id` (which is a dictionary string to int) and the
*language id to language name* mapping is in `model.config.id2lang` (dictionary int to string).
See usage examples detailed in the [multilingual documentation](../multilingual).
token_type_ids (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
1]`:
- 0 corresponds to a *sentence A* token,
- 1 corresponds to a *sentence B* token.
[What are token type IDs?](../glossary#token-type-ids)
position_ids (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
lengths (`tf.Tensor` or `Numpy array` of shape `(batch_size,)`, *optional*):
Length of each sentence that can be used to avoid performing attention on padding token indices. You can
also use *attention_mask* for the same result (see above), kept here for compatibility. Indices selected in
`[0, ..., input_ids.size(-1)]`.
cache (`Dict[str, tf.Tensor]`, *optional*):
Dictionary string to `tf.Tensor` that contains precomputed hidden states (key and values in the attention
blocks) as computed by the model (see `cache` output below). Can be used to speed up sequential decoding.
The dictionary object will be modified in-place during the forward pass to add newly computed
hidden-states.
head_mask (`Numpy array` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`tf.Tensor` of shape `({0}, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
training (`bool`, *optional*, defaults to `False`):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
@add_start_docstrings(
"The bare XLM Model transformer outputting raw hidden-states without any specific head on top.",
XLM_START_DOCSTRING,
)
class TFXLMModel(TFXLMPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.transformer = TFXLMMainLayer(config, name="transformer")
@unpack_inputs
@add_start_docstrings_to_model_forward(XLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFBaseModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: tf.Tensor | None = None,
langs: tf.Tensor | None = None,
token_type_ids: tf.Tensor | None = None,
position_ids: tf.Tensor | None = None,
lengths: tf.Tensor | None = None,
cache: Dict[str, tf.Tensor] | None = None,
head_mask: tf.Tensor | None = None,
inputs_embeds: tf.Tensor | None = None,
output_attentions: bool | None = None,
output_hidden_states: bool | None = None,
return_dict: bool | None = None,
training: bool = False,
) -> TFBaseModelOutput | Tuple[tf.Tensor]:
outputs = self.transformer(
input_ids=input_ids,
attention_mask=attention_mask,
langs=langs,
token_type_ids=token_type_ids,
position_ids=position_ids,
lengths=lengths,
cache=cache,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
return outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "transformer", None) is not None:
with tf.name_scope(self.transformer.name):
self.transformer.build(None)
class TFXLMPredLayer(keras.layers.Layer):
"""
Prediction layer (cross_entropy or adaptive_softmax).
"""
def __init__(self, config, input_embeddings, **kwargs):
super().__init__(**kwargs)
self.asm = config.asm
self.n_words = config.n_words
self.pad_index = config.pad_index
if config.asm is False:
self.input_embeddings = input_embeddings
else:
raise NotImplementedError
# self.proj = nn.AdaptiveLogSoftmaxWithLoss(
# in_features=dim,
# n_classes=config.n_words,
# cutoffs=config.asm_cutoffs,
# div_value=config.asm_div_value,
# head_bias=True, # default is False
# )
def build(self, input_shape):
# The output weights are the same as the input embeddings, but there is an output-only bias for each token.
self.bias = self.add_weight(shape=(self.n_words,), initializer="zeros", trainable=True, name="bias")
super().build(input_shape)
def get_output_embeddings(self):
return self.input_embeddings
def set_output_embeddings(self, value):
self.input_embeddings.weight = value
self.input_embeddings.vocab_size = shape_list(value)[0]
def get_bias(self):
return {"bias": self.bias}
def set_bias(self, value):
self.bias = value["bias"]
self.vocab_size = shape_list(value["bias"])[0]
def call(self, hidden_states):
hidden_states = self.input_embeddings(hidden_states, mode="linear")
hidden_states = hidden_states + self.bias
return hidden_states
@add_start_docstrings(
"""
The XLM Model transformer with a language modeling head on top (linear layer with weights tied to the input
embeddings).
""",
XLM_START_DOCSTRING,
)
class TFXLMWithLMHeadModel(TFXLMPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.transformer = TFXLMMainLayer(config, name="transformer")
self.pred_layer = TFXLMPredLayer(config, self.transformer.embeddings, name="pred_layer_._proj")
# XLM does not have past caching features
self.supports_xla_generation = False
def get_lm_head(self):
return self.pred_layer
def get_prefix_bias_name(self):
warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning)
return self.name + "/" + self.pred_layer.name
def prepare_inputs_for_generation(self, inputs, **kwargs):
mask_token_id = self.config.mask_token_id
lang_id = self.config.lang_id
effective_batch_size = inputs.shape[0]
mask_token = tf.fill((effective_batch_size, 1), 1) * mask_token_id
inputs = tf.concat([inputs, mask_token], axis=1)
if lang_id is not None:
langs = tf.ones_like(inputs) * lang_id
else:
langs = None
return {"input_ids": inputs, "langs": langs}
@unpack_inputs
@add_start_docstrings_to_model_forward(XLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFXLMWithLMHeadModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
langs: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
lengths: np.ndarray | tf.Tensor | None = None,
cache: Optional[Dict[str, tf.Tensor]] = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> Union[TFXLMWithLMHeadModelOutput, Tuple[tf.Tensor]]:
transformer_outputs = self.transformer(
input_ids=input_ids,
attention_mask=attention_mask,
langs=langs,
token_type_ids=token_type_ids,
position_ids=position_ids,
lengths=lengths,
cache=cache,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
output = transformer_outputs[0]
outputs = self.pred_layer(output)
if not return_dict:
return (outputs,) + transformer_outputs[1:]
return TFXLMWithLMHeadModelOutput(
logits=outputs, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "transformer", None) is not None:
with tf.name_scope(self.transformer.name):
self.transformer.build(None)
if getattr(self, "pred_layer", None) is not None:
with tf.name_scope(self.pred_layer.name):
self.pred_layer.build(None)
@add_start_docstrings(
"""
XLM Model with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g.
for GLUE tasks.
""",
XLM_START_DOCSTRING,
)
class TFXLMForSequenceClassification(TFXLMPreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.transformer = TFXLMMainLayer(config, name="transformer")
self.sequence_summary = TFSequenceSummary(config, initializer_range=config.init_std, name="sequence_summary")
@unpack_inputs
@add_start_docstrings_to_model_forward(XLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFSequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
langs: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
lengths: np.ndarray | tf.Tensor | None = None,
cache: Optional[Dict[str, tf.Tensor]] = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: np.ndarray | tf.Tensor | None = None,
training: bool = False,
) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
r"""
labels (`tf.Tensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
transformer_outputs = self.transformer(
input_ids=input_ids,
attention_mask=attention_mask,
langs=langs,
token_type_ids=token_type_ids,
position_ids=position_ids,
lengths=lengths,
cache=cache,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
output = transformer_outputs[0]
logits = self.sequence_summary(output)
loss = None if labels is None else self.hf_compute_loss(labels, logits)
if not return_dict:
output = (logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "transformer", None) is not None:
with tf.name_scope(self.transformer.name):
self.transformer.build(None)
if getattr(self, "sequence_summary", None) is not None:
with tf.name_scope(self.sequence_summary.name):
self.sequence_summary.build(None)
@add_start_docstrings(
"""
XLM Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
softmax) e.g. for RocStories/SWAG tasks.
""",
XLM_START_DOCSTRING,
)
class TFXLMForMultipleChoice(TFXLMPreTrainedModel, TFMultipleChoiceLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.transformer = TFXLMMainLayer(config, name="transformer")
self.sequence_summary = TFSequenceSummary(config, initializer_range=config.init_std, name="sequence_summary")
self.logits_proj = keras.layers.Dense(
1, kernel_initializer=get_initializer(config.initializer_range), name="logits_proj"
)
self.config = config
@property
def dummy_inputs(self):
"""
Dummy inputs to build the network.
Returns:
tf.Tensor with dummy inputs
"""
# Sometimes XLM has language embeddings so don't forget to build them as well if needed
if self.config.use_lang_emb and self.config.n_langs > 1:
return {
"input_ids": tf.constant(MULTIPLE_CHOICE_DUMMY_INPUTS, dtype=tf.int32),
"langs": tf.constant(MULTIPLE_CHOICE_DUMMY_INPUTS, dtype=tf.int32),
}
else:
return {
"input_ids": tf.constant(MULTIPLE_CHOICE_DUMMY_INPUTS, dtype=tf.int32),
}
@unpack_inputs
@add_start_docstrings_to_model_forward(XLM_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFMultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
langs: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
lengths: np.ndarray | tf.Tensor | None = None,
cache: Optional[Dict[str, tf.Tensor]] = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: np.ndarray | tf.Tensor | None = None,
training: bool = False,
) -> Union[TFMultipleChoiceModelOutput, Tuple[tf.Tensor]]:
if input_ids is not None:
num_choices = shape_list(input_ids)[1]
seq_length = shape_list(input_ids)[2]
else:
num_choices = shape_list(inputs_embeds)[1]
seq_length = shape_list(inputs_embeds)[2]
flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None
flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None
flat_token_type_ids = tf.reshape(token_type_ids, (-1, seq_length)) if token_type_ids is not None else None
flat_position_ids = tf.reshape(position_ids, (-1, seq_length)) if position_ids is not None else None
flat_langs = tf.reshape(langs, (-1, seq_length)) if langs is not None else None
flat_inputs_embeds = (
tf.reshape(inputs_embeds, (-1, seq_length, shape_list(inputs_embeds)[3]))
if inputs_embeds is not None
else None
)
if lengths is not None:
logger.warning(
"The `lengths` parameter cannot be used with the XLM multiple choice models. Please use the "
"attention mask instead.",
)
lengths = None
transformer_outputs = self.transformer(
flat_input_ids,
flat_attention_mask,
flat_langs,
flat_token_type_ids,
flat_position_ids,
lengths,
cache,
head_mask,
flat_inputs_embeds,
output_attentions,
output_hidden_states,
return_dict=return_dict,
training=training,
)
output = transformer_outputs[0]
logits = self.sequence_summary(output)
logits = self.logits_proj(logits)
reshaped_logits = tf.reshape(logits, (-1, num_choices))
loss = None if labels is None else self.hf_compute_loss(labels, reshaped_logits)
if not return_dict:
output = (reshaped_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return TFMultipleChoiceModelOutput(
loss=loss,
logits=reshaped_logits,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "transformer", None) is not None:
with tf.name_scope(self.transformer.name):
self.transformer.build(None)
if getattr(self, "sequence_summary", None) is not None:
with tf.name_scope(self.sequence_summary.name):
self.sequence_summary.build(None)
if getattr(self, "logits_proj", None) is not None:
with tf.name_scope(self.logits_proj.name):
self.logits_proj.build([None, None, self.config.num_labels])
@add_start_docstrings(
"""
XLM Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.
""",
XLM_START_DOCSTRING,
)
class TFXLMForTokenClassification(TFXLMPreTrainedModel, TFTokenClassificationLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.transformer = TFXLMMainLayer(config, name="transformer")
self.dropout = keras.layers.Dropout(config.dropout)
self.classifier = keras.layers.Dense(
config.num_labels, kernel_initializer=get_initializer(config.init_std), name="classifier"
)
self.config = config
@unpack_inputs
@add_start_docstrings_to_model_forward(XLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFTokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
langs: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
lengths: np.ndarray | tf.Tensor | None = None,
cache: Optional[Dict[str, tf.Tensor]] = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: np.ndarray | tf.Tensor | None = None,
training: bool = False,
) -> Union[TFTokenClassifierOutput, Tuple[tf.Tensor]]:
r"""
labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
"""
transformer_outputs = self.transformer(
input_ids=input_ids,
attention_mask=attention_mask,
langs=langs,
token_type_ids=token_type_ids,
position_ids=position_ids,
lengths=lengths,
cache=cache,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
sequence_output = transformer_outputs[0]
sequence_output = self.dropout(sequence_output, training=training)
logits = self.classifier(sequence_output)
loss = None if labels is None else self.hf_compute_loss(labels, logits)
if not return_dict:
output = (logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return TFTokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "transformer", None) is not None:
with tf.name_scope(self.transformer.name):
self.transformer.build(None)
if getattr(self, "classifier", None) is not None:
with tf.name_scope(self.classifier.name):
self.classifier.build([None, None, self.config.hidden_size])
@add_start_docstrings(
"""
XLM Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer
on top of the hidden-states output to compute `span start logits` and `span end logits`).
""",
XLM_START_DOCSTRING,
)
class TFXLMForQuestionAnsweringSimple(TFXLMPreTrainedModel, TFQuestionAnsweringLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.transformer = TFXLMMainLayer(config, name="transformer")
self.qa_outputs = keras.layers.Dense(
config.num_labels, kernel_initializer=get_initializer(config.init_std), name="qa_outputs"
)
self.config = config
@unpack_inputs
@add_start_docstrings_to_model_forward(XLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFQuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
langs: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
lengths: np.ndarray | tf.Tensor | None = None,
cache: Optional[Dict[str, tf.Tensor]] = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
start_positions: np.ndarray | tf.Tensor | None = None,
end_positions: np.ndarray | tf.Tensor | None = None,
training: bool = False,
) -> Union[TFQuestionAnsweringModelOutput, Tuple[tf.Tensor]]:
r"""
start_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
"""
transformer_outputs = self.transformer(
input_ids=input_ids,
attention_mask=attention_mask,
langs=langs,
token_type_ids=token_type_ids,
position_ids=position_ids,
lengths=lengths,
cache=cache,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
sequence_output = transformer_outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = tf.split(logits, 2, axis=-1)
start_logits = tf.squeeze(start_logits, axis=-1)
end_logits = tf.squeeze(end_logits, axis=-1)
loss = None
if start_positions is not None and end_positions is not None:
labels = {"start_position": start_positions}
labels["end_position"] = end_positions
loss = self.hf_compute_loss(labels, (start_logits, end_logits))
if not return_dict:
output = (start_logits, end_logits) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return TFQuestionAnsweringModelOutput(
loss=loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "transformer", None) is not None:
with tf.name_scope(self.transformer.name):
self.transformer.build(None)
if getattr(self, "qa_outputs", None) is not None:
with tf.name_scope(self.qa_outputs.name):
self.qa_outputs.build([None, None, self.config.hidden_size])
| transformers/src/transformers/models/xlm/modeling_tf_xlm.py/0 | {
"file_path": "transformers/src/transformers/models/xlm/modeling_tf_xlm.py",
"repo_id": "transformers",
"token_count": 25225
} | 349 |
# coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert RoBERTa checkpoint."""
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertSelfAttention,
BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse("1.0.0a"):
raise Exception("requires fairseq >= 1.0.0a")
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
SAMPLE_TEXT = "Hello world! cécé herlolip"
def convert_xlm_roberta_xl_checkpoint_to_pytorch(
roberta_checkpoint_path: str, pytorch_dump_folder_path: str, classification_head: bool
):
"""
Copy/paste/tweak roberta's weights to our BERT structure.
"""
roberta = FairseqRobertaModel.from_pretrained(roberta_checkpoint_path)
roberta.eval() # disable dropout
roberta_sent_encoder = roberta.model.encoder.sentence_encoder
config = XLMRobertaConfig(
vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings,
hidden_size=roberta.cfg.model.encoder_embed_dim,
num_hidden_layers=roberta.cfg.model.encoder_layers,
num_attention_heads=roberta.cfg.model.encoder_attention_heads,
intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim,
max_position_embeddings=514,
type_vocab_size=1,
layer_norm_eps=1e-5, # PyTorch default used in fairseq
)
if classification_head:
config.num_labels = roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
print("Our RoBERTa config:", config)
model = XLMRobertaXLForSequenceClassification(config) if classification_head else XLMRobertaXLForMaskedLM(config)
model.eval()
# Now let's copy all the weights.
# Embeddings
model.roberta.embeddings.word_embeddings.weight = roberta_sent_encoder.embed_tokens.weight
model.roberta.embeddings.position_embeddings.weight = roberta_sent_encoder.embed_positions.weight
model.roberta.embeddings.token_type_embeddings.weight.data = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight
) # just zero them out b/c RoBERTa doesn't use them.
model.roberta.encoder.LayerNorm.weight = roberta_sent_encoder.layer_norm.weight
model.roberta.encoder.LayerNorm.bias = roberta_sent_encoder.layer_norm.bias
for i in range(config.num_hidden_layers):
# Encoder: start of layer
layer: BertLayer = model.roberta.encoder.layer[i]
roberta_layer: TransformerSentenceEncoderLayer = roberta_sent_encoder.layers[i]
attention: RobertaAttention = layer.attention
attention.self_attn_layer_norm.weight = roberta_layer.self_attn_layer_norm.weight
attention.self_attn_layer_norm.bias = roberta_layer.self_attn_layer_norm.bias
# self attention
self_attn: BertSelfAttention = layer.attention.self
assert (
roberta_layer.self_attn.k_proj.weight.data.shape
== roberta_layer.self_attn.q_proj.weight.data.shape
== roberta_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size))
)
self_attn.query.weight.data = roberta_layer.self_attn.q_proj.weight
self_attn.query.bias.data = roberta_layer.self_attn.q_proj.bias
self_attn.key.weight.data = roberta_layer.self_attn.k_proj.weight
self_attn.key.bias.data = roberta_layer.self_attn.k_proj.bias
self_attn.value.weight.data = roberta_layer.self_attn.v_proj.weight
self_attn.value.bias.data = roberta_layer.self_attn.v_proj.bias
# self-attention output
self_output: BertSelfOutput = layer.attention.output
assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
self_output.dense.weight = roberta_layer.self_attn.out_proj.weight
self_output.dense.bias = roberta_layer.self_attn.out_proj.bias
# this one is final layer norm
layer.LayerNorm.weight = roberta_layer.final_layer_norm.weight
layer.LayerNorm.bias = roberta_layer.final_layer_norm.bias
# intermediate
intermediate: BertIntermediate = layer.intermediate
assert intermediate.dense.weight.shape == roberta_layer.fc1.weight.shape
intermediate.dense.weight = roberta_layer.fc1.weight
intermediate.dense.bias = roberta_layer.fc1.bias
# output
bert_output: BertOutput = layer.output
assert bert_output.dense.weight.shape == roberta_layer.fc2.weight.shape
bert_output.dense.weight = roberta_layer.fc2.weight
bert_output.dense.bias = roberta_layer.fc2.bias
# end of layer
if classification_head:
model.classifier.dense.weight = roberta.model.classification_heads["mnli"].dense.weight
model.classifier.dense.bias = roberta.model.classification_heads["mnli"].dense.bias
model.classifier.out_proj.weight = roberta.model.classification_heads["mnli"].out_proj.weight
model.classifier.out_proj.bias = roberta.model.classification_heads["mnli"].out_proj.bias
else:
# LM Head
model.lm_head.dense.weight = roberta.model.encoder.lm_head.dense.weight
model.lm_head.dense.bias = roberta.model.encoder.lm_head.dense.bias
model.lm_head.layer_norm.weight = roberta.model.encoder.lm_head.layer_norm.weight
model.lm_head.layer_norm.bias = roberta.model.encoder.lm_head.layer_norm.bias
model.lm_head.decoder.weight = roberta.model.encoder.lm_head.weight
model.lm_head.decoder.bias = roberta.model.encoder.lm_head.bias
# Let's check that we get the same results.
input_ids: torch.Tensor = roberta.encode(SAMPLE_TEXT).unsqueeze(0) # batch of size 1
our_output = model(input_ids)[0]
if classification_head:
their_output = roberta.model.classification_heads["mnli"](roberta.extract_features(input_ids))
else:
their_output = roberta.model(input_ids)[0]
print(our_output.shape, their_output.shape)
max_absolute_diff = torch.max(torch.abs(our_output - their_output)).item()
print(f"max_absolute_diff = {max_absolute_diff}") # ~ 1e-7
success = torch.allclose(our_output, their_output, atol=1e-3)
print("Do both models output the same tensors?", "🔥" if success else "💩")
if not success:
raise Exception("Something went wRoNg")
pathlib.Path(pytorch_dump_folder_path).mkdir(parents=True, exist_ok=True)
print(f"Saving model to {pytorch_dump_folder_path}")
model.save_pretrained(pytorch_dump_folder_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--roberta_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--classification_head", action="store_true", help="Whether to convert a final classification head."
)
args = parser.parse_args()
convert_xlm_roberta_xl_checkpoint_to_pytorch(
args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| transformers/src/transformers/models/xlm_roberta_xl/convert_xlm_roberta_xl_original_pytorch_checkpoint_to_pytorch.py/0 | {
"file_path": "transformers/src/transformers/models/xlm_roberta_xl/convert_xlm_roberta_xl_original_pytorch_checkpoint_to_pytorch.py",
"repo_id": "transformers",
"token_count": 3308
} | 350 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import subprocess
from typing import Union
import numpy as np
import requests
from ..utils import add_end_docstrings, is_torch_available, is_torchaudio_available, logging
from .base import Pipeline, build_pipeline_init_args
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
logger = logging.get_logger(__name__)
def ffmpeg_read(bpayload: bytes, sampling_rate: int) -> np.array:
"""
Helper function to read an audio file through ffmpeg.
"""
ar = f"{sampling_rate}"
ac = "1"
format_for_conversion = "f32le"
ffmpeg_command = [
"ffmpeg",
"-i",
"pipe:0",
"-ac",
ac,
"-ar",
ar,
"-f",
format_for_conversion,
"-hide_banner",
"-loglevel",
"quiet",
"pipe:1",
]
try:
ffmpeg_process = subprocess.Popen(ffmpeg_command, stdin=subprocess.PIPE, stdout=subprocess.PIPE)
except FileNotFoundError:
raise ValueError("ffmpeg was not found but is required to load audio files from filename")
output_stream = ffmpeg_process.communicate(bpayload)
out_bytes = output_stream[0]
audio = np.frombuffer(out_bytes, np.float32)
if audio.shape[0] == 0:
raise ValueError("Malformed soundfile")
return audio
@add_end_docstrings(build_pipeline_init_args(has_feature_extractor=True))
class AudioClassificationPipeline(Pipeline):
"""
Audio classification pipeline using any `AutoModelForAudioClassification`. This pipeline predicts the class of a
raw waveform or an audio file. In case of an audio file, ffmpeg should be installed to support multiple audio
formats.
Example:
```python
>>> from transformers import pipeline
>>> classifier = pipeline(model="superb/wav2vec2-base-superb-ks")
>>> classifier("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/1.flac")
[{'score': 0.997, 'label': '_unknown_'}, {'score': 0.002, 'label': 'left'}, {'score': 0.0, 'label': 'yes'}, {'score': 0.0, 'label': 'down'}, {'score': 0.0, 'label': 'stop'}]
```
Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
This pipeline can currently be loaded from [`pipeline`] using the following task identifier:
`"audio-classification"`.
See the list of available models on
[huggingface.co/models](https://huggingface.co/models?filter=audio-classification).
"""
def __init__(self, *args, **kwargs):
# Default, might be overriden by the model.config.
kwargs["top_k"] = 5
super().__init__(*args, **kwargs)
if self.framework != "pt":
raise ValueError(f"The {self.__class__} is only available in PyTorch.")
self.check_model_type(MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES)
def __call__(
self,
inputs: Union[np.ndarray, bytes, str],
**kwargs,
):
"""
Classify the sequence(s) given as inputs. See the [`AutomaticSpeechRecognitionPipeline`] documentation for more
information.
Args:
inputs (`np.ndarray` or `bytes` or `str` or `dict`):
The inputs is either :
- `str` that is the filename of the audio file, the file will be read at the correct sampling rate
to get the waveform using *ffmpeg*. This requires *ffmpeg* to be installed on the system.
- `bytes` it is supposed to be the content of an audio file and is interpreted by *ffmpeg* in the
same way.
- (`np.ndarray` of shape (n, ) of type `np.float32` or `np.float64`)
Raw audio at the correct sampling rate (no further check will be done)
- `dict` form can be used to pass raw audio sampled at arbitrary `sampling_rate` and let this
pipeline do the resampling. The dict must be either be in the format `{"sampling_rate": int,
"raw": np.array}`, or `{"sampling_rate": int, "array": np.array}`, where the key `"raw"` or
`"array"` is used to denote the raw audio waveform.
top_k (`int`, *optional*, defaults to None):
The number of top labels that will be returned by the pipeline. If the provided number is `None` or
higher than the number of labels available in the model configuration, it will default to the number of
labels.
Return:
A list of `dict` with the following keys:
- **label** (`str`) -- The label predicted.
- **score** (`float`) -- The corresponding probability.
"""
return super().__call__(inputs, **kwargs)
def _sanitize_parameters(self, top_k=None, **kwargs):
# No parameters on this pipeline right now
postprocess_params = {}
if top_k is not None:
if top_k > self.model.config.num_labels:
top_k = self.model.config.num_labels
postprocess_params["top_k"] = top_k
return {}, {}, postprocess_params
def preprocess(self, inputs):
if isinstance(inputs, str):
if inputs.startswith("http://") or inputs.startswith("https://"):
# We need to actually check for a real protocol, otherwise it's impossible to use a local file
# like http_huggingface_co.png
inputs = requests.get(inputs).content
else:
with open(inputs, "rb") as f:
inputs = f.read()
if isinstance(inputs, bytes):
inputs = ffmpeg_read(inputs, self.feature_extractor.sampling_rate)
if isinstance(inputs, dict):
# Accepting `"array"` which is the key defined in `datasets` for
# better integration
if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)):
raise ValueError(
"When passing a dictionary to AudioClassificationPipeline, the dict needs to contain a "
'"raw" key containing the numpy array representing the audio and a "sampling_rate" key, '
"containing the sampling_rate associated with that array"
)
_inputs = inputs.pop("raw", None)
if _inputs is None:
# Remove path which will not be used from `datasets`.
inputs.pop("path", None)
_inputs = inputs.pop("array", None)
in_sampling_rate = inputs.pop("sampling_rate")
inputs = _inputs
if in_sampling_rate != self.feature_extractor.sampling_rate:
import torch
if is_torchaudio_available():
from torchaudio import functional as F
else:
raise ImportError(
"torchaudio is required to resample audio samples in AudioClassificationPipeline. "
"The torchaudio package can be installed through: `pip install torchaudio`."
)
inputs = F.resample(
torch.from_numpy(inputs), in_sampling_rate, self.feature_extractor.sampling_rate
).numpy()
if not isinstance(inputs, np.ndarray):
raise ValueError("We expect a numpy ndarray as input")
if len(inputs.shape) != 1:
raise ValueError("We expect a single channel audio input for AudioClassificationPipeline")
processed = self.feature_extractor(
inputs, sampling_rate=self.feature_extractor.sampling_rate, return_tensors="pt"
)
return processed
def _forward(self, model_inputs):
model_outputs = self.model(**model_inputs)
return model_outputs
def postprocess(self, model_outputs, top_k=5):
probs = model_outputs.logits[0].softmax(-1)
scores, ids = probs.topk(top_k)
scores = scores.tolist()
ids = ids.tolist()
labels = [{"score": score, "label": self.model.config.id2label[_id]} for score, _id in zip(scores, ids)]
return labels
| transformers/src/transformers/pipelines/audio_classification.py/0 | {
"file_path": "transformers/src/transformers/pipelines/audio_classification.py",
"repo_id": "transformers",
"token_count": 3711
} | 351 |
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class PipelineDataset(Dataset):
def __init__(self, dataset, process, params):
self.dataset = dataset
self.process = process
self.params = params
def __len__(self):
return len(self.dataset)
def __getitem__(self, i):
item = self.dataset[i]
processed = self.process(item, **self.params)
return processed
class PipelineIterator(IterableDataset):
def __init__(self, loader, infer, params, loader_batch_size=None):
"""
Roughly equivalent to
```
for item in loader:
yield infer(item, **params)
```
Arguments:
loader (`torch.utils.data.DataLoader` or any iterator):
The iterator that will be used to apply `infer` on.
infer (any function):
The function to apply of each element of `loader`.
params (`dict`):
The parameters passed to `infer` along with every item
loader_batch_size (`int`, *optional*):
If specified, the items of `loader` are supposed to come as batch, and are loader_batched here
making it roughly behave as
```
for items in loader:
for i in loader_batch_size:
item = items[i]
yield infer(item, **params)
```"""
self.loader = loader
self.infer = infer
self.params = params
if loader_batch_size == 1:
# Let's spare some time by deactivating altogether
loader_batch_size = None
self.loader_batch_size = loader_batch_size
# Internal bookkeeping
self._loader_batch_index = None
self._loader_batch_data = None
def __len__(self):
return len(self.loader)
def __iter__(self):
self.iterator = iter(self.loader)
return self
def loader_batch_item(self):
"""
Return item located at `loader_batch_index` within the current `loader_batch_data`.
"""
if isinstance(self._loader_batch_data, torch.Tensor):
# Batch data is simple tensor, just fetch the slice
result = self._loader_batch_data[self._loader_batch_index].unsqueeze(0)
else:
# Batch data is assumed to be BaseModelOutput (or dict)
loader_batched = {}
for k, element in self._loader_batch_data.items():
if isinstance(element, ModelOutput):
# Convert ModelOutput to tuple first
element = element.to_tuple()
if isinstance(element[0], torch.Tensor):
loader_batched[k] = tuple(el[self._loader_batch_index].unsqueeze(0) for el in element)
elif isinstance(element[0], np.ndarray):
loader_batched[k] = tuple(np.expand_dims(el[self._loader_batch_index], 0) for el in element)
continue
if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(element, tuple):
# Those are stored as lists of tensors so need specific unbatching.
if isinstance(element[0], torch.Tensor):
loader_batched[k] = tuple(el[self._loader_batch_index].unsqueeze(0) for el in element)
elif isinstance(element[0], np.ndarray):
loader_batched[k] = tuple(np.expand_dims(el[self._loader_batch_index], 0) for el in element)
continue
if element is None:
# This can happen for optional data that get passed around
loader_batched[k] = None
elif isinstance(element[self._loader_batch_index], torch.Tensor):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
loader_batched[k] = element[self._loader_batch_index].unsqueeze(0)
elif isinstance(element[self._loader_batch_index], np.ndarray):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
loader_batched[k] = np.expand_dims(element[self._loader_batch_index], 0)
else:
# This is typically a list, so no need to `unsqueeze`.
loader_batched[k] = element[self._loader_batch_index]
# Recreate the element by reusing the original class to make it look
# batch_size=1
result = self._loader_batch_data.__class__(loader_batched)
self._loader_batch_index += 1
return result
def __next__(self):
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
# We are currently unrolling a batch so we just need to return
# the current item within a batch
return self.loader_batch_item()
# We're out of items within a batch
item = next(self.iterator)
processed = self.infer(item, **self.params)
# We now have a batch of "inferred things".
if self.loader_batch_size is not None:
# Try to infer the size of the batch
if isinstance(processed, torch.Tensor):
first_tensor = processed
else:
key = list(processed.keys())[0]
first_tensor = processed[key]
if isinstance(first_tensor, list):
observed_batch_size = len(first_tensor)
else:
observed_batch_size = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
self.loader_batch_size = observed_batch_size
# Setting internal index to unwrap the batch
self._loader_batch_data = processed
self._loader_batch_index = 0
return self.loader_batch_item()
else:
# We're not unrolling batches
return processed
class PipelineChunkIterator(PipelineIterator):
def __init__(self, loader, infer, params, loader_batch_size=None):
"""
Roughly equivalent to
```
for iterator in loader:
for item in iterator:
yield infer(item, **params)
```
Arguments:
loader (`torch.utils.data.DataLoader` or any iterator):
The iterator that will be used to apply `infer` on.
infer (any function):
The function to apply of each element of `loader`.
params (`dict`):
The parameters passed to `infer` along with every item
"""
super().__init__(loader, infer, params)
def __iter__(self):
self.iterator = iter(self.loader)
self.subiterator = None
return self
def __next__(self):
if self.subiterator is None:
"Subiterator None means we haven't started a `preprocess` iterator. so start it"
self.subiterator = self.infer(next(self.iterator), **self.params)
try:
# Try to return next item
processed = next(self.subiterator)
except StopIteration:
# When a preprocess iterator ends, we can start lookig at the next item
# ChunkIterator will keep feeding until ALL elements of iterator
# all have created their subiterator and have been iterating against.
#
# Another way to look at it, is we're basically flattening lists of lists
# into a single list, but with generators
self.subiterator = self.infer(next(self.iterator), **self.params)
processed = next(self.subiterator)
return processed
class PipelinePackIterator(PipelineIterator):
"""
Roughly equivalent to
```
packed = []
for item in loader:
packed.append(item)
if item["is_last"]:
yield packed
packed = []
```
but it also handles cases where `item` are batched (meaning it's a dict of Tensor with first dimension > 1. In
that case it does
```
packed = []
for batch in loader:
# item is batched
for item in batch:
packed.append(item)
if item["is_last"]:
yield packed
packed = []
```
Arguments:
loader (`torch.utils.data.DataLoader` or any iterator):
The iterator that will be used to apply `infer` on.
infer (any function):
The function to apply of each element of `loader`.
params (`dict`):
The parameters passed to `infer` along with every item
loader_batch_size (`int`, *optional*):
If specified, the items of `loader` are supposed to come as batch, and are loader_batched here making
it roughly behave as
```
for items in loader:
for i in loader_batch_size:
item = items[i]
yield infer(item, **params)
```"""
def __iter__(self):
self.iterator = iter(self.loader)
return self
def __next__(self):
# Extremely similar to PipelineIterator in its unpacking mechanism
# BUT, we have an extra required item which is the presence of `is_last`
# That is because everything is flattened by `PipelineChunkIterator` we
# need to keep track of how to regroup here in the original `process`
# boundaries so that `process` and `postprocess` see the same data.
# This iterator accumulates items (possibly while unbatching) until it
# its a `is_last` and then just passes it on to the caller.
is_last = False
accumulator = []
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
while self._loader_batch_index < self.loader_batch_size:
item = self.loader_batch_item()
is_last = item.pop("is_last")
accumulator.append(item)
if is_last:
return accumulator
while not is_last:
processed = self.infer(next(self.iterator), **self.params)
if self.loader_batch_size is not None:
if isinstance(processed, torch.Tensor):
first_tensor = processed
else:
key = list(processed.keys())[0]
first_tensor = processed[key]
if isinstance(first_tensor, list):
observed_batch_size = len(first_tensor)
else:
observed_batch_size = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
self.loader_batch_size = observed_batch_size
self._loader_batch_data = processed
self._loader_batch_index = 0
while self._loader_batch_index < self.loader_batch_size:
item = self.loader_batch_item()
is_last = item.pop("is_last")
accumulator.append(item)
if is_last:
return accumulator
else:
item = processed
is_last = item.pop("is_last")
accumulator.append(item)
return accumulator
class KeyDataset(Dataset):
def __init__(self, dataset: Dataset, key: str):
self.dataset = dataset
self.key = key
def __len__(self):
return len(self.dataset)
def __getitem__(self, i):
return self.dataset[i][self.key]
class KeyPairDataset(Dataset):
def __init__(self, dataset: Dataset, key1: str, key2: str):
self.dataset = dataset
self.key1 = key1
self.key2 = key2
def __len__(self):
return len(self.dataset)
def __getitem__(self, i):
return {"text": self.dataset[i][self.key1], "text_pair": self.dataset[i][self.key2]}
| transformers/src/transformers/pipelines/pt_utils.py/0 | {
"file_path": "transformers/src/transformers/pipelines/pt_utils.py",
"repo_id": "transformers",
"token_count": 5891
} | 352 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Time series distributional output classes and utilities.
"""
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class AffineTransformed(TransformedDistribution):
def __init__(self, base_distribution: Distribution, loc=None, scale=None, event_dim=0):
self.scale = 1.0 if scale is None else scale
self.loc = 0.0 if loc is None else loc
super().__init__(base_distribution, [AffineTransform(loc=self.loc, scale=self.scale, event_dim=event_dim)])
@property
def mean(self):
"""
Returns the mean of the distribution.
"""
return self.base_dist.mean * self.scale + self.loc
@property
def variance(self):
"""
Returns the variance of the distribution.
"""
return self.base_dist.variance * self.scale**2
@property
def stddev(self):
"""
Returns the standard deviation of the distribution.
"""
return self.variance.sqrt()
class ParameterProjection(nn.Module):
def __init__(
self, in_features: int, args_dim: Dict[str, int], domain_map: Callable[..., Tuple[torch.Tensor]], **kwargs
) -> None:
super().__init__(**kwargs)
self.args_dim = args_dim
self.proj = nn.ModuleList([nn.Linear(in_features, dim) for dim in args_dim.values()])
self.domain_map = domain_map
def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor]:
params_unbounded = [proj(x) for proj in self.proj]
return self.domain_map(*params_unbounded)
class LambdaLayer(nn.Module):
def __init__(self, function):
super().__init__()
self.function = function
def forward(self, x, *args):
return self.function(x, *args)
class DistributionOutput:
distribution_class: type
in_features: int
args_dim: Dict[str, int]
def __init__(self, dim: int = 1) -> None:
self.dim = dim
self.args_dim = {k: dim * self.args_dim[k] for k in self.args_dim}
def _base_distribution(self, distr_args):
if self.dim == 1:
return self.distribution_class(*distr_args)
else:
return Independent(self.distribution_class(*distr_args), 1)
def distribution(
self,
distr_args,
loc: Optional[torch.Tensor] = None,
scale: Optional[torch.Tensor] = None,
) -> Distribution:
distr = self._base_distribution(distr_args)
if loc is None and scale is None:
return distr
else:
return AffineTransformed(distr, loc=loc, scale=scale, event_dim=self.event_dim)
@property
def event_shape(self) -> Tuple:
r"""
Shape of each individual event contemplated by the distributions that this object constructs.
"""
return () if self.dim == 1 else (self.dim,)
@property
def event_dim(self) -> int:
r"""
Number of event dimensions, i.e., length of the `event_shape` tuple, of the distributions that this object
constructs.
"""
return len(self.event_shape)
@property
def value_in_support(self) -> float:
r"""
A float that will have a valid numeric value when computing the log-loss of the corresponding distribution. By
default 0.0. This value will be used when padding data series.
"""
return 0.0
def get_parameter_projection(self, in_features: int) -> nn.Module:
r"""
Return the parameter projection layer that maps the input to the appropriate parameters of the distribution.
"""
return ParameterProjection(
in_features=in_features,
args_dim=self.args_dim,
domain_map=LambdaLayer(self.domain_map),
)
def domain_map(self, *args: torch.Tensor):
r"""
Converts arguments to the right shape and domain. The domain depends on the type of distribution, while the
correct shape is obtained by reshaping the trailing axis in such a way that the returned tensors define a
distribution of the right event_shape.
"""
raise NotImplementedError()
@staticmethod
def squareplus(x: torch.Tensor) -> torch.Tensor:
r"""
Helper to map inputs to the positive orthant by applying the square-plus operation. Reference:
https://twitter.com/jon_barron/status/1387167648669048833
"""
return (x + torch.sqrt(torch.square(x) + 4.0)) / 2.0
class StudentTOutput(DistributionOutput):
"""
Student-T distribution output class.
"""
args_dim: Dict[str, int] = {"df": 1, "loc": 1, "scale": 1}
distribution_class: type = StudentT
@classmethod
def domain_map(cls, df: torch.Tensor, loc: torch.Tensor, scale: torch.Tensor):
scale = cls.squareplus(scale).clamp_min(torch.finfo(scale.dtype).eps)
df = 2.0 + cls.squareplus(df)
return df.squeeze(-1), loc.squeeze(-1), scale.squeeze(-1)
class NormalOutput(DistributionOutput):
"""
Normal distribution output class.
"""
args_dim: Dict[str, int] = {"loc": 1, "scale": 1}
distribution_class: type = Normal
@classmethod
def domain_map(cls, loc: torch.Tensor, scale: torch.Tensor):
scale = cls.squareplus(scale).clamp_min(torch.finfo(scale.dtype).eps)
return loc.squeeze(-1), scale.squeeze(-1)
class NegativeBinomialOutput(DistributionOutput):
"""
Negative Binomial distribution output class.
"""
args_dim: Dict[str, int] = {"total_count": 1, "logits": 1}
distribution_class: type = NegativeBinomial
@classmethod
def domain_map(cls, total_count: torch.Tensor, logits: torch.Tensor):
total_count = cls.squareplus(total_count)
return total_count.squeeze(-1), logits.squeeze(-1)
def _base_distribution(self, distr_args) -> Distribution:
total_count, logits = distr_args
if self.dim == 1:
return self.distribution_class(total_count=total_count, logits=logits)
else:
return Independent(self.distribution_class(total_count=total_count, logits=logits), 1)
# Overwrites the parent class method. We cannot scale using the affine
# transformation since negative binomial should return integers. Instead
# we scale the parameters.
def distribution(
self, distr_args, loc: Optional[torch.Tensor] = None, scale: Optional[torch.Tensor] = None
) -> Distribution:
total_count, logits = distr_args
if scale is not None:
# See scaling property of Gamma.
logits += scale.log()
return self._base_distribution((total_count, logits))
| transformers/src/transformers/time_series_utils.py/0 | {
"file_path": "transformers/src/transformers/time_series_utils.py",
"repo_id": "transformers",
"token_count": 2916
} | 353 |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class TextClassificationTool(PipelineTool):
"""
Example:
```py
from transformers.tools import TextClassificationTool
classifier = TextClassificationTool()
classifier("This is a super nice API!", labels=["positive", "negative"])
```
"""
default_checkpoint = "facebook/bart-large-mnli"
description = (
"This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which "
"should be the text to classify, and `labels`, which should be the list of labels to use for classification. "
"It returns the most likely label in the list of provided `labels` for the input text."
)
name = "text_classifier"
pre_processor_class = AutoTokenizer
model_class = AutoModelForSequenceClassification
inputs = ["text", ["text"]]
outputs = ["text"]
def setup(self):
super().setup()
config = self.model.config
self.entailment_id = -1
for idx, label in config.id2label.items():
if label.lower().startswith("entail"):
self.entailment_id = int(idx)
if self.entailment_id == -1:
raise ValueError("Could not determine the entailment ID from the model config, please pass it at init.")
def encode(self, text, labels):
self._labels = labels
return self.pre_processor(
[text] * len(labels),
[f"This example is {label}" for label in labels],
return_tensors="pt",
padding="max_length",
)
def decode(self, outputs):
logits = outputs.logits
label_id = torch.argmax(logits[:, 2]).item()
return self._labels[label_id]
| transformers/src/transformers/tools/text_classification.py/0 | {
"file_path": "transformers/src/transformers/tools/text_classification.py",
"repo_id": "transformers",
"token_count": 874
} | 354 |
IMAGENET_DEFAULT_MEAN = [0.485, 0.456, 0.406]
IMAGENET_DEFAULT_STD = [0.229, 0.224, 0.225]
IMAGENET_STANDARD_MEAN = [0.5, 0.5, 0.5]
IMAGENET_STANDARD_STD = [0.5, 0.5, 0.5]
OPENAI_CLIP_MEAN = [0.48145466, 0.4578275, 0.40821073]
OPENAI_CLIP_STD = [0.26862954, 0.26130258, 0.27577711]
| transformers/src/transformers/utils/constants.py/0 | {
"file_path": "transformers/src/transformers/utils/constants.py",
"repo_id": "transformers",
"token_count": 162
} | 355 |
# coding=utf-8
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import builtins
import collections
import functools
import inspect
import math
import operator
import os
import random
import warnings
from typing import Any, Callable, Dict, List, Optional, Type, Union
import torch
from torch import nn
from torch.fx import Graph, GraphModule, Proxy, Tracer
from torch.fx._compatibility import compatibility
from torch.fx.proxy import ParameterProxy
from .. import PretrainedConfig, PreTrainedModel, logging
from ..models.auto import get_values
from ..models.auto.modeling_auto import (
MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_BACKBONE_MAPPING_NAMES,
MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
MODEL_FOR_CTC_MAPPING_NAMES,
MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_IMAGE_MAPPING_NAMES,
MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING_NAMES,
MODEL_FOR_MASKED_LM_MAPPING_NAMES,
MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES,
MODEL_FOR_PRETRAINING_MAPPING_NAMES,
MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES,
MODEL_MAPPING_NAMES,
)
from ..pytorch_utils import is_torch_greater_or_equal_than_2_0
from ..utils import (
ENV_VARS_TRUE_VALUES,
TORCH_FX_REQUIRED_VERSION,
get_torch_version,
is_peft_available,
is_torch_fx_available,
)
if is_peft_available():
from peft import PeftModel
logger = logging.get_logger(__name__)
_IS_IN_DEBUG_MODE = os.environ.get("FX_DEBUG_MODE", "").upper() in ENV_VARS_TRUE_VALUES
def _generate_supported_model_class_names(
model_name: Type[PretrainedConfig],
supported_tasks: Optional[Union[str, List[str]]] = None,
) -> List[str]:
task_mapping = {
"default": MODEL_MAPPING_NAMES,
"pretraining": MODEL_FOR_PRETRAINING_MAPPING_NAMES,
"next-sentence-prediction": MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES,
"masked-lm": MODEL_FOR_MASKED_LM_MAPPING_NAMES,
"causal-lm": MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
"seq2seq-lm": MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
"speech-seq2seq": MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES,
"multiple-choice": MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
"document-question-answering": MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
"question-answering": MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
"sequence-classification": MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
"token-classification": MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
"masked-image-modeling": MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING_NAMES,
"image-classification": MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
"zero-shot-image-classification": MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES,
"ctc": MODEL_FOR_CTC_MAPPING_NAMES,
"audio-classification": MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
"semantic-segmentation": MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
"backbone": MODEL_FOR_BACKBONE_MAPPING_NAMES,
"image-feature-extraction": MODEL_FOR_IMAGE_MAPPING_NAMES,
}
if supported_tasks is None:
supported_tasks = task_mapping.keys()
if isinstance(supported_tasks, str):
supported_tasks = [supported_tasks]
model_class_names = []
for task in supported_tasks:
class_name = task_mapping[task].get(model_name, None)
if class_name:
model_class_names.append(class_name)
return model_class_names
_REGULAR_SUPPORTED_MODEL_NAMES_AND_TASKS = [
"altclip",
"albert",
"bart",
"bert",
"blenderbot",
"blenderbot-small",
"bloom",
"clip",
"convnext",
"deberta",
"deberta-v2",
"dinov2",
"distilbert",
"donut-swin",
"electra",
"gpt2",
"gpt_neo",
"gptj",
"hubert",
"layoutlm",
"llama",
"cohere",
"lxmert",
"m2m_100",
"marian",
"mbart",
"megatron-bert",
"mobilebert",
"mt5",
"nezha",
"opt",
"pegasus",
"plbart",
"resnet",
"roberta",
"segformer",
"speech_to_text",
"speech_to_text_2",
"swin",
"t5",
"trocr",
"vit",
"xglm",
"wav2vec2",
# "xlnet",
]
_FX_SUPPORTED_MODELS_WITH_KV_CACHE = ["llama", "opt"]
_REGULAR_SUPPORTED_MODELS = []
for item in _REGULAR_SUPPORTED_MODEL_NAMES_AND_TASKS:
if isinstance(item, dict):
_REGULAR_SUPPORTED_MODELS.extend(_generate_supported_model_class_names(**item))
else:
_REGULAR_SUPPORTED_MODELS.extend(_generate_supported_model_class_names(item))
_SPECIAL_SUPPORTED_MODELS = [
"CLIPTextModel",
"CLIPTextModelWithProjection",
"CLIPVisionModel",
"CLIPVisionModelWithProjection",
"AltCLIPTextModel",
"AltCLIPVisionModel",
"GitVisionModel",
"GPT2DoubleHeadsModel",
"Speech2Text2Decoder",
"TrOCRDecoder",
"PeftModelForCausalLM",
"PeftModelForSeq2SeqLM",
# TODO: add support for them as it should be quite easy to do so (small blocking issues).
# XLNetForQuestionAnswering,
]
_SUPPORTED_MODELS = tuple(sorted(set(_REGULAR_SUPPORTED_MODELS + _SPECIAL_SUPPORTED_MODELS)))
def torch_nn_embedding(self, input):
return torch.empty(*input.shape, self.weight.shape[-1], device="meta", dtype=self.weight.dtype)
def torch_nn_functional_embedding(
input, weight, padding_idx=None, max_norm=None, norm_type=2.0, scale_grad_by_freq=False, sparse=False
):
return torch.empty(*input.shape, weight.shape[-1], device="meta", dtype=weight.dtype)
def torch_nn_layernorm(self, input):
return input
def torch_nn_groupnorm(self, input):
return input
def torch_nn_linear(self, input):
return torch.empty(input.shape[:-1] + (self.out_features,), device="meta")
def torch_relu(x):
return x
def torch_nn_relu(self, x):
return x
def torch_nn_functional_relu(x, inplace=False):
if not inplace:
raise ValueError("Don't support in-place functional.relu for MetaTensor analysis")
return x
def torch_where(condition, x, y):
# torch.where returns the broadcasted tensor of condition, x, and y,
# so hack it by using addition
return condition.to(device="meta") + x.to(device="meta") + y.to(device="meta")
def torch_abs(input, *, out=None):
if out is not None:
raise ValueError("Don't support in-place abs for MetaTensor analysis")
return input
def torch_arange(*args, **kwargs):
n = len(args)
step = 1
if n == 1:
start = 0
end = args[0]
elif n == 2:
start, end = args
else:
start, end, step = args
if isinstance(start, float):
start = int(start)
if isinstance(end, float):
start = int(end)
if isinstance(step, float):
step = int(step)
step = kwargs.get("step", step)
dtype = kwargs.get("dtype")
return torch.empty((end - start) // step, dtype=dtype, device="meta")
def torch_full(*args, **kwargs):
args = list(args)
if isinstance(args[1], torch.Tensor) and args[1].device == torch.device("meta"):
args[1] = 1 # Any value.
kwargs_without_device = dict(kwargs)
kwargs_without_device.pop("device", None)
return torch.full(*args, **kwargs_without_device)
def torch_cat(tensors, dim=None, axis=None, *, out=None):
if dim is None and axis is None:
dim = 0
if dim is None and axis is not None:
dim = axis
if dim < 0:
dim = tensors[0].dim() + dim
shapes = [t.shape for t in tensors]
shape = list(shapes[0])
concatenated_dim = sum(shape[dim] for shape in shapes)
final_shape = shape[:dim] + [concatenated_dim] + shape[dim + 1 :]
return torch.empty(final_shape, device="meta")
def torch_stack(tensors, dim=None, axis=None, *, out=None):
if dim is None and axis is None:
dim = 0
if dim is None and axis is not None:
dim = axis
if dim < 0:
dim = tensors[0].dim() + 1 + dim
shape = list(tensors[0].shape)
shape.insert(dim, len(tensors))
return torch.empty(shape, device="meta")
def torch_add(input, other, *, alpha=1, out=None):
if not isinstance(input, torch.Tensor):
return torch.empty_like(other, device="meta")
if not isinstance(other, torch.Tensor):
return torch.empty_like(input, device="meta")
max_length = max(input.dim(), other.dim())
input_shape = list(input.shape) + [1] * (max_length - input.dim())
other_shape = list(other.shape) + [1] * (max_length - other.dim())
shape = []
for i in range(max_length):
shape.append(max(input_shape[i], other_shape[i]))
return torch.empty(shape, device="meta")
def torch_mul(input, other, *, out=None):
return torch_add(input, other, out=out)
def torch_tensor_mul(self, other):
return torch_mul(self, other)
def torch_matmul(input, other, *, out=None):
d1 = input.dim()
d2 = other.dim()
shape = None
if d1 == 1 and d2 == 1:
shape = None
elif d1 == 2 and d2 == 2:
shape = (input.size(0), other.size(1))
elif d1 == 1 and d2 == 2:
shape = (other.size(1),)
elif d1 == 2 and d1 == 1:
shape = (input.size(0),)
else:
max_length = max(input.dim(), other.dim())
shape1 = list(input.shape)
shape2 = list(other.shape)
if d1 == 1:
shape1 = [1] + shape1
if d2 == 1:
shape2.append(1)
shape1 = [-1] * (max_length - d1) + list(input.shape)
shape2 = [-1] * (max_length - d2) + list(other.shape)
shape = []
for i in range(max_length):
shape.append(max(shape1[i], shape2[i]))
shape[-2] = shape1[-2]
shape[-1] = shape2[-1]
if d1 == 1:
shape.pop(-2)
if d2 == 1:
shape.pop(-1)
if shape is None:
return torch.tensor(0.0, device="meta")
return torch.empty(*shape, device="meta")
def torch_bmm(input, mat2, *, out=None):
if out is not None:
raise ValueError("Don't support in-place bmm for MetaTensor analysis")
batch_size, n, m = input.shape
_, _, p = mat2.shape
return torch.empty(batch_size, n, p, device="meta")
def torch_baddbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None):
if out is not None:
raise ValueError("Don't support in-place baddbmm for MetaTensor analysis")
return torch_bmm(batch1, batch2)
def torch_tensor_baddbmm(self, batch1, batch2, *, beta=1, alpha=1, out=None):
return torch_baddbmm(self, batch1, batch2, beta=beta, alpha=alpha, out=out)
def torch_einsum(equation, *operands):
# TODO: infer shape without performing the computation, this might be quite hard.
concrete_operands = (torch.empty_like(operand, device="cpu") for operand in operands)
return torch.einsum(equation, *concrete_operands).to("meta")
def torch_tensor_repeat(self, *sizes):
shape = list(self.shape)
for i, x in enumerate(sizes):
shape[i] *= x
return torch.empty(shape, device="meta")
def torch_repeat_interleave(*args, dim=None, output_size=None):
num_args = len(args)
if num_args == 1:
shape = [output_size if output_size is not None else args[0].sum()]
else:
shape = list(args[0].shape)
if dim is None:
if num_args > 2:
dim = args[2]
else:
shape = [sum(shape)]
dim = 0
repeats = args[1]
if isinstance(repeats, int) or torch.numel(repeats) == 1:
shape[dim] *= int(repeats)
else:
shape[dim] = output_size if output_size is not None else repeats.sum()
return torch.empty(*shape, device="meta")
def torch_index_select(input, dim, index, *, out=None):
shape = list(input.shape)
shape[dim] = len(index)
return torch.empty(*shape, device="meta")
def torch_tensor_index_select(self, dim, index):
return torch_index_select(self, dim, index)
def torch_gather(input, dim, index, *, sparse_grad=False, out=None):
shape = list(input.shape)
shape[dim] = index.shape[dim]
return torch.empty(*shape, device="meta")
def torch_tensor_gather(self, dim, index):
return torch_gather(self, dim, index)
def torch_roll(input, shifts, dims=None):
return input
def torch_flip(input, dims):
return input
def torch_tensor_flip(self, dims):
return self
def torch_nn_conv1d(self, input):
l_in = input.shape[-1]
shape = None
padding = self.padding
if padding == "valid":
padding = (0, 0)
if padding == "same":
shape = list(input.shape)
if shape is None:
shape = list(input.shape)
l_out = math.floor(
(l_in + 2 * padding[0] - self.dilation[0] * (self.kernel_size[0] - 1) - 1) / self.stride[0] + 1
)
shape[-1] = l_out
shape[-2] = self.out_channels
return torch.empty(shape, device="meta")
def torch_nn_conv2d(self, input):
h_in, w_in = input.shape[-2:]
shape = None
padding = self.padding
if padding == "valid":
padding = (0, 0)
if padding == "same":
shape = list(input.shape)
if shape is None:
shape = list(input.shape)
h_out = math.floor(
(h_in + 2 * padding[0] - self.dilation[0] * (self.kernel_size[0] - 1) - 1) / self.stride[0] + 1
)
w_out = math.floor(
(w_in + 2 * padding[1] - self.dilation[1] * (self.kernel_size[1] - 1) - 1) / self.stride[1] + 1
)
shape[-2:] = [h_out, w_out]
shape[-3] = self.out_channels
return torch.empty(shape, device="meta")
def torch_squeeze(input, dim=None):
shape = list(input.shape)
if dim is not None:
if dim < 0:
dim = input.dim() + dim
if shape[dim] == 1:
shape.pop(dim)
else:
new_shape = []
for dim_value in shape:
if dim_value == 1:
continue
new_shape.append(dim_value)
shape = new_shape
return torch.empty(shape, device="meta")
def torch_tensor_squeeze(self, dim=None):
return torch_squeeze(self, dim)
def torch_unsqueeze(input, dim):
shape = list(input.shape)
if dim < 0:
dim = input.dim() + 1 + dim
shape.insert(dim, 1)
return torch.empty(shape, device="meta")
def torch_tensor_unsqueeze(self, dim):
return torch_unsqueeze(self, dim)
def torch_unique_consecutive(input, **kwargs):
output = torch.unique_consecutive(torch.zeros_like(input, device="cpu"), **kwargs)
if isinstance(output, torch.Tensor):
return output.to("meta")
else:
return tuple(map(output, lambda x: x.to("meta")))
def torch_nn_functional_one_hot(tensor, num_classes=-1):
if num_classes < 0:
raise ValueError("Don't support automatic num_classes inference for MetaTensor analysis")
shape = list(tensor.shape) + [num_classes]
return torch.empty(shape, device="meta")
def torch_nn_functional_scaled_dot_product_attention(
query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, scale=None
):
target_length = query.shape[-2]
head_dim = value.shape[-1]
return torch.empty((*query.shape[:-2], target_length, head_dim), device="meta")
def torch_nn_mseloss(self, input, target):
if self.reduction == "none":
shape = target.shape
else:
shape = (1,)
return torch.empty(shape, device="meta")
def torch_nn_crossentropyloss(self, input, target):
if self.reduction == "none":
shape = target.shape
else:
shape = (1,)
return torch.empty(shape, device="meta")
def torch_nn_bcewithlogitsloss(self, input, target):
if self.reduction == "none":
shape = target.shape
else:
shape = (1,)
return torch.empty(shape, device="meta")
def operator_getitem(a, b):
def to_concrete(t):
if isinstance(t, torch.Tensor):
concrete = torch.ones_like(t, device="cpu")
if concrete.dtype in [torch.float16, torch.float32, torch.float64, torch.int32]:
concrete = concrete.to(torch.int64)
return concrete
return t
if isinstance(a, torch.Tensor):
# TODO: infer shape without performing the computation.
if isinstance(b, tuple):
b = tuple(map(to_concrete, b))
else:
b = to_concrete(b)
return operator.getitem(torch.empty_like(a, device="cpu"), b).to("meta")
return operator.getitem(a, b)
_MANUAL_META_OVERRIDES: Dict[Callable, Callable] = {
torch.nn.Embedding: torch_nn_embedding,
torch.nn.functional.embedding: torch_nn_functional_embedding,
torch.nn.LayerNorm: torch_nn_layernorm,
torch.nn.GroupNorm: torch_nn_groupnorm,
torch.nn.Linear: torch_nn_linear,
torch.relu: torch_relu,
torch.nn.functional.relu: torch_nn_functional_relu,
torch.nn.ReLU: torch_nn_relu,
torch.where: torch_where,
torch.abs: torch_abs,
torch.arange: torch_arange,
torch.full: torch_full,
torch.cat: torch_cat,
torch.stack: torch_stack,
torch.add: torch_add,
torch.mul: torch_mul,
torch.Tensor.mul: torch_tensor_mul,
torch.matmul: torch_matmul,
torch.bmm: torch_bmm,
torch.baddbmm: torch_baddbmm,
torch.Tensor.baddbmm: torch_tensor_baddbmm,
torch.einsum: torch_einsum,
torch.Tensor.repeat: torch_tensor_repeat,
torch.repeat_interleave: torch_repeat_interleave,
torch.roll: torch_roll,
torch.flip: torch_flip,
torch.Tensor.flip: torch_tensor_flip,
torch.index_select: torch_index_select,
torch.Tensor.index_select: torch_tensor_index_select,
torch.gather: torch_gather,
torch.Tensor.gather: torch_tensor_gather,
torch.nn.Conv1d: torch_nn_conv1d,
torch.nn.Conv2d: torch_nn_conv2d,
torch.squeeze: torch_squeeze,
torch.Tensor.squeeze: torch_tensor_squeeze,
torch.unsqueeze: torch_unsqueeze,
torch.Tensor.unsqueeze: torch_tensor_unsqueeze,
torch.unique_consecutive: torch_unique_consecutive,
torch.nn.functional.one_hot: torch_nn_functional_one_hot,
torch.nn.MSELoss: torch_nn_mseloss,
torch.nn.CrossEntropyLoss: torch_nn_crossentropyloss,
torch.nn.BCEWithLogitsLoss: torch_nn_bcewithlogitsloss,
operator.getitem: operator_getitem,
}
if is_torch_greater_or_equal_than_2_0:
_MANUAL_META_OVERRIDES[
torch.nn.functional.scaled_dot_product_attention
] = torch_nn_functional_scaled_dot_product_attention
class HFProxy(Proxy):
"""
Proxy that uses metadata to handle data-dependent control-flow.
"""
def install_metadata(self, metadata):
self._metadata = metadata
@property
def shape(self):
return self.tracer.create_proxy("call_method", "size", (self,), {})
@property
def device(self):
# Hack so we can track when devices are used. During meta-tensor propagation,
# replace these values with a constant 'meta'
return MetaDeviceAttribute(self, "device")
def __len__(self):
if hasattr(self, "_metadata") and self._metadata is not None:
return len(self._metadata)
return super().__len__()
def __bool__(self):
if hasattr(self, "_metadata") and self._metadata is not None:
return self._metadata
return super().__bool__()
def __getattr__(self, k):
if k == "_metadata":
return self.__getattribute__(k)
# note: not added to the graph yet, if this is a method call
# we peephole optimize to the method invocation
return HFAttribute(self, k)
def __setitem__(self, indices, values):
return self.tracer.create_proxy("call_function", operator.setitem, (self, indices, values), {})
def __contains__(self, key):
if hasattr(self, "_metadata") and self._metadata is not None:
return key in self._metadata
return super().__contains__(key)
class HFAttribute(HFProxy):
def __init__(self, root, attr: str):
self.root = root
self.attr = attr
self.tracer = root.tracer
self._node = None
if hasattr(self.root, "_metadata"):
self.install_metadata(getattr(self.root._metadata, attr))
@property
def node(self):
# the node for attributes is added lazily, since most will just be method calls
# which do not rely on the getitem call
if self._node is None:
self._node = self.tracer.create_proxy("call_function", builtins.getattr, (self.root, self.attr), {}).node
return self._node
def __call__(self, *args, **kwargs):
return self.tracer.create_proxy("call_method", self.attr, (self.root,) + args, kwargs)
class MetaDeviceAttribute(HFAttribute):
pass
def _proxies_to_metas(v):
"""Returns the underlying metadata for HFProxies, and behaves like the identity for the others."""
if isinstance(v, MetaDeviceAttribute):
return "meta"
if isinstance(v, torch.fx.Proxy):
if not (isinstance(v, HFProxy) and hasattr(v, "_metadata")):
raise RuntimeError(f"No metadata was found for {v}")
return v._metadata
return v
def _gen_constructor_wrapper(target):
@functools.wraps(target)
def wrapper(*args, **kwargs):
proxy = None
def check_has_proxy(v):
if isinstance(v, Proxy):
nonlocal proxy
proxy = v
torch.fx.node.map_aggregate(args, check_has_proxy)
torch.fx.node.map_aggregate(kwargs, check_has_proxy)
if proxy is not None:
return proxy.tracer.create_proxy("call_function", target, args, kwargs)
else:
return target(*args, **kwargs)
return wrapper, target
def _generate_random_int(low: int = 10, high: int = 20, forbidden_values: Optional[List[int]] = None):
if forbidden_values is None:
forbidden_values = []
value = random.randint(low, high)
while value in forbidden_values:
value = random.randint(low, high)
return value
class HFTracer(Tracer):
"""
Tracer that is able to symbolically trace models from the library. To do that, it uses the HFProxy instead of the
regular PyTorch torch.fx.Proxy.
"""
# Feature flag for proxying accesses to buffer values
proxy_buffer_attributes: bool = True
allow_insert_stateless_mods: bool = True
_TORCH_METHODS_TO_PATCH = [
"arange",
"zeros",
"ones",
"full",
"full_like",
"eye",
"empty",
"tensor",
"clamp",
"finfo",
]
supported_archs = (PreTrainedModel,) if not is_peft_available() else (PreTrainedModel, PeftModel)
def __init__(self, autowrap_modules=(math,), autowrap_functions=()):
super().__init__(autowrap_modules=autowrap_modules, autowrap_functions=autowrap_functions)
if not is_torch_fx_available():
raise ImportError(
f"Found an incompatible version of torch. Found version {get_torch_version()}, but only version "
f"{TORCH_FX_REQUIRED_VERSION} is supported."
)
def _generate_dummy_input(
self, model: PreTrainedModel, input_name: str, shape: List[int], input_names: List[str]
) -> Dict[str, torch.Tensor]:
"""Generates dummy input for model inference recording."""
# Retrieving the model class, either from the "class_for_deserialization" attribute if the model was restored
# from pickle, or from the "__class__" attribute in the general case.
model_class_name = getattr(model, "class_for_deserialization", model.__class__).__name__
device = model.device
inputs_dict = {}
# when tracing a model with KV cache, we simply need to unsure that the KV cache length is larger than one to
# rightfully pass certain controlflows (Example: https://github.com/huggingface/transformers/blob/5c8d941d66734811d2ef6f57f15b44f7fb7a98c4/src/transformers/modeling_attn_mask_utils.py#L162).
# After tracing, the model can then still be used with arbitrary lengths different than the one used during tracing.
kv_cache_length = 5
if input_name in ["labels", "start_positions", "end_positions"]:
batch_size = shape[0]
if model_class_name in [
*get_values(MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES),
*get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES),
*get_values(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES),
*get_values(MODEL_FOR_BACKBONE_MAPPING_NAMES),
*get_values(MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES),
]:
inputs_dict["labels"] = torch.zeros(batch_size, dtype=torch.long, device=device)
elif model_class_name in [
*get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES),
*get_values(MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES),
"XLNetForQuestionAnswering",
]:
inputs_dict["start_positions"] = torch.zeros(batch_size, dtype=torch.long, device=device)
inputs_dict["end_positions"] = torch.zeros(batch_size, dtype=torch.long, device=device)
elif model_class_name in get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES):
if not hasattr(model.config, "problem_type") or model.config.problem_type is None:
raise ValueError(
"Could not retrieve the problem type for the sequence classification task, please set "
'model.config.problem_type to one of the following values: "regression", '
'"single_label_classification", or "multi_label_classification".'
)
if model.config.problem_type == "regression":
labels_shape = (batch_size, model.config.num_labels)
labels_dtype = torch.float32
elif model.config.problem_type == "single_label_classification":
labels_shape = (batch_size,)
labels_dtype = torch.long
elif model.config.problem_type == "multi_label_classification":
labels_shape = (batch_size, model.config.num_labels)
labels_dtype = torch.float32
else:
raise ValueError(
'Expected model.config.problem_type to be either: "regression", "single_label_classification"'
f', or "multi_label_classification", but "{model.config.problem_type}" was provided.'
)
inputs_dict["labels"] = torch.zeros(*labels_shape, dtype=labels_dtype, device=device)
elif model_class_name in [
*get_values(MODEL_FOR_PRETRAINING_MAPPING_NAMES),
*get_values(MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES),
*get_values(MODEL_FOR_CAUSAL_LM_MAPPING_NAMES),
*get_values(MODEL_FOR_MASKED_LM_MAPPING_NAMES),
*get_values(MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES),
*get_values(MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES),
"GPT2DoubleHeadsModel",
"PeftModelForCausalLM",
"PeftModelForSeq2SeqLM",
]:
inputs_dict["labels"] = torch.zeros(shape, dtype=torch.long, device=device)
elif model_class_name in [*get_values(MODEL_FOR_CTC_MAPPING_NAMES)]:
inputs_dict["labels"] = torch.zeros(shape, dtype=torch.float32, device=device)
else:
raise NotImplementedError(
f"Generating the dummy input named {input_name} for {model_class_name} is not supported yet."
)
elif "pixel_values" in input_name:
batch_size = shape[0]
image_size = getattr(model.config, "image_size", None)
if image_size is None:
if hasattr(model.config, "vision_config"):
image_size = model.config.vision_config.image_size
elif hasattr(model.config, "encoder"):
image_size = model.config.encoder.image_size
else:
image_size = (_generate_random_int(), _generate_random_int())
# If no num_channels is in the config, use some arbitrary value.
num_channels = getattr(model.config, "num_channels", 3)
if not isinstance(image_size, collections.abc.Iterable):
image_size = (image_size, image_size)
height, width = image_size
inputs_dict[input_name] = torch.zeros(
batch_size, num_channels, height, width, dtype=torch.float32, device=device
)
elif "bbox" in input_name:
inputs_dict[input_name] = torch.zeros(*shape, 4, dtype=torch.float, device=device)
elif "input_features" in input_name:
inputs_dict[input_name] = torch.zeros(
*shape, model.config.input_feat_per_channel, dtype=torch.float, device=device
)
elif "visual_feats" in input_name:
inputs_dict[input_name] = torch.zeros(
shape
+ [
model.config.visual_feat_dim,
],
dtype=torch.float,
device=device,
)
elif "visual_pos" in input_name:
inputs_dict[input_name] = torch.zeros(
shape
+ [
model.config.visual_pos_dim,
],
dtype=torch.float,
device=device,
)
elif "inputs" in input_name:
inputs_dict[input_name] = torch.zeros(*shape, dtype=torch.float, device=device)
elif "input_values" in input_name:
batch_size, _ = shape
# Generating big sequence length for audio inputs.
seq_length = _generate_random_int(low=10000, high=20000)
inputs_dict[input_name] = torch.zeros(batch_size, seq_length, dtype=torch.float, device=device)
elif "mask" in input_name:
if "past_key_values" in input_names:
mask_shape = [shape[0], shape[1] + kv_cache_length]
else:
mask_shape = shape
inputs_dict[input_name] = torch.zeros(mask_shape, dtype=torch.long, device=device)
elif "ids" in input_name:
inputs_dict[input_name] = torch.zeros(shape, dtype=torch.long, device=device)
elif "past_key_values" in input_name:
if model.config.model_type not in _FX_SUPPORTED_MODELS_WITH_KV_CACHE:
raise NotImplementedError(
f"Symbolic trace with past_key_values input is not supported yet for the model {model.config.model_type}. Please open an issue or a PR in Transformers repository if you would like to see the support added."
)
num_heads = model.config.num_attention_heads
head_dim = model.config.hidden_size // model.config.num_attention_heads
cache_shape = (shape[0], num_heads, kv_cache_length, head_dim)
pkv = tuple(
(
torch.rand(cache_shape, dtype=torch.float, device=device),
torch.rand(cache_shape, dtype=torch.float, device=device),
)
for i in range(model.config.num_hidden_layers)
)
inputs_dict[input_name] = pkv
else:
shape_with_hidden_size = shape + [model.config.hidden_size]
inputs_dict[input_name] = torch.zeros(shape_with_hidden_size, dtype=torch.float, device=device)
return inputs_dict
def create_proxy(self, kind, target, args, kwargs, name=None, type_expr=None, proxy_factory_fn=None):
rv = super().create_proxy(kind, target, args, kwargs, name, type_expr, proxy_factory_fn)
if kind == "placeholder" and target in self.meta_args:
rv.install_metadata(self.meta_args[target])
return rv
if target in self.orig_fns:
# NOTE: tensor constructors in PyTorch define the `device` argument as
# *kwargs-only*. That is why this works. If you add methods to
# _TORCH_METHODS_TO_PATCH that do not define `device` as kwarg-only,
# this will break and you will likely see issues where we cannot infer
# the size of the output.
if "device" in kwargs:
kwargs["device"] = "meta"
try:
args_metas = torch.fx.node.map_aggregate(args, _proxies_to_metas)
kwargs_metas = torch.fx.node.map_aggregate(kwargs, _proxies_to_metas)
if kind == "call_function":
meta_target = _MANUAL_META_OVERRIDES.get(target, target)
meta_out = meta_target(*args_metas, **kwargs_metas)
if isinstance(meta_out, torch.Tensor):
meta_out = meta_out.to(device="meta")
elif kind == "call_method":
method = getattr(args_metas[0].__class__, target)
meta_target = _MANUAL_META_OVERRIDES.get(method, method)
meta_out = meta_target(*args_metas, **kwargs_metas)
elif kind == "call_module":
if not hasattr(self, "orig_forward"):
raise AttributeError(f"{self} does not have an attribute called orig_forward")
self._disable_module_getattr = True
try:
mod = self.root.get_submodule(target)
mod_type = type(mod)
if mod_type in _MANUAL_META_OVERRIDES:
meta_out = _MANUAL_META_OVERRIDES[mod_type](mod, *args_metas, **kwargs_metas)
else:
meta_out = self.orig_forward(*args_metas, **kwargs_metas)
finally:
self._disable_module_getattr = False
elif kind == "get_attr":
self._disable_module_getattr = True
try:
attr_itr = self.root
atoms = target.split(".")
for atom in atoms:
attr_itr = getattr(attr_itr, atom)
if isinstance(attr_itr, torch.Tensor):
meta_out = attr_itr.to(device="meta")
else:
meta_out = attr_itr
finally:
self._disable_module_getattr = False
else:
return rv
if not isinstance(rv, Proxy):
raise ValueError("Don't support composite output yet")
rv.install_metadata(meta_out)
except Exception as e:
if _IS_IN_DEBUG_MODE:
warnings.warn(f"Could not compute metadata for {kind} target {target}: {e}")
return rv
# Replaced by .getattr from PyTorch 1.13
def _module_getattr(self, attr, attr_val, parameter_proxy_cache):
if getattr(self, "_disable_module_getattr", False):
return attr_val
else:
def maybe_get_proxy_for_attr(attr_val, collection_to_search, parameter_proxy_cache):
for n, p in collection_to_search:
if attr_val is p:
if n not in parameter_proxy_cache:
kwargs = {}
if "proxy_factory_fn" in inspect.signature(self.create_proxy).parameters:
kwargs["proxy_factory_fn"] = (
None
if not self.param_shapes_constant
else lambda node: ParameterProxy(self, node, n, attr_val)
)
val_proxy = self.create_proxy("get_attr", n, (), {}, **kwargs) # type: ignore[arg-type]
parameter_proxy_cache[n] = val_proxy
return parameter_proxy_cache[n]
return None
if isinstance(attr_val, torch.nn.Parameter):
maybe_parameter_proxy = maybe_get_proxy_for_attr(
attr_val, self.root.named_parameters(), parameter_proxy_cache
)
if maybe_parameter_proxy is not None:
return maybe_parameter_proxy
if self.proxy_buffer_attributes and isinstance(attr_val, torch.Tensor):
maybe_buffer_proxy = maybe_get_proxy_for_attr(
attr_val, self.root.named_buffers(), parameter_proxy_cache
)
if maybe_buffer_proxy is not None:
return maybe_buffer_proxy
return attr_val
# Needed for PyTorch 1.13+
def getattr(self, attr: str, attr_val: Any, parameter_proxy_cache: Dict[str, Any]):
return self._module_getattr(attr, attr_val, parameter_proxy_cache)
def call_module(self, m, forward, args, kwargs):
self.orig_forward = forward
return super().call_module(m, forward, args, kwargs)
def proxy(self, node):
return HFProxy(node, self)
def trace(
self,
root: Union[torch.nn.Module, Callable[..., Any]],
concrete_args: Optional[Dict[str, Any]] = None,
dummy_inputs: Optional[Dict[str, Any]] = None,
complete_concrete_args_with_inputs_not_in_dummy_inputs: bool = True,
) -> Graph:
"""
Traces `root` and returns the corresponding FX `torch.fx.Graph` representation. `root` can either be a
`torch.nn.Module` instance or a Python callable. Note that after this call, `self.root` may be different from
the `root` passed in here. For example, when a free function is passed to `trace()`, we will create a
`torch.nn.Module` instance to use as the root and add embedded constants to.
Args:
root (`torch.nn.Module` or `Callable`):
Either a `torch.nn.Module`` or a function to be traced through. If root is not a
[`~transformers.PreTrainedModel`], then `dummy_inputs` must be passed, otherwise tracing will fail.
concrete_args (`Dict[str, Any], *optional*):
Concrete arguments that should not be treated as Proxies
dummy_inputs (`Dict[str, Any]`, *optional*):
The dummy inputs needed to handle data-dependent control-flow if `root` is not a
[`~transformers.PreTrainedModel`]. It can also be used when `root` is a
[`~transformers.PreTrainedModel`] to specify custom dummy inputs for a subset or all the model inputs.
complete_concrete_args_with_inputs_not_in_dummy_inputs (`bool`, *optional*, defaults to `True`):
If `True`, and `dummy_inputs` is specified, every argument that `root` can take that is not in
`dummy_inputs` and not in `concrete_args` will be added to `concrete_args`, otherwise does nothing.
Returns:
`torch.fx.Graph`:
A FX `torch.fx.Graph` representing the semantics of the passed-in `root`.
"""
sig = inspect.signature(root.forward if isinstance(root, torch.nn.Module) else root)
if concrete_args is None:
concrete_args = {}
if dummy_inputs is not None and complete_concrete_args_with_inputs_not_in_dummy_inputs:
for param in sig.parameters.values():
if param.name in dummy_inputs:
continue
if param.default is inspect.Parameter.empty:
raise ValueError(f"You need to specify a default value for the parameter {param.name}.")
concrete_args.update(
{
p.name: p.default
for p in sig.parameters.values()
if (p.name not in dummy_inputs and p.name not in concrete_args)
}
)
input_names = sig.parameters.keys() - concrete_args.keys()
# Creating a random input shape to generate dummy inputs.
batch_size = _generate_random_int()
sequence_length = _generate_random_int()
shape = [batch_size, sequence_length]
if root.__class__.__name__ in get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES):
num_choices = _generate_random_int(low=2, high=5)
shape.insert(1, num_choices)
inputs = dict(dummy_inputs) if dummy_inputs is not None else {}
for input_name in input_names:
if input_name in inputs:
continue
# We enforce that root must either be a PreTrainedModel or deserialized from a serialized traced model to
# be able to use HFTracer._generate_dummy_input.
if isinstance(root, self.supported_archs) or type(root).__qualname__.startswith(
("_deserialize_graph_module", "_CodeOnlyModule")
):
inputs.update(self._generate_dummy_input(root, input_name, shape, input_names=input_names))
else:
raise RuntimeError(
f"Could not generate input named {input_name} for because root is not a"
" transformers.PreTrainedModel."
)
concrete_metas = {
input_name: input_.to("meta") if isinstance(input_, torch.Tensor) else input_
for input_name, input_ in inputs.items()
}
for param in sig.parameters.values():
if param.kind == inspect.Parameter.VAR_KEYWORD and param.name not in input_names:
concrete_metas[f"**{param.name}"] = {}
self.meta_args = concrete_metas
self.patched_torch_methods = {
target: _gen_constructor_wrapper(getattr(torch, target)) for target in self._TORCH_METHODS_TO_PATCH
}
self.orig_fns = set()
for name, (wrapper, orig) in self.patched_torch_methods.items():
setattr(torch, name, wrapper)
self.orig_fns.add(orig)
try:
self.graph = super().trace(root, concrete_args=concrete_args)
finally:
for name, (_, orig) in self.patched_torch_methods.items():
setattr(torch, name, orig)
# This is necessary because concrete args are added as input to the traced module since
# https://github.com/pytorch/pytorch/pull/55888.
for node in self.graph.nodes:
if node.op == "placeholder":
# Removing default values for inputs as the forward pass will fail with them.
if node.target in input_names:
node.args = ()
# Without this, torch.jit.script fails because the inputs type is Optional[torch.Tensor].
# It cannot infer on the attributes and methods the input should have, and fails.
node.type = torch.Tensor
# It is a concrete arg so it is not used and should be removed.
else:
to_visit = [node]
to_delete = collections.OrderedDict()
while to_visit:
n = to_visit.pop(0)
to_delete[n] = None
to_visit += list(n.users.keys())
for user in reversed(to_delete.keys()):
self.graph.erase_node(user)
# TODO: solves GraphModule creation.
# Without this, return type annotation "Tuple" is causing code execution failure.
if node.op == "output":
node.type = None
return self.graph
def _stateless_mod_instanciation_depends_on_proxies(self, mod: nn.Module) -> bool:
"""
Whether the module was instantiated with Proxies. If that is the case, such module cannot be a leaf module
because its attributes are input-dependent.
"""
return any(isinstance(attr, Proxy) for attr in mod.__dict__.values())
def _insert_module_as_submodule(self, mod: nn.Module) -> str:
"""
Helper method which tries to insert a module that was not declared as submodule.
"""
# If one of the module attributes is a Proxy, it means that its instantiation is input-dependent.
# It is not possible to insert such modules, those should be traced through.
if self._stateless_mod_instanciation_depends_on_proxies(mod):
return ""
idx = 0
mod_name = mod.__class__.__name__.lower()
path = f"{mod_name}_{idx}"
already_inserted = False
while hasattr(self.root, path):
if getattr(self.root, path) is mod:
already_inserted = True
break
path = f"{mod_name}_{idx}"
idx += 1
# No need to add multiple instances of the same module.
if not already_inserted:
self.root.add_module(path, mod)
return path
def path_of_module(self, mod: nn.Module) -> str:
"""
Helper method to find the qualified name of `mod` in the Module hierarchy of `root`. For example, if `root` has
a submodule named `foo`, which has a submodule named `bar`, passing `bar` into this function will return the
string "foo.bar".
Args:
mod (str): The `Module` to retrieve the qualified name for.
"""
try:
return super().path_of_module(mod)
except NameError as e:
if self.allow_insert_stateless_mods and len(list(mod.parameters())) == 0 and len(list(mod.buffers())) == 0:
path = self._insert_module_as_submodule(mod)
return path
raise e
def is_leaf_module(self, m: torch.nn.Module, module_qualified_name: str) -> bool:
return (not self._stateless_mod_instanciation_depends_on_proxies(m)) and super().is_leaf_module(
m, module_qualified_name
)
@compatibility(is_backward_compatible=True)
def keys(self, obj: "Proxy") -> Any:
"""Called when a proxy object is has the keys() method called.
This is what happens when ** is called on a proxy. This should return an iterator if ** is supposed to work in
your custom tracer.
"""
attribute = HFAttribute(obj, "keys")()
if obj.node.target == "**kwargs":
return attribute._metadata
return attribute
def get_concrete_args(model: nn.Module, input_names: List[str]):
sig = inspect.signature(model.forward)
if not (set(input_names) <= set(sig.parameters.keys())):
formatted_input_names = input_names[0] if len(input_names) == 1 else ", ".join(input_names)
formatted_allowed_input_names = ", ".join(sig.parameters.keys())
raise ValueError(
f"The model does not have input(s) named: {formatted_input_names}, expected a subset of the following:"
f" {formatted_allowed_input_names}"
)
return {p.name: p.default for p in sig.parameters.values() if p.name not in input_names}
def is_model_supported(model: PreTrainedModel):
return model.__class__.__name__ in _SUPPORTED_MODELS
def check_if_model_is_supported(model: PreTrainedModel):
if not is_model_supported(model):
supported_model_names = ", ".join(_SUPPORTED_MODELS)
raise NotImplementedError(
f"Model {model.__class__.__name__} is not supported yet, supported models: {supported_model_names}"
)
def symbolic_trace(
model: PreTrainedModel,
input_names: Optional[List[str]] = None,
disable_check: bool = False,
tracer_cls: Type[HFTracer] = HFTracer,
) -> GraphModule:
"""
Performs symbolic tracing on the model.
Args:
model ([`PretrainedModel`]):
The model to trace.
input_names (`List[str]`, *optional*):
The names of the inputs of the traced model. If unset, model.dummy_inputs.keys() are used instead.
disable_check (`bool`, *optional*, defaults to `False`):
If `True`, no check is done before trying to trace the model, this is mostly usesul for debugging purposes.
tracer_cls (`Type[HFTracer]`, *optional*, defaults to `HFTracer`):
The tracer class to use for instantiating the tracer. If unset, `HFTracer` is used instead.
Returns:
`torch.fx.GraphModule`: A GraphModule constructed by recording operations seen while tracing the model.
Example:
```python
from transformers.utils.fx import symbolic_trace
traced_model = symbolic_trace(model, input_names=["input_ids", "attention_mask", "token_type_ids"])
```
"""
if input_names is None:
input_names = model.dummy_inputs.keys()
input_names = list(input_names)
concrete_args = get_concrete_args(model, input_names)
if not disable_check:
check_if_model_is_supported(model)
# Tracing.
tracer = tracer_cls()
traced_graph = tracer.trace(model, concrete_args=concrete_args)
traced = torch.fx.GraphModule(model, traced_graph)
traced.config = model.config
# The model class must be stored as an attribute to allow model deserialization, which uses trace, and thus
# _generate_dummy_input, where the model class is needed.
traced.class_for_deserialization = model.__class__
traced.device = model.device
return traced
| transformers/src/transformers/utils/fx.py/0 | {
"file_path": "transformers/src/transformers/utils/fx.py",
"repo_id": "transformers",
"token_count": 22812
} | 356 |
<!---
Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->
# How to add a new example script in 🤗 Transformers
This folder provide a template for adding a new example script implementing a training or inference task with the
models in the 🤗 Transformers library. To use it, you will need to install cookiecutter:
```bash
pip install cookiecutter
```
or refer to the installation page of the [cookiecutter documentation](https://cookiecutter.readthedocs.io/).
You can then run the following command inside the `examples` folder of the transformers repo:
```bash
cookiecutter ../templates/adding_a_new_example_script/
```
and answer the questions asked, which will generate a new folder where you will find a pre-filled template for your
example following the best practices we recommend for them.
Adjust the way the data is preprocessed, the model is loaded or the Trainer is instantiated then when you're happy, add
a `README.md` in the folder (or complete the existing one if you added a script to an existing folder) telling a user
how to run your script.
Make a PR to the 🤗 Transformers repo. Don't forget to tweet about your new example with a carbon screenshot of how to
run it and tag @huggingface!
| transformers/templates/adding_a_new_example_script/README.md/0 | {
"file_path": "transformers/templates/adding_a_new_example_script/README.md",
"repo_id": "transformers",
"token_count": 444
} | 357 |
# coding=utf-8
# Copyright 2022 {{cookiecutter.authors}} and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization classes for {{cookiecutter.modelname}}."""
{%- if cookiecutter.tokenizer_type == "Based on BERT" %}
from ...utils import logging
from ..bert.tokenization_bert import BertTokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"{{cookiecutter.checkpoint_identifier}}": "https://huggingface.co/{{cookiecutter.checkpoint_identifier}}/resolve/main/vocab.txt",
}
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"{{cookiecutter.checkpoint_identifier}}": 512,
}
PRETRAINED_INIT_CONFIGURATION = {
"{{cookiecutter.checkpoint_identifier}}": {"do_lower_case": False},
}
class {{cookiecutter.camelcase_modelname}}Tokenizer(BertTokenizer):
r"""
Construct a {{cookiecutter.modelname}} tokenizer.
[`~{{cookiecutter.camelcase_modelname}}Tokenizer`] is identical to [`BertTokenizer`] and runs end-to-end
tokenization: punctuation splitting and wordpiece.
Refer to superclass [`BertTokenizer`] for usage examples and documentation concerning
parameters.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
{%- elif cookiecutter.tokenizer_type == "Based on BART" %}
from ...utils import logging
from ..bart.tokenization_bart import BartTokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"{{cookiecutter.checkpoint_identifier}}": "https://huggingface.co/{{cookiecutter.checkpoint_identifier}}/resolve/main/vocab.json",
},
"merges_file": {
"{{cookiecutter.checkpoint_identifier}}": "https://huggingface.co/{{cookiecutter.checkpoint_identifier}}/resolve/main/merges.txt",
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"{{cookiecutter.checkpoint_identifier}}": 1024,
}
class {{cookiecutter.camelcase_modelname}}Tokenizer(BartTokenizer):
"""
Construct a {{cookiecutter.modelname}} tokenizer.
[`~{{cookiecutter.camelcase_modelname}}Tokenizer`] is identical to [`BartTokenizer`] and runs end-to-end
tokenization: punctuation splitting and wordpiece.
Refer to superclass [`BartTokenizer`] for usage examples and documentation concerning
parameters.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
{%- elif cookiecutter.tokenizer_type == "Standalone" %}
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"{{cookiecutter.checkpoint_identifier}}": "https://huggingface.co/{{cookiecutter.checkpoint_identifier}}/resolve/main/vocab.txt",
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"{{cookiecutter.checkpoint_identifier}}": 1024,
}
class {{cookiecutter.camelcase_modelname}}Tokenizer(PreTrainedTokenizer):
"""
Construct a {{cookiecutter.modelname}} tokenizer. Based on byte-level Byte-Pair-Encoding.
Args:
vocab_file (`str`):
Path to the vocabulary file.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
vocab_file,
unk_token="<|endoftext|>",
bos_token="<|endoftext|>",
eos_token="<|endoftext|>",
**kwargs
):
bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
super().__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, **kwargs)
""" Initialisation """
@property
def vocab_size(self):
""" Returns vocab size """
def get_vocab(self):
""" Returns vocab as a dict """
def _tokenize(self, text):
""" Returns a tokenized string. """
def _convert_token_to_id(self, token):
""" Converts a token (str) in an id using the vocab. """
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
def convert_tokens_to_string(self, tokens):
""" Converts a sequence of tokens (string) in a single string. """
def save_vocabulary(self, save_directory):
"""
Save the vocabulary and special tokens file to a directory.
Args:
save_directory (`str`):
The directory in which to save the vocabulary.
Returns:
`Tuple(str)`: Paths to the files saved.
"""
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks
by concatenating and adding special tokens.
A {{cookiecutter.modelname}} sequence has the following format:
- single sequence: `<s> X </s>`
- pair of sequences: `<s> A </s></s> B </s>`
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
if token_ids_1 is None:
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
cls = [self.cls_token_id]
sep = [self.sep_token_id]
return cls + token_ids_0 + sep + sep + token_ids_1 + sep
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
)
if token_ids_1 is None:
return [1] + ([0] * len(token_ids_0)) + [1]
return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task.
{{cookiecutter.modelname}} does not make use of token type ids, therefore a list of zeros is returned.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of zeros.
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space)
if (is_split_into_words or add_prefix_space) and (len(text) > 0 and not text[0].isspace()):
text = " " + text
return (text, kwargs)
class {{cookiecutter.camelcase_modelname}}TokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" {{cookiecutter.modelname}} tokenizer (backed by HuggingFace's *tokenizers* library).
Args:
vocab_file (`str`):
Path to the vocabulary file.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
vocab_file,
merges_file,
unk_token="<|endoftext|>",
bos_token="<|endoftext|>",
eos_token="<|endoftext|>",
add_prefix_space=False,
trim_offsets=True,
**kwargs
):
super().__init__(
ByteLevelBPETokenizer(
vocab_file=vocab_file,
merges_file=merges_file,
add_prefix_space=add_prefix_space,
trim_offsets=trim_offsets,
),
bos_token=bos_token,
eos_token=eos_token,
unk_token=unk_token,
**kwargs,
)
self.add_prefix_space = add_prefix_space
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
output = [self.bos_token_id] + token_ids_0 + [self.eos_token_id]
if token_ids_1 is None:
return output
return output + [self.eos_token_id] + token_ids_1 + [self.eos_token_id]
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task.
{{cookiecutter.modelname}} does not make use of token type ids, therefore a list of zeros is returned.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of zeros.
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
{% endif %}
| transformers/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/tokenization_{{cookiecutter.lowercase_modelname}}.py/0 | {
"file_path": "transformers/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/tokenization_{{cookiecutter.lowercase_modelname}}.py",
"repo_id": "transformers",
"token_count": 5165
} | 358 |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class TFBenchmarkTest(unittest.TestCase):
def check_results_dict_not_empty(self, results):
for model_result in results.values():
for batch_size, sequence_length in zip(model_result["bs"], model_result["ss"]):
result = model_result["result"][batch_size][sequence_length]
self.assertIsNotNone(result)
def test_inference_no_configs_eager(self):
MODEL_ID = "sshleifer/tiny-gpt2"
benchmark_args = TensorFlowBenchmarkArguments(
models=[MODEL_ID],
training=False,
inference=True,
sequence_lengths=[8],
batch_sizes=[1],
eager_mode=True,
multi_process=False,
)
benchmark = TensorFlowBenchmark(benchmark_args)
results = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def test_inference_no_configs_only_pretrain(self):
MODEL_ID = "sgugger/tiny-distilbert-classification"
benchmark_args = TensorFlowBenchmarkArguments(
models=[MODEL_ID],
training=False,
inference=True,
sequence_lengths=[8],
batch_sizes=[1],
multi_process=False,
only_pretrain_model=True,
)
benchmark = TensorFlowBenchmark(benchmark_args)
results = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def test_inference_no_configs_graph(self):
MODEL_ID = "sshleifer/tiny-gpt2"
benchmark_args = TensorFlowBenchmarkArguments(
models=[MODEL_ID],
training=False,
inference=True,
sequence_lengths=[8],
batch_sizes=[1],
multi_process=False,
)
benchmark = TensorFlowBenchmark(benchmark_args)
results = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def test_inference_with_configs_eager(self):
MODEL_ID = "sshleifer/tiny-gpt2"
config = AutoConfig.from_pretrained(MODEL_ID)
benchmark_args = TensorFlowBenchmarkArguments(
models=[MODEL_ID],
training=False,
inference=True,
sequence_lengths=[8],
batch_sizes=[1],
eager_mode=True,
multi_process=False,
)
benchmark = TensorFlowBenchmark(benchmark_args, [config])
results = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def test_inference_with_configs_graph(self):
MODEL_ID = "sshleifer/tiny-gpt2"
config = AutoConfig.from_pretrained(MODEL_ID)
benchmark_args = TensorFlowBenchmarkArguments(
models=[MODEL_ID],
training=False,
inference=True,
sequence_lengths=[8],
batch_sizes=[1],
multi_process=False,
)
benchmark = TensorFlowBenchmark(benchmark_args, [config])
results = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def test_train_no_configs(self):
MODEL_ID = "sshleifer/tiny-gpt2"
benchmark_args = TensorFlowBenchmarkArguments(
models=[MODEL_ID],
training=True,
inference=False,
sequence_lengths=[8],
batch_sizes=[1],
multi_process=False,
)
benchmark = TensorFlowBenchmark(benchmark_args)
results = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result)
self.check_results_dict_not_empty(results.memory_train_result)
def test_train_with_configs(self):
MODEL_ID = "sshleifer/tiny-gpt2"
config = AutoConfig.from_pretrained(MODEL_ID)
benchmark_args = TensorFlowBenchmarkArguments(
models=[MODEL_ID],
training=True,
inference=False,
sequence_lengths=[8],
batch_sizes=[1],
multi_process=False,
)
benchmark = TensorFlowBenchmark(benchmark_args, [config])
results = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result)
self.check_results_dict_not_empty(results.memory_train_result)
def test_inference_encoder_decoder_with_configs(self):
MODEL_ID = "patrickvonplaten/t5-tiny-random"
config = AutoConfig.from_pretrained(MODEL_ID)
benchmark_args = TensorFlowBenchmarkArguments(
models=[MODEL_ID],
training=False,
inference=True,
sequence_lengths=[8],
batch_sizes=[1],
multi_process=False,
)
benchmark = TensorFlowBenchmark(benchmark_args, configs=[config])
results = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("GPU")) == 0, "Cannot do xla on CPU.")
def test_inference_no_configs_xla(self):
MODEL_ID = "sshleifer/tiny-gpt2"
benchmark_args = TensorFlowBenchmarkArguments(
models=[MODEL_ID],
training=False,
inference=True,
sequence_lengths=[8],
batch_sizes=[1],
use_xla=True,
multi_process=False,
)
benchmark = TensorFlowBenchmark(benchmark_args)
results = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def test_save_csv_files(self):
MODEL_ID = "sshleifer/tiny-gpt2"
with tempfile.TemporaryDirectory() as tmp_dir:
benchmark_args = TensorFlowBenchmarkArguments(
models=[MODEL_ID],
inference=True,
save_to_csv=True,
sequence_lengths=[8],
batch_sizes=[1],
inference_time_csv_file=os.path.join(tmp_dir, "inf_time.csv"),
inference_memory_csv_file=os.path.join(tmp_dir, "inf_mem.csv"),
env_info_csv_file=os.path.join(tmp_dir, "env.csv"),
multi_process=False,
)
benchmark = TensorFlowBenchmark(benchmark_args)
benchmark.run()
self.assertTrue(Path(os.path.join(tmp_dir, "inf_time.csv")).exists())
self.assertTrue(Path(os.path.join(tmp_dir, "inf_mem.csv")).exists())
self.assertTrue(Path(os.path.join(tmp_dir, "env.csv")).exists())
def test_trace_memory(self):
MODEL_ID = "sshleifer/tiny-gpt2"
def _check_summary_is_not_empty(summary):
self.assertTrue(hasattr(summary, "sequential"))
self.assertTrue(hasattr(summary, "cumulative"))
self.assertTrue(hasattr(summary, "current"))
self.assertTrue(hasattr(summary, "total"))
with tempfile.TemporaryDirectory() as tmp_dir:
benchmark_args = TensorFlowBenchmarkArguments(
models=[MODEL_ID],
inference=True,
sequence_lengths=[8],
batch_sizes=[1],
log_filename=os.path.join(tmp_dir, "log.txt"),
log_print=True,
trace_memory_line_by_line=True,
eager_mode=True,
multi_process=False,
)
benchmark = TensorFlowBenchmark(benchmark_args)
result = benchmark.run()
_check_summary_is_not_empty(result.inference_summary)
self.assertTrue(Path(os.path.join(tmp_dir, "log.txt")).exists())
| transformers/tests/benchmark/test_benchmark_tf.py/0 | {
"file_path": "transformers/tests/benchmark/test_benchmark_tf.py",
"repo_id": "transformers",
"token_count": 4131
} | 359 |
# coding=utf-8
# Copyright 2020 The HuggingFace Team Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a clone of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import tempfile
import unittest
import warnings
import numpy as np
from parameterized import parameterized
from transformers import is_torch_available, pipeline, set_seed
from transformers.testing_utils import (
is_flaky,
require_accelerate,
require_torch,
require_torch_multi_accelerator,
slow,
torch_device,
)
from ..test_modeling_common import floats_tensor, ids_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin
if is_torch_available():
import torch
from transformers import (
AutoModelForCausalLM,
AutoModelForSeq2SeqLM,
AutoModelForSpeechSeq2Seq,
AutoModelForVision2Seq,
AutoTokenizer,
BartForCausalLM,
BartForConditionalGeneration,
BartTokenizer,
GPT2LMHeadModel,
GPT2Tokenizer,
ImageGPTForCausalImageModeling,
SpeechEncoderDecoderModel,
)
from transformers.cache_utils import DynamicCache
from transformers.generation import (
BeamSampleDecoderOnlyOutput,
BeamSampleEncoderDecoderOutput,
BeamSearchDecoderOnlyOutput,
BeamSearchEncoderDecoderOutput,
DisjunctiveConstraint,
GenerateBeamDecoderOnlyOutput,
GenerateBeamEncoderDecoderOutput,
GenerateDecoderOnlyOutput,
GenerateEncoderDecoderOutput,
GreedySearchDecoderOnlyOutput,
GreedySearchEncoderDecoderOutput,
LogitsProcessorList,
MaxLengthCriteria,
MinLengthLogitsProcessor,
PhrasalConstraint,
SampleDecoderOnlyOutput,
SampleEncoderDecoderOutput,
StoppingCriteria,
StoppingCriteriaList,
)
from transformers.generation.utils import _speculative_sampling
class GenerationTesterMixin:
model_tester = None
all_generative_model_classes = ()
input_name = "input_ids"
def _get_input_ids_and_config(self, batch_size=2):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
input_ids = inputs_dict[self.input_name]
# cut to half length & take max batch_size 3
sequence_length = input_ids.shape[-1] // 2
input_ids = input_ids[:batch_size, :sequence_length]
# generate max 3 tokens
if config.is_encoder_decoder:
max_length = 4
else:
max_length = input_ids.shape[-1] + 3
if config.eos_token_id is not None and config.pad_token_id is None:
# hack to allow generate for models such as GPT2 as is done in `generate()`
if isinstance(config.eos_token_id, int):
config.eos_token_id = [config.eos_token_id]
config.pad_token_id = config.eos_token_id[0]
attention_mask = torch.ones_like(input_ids, dtype=torch.long)[:batch_size, :sequence_length]
# It is important set set the eos_token_id to None to ensure that no sequences
# shorter than `max_length` can be generated
config.eos_token_id = None
config.forced_eos_token_id = None
return config, input_ids, attention_mask, max_length
@staticmethod
def _get_logits_processor_and_warper_kwargs(
input_length,
forced_bos_token_id=None,
forced_eos_token_id=None,
max_length=None,
):
process_kwargs = {
"min_length": input_length + 1 if max_length is None else max_length - 1,
"bad_words_ids": [[1, 0]],
"repetition_penalty": 1.2,
"remove_invalid_values": True,
}
# NoRepeatNGramLogitsProcessor + forced tokens may result in no valid continuations
if forced_bos_token_id is None and forced_eos_token_id is None:
process_kwargs["no_repeat_ngram_size"] = 2
warp_kwargs = {"top_k": 10, "top_p": 0.7, "temperature": 0.7}
return process_kwargs, warp_kwargs
@staticmethod
def _get_beam_kwargs(num_return_sequences=1):
beam_kwargs = {
"early_stopping": False,
"length_penalty": 2.0,
"num_beams": 2,
"num_return_sequences": num_return_sequences,
}
return beam_kwargs
@staticmethod
def _get_diverse_beam_kwargs(num_return_sequences=1):
beam_kwargs = {
"early_stopping": False,
"length_penalty": 2.0,
"num_beams": 2,
"num_return_sequences": num_return_sequences,
"num_beam_groups": 2, # one beam per group
"diversity_penalty": 2.0,
}
return beam_kwargs
@staticmethod
def _get_constrained_beam_kwargs(num_return_sequences=1):
beam_kwargs = {
"early_stopping": False,
"length_penalty": 2.0,
"num_beams": num_return_sequences * 4,
"num_return_sequences": num_return_sequences,
}
return beam_kwargs
@staticmethod
def _get_encoder_outputs(
model, input_ids, attention_mask, output_attentions=None, output_hidden_states=None, num_interleave=1
):
encoder = model.get_encoder()
encoder_outputs = encoder(
input_ids,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
encoder_outputs["last_hidden_state"] = encoder_outputs.last_hidden_state.repeat_interleave(
num_interleave, dim=0
)
input_ids = torch.zeros_like(input_ids[:, :1]) + model._get_decoder_start_token_id()
attention_mask = None
return encoder_outputs, input_ids, attention_mask
def _greedy_generate(
self,
model,
input_ids,
attention_mask,
max_length,
output_scores=False,
output_logits=False,
output_attentions=False,
output_hidden_states=False,
return_dict_in_generate=False,
):
logits_process_kwargs, _ = self._get_logits_processor_and_warper_kwargs(
input_ids.shape[-1],
forced_bos_token_id=model.config.forced_bos_token_id,
forced_eos_token_id=model.config.forced_eos_token_id,
max_length=max_length,
)
model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
output_generate = model.generate(
input_ids,
do_sample=False,
num_beams=1,
max_length=max_length,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
output_scores=output_scores,
output_logits=output_logits,
return_dict_in_generate=return_dict_in_generate,
**logits_process_kwargs,
**model_kwargs,
)
return output_generate
def _sample_generate(
self,
model,
input_ids,
attention_mask,
max_length,
num_return_sequences,
logits_warper_kwargs,
process_kwargs,
output_scores=False,
output_logits=False,
output_attentions=False,
output_hidden_states=False,
return_dict_in_generate=False,
):
torch.manual_seed(0)
model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
output_generate = model.generate(
input_ids,
do_sample=True,
num_beams=1,
max_length=max_length,
num_return_sequences=num_return_sequences,
output_scores=output_scores,
output_logits=output_logits,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict_in_generate=return_dict_in_generate,
**logits_warper_kwargs,
**process_kwargs,
**model_kwargs,
)
return output_generate
def _beam_search_generate(
self,
model,
input_ids,
attention_mask,
max_length,
beam_kwargs,
logits_process_kwargs,
output_scores=False,
output_logits=False,
output_attentions=False,
output_hidden_states=False,
return_dict_in_generate=False,
):
model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
output_generate = model.generate(
input_ids,
do_sample=False,
max_length=max_length,
output_scores=output_scores,
output_logits=output_logits,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict_in_generate=return_dict_in_generate,
**beam_kwargs,
**logits_process_kwargs,
**model_kwargs,
)
return output_generate
def _beam_sample_generate(
self,
model,
input_ids,
attention_mask,
max_length,
beam_kwargs,
logits_warper_kwargs,
output_scores=False,
output_logits=False,
output_attentions=False,
output_hidden_states=False,
return_dict_in_generate=False,
):
torch.manual_seed(0)
model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
output_generate = model.generate(
input_ids,
do_sample=True,
max_length=max_length,
output_scores=output_scores,
output_logits=output_logits,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict_in_generate=return_dict_in_generate,
**beam_kwargs,
**logits_warper_kwargs,
**model_kwargs,
)
return output_generate
def _group_beam_search_generate(
self,
model,
input_ids,
attention_mask,
max_length,
beam_kwargs,
logits_process_kwargs,
output_scores=False,
output_logits=False,
output_attentions=False,
output_hidden_states=False,
return_dict_in_generate=False,
):
model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
output_generate = model.generate(
input_ids,
do_sample=False,
max_length=max_length,
output_scores=output_scores,
output_logits=output_logits,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict_in_generate=return_dict_in_generate,
**beam_kwargs,
**logits_process_kwargs,
**model_kwargs,
)
return output_generate
def _constrained_beam_search_generate(
self,
model,
input_ids,
attention_mask,
max_length,
constraints,
beam_kwargs,
logits_process_kwargs,
output_scores=False,
output_logits=False,
output_attentions=False,
output_hidden_states=False,
return_dict_in_generate=False,
):
model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
output_generate = model.generate(
input_ids,
do_sample=False,
max_length=max_length,
output_scores=output_scores,
output_logits=output_logits,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict_in_generate=return_dict_in_generate,
constraints=constraints,
**beam_kwargs,
**logits_process_kwargs,
**model_kwargs,
)
return output_generate
def _contrastive_generate(
self,
model,
input_ids,
attention_mask,
max_length,
output_scores=False,
output_logits=False,
output_attentions=False,
output_hidden_states=False,
return_dict_in_generate=False,
):
contrastive_search_kwargs = {
"penalty_alpha": 0.6,
"top_k": 5,
}
logits_process_kwargs, _ = self._get_logits_processor_and_warper_kwargs(
input_ids.shape[-1],
forced_bos_token_id=model.config.forced_bos_token_id,
forced_eos_token_id=model.config.forced_eos_token_id,
max_length=max_length,
)
model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
output_generate = model.generate(
input_ids,
do_sample=False,
num_beams=1,
max_length=max_length,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
output_scores=output_scores,
output_logits=output_logits,
return_dict_in_generate=return_dict_in_generate,
**logits_process_kwargs,
**model_kwargs,
**contrastive_search_kwargs,
)
return output_generate
def test_greedy_generate(self):
for model_class in self.all_generative_model_classes:
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
model = model_class(config).to(torch_device).eval()
output_generate = self._greedy_generate(
model=model, input_ids=input_ids, attention_mask=attention_mask, max_length=max_length
)
self.assertTrue(output_generate.shape[-1] == max_length)
def test_greedy_generate_dict_outputs(self):
for model_class in self.all_generative_model_classes:
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
config.use_cache = False
model = model_class(config).to(torch_device).eval()
output_generate = self._greedy_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
max_length=max_length,
output_scores=True,
output_logits=True,
output_hidden_states=True,
output_attentions=True,
return_dict_in_generate=True,
)
if model.config.is_encoder_decoder:
self.assertIsInstance(output_generate, GenerateEncoderDecoderOutput)
# Retrocompatibility check
self.assertIsInstance(output_generate, GreedySearchEncoderDecoderOutput)
else:
self.assertIsInstance(output_generate, GenerateDecoderOnlyOutput)
# Retrocompatibility check
self.assertIsInstance(output_generate, GreedySearchDecoderOnlyOutput)
self.assertTrue(output_generate.sequences.shape[-1] == max_length)
self._check_outputs(output_generate, input_ids, model.config)
def test_greedy_generate_dict_outputs_use_cache(self):
for model_class in self.all_generative_model_classes:
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
if not hasattr(config, "use_cache"):
self.skipTest("This model doesn't support caching")
config.use_cache = True
config.is_decoder = True
model = model_class(config).to(torch_device).eval()
output_generate = self._greedy_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
max_length=max_length,
output_scores=True,
output_logits=True,
output_hidden_states=True,
output_attentions=True,
return_dict_in_generate=True,
)
self.assertTrue(output_generate.sequences.shape[-1] == max_length)
self._check_outputs(output_generate, input_ids, model.config, use_cache=True)
def test_sample_generate(self):
for model_class in self.all_generative_model_classes:
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
model = model_class(config).to(torch_device).eval()
if model.config.is_encoder_decoder:
max_length = 4
process_kwargs, logits_warper_kwargs = self._get_logits_processor_and_warper_kwargs(
input_ids.shape[-1],
forced_bos_token_id=model.config.forced_bos_token_id,
forced_eos_token_id=model.config.forced_eos_token_id,
max_length=max_length,
)
output_generate = self._sample_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
max_length=max_length,
num_return_sequences=1,
logits_warper_kwargs=logits_warper_kwargs,
process_kwargs=process_kwargs,
)
self.assertTrue(output_generate.shape[-1] == max_length)
def test_sample_generate_dict_output(self):
for model_class in self.all_generative_model_classes:
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
config.use_cache = False
model = model_class(config).to(torch_device).eval()
if model.config.is_encoder_decoder:
max_length = 4
process_kwargs, logits_warper_kwargs = self._get_logits_processor_and_warper_kwargs(
input_ids.shape[-1],
forced_bos_token_id=model.config.forced_bos_token_id,
forced_eos_token_id=model.config.forced_eos_token_id,
max_length=max_length,
)
output_generate = self._sample_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
max_length=max_length,
num_return_sequences=2,
logits_warper_kwargs=logits_warper_kwargs,
process_kwargs=process_kwargs,
output_scores=True,
output_logits=True,
output_hidden_states=True,
output_attentions=True,
return_dict_in_generate=True,
)
if model.config.is_encoder_decoder:
self.assertIsInstance(output_generate, GenerateEncoderDecoderOutput)
# Retrocompatibility check
self.assertIsInstance(output_generate, SampleEncoderDecoderOutput)
else:
self.assertIsInstance(output_generate, GenerateDecoderOnlyOutput)
# Retrocompatibility check
self.assertIsInstance(output_generate, SampleDecoderOnlyOutput)
self.assertTrue(output_generate.sequences.shape[-1] == max_length)
self._check_outputs(output_generate, input_ids, model.config, num_return_sequences=2)
def test_beam_search_generate(self):
for model_class in self.all_generative_model_classes:
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
model = model_class(config).to(torch_device).eval()
if model.config.is_encoder_decoder:
max_length = 4
logits_process_kwargs, _ = self._get_logits_processor_and_warper_kwargs(
input_ids.shape[-1],
config.forced_bos_token_id,
config.forced_eos_token_id,
max_length,
)
beam_kwargs = self._get_beam_kwargs()
output_generate = self._beam_search_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
max_length=max_length,
beam_kwargs=beam_kwargs,
logits_process_kwargs=logits_process_kwargs,
)
self.assertTrue(output_generate.shape[-1] == max_length)
def test_beam_search_generate_dict_output(self):
for model_class in self.all_generative_model_classes:
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
# disable cache
config.use_cache = False
model = model_class(config).to(torch_device).eval()
if model.config.is_encoder_decoder:
max_length = 4
logits_process_kwargs, _ = self._get_logits_processor_and_warper_kwargs(
input_ids.shape[-1],
config.forced_bos_token_id,
config.forced_eos_token_id,
max_length,
)
beam_kwargs = self._get_beam_kwargs()
output_generate = self._beam_search_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
max_length=max_length,
beam_kwargs=beam_kwargs,
logits_process_kwargs=logits_process_kwargs,
output_scores=True,
output_logits=True,
output_hidden_states=True,
output_attentions=True,
return_dict_in_generate=True,
)
if model.config.is_encoder_decoder:
self.assertIsInstance(output_generate, GenerateBeamEncoderDecoderOutput)
# Retrocompatibility check
self.assertIsInstance(output_generate, BeamSearchEncoderDecoderOutput)
else:
self.assertIsInstance(output_generate, GenerateBeamDecoderOnlyOutput)
# Retrocompatibility check
self.assertIsInstance(output_generate, BeamSearchDecoderOnlyOutput)
self.assertTrue(output_generate.sequences.shape[-1] == max_length)
self._check_outputs(
output_generate, input_ids, model.config, num_return_sequences=beam_kwargs["num_beams"]
)
def test_beam_search_generate_dict_outputs_use_cache(self):
for model_class in self.all_generative_model_classes:
# enable cache
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
if not hasattr(config, "use_cache"):
self.skipTest("This model doesn't support caching")
model = model_class(config).to(torch_device).eval()
if model.config.is_encoder_decoder:
max_length = 4
logits_process_kwargs, _ = self._get_logits_processor_and_warper_kwargs(
input_ids.shape[-1],
config.forced_bos_token_id,
config.forced_eos_token_id,
max_length,
)
beam_kwargs = self._get_beam_kwargs()
config.use_cache = True
config.is_decoder = True
model = model_class(config).to(torch_device).eval()
output_generate = self._beam_search_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
max_length=max_length,
beam_kwargs=beam_kwargs,
logits_process_kwargs=logits_process_kwargs,
output_scores=True,
output_logits=True,
output_hidden_states=True,
output_attentions=True,
return_dict_in_generate=True,
)
self.assertTrue(output_generate.sequences.shape[-1] == max_length)
self._check_outputs(
output_generate, input_ids, model.config, use_cache=True, num_return_sequences=beam_kwargs["num_beams"]
)
@require_accelerate
@require_torch_multi_accelerator
def test_model_parallel_beam_search(self):
for model_class in self.all_generative_model_classes:
if "xpu" in torch_device:
return unittest.skip("device_map='auto' does not work with XPU devices")
if model_class._no_split_modules is None:
continue
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
model = model_class(config).eval()
with tempfile.TemporaryDirectory() as tmp_dir:
model.cpu().save_pretrained(tmp_dir)
new_model = model_class.from_pretrained(tmp_dir, device_map="auto")
new_model.generate(
input_ids,
attention_mask=attention_mask,
max_length=max_length,
num_beams=2,
)
def test_beam_sample_generate(self):
for model_class in self.all_generative_model_classes:
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
_, logits_warper_kwargs = self._get_logits_processor_and_warper_kwargs(input_ids.shape[-1])
model = model_class(config).to(torch_device).eval()
if model.config.is_encoder_decoder:
max_length = 4
beam_kwargs = self._get_beam_kwargs()
output_generate = self._beam_sample_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
max_length=max_length,
beam_kwargs=beam_kwargs,
logits_warper_kwargs=logits_warper_kwargs,
)
self.assertTrue(output_generate.shape[-1] == max_length)
def test_beam_sample_generate_dict_output(self):
for model_class in self.all_generative_model_classes:
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
# disable cache
config.use_cache = False
model = model_class(config).to(torch_device).eval()
_, logits_warper_kwargs = self._get_logits_processor_and_warper_kwargs(input_ids.shape[-1])
if model.config.is_encoder_decoder:
max_length = 4
beam_kwargs = self._get_beam_kwargs()
output_generate = self._beam_sample_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
max_length=max_length,
beam_kwargs=beam_kwargs,
logits_warper_kwargs=logits_warper_kwargs,
output_scores=True,
output_logits=True,
output_hidden_states=True,
output_attentions=True,
return_dict_in_generate=True,
)
if model.config.is_encoder_decoder:
self.assertIsInstance(output_generate, GenerateBeamEncoderDecoderOutput)
# Retrocompatibility check
self.assertIsInstance(output_generate, BeamSampleEncoderDecoderOutput)
else:
self.assertIsInstance(output_generate, GenerateBeamDecoderOnlyOutput)
# Retrocompatibility check
self.assertIsInstance(output_generate, BeamSampleDecoderOnlyOutput)
self.assertTrue(output_generate.sequences.shape[-1] == max_length)
self._check_outputs(
output_generate, input_ids, model.config, num_return_sequences=beam_kwargs["num_beams"]
)
def test_generate_without_input_ids(self):
config, _, _, max_length = self._get_input_ids_and_config()
# if no bos token id => cannot generate from None
if config.bos_token_id is None:
return
# hack in case they are equal, otherwise the attn mask will be [0]
if config.bos_token_id == config.pad_token_id:
config.pad_token_id = None
for model_class in self.all_generative_model_classes:
model = model_class(config).to(torch_device)
model.eval()
output_ids_generate = model.generate(do_sample=False, max_length=max_length, remove_invalid_values=True)
self.assertIsNotNone(output_ids_generate)
def test_group_beam_search_generate(self):
for model_class in self.all_generative_model_classes:
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
model = model_class(config).to(torch_device).eval()
if model.config.is_encoder_decoder:
max_length = 4
logits_process_kwargs, _ = self._get_logits_processor_and_warper_kwargs(
input_ids.shape[-1],
config.forced_bos_token_id,
config.forced_eos_token_id,
max_length,
)
# check `generate()` and `group_beam_search()` are equal
beam_kwargs = self._get_diverse_beam_kwargs()
output_generate = self._group_beam_search_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
max_length=max_length,
beam_kwargs=beam_kwargs,
logits_process_kwargs=logits_process_kwargs,
)
self.assertTrue(output_generate.shape[-1] == max_length)
# check `group_beam_search` for higher than 1 `num_return_sequences`
num_return_sequences = 2
beam_kwargs = self._get_diverse_beam_kwargs(num_return_sequences=num_return_sequences)
output_generate = self._group_beam_search_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
max_length=max_length,
beam_kwargs=beam_kwargs,
logits_process_kwargs=logits_process_kwargs,
)
self.assertTrue(output_generate.shape[-1] == max_length)
def test_group_beam_search_generate_dict_output(self):
for model_class in self.all_generative_model_classes:
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
config.use_cache = False
model = model_class(config).to(torch_device).eval()
if model.config.is_encoder_decoder:
max_length = 4
logits_process_kwargs, _ = self._get_logits_processor_and_warper_kwargs(
input_ids.shape[-1],
config.forced_bos_token_id,
config.forced_eos_token_id,
max_length,
)
beam_kwargs = self._get_diverse_beam_kwargs()
output_generate = self._group_beam_search_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
max_length=max_length,
beam_kwargs=beam_kwargs,
logits_process_kwargs=logits_process_kwargs,
output_scores=True,
output_logits=True,
output_hidden_states=True,
output_attentions=True,
return_dict_in_generate=True,
)
if model.config.is_encoder_decoder:
self.assertIsInstance(output_generate, GenerateBeamEncoderDecoderOutput)
# Retrocompatibility check
self.assertIsInstance(output_generate, BeamSearchEncoderDecoderOutput)
else:
self.assertIsInstance(output_generate, GenerateBeamDecoderOnlyOutput)
# Retrocompatibility check
self.assertIsInstance(output_generate, BeamSearchDecoderOnlyOutput)
self.assertTrue(output_generate.sequences.shape[-1] == max_length)
self._check_outputs(
output_generate, input_ids, model.config, num_return_sequences=beam_kwargs["num_beams"]
)
# TODO: @gante
@is_flaky()
def test_constrained_beam_search_generate(self):
for model_class in self.all_generative_model_classes:
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
model = model_class(config).to(torch_device).eval()
max_length = 20
logits_process_kwargs, _ = self._get_logits_processor_and_warper_kwargs(
input_ids.shape[-1],
config.forced_bos_token_id,
config.forced_eos_token_id,
max_length,
)
# Sample constraints
min_id = 3
max_id = config.vocab_size
force_tokens = torch.randint(min_id, max_id, (1, 2)).tolist()[0]
constraints = [
PhrasalConstraint(force_tokens),
]
beam_kwargs = self._get_constrained_beam_kwargs()
output_generate = self._constrained_beam_search_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
max_length=max_length,
constraints=constraints,
beam_kwargs=beam_kwargs,
logits_process_kwargs=logits_process_kwargs,
)
self.assertTrue(output_generate.shape[-1] == max_length)
for generation_output in output_generate:
self._check_sequence_inside_sequence(force_tokens, generation_output)
# check`constrained_beam_search` for higher than 1 `num_return_sequences`
# Sample constraints
force_tokens = torch.randint(min_id, max_id, (1, 2)).tolist()[0]
constraints = [
PhrasalConstraint(force_tokens),
]
max_length = 20
beam_kwargs = self._get_constrained_beam_kwargs(num_return_sequences=2)
output_generate = self._constrained_beam_search_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
max_length=max_length,
constraints=constraints,
beam_kwargs=beam_kwargs,
logits_process_kwargs=logits_process_kwargs,
)
self.assertTrue(output_generate.shape[-1] == max_length)
for generation_output in output_generate:
self._check_sequence_inside_sequence(force_tokens, generation_output)
def test_constrained_beam_search_generate_dict_output(self):
for model_class in self.all_generative_model_classes:
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
# disable cache
config.use_cache = False
model = model_class(config).to(torch_device).eval()
if model.config.is_encoder_decoder:
max_length = 20
logits_process_kwargs, _ = self._get_logits_processor_and_warper_kwargs(
input_ids.shape[-1],
config.forced_bos_token_id,
config.forced_eos_token_id,
max_length,
)
# Sample constraints
min_id = 3
max_id = model.config.vocab_size
force_tokens = torch.randint(min_id, max_id, (1, 2)).tolist()[0]
constraints = [
PhrasalConstraint(force_tokens),
]
beam_kwargs = self._get_constrained_beam_kwargs()
output_generate = self._constrained_beam_search_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
max_length=max_length,
constraints=constraints,
beam_kwargs=beam_kwargs,
logits_process_kwargs=logits_process_kwargs,
output_scores=True,
output_logits=True,
output_hidden_states=True,
output_attentions=True,
return_dict_in_generate=True,
)
if model.config.is_encoder_decoder:
self.assertIsInstance(output_generate, GenerateBeamEncoderDecoderOutput)
# Retrocompatibility check
self.assertIsInstance(output_generate, BeamSearchEncoderDecoderOutput)
else:
self.assertIsInstance(output_generate, GenerateBeamDecoderOnlyOutput)
# Retrocompatibility check
self.assertIsInstance(output_generate, BeamSearchDecoderOnlyOutput)
self.assertTrue(output_generate.sequences.shape[-1] == max_length)
self._check_outputs(
output_generate, input_ids, model.config, num_return_sequences=beam_kwargs["num_beams"]
)
def test_contrastive_generate(self):
for model_class in self.all_generative_model_classes:
# won't fix: FSMT and Reformer have a different cache variable type (and format).
if any(model_name in model_class.__name__.lower() for model_name in ["fsmt", "reformer"]):
self.skipTest("Won't fix: old model with different cache format")
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
# NOTE: contrastive search only works with cache on at the moment.
if not hasattr(config, "use_cache"):
self.skipTest("This model doesn't support caching")
config.use_cache = True
config.is_decoder = True
# test old generation output for backwards compatibility
model = model_class(config).to(torch_device).eval()
output_generate = self._contrastive_generate(
model=model, input_ids=input_ids, attention_mask=attention_mask, max_length=max_length
)
self.assertTrue(output_generate.shape[-1] == max_length)
def test_contrastive_generate_dict_outputs_use_cache(self):
for model_class in self.all_generative_model_classes:
# won't fix: FSMT and Reformer have a different cache variable type (and format).
if any(model_name in model_class.__name__.lower() for model_name in ["fsmt", "reformer"]):
self.skipTest("Won't fix: old model with different cache format")
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
# NOTE: contrastive search only works with cache on at the moment.
if not hasattr(config, "use_cache"):
self.skipTest("This model doesn't support caching")
config.use_cache = True
config.is_decoder = True
model = model_class(config).to(torch_device).eval()
output_generate = self._contrastive_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
max_length=max_length,
output_scores=True,
output_logits=True,
output_hidden_states=True,
output_attentions=True,
return_dict_in_generate=True,
)
self.assertTrue(output_generate.sequences.shape[-1] == max_length)
self._check_outputs(output_generate, input_ids, model.config, use_cache=True)
def test_contrastive_generate_low_memory(self):
# Check that choosing 'low_memory' does not change the model output
for model_class in self.all_generative_model_classes:
if any(model_name in model_class.__name__.lower() for model_name in ["fsmt", "reformer", "speech2text"]):
self.skipTest("Won't fix: old model with different cache format")
if any(model_name in model_class.__name__.lower() for model_name in ["gptbigcode"]):
self.skipTest("TODO: fix me")
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config(batch_size=1)
# NOTE: contrastive search only works with cache on at the moment.
if not hasattr(config, "use_cache"):
self.skipTest("This model doesn't support caching")
config.use_cache = True
config.is_decoder = True
# test output equality of low versus high memory
model = model_class(config).to(torch_device).eval()
low_output = model.generate(
input_ids,
top_k=4,
penalty_alpha=0.6,
low_memory=True,
max_length=max_length,
attention_mask=attention_mask,
)
high_output = model.generate(
input_ids,
top_k=4,
penalty_alpha=0.6,
low_memory=False,
max_length=max_length,
attention_mask=attention_mask,
)
self.assertListEqual(low_output.tolist(), high_output.tolist())
def test_beam_search_low_memory(self):
# Check that choosing 'low_memory' does not change the model output
for model_class in self.all_generative_model_classes:
if any(model_name in model_class.__name__.lower() for model_name in ["fsmt", "reformer"]):
self.skipTest("Won't fix: old model with different cache format")
if any(
model_name in model_class.__name__.lower()
for model_name in [
"bloom",
"ctrl",
"gptbigcode",
"transo_xl",
"xlnet",
"cpm",
]
):
self.skipTest("May fix in the future: need model-specific fixes")
config, input_ids, _, _ = self._get_input_ids_and_config(batch_size=2)
# batch_size=1 is ok, but batch_size>1 will cause non-identical output
config.use_cache = True
config.is_decoder = True
# test output equality of low versus high memory
model = model_class(config).to(torch_device).eval()
low_output = model.generate(input_ids, max_new_tokens=8, num_beams=5, early_stopping=True, low_memory=True)
high_output = model.generate(
input_ids, max_new_tokens=8, num_beams=5, early_stopping=True, low_memory=False
)
self.assertListEqual(low_output.tolist(), high_output.tolist())
@is_flaky() # Read NOTE (1) below. If there are API issues, all attempts will fail.
def test_assisted_decoding_matches_greedy_search(self):
# This test ensures that the assisted generation does not introduce output changes over greedy search.
# NOTE (1): The sentence above is true most of the time, there is a tiny difference in the logits due to matmul
# shape differences -- and it may result in a different output. The input shape difference happens in the
# main model, that runs the forward pass with several candidates at once (as opposed to generating one token at
# a time). See https://github.com/huggingface/transformers/issues/25420#issuecomment-1775317535 for more info.
# NOTE (2): It breaks the pattern in the tests above, for multiple reasons:
# - assisted_decoding, contrarily to the other methods, can't be called on its own (e.g. needs to
# prepare the assistant encoder outputs in the main generate body);
# - assisted_decoding does not support `use_cache = False`
# - assisted_decoding does not support `batch_size > 1`
for model_class in self.all_generative_model_classes:
if any(model_name in model_class.__name__.lower() for model_name in ["fsmt", "reformer"]):
self.skipTest("Won't fix: old model with different cache format")
if any(
model_name in model_class.__name__.lower()
for model_name in [
"bigbirdpegasus",
"led",
"mega",
"speech2text",
"git",
"prophetnet",
"seamlessm4t",
"clvp",
]
):
self.skipTest("May fix in the future: need model-specific fixes")
# enable cache
config, input_ids, attention_mask, _ = self._get_input_ids_and_config(batch_size=1)
# NOTE: assisted generation only works with cache on at the moment.
if not hasattr(config, "use_cache"):
self.skipTest("This model doesn't support caching")
config.use_cache = True
config.is_decoder = True
model = model_class(config).to(torch_device).eval()
# Sets assisted generation arguments such that:
# a) no EOS is generated, to ensure generation doesn't break early
# b) the assistant model always generates two tokens when it is called, to ensure the input preparation of
# the assistant model is correct
# c) there are at least two forward passes in the main model, to ensure the input preparation of
# the main model is correct
generation_kwargs = {
"eos_token_id": -1, # see a)
"max_new_tokens": 4, # see c)
"num_beams": 1,
"do_sample": False,
"output_scores": True,
"output_logits": True,
"output_hidden_states": True,
"output_attentions": True,
"return_dict_in_generate": True,
}
output_greedy = model.generate(input_ids, attention_mask=attention_mask, **generation_kwargs)
assistant_model = model
assistant_model.generation_config.num_assistant_tokens = 2 # see b)
assistant_model.generation_config.num_assistant_tokens_schedule = "constant" # see b)
generation_kwargs.update({"assistant_model": assistant_model})
output_assisted = model.generate(input_ids, attention_mask=attention_mask, **generation_kwargs)
# The two outputs must match and their shape must be as expected
self.assertListEqual(output_greedy.sequences.tolist(), output_assisted.sequences.tolist())
for output in (output_greedy, output_assisted):
self._check_outputs(output, input_ids, model.config, use_cache=True)
@is_flaky()
def test_prompt_lookup_decoding_matches_greedy_search(self):
# This test ensures that the prompt lookup generation does not introduce output changes over greedy search.
# This test is mostly a copy of test_assisted_decoding_matches_greedy_search
for model_class in self.all_generative_model_classes:
if any(model_name in model_class.__name__.lower() for model_name in ["fsmt", "reformer"]):
self.skipTest("Won't fix: old model with different cache format")
if any(
model_name in model_class.__name__.lower()
for model_name in [
"bigbirdpegasus",
"led",
"mega",
"speech2text",
"git",
"prophetnet",
"seamlessm4t",
"clvp",
]
):
self.skipTest("May fix in the future: need model-specific fixes")
# enable cache
config, input_ids, attention_mask, _ = self._get_input_ids_and_config(batch_size=1)
# NOTE: assisted generation only works with cache on at the moment.
if not hasattr(config, "use_cache"):
self.skipTest("This model doesn't support caching")
config.use_cache = True
config.is_decoder = True
model = model_class(config).to(torch_device).eval()
# Sets assisted generation arguments such that:
# a) no EOS is generated, to ensure generation doesn't break early
# b) the prompt lookup tries to give the model 2 tokens, to ensure the input preparation of
# prompt lookup is correct
# c) there are at least two forward passes in the main model, to ensure the input preparation of
# the main model is correct
generation_kwargs = {
"eos_token_id": -1, # see a)
"max_new_tokens": 4, # see c)
"num_beams": 1,
"do_sample": False,
"output_scores": True,
"output_logits": True,
"output_hidden_states": True,
"output_attentions": True,
"return_dict_in_generate": True,
}
output_greedy = model.generate(input_ids, attention_mask=attention_mask, **generation_kwargs)
generation_kwargs.update({"prompt_lookup_num_tokens": 2}) # see b)
output_prompt_lookup = model.generate(input_ids, attention_mask=attention_mask, **generation_kwargs)
# The two outputs must match and their shape must be as expected
self.assertListEqual(output_greedy.sequences.tolist(), output_prompt_lookup.sequences.tolist())
for output in (output_greedy, output_prompt_lookup):
self._check_outputs(output, input_ids, model.config, use_cache=True)
def test_assisted_decoding_sample(self):
# In this test we don't check assisted vs non-assisted output -- seeded assisted decoding with sample will not
# match sample for the same seed, as the forward pass does not return the exact same logits (due to matmul with
# different shapes, see https://github.com/huggingface/transformers/issues/25420#issuecomment-1775317535).
for model_class in self.all_generative_model_classes:
if any(model_name in model_class.__name__.lower() for model_name in ["fsmt", "reformer"]):
self.skipTest("Won't fix: old model with different cache format")
if any(
model_name in model_class.__name__.lower()
for model_name in [
"bigbirdpegasus",
"led",
"mega",
"speech2text",
"git",
"prophetnet",
"seamlessm4t",
"clvp",
]
):
self.skipTest("May fix in the future: need model-specific fixes")
# enable cache
config, input_ids, attention_mask, _ = self._get_input_ids_and_config(batch_size=1)
# NOTE: assisted generation only works with cache on at the moment.
if not hasattr(config, "use_cache"):
self.skipTest("This model doesn't support caching")
config.use_cache = True
config.is_decoder = True
model = model_class(config).to(torch_device).eval()
# Sets assisted generation arguments such that:
# a) no EOS is generated, to ensure generation doesn't break early
# b) the assistant model always generates two tokens when it is called, to ensure the input preparation of
# the assistant model is correct
# c) there are at least two forward passes in the main model, to ensure the input preparation of
# the main model is correct
assistant_model = model
assistant_model.generation_config.num_assistant_tokens = 2 # see b)
assistant_model.generation_config.num_assistant_tokens_schedule = "constant" # see b)
generation_kwargs = {
"eos_token_id": -1, # see a)
"max_new_tokens": 4, # see c)
"num_beams": 1,
"do_sample": True,
"assistant_model": assistant_model,
"output_scores": True,
"output_logits": True,
"output_hidden_states": True,
"output_attentions": True,
"return_dict_in_generate": True,
}
output_assisted = model.generate(input_ids, attention_mask=attention_mask, **generation_kwargs)
self._check_outputs(output_assisted, input_ids, model.config, use_cache=True)
def test_generate_with_head_masking(self):
"""Test designed for encoder-decoder models to ensure the attention head masking is used."""
attention_names = ["encoder_attentions", "decoder_attentions", "cross_attentions"]
for model_class in self.all_generative_model_classes:
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
# We want to test only encoder-decoder models
if not config.is_encoder_decoder:
continue
model = model_class(config).to(torch_device)
head_masking = {
"head_mask": torch.zeros(config.encoder_layers, config.encoder_attention_heads, device=torch_device),
"decoder_head_mask": torch.zeros(
config.decoder_layers, config.decoder_attention_heads, device=torch_device
),
"cross_attn_head_mask": torch.zeros(
config.decoder_layers, config.decoder_attention_heads, device=torch_device
),
}
signature = inspect.signature(model.forward)
# We want to test only models where encoder/decoder head masking is implemented
if not set(head_masking.keys()) < {*signature.parameters.keys()}:
continue
for attn_name, (name, mask) in zip(attention_names, head_masking.items()):
out = model.generate(
input_ids,
attention_mask=attention_mask,
num_beams=1,
output_attentions=True,
return_dict_in_generate=True,
remove_invalid_values=True,
**{name: mask},
)
# We check the state of decoder_attentions and cross_attentions just from the last step
attn_weights = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
self.assertEqual(sum([w.sum().item() for w in attn_weights]), 0.0)
def test_left_padding_compatibility(self):
# NOTE: left-padding results in small numerical differences. This is expected.
# See https://github.com/huggingface/transformers/issues/25420#issuecomment-1775317535
# First, filter out models that don't support left padding
# - The model must have generative capabilities
if len(self.all_generative_model_classes) == 0:
self.skipTest(reason="No generative architecture available for this model.")
# - The model must be a decoder-only architecture (encoder-based architectures use right-padding)
decoder_only_classes = []
for model_class in self.all_generative_model_classes:
config, _, _, _ = self._get_input_ids_and_config()
if config.is_encoder_decoder:
continue
else:
decoder_only_classes.append(model_class)
if len(decoder_only_classes) == 0:
self.skipTest(reason="No decoder-only architecture available for this model.")
# - Decoder-only architectures derived from encoder-decoder models could support it in theory, but we haven't
# added support for it yet. We skip these models for now.
has_encoder_attributes = any(
attr_name
for attr_name in config.to_dict().keys()
if attr_name.startswith("encoder") and attr_name != "encoder_no_repeat_ngram_size"
)
if has_encoder_attributes:
self.skipTest(
reason="The decoder-only derived from encoder-decoder models are not expected to support left-padding."
)
# Then, test left-padding
def _prepare_model_kwargs(input_ids, attention_mask, signature):
model_kwargs = {"input_ids": input_ids, "attention_mask": attention_mask}
if "position_ids" in signature:
position_ids = torch.cumsum(attention_mask, dim=-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
model_kwargs["position_ids"] = position_ids
if "cache_position" in signature:
cache_position = torch.arange(input_ids.shape[-1], device=torch_device)
model_kwargs["cache_position"] = cache_position
return model_kwargs
for model_class in decoder_only_classes:
config, input_ids, attention_mask, _ = self._get_input_ids_and_config()
model = model_class(config).to(torch_device).eval()
signature = inspect.signature(model.forward).parameters.keys()
# Without padding
model_kwargs = _prepare_model_kwargs(input_ids, attention_mask, signature)
next_logits_wo_padding = model(**model_kwargs).logits[:, -1, :]
# With left-padding (length 32)
pad_size = (input_ids.shape[0], 32)
padding = torch.ones(pad_size, dtype=input_ids.dtype, device=torch_device) * config.pad_token_id
padded_input_ids = torch.cat((padding, input_ids), dim=1)
padded_attention_mask = torch.cat((torch.zeros_like(padding), attention_mask), dim=1)
model_kwargs = _prepare_model_kwargs(padded_input_ids, padded_attention_mask, signature)
next_logits_with_padding = model(**model_kwargs).logits[:, -1, :]
# They should result in very similar logits
self.assertTrue(torch.allclose(next_logits_wo_padding, next_logits_with_padding, atol=1e-5))
def test_past_key_values_format(self):
# Test that the KV cache is formatted correctly. Exceptions need to explicitly overwrite this test. Having a
# standard KV cache format is important for a consistent API (and for advanced generation methods).
for model_class in self.all_generative_model_classes:
config, inputs = self.model_tester.prepare_config_and_inputs_for_common()
# If it doesn't support cache, pass the test
if not hasattr(config, "use_cache"):
self.skipTest("This model doesn't support caching")
model = model_class(config).to(torch_device)
if "use_cache" not in inputs:
inputs["use_cache"] = True
outputs = model(**inputs)
# If "past_key_values" is not returned, pass the test (e.g. RWKV uses a different cache name and format)
if "past_key_values" not in outputs:
self.skipTest("This model doesn't return `past_key_values`")
num_hidden_layers = (
getattr(config, "decoder_layers", None)
or getattr(config, "num_decoder_layers", None)
or config.num_hidden_layers
)
num_attention_heads = getattr(config, "decoder_attention_heads", config.num_attention_heads)
embed_dim = getattr(config, "d_model", config.hidden_size)
per_head_embed_dim = embed_dim // num_attention_heads
past_kv = outputs["past_key_values"]
self.assertEqual(len(past_kv), num_hidden_layers)
# Encoder-Decoder checks
if config.is_encoder_decoder:
encoder_num_attention_heads = config.encoder_attention_heads
encoder_per_head_embed_dim = embed_dim // encoder_num_attention_heads
batch_size, seq_length = inputs["decoder_input_ids"].shape
for i in range(num_hidden_layers):
self.assertEqual(len(past_kv[i]), 4) # K V for the decoder + K V for the encoder = 4
self.assertEqual(
past_kv[i][0].shape, (batch_size, num_attention_heads, seq_length, per_head_embed_dim)
)
self.assertEqual(
past_kv[i][1].shape, (batch_size, num_attention_heads, seq_length, per_head_embed_dim)
)
# The sequence length for the encoder K V depends on the model. Since it is not manipulated in
# autoregressive generation, I'm keeping the test general and not checking the 3rd dim
self.assertEqual(
(past_kv[i][2].shape[0], past_kv[i][2].shape[1], past_kv[i][2].shape[3]),
(batch_size, encoder_num_attention_heads, encoder_per_head_embed_dim),
)
self.assertEqual(
(past_kv[i][3].shape[0], past_kv[i][3].shape[1], past_kv[i][3].shape[3]),
(batch_size, encoder_num_attention_heads, encoder_per_head_embed_dim),
)
# Decoder-only checks
else:
# TODO: this line is only needed because of imagegpt, where "pixel_values" = "input_ids". Fix the
# tests in imagegpt such that `prepare_config_and_inputs_for_common` returns the later (and the other
# tests use it)
key = "input_ids" if "input_ids" in inputs else "pixel_values"
batch_size, seq_length = inputs[key].shape
for i in range(num_hidden_layers):
self.assertEqual(len(past_kv[0]), 2) # K V for the decoder = 2
self.assertEqual(
past_kv[i][0].shape, (batch_size, num_attention_heads, seq_length, per_head_embed_dim)
)
self.assertEqual(
past_kv[i][1].shape, (batch_size, num_attention_heads, seq_length, per_head_embed_dim)
)
def test_generate_from_inputs_embeds_decoder_only(self):
# When supported, tests that the decoder model can generate from `inputs_embeds` instead of `input_ids`
# if fails, you should probably update the `prepare_inputs_for_generation` function
for model_class in self.all_generative_model_classes:
config, input_ids, _, _ = self._get_input_ids_and_config()
# Ignore:
# a) eos (to always output 20 tokens) and pad (so we don't try to infer the attn mask from the input_ids,
# which would cause a mismatch),
config.pad_token_id = config.eos_token_id = -1
# b) embedding scaling, the scaling factor applied after embeding from input_ids (requires knowledge of the
# variable that holds the scaling factor, which is model-dependent)
if hasattr(config, "scale_embedding"):
config.scale_embedding = False
# This test is for decoder-only models (encoder-decoder models have native input embeddings support in the
# decoder)
if config.is_encoder_decoder:
continue
# Skip models without explicit support
model = model_class(config).to(torch_device).eval()
if "inputs_embeds" not in inspect.signature(model.prepare_inputs_for_generation).parameters.keys():
continue
# Traditional way of generating text
outputs_from_ids = model.generate(input_ids)
self.assertEqual(outputs_from_ids.shape, (2, 20))
# Same thing, but from input embeddings (`input_ids` is passed so the prompt is present in the output)
inputs_embeds = model.get_input_embeddings()(input_ids)
outputs_from_embeds = model.generate(input_ids, inputs_embeds=inputs_embeds)
self.assertListEqual(outputs_from_ids.tolist(), outputs_from_embeds.tolist())
# But if we pass different inputs_embeds, we should get different outputs
torch.manual_seed(0)
random_embeds = torch.rand_like(inputs_embeds)
outputs_from_rand_embeds = model.generate(input_ids, inputs_embeds=random_embeds)
with self.assertRaises(AssertionError):
self.assertListEqual(outputs_from_rand_embeds.tolist(), outputs_from_embeds.tolist())
# input_ids is not a required input -- if we don't pass it, the newly generated tokens will be the same
outputs_from_embeds_wo_ids = model.generate(
inputs_embeds=inputs_embeds, max_new_tokens=20 - inputs_embeds.shape[1]
)
self.assertListEqual(
outputs_from_embeds[:, inputs_embeds.shape[1] :].tolist(),
outputs_from_embeds_wo_ids.tolist(),
)
def test_generate_continue_from_past_key_values(self):
# Tests that we can continue generating from past key values, returned from a previous `generate` call
for model_class in self.all_generative_model_classes:
if any(model_name in model_class.__name__.lower() for model_name in ["imagegpt"]):
self.skipTest("Won't fix: old model with unique inputs/caches/other")
if any(model_name in model_class.__name__.lower() for model_name in ["umt5"]):
self.skipTest("TODO: needs modeling or test input preparation fixes for compatibility")
config, inputs = self.model_tester.prepare_config_and_inputs_for_common()
if not hasattr(config, "use_cache"):
self.skipTest("This model doesn't support caching")
# Let's make it always:
# 1. use cache (for obvious reasons)
# 2. generate to max length (which can be achieved by setting the eos token to an invalid value), which
# would make the test flaky (e.g. EOS is generated on iteration 1 on both generations, but the
# continuation would force it to generate beyond an EOS token)
# 3. ignore `token_type_ids` for simplicity
# 4. ignore `forced_eos_token_id`, which requires further manipulation of the continuation inputs and is
# active by default on some models
config.use_cache = True
if "token_type_ids" in inputs:
del inputs["token_type_ids"]
model = model_class(config).to(torch_device)
model.eval()
model.generation_config.pad_token_id = model.generation_config.eos_token_id = -1
model.generation_config.forced_eos_token_id = None
# If "past_key_values" is not returned, skip the test (e.g. RWKV uses a different cache name and format)
outputs = model(**inputs)
if "past_key_values" not in outputs:
self.skipTest("This model doesn't return `past_key_values`")
# Traditional way of generating text, with `return_dict_in_generate` to return the past key values
outputs = model.generate(**inputs, do_sample=False, max_new_tokens=4, return_dict_in_generate=True)
# Let's generate again, but passing the past key values in between (3 + 1 = 4 tokens). Note that the
# inputs may need to be tweaked across `generate` calls (like the attention mask).
outputs_cached = model.generate(**inputs, do_sample=False, max_new_tokens=3, return_dict_in_generate=True)
# Continue from the tokens generated above, preparing the inputs accordingly
inputs["past_key_values"] = outputs_cached.past_key_values
new_attention_len = outputs_cached.sequences.shape[-1]
if config.is_encoder_decoder:
inputs["decoder_input_ids"] = outputs_cached.sequences
if "decoder_attention_mask" in inputs:
inputs["decoder_attention_mask"] = torch.nn.functional.pad(
inputs["decoder_attention_mask"],
(0, new_attention_len - inputs["decoder_attention_mask"].shape[1]),
mode="constant",
value=1,
)
else:
inputs["input_ids"] = outputs_cached.sequences
if "attention_mask" in inputs:
inputs["attention_mask"] = torch.nn.functional.pad(
inputs["attention_mask"],
(0, new_attention_len - inputs["attention_mask"].shape[1]),
mode="constant",
value=1,
)
outputs_cached = model.generate(**inputs, do_sample=False, max_new_tokens=1, return_dict_in_generate=True)
# The two sets of generated text and past kv should be equal to each other
self.assertListEqual(outputs.sequences.tolist(), outputs_cached.sequences.tolist())
for layer_idx in range(len(outputs_cached.past_key_values)):
for kv_idx in range(len(outputs_cached.past_key_values[layer_idx])):
self.assertTrue(
torch.allclose(
outputs.past_key_values[layer_idx][kv_idx],
outputs_cached.past_key_values[layer_idx][kv_idx],
)
)
@parameterized.expand([(1, False), (1, True), (4, False)])
def test_new_cache_format(self, num_beams, do_sample):
# Tests that generating with the new format is exactly the same as the legacy one (for models that support it).
# 👉 tests with and without beam search so that we can test with and without cache reordering.
# 👉 tests with and without sampling so we can cover the most common use cases.
for model_class in self.all_generative_model_classes:
if not model_class._supports_cache_class:
self.skipTest("This model does not support the new cache format")
config, input_ids, attention_mask, _ = self._get_input_ids_and_config()
config.use_cache = True
config.is_decoder = True
model = model_class(config).to(torch_device).eval()
generation_kwargs = {
"max_new_tokens": 5,
"do_sample": do_sample,
"num_beams": num_beams,
"num_return_sequences": num_beams,
"return_dict_in_generate": True, # Required to return `past_key_values`
}
# Sets seed before calling `generate` for the case with do_sample=True
seed = torch.randint(0, 1000000, (1,)).item()
set_seed(seed)
legacy_results = model.generate(input_ids, attention_mask=attention_mask, **generation_kwargs)
set_seed(seed)
new_results = model.generate(
input_ids, attention_mask=attention_mask, past_key_values=DynamicCache(), **generation_kwargs
)
# The two sets of generated sequences must match, despite the cache format between forward passes being
# different
self.assertListEqual(legacy_results.sequences.tolist(), new_results.sequences.tolist())
self.assertTrue(isinstance(legacy_results.past_key_values, tuple))
self.assertTrue(isinstance(new_results.past_key_values, DynamicCache))
# The contents of the two caches, when converted to the same format (in both directions!), must match
legacy_cache = legacy_results.past_key_values
new_cache_converted = new_results.past_key_values.to_legacy_cache()
for layer_idx in range(len(legacy_cache)):
for kv_idx in range(len(legacy_cache[layer_idx])):
self.assertTrue(
torch.allclose(
legacy_cache[layer_idx][kv_idx],
new_cache_converted[layer_idx][kv_idx],
)
)
new_cache = new_results.past_key_values
legacy_cache_converted = DynamicCache.from_legacy_cache(legacy_results.past_key_values)
for layer_idx in range(len(new_cache)):
for kv_idx in range(len(new_cache[layer_idx])):
self.assertTrue(
torch.allclose(
new_cache[layer_idx][kv_idx],
legacy_cache_converted[layer_idx][kv_idx],
)
)
def _check_outputs(self, output, input_ids, config, use_cache=False, num_return_sequences=1):
batch_size, seq_length = input_ids.shape
num_sequences_in_output = batch_size * num_return_sequences
gen_len = (
output.sequences.shape[-1] - 1 if config.is_encoder_decoder else output.sequences.shape[-1] - seq_length
)
# scores
self._check_scores(num_sequences_in_output, output.scores, length=gen_len, config=config)
# unprocessed logits
self._check_logits(num_sequences_in_output, output.logits, config=config)
# Attentions
if config.is_encoder_decoder:
# encoder
self._check_encoder_attention_for_generate(output.encoder_attentions, batch_size, config, seq_length)
# decoder
self._check_attentions_for_generate(
num_sequences_in_output,
output.decoder_attentions,
min_length=1,
max_length=output.sequences.shape[-1],
config=config,
use_cache=use_cache,
)
else:
# if use_cache first input is equal to no use_cache, so skip here
attentions = output.attentions if not use_cache else output.attentions[1:]
min_length = seq_length if not use_cache else seq_length + 1
self._check_attentions_for_generate(
num_sequences_in_output,
attentions=attentions,
min_length=min_length,
max_length=output.sequences.shape[-1],
config=config,
use_cache=use_cache,
)
# Hidden States
if config.is_encoder_decoder:
# encoder
self._check_encoder_hidden_states_for_generate(
output.encoder_hidden_states, batch_size, config, seq_length
)
# decoder
self._check_hidden_states_for_generate(
num_sequences_in_output,
output.decoder_hidden_states,
min_length=1,
max_length=output.sequences.shape[-1],
config=config,
use_cache=use_cache,
)
else:
# if use_cache first input is equal to no use_cache, so skip here
hidden_states = output.hidden_states if not use_cache else output.hidden_states[1:]
min_length = seq_length if not use_cache else seq_length + 1
self._check_hidden_states_for_generate(
num_sequences_in_output,
hidden_states,
min_length=min_length,
max_length=output.sequences.shape[-1],
config=config,
use_cache=use_cache,
)
# Past Key Value States -- two notes here:
# 1. Its inner sequence length is with respect to the inputs of the latest forward pass, hence the "-1"
# 2. Some old models still return `output.past_key_values` even without `use_cache=True`
# 3. TODO (joao): A few models have different formats, skipping those until the cache refactor is complete
models_without_standard_cache = ("bloom", "ctrl", "fsmt", "gptbigcode", "mega", "reformer")
has_standard_cache = not any(
model_name in config.__class__.__name__.lower() for model_name in models_without_standard_cache
)
if use_cache and has_standard_cache:
past_key_values = output.past_key_values
past_sequence_length = output.sequences.shape[-1] - 1
self._check_past_key_values_for_generate(
num_sequences_in_output,
past_key_values,
seq_length=past_sequence_length,
config=config,
)
def _check_scores(self, batch_size, scores, length, config):
expected_shape = (batch_size, config.vocab_size)
self.assertIsInstance(scores, tuple)
self.assertEqual(len(scores), length)
self.assertListEqual([iter_scores.shape for iter_scores in scores], [expected_shape] * len(scores))
def _check_logits(self, batch_size, scores, config):
self.assertIsInstance(scores, tuple)
self.assertListEqual([iter_scores.shape[0] for iter_scores in scores], [batch_size] * len(scores))
# vocabulary difference equal to one (imagegptmodel?) or zero (all other models)
vocab_diff = config.vocab_size - scores[0].shape[-1]
self.assertTrue(vocab_diff in [0, 1])
self.assertListEqual([config.vocab_size - score.shape[-1] for score in scores], [vocab_diff] * len(scores))
def _check_attentions_for_generate(
self, batch_size, attentions, min_length, max_length, config, use_cache=False, num_beam_groups=1
):
self.assertIsInstance(attentions, tuple)
self.assertListEqual(
[isinstance(iter_attentions, tuple) for iter_attentions in attentions], [True] * len(attentions)
)
self.assertEqual(len(attentions), (max_length - min_length) * num_beam_groups)
for idx, iter_attentions in enumerate(attentions):
tgt_len = min_length + idx if not use_cache else 1
src_len = min_length + idx
expected_shape = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions], [expected_shape] * len(iter_attentions)
)
def _check_encoder_attention_for_generate(self, attentions, batch_size, config, seq_length):
encoder_expected_shape = (batch_size, config.num_attention_heads, seq_length, seq_length)
self.assertIsInstance(attentions, tuple)
self.assertListEqual(
[layer_attentions.shape for layer_attentions in attentions],
[encoder_expected_shape] * len(attentions),
)
def _check_hidden_states_for_generate(
self, batch_size, hidden_states, min_length, max_length, config, use_cache=False, num_beam_groups=1
):
self.assertIsInstance(hidden_states, tuple)
self.assertListEqual(
[isinstance(iter_hidden_states, tuple) for iter_hidden_states in hidden_states],
[True] * len(hidden_states),
)
self.assertEqual(len(hidden_states), (max_length - min_length) * num_beam_groups)
for idx, iter_hidden_states in enumerate(hidden_states):
seq_len = min_length + idx if not use_cache else 1
expected_shape = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states],
[expected_shape] * len(iter_hidden_states),
)
def _check_encoder_hidden_states_for_generate(self, hidden_states, batch_size, config, seq_length):
encoder_expected_shape = (batch_size, seq_length, config.hidden_size)
self.assertIsInstance(hidden_states, tuple)
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in hidden_states],
[encoder_expected_shape] * len(hidden_states),
)
def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config, num_beam_groups=1):
self.assertIsInstance(past_key_values, tuple)
self.assertListEqual(
[isinstance(iter_past_key_values, tuple) for iter_past_key_values in past_key_values],
[True] * len(past_key_values),
)
# (batch, head, seq_length, head_features)
expected_shape = (
batch_size * num_beam_groups,
config.num_key_value_heads if hasattr(config, "num_key_value_heads") else config.num_attention_heads,
seq_length,
config.hidden_size // config.num_attention_heads,
)
# check shape key, value
self.assertListEqual(
[layer_past_key_values[0].shape for layer_past_key_values in past_key_values],
[expected_shape] * len(past_key_values),
)
self.assertListEqual(
[layer_past_key_values[1].shape for layer_past_key_values in past_key_values],
[expected_shape] * len(past_key_values),
)
def _check_sequence_inside_sequence(self, tensor_1, tensor_2):
# check if tensor_1 inside tensor_2 or tensor_2 inside tensor_1.
# set to same device. we don't care what device.
if not isinstance(tensor_1, list):
tensor_1 = tensor_1.cpu().tolist()
if not isinstance(tensor_2, list):
tensor_2 = tensor_2.cpu().tolist()
in_order = len(tensor_1) <= len(tensor_2)
longer = tensor_2 if in_order else tensor_1
shorter = tensor_1 if in_order else tensor_2
flag = False
chunk_size = len(shorter)
for chunk_idx in range(len(longer) - chunk_size + 1):
subseq = longer[chunk_idx : chunk_idx + chunk_size]
if subseq == shorter:
flag = True
break
self.assertTrue(flag)
@require_torch
class UtilsFunctionsTest(unittest.TestCase):
def test_speculative_sampling(self):
# assume vocab size 10, input length 5 + 3 generated candidates
candidate_input_ids = torch.tensor([[8, 0, 3, 9, 8, 1, 4, 5]]) # input tokens
candidate_logits = torch.tensor(
[
[
[-10.0, 10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0], # generated 1
[-10.0, -10.0, -10.0, -10.0, 10.0, -10.0, -10.0, -10.0, -10.0, -10.0], # generated 4
[-10.0, -10.0, -10.0, -10.0, -10.0, 10.0, -10.0, -10.0, -10.0, -10.0], # generated 5
]
]
)
candidate_length = 3
inf = float("inf")
new_logits = torch.tensor(
[
[
[-10.0, 10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0], # accepts 1
[-10.0, -10.0, -10.0, -10.0, 10.0, -10.0, -10.0, -10.0, -10.0, -10.0], # accepts 4
[-inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, 10.0, -inf], # rejects 5, accepts 8
[-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0], # N/A
]
]
)
last_assistant_token_is_eos = False
max_matches = 5
validated_tokens, n_matches = _speculative_sampling(
candidate_input_ids,
candidate_logits,
candidate_length,
new_logits,
last_assistant_token_is_eos,
max_matches,
)
self.assertTrue(n_matches.item() == 2)
self.assertTrue(validated_tokens.tolist()[0] == [1, 4, 8])
@require_torch
class GenerationIntegrationTests(unittest.TestCase, GenerationIntegrationTestsMixin):
# setting framework_dependent_parameters needs to be gated, just like its contents' imports
if is_torch_available():
framework_dependent_parameters = {
"AutoModelForCausalLM": AutoModelForCausalLM,
"AutoModelForSpeechSeq2Seq": AutoModelForSpeechSeq2Seq,
"AutoModelForSeq2SeqLM": AutoModelForSeq2SeqLM,
"AutoModelForVision2Seq": AutoModelForVision2Seq,
"LogitsProcessorList": LogitsProcessorList,
"MinLengthLogitsProcessor": MinLengthLogitsProcessor,
"create_tensor_fn": torch.tensor,
"floats_tensor": floats_tensor,
"return_tensors": "pt",
}
@slow
def test_diverse_beam_search(self):
# PT-only test: TF doesn't have a diverse beam search implementation
article = """Justin Timberlake and Jessica Biel, welcome to parenthood.
The celebrity couple announced the arrival of their son, Silas Randall Timberlake, in statements to People.
"Silas was the middle name of Timberlake's maternal grandfather Bill Bomar, who died in 2012, while Randall is the musician's own middle name, as well as his father's first," People reports.
The couple announced the pregnancy in January, with an Instagram post. It is the first baby for both."""
bart_tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn")
bart_model = BartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn").to(torch_device)
input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
outputs = bart_model.generate(
input_ids,
num_beams=4,
num_return_sequences=2,
num_beam_groups=4,
diversity_penalty=2.0,
remove_invalid_values=True,
)
generated_text = bart_tokenizer.batch_decode(outputs, skip_special_tokens=True)
self.assertListEqual(
generated_text,
[
"The couple announced the birth of their son, Silas Randall Timberlake, in a statement. Silas was the"
" middle name of Timberlake's maternal grandfather Bill Bomar. Randall is the musician's own middle"
" name, as well as his father's first. It is the first baby for both of them.",
"Justin Timberlake and Jessica Biel have a son. The baby is named Silas Randall Timberlake. It is the"
" first child for both. The couple announced the pregnancy in January. The name Silas is the middle"
" name of Timberlake's maternal grandfather. It's also his own middle name.",
],
)
def test_max_length_if_input_embeds(self):
# PT-only test: TF doesn't have StoppingCriteria
article = "Today a dragon flew over Paris."
model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
input_ids = tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
inputs_embeds = model.get_input_embeddings()(input_ids)
max_length = 20
input_len = input_ids.shape[-1]
out_gen = model.generate(input_ids=input_ids, max_length=max_length)
out_gen_embeds = model.generate(inputs_embeds=inputs_embeds, max_length=max_length)
self.assertEqual(out_gen.shape[-1], input_len + out_gen_embeds.shape[-1])
def test_custom_stopping_criteria_overload_error(self):
# PT-only test: TF doesn't have StoppingCriteria
article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
bart_tokenizer = BartTokenizer.from_pretrained("sshleifer/bart-tiny-random")
bart_model = BartForConditionalGeneration.from_pretrained("sshleifer/bart-tiny-random").to(torch_device)
input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
stopping_criteria = StoppingCriteriaList()
stopping_criteria.append(MaxLengthCriteria(max_length=42))
with self.assertRaises(ValueError):
bart_model.generate(input_ids, stopping_criteria=stopping_criteria)
with self.assertRaises(ValueError):
bart_model.generate(input_ids, stopping_criteria=stopping_criteria, max_length=32)
def test_custom_stopping_criteria(self):
# PT-only test: TF doesn't have StoppingCriteria
article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
bart_tokenizer = BartTokenizer.from_pretrained("sshleifer/bart-tiny-random")
bart_model = BartForConditionalGeneration.from_pretrained("sshleifer/bart-tiny-random").to(torch_device)
input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
class DummyCriteria(StoppingCriteria):
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
return input_ids.shape[-1] >= 20
stopping_criteria = StoppingCriteriaList()
stopping_criteria.append(DummyCriteria())
self.assertEqual(
list(bart_model.generate(input_ids, stopping_criteria=stopping_criteria, max_length=22).shape),
[1, 20],
)
self.assertEqual(
list(bart_model.generate(input_ids, stopping_criteria=stopping_criteria, max_length=18).shape),
[1, 18],
)
def test_stop_sequence_stopping_criteria(self):
# PT-only test: TF doesn't have StoppingCriteria
prompt = """Hello I believe in"""
generator = pipeline("text-generation", model="hf-internal-testing/tiny-random-bart")
output = generator(prompt)
self.assertEqual(
output,
[
{
"generated_text": (
"Hello I believe in in in number number number number number number number number number"
)
}
],
)
output = generator(prompt, stop_sequence=" number")
self.assertEqual(output, [{"generated_text": "Hello I believe in in in number"}])
def test_generate_non_nlp_input_ids_as_kwarg(self):
# PT-only test: AFAIK there's no non-NLP model architecture in TF that supports `input_ids` as its only input
model = ImageGPTForCausalImageModeling.from_pretrained(
"hf-internal-testing/tiny-random-imagegpt", max_length=10
).to(torch_device)
input_ids = ids_tensor((3, 5), vocab_size=10)
output_sequences_kwargs = model.generate(input_ids=input_ids).cpu()
output_sequences = model.generate(input_ids).cpu()
self.assertListEqual(output_sequences.tolist(), output_sequences_kwargs.tolist())
self.assertEqual(output_sequences.shape, (3, 10))
def test_generate_input_values_as_encoder_kwarg(self):
# PT-only test: AFAIK there's no generate-capable architecture in TF that supports `input_values` as its input
input_values = floats_tensor((2, 250))
model = SpeechEncoderDecoderModel.from_pretrained("hf-internal-testing/tiny-random-speech-encoder-decoder")
model = model.to(torch_device)
output_sequences_kwargs = model.generate(input_values=input_values, max_length=5).cpu()
output_sequences = model.generate(input_values, max_length=5).cpu()
self.assertListEqual(output_sequences.tolist(), output_sequences_kwargs.tolist())
self.assertEqual(output_sequences.shape, (2, 5))
def test_transition_scores_group_beam_search_encoder_decoder(self):
# PT-only test: TF doesn't have group beam search
articles = [
"Justin Timberlake and Jessica Biel, welcome to parenthood.",
"Michael Phelps is arguably the most decorated Olympian of all time.",
]
tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
model = BartForConditionalGeneration.from_pretrained(
"hf-internal-testing/tiny-random-bart",
max_length=10,
num_beams=2,
num_beam_groups=2,
num_return_sequences=2,
diversity_penalty=1.0,
eos_token_id=None,
return_dict_in_generate=True,
output_scores=True,
length_penalty=0.0,
)
model = model.to(torch_device)
input_ids = tokenizer(articles, return_tensors="pt", padding=True).input_ids.to(torch_device)
outputs = model.generate(input_ids=input_ids)
transition_scores = model.compute_transition_scores(outputs.sequences, outputs.scores, outputs.beam_indices)
transition_scores_sum = transition_scores.sum(-1)
self.assertTrue(torch.allclose(transition_scores_sum, outputs.sequences_scores, atol=1e-3))
def test_beam_search_low_memory(self):
tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2")
model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
tokenizer.pad_token_id = tokenizer.eos_token_id
model_inputs = tokenizer("I", return_tensors="pt")["input_ids"]
low_output = model.generate(model_inputs, max_new_tokens=40, num_beams=5, early_stopping=True, low_memory=True)
high_output = model.generate(
model_inputs, max_new_tokens=40, num_beams=5, early_stopping=True, low_memory=False
)
self.assertListEqual(low_output.tolist(), high_output.tolist())
@slow
def test_beam_search_example_integration(self):
# PT-only test: TF doesn't have a BeamSearchScorer
# exactly the example provided in the docstrings of beam search, which previously
# failed after directly copying from it. Refer to PR #15555
tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-base")
model = AutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-base")
encoder_input_str = "translate English to German: How old are you?"
encoder_input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids
# lets run beam search using 3 beams
num_beams = 3
# define decoder start token ids
input_ids = torch.ones((1, 1), device=model.device, dtype=torch.long)
input_ids = input_ids * model.config.decoder_start_token_id
# add encoder_outputs to model keyword arguments
model_kwargs = {"encoder_outputs": model.get_encoder()(encoder_input_ids, return_dict=True)}
outputs = model.generate(
input_ids, num_beams=num_beams, min_length=5, eos_token_id=model.config.eos_token_id, **model_kwargs
)
outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)
self.assertListEqual(outputs, ["Wie alt bist du?"])
@slow
def test_constrained_beam_search(self):
# PT-only test: TF doesn't have constrained beam search
model = GPT2LMHeadModel.from_pretrained("openai-community/gpt2").to(torch_device)
tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2")
force_tokens = tokenizer("scared", add_prefix_space=True, add_special_tokens=False).input_ids
force_tokens_2 = tokenizer("big weapons", add_prefix_space=True, add_special_tokens=False).input_ids
constraints = [
PhrasalConstraint(force_tokens),
PhrasalConstraint(force_tokens_2),
]
starting_text = ["The soldiers were not prepared and"]
input_ids = tokenizer(starting_text, return_tensors="pt").input_ids.to(torch_device)
outputs = model.generate(
input_ids,
constraints=constraints,
num_beams=10,
num_return_sequences=1,
no_repeat_ngram_size=1,
max_length=30,
remove_invalid_values=True,
)
generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)
self.assertListEqual(
generated_text,
[
"The soldiers were not prepared and didn't know what to do. They had no idea how they would react if"
" the enemy attacked them, big weapons scared"
],
)
@slow
def test_constrained_beam_search_mixed(self):
# PT-only test: TF doesn't have constrained beam search
model = GPT2LMHeadModel.from_pretrained("openai-community/gpt2").to(torch_device)
tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2")
force_phrase = tokenizer("scared", add_prefix_space=True, add_special_tokens=False).input_ids
flexible_phrases = tokenizer(
["scream", "screams", "screaming", "screamed"], add_prefix_space=True, add_special_tokens=False
).input_ids
constraints = [
PhrasalConstraint(force_phrase),
DisjunctiveConstraint(flexible_phrases),
]
starting_text = ["The soldiers", "The child"]
input_ids = tokenizer(starting_text, return_tensors="pt").input_ids.to(torch_device)
outputs = model.generate(
input_ids,
constraints=constraints,
num_beams=10,
num_return_sequences=1,
no_repeat_ngram_size=1,
# max_length=20,
remove_invalid_values=True,
)
generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)
self.assertListEqual(
generated_text,
[
"The soldiers, who had been stationed at the base for more than a year before being evacuated"
" screaming scared",
"The child was taken to a local hospital where he died.\n 'I don't think screaming scared",
],
)
@slow
def test_constrained_beam_search_mixed_mixin(self):
# PT-only test: TF doesn't have constrained beam search
model = GPT2LMHeadModel.from_pretrained("openai-community/gpt2").to(torch_device)
tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2")
force_word = "scared"
force_flexible = ["scream", "screams", "screaming", "screamed"]
force_words_ids = [
tokenizer([force_word], add_prefix_space=True, add_special_tokens=False).input_ids,
tokenizer(force_flexible, add_prefix_space=True, add_special_tokens=False).input_ids,
]
starting_text = ["The soldiers", "The child"]
input_ids = tokenizer(starting_text, return_tensors="pt").input_ids.to(torch_device)
outputs = model.generate(
input_ids,
force_words_ids=force_words_ids,
num_beams=10,
num_return_sequences=1,
no_repeat_ngram_size=1,
remove_invalid_values=True,
)
generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)
self.assertListEqual(
generated_text,
[
"The soldiers, who had been stationed at the base for more than a year before being evacuated"
" screaming scared",
"The child was taken to a local hospital where he died.\n 'I don't think screaming scared",
],
)
@slow
def test_cfg_mixin(self):
model = GPT2LMHeadModel.from_pretrained("openai-community/gpt2").to(torch_device)
tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2")
input = tokenizer(["The dragon flew over Paris,"], return_tensors="pt", return_attention_mask=True)
input["input_ids"] = input["input_ids"].to(torch_device)
input["attention_mask"] = input["attention_mask"].to(torch_device)
outputs = model.generate(**input, max_new_tokens=32, guidance_scale=1.5)
generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)
self.assertListEqual(
generated_text,
[
"The dragon flew over Paris, landing in the Rue de la Bastille. The crowd was so excited "
'that they had to leave the city.\n\n"We\'re going to Paris!"\n'
],
)
neg = tokenizer(["France,"], return_tensors="pt", return_attention_mask=True)
neg["input_ids"] = neg["input_ids"].to(torch_device)
neg["attention_mask"] = neg["attention_mask"].to(torch_device)
outputs = model.generate(
**input,
max_new_tokens=32,
guidance_scale=1.5,
negative_prompt_ids=neg["input_ids"],
negative_prompt_attention_mask=neg["attention_mask"],
)
generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)
self.assertListEqual(
generated_text,
[
'The dragon flew over Paris, landing on the pavement.\n\n"Paris!"\n\n"Paris!"\n\n"'
'Paris!"\n\n"Paris!"\n\n"Paris!"\n\n'
],
)
@slow
def test_constrained_beam_search_example_translation_mixin(self):
# PT-only test: TF doesn't have constrained beam search
tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-base")
model = AutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-base")
encoder_input_str = "translate English to German: How old are you?"
force_words = ["sind"]
input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids
force_words_ids = tokenizer(force_words, add_special_tokens=False).input_ids
outputs = model.generate(
input_ids,
force_words_ids=force_words_ids,
num_beams=10,
num_return_sequences=1,
no_repeat_ngram_size=1,
remove_invalid_values=True,
)
outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)
self.assertListEqual(outputs, ["Wie alt sind Sie?"])
@slow
def test_constrained_beam_search_example_integration(self):
# PT-only test: TF doesn't have constrained beam search
tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-base")
model = AutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-base")
encoder_input_str = "translate English to German: How old are you?"
encoder_input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids
# lets run beam search using 5 beams
num_beams = 5
# define decoder start token ids
input_ids = torch.ones((1, 1), device=model.device, dtype=torch.long)
input_ids = input_ids * model.config.decoder_start_token_id
# add encoder_outputs to model keyword arguments
model_kwargs = {"encoder_outputs": model.get_encoder()(encoder_input_ids, return_dict=True)}
constraint_str = "sind"
constraint_token_ids = tokenizer.encode(constraint_str)[:-1] # remove eos token
outputs = model.generate(
input_ids,
num_beams=num_beams,
force_words_ids=[constraint_token_ids],
min_length=5,
eos_token_id=model.config.eos_token_id,
**model_kwargs,
)
outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)
self.assertListEqual(outputs, ["Wie alt sind Sie?"])
def test_constrained_beam_search_mixin_type_checks(self):
# PT-only test: TF doesn't have constrained beam search
tokenizer = AutoTokenizer.from_pretrained("patrickvonplaten/t5-tiny-random")
model = AutoModelForSeq2SeqLM.from_pretrained("patrickvonplaten/t5-tiny-random")
encoder_input_str = "translate English to German: How old are you?"
input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids
with self.assertRaises(ValueError):
force_words = ["sind"]
force_words_ids = tokenizer(force_words, return_tensors="pt").input_ids
model.generate(
input_ids,
force_words_ids=force_words_ids,
num_beams=10,
num_return_sequences=1,
no_repeat_ngram_size=1,
remove_invalid_values=True,
)
with self.assertRaises(ValueError):
force_words = ["sind"]
force_words_ids = [tokenizer(force_words, return_tensors="pt").input_ids]
model.generate(
input_ids,
force_words_ids=force_words_ids,
num_beams=10,
num_return_sequences=1,
no_repeat_ngram_size=1,
remove_invalid_values=True,
)
with self.assertRaises(ValueError):
model.generate(input_ids, force_words_ids=[])
with self.assertRaises(ValueError):
model.generate(input_ids, force_words_ids=[[-1]])
with self.assertRaises(ValueError):
model.generate(input_ids, force_words_ids=[[[-1]]])
def test_batched_decoder_start_id(self):
# PT-only test: TF doesn't support batched_decoder_start_id
articles = [
"Justin Timberlake and Jessica Biel, welcome to parenthood.",
"Michael Phelps is arguably the most decorated Olympian of all time.",
]
bart_tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
bart_model = BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart").to(
torch_device
)
input_ids = bart_tokenizer(articles, return_tensors="pt", padding=True).input_ids.to(torch_device)
decoder_start_token_id = bart_model.generation_config.decoder_start_token_id
decoder_start_token_id_batch = [decoder_start_token_id] * input_ids.shape[0]
outputs = bart_model.generate(input_ids, decoder_start_token_id=decoder_start_token_id)
outputs_batched_ids = bart_model.generate(input_ids, decoder_start_token_id=decoder_start_token_id_batch)
self.assertListEqual(outputs.tolist(), outputs_batched_ids.tolist())
def test_contrastive_search_batched(self):
# PT-only test: TF doesn't have constrained beam search
# Tests that contrastive search works with batched inputs (i.e. has the same output as for non-batched inputs)
articles = ["Foo", "Bar Baz"]
tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
model = BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart").to(torch_device)
model.config.eos_token_id = None
input_ids_batched = tokenizer(articles, padding=True, return_tensors="pt").input_ids.to(torch_device)
input_ids = tokenizer(articles[1], return_tensors="pt").input_ids.to(torch_device)
output_sequences_batched = model.generate(
input_ids=input_ids_batched, penalty_alpha=0.6, top_k=4, return_dict_in_generate=True, output_scores=True
)
output_sequences = model.generate(
input_ids=input_ids, penalty_alpha=0.6, top_k=4, return_dict_in_generate=True, output_scores=True
)
batched_out = tokenizer.decode(output_sequences_batched.sequences[1], skip_special_tokens=True)
out = tokenizer.decode(output_sequences.sequences[0], skip_special_tokens=True)
self.assertEqual(batched_out, out)
# output_sequences_batched.scores[0][1] -> 1st set of logits, 2nd sequence
max_score_diff = (output_sequences_batched.scores[0][1] - output_sequences.scores[0][0]).abs().max()
self.assertTrue(max_score_diff < 1e-5)
def test_eos_token_id_int_and_list_top_k_top_sampling(self):
# Has TF equivalent: this test relies on random sampling
generation_kwargs = {
"do_sample": True,
"num_beams": 1,
"top_p": 0.7,
"top_k": 10,
"temperature": 0.7,
}
expectation = 20
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
text = """Hello, my dog is cute and"""
tokens = tokenizer(text, return_tensors="pt").to(torch_device)
model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
# Only some seeds will work both on CPU/GPU for a fixed `expectation` value.
# The selected seed is not guaranteed to work on all torch versions.
torch.manual_seed(1)
eos_token_id = 846
generated_tokens = model.generate(**tokens, eos_token_id=eos_token_id, **generation_kwargs)
self.assertTrue(expectation == len(generated_tokens[0]))
torch.manual_seed(1)
eos_token_id = [846, 198]
generated_tokens = model.generate(**tokens, eos_token_id=eos_token_id, **generation_kwargs)
self.assertTrue(expectation == len(generated_tokens[0]))
def test_model_kwarg_encoder_signature_filtering(self):
# Has TF equivalent: ample use of framework-specific code
bart_tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
article = """Hugging Face is a technology company based in New York and Paris."""
input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
bart_model = BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart").to(
torch_device
)
output = bart_model.generate(input_ids).cpu().numpy()
# Let's create a fake model that has a different signature. In particular, this fake model accepts "foo" as an
# argument. Because "foo" is not in the encoder signature and doesn't start with "decoder_", it will be part of
# the encoder kwargs prior to signature filtering, which would lead to an exception. But filtering kicks in and
# saves the day.
class FakeBart(BartForConditionalGeneration):
def forward(self, input_ids, foo=None, **kwargs):
return super().forward(input_ids, **kwargs)
bart_model = FakeBart.from_pretrained("hf-internal-testing/tiny-random-bart").to(torch_device)
fake_output = bart_model.generate(input_ids, foo="bar").cpu().numpy()
self.assertTrue(np.array_equal(output, fake_output))
# Encoder signature filtering only kicks in if it doesn't accept wildcard kwargs. The following test will fail
# because it doesn't do signature filtering.
class FakeEncoder(bart_model.model.encoder.__class__):
def forward(self, input_ids, **kwargs):
return super().forward(input_ids, **kwargs)
fake_encoder = FakeEncoder(bart_model.config, bart_model.model.shared).to(torch_device)
bart_model.model.encoder = fake_encoder
# Normal generation still works (the output will be different because the encoder weights are different)
fake_output = bart_model.generate(input_ids).cpu().numpy()
with self.assertRaises(TypeError):
# FakeEncoder.forward() accepts **kwargs -> no filtering -> type error due to unexpected input "foo"
bart_model.generate(input_ids, foo="bar")
def test_default_max_length_warning(self):
model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
model.config.pad_token_id = tokenizer.eos_token_id
text = "Hello world"
tokenized_inputs = tokenizer([text], return_tensors="pt")
input_ids = tokenized_inputs.input_ids.to(torch_device)
# Default generation config value of 20 -> emits warning
with self.assertWarns(UserWarning):
model.generate(input_ids)
# Explicitly setting max_length to 20 -> no warning
with warnings.catch_warnings(record=True) as warning_list:
model.generate(input_ids, max_length=20)
self.assertEqual(len(warning_list), 0)
# Generation config max_length != 20 -> no warning
with warnings.catch_warnings(record=True) as warning_list:
# generation_config is modified -> legacy mode is disabled = generation_config takes precedence
model.generation_config.max_length = 10
model.generate(input_ids)
self.assertEqual(len(warning_list), 0)
def test_model_kwarg_assisted_decoding_decoder_only(self):
# PT-only test: TF doesn't support assisted decoding yet.
model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
model.config.pad_token_id = tokenizer.eos_token_id
text = "Hello world"
tokenized_inputs = tokenizer([text], return_tensors="pt")
input_ids = tokenized_inputs.input_ids.to(torch_device)
# Traditional way of generating text
outputs_normal = model.generate(input_ids)
self.assertEqual(outputs_normal.shape, (1, 20))
# Should be different with token_type_ids
outputs_tti = model.generate(
input_ids,
token_type_ids=torch.zeros(input_ids.shape, dtype=torch.long).to(torch_device),
)
with self.assertRaises(AssertionError):
self.assertListEqual(outputs_tti.tolist(), outputs_normal.tolist())
# Assistant model
assistant = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
assistant.config.pad_token_id = tokenizer.eos_token_id
# If assisted generation passes model_kwargs correctly, should be same as previous
outputs_assisted = model.generate(
input_ids,
token_type_ids=torch.zeros(input_ids.shape, dtype=torch.long).to(torch_device),
assistant_model=assistant,
)
self.assertListEqual(outputs_assisted.tolist(), outputs_tti.tolist())
def test_model_kwarg_assisted_decoding_encoder_decoder(self):
"""
Tests that the following scenario is compatible with assisted generation:
1. encoder-decoder main model
2. encoder-decoder assistant model
3. both have a custom input
(e.g. Whisper)
"""
# PT-only test: TF doesn't support assisted decoding yet.
# Bart subclass with a kwarg that distorts the output
class FakeBart(BartForConditionalGeneration):
def forward(self, input_ids, past_key_values, foo=False, **kwargs):
outs = super().forward(input_ids, past_key_values=past_key_values, **kwargs)
if foo:
outs["logits"][:, :, :] = 0.0
return outs
def prepare_inputs_for_generation(self, *args, foo=False, encoder_outputs=None, **kwargs):
kwargs["encoder_outputs"] = encoder_outputs
inputs = super().prepare_inputs_for_generation(*args, **kwargs)
inputs["foo"] = foo
return inputs
model = FakeBart.from_pretrained("hf-internal-testing/tiny-random-BartForConditionalGeneration").to(
torch_device
)
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-BartForConditionalGeneration")
text = "Hello world"
tokenized_inputs = tokenizer([text], return_tensors="pt")
input_ids = tokenized_inputs.input_ids.to(torch_device)
# Traditional way of generating text
outputs_normal = model.generate(input_ids)
self.assertEqual(outputs_normal.shape, (1, 20))
# Should be different with foo
outputs_foo = model.generate(input_ids, foo=True)
with self.assertRaises(AssertionError):
self.assertListEqual(outputs_foo.tolist(), outputs_normal.tolist())
# Assistant model
assistant = FakeBart.from_pretrained("hf-internal-testing/tiny-random-BartForConditionalGeneration").to(
torch_device
)
# If assisted generation passes model_kwargs correctly, should be same as previous
outputs_assisted = model.generate(
input_ids,
foo=True,
assistant_model=assistant,
)
self.assertListEqual(outputs_assisted.tolist(), outputs_foo.tolist())
# Check that passing encoder_outputs directly also works as expected
encoder_outputs = assistant.get_encoder()(input_ids)
outputs_assisted = model.generate(
foo=True,
assistant_model=assistant,
encoder_outputs=encoder_outputs,
assistant_encoder_outputs=encoder_outputs,
)
self.assertListEqual(outputs_assisted.tolist(), outputs_foo.tolist())
def test_assisted_decoding_encoder_decoder_shared_encoder(self):
"""
Tests that the following scenario is compatible with assisted generation:
1. encoder-decoder main model
2. decoder-only assistant model
3. both have a custom input
(e.g. DistilWhisper)
"""
# PT-only test: TF doesn't support assisted decoding yet.
# Bart subclass with a kwarg called foo that distorts the output
class FakeBartSeq2Seq(BartForConditionalGeneration):
def forward(self, input_ids, foo=False, **kwargs):
outs = super().forward(input_ids, **kwargs)
if foo:
outs["logits"][:, :, :] = 0.0
return outs
def prepare_inputs_for_generation(self, *args, foo=False, encoder_outputs=None, **kwargs):
kwargs["encoder_outputs"] = encoder_outputs
inputs = super().prepare_inputs_for_generation(*args, **kwargs)
inputs["foo"] = foo
return inputs
class FakeBartCausalLM(BartForCausalLM):
def forward(self, input_ids, attention_mask, past_key_values, foo=False, **kwargs):
outs = super().forward(input_ids, attention_mask, past_key_values=past_key_values, **kwargs)
if foo:
outs["logits"][:, :, :] = 0.0
return outs
def prepare_inputs_for_generation(self, *args, foo=False, encoder_outputs=None, **kwargs):
kwargs["encoder_outputs"] = encoder_outputs
inputs = super().prepare_inputs_for_generation(*args, **kwargs)
inputs["foo"] = foo
return inputs
model = FakeBartSeq2Seq.from_pretrained("hf-internal-testing/tiny-random-BartForConditionalGeneration").to(
torch_device
)
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-BartForConditionalGeneration")
text = "Hello world"
tokenized_inputs = tokenizer([text], return_tensors="pt")
input_ids = tokenized_inputs.input_ids.to(torch_device)
# Traditional way of generating text
outputs_normal = model.generate(input_ids)
self.assertEqual(outputs_normal.shape, (1, 20))
# Should be different with foo
outputs_foo = model.generate(input_ids, foo=True)
with self.assertRaises(AssertionError):
self.assertListEqual(outputs_foo.tolist(), outputs_normal.tolist())
# Assistant model
assistant = FakeBartCausalLM.from_pretrained(
"hf-internal-testing/tiny-random-BartForConditionalGeneration"
).to(torch_device)
# If assisted generation passes model_kwargs correctly, should be same as previous
outputs_assisted = model.generate(
input_ids,
foo=True,
assistant_model=assistant,
)
self.assertListEqual(outputs_assisted.tolist(), outputs_foo.tolist())
# Check that passing encoder_outputs directly also works as expected
encoder_outputs = model.get_encoder()(input_ids)
outputs_assisted = model.generate(
foo=True,
assistant_model=assistant,
encoder_outputs=encoder_outputs,
)
self.assertListEqual(outputs_assisted.tolist(), outputs_foo.tolist())
def test_assisted_decoding_num_assistant_tokens_heuristic_schedule(self):
# This test ensures that the assisted generation num_assistant_tokens 'heuristic' schedule works properly.
prompt = "Alice and Bob"
checkpoint = "EleutherAI/pythia-160m-deduped"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
inputs = tokenizer(prompt, return_tensors="pt")
model = AutoModelForCausalLM.from_pretrained(checkpoint)
assistant_model = model
assistant_model.generation_config.num_assistant_tokens = 5
assistant_model.generation_config.num_assistant_tokens_schedule = "heuristic"
generation_kwargs = {
"eos_token_id": -1,
"max_new_tokens": 5,
"do_sample": False,
"assistant_model": assistant_model,
}
model.generate(**inputs, **generation_kwargs)
# update_candidate_strategy is called only once and therefore, assistant_model.generation_config.num_assistant_tokens should be either 4 or 7
self.assertTrue(assistant_model.generation_config.num_assistant_tokens in (4, 7))
def test_assisted_decoding_num_assistant_tokens_heuristic_transient_schedule(self):
# This test ensures that the assisted generation num_assistant_tokens 'heuristic' schedule works properly.
prompt = "Alice and Bob"
checkpoint = "EleutherAI/pythia-160m-deduped"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
inputs = tokenizer(prompt, return_tensors="pt")
model = AutoModelForCausalLM.from_pretrained(checkpoint)
assistant_model = model
assistant_model.generation_config.num_assistant_tokens = 5
assistant_model.generation_config.num_assistant_tokens_schedule = "heuristic_transient"
generation_kwargs = {
"eos_token_id": -1,
"max_new_tokens": 5,
"do_sample": False,
"assistant_model": assistant_model,
}
model.generate(**inputs, **generation_kwargs)
# update_candidate_strategy is called once but assistant_model.generation_config.num_assistant_tokens should stay 5
self.assertEqual(assistant_model.generation_config.num_assistant_tokens, 5)
def test_compare_unprocessed_logit_scores(self):
# Get unprocessed logit scores back from model generate function.
# Assert that unprocessed logits from generate() are same as those from modal eval()
# tell model to generate text and return unprocessed/unwarped logit scores
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
text = "generate yes or no: "
input_ids = tokenizer([text], return_tensors="pt").input_ids.to(torch_device)
model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
with torch.no_grad():
# Get logits for the next token from fwd pass
logits_fwd = model(input_ids).logits[:, -1, :][0]
# Get logits for the next token from generate function
outputs = model.generate(
input_ids=input_ids,
return_dict_in_generate=True,
output_logits=True,
max_new_tokens=1,
do_sample=True,
)
logits_gen = outputs.logits[0][0]
# assert that unprocessed logits from generate() are same as those from modal eval()
self.assertListEqual(logits_fwd.tolist(), logits_gen.tolist())
def test_return_unprocessed_logit_scores(self):
# tell model to generate text and return unprocessed/unwarped logit scores
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
text = "generate yes or no: "
input_ids = tokenizer([text], return_tensors="pt").input_ids.to(torch_device)
model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
outputs = model.generate(
input_ids=input_ids, return_dict_in_generate=True, output_logits=True, max_new_tokens=3
)
# perform dummy check if unpreprocessed logits make sense.
# do preselection on high probabilities; find scores of y and n tokens
probs_all = torch.nn.functional.softmax(outputs.logits[2][0], dim=-1)
indices = torch.argwhere(probs_all > 0.001)
indices = indices[:, -1]
tokens_max = tokenizer.batch_decode(indices, skip_special_tokens=True)
probs_max = probs_all[probs_all > 0.001]
self.assertTrue(len(indices) >= 2)
next_token_dict = {str(t): p for t, p in zip(tokens_max, probs_max)}
self.assertTrue("n" in next_token_dict)
self.assertTrue("y" in next_token_dict)
y_prob = next_token_dict["y"]
n_prob = next_token_dict["n"]
self.assertTrue(y_prob > 0.001 and n_prob > 0.001)
self.assertTrue(y_prob <= 1.0 and n_prob <= 1.0)
| transformers/tests/generation/test_utils.py/0 | {
"file_path": "transformers/tests/generation/test_utils.py",
"repo_id": "transformers",
"token_count": 58342
} | 360 |
# coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir
sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
SAMPLE_ROBERTA_CONFIG = get_tests_dir("fixtures/dummy-config.json")
class AutoConfigTest(unittest.TestCase):
def setUp(self):
transformers.dynamic_module_utils.TIME_OUT_REMOTE_CODE = 0
def test_module_spec(self):
self.assertIsNotNone(transformers.models.auto.__spec__)
self.assertIsNotNone(importlib.util.find_spec("transformers.models.auto"))
def test_config_from_model_shortcut(self):
config = AutoConfig.from_pretrained("google-bert/bert-base-uncased")
self.assertIsInstance(config, BertConfig)
def test_config_model_type_from_local_file(self):
config = AutoConfig.from_pretrained(SAMPLE_ROBERTA_CONFIG)
self.assertIsInstance(config, RobertaConfig)
def test_config_model_type_from_model_identifier(self):
config = AutoConfig.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER)
self.assertIsInstance(config, RobertaConfig)
def test_config_for_model_str(self):
config = AutoConfig.for_model("roberta")
self.assertIsInstance(config, RobertaConfig)
def test_pattern_matching_fallback(self):
with tempfile.TemporaryDirectory() as tmp_dir:
# This model name contains bert and roberta, but roberta ends up being picked.
folder = os.path.join(tmp_dir, "fake-roberta")
os.makedirs(folder, exist_ok=True)
with open(os.path.join(folder, "config.json"), "w") as f:
f.write(json.dumps({}))
config = AutoConfig.from_pretrained(folder)
self.assertEqual(type(config), RobertaConfig)
def test_new_config_registration(self):
try:
AutoConfig.register("custom", CustomConfig)
# Wrong model type will raise an error
with self.assertRaises(ValueError):
AutoConfig.register("model", CustomConfig)
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(ValueError):
AutoConfig.register("bert", BertConfig)
# Now that the config is registered, it can be used as any other config with the auto-API
config = CustomConfig()
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(tmp_dir)
new_config = AutoConfig.from_pretrained(tmp_dir)
self.assertIsInstance(new_config, CustomConfig)
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
def test_repo_not_found(self):
with self.assertRaisesRegex(
EnvironmentError, "bert-base is not a local folder and is not a valid model identifier"
):
_ = AutoConfig.from_pretrained("bert-base")
def test_revision_not_found(self):
with self.assertRaisesRegex(
EnvironmentError, r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"
):
_ = AutoConfig.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER, revision="aaaaaa")
def test_configuration_not_found(self):
with self.assertRaisesRegex(
EnvironmentError,
"hf-internal-testing/no-config-test-repo does not appear to have a file named config.json.",
):
_ = AutoConfig.from_pretrained("hf-internal-testing/no-config-test-repo")
def test_from_pretrained_dynamic_config(self):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(ValueError):
config = AutoConfig.from_pretrained("hf-internal-testing/test_dynamic_model")
# If remote code is disabled, we can't load this config.
with self.assertRaises(ValueError):
config = AutoConfig.from_pretrained("hf-internal-testing/test_dynamic_model", trust_remote_code=False)
config = AutoConfig.from_pretrained("hf-internal-testing/test_dynamic_model", trust_remote_code=True)
self.assertEqual(config.__class__.__name__, "NewModelConfig")
# Test config can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(tmp_dir)
reloaded_config = AutoConfig.from_pretrained(tmp_dir, trust_remote_code=True)
self.assertEqual(reloaded_config.__class__.__name__, "NewModelConfig")
def test_from_pretrained_dynamic_config_conflict(self):
class NewModelConfigLocal(BertConfig):
model_type = "new-model"
try:
AutoConfig.register("new-model", NewModelConfigLocal)
# If remote code is not set, the default is to use local
config = AutoConfig.from_pretrained("hf-internal-testing/test_dynamic_model")
self.assertEqual(config.__class__.__name__, "NewModelConfigLocal")
# If remote code is disabled, we load the local one.
config = AutoConfig.from_pretrained("hf-internal-testing/test_dynamic_model", trust_remote_code=False)
self.assertEqual(config.__class__.__name__, "NewModelConfigLocal")
# If remote is enabled, we load from the Hub
config = AutoConfig.from_pretrained("hf-internal-testing/test_dynamic_model", trust_remote_code=True)
self.assertEqual(config.__class__.__name__, "NewModelConfig")
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
| transformers/tests/models/auto/test_configuration_auto.py/0 | {
"file_path": "transformers/tests/models/auto/test_configuration_auto.py",
"repo_id": "transformers",
"token_count": 2613
} | 361 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BartConfig, BartTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
os.environ["XLA_PYTHON_CLIENT_ALLOCATOR"] = "platform"
import jax
import jax.numpy as jnp
from transformers.models.bart.modeling_flax_bart import (
FlaxBartForConditionalGeneration,
FlaxBartForQuestionAnswering,
FlaxBartForSequenceClassification,
FlaxBartModel,
shift_tokens_right,
)
def prepare_bart_inputs_dict(
config,
input_ids,
decoder_input_ids=None,
attention_mask=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
):
if attention_mask is None:
attention_mask = np.where(input_ids != config.pad_token_id, 1, 0)
if decoder_attention_mask is None:
decoder_attention_mask = np.where(decoder_input_ids != config.pad_token_id, 1, 0)
if head_mask is None:
head_mask = np.ones((config.encoder_layers, config.encoder_attention_heads))
if decoder_head_mask is None:
decoder_head_mask = np.ones((config.decoder_layers, config.decoder_attention_heads))
if cross_attn_head_mask is None:
cross_attn_head_mask = np.ones((config.decoder_layers, config.decoder_attention_heads))
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
}
class FlaxBartModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_labels=False,
vocab_size=99,
hidden_size=16,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=4,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=32,
eos_token_id=2,
pad_token_id=1,
bos_token_id=0,
initializer_range=0.02,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
self.initializer_range = initializer_range
def prepare_config_and_inputs(self):
input_ids = np.clip(ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size), 3, self.vocab_size)
input_ids = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1), dtype=np.int64)), -1)
decoder_input_ids = shift_tokens_right(input_ids, 1, 2)
config = BartConfig(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
encoder_ffn_dim=self.intermediate_size,
decoder_ffn_dim=self.intermediate_size,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
pad_token_id=self.pad_token_id,
initializer_range=self.initializer_range,
use_cache=False,
)
inputs_dict = prepare_bart_inputs_dict(config, input_ids, decoder_input_ids)
return config, inputs_dict
def prepare_config_and_inputs_for_common(self):
config, inputs_dict = self.prepare_config_and_inputs()
return config, inputs_dict
def check_use_cache_forward(self, model_class_name, config, inputs_dict):
max_decoder_length = 20
model = model_class_name(config)
encoder_outputs = model.encode(inputs_dict["input_ids"])
decoder_input_ids, decoder_attention_mask = (
inputs_dict["decoder_input_ids"],
inputs_dict["decoder_attention_mask"],
)
past_key_values = model.init_cache(decoder_input_ids.shape[0], max_decoder_length, encoder_outputs)
decoder_attention_mask = jnp.ones((decoder_input_ids.shape[0], max_decoder_length), dtype="i4")
decoder_position_ids = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :],
(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),
)
outputs_cache = model.decode(
decoder_input_ids[:, :-1],
encoder_outputs,
decoder_attention_mask=decoder_attention_mask,
past_key_values=past_key_values,
decoder_position_ids=decoder_position_ids,
)
decoder_position_ids = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]], dtype="i4")
outputs_cache_next = model.decode(
decoder_input_ids[:, -1:],
encoder_outputs,
decoder_attention_mask=decoder_attention_mask,
past_key_values=outputs_cache.past_key_values,
decoder_position_ids=decoder_position_ids,
)
outputs = model.decode(decoder_input_ids, encoder_outputs)
diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}")
def check_use_cache_forward_with_attn_mask(self, model_class_name, config, inputs_dict):
max_decoder_length = 20
model = model_class_name(config)
encoder_outputs = model.encode(inputs_dict["input_ids"])
decoder_input_ids, decoder_attention_mask = (
inputs_dict["decoder_input_ids"],
inputs_dict["decoder_attention_mask"],
)
decoder_attention_mask_cache = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
],
axis=-1,
)
past_key_values = model.init_cache(decoder_input_ids.shape[0], max_decoder_length, encoder_outputs)
decoder_position_ids = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :],
(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),
)
outputs_cache = model.decode(
decoder_input_ids[:, :-1],
encoder_outputs,
decoder_attention_mask=decoder_attention_mask_cache,
past_key_values=past_key_values,
decoder_position_ids=decoder_position_ids,
)
decoder_position_ids = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]], dtype="i4")
outputs_cache_next = model.decode(
decoder_input_ids[:, -1:],
encoder_outputs,
past_key_values=outputs_cache.past_key_values,
decoder_attention_mask=decoder_attention_mask_cache,
decoder_position_ids=decoder_position_ids,
)
outputs = model.decode(decoder_input_ids, encoder_outputs, decoder_attention_mask=decoder_attention_mask)
diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}")
@require_flax
class BartHeadTests(unittest.TestCase):
vocab_size = 99
def _get_config_and_data(self):
input_ids = np.array(
[
[71, 82, 18, 33, 46, 91, 2],
[68, 34, 26, 58, 30, 82, 2],
[5, 97, 17, 39, 94, 40, 2],
[76, 83, 94, 25, 70, 78, 2],
[87, 59, 41, 35, 48, 66, 2],
[55, 13, 16, 58, 5, 2, 1], # note padding
[64, 27, 31, 51, 12, 75, 2],
[52, 64, 86, 17, 83, 39, 2],
[48, 61, 9, 24, 71, 82, 2],
[26, 1, 60, 48, 22, 13, 2],
[21, 5, 62, 28, 14, 76, 2],
[45, 98, 37, 86, 59, 48, 2],
[70, 70, 50, 9, 28, 0, 2],
],
dtype=np.int64,
)
batch_size = input_ids.shape[0]
config = BartConfig(
vocab_size=self.vocab_size,
d_model=24,
encoder_layers=2,
decoder_layers=2,
encoder_attention_heads=2,
decoder_attention_heads=2,
encoder_ffn_dim=32,
decoder_ffn_dim=32,
max_position_embeddings=48,
eos_token_id=2,
pad_token_id=1,
bos_token_id=0,
)
return config, input_ids, batch_size
def test_sequence_classification_forward(self):
config, input_ids, batch_size = self._get_config_and_data()
model = FlaxBartForSequenceClassification(config)
outputs = model(input_ids=input_ids, decoder_input_ids=input_ids)
expected_shape = (batch_size, config.num_labels)
self.assertEqual(outputs["logits"].shape, expected_shape)
def test_question_answering_forward(self):
config, input_ids, batch_size = self._get_config_and_data()
model = FlaxBartForQuestionAnswering(config)
outputs = model(input_ids=input_ids)
self.assertEqual(outputs["start_logits"].shape, input_ids.shape)
self.assertEqual(outputs["end_logits"].shape, input_ids.shape)
# @timeout_decorator.timeout(1) # not working with the decorator so far
def test_lm_forward(self):
config, input_ids, batch_size = self._get_config_and_data()
lm_model = FlaxBartForConditionalGeneration(config)
outputs = lm_model(input_ids=input_ids)
expected_shape = (batch_size, input_ids.shape[1], config.vocab_size)
self.assertEqual(outputs["logits"].shape, expected_shape)
def test_lm_uneven_forward(self):
config = BartConfig(
vocab_size=self.vocab_size,
d_model=14,
encoder_layers=2,
decoder_layers=2,
encoder_attention_heads=2,
decoder_attention_heads=2,
encoder_ffn_dim=8,
decoder_ffn_dim=8,
max_position_embeddings=48,
)
lm_model = FlaxBartForConditionalGeneration(config)
context = np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]], dtype=np.int64)
summary = np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]], dtype=np.int64)
outputs = lm_model(input_ids=context, decoder_input_ids=summary)
expected_shape = (*summary.shape, config.vocab_size)
self.assertEqual(outputs["logits"].shape, expected_shape)
def test_shift_tokens_right(self):
input_ids = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]], dtype=np.int64)
shifted = shift_tokens_right(input_ids, 1, 2)
n_pad_before = np.equal(input_ids, 1).astype(np.float32).sum()
n_pad_after = np.equal(shifted, 1).astype(np.float32).sum()
self.assertEqual(shifted.shape, input_ids.shape)
self.assertEqual(n_pad_after, n_pad_before - 1)
self.assertTrue(np.equal(shifted[:, 0], 2).all())
@require_flax
class FlaxBartModelTest(FlaxModelTesterMixin, unittest.TestCase, FlaxGenerationTesterMixin):
is_encoder_decoder = True
all_model_classes = (
(
FlaxBartModel,
FlaxBartForConditionalGeneration,
FlaxBartForSequenceClassification,
FlaxBartForQuestionAnswering,
)
if is_flax_available()
else ()
)
all_generative_model_classes = (FlaxBartForConditionalGeneration,) if is_flax_available() else ()
def setUp(self):
self.model_tester = FlaxBartModelTester(self)
def test_use_cache_forward(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(model_class, config, inputs_dict)
def test_use_cache_forward_with_attn_mask(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(model_class, config, inputs_dict)
def test_encode(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
model = model_class(config)
@jax.jit
def encode_jitted(input_ids, attention_mask=None, **kwargs):
return model.encode(input_ids=input_ids, attention_mask=attention_mask)
with self.subTest("JIT Enabled"):
jitted_outputs = encode_jitted(**prepared_inputs_dict).to_tuple()
with self.subTest("JIT Disabled"):
with jax.disable_jit():
outputs = encode_jitted(**prepared_inputs_dict).to_tuple()
self.assertEqual(len(outputs), len(jitted_outputs))
for jitted_output, output in zip(jitted_outputs, outputs):
self.assertEqual(jitted_output.shape, output.shape)
def test_decode(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
model = model_class(config)
encoder_outputs = model.encode(inputs_dict["input_ids"], inputs_dict["attention_mask"])
prepared_inputs_dict = {
"decoder_input_ids": inputs_dict["decoder_input_ids"],
"decoder_attention_mask": inputs_dict["decoder_attention_mask"],
"encoder_outputs": encoder_outputs,
}
@jax.jit
def decode_jitted(decoder_input_ids, decoder_attention_mask, encoder_outputs):
return model.decode(
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
encoder_outputs=encoder_outputs,
)
with self.subTest("JIT Enabled"):
jitted_outputs = decode_jitted(**prepared_inputs_dict).to_tuple()
with self.subTest("JIT Disabled"):
with jax.disable_jit():
outputs = decode_jitted(**prepared_inputs_dict).to_tuple()
self.assertEqual(len(outputs), len(jitted_outputs))
for jitted_output, output in zip(jitted_outputs, outputs):
self.assertEqual(jitted_output.shape, output.shape)
@slow
def test_model_from_pretrained(self):
for model_class_name in self.all_model_classes:
model = model_class_name.from_pretrained("facebook/bart-base", from_pt=True)
# FlaxBartForSequenceClassification expects eos token in input_ids
input_ids = np.ones((1, 1)) * model.config.eos_token_id
outputs = model(input_ids)
self.assertIsNotNone(outputs)
@slow
def test_summarization_fast(self):
model = FlaxBartForConditionalGeneration.from_pretrained("sshleifer/distilbart-cnn-6-6")
tokenizer = BartTokenizer.from_pretrained("sshleifer/distilbart-cnn-6-6")
input_str = (
"This sentence is made of three parts. Each part is important on its own. One part is about animals, the"
" other part about planes, and the last part about housing."
)
input_ids = tokenizer(input_str, return_tensors="np").input_ids
sequences = model.generate(input_ids, num_beams=2, min_length=None, max_length=20).sequences
output_str = tokenizer.batch_decode(sequences)[0]
assert (
output_str == "</s><s>This sentence is made of three parts. One part is about animals, the other part</s>"
)
@slow
def test_cnn_summarization_same_as_fairseq(self):
model = FlaxBartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn")
tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn")
FRANCE_ARTICLE = ( # @noq
" Marseille, France (CNN)The French prosecutor leading an investigation into the crash of Germanwings"
" Flight 9525 insisted Wednesday that he was not aware of any video footage from on board the plane."
' Marseille prosecutor Brice Robin told CNN that "so far no videos were used in the crash investigation."'
' He added, "A person who has such a video needs to immediately give it to the investigators." Robin\'s'
" comments follow claims by two magazines, German daily Bild and French Paris Match, of a cell phone video"
" showing the harrowing final seconds from on board Germanwings Flight 9525 as it crashed into the French"
" Alps. All 150 on board were killed. Paris Match and Bild reported that the video was recovered from a"
" phone at the wreckage site. The two publications described the supposed video, but did not post it on"
" their websites. The publications said that they watched the video, which was found by a source close to"
" the investigation. \"One can hear cries of 'My God' in several languages,\" Paris Match reported."
' "Metallic banging can also be heard more than three times, perhaps of the pilot trying to open the'
" cockpit door with a heavy object. Towards the end, after a heavy shake, stronger than the others, the"
' screaming intensifies. Then nothing." "It is a very disturbing scene," said Julian Reichelt,'
" editor-in-chief of Bild online. An official with France's accident investigation agency, the BEA, said"
" the agency is not aware of any such video. Lt. Col. Jean-Marc Menichini, a French Gendarmerie spokesman"
" in charge of communications on rescue efforts around the Germanwings crash site, told CNN that the"
' reports were "completely wrong" and "unwarranted." Cell phones have been collected at the site, he said,'
' but that they "hadn\'t been exploited yet." Menichini said he believed the cell phones would need to be'
" sent to the Criminal Research Institute in Rosny sous-Bois, near Paris, in order to be analyzed by"
" specialized technicians working hand-in-hand with investigators. But none of the cell phones found so"
" far have been sent to the institute, Menichini said. Asked whether staff involved in the search could"
' have leaked a memory card to the media, Menichini answered with a categorical "no." Reichelt told "Erin'
' Burnett: Outfront" that he had watched the video and stood by the report, saying Bild and Paris Match'
' are "very confident" that the clip is real. He noted that investigators only revealed they\'d recovered'
' cell phones from the crash site after Bild and Paris Match published their reports. "That is something'
" we did not know before. ... Overall we can say many things of the investigation weren't revealed by the"
' investigation at the beginning," he said. What was mental state of Germanwings co-pilot? German airline'
" Lufthansa confirmed Tuesday that co-pilot Andreas Lubitz had battled depression years before he took the"
" controls of Germanwings Flight 9525, which he's accused of deliberately crashing last week in the"
' French Alps. Lubitz told his Lufthansa flight training school in 2009 that he had a "previous episode of'
' severe depression," the airline said Tuesday. Email correspondence between Lubitz and the school'
" discovered in an internal investigation, Lufthansa said, included medical documents he submitted in"
" connection with resuming his flight training. The announcement indicates that Lufthansa, the parent"
" company of Germanwings, knew of Lubitz's battle with depression, allowed him to continue training and"
" ultimately put him in the cockpit. Lufthansa, whose CEO Carsten Spohr previously said Lubitz was 100%"
' fit to fly, described its statement Tuesday as a "swift and seamless clarification" and said it was'
" sharing the information and documents -- including training and medical records -- with public"
" prosecutors. Spohr traveled to the crash site Wednesday, where recovery teams have been working for the"
" past week to recover human remains and plane debris scattered across a steep mountainside. He saw the"
" crisis center set up in Seyne-les-Alpes, laid a wreath in the village of Le Vernet, closer to the crash"
" site, where grieving families have left flowers at a simple stone memorial. Menichini told CNN late"
" Tuesday that no visible human remains were left at the site but recovery teams would keep searching."
" French President Francois Hollande, speaking Tuesday, said that it should be possible to identify all"
" the victims using DNA analysis by the end of the week, sooner than authorities had previously suggested."
" In the meantime, the recovery of the victims' personal belongings will start Wednesday, Menichini said."
" Among those personal belongings could be more cell phones belonging to the 144 passengers and six crew"
" on board. Check out the latest from our correspondents . The details about Lubitz's correspondence with"
" the flight school during his training were among several developments as investigators continued to"
" delve into what caused the crash and Lubitz's possible motive for downing the jet. A Lufthansa"
" spokesperson told CNN on Tuesday that Lubitz had a valid medical certificate, had passed all his"
' examinations and "held all the licenses required." Earlier, a spokesman for the prosecutor\'s office in'
" Dusseldorf, Christoph Kumpa, said medical records reveal Lubitz suffered from suicidal tendencies at"
" some point before his aviation career and underwent psychotherapy before he got his pilot's license."
" Kumpa emphasized there's no evidence suggesting Lubitz was suicidal or acting aggressively before the"
" crash. Investigators are looking into whether Lubitz feared his medical condition would cause him to"
" lose his pilot's license, a European government official briefed on the investigation told CNN on"
' Tuesday. While flying was "a big part of his life," the source said, it\'s only one theory being'
" considered. Another source, a law enforcement official briefed on the investigation, also told CNN that"
" authorities believe the primary motive for Lubitz to bring down the plane was that he feared he would"
" not be allowed to fly because of his medical problems. Lubitz's girlfriend told investigators he had"
" seen an eye doctor and a neuropsychologist, both of whom deemed him unfit to work recently and concluded"
" he had psychological issues, the European government official said. But no matter what details emerge"
" about his previous mental health struggles, there's more to the story, said Brian Russell, a forensic"
' psychologist. "Psychology can explain why somebody would turn rage inward on themselves about the fact'
" that maybe they weren't going to keep doing their job and they're upset about that and so they're"
' suicidal," he said. "But there is no mental illness that explains why somebody then feels entitled to'
" also take that rage and turn it outward on 149 other people who had nothing to do with the person's"
' problems." Germanwings crash compensation: What we know . Who was the captain of Germanwings Flight'
" 9525? CNN's Margot Haddad reported from Marseille and Pamela Brown from Dusseldorf, while Laura"
" Smith-Spark wrote from London. CNN's Frederik Pleitgen, Pamela Boykoff, Antonia Mortensen, Sandrine"
" Amiel and Anna-Maja Rappard contributed to this report."
)
SHORTER_ARTICLE = (
" (CNN)The Palestinian Authority officially became the 123rd member of the International Criminal Court on"
" Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The"
" formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based."
" The Palestinians signed the ICC's founding Rome Statute in January, when they also accepted its"
' jurisdiction over alleged crimes committed "in the occupied Palestinian territory, including East'
' Jerusalem, since June 13, 2014." Later that month, the ICC opened a preliminary examination into the'
" situation in Palestinian territories, paving the way for possible war crimes investigations against"
" Israelis. As members of the court, Palestinians may be subject to counter-charges as well. Israel and"
" the United States, neither of which is an ICC member, opposed the Palestinians' efforts to join the"
" body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday's ceremony, said it was a"
' move toward greater justice. "As Palestine formally becomes a State Party to the Rome Statute today, the'
' world is also a step closer to ending a long era of impunity and injustice," he said, according to an'
' ICC news release. "Indeed, today brings us closer to our shared goals of justice and peace." Judge'
" Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was just the first step for the"
' Palestinians. "As the Rome Statute today enters into force for the State of Palestine, Palestine'
" acquires all the rights as well as responsibilities that come with being a State Party to the Statute."
' These are substantive commitments, which cannot be taken lightly," she said. Rights group Human Rights'
' Watch welcomed the development. "Governments seeking to penalize Palestine for joining the ICC should'
" immediately end their pressure, and countries that support universal acceptance of the court's treaty"
' should speak out to welcome its membership," said Balkees Jarrah, international justice counsel for the'
" group. \"What's objectionable is the attempts to undermine international justice, not Palestine's"
' decision to join a treaty to which over 100 countries around the world are members." In January, when'
" the preliminary ICC examination was opened, Israeli Prime Minister Benjamin Netanyahu described it as an"
' outrage, saying the court was overstepping its boundaries. The United States also said it "strongly"'
" disagreed with the court's decision. \"As we have said repeatedly, we do not believe that Palestine is a"
' state and therefore we do not believe that it is eligible to join the ICC," the State Department said in'
' a statement. It urged the warring sides to resolve their differences through direct negotiations. "We'
' will continue to oppose actions against Israel at the ICC as counterproductive to the cause of peace,"'
" it said. But the ICC begs to differ with the definition of a state for its purposes and refers to the"
' territories as "Palestine." While a preliminary examination is not a formal investigation, it allows the'
" court to review evidence and determine whether to investigate suspects on both sides. Prosecutor Fatou"
' Bensouda said her office would "conduct its analysis in full independence and impartiality." The war'
" between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The inquiry"
" will include alleged war crimes committed since June. The International Criminal Court was set up in"
" 2002 to prosecute genocide, crimes against humanity and war crimes. CNN's Vasco Cotovio, Kareem Khadder"
" and Faith Karimi contributed to this report."
)
# The below article tests that we don't add any hypotheses outside of the top n_beams
IRAN_ARTICLE = (
" (CNN)The United States and its negotiating partners reached a very strong framework agreement with Iran"
" in Lausanne, Switzerland, on Thursday that limits Iran's nuclear program in such a way as to effectively"
" block it from building a nuclear weapon. Expect pushback anyway, if the recent past is any harbinger."
" Just last month, in an attempt to head off such an agreement, House Speaker John Boehner invited Israeli"
" Prime Minister Benjamin Netanyahu to preemptively blast it before Congress, and 47 senators sent a"
" letter to the Iranian leadership warning them away from a deal. The debate that has already begun since"
" the announcement of the new framework will likely result in more heat than light. It will not be helped"
" by the gathering swirl of dubious assumptions and doubtful assertions. Let us address some of these: ."
" The most misleading assertion, despite universal rejection by experts, is that the negotiations'"
" objective at the outset was the total elimination of any nuclear program in Iran. That is the position"
" of Netanyahu and his acolytes in the U.S. Congress. But that is not and never was the objective. If it"
" had been, there would have been no Iranian team at the negotiating table. Rather, the objective has"
" always been to structure an agreement or series of agreements so that Iran could not covertly develop a"
" nuclear arsenal before the United States and its allies could respond. The new framework has exceeded"
" expectations in achieving that goal. It would reduce Iran's low-enriched uranium stockpile, cut by"
" two-thirds its number of installed centrifuges and implement a rigorous inspection regime. Another"
" dubious assumption of opponents is that the Iranian nuclear program is a covert weapons program. Despite"
" sharp accusations by some in the United States and its allies, Iran denies having such a program, and"
" U.S. intelligence contends that Iran has not yet made the decision to build a nuclear weapon. Iran's"
" continued cooperation with International Atomic Energy Agency inspections is further evidence on this"
" point, and we'll know even more about Iran's program in the coming months and years because of the deal."
" In fact, the inspections provisions that are part of this agreement are designed to protect against any"
" covert action by the Iranians. What's more, the rhetoric of some members of Congress has implied that"
" the negotiations have been between only the United States and Iran (i.e., the 47 senators' letter"
" warning that a deal might be killed by Congress or a future president). This of course is not the case."
" The talks were between Iran and the five permanent members of the U.N. Security Council (United States,"
" United Kingdom, France, China and Russia) plus Germany, dubbed the P5+1. While the United States has"
" played a leading role in the effort, it negotiated the terms alongside its partners. If the agreement"
" reached by the P5+1 is rejected by Congress, it could result in an unraveling of the sanctions on Iran"
" and threaten NATO cohesion in other areas. Another questionable assertion is that this agreement"
" contains a sunset clause, after which Iran will be free to do as it pleases. Again, this is not the"
" case. Some of the restrictions on Iran's nuclear activities, such as uranium enrichment, will be eased"
" or eliminated over time, as long as 15 years. But most importantly, the framework agreement includes"
" Iran's ratification of the Additional Protocol, which allows IAEA inspectors expanded access to nuclear"
" sites both declared and nondeclared. This provision will be permanent. It does not sunset. Thus, going"
" forward, if Iran decides to enrich uranium to weapons-grade levels, monitors will be able to detect such"
" a move in a matter of days and alert the U.N. Security Council. Many in Congress have said that the"
' agreement should be a formal treaty requiring the Senate to "advise and consent." But the issue is not'
" suited for a treaty. Treaties impose equivalent obligations on all signatories. For example, the New"
" START treaty limits Russia and the United States to 1,550 deployed strategic warheads. But any agreement"
" with Iran will not be so balanced. The restrictions and obligations in the final framework agreement"
" will be imposed almost exclusively on Iran. The P5+1 are obligated only to ease and eventually remove"
" most but not all economic sanctions, which were imposed as leverage to gain this final deal. Finally"
" some insist that any agreement must address Iranian missile programs, human rights violations or support"
" for Hamas or Hezbollah. As important as these issues are, and they must indeed be addressed, they are"
" unrelated to the most important aim of a nuclear deal: preventing a nuclear Iran. To include them in"
" the negotiations would be a poison pill. This agreement should be judged on its merits and on how it"
" affects the security of our negotiating partners and allies, including Israel. Those judgments should be"
" fact-based, not based on questionable assertions or dubious assumptions."
)
ARTICLE_SUBWAY = (
" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York. A"
" year later, she got married again in Westchester County, but to a different man and without divorcing"
" her first husband. Only 18 days after that marriage, she got hitched yet again. Then, Barrientos"
' declared "I do" five more times, sometimes only within two weeks of each other. In 2010, she married'
" once more, this time in the Bronx. In an application for a marriage license, she stated it was her"
' "first and only" marriage. Barrientos, now 39, is facing two criminal counts of "offering a false'
' instrument for filing in the first degree," referring to her false statements on the 2010 marriage'
" license application, according to court documents. Prosecutors said the marriages were part of an"
" immigration scam. On Friday, she pleaded not guilty at State Supreme Court in the Bronx, according to"
" her attorney, Christopher Wright, who declined to comment further. After leaving court, Barrientos was"
" arrested and charged with theft of service and criminal trespass for allegedly sneaking into the New"
" York subway through an emergency exit, said Detective Annette Markowski, a police spokeswoman. In total,"
" Barrientos has been married 10 times, with nine of her marriages occurring between 1999 and 2002. All"
" occurred either in Westchester County, Long Island, New Jersey or the Bronx. She is believed to still be"
" married to four men, and at one time, she was married to eight men at once, prosecutors say. Prosecutors"
" said the immigration scam involved some of her husbands, who filed for permanent residence status"
" shortly after the marriages. Any divorces happened only after such filings were approved. It was"
" unclear whether any of the men will be prosecuted. The case was referred to the Bronx District"
" Attorney's Office by Immigration and Customs Enforcement and the Department of Homeland Security's"
' Investigation Division. Seven of the men are from so-called "red-flagged" countries, including Egypt,'
" Turkey, Georgia, Pakistan and Mali. Her eighth husband, Rashid Rajput, was deported in 2006 to his"
" native Pakistan after an investigation by the Joint Terrorism Task Force. If convicted, Barrientos faces"
" up to four years in prison. Her next court appearance is scheduled for May 18."
)
dct = tokenizer.batch_encode_plus(
[FRANCE_ARTICLE, SHORTER_ARTICLE, IRAN_ARTICLE, ARTICLE_SUBWAY],
max_length=1024,
padding="max_length",
truncation_strategy="only_first",
truncation=True,
return_tensors="np",
)
self.assertEqual(1024, dct["input_ids"].shape[1])
hypotheses_batch = model.generate(
input_ids=dct["input_ids"],
attention_mask=dct["attention_mask"],
num_beams=2,
).sequences
assert (hypotheses_batch[:, 1] == 0).all().item()
EXPECTED = [
"A French prosecutor says he is not aware of any video footage from on board the plane. Two German"
" magazines claim to have found a cell phone video showing the crash. The publications say they watched"
" the video, which was found by a source close to the investigation. All 150 on board the Germanwings"
" flight were killed.",
"Palestinian Authority becomes 123rd member of the International Criminal Court. The move gives the court"
" jurisdiction over alleged crimes in Palestinian territories. Israel and the United States opposed the"
" Palestinians' efforts to join the body. But Palestinian Foreign Minister Riad al-Malki said it was a"
" move toward greater justice.",
"U.S. and its negotiating partners reached a strong framework agreement with Iran. Peter Bergen: The"
" debate that has already begun will likely result in more heat than light. Bergen: The most misleading"
" assertion is that the negotiations' objective at the outset was the total elimination of any nuclear"
" program.",
"Liana Barrientos, 39, has been married 10 times, sometimes within two weeks of each other. Prosecutors"
" say the marriages were part of an immigration scam. She pleaded not guilty at State Supreme Court in the"
" Bronx on Friday. If convicted, Barrientos faces up to four years in prison.",
]
generated_summaries = tokenizer.batch_decode(
hypotheses_batch.tolist(), clean_up_tokenization_spaces=True, skip_special_tokens=True
)
assert generated_summaries == EXPECTED
class FlaxBartStandaloneDecoderModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_attention_mask=True,
use_labels=False,
vocab_size=99,
hidden_size=16,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=4,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=32,
eos_token_id=2,
pad_token_id=1,
bos_token_id=0,
initializer_range=0.02,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_attention_mask = use_attention_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
self.initializer_range = initializer_range
def prepare_config_and_inputs(self):
input_ids = jnp.clip(ids_tensor([self.batch_size, self.seq_length], self.vocab_size), 3, self.vocab_size)
attention_mask = None
if self.use_attention_mask:
attention_mask = random_attention_mask([self.batch_size, self.seq_length])
config = BartConfig(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
encoder_ffn_dim=self.intermediate_size,
decoder_ffn_dim=self.intermediate_size,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
pad_token_id=self.pad_token_id,
initializer_range=self.initializer_range,
use_cache=False,
)
return config, input_ids, attention_mask
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, input_ids, attention_mask = config_and_inputs
inputs_dict = {"input_ids": input_ids, "attention_mask": attention_mask}
return config, inputs_dict
def prepare_config_and_inputs_for_decoder(self):
config, input_ids, attention_mask = self.prepare_config_and_inputs()
encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
return (
config,
input_ids,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
)
| transformers/tests/models/bart/test_modeling_flax_bart.py/0 | {
"file_path": "transformers/tests/models/bart/test_modeling_flax_bart.py",
"repo_id": "transformers",
"token_count": 17353
} | 362 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
if is_tf_available():
import tensorflow as tf
from transformers.modeling_tf_utils import keras
if is_tensorflow_text_available():
from transformers.models.bert import TFBertTokenizer
TOKENIZER_CHECKPOINTS = ["google-bert/bert-base-uncased", "google-bert/bert-base-cased"]
TINY_MODEL_CHECKPOINT = "hf-internal-testing/tiny-bert-tf-only"
if is_tf_available():
from transformers.modeling_tf_utils import keras
class ModelToSave(keras.Model):
def __init__(self, tokenizer):
super().__init__()
self.tokenizer = tokenizer
config = AutoConfig.from_pretrained(TINY_MODEL_CHECKPOINT)
self.bert = TFAutoModel.from_config(config)
def call(self, inputs):
tokenized = self.tokenizer(inputs)
out = self.bert(tokenized)
return out["pooler_output"]
@require_tf
@require_tensorflow_text
class BertTokenizationTest(unittest.TestCase):
# The TF tokenizers are usually going to be used as pretrained tokenizers from existing model checkpoints,
# so that's what we focus on here.
def setUp(self):
super().setUp()
self.tokenizers = [BertTokenizer.from_pretrained(checkpoint) for checkpoint in TOKENIZER_CHECKPOINTS]
self.tf_tokenizers = [TFBertTokenizer.from_pretrained(checkpoint) for checkpoint in TOKENIZER_CHECKPOINTS]
assert len(self.tokenizers) == len(self.tf_tokenizers)
self.test_sentences = [
"This is a straightforward English test sentence.",
"This one has some weird characters\rto\nsee\r\nif those\u00E9break things.",
"Now we're going to add some Chinese: 一 二 三 一二三",
"And some much more rare Chinese: 齉 堃 齉堃",
"Je vais aussi écrire en français pour tester les accents",
"Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ",
]
self.paired_sentences = list(zip(self.test_sentences, self.test_sentences[::-1]))
def test_output_equivalence(self):
for tokenizer, tf_tokenizer in zip(self.tokenizers, self.tf_tokenizers):
for test_inputs in (self.test_sentences, self.paired_sentences):
python_outputs = tokenizer(test_inputs, return_tensors="tf", padding="longest")
tf_outputs = tf_tokenizer(test_inputs)
for key in python_outputs.keys():
self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape))
self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key], tf.int64) == tf_outputs[key]))
@slow
def test_different_pairing_styles(self):
for tf_tokenizer in self.tf_tokenizers:
merged_outputs = tf_tokenizer(self.paired_sentences)
separated_outputs = tf_tokenizer(
text=[sentence[0] for sentence in self.paired_sentences],
text_pair=[sentence[1] for sentence in self.paired_sentences],
)
for key in merged_outputs.keys():
self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key], tf.int64) == separated_outputs[key]))
@slow
def test_graph_mode(self):
for tf_tokenizer in self.tf_tokenizers:
compiled_tokenizer = tf.function(tf_tokenizer)
for test_inputs in (self.test_sentences, self.paired_sentences):
test_inputs = tf.constant(test_inputs)
compiled_outputs = compiled_tokenizer(test_inputs)
eager_outputs = tf_tokenizer(test_inputs)
for key in eager_outputs.keys():
self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]))
@slow
def test_export_for_inference(self):
for tf_tokenizer in self.tf_tokenizers:
model = ModelToSave(tokenizer=tf_tokenizer)
test_inputs = tf.convert_to_tensor(self.test_sentences)
out = model(test_inputs) # Build model with some sample inputs
with TemporaryDirectory() as tempdir:
save_path = Path(tempdir) / "saved.model"
model.export(save_path)
loaded_model = tf.saved_model.load(save_path)
loaded_output = loaded_model.serve(test_inputs)
# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output)), 1e-5)
| transformers/tests/models/bert/test_tokenization_bert_tf.py/0 | {
"file_path": "transformers/tests/models/bert/test_tokenization_bert_tf.py",
"repo_id": "transformers",
"token_count": 2067
} | 363 |
# coding=utf-8
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import require_sacremoses, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_sacremoses
class BioGptTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
from_pretrained_id = "microsoft/biogpt"
tokenizer_class = BioGptTokenizer
test_rust_tokenizer = False
def setUp(self):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
vocab = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"w</w>",
"r</w>",
"t</w>",
"lo",
"low",
"er</w>",
"low</w>",
"lowest</w>",
"newer</w>",
"wider</w>",
"<unk>",
]
vocab_tokens = dict(zip(vocab, range(len(vocab))))
merges = ["l o 123", "lo w 1456", "e r</w> 1789", ""]
self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
with open(self.vocab_file, "w") as fp:
fp.write(json.dumps(vocab_tokens))
with open(self.merges_file, "w") as fp:
fp.write("\n".join(merges))
def get_input_output_texts(self, tokenizer):
input_text = "lower newer"
output_text = "lower newer"
return input_text, output_text
def test_full_tokenizer(self):
"""Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt"""
tokenizer = BioGptTokenizer(self.vocab_file, self.merges_file)
text = "lower"
bpe_tokens = ["low", "er</w>"]
tokens = tokenizer.tokenize(text)
self.assertListEqual(tokens, bpe_tokens)
input_tokens = tokens + ["<unk>"]
input_bpe_tokens = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
@slow
def test_sequence_builders(self):
tokenizer = BioGptTokenizer.from_pretrained("microsoft/biogpt")
text = tokenizer.encode("sequence builders", add_special_tokens=False)
text_2 = tokenizer.encode("multi-sequence build", add_special_tokens=False)
encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
self.assertTrue(encoded_sentence == [2] + text)
self.assertTrue(encoded_pair == [2] + text + [2] + text_2)
| transformers/tests/models/biogpt/test_tokenization_biogpt.py/0 | {
"file_path": "transformers/tests/models/biogpt/test_tokenization_biogpt.py",
"repo_id": "transformers",
"token_count": 1540
} | 364 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sentencepiece
class ClapProcessorTest(unittest.TestCase):
def setUp(self):
self.checkpoint = "laion/clap-htsat-unfused"
self.tmpdirname = tempfile.mkdtemp()
def get_tokenizer(self, **kwargs):
return RobertaTokenizer.from_pretrained(self.checkpoint, **kwargs)
def get_feature_extractor(self, **kwargs):
return ClapFeatureExtractor.from_pretrained(self.checkpoint, **kwargs)
def tearDown(self):
shutil.rmtree(self.tmpdirname)
def test_save_load_pretrained_default(self):
tokenizer = self.get_tokenizer()
feature_extractor = self.get_feature_extractor()
processor = ClapProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
processor.save_pretrained(self.tmpdirname)
processor = ClapProcessor.from_pretrained(self.tmpdirname)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab())
self.assertIsInstance(processor.tokenizer, RobertaTokenizerFast)
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string())
self.assertIsInstance(processor.feature_extractor, ClapFeatureExtractor)
def test_save_load_pretrained_additional_features(self):
processor = ClapProcessor(tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor())
processor.save_pretrained(self.tmpdirname)
tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
feature_extractor_add_kwargs = self.get_feature_extractor(do_normalize=False, padding_value=1.0)
processor = ClapProcessor.from_pretrained(
self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
self.assertIsInstance(processor.tokenizer, RobertaTokenizerFast)
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string())
self.assertIsInstance(processor.feature_extractor, ClapFeatureExtractor)
def test_feature_extractor(self):
feature_extractor = self.get_feature_extractor()
tokenizer = self.get_tokenizer()
processor = ClapProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
raw_speech = floats_list((3, 1000))
input_feat_extract = feature_extractor(raw_speech, return_tensors="np")
input_processor = processor(audios=raw_speech, return_tensors="np")
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
def test_tokenizer(self):
feature_extractor = self.get_feature_extractor()
tokenizer = self.get_tokenizer()
processor = ClapProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
input_str = "This is a test string"
encoded_processor = processor(text=input_str)
encoded_tok = tokenizer(input_str)
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key], encoded_processor[key])
def test_tokenizer_decode(self):
feature_extractor = self.get_feature_extractor()
tokenizer = self.get_tokenizer()
processor = ClapProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
decoded_processor = processor.batch_decode(predicted_ids)
decoded_tok = tokenizer.batch_decode(predicted_ids)
self.assertListEqual(decoded_tok, decoded_processor)
def test_model_input_names(self):
feature_extractor = self.get_feature_extractor()
tokenizer = self.get_tokenizer()
processor = ClapProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
self.assertListEqual(
processor.model_input_names[2:],
feature_extractor.model_input_names,
msg="`processor` and `feature_extractor` model input names do not match",
)
| transformers/tests/models/clap/test_processor_clap.py/0 | {
"file_path": "transformers/tests/models/clap/test_processor_clap.py",
"repo_id": "transformers",
"token_count": 1895
} | 365 |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch ConvNext model. """
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class ConvNextModelTester:
def __init__(
self,
parent,
batch_size=13,
image_size=32,
num_channels=3,
num_stages=4,
hidden_sizes=[10, 20, 30, 40],
depths=[2, 2, 3, 2],
is_training=True,
use_labels=True,
intermediate_size=37,
hidden_act="gelu",
num_labels=10,
initializer_range=0.02,
out_features=["stage2", "stage3", "stage4"],
out_indices=[2, 3, 4],
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.num_channels = num_channels
self.num_stages = num_stages
self.hidden_sizes = hidden_sizes
self.depths = depths
self.is_training = is_training
self.use_labels = use_labels
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.num_labels = num_labels
self.initializer_range = initializer_range
self.out_features = out_features
self.out_indices = out_indices
self.scope = scope
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size], self.num_labels)
config = self.get_config()
return config, pixel_values, labels
def get_config(self):
return ConvNextConfig(
num_channels=self.num_channels,
hidden_sizes=self.hidden_sizes,
depths=self.depths,
num_stages=self.num_stages,
hidden_act=self.hidden_act,
is_decoder=False,
initializer_range=self.initializer_range,
out_features=self.out_features,
out_indices=self.out_indices,
num_labels=self.num_labels,
)
def create_and_check_model(self, config, pixel_values, labels):
model = ConvNextModel(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape,
(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),
)
def create_and_check_for_image_classification(self, config, pixel_values, labels):
model = ConvNextForImageClassification(config)
model.to(torch_device)
model.eval()
result = model(pixel_values, labels=labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_backbone(self, config, pixel_values, labels):
model = ConvNextBackbone(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
# verify hidden states
self.parent.assertEqual(len(result.feature_maps), len(config.out_features))
self.parent.assertListEqual(list(result.feature_maps[0].shape), [self.batch_size, self.hidden_sizes[1], 4, 4])
# verify channels
self.parent.assertEqual(len(model.channels), len(config.out_features))
self.parent.assertListEqual(model.channels, config.hidden_sizes[1:])
# verify backbone works with out_features=None
config.out_features = None
model = ConvNextBackbone(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
# verify feature maps
self.parent.assertEqual(len(result.feature_maps), 1)
self.parent.assertListEqual(list(result.feature_maps[0].shape), [self.batch_size, self.hidden_sizes[-1], 1, 1])
# verify channels
self.parent.assertEqual(len(model.channels), 1)
self.parent.assertListEqual(model.channels, [config.hidden_sizes[-1]])
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, labels = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class ConvNextModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
"""
Here we also overwrite some of the tests of test_modeling_common.py, as ConvNext does not use input_ids, inputs_embeds,
attention_mask and seq_length.
"""
all_model_classes = (
(
ConvNextModel,
ConvNextForImageClassification,
ConvNextBackbone,
)
if is_torch_available()
else ()
)
pipeline_model_mapping = (
{"image-feature-extraction": ConvNextModel, "image-classification": ConvNextForImageClassification}
if is_torch_available()
else {}
)
fx_compatible = True
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
has_attentions = False
def setUp(self):
self.model_tester = ConvNextModelTester(self)
self.config_tester = ConfigTester(self, config_class=ConvNextConfig, has_text_modality=False, hidden_size=37)
def test_config(self):
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def create_and_test_config_common_properties(self):
return
@unittest.skip(reason="ConvNext does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="ConvNext does not support input and output embeddings")
def test_model_common_attributes(self):
pass
@unittest.skip(reason="ConvNext does not use feedforward chunking")
def test_feed_forward_chunking(self):
pass
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_backbone(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*config_and_inputs)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
expected_num_stages = self.model_tester.num_stages
self.assertEqual(len(hidden_states), expected_num_stages + 1)
# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[self.model_tester.image_size // 4, self.model_tester.image_size // 4],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
def test_for_image_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = ConvNextModel.from_pretrained(model_name)
self.assertIsNotNone(model)
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_torch
@require_vision
class ConvNextModelIntegrationTest(unittest.TestCase):
@cached_property
def default_image_processor(self):
return AutoImageProcessor.from_pretrained("facebook/convnext-tiny-224") if is_vision_available() else None
@slow
def test_inference_image_classification_head(self):
model = ConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224").to(torch_device)
image_processor = self.default_image_processor
image = prepare_img()
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
# verify the logits
expected_shape = torch.Size((1, 1000))
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = torch.tensor([-0.0260, -0.4739, 0.1911]).to(torch_device)
self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))
@require_torch
class ConvNextBackboneTest(unittest.TestCase, BackboneTesterMixin):
all_model_classes = (ConvNextBackbone,) if is_torch_available() else ()
config_class = ConvNextConfig
has_attentions = False
def setUp(self):
self.model_tester = ConvNextModelTester(self)
| transformers/tests/models/convnext/test_modeling_convnext.py/0 | {
"file_path": "transformers/tests/models/convnext/test_modeling_convnext.py",
"repo_id": "transformers",
"token_count": 4658
} | 366 |
""" Testing suite for the Tensorflow CvT model. """
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFCvtForImageClassification, TFCvtModel
from transformers.modeling_tf_utils import keras
from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class TFCvtConfigTester(ConfigTester):
def create_and_test_config_common_properties(self):
config = self.config_class(**self.inputs_dict)
self.parent.assertTrue(hasattr(config, "embed_dim"))
self.parent.assertTrue(hasattr(config, "num_heads"))
class TFCvtModelTester:
def __init__(
self,
parent,
batch_size=13,
image_size=64,
num_channels=3,
embed_dim=[16, 32, 48],
num_heads=[1, 2, 3],
depth=[1, 2, 10],
patch_sizes=[7, 3, 3],
patch_stride=[4, 2, 2],
patch_padding=[2, 1, 1],
stride_kv=[2, 2, 2],
cls_token=[False, False, True],
attention_drop_rate=[0.0, 0.0, 0.0],
initializer_range=0.02,
layer_norm_eps=1e-12,
is_training=True,
use_labels=True,
num_labels=2,
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.patch_sizes = patch_sizes
self.patch_stride = patch_stride
self.patch_padding = patch_padding
self.is_training = is_training
self.use_labels = use_labels
self.num_labels = num_labels
self.num_channels = num_channels
self.embed_dim = embed_dim
self.num_heads = num_heads
self.stride_kv = stride_kv
self.depth = depth
self.cls_token = cls_token
self.attention_drop_rate = attention_drop_rate
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
labels = None
if self.use_labels:
# create a random int32 tensor of given shape
labels = ids_tensor([self.batch_size], self.num_labels)
config = self.get_config()
return config, pixel_values, labels
def get_config(self):
return CvtConfig(
image_size=self.image_size,
num_labels=self.num_labels,
num_channels=self.num_channels,
embed_dim=self.embed_dim,
num_heads=self.num_heads,
patch_sizes=self.patch_sizes,
patch_padding=self.patch_padding,
patch_stride=self.patch_stride,
stride_kv=self.stride_kv,
depth=self.depth,
cls_token=self.cls_token,
attention_drop_rate=self.attention_drop_rate,
initializer_range=self.initializer_range,
)
def create_and_check_model(self, config, pixel_values, labels):
model = TFCvtModel(config=config)
result = model(pixel_values, training=False)
image_size = (self.image_size, self.image_size)
height, width = image_size[0], image_size[1]
for i in range(len(self.depth)):
height = floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1)
width = floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.embed_dim[-1], height, width))
def create_and_check_for_image_classification(self, config, pixel_values, labels):
config.num_labels = self.num_labels
model = TFCvtForImageClassification(config)
result = model(pixel_values, labels=labels, training=False)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, labels = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class TFCvtModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
"""
Here we also overwrite some of the tests of test_modeling_common.py, as Cvt
does not use input_ids, inputs_embeds, attention_mask and seq_length.
"""
all_model_classes = (TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
pipeline_model_mapping = (
{"feature-extraction": TFCvtModel, "image-classification": TFCvtForImageClassification}
if is_tf_available()
else {}
)
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
has_attentions = False
test_onnx = False
def setUp(self):
self.model_tester = TFCvtModelTester(self)
self.config_tester = TFCvtConfigTester(self, config_class=CvtConfig, has_text_modality=False, hidden_size=37)
def test_config(self):
self.config_tester.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
@unittest.skip(reason="Cvt does not output attentions")
def test_attention_outputs(self):
pass
@unittest.skip(reason="Cvt does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="Cvt does not support input and output embeddings")
def test_model_common_attributes(self):
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0,
reason="TF does not support backprop for grouped convolutions on CPU.",
)
def test_dataset_conversion(self):
super().test_dataset_conversion()
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0,
reason="TF does not support backprop for grouped convolutions on CPU.",
)
@slow
def test_keras_fit(self):
super().test_keras_fit()
@unittest.skip(reason="Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8")
def test_keras_fit_mixed_precision(self):
policy = keras.mixed_precision.Policy("mixed_float16")
keras.mixed_precision.set_global_policy(policy)
super().test_keras_fit()
keras.mixed_precision.set_global_policy("float32")
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.call)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.hidden_states
expected_num_layers = len(self.model_tester.depth)
self.assertEqual(len(hidden_states), expected_num_layers)
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:]),
[
self.model_tester.embed_dim[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_image_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = TFCvtModel.from_pretrained(model_name)
self.assertIsNotNone(model)
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_tf
@require_vision
class TFCvtModelIntegrationTest(unittest.TestCase):
@cached_property
def default_image_processor(self):
return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0])
@slow
def test_inference_image_classification_head(self):
model = TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0])
image_processor = self.default_image_processor
image = prepare_img()
inputs = image_processor(images=image, return_tensors="tf")
# forward pass
outputs = model(**inputs)
# verify the logits
expected_shape = tf.TensorShape((1, 1000))
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = tf.constant([0.9285, 0.9015, -0.3150])
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy(), expected_slice, atol=1e-4))
| transformers/tests/models/cvt/test_modeling_tf_cvt.py/0 | {
"file_path": "transformers/tests/models/cvt/test_modeling_tf_cvt.py",
"repo_id": "transformers",
"token_count": 4675
} | 367 |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch Dinat model. """
import collections
import unittest
from transformers import DinatConfig
from transformers.testing_utils import require_natten, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import DinatBackbone, DinatForImageClassification, DinatModel
from transformers.models.dinat.modeling_dinat import DINAT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class DinatModelTester:
def __init__(
self,
parent,
batch_size=13,
image_size=64,
patch_size=4,
num_channels=3,
embed_dim=16,
depths=[1, 2, 1],
num_heads=[2, 4, 8],
kernel_size=3,
dilations=[[3], [1, 2], [1]],
mlp_ratio=2.0,
qkv_bias=True,
hidden_dropout_prob=0.0,
attention_probs_dropout_prob=0.0,
drop_path_rate=0.1,
hidden_act="gelu",
patch_norm=True,
initializer_range=0.02,
layer_norm_eps=1e-5,
is_training=True,
scope=None,
use_labels=True,
num_labels=10,
out_features=["stage1", "stage2"],
out_indices=[1, 2],
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.embed_dim = embed_dim
self.depths = depths
self.num_heads = num_heads
self.kernel_size = kernel_size
self.dilations = dilations
self.mlp_ratio = mlp_ratio
self.qkv_bias = qkv_bias
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.drop_path_rate = drop_path_rate
self.hidden_act = hidden_act
self.patch_norm = patch_norm
self.layer_norm_eps = layer_norm_eps
self.initializer_range = initializer_range
self.is_training = is_training
self.scope = scope
self.use_labels = use_labels
self.num_labels = num_labels
self.out_features = out_features
self.out_indices = out_indices
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size], self.num_labels)
config = self.get_config()
return config, pixel_values, labels
def get_config(self):
return DinatConfig(
num_labels=self.num_labels,
image_size=self.image_size,
patch_size=self.patch_size,
num_channels=self.num_channels,
embed_dim=self.embed_dim,
depths=self.depths,
num_heads=self.num_heads,
kernel_size=self.kernel_size,
dilations=self.dilations,
mlp_ratio=self.mlp_ratio,
qkv_bias=self.qkv_bias,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
drop_path_rate=self.drop_path_rate,
hidden_act=self.hidden_act,
patch_norm=self.patch_norm,
layer_norm_eps=self.layer_norm_eps,
initializer_range=self.initializer_range,
out_features=self.out_features,
out_indices=self.out_indices,
)
def create_and_check_model(self, config, pixel_values, labels):
model = DinatModel(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
expected_height = expected_width = (config.image_size // config.patch_size) // (2 ** (len(config.depths) - 1))
expected_dim = int(config.embed_dim * 2 ** (len(config.depths) - 1))
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, expected_height, expected_width, expected_dim)
)
def create_and_check_for_image_classification(self, config, pixel_values, labels):
model = DinatForImageClassification(config)
model.to(torch_device)
model.eval()
result = model(pixel_values, labels=labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
# test greyscale images
config.num_channels = 1
model = DinatForImageClassification(config)
model.to(torch_device)
model.eval()
pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
result = model(pixel_values)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_backbone(self, config, pixel_values, labels):
model = DinatBackbone(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
# verify hidden states
self.parent.assertEqual(len(result.feature_maps), len(config.out_features))
self.parent.assertListEqual(list(result.feature_maps[0].shape), [self.batch_size, model.channels[0], 16, 16])
# verify channels
self.parent.assertEqual(len(model.channels), len(config.out_features))
# verify backbone works with out_features=None
config.out_features = None
model = DinatBackbone(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
# verify feature maps
self.parent.assertEqual(len(result.feature_maps), 1)
self.parent.assertListEqual(list(result.feature_maps[0].shape), [self.batch_size, model.channels[-1], 4, 4])
# verify channels
self.parent.assertEqual(len(model.channels), 1)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, labels = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_natten
@require_torch
class DinatModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
DinatModel,
DinatForImageClassification,
DinatBackbone,
)
if is_torch_available()
else ()
)
pipeline_model_mapping = (
{"image-feature-extraction": DinatModel, "image-classification": DinatForImageClassification}
if is_torch_available()
else {}
)
fx_compatible = False
test_torchscript = False
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
def setUp(self):
self.model_tester = DinatModelTester(self)
self.config_tester = ConfigTester(self, config_class=DinatConfig, embed_dim=37)
def test_config(self):
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def create_and_test_config_common_properties(self):
return
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_image_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
def test_backbone(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*config_and_inputs)
@unittest.skip(reason="Dinat does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="Dinat does not use feedforward chunking")
def test_feed_forward_chunking(self):
pass
def test_model_common_attributes(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
x = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(x, nn.Linear))
def test_attention_outputs(self):
self.skipTest("Dinat's attention operation is handled entirely by NATTEN.")
def check_hidden_states_output(self, inputs_dict, config, model_class, image_size):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.hidden_states
expected_num_layers = getattr(
self.model_tester, "expected_num_hidden_layers", len(self.model_tester.depths) + 1
)
self.assertEqual(len(hidden_states), expected_num_layers)
# Dinat has a different seq_length
patch_size = (
config.patch_size
if isinstance(config.patch_size, collections.abc.Iterable)
else (config.patch_size, config.patch_size)
)
height = image_size[0] // patch_size[0]
width = image_size[1] // patch_size[1]
self.assertListEqual(
list(hidden_states[0].shape[-3:]),
[height, width, self.model_tester.embed_dim],
)
if model_class.__name__ != "DinatBackbone":
reshaped_hidden_states = outputs.reshaped_hidden_states
self.assertEqual(len(reshaped_hidden_states), expected_num_layers)
batch_size, num_channels, height, width = reshaped_hidden_states[0].shape
reshaped_hidden_states = (
reshaped_hidden_states[0].view(batch_size, num_channels, height, width).permute(0, 2, 3, 1)
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-3:]),
[height, width, self.model_tester.embed_dim],
)
def test_hidden_states_output(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
image_size = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size, collections.abc.Iterable)
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
self.check_hidden_states_output(inputs_dict, config, model_class, image_size)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
self.check_hidden_states_output(inputs_dict, config, model_class, image_size)
@slow
def test_model_from_pretrained(self):
for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = DinatModel.from_pretrained(model_name)
self.assertIsNotNone(model)
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
if "embeddings" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
@require_natten
@require_vision
@require_torch
class DinatModelIntegrationTest(unittest.TestCase):
@cached_property
def default_image_processor(self):
return AutoImageProcessor.from_pretrained("shi-labs/dinat-mini-in1k-224") if is_vision_available() else None
@slow
def test_inference_image_classification_head(self):
model = DinatForImageClassification.from_pretrained("shi-labs/dinat-mini-in1k-224").to(torch_device)
image_processor = self.default_image_processor
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
# verify the logits
expected_shape = torch.Size((1, 1000))
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = torch.tensor([-0.1545, -0.7667, 0.4642]).to(torch_device)
self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))
@require_torch
@require_natten
class DinatBackboneTest(unittest.TestCase, BackboneTesterMixin):
all_model_classes = (DinatBackbone,) if is_torch_available() else ()
config_class = DinatConfig
def setUp(self):
self.model_tester = DinatModelTester(self)
| transformers/tests/models/dinat/test_modeling_dinat.py/0 | {
"file_path": "transformers/tests/models/dinat/test_modeling_dinat.py",
"repo_id": "transformers",
"token_count": 6347
} | 368 |
# coding=utf-8
# Copyright 2020 Huggingface
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import numpy
import tensorflow as tf
from transformers import (
TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
BertConfig,
DPRConfig,
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
)
class TFDPRModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
projection_dim=0,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
self.projection_dim = projection_dim
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
# follow test_modeling_tf_ctrl.py
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = BertConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
is_decoder=False,
initializer_range=self.initializer_range,
)
config = DPRConfig(projection_dim=self.projection_dim, **config.to_dict())
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def create_and_check_dpr_context_encoder(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = TFDPRContextEncoder(config=config)
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.projection_dim or self.hidden_size))
def create_and_check_dpr_question_encoder(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = TFDPRQuestionEncoder(config=config)
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.projection_dim or self.hidden_size))
def create_and_check_dpr_reader(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = TFDPRReader(config=config)
result = model(input_ids, attention_mask=input_mask)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.relevance_logits.shape, (self.batch_size,))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids}
return config, inputs_dict
@require_tf
class TFDPRModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
)
if is_tf_available()
else ()
)
pipeline_model_mapping = {"feature-extraction": TFDPRQuestionEncoder} if is_tf_available() else {}
test_resize_embeddings = False
test_missing_keys = False
test_pruning = False
test_head_masking = False
test_onnx = False
def setUp(self):
self.model_tester = TFDPRModelTester(self)
self.config_tester = ConfigTester(self, config_class=DPRConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_dpr_context_encoder_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_context_encoder(*config_and_inputs)
def test_dpr_question_encoder_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_question_encoder(*config_and_inputs)
def test_dpr_reader_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_reader(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = TFDPRContextEncoder.from_pretrained(model_name)
self.assertIsNotNone(model)
for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = TFDPRContextEncoder.from_pretrained(model_name)
self.assertIsNotNone(model)
for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = TFDPRQuestionEncoder.from_pretrained(model_name)
self.assertIsNotNone(model)
for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = TFDPRReader.from_pretrained(model_name)
self.assertIsNotNone(model)
@require_tf
class TFDPRModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_no_head(self):
model = TFDPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-single-nq-base")
input_ids = tf.constant(
[[101, 7592, 1010, 2003, 2026, 3899, 10140, 1029, 102]]
) # [CLS] hello, is my dog cute? [SEP]
output = model(input_ids)[0] # embedding shape = (1, 768)
# compare the actual values for a slice.
expected_slice = tf.constant(
[
[
0.03236253,
0.12753335,
0.16818509,
0.00279786,
0.3896933,
0.24264945,
0.2178971,
-0.02335227,
-0.08481959,
-0.14324117,
]
]
)
self.assertTrue(numpy.allclose(output[:, :10].numpy(), expected_slice.numpy(), atol=1e-4))
| transformers/tests/models/dpr/test_modeling_tf_dpr.py/0 | {
"file_path": "transformers/tests/models/dpr/test_modeling_tf_dpr.py",
"repo_id": "transformers",
"token_count": 4568
} | 369 |
import unittest
import numpy as np
from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.electra.modeling_flax_electra import (
FlaxElectraForCausalLM,
FlaxElectraForMaskedLM,
FlaxElectraForMultipleChoice,
FlaxElectraForPreTraining,
FlaxElectraForQuestionAnswering,
FlaxElectraForSequenceClassification,
FlaxElectraForTokenClassification,
FlaxElectraModel,
)
class FlaxElectraModelTester(unittest.TestCase):
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_attention_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
embedding_size=24,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_choices=4,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_attention_mask = use_attention_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.embedding_size = embedding_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_choices = num_choices
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
attention_mask = None
if self.use_attention_mask:
attention_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
config = ElectraConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
embedding_size=self.embedding_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range,
)
return config, input_ids, token_type_ids, attention_mask
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, input_ids, token_type_ids, attention_mask = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
return config, inputs_dict
@require_flax
class FlaxElectraModelTest(FlaxModelTesterMixin, unittest.TestCase):
test_head_masking = True
all_model_classes = (
(
FlaxElectraModel,
FlaxElectraForCausalLM,
FlaxElectraForMaskedLM,
FlaxElectraForPreTraining,
FlaxElectraForTokenClassification,
FlaxElectraForQuestionAnswering,
FlaxElectraForMultipleChoice,
FlaxElectraForSequenceClassification,
)
if is_flax_available()
else ()
)
def setUp(self):
self.model_tester = FlaxElectraModelTester(self)
@slow
def test_model_from_pretrained(self):
for model_class_name in self.all_model_classes:
if model_class_name == FlaxElectraForMaskedLM:
model = model_class_name.from_pretrained("google/electra-small-generator")
else:
model = model_class_name.from_pretrained("google/electra-small-discriminator")
outputs = model(np.ones((1, 1)))
self.assertIsNotNone(outputs)
| transformers/tests/models/electra/test_modeling_flax_electra.py/0 | {
"file_path": "transformers/tests/models/electra/test_modeling_flax_electra.py",
"repo_id": "transformers",
"token_count": 2277
} | 370 |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch ESM model. """
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_bitsandbytes, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
from transformers.models.esm.modeling_esm import (
ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
EsmEmbeddings,
create_position_ids_from_input_ids,
)
# copied from tests.test_modeling_roberta
class EsmModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=False,
use_input_mask=True,
use_token_type_ids=False,
use_labels=True,
vocab_size=33,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
return EsmConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
pad_token_id=1,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range,
)
def create_and_check_model(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels):
model = EsmModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask)
result = model(input_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def create_and_check_for_masked_lm(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = EsmForMaskedLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_token_classification(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = EsmForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_forward_and_backwards(
self,
config,
input_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
gradient_checkpointing=False,
):
model = EsmForMaskedLM(config)
if gradient_checkpointing:
model.gradient_checkpointing_enable()
model.to(torch_device)
result = model(input_ids, attention_mask=input_mask, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
result.loss.backward()
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class EsmModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
test_mismatched_shapes = False
all_model_classes = (
(
EsmForMaskedLM,
EsmModel,
EsmForSequenceClassification,
EsmForTokenClassification,
)
if is_torch_available()
else ()
)
all_generative_model_classes = ()
pipeline_model_mapping = (
{
"feature-extraction": EsmModel,
"fill-mask": EsmForMaskedLM,
"text-classification": EsmForSequenceClassification,
"token-classification": EsmForTokenClassification,
"zero-shot": EsmForSequenceClassification,
}
if is_torch_available()
else {}
)
test_sequence_classification_problem_types = True
model_split_percents = [0.5, 0.8, 0.9]
def setUp(self):
self.model_tester = EsmModelTester(self)
self.config_tester = ConfigTester(self, config_class=EsmConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_various_embeddings(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
config_and_inputs[0].position_embedding_type = type
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
def test_esm_gradient_checkpointing(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs, gradient_checkpointing=True)
@slow
def test_model_from_pretrained(self):
for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = EsmModel.from_pretrained(model_name)
self.assertIsNotNone(model)
def test_create_position_ids_respects_padding_index(self):
"""Ensure that the default position ids only assign a sequential . This is a regression
test for https://github.com/huggingface/transformers/issues/1761
The position ids should be masked with the embedding object's padding index. Therefore, the
first available non-padding position index is EsmEmbeddings.padding_idx + 1
"""
config = self.model_tester.prepare_config_and_inputs()[0]
model = EsmEmbeddings(config=config)
input_ids = torch.as_tensor([[12, 31, 13, model.padding_idx]])
expected_positions = torch.as_tensor(
[
[
0 + model.padding_idx + 1,
1 + model.padding_idx + 1,
2 + model.padding_idx + 1,
model.padding_idx,
]
]
)
position_ids = create_position_ids_from_input_ids(input_ids, model.padding_idx)
self.assertEqual(position_ids.shape, expected_positions.shape)
self.assertTrue(torch.all(torch.eq(position_ids, expected_positions)))
def test_create_position_ids_from_inputs_embeds(self):
"""Ensure that the default position ids only assign a sequential . This is a regression
test for https://github.com/huggingface/transformers/issues/1761
The position ids should be masked with the embedding object's padding index. Therefore, the
first available non-padding position index is EsmEmbeddings.padding_idx + 1
"""
config = self.model_tester.prepare_config_and_inputs()[0]
embeddings = EsmEmbeddings(config=config)
inputs_embeds = torch.empty(2, 4, 30)
expected_single_positions = [
0 + embeddings.padding_idx + 1,
1 + embeddings.padding_idx + 1,
2 + embeddings.padding_idx + 1,
3 + embeddings.padding_idx + 1,
]
expected_positions = torch.as_tensor([expected_single_positions, expected_single_positions])
position_ids = embeddings.create_position_ids_from_inputs_embeds(inputs_embeds)
self.assertEqual(position_ids.shape, expected_positions.shape)
self.assertTrue(torch.all(torch.eq(position_ids, expected_positions)))
@unittest.skip("Esm does not support embedding resizing")
def test_resize_embeddings_untied(self):
pass
@unittest.skip("Esm does not support embedding resizing")
def test_resize_tokens_embeddings(self):
pass
@slow
@require_torch
class EsmModelIntegrationTest(TestCasePlus):
def test_inference_masked_lm(self):
with torch.no_grad():
model = EsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D")
model.eval()
input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]])
output = model(input_ids)[0]
vocab_size = 33
expected_shape = torch.Size((1, 6, vocab_size))
self.assertEqual(output.shape, expected_shape)
expected_slice = torch.tensor(
[[[8.9215, -10.5898, -6.4671], [-6.3967, -13.9114, -1.1212], [-7.7812, -13.9516, -3.7406]]]
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
def test_inference_no_head(self):
with torch.no_grad():
model = EsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D")
model.eval()
input_ids = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]])
output = model(input_ids)[0]
# compare the actual values for a slice.
expected_slice = torch.tensor(
[[[0.1444, 0.5413, 0.3248], [0.3034, 0.0053, 0.3108], [0.3228, -0.2499, 0.3415]]]
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
@require_bitsandbytes
def test_inference_bitsandbytes(self):
model = EsmForMaskedLM.from_pretrained("facebook/esm2_t36_3B_UR50D", load_in_8bit=True)
input_ids = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]])
# Just test if inference works
with torch.no_grad():
_ = model(input_ids)[0]
model = EsmForMaskedLM.from_pretrained("facebook/esm2_t36_3B_UR50D", load_in_4bit=True)
input_ids = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]])
# Just test if inference works
_ = model(input_ids)[0]
| transformers/tests/models/esm/test_modeling_esm.py/0 | {
"file_path": "transformers/tests/models/esm/test_modeling_esm.py",
"repo_id": "transformers",
"token_count": 6245
} | 371 |
# coding=utf-8
# Copyright 2021 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers.testing_utils import require_torch, require_torchvision, require_vision
from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available
from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_torchvision_available():
import torchvision.transforms as transforms
if is_vision_available():
from PIL import Image
from transformers import IdeficsImageProcessor
class IdeficsImageProcessingTester(unittest.TestCase):
def __init__(
self,
parent,
batch_size=7,
num_channels=3,
image_size=18,
min_resolution=30,
max_resolution=400,
size=None,
image_mean=[0.48145466, 0.4578275, 0.40821073],
image_std=[0.26862954, 0.26130258, 0.27577711],
):
size = size if size is not None else {"shortest_edge": 30}
self.parent = parent
self.batch_size = batch_size
self.num_channels = num_channels
self.image_size = image_size
self.min_resolution = min_resolution
self.max_resolution = max_resolution
# self.size = size
self.image_mean = image_mean
self.image_std = image_std
def prepare_image_processor_dict(self):
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"image_size": self.image_size,
}
def get_expected_values(self, image_inputs, batched=False):
"""
This function computes the expected height and width when providing images to IdeficsImageProcessor,
assuming do_resize is set to True with a scalar size and size_divisor.
"""
if not batched:
size = self.image_size
image = image_inputs[0]
if isinstance(image, Image.Image):
w, h = image.size
else:
h, w = image.shape[1], image.shape[2]
scale = size / min(w, h)
if h < w:
newh, neww = size, scale * w
else:
newh, neww = scale * h, size
max_size = int((1333 / 800) * size)
if max(newh, neww) > max_size:
scale = max_size / max(newh, neww)
newh = newh * scale
neww = neww * scale
newh, neww = int(newh + 0.5), int(neww + 0.5)
expected_height, expected_width = (
newh // self.size_divisor * self.size_divisor,
neww // self.size_divisor * self.size_divisor,
)
else:
expected_values = []
for image in image_inputs:
expected_height, expected_width = self.get_expected_values([image])
expected_values.append((expected_height, expected_width))
expected_height = max(expected_values, key=lambda item: item[0])[0]
expected_width = max(expected_values, key=lambda item: item[1])[1]
return expected_height, expected_width
def expected_output_image_shape(self, images):
height, width = self.get_expected_values(images, batched=True)
return (self.num_channels, height, width)
def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
return prepare_image_inputs(
batch_size=self.batch_size,
num_channels=self.num_channels,
min_resolution=self.min_resolution,
max_resolution=self.max_resolution,
equal_resolution=equal_resolution,
numpify=numpify,
torchify=torchify,
)
@require_torch
@require_vision
class IdeficsImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = IdeficsImageProcessor if is_vision_available() else None
def setUp(self):
self.image_processor_tester = IdeficsImageProcessingTester(self)
@property
def image_processor_dict(self):
return self.image_processor_tester.prepare_image_processor_dict()
def test_image_processor_properties(self):
image_processing = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(image_processing, "image_mean"))
self.assertTrue(hasattr(image_processing, "image_std"))
self.assertTrue(hasattr(image_processing, "image_size"))
def test_image_processor_from_dict_with_kwargs(self):
image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
self.assertNotEqual(image_processor.image_size, 30)
image_processor = self.image_processing_class.from_dict(self.image_processor_dict, image_size=42)
self.assertEqual(image_processor.image_size, 42)
@require_torchvision
def test_torchvision_numpy_transforms_equivalency(self):
# as we had to reimplement the torchvision transforms using transformers utils we must check
# they both do the same
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
image_processor = self.image_processing_class(**self.image_processor_dict)
print(image_inputs)
def convert_to_rgb(image):
# `image.convert("RGB")` would only work for .jpg images, as it creates a wrong background
# for transparent images. The call to `alpha_composite` handles this case
if image.mode == "RGB":
return image
image_rgba = image.convert("RGBA")
background = Image.new("RGBA", image_rgba.size, (255, 255, 255))
alpha_composite = Image.alpha_composite(background, image_rgba)
alpha_composite = alpha_composite.convert("RGB")
return alpha_composite
image_size = image_processor.image_size
image_mean = image_processor.image_mean
image_std = image_processor.image_std
transform = transforms.Compose(
[
convert_to_rgb,
transforms.Resize((image_size, image_size), interpolation=transforms.InterpolationMode.BICUBIC),
transforms.ToTensor(),
transforms.Normalize(mean=image_mean, std=image_std),
]
)
pixel_values_transform_implied = image_processor(image_inputs, transform=None)
pixel_values_transform_supplied = image_processor(image_inputs, transform=transform)
torch.testing.assert_close(pixel_values_transform_implied, pixel_values_transform_supplied, rtol=0.0, atol=0.0)
@unittest.skip("not supported")
def test_call_numpy(self):
pass
@unittest.skip("not supported")
def test_call_numpy_4_channels(self):
pass
@unittest.skip("not supported")
def test_call_pil(self):
pass
@unittest.skip("not supported")
def test_call_pytorch(self):
pass
| transformers/tests/models/idefics/test_image_processing_idefics.py/0 | {
"file_path": "transformers/tests/models/idefics/test_image_processing_idefics.py",
"repo_id": "transformers",
"token_count": 3182
} | 372 |
# coding=utf-8
# Copyright 2023 Microsoft Research and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
import requests
from transformers.testing_utils import (
get_tests_dir,
require_sentencepiece,
require_tokenizers,
require_torch,
require_vision,
)
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
CLIPImageProcessor,
Kosmos2Processor,
PreTrainedTokenizerFast,
XLMRobertaTokenizer,
XLMRobertaTokenizerFast,
)
SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
@require_vision
class Kosmos2ProcessorTest(unittest.TestCase):
def setUp(self):
self.tmpdirname = tempfile.mkdtemp()
image_processor = CLIPImageProcessor()
# We have a SentencePiece fixture for testing
slow_tokenizer = XLMRobertaTokenizer(SAMPLE_VOCAB)
fast_tokenizer = XLMRobertaTokenizerFast(__slow_tokenizer=slow_tokenizer)
processor = Kosmos2Processor(image_processor, fast_tokenizer)
processor.save_pretrained(self.tmpdirname)
def get_tokenizer(self, **kwargs):
return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).tokenizer
def get_image_processor(self, **kwargs):
return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).image_processor
def tearDown(self):
shutil.rmtree(self.tmpdirname)
def prepare_image_inputs(self):
"""This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
or a list of PyTorch tensors if one specifies torchify=True.
"""
image_inputs = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)]
image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]
return image_inputs
def test_save_load_pretrained_additional_features(self):
processor = Kosmos2Processor(tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor())
processor.save_pretrained(self.tmpdirname)
tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
image_processor_add_kwargs = self.get_image_processor(do_normalize=False, padding_value=1.0)
processor = Kosmos2Processor.from_pretrained(
self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
self.assertIsInstance(processor.tokenizer, PreTrainedTokenizerFast)
self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string())
self.assertIsInstance(processor.image_processor, CLIPImageProcessor)
def test_image_processor(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = Kosmos2Processor(tokenizer=tokenizer, image_processor=image_processor)
image_input = self.prepare_image_inputs()
input_image_processor = image_processor(image_input, return_tensors="np")
input_processor = processor(images=image_input, return_tensors="np")
for key in input_image_processor.keys():
self.assertAlmostEqual(input_image_processor[key].sum(), input_processor[key].sum(), delta=1e-2)
def test_tokenizer(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = Kosmos2Processor(tokenizer=tokenizer, image_processor=image_processor)
input_str = "This is a test"
encoded_processor = processor(text=input_str, add_eos_token=True)
encoded_tok = tokenizer(input_str, return_token_type_ids=False)
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key], encoded_processor[key])
def test_processor(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = Kosmos2Processor(tokenizer=tokenizer, image_processor=image_processor)
input_str = "This is a test"
image_input = self.prepare_image_inputs()
inputs = processor(text=input_str, images=image_input)
self.assertListEqual(
list(inputs.keys()), ["pixel_values", "input_ids", "attention_mask", "image_embeds_position_mask"]
)
# test if it raises when no input is passed
with pytest.raises(ValueError):
processor()
def test_tokenizer_decode(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = Kosmos2Processor(tokenizer=tokenizer, image_processor=image_processor)
predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
decoded_processor = processor.batch_decode(predicted_ids)
decoded_tok = tokenizer.batch_decode(predicted_ids)
self.assertListEqual(decoded_tok, decoded_processor)
def test_model_input_names(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = Kosmos2Processor(tokenizer=tokenizer, image_processor=image_processor)
input_str = "This is a test"
image_input = self.prepare_image_inputs()
# both image and text
inputs = processor(text=input_str, images=image_input)
self.assertListEqual(
list(inputs.keys()), ["pixel_values", "input_ids", "attention_mask", "image_embeds_position_mask"]
)
# only text
inputs = processor(text=input_str)
self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask"])
# only image
inputs = processor(images=image_input)
self.assertListEqual(list(inputs.keys()), ["pixel_values"])
@require_torch
def test_full_processor(self):
url = "https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/two_dogs.jpg"
processor = Kosmos2Processor.from_pretrained("microsoft/kosmos-2-patch14-224")
# test with different input formats.
# fmt: off
texts = [
# no phrase
"<grounding> Two puppies sit in a field of grass.",
# 1 phrase
"<grounding> <phrase> Two puppies </phrase> sit in a field of grass.",
# 2 phrases
"<grounding> <phrase> Two puppies </phrase> sit in a field of <phrase> grass </phrase>.",
# 2 phrases: bboxes already specified for the 1st phrase
"<grounding> <phrase> Two puppies </phrase> <object> <patch_index_0079> <patch_index_1016> </delimiter_of_multi_objects/> <patch_index_0135> <patch_index_1008> </object> sit in a field of <phrase> grass </phrase>.",
]
# fmt: on
image = Image.open(requests.get(url, stream=True).raw)
# To match the official (microsoft) Kosmos-2 demo from which the expected values here are grabbed
image_path = os.path.join(self.tmpdirname, "image.jpg")
image.save(image_path)
image = Image.open(image_path)
# fmt: off
bboxes = [
[None, []],
[[None], [[]], [(79, 1016)], [[(79, 1016)]], [[(79, 1016), (135, 1008)]]],
[[[(79, 1016), (135, 1008)], None], [[(79, 1016), (135, 1008)], []], [[(79, 1016), (135, 1008)], (480, 1023)], [[(79, 1016), (135, 1008)], [(480, 1023)]]],
[[None, [(480, 1023)]]],
]
# fmt: on
batch_image = [image] * 4
batch_text = [texts[0], texts[1], texts[1], texts[2]]
batch_bboxes = [
None, # no phrase
[[]], # 1 phrase: no bbox
[(79, 1016)], # 1 phrase: 1 bbox
[[(79, 1016), (135, 1008)], (480, 1023)], # 2 phrase: 2 bboxes + 1 bbox
]
# fmt: off
expected_input_ids = [
[0, 64012, 1264, 17772, 1357, 12, 10, 770, 9, 4464, 4, 2],
[0, 64012, 64007, 1264, 17772, 64008, 1357, 12, 10, 770, 9, 4464, 4, 2],
[0, 64012, 64007, 1264, 17772, 64008, 64009, 64092, 65029, 64010, 1357, 12, 10, 770, 9, 4464, 4, 2],
[0, 64012, 64007, 1264, 17772, 64008, 64009, 64092, 65029, 64011, 64148, 65021, 64010, 1357, 12, 10, 770, 9, 4464, 4, 2],
[0, 64012, 64007, 1264, 17772, 64008, 64009, 64092, 65029, 64011, 64148, 65021, 64010, 1357, 12, 10, 770, 9, 64007, 4464, 64008, 106, 4, 2],
[0, 64012, 64007, 1264, 17772, 64008, 64009, 64092, 65029, 64011, 64148, 65021, 64010, 1357, 12, 10, 770, 9, 64007, 4464, 64008, 64009, 64493, 65036, 64010, 106, 4, 2],
]
# fmt: on
EXPECTED_PIXEL_VALUES_1 = np.array(
[
[
[-0.6535852551460266, -0.6389868259429932, -0.6243883967399597],
[-0.6535852551460266, -0.6389868259429932, -0.6243883967399597],
[-0.6243883967399597, -0.6243883967399597, -0.5951915383338928],
],
[
[-0.20629698038101196, -0.19128920137882233, -0.19128920137882233],
[-0.20629698038101196, -0.19128920137882233, -0.17628143727779388],
[-0.2213047444820404, -0.20629698038101196, -0.16127367317676544],
],
[
[-0.5843556523323059, -0.5701355338096619, -0.5701355338096619],
[-0.5843556523323059, -0.5701355338096619, -0.5559154152870178],
[-0.5843556523323059, -0.5559154152870178, -0.5416953563690186],
],
]
)
EXPECTED_PIXEL_VALUES_2 = np.array(
[
[
[-0.4346088469028473, -0.47840413451194763, -0.7849710583686829],
[-0.5221993923187256, -0.5076009631156921, -0.755774199962616],
[-0.5221993923187256, -0.5076009631156921, -0.7411757707595825],
],
[
[-0.2813358008861542, -0.2963435649871826, -0.431413471698761],
[-0.26632803678512573, -0.2963435649871826, -0.4764367938041687],
[-0.2213047444820404, -0.2813358008861542, -0.49144455790519714],
],
[
[-0.5701355338096619, -0.641235888004303, -0.7549964189529419],
[-0.5843556523323059, -0.641235888004303, -0.7834365367889404],
[-0.5559154152870178, -0.641235888004303, -0.7834365367889404],
],
]
)
def check(texts, bboxes, expected_input_ids):
outputs = processor(images=None, text=texts, bboxes=bboxes, add_eos_token=True)
self.assertListEqual(outputs.input_ids, expected_input_ids)
# no phrase
check(texts[0], bboxes[0][0], expected_input_ids[0])
# no phrase
check(texts[0], bboxes[0][1], expected_input_ids[0])
# 1 phrase: no bbox
check(texts[1], bboxes[1][0], expected_input_ids[1])
# 1 phrase: no bbox
check(texts[1], bboxes[1][1], expected_input_ids[1])
# 1 phrase: 1 bbox
check(texts[1], bboxes[1][2], expected_input_ids[2])
# 1 phrase: 1 bbox
check(texts[1], bboxes[1][3], expected_input_ids[2])
# 1 phrase: 2 bboxes
check(texts[1], bboxes[1][4], expected_input_ids[3])
# could not contain `[None]`
with pytest.raises(ValueError):
_ = processor.preprocess_examples(images=None, texts=texts[1], bboxes=[[None]])
# 2 phrase: 2 bboxes + no bbox
check(texts[2], bboxes[2][0], expected_input_ids[4])
# 2 phrase: 2 bboxes + no bbox
check(texts[2], bboxes[2][1], expected_input_ids[4])
# 2 phrase: 2 bboxes + 1 bbox
check(texts[2], bboxes[2][2], expected_input_ids[5])
# 2 phrase: 2 bboxes + 1 bbox
check(texts[2], bboxes[2][3], expected_input_ids[5])
# 2 phrase: no box (as already specified in the text) + 1 bbox
check(texts[3], bboxes[3][0], expected_input_ids[5])
# could not contain `[None]`
with pytest.raises(ValueError):
_ = processor.preprocess_examples(images=None, texts=texts[2], bboxes=[[(79, 1016), (135, 1008)], [None]])
# test batch
outputs = processor(
images=None,
text=batch_text,
bboxes=batch_bboxes,
add_eos_token=True,
)
self.assertListEqual(
outputs.input_ids,
[expected_input_ids[0], expected_input_ids[1], expected_input_ids[2], expected_input_ids[5]],
)
# test batch with padding (without `return_tensors`)
outputs = processor(
images=None,
text=batch_text,
bboxes=batch_bboxes,
padding=True,
add_eos_token=True,
)
# padding on the right
self.assertListEqual(
outputs.input_ids[0],
expected_input_ids[0] + [1] * (len(expected_input_ids[5]) - len(expected_input_ids[0])),
)
self.assertListEqual(
outputs.attention_mask[0],
[1] * len(expected_input_ids[0]) + [0] * (len(expected_input_ids[5]) - len(expected_input_ids[0])),
)
# no padding for the longest sequence
self.assertListEqual(outputs.input_ids[-1], expected_input_ids[5])
self.assertListEqual(outputs.attention_mask[-1], [1] * len(expected_input_ids[5]))
# test batch with padding (with `return_tensors`)
outputs = processor(
images=None,
text=batch_text,
bboxes=batch_bboxes,
return_tensors="pt",
padding=True,
add_eos_token=True,
)
# padding on the right
self.assertListEqual(
outputs.input_ids.numpy().tolist()[0],
expected_input_ids[0] + [1] * (len(expected_input_ids[5]) - len(expected_input_ids[0])),
)
self.assertListEqual(
outputs.attention_mask.numpy().tolist()[0],
[1] * len(expected_input_ids[0]) + [0] * (len(expected_input_ids[5]) - len(expected_input_ids[0])),
)
# no padding for the longest sequence
self.assertListEqual(outputs.input_ids.numpy().tolist()[-1], expected_input_ids[5])
self.assertListEqual(outputs.attention_mask.numpy().tolist()[-1], [1] * len(expected_input_ids[5]))
# test with image
num_image_tokens = 64
outputs = processor(images=image, text=texts[0], bboxes=None, add_eos_token=True)
self.assertTupleEqual(outputs.pixel_values[0].shape, (3, 224, 224))
self.assertListEqual(
outputs.input_ids,
[0, 64003] + list(range(4, 4 + num_image_tokens)) + [64004] + expected_input_ids[0][1:],
)
self.assertListEqual(
outputs.image_embeds_position_mask,
[0] * 2 + [1] * num_image_tokens + [0] + [0] * (len(expected_input_ids[0]) - 1),
)
np.testing.assert_allclose(outputs.pixel_values[0][:3, :3, :3], EXPECTED_PIXEL_VALUES_1, atol=1e-9)
np.testing.assert_allclose(outputs.pixel_values[0][:3, -3:, -3:], EXPECTED_PIXEL_VALUES_2, atol=1e-9)
# test with image in batch (right padding)
outputs = processor(
images=batch_image,
text=batch_text,
bboxes=batch_bboxes,
return_tensors="pt",
padding=True,
add_eos_token=True,
)
self.assertTupleEqual(outputs.pixel_values.shape, (4, 3, 224, 224))
np.testing.assert_allclose(
outputs.pixel_values[:, :3, :3, :3].numpy(), [EXPECTED_PIXEL_VALUES_1] * len(batch_image), atol=1e-9
)
np.testing.assert_allclose(
outputs.pixel_values[:, :3, -3:, -3:].numpy(), [EXPECTED_PIXEL_VALUES_2] * len(batch_image), atol=1e-9
)
# padding on the right: the `[1:]` below is because the part for `BOS` is already added in the beginning of each (dynamically computed) expected value # noqa
# fmt: off
EXPECTED_IDS_BATCH_RIGHT_PADDING = [
[0, 64003] + list(range(4, 4 + num_image_tokens)) + [64004] + expected_input_ids[0][1:] + [1] * (len(expected_input_ids[5]) - len(expected_input_ids[0])),
[0, 64003] + list(range(4, 4 + num_image_tokens)) + [64004] + expected_input_ids[5][1:],
]
EXPECTED_MASK_BATCH_RIGHT_PADDING = [
[1, 1] + [1] * num_image_tokens + [1] + [1] * len(expected_input_ids[0][1:]) + [0] * (len(expected_input_ids[5]) - len(expected_input_ids[0])),
[1] * (2 + num_image_tokens + len(expected_input_ids[5])),
]
# fmt: on
self.assertListEqual(outputs.input_ids.numpy().tolist()[0], EXPECTED_IDS_BATCH_RIGHT_PADDING[0])
self.assertListEqual(outputs.attention_mask.numpy().tolist()[0], EXPECTED_MASK_BATCH_RIGHT_PADDING[0])
self.assertListEqual(outputs.input_ids.numpy().tolist()[-1], EXPECTED_IDS_BATCH_RIGHT_PADDING[-1])
self.assertListEqual(outputs.attention_mask.numpy().tolist()[-1], EXPECTED_MASK_BATCH_RIGHT_PADDING[-1])
self.assertListEqual(
outputs.image_embeds_position_mask.numpy().tolist(),
[[0, 0] + [1] * num_image_tokens + [0] + [0] * (len(expected_input_ids[5]) - 1)] * len(batch_image),
)
processor = Kosmos2Processor.from_pretrained("microsoft/kosmos-2-patch14-224", padding_side="left")
# test with image in batch (left padding)
outputs = processor(
images=batch_image,
text=batch_text,
bboxes=batch_bboxes,
return_tensors="pt",
padding=True,
add_eos_token=True,
)
# padding on the left: the `[1:]` below is because the part for `BOS` is already added in the beginning of each (dynamically computed) expected value # noqa
# fmt: off
EXPECTED_IDS_BATCH = [
[1] * (len(expected_input_ids[5]) - len(expected_input_ids[0])) + [0, 64003] + list(range(4, 4 + num_image_tokens)) + [64004] + expected_input_ids[0][1:],
[0, 64003] + list(range(4, 4 + num_image_tokens)) + [64004] + expected_input_ids[5][1:],
]
EXPECTED_MASK_BATCH =[
[0] * (len(expected_input_ids[5]) - len(expected_input_ids[0])) + [1, 1] + [1] * num_image_tokens + [1] + [1] * len(expected_input_ids[0][1:]),
[1] * (2 + num_image_tokens + len(expected_input_ids[5])),
]
EXPECTED_IMG_POS_MASK_BATCH = [
[0] * (len(expected_input_ids[5]) - len(expected_input_ids[0])) + [0, 0] + [1] * num_image_tokens + [0] + [0] * len(expected_input_ids[0][1:]),
[0, 0] + [1] * num_image_tokens + [0] + [0] * (len(expected_input_ids[5]) - 1),
]
# fmt: on
self.assertListEqual(outputs.input_ids.numpy().tolist()[0], EXPECTED_IDS_BATCH[0])
self.assertListEqual(outputs.attention_mask.numpy().tolist()[0], EXPECTED_MASK_BATCH[0])
self.assertListEqual(outputs.image_embeds_position_mask.numpy().tolist()[0], EXPECTED_IMG_POS_MASK_BATCH[0])
# no padding for the longest sequence
self.assertListEqual(outputs.input_ids.numpy().tolist()[-1], EXPECTED_IDS_BATCH[-1])
self.assertListEqual(outputs.attention_mask.numpy().tolist()[-1], EXPECTED_MASK_BATCH[-1])
self.assertListEqual(outputs.image_embeds_position_mask.numpy().tolist()[-1], EXPECTED_IMG_POS_MASK_BATCH[-1])
| transformers/tests/models/kosmos2/test_processor_kosmos2.py/0 | {
"file_path": "transformers/tests/models/kosmos2/test_processor_kosmos2.py",
"repo_id": "transformers",
"token_count": 9619
} | 373 |
# coding=utf-8
# Copyright 2018 LXMERT Authors, The Hugging Face Team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import unittest
import numpy as np
from transformers import LxmertConfig, is_tf_available, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MODEL_FOR_QUESTION_ANSWERING_MAPPING,
LxmertForPreTraining,
LxmertForQuestionAnswering,
LxmertModel,
)
from transformers.models.lxmert.modeling_lxmert import LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_tf_available():
import tensorflow as tf
class LxmertModelTester:
def __init__(
self,
parent,
vocab_size=300,
hidden_size=28,
num_attention_heads=2,
num_labels=2,
intermediate_size=64,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02,
layer_norm_eps=1e-12,
pad_token_id=0,
num_qa_labels=30,
num_object_labels=16,
num_attr_labels=4,
num_visual_features=10,
l_layers=2,
x_layers=1,
r_layers=1,
visual_feat_dim=128,
visual_pos_dim=4,
visual_loss_normalizer=6.67,
seq_length=20,
batch_size=4,
is_training=True,
task_matched=True,
task_mask_lm=True,
task_obj_predict=True,
task_qa=True,
visual_obj_loss=True,
visual_attr_loss=True,
visual_feat_loss=True,
use_token_type_ids=True,
use_lang_mask=True,
output_attentions=False,
output_hidden_states=False,
scope=None,
):
self.parent = parent
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_attention_heads = num_attention_heads
self.num_labels = num_labels
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.pad_token_id = pad_token_id
self.num_qa_labels = num_qa_labels
self.num_object_labels = num_object_labels
self.num_attr_labels = num_attr_labels
self.l_layers = l_layers
self.x_layers = x_layers
self.r_layers = r_layers
self.visual_feat_dim = visual_feat_dim
self.visual_pos_dim = visual_pos_dim
self.visual_loss_normalizer = visual_loss_normalizer
self.seq_length = seq_length
self.batch_size = batch_size
self.is_training = is_training
self.use_lang_mask = use_lang_mask
self.task_matched = task_matched
self.task_mask_lm = task_mask_lm
self.task_obj_predict = task_obj_predict
self.task_qa = task_qa
self.visual_obj_loss = visual_obj_loss
self.visual_attr_loss = visual_attr_loss
self.visual_feat_loss = visual_feat_loss
self.num_visual_features = num_visual_features
self.use_token_type_ids = use_token_type_ids
self.output_attentions = output_attentions
self.output_hidden_states = output_hidden_states
self.scope = scope
self.num_hidden_layers = {"vision": r_layers, "cross_encoder": x_layers, "language": l_layers}
def prepare_config_and_inputs(self):
output_attentions = self.output_attentions
input_ids = ids_tensor([self.batch_size, self.seq_length], vocab_size=self.vocab_size)
visual_feats = torch.rand(self.batch_size, self.num_visual_features, self.visual_feat_dim, device=torch_device)
bounding_boxes = torch.rand(self.batch_size, self.num_visual_features, 4, device=torch_device)
input_mask = None
if self.use_lang_mask:
input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
obj_labels = None
if self.task_obj_predict:
obj_labels = {}
if self.visual_attr_loss and self.task_obj_predict:
obj_labels["attr"] = (
ids_tensor([self.batch_size, self.num_visual_features], self.num_attr_labels),
ids_tensor([self.batch_size, self.num_visual_features], self.num_attr_labels),
)
if self.visual_feat_loss and self.task_obj_predict:
obj_labels["feat"] = (
ids_tensor(
[self.batch_size, self.num_visual_features, self.visual_feat_dim], self.num_visual_features
),
ids_tensor([self.batch_size, self.num_visual_features], self.num_visual_features),
)
if self.visual_obj_loss and self.task_obj_predict:
obj_labels["obj"] = (
ids_tensor([self.batch_size, self.num_visual_features], self.num_object_labels),
ids_tensor([self.batch_size, self.num_visual_features], self.num_object_labels),
)
ans = None
if self.task_qa:
ans = ids_tensor([self.batch_size], self.num_qa_labels)
masked_lm_labels = None
if self.task_mask_lm:
masked_lm_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
matched_label = None
if self.task_matched:
matched_label = ids_tensor([self.batch_size], self.num_labels)
config = self.get_config()
return (
config,
input_ids,
visual_feats,
bounding_boxes,
token_type_ids,
input_mask,
obj_labels,
masked_lm_labels,
matched_label,
ans,
output_attentions,
)
def get_config(self):
return LxmertConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_attention_heads=self.num_attention_heads,
num_labels=self.num_labels,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range,
layer_norm_eps=self.layer_norm_eps,
pad_token_id=self.pad_token_id,
num_qa_labels=self.num_qa_labels,
num_object_labels=self.num_object_labels,
num_attr_labels=self.num_attr_labels,
l_layers=self.l_layers,
x_layers=self.x_layers,
r_layers=self.r_layers,
visual_feat_dim=self.visual_feat_dim,
visual_pos_dim=self.visual_pos_dim,
visual_loss_normalizer=self.visual_loss_normalizer,
task_matched=self.task_matched,
task_mask_lm=self.task_mask_lm,
task_obj_predict=self.task_obj_predict,
task_qa=self.task_qa,
visual_obj_loss=self.visual_obj_loss,
visual_attr_loss=self.visual_attr_loss,
visual_feat_loss=self.visual_feat_loss,
output_attentions=self.output_attentions,
output_hidden_states=self.output_hidden_states,
)
def create_and_check_lxmert_model(
self,
config,
input_ids,
visual_feats,
bounding_boxes,
token_type_ids,
input_mask,
obj_labels,
masked_lm_labels,
matched_label,
ans,
output_attentions,
):
model = LxmertModel(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
output_attentions=output_attentions,
)
result = model(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
output_attentions=not output_attentions,
)
result = model(input_ids, visual_feats, bounding_boxes, return_dict=False)
result = model(input_ids, visual_feats, bounding_boxes, return_dict=True)
self.parent.assertEqual(result.language_output.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(
result.vision_output.shape, (self.batch_size, self.num_visual_features, self.hidden_size)
)
self.parent.assertEqual(result.pooled_output.shape, (self.batch_size, self.hidden_size))
def create_and_check_lxmert_for_question_answering(
self,
config,
input_ids,
visual_feats,
bounding_boxes,
token_type_ids,
input_mask,
obj_labels,
masked_lm_labels,
matched_label,
ans,
output_attentions,
):
model = LxmertForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
labels=ans,
output_attentions=output_attentions,
)
result = model(input_ids, visual_feats, bounding_boxes, labels=ans)
result = model(
input_ids,
visual_feats,
bounding_boxes,
labels=ans,
token_type_ids=token_type_ids,
attention_mask=input_mask,
output_attentions=output_attentions,
)
result = model(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
labels=ans,
output_attentions=not output_attentions,
)
self.parent.assertEqual(result.question_answering_score.shape, (self.batch_size, self.num_qa_labels))
def create_and_check_lxmert_for_pretraining(
self,
config,
input_ids,
visual_feats,
bounding_boxes,
token_type_ids,
input_mask,
obj_labels,
masked_lm_labels,
matched_label,
ans,
output_attentions,
):
model = LxmertForPreTraining(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
masked_lm_labels=masked_lm_labels,
obj_labels=obj_labels,
matched_label=matched_label,
ans=ans,
output_attentions=output_attentions,
)
result = model(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
masked_lm_labels=masked_lm_labels,
output_attentions=not output_attentions,
return_dict=False,
)
result = model(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
masked_lm_labels=masked_lm_labels,
)
result = model(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
obj_labels=obj_labels,
)
result = model(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
matched_label=matched_label,
)
result = model(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
ans=ans,
)
result = model(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
masked_lm_labels=masked_lm_labels,
obj_labels=obj_labels,
matched_label=matched_label,
ans=ans,
output_attentions=not output_attentions,
)
self.parent.assertEqual(result.prediction_logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def resize_lxmert_num_qa_labels(
self,
config,
input_ids,
visual_feats,
bounding_boxes,
token_type_ids,
input_mask,
obj_labels,
masked_lm_labels,
matched_label,
ans,
output_attentions,
):
start_labels = config.num_qa_labels
num_large_labels = config.num_qa_labels * 2
num_small_labels = int(config.num_qa_labels * 2)
less_labels_ans = ids_tensor([self.batch_size], num_small_labels)
more_labels_ans = ids_tensor([self.batch_size], num_large_labels)
model_pretrain = LxmertForPreTraining(config=config).to(torch_device)
model_qa = LxmertForQuestionAnswering(config=config).to(torch_device)
config.num_labels = num_small_labels
end_labels = config.num_labels
result_pretrain = model_pretrain(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
ans=ans,
)
result_qa = model_qa(
input_ids,
visual_feats,
bounding_boxes,
labels=ans,
token_type_ids=token_type_ids,
attention_mask=input_mask,
)
model_pretrain.resize_num_qa_labels(num_small_labels)
model_qa.resize_num_qa_labels(num_small_labels)
result_pretrain_less = model_pretrain(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
ans=less_labels_ans,
)
result_qa_less = model_qa(
input_ids,
visual_feats,
bounding_boxes,
labels=less_labels_ans,
token_type_ids=token_type_ids,
attention_mask=input_mask,
)
model_pretrain.resize_num_qa_labels(num_large_labels)
model_qa.resize_num_qa_labels(num_large_labels)
result_pretrain_more = model_pretrain(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
ans=more_labels_ans,
)
result_qa_more = model_qa(
input_ids,
visual_feats,
bounding_boxes,
labels=more_labels_ans,
token_type_ids=token_type_ids,
attention_mask=input_mask,
)
model_qa_labels = model_qa.num_qa_labels
self.parent.assertNotEqual(start_labels, end_labels)
self.parent.assertNotEqual(model_qa_labels, start_labels)
self.parent.assertEqual(result_qa.question_answering_score.shape, (self.batch_size, start_labels))
self.parent.assertEqual(result_pretrain.question_answering_score.shape, (self.batch_size, start_labels))
self.parent.assertEqual(result_qa_less.question_answering_score.shape, (self.batch_size, num_small_labels))
self.parent.assertEqual(
result_pretrain_less.question_answering_score.shape, (self.batch_size, num_small_labels)
)
self.parent.assertEqual(result_qa_more.question_answering_score.shape, (self.batch_size, num_large_labels))
self.parent.assertEqual(
result_pretrain_more.question_answering_score.shape, (self.batch_size, num_large_labels)
)
def prepare_config_and_inputs_for_common(self, return_obj_labels=False):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
visual_feats,
bounding_boxes,
token_type_ids,
input_mask,
obj_labels,
masked_lm_labels,
matched_label,
ans,
output_attentions,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"visual_feats": visual_feats,
"visual_pos": bounding_boxes,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
if return_obj_labels:
inputs_dict["obj_labels"] = obj_labels
else:
config.task_obj_predict = False
return config, inputs_dict
@require_torch
class LxmertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (LxmertModel, LxmertForPreTraining, LxmertForQuestionAnswering) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": LxmertModel, "question-answering": LxmertForQuestionAnswering}
if is_torch_available()
else {}
)
fx_compatible = True
test_head_masking = False
test_pruning = False
test_torchscript = False
# overwrite function because qa models takes different input label shape
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = copy.deepcopy(inputs_dict)
if return_labels:
if model_class in get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING):
inputs_dict["labels"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device
)
elif model_class in get_values(MODEL_FOR_PRETRAINING_MAPPING):
# special case for models like BERT that use multi-loss training for PreTraining
inputs_dict["labels"] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
)
return inputs_dict
def setUp(self):
self.model_tester = LxmertModelTester(self)
self.config_tester = ConfigTester(self, config_class=LxmertConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_lxmert_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lxmert_model(*config_and_inputs)
def test_lxmert_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lxmert_for_question_answering(*config_and_inputs)
def test_lxmert_pretraining(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lxmert_for_pretraining(*config_and_inputs)
def test_lxmert_question_answering_labels_resize(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.resize_lxmert_num_qa_labels(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = LxmertModel.from_pretrained(model_name)
model.to(torch_device)
self.assertIsNotNone(model)
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
seq_len = getattr(self.model_tester, "seq_length", None)
encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
chunk_length = getattr(self.model_tester, "chunk_length", None)
if chunk_length is not None and hasattr(self.model_tester, "num_hashes"):
encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
language_attentions, vision_attentions, cross_encoder_attentions = (outputs[-3], outputs[-2], outputs[-1])
self.assertEqual(len(language_attentions), self.model_tester.num_hidden_layers["language"])
self.assertEqual(len(vision_attentions), self.model_tester.num_hidden_layers["vision"])
self.assertEqual(len(cross_encoder_attentions), self.model_tester.num_hidden_layers["cross_encoder"])
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
language_attentions, vision_attentions, cross_encoder_attentions = (outputs[-3], outputs[-2], outputs[-1])
self.assertEqual(len(language_attentions), self.model_tester.num_hidden_layers["language"])
self.assertEqual(len(vision_attentions), self.model_tester.num_hidden_layers["vision"])
self.assertEqual(len(cross_encoder_attentions), self.model_tester.num_hidden_layers["cross_encoder"])
attentions = [language_attentions, vision_attentions, cross_encoder_attentions]
attention_shapes = [
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
[
self.model_tester.num_attention_heads,
self.model_tester.num_visual_features,
self.model_tester.num_visual_features,
],
[self.model_tester.num_attention_heads, encoder_key_length, self.model_tester.num_visual_features],
]
for attention, attention_shape in zip(attentions, attention_shapes):
self.assertListEqual(list(attention[0].shape[-3:]), attention_shape)
out_len = len(outputs)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
# 2 hidden states were added
self.assertEqual(out_len + 2, len(outputs))
language_attentions, vision_attentions, cross_encoder_attentions = (outputs[-3], outputs[-2], outputs[-1])
self.assertEqual(len(language_attentions), self.model_tester.num_hidden_layers["language"])
self.assertEqual(len(vision_attentions), self.model_tester.num_hidden_layers["vision"])
self.assertEqual(len(cross_encoder_attentions), self.model_tester.num_hidden_layers["cross_encoder"])
attentions = [language_attentions, vision_attentions, cross_encoder_attentions]
attention_shapes = [
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
[
self.model_tester.num_attention_heads,
self.model_tester.num_visual_features,
self.model_tester.num_visual_features,
],
[self.model_tester.num_attention_heads, encoder_key_length, self.model_tester.num_visual_features],
]
for attention, attention_shape in zip(attentions, attention_shapes):
self.assertListEqual(list(attention[0].shape[-3:]), attention_shape)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
language_hidden_states, vision_hidden_states = outputs[-2], outputs[-1]
self.assertEqual(len(language_hidden_states), self.model_tester.num_hidden_layers["language"] + 1)
self.assertEqual(len(vision_hidden_states), self.model_tester.num_hidden_layers["vision"] + 1)
seq_length = self.model_tester.seq_length
num_visual_features = self.model_tester.num_visual_features
self.assertListEqual(
list(language_hidden_states[0].shape[-2:]),
[seq_length, self.model_tester.hidden_size],
)
self.assertListEqual(
list(vision_hidden_states[0].shape[-2:]),
[num_visual_features, self.model_tester.hidden_size],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
def test_retain_grad_hidden_states_attentions(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_hidden_states = True
config.output_attentions = True
# no need to test all models as different heads yield the same functionality
model_class = self.all_model_classes[0]
model = model_class(config)
model.to(torch_device)
inputs = self._prepare_for_class(inputs_dict, model_class)
outputs = model(**inputs)
hidden_states_lang = outputs.language_hidden_states[0]
attentions_lang = outputs.language_attentions[0]
hidden_states_vision = outputs.vision_hidden_states[0]
attentions_vision = outputs.vision_attentions[0]
hidden_states_lang.retain_grad()
attentions_lang.retain_grad()
hidden_states_vision.retain_grad()
attentions_vision.retain_grad()
outputs.language_output.flatten()[0].backward(retain_graph=True)
outputs.vision_output.flatten()[0].backward(retain_graph=True)
self.assertIsNotNone(hidden_states_lang.grad)
self.assertIsNotNone(attentions_vision.grad)
self.assertIsNotNone(hidden_states_vision.grad)
self.assertIsNotNone(attentions_vision.grad)
def prepare_tf_inputs_from_pt_inputs(self, pt_inputs_dict):
tf_inputs_dict = {}
for key, value in pt_inputs_dict.items():
# skip key that does not exist in tf
if isinstance(value, dict):
tf_inputs_dict[key] = self.prepare_pt_inputs_from_tf_inputs(value)
elif isinstance(value, (list, tuple)):
tf_inputs_dict[key] = (self.prepare_pt_inputs_from_tf_inputs(iter_value) for iter_value in value)
elif isinstance(value, bool):
tf_inputs_dict[key] = value
elif key == "input_values":
tf_inputs_dict[key] = tf.convert_to_tensor(value.cpu().numpy(), dtype=tf.float32)
elif key == "pixel_values":
tf_inputs_dict[key] = tf.convert_to_tensor(value.cpu().numpy(), dtype=tf.float32)
elif key == "input_features":
tf_inputs_dict[key] = tf.convert_to_tensor(value.cpu().numpy(), dtype=tf.float32)
# other general float inputs
elif value.is_floating_point():
tf_inputs_dict[key] = tf.convert_to_tensor(value.cpu().numpy(), dtype=tf.float32)
else:
tf_inputs_dict[key] = tf.convert_to_tensor(value.cpu().numpy(), dtype=tf.int32)
return tf_inputs_dict
@require_torch
class LxmertModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_no_head_absolute_embedding(self):
model = LxmertModel.from_pretrained(LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST[0])
input_ids = torch.tensor([[101, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 102]])
num_visual_features = 10
_, visual_feats = np.random.seed(0), np.random.rand(1, num_visual_features, model.config.visual_feat_dim)
_, visual_pos = np.random.seed(0), np.random.rand(1, num_visual_features, 4)
visual_feats = torch.as_tensor(visual_feats, dtype=torch.float32)
visual_pos = torch.as_tensor(visual_pos, dtype=torch.float32)
output = model(input_ids, visual_feats=visual_feats, visual_pos=visual_pos)[0]
expected_shape = torch.Size([1, 11, 768])
self.assertEqual(expected_shape, output.shape)
expected_slice = torch.tensor(
[[[0.2417, -0.9807, 0.1480], [1.2541, -0.8320, 0.5112], [1.4070, -1.1052, 0.6990]]]
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
| transformers/tests/models/lxmert/test_modeling_lxmert.py/0 | {
"file_path": "transformers/tests/models/lxmert/test_modeling_lxmert.py",
"repo_id": "transformers",
"token_count": 15109
} | 374 |
# coding=utf-8
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from typing import Tuple
from transformers.models.mluke.tokenization_mluke import MLukeTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
SAMPLE_ENTITY_VOCAB = get_tests_dir("fixtures/test_entity_vocab.json")
class MLukeTokenizerTest(TokenizerTesterMixin, unittest.TestCase):
from_pretrained_id = "studio-ousia/mluke-base"
tokenizer_class = MLukeTokenizer
test_rust_tokenizer = False
from_pretrained_kwargs = {"cls_token": "<s>"}
def setUp(self):
super().setUp()
self.special_tokens_map = {"entity_token_1": "<ent>", "entity_token_2": "<ent2>"}
def get_tokenizer(self, task=None, **kwargs):
kwargs.update(self.special_tokens_map)
kwargs.update({"task": task})
tokenizer = MLukeTokenizer(vocab_file=SAMPLE_VOCAB, entity_vocab_file=SAMPLE_ENTITY_VOCAB, **kwargs)
return tokenizer
def get_input_output_texts(self, tokenizer):
input_text = "lower newer"
output_text = "lower newer"
return input_text, output_text
def test_full_tokenizer(self):
tokenizer = self.get_tokenizer()
text = "lower newer"
spm_tokens = ["▁l", "ow", "er", "▁new", "er"]
tokens = tokenizer.tokenize(text)
self.assertListEqual(tokens, spm_tokens)
input_tokens = tokens + [tokenizer.unk_token]
input_spm_tokens = [149, 116, 40, 410, 40] + [3]
self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_spm_tokens)
def mluke_dict_integration_testing(self):
tokenizer = self.get_tokenizer()
self.assertListEqual(tokenizer.encode("Hello world!", add_special_tokens=False), [35378, 8999, 38])
self.assertListEqual(
tokenizer.encode("Hello world! cécé herlolip 418", add_special_tokens=False),
[35378, 8999, 38, 33273, 11676, 604, 365, 21392, 201, 1819],
)
def test_sequence_builders(self):
tokenizer = self.tokenizer_class.from_pretrained("hf-internal-testing/tiny-random-mluke")
text = tokenizer.encode("sequence builders", add_special_tokens=False)
text_2 = tokenizer.encode("multi-sequence build", add_special_tokens=False)
encoded_text_from_decode = tokenizer.encode(
"sequence builders", add_special_tokens=True, add_prefix_space=False
)
encoded_pair_from_decode = tokenizer.encode(
"sequence builders", "multi-sequence build", add_special_tokens=True, add_prefix_space=False
)
encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
self.assertEqual(encoded_sentence, encoded_text_from_decode)
self.assertEqual(encoded_pair, encoded_pair_from_decode)
def get_clean_sequence(self, tokenizer, max_length=20) -> Tuple[str, list]:
txt = "Beyonce lives in Los Angeles"
ids = tokenizer.encode(txt, add_special_tokens=False)
return txt, ids
def test_pretokenized_inputs(self):
pass
def test_embeded_special_tokens(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest("{} ({})".format(tokenizer.__class__.__name__, pretrained_name)):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
sentence = "A, <mask> AllenNLP sentence."
tokens_r = tokenizer_r.encode_plus(sentence, add_special_tokens=True, return_token_type_ids=True)
tokens_p = tokenizer_p.encode_plus(sentence, add_special_tokens=True, return_token_type_ids=True)
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r["token_type_ids"]), sum(tokens_p["token_type_ids"]))
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r["token_type_ids"]), sum(tokens_p["token_type_ids"]))
# attention_mask should put 1 everywhere, so sum over length should be 1
self.assertEqual(
sum(tokens_p["attention_mask"]) / len(tokens_p["attention_mask"]),
)
tokens_p_str = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"])
# Rust correctly handles the space before the mask while python doesnt
self.assertSequenceEqual(tokens_p["input_ids"], [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2])
self.assertSequenceEqual(
tokens_p_str, ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]
)
def test_padding_entity_inputs(self):
tokenizer = self.get_tokenizer()
sentence = "Japanese is an East Asian language spoken by about 128 million people, primarily in Japan."
span = (15, 34)
pad_id = tokenizer.entity_vocab["[PAD]"]
mask_id = tokenizer.entity_vocab["[MASK]"]
encoding = tokenizer([sentence, sentence], entity_spans=[[span], [span, span]], padding=True)
self.assertEqual(encoding["entity_ids"], [[mask_id, pad_id], [mask_id, mask_id]])
# test with a sentence with no entity
encoding = tokenizer([sentence, sentence], entity_spans=[[], [span, span]], padding=True)
self.assertEqual(encoding["entity_ids"], [[pad_id, pad_id], [mask_id, mask_id]])
def test_if_tokenize_single_text_raise_error_with_invalid_inputs(self):
tokenizer = self.get_tokenizer()
sentence = "ISO 639-3 uses the code fas for the dialects spoken across Iran and Afghanistan."
entities = ["DUMMY"]
spans = [(0, 9)]
with self.assertRaises(ValueError):
tokenizer(sentence, entities=tuple(entities), entity_spans=spans)
with self.assertRaises(ValueError):
tokenizer(sentence, entities=entities, entity_spans=tuple(spans))
with self.assertRaises(ValueError):
tokenizer(sentence, entities=[0], entity_spans=spans)
with self.assertRaises(ValueError):
tokenizer(sentence, entities=entities, entity_spans=[0])
with self.assertRaises(ValueError):
tokenizer(sentence, entities=entities, entity_spans=spans + [(0, 9)])
def test_if_tokenize_entity_classification_raise_error_with_invalid_inputs(self):
tokenizer = self.get_tokenizer(task="entity_classification")
sentence = "Japanese is an East Asian language spoken by about 128 million people, primarily in Japan."
span = (15, 34)
with self.assertRaises(ValueError):
tokenizer(sentence, entity_spans=[])
with self.assertRaises(ValueError):
tokenizer(sentence, entity_spans=[span, span])
with self.assertRaises(ValueError):
tokenizer(sentence, entity_spans=[0])
def test_if_tokenize_entity_pair_classification_raise_error_with_invalid_inputs(self):
tokenizer = self.get_tokenizer(task="entity_pair_classification")
sentence = "Japanese is an East Asian language spoken by about 128 million people, primarily in Japan."
# head and tail information
with self.assertRaises(ValueError):
tokenizer(sentence, entity_spans=[])
with self.assertRaises(ValueError):
tokenizer(sentence, entity_spans=[0, 0])
def test_if_tokenize_entity_span_classification_raise_error_with_invalid_inputs(self):
tokenizer = self.get_tokenizer(task="entity_span_classification")
sentence = "Japanese is an East Asian language spoken by about 128 million people, primarily in Japan."
with self.assertRaises(ValueError):
tokenizer(sentence, entity_spans=[])
with self.assertRaises(ValueError):
tokenizer(sentence, entity_spans=[0, 0, 0])
@slow
@require_torch
class MLukeTokenizerIntegrationTests(unittest.TestCase):
tokenizer_class = MLukeTokenizer
from_pretrained_kwargs = {"cls_token": "<s>"}
@classmethod
def setUpClass(cls):
cls.tokenizer = MLukeTokenizer.from_pretrained("studio-ousia/mluke-base", return_token_type_ids=True)
cls.entity_classification_tokenizer = MLukeTokenizer.from_pretrained(
"studio-ousia/mluke-base", return_token_type_ids=True, task="entity_classification"
)
cls.entity_pair_tokenizer = MLukeTokenizer.from_pretrained(
"studio-ousia/mluke-base", return_token_type_ids=True, task="entity_pair_classification"
)
cls.entity_span_tokenizer = MLukeTokenizer.from_pretrained(
"studio-ousia/mluke-base", return_token_type_ids=True, task="entity_span_classification"
)
def test_single_text_no_padding_or_truncation(self):
tokenizer = self.tokenizer
sentence = "ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."
entities = ["en:ISO 639-3", "DUMMY_ENTITY", "ja:アフガニスタン", "en:Afghanistan"]
spans = [(0, 9), (59, 63), (68, 75), (77, 88)]
encoding = tokenizer(sentence, entities=entities, entity_spans=spans, return_token_type_ids=True)
self.assertEqual(
tokenizer.decode(encoding["input_ids"], spaces_between_special_tokens=False),
"<s> ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン ( Afghanistan ).</s>",
)
self.assertEqual(
tokenizer.decode(encoding["input_ids"][1:5], spaces_between_special_tokens=False), "ISO 639-3"
)
self.assertEqual(tokenizer.decode(encoding["input_ids"][17], spaces_between_special_tokens=False), "Iran")
self.assertEqual(
tokenizer.decode(encoding["input_ids"][19:25], spaces_between_special_tokens=False), "アフガニスタン"
)
self.assertEqual(
tokenizer.decode(encoding["input_ids"][26], spaces_between_special_tokens=False), "Afghanistan"
)
self.assertEqual(
encoding["entity_ids"],
[
tokenizer.entity_vocab["en:ISO 639-3"],
tokenizer.entity_vocab["[UNK]"],
tokenizer.entity_vocab["ja:アフガニスタン"],
tokenizer.entity_vocab["en:Afghanistan"],
],
)
self.assertEqual(encoding["entity_attention_mask"], [1, 1, 1, 1])
self.assertEqual(encoding["entity_token_type_ids"], [0, 0, 0, 0])
# fmt: off
self.assertEqual(
encoding["entity_position_ids"],
[
[1, 2, 3, 4, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
[17, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
[19, 20, 21, 22, 23, 24, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
[26, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]
]
)
# fmt: on
def test_single_text_only_entity_spans_no_padding_or_truncation(self):
tokenizer = self.tokenizer
sentence = "ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."
entities = ["en:ISO 639-3", "DUMMY_ENTITY", "ja:アフガニスタン", "en:Afghanistan"]
spans = [(0, 9), (59, 63), (68, 75), (77, 88)]
encoding = tokenizer(sentence, entities=entities, entity_spans=spans, return_token_type_ids=True)
self.assertEqual(
tokenizer.decode(encoding["input_ids"], spaces_between_special_tokens=False),
"<s> ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン ( Afghanistan ).</s>",
)
self.assertEqual(
tokenizer.decode(encoding["input_ids"][1:5], spaces_between_special_tokens=False), "ISO 639-3"
)
self.assertEqual(tokenizer.decode(encoding["input_ids"][17], spaces_between_special_tokens=False), "Iran")
self.assertEqual(
tokenizer.decode(encoding["input_ids"][20:25], spaces_between_special_tokens=False), "アフガニスタン"
)
self.assertEqual(
tokenizer.decode(encoding["input_ids"][26], spaces_between_special_tokens=False), "Afghanistan"
)
self.assertEqual(
encoding["entity_ids"],
[
tokenizer.entity_vocab["en:ISO 639-3"],
tokenizer.entity_vocab["[UNK]"],
tokenizer.entity_vocab["ja:アフガニスタン"],
tokenizer.entity_vocab["en:Afghanistan"],
],
)
self.assertEqual(encoding["entity_attention_mask"], [1, 1, 1, 1])
self.assertEqual(encoding["entity_token_type_ids"], [0, 0, 0, 0])
# fmt: off
self.assertEqual(
encoding["entity_position_ids"],
[
[1, 2, 3, 4, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
[17, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
[19, 20, 21, 22, 23, 24, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
[26, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]
]
)
# fmt: on
def test_single_text_padding_pytorch_tensors(self):
tokenizer = self.tokenizer
sentence = "ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."
entities = ["en:ISO 639-3", "DUMMY_ENTITY", "ja:アフガニスタン", "en:Afghanistan"]
spans = [(0, 9), (59, 63), (68, 75), (77, 88)]
encoding = tokenizer(
sentence,
entities=entities,
entity_spans=spans,
return_token_type_ids=True,
padding="max_length",
max_length=30,
max_entity_length=16,
return_tensors="pt",
)
# test words
self.assertEqual(encoding["input_ids"].shape, (1, 30))
self.assertEqual(encoding["attention_mask"].shape, (1, 30))
self.assertEqual(encoding["token_type_ids"].shape, (1, 30))
# test entities
self.assertEqual(encoding["entity_ids"].shape, (1, 16))
self.assertEqual(encoding["entity_attention_mask"].shape, (1, 16))
self.assertEqual(encoding["entity_token_type_ids"].shape, (1, 16))
self.assertEqual(encoding["entity_position_ids"].shape, (1, 16, tokenizer.max_mention_length))
def test_text_pair_no_padding_or_truncation(self):
tokenizer = self.tokenizer
sentence = "ISO 639-3 uses the code fas"
sentence_pair = "for the dialects spoken across Iran and アフガニスタン (Afghanistan)."
entities = ["en:ISO 639-3"]
entities_pair = ["DUMMY_ENTITY", "ja:アフガニスタン", "en:Afghanistan"]
spans = [(0, 9)]
spans_pair = [(31, 35), (40, 47), (49, 60)]
encoding = tokenizer(
sentence,
sentence_pair,
entities=entities,
entities_pair=entities_pair,
entity_spans=spans,
entity_spans_pair=spans_pair,
return_token_type_ids=True,
)
self.assertEqual(
tokenizer.decode(encoding["input_ids"], spaces_between_special_tokens=False),
"<s> ISO 639-3 uses the code fas</s></s> for the dialects spoken across Iran and アフガニスタン ( Afghanistan"
" ).</s>",
)
self.assertEqual(
tokenizer.decode(encoding["input_ids"][1:5], spaces_between_special_tokens=False), "ISO 639-3"
)
self.assertEqual(tokenizer.decode(encoding["input_ids"][19], spaces_between_special_tokens=False), "Iran")
self.assertEqual(
tokenizer.decode(encoding["input_ids"][21:27], spaces_between_special_tokens=False), "アフガニスタン"
)
self.assertEqual(
tokenizer.decode(encoding["input_ids"][28], spaces_between_special_tokens=False), "Afghanistan"
)
self.assertEqual(
encoding["entity_ids"],
[
tokenizer.entity_vocab["en:ISO 639-3"],
tokenizer.entity_vocab["[UNK]"],
tokenizer.entity_vocab["ja:アフガニスタン"],
tokenizer.entity_vocab["en:Afghanistan"],
],
)
self.assertEqual(encoding["entity_attention_mask"], [1, 1, 1, 1])
self.assertEqual(encoding["entity_token_type_ids"], [0, 0, 0, 0])
# fmt: off
self.assertEqual(
encoding["entity_position_ids"],
[
[1, 2, 3, 4, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
[19, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
[21, 22, 23, 24, 25, 26, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
[28, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]
]
)
# fmt: on
def test_text_pair_only_entity_spans_no_padding_or_truncation(self):
tokenizer = self.tokenizer
sentence = "ISO 639-3 uses the code fas"
sentence_pair = "for the dialects spoken across Iran and アフガニスタン (Afghanistan)."
entities = ["en:ISO 639-3"]
entities_pair = ["DUMMY_ENTITY", "ja:アフガニスタン", "en:Afghanistan"]
spans = [(0, 9)]
spans_pair = [(31, 35), (40, 47), (49, 60)]
encoding = tokenizer(
sentence,
sentence_pair,
entities=entities,
entities_pair=entities_pair,
entity_spans=spans,
entity_spans_pair=spans_pair,
return_token_type_ids=True,
)
self.assertEqual(
tokenizer.decode(encoding["input_ids"], spaces_between_special_tokens=False),
"<s> ISO 639-3 uses the code fas</s></s> for the dialects spoken across Iran and アフガニスタン ( Afghanistan"
" ).</s>",
)
self.assertEqual(
tokenizer.decode(encoding["input_ids"][1:5], spaces_between_special_tokens=False), "ISO 639-3"
)
self.assertEqual(tokenizer.decode(encoding["input_ids"][19], spaces_between_special_tokens=False), "Iran")
self.assertEqual(
tokenizer.decode(encoding["input_ids"][21:27], spaces_between_special_tokens=False), "アフガニスタン"
)
self.assertEqual(
tokenizer.decode(encoding["input_ids"][28], spaces_between_special_tokens=False), "Afghanistan"
)
self.assertEqual(
encoding["entity_ids"],
[
tokenizer.entity_vocab["en:ISO 639-3"],
tokenizer.entity_vocab["[UNK]"],
tokenizer.entity_vocab["ja:アフガニスタン"],
tokenizer.entity_vocab["en:Afghanistan"],
],
)
# fmt: off
self.assertEqual(
encoding["entity_position_ids"],
[
[1, 2, 3, 4, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
[19, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
[21, 22, 23, 24, 25, 26, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
[28, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]
]
)
# fmt: on
def test_text_pair_padding_pytorch_tensors(self):
tokenizer = self.tokenizer
sentence = "ISO 639-3 uses the code fas"
sentence_pair = "for the dialects spoken across Iran and アフガニスタン (Afghanistan)."
entities = ["en:ISO 639-3"]
entities_pair = ["DUMMY_ENTITY", "ja:アフガニスタン", "en:Afghanistan"]
spans = [(0, 9)]
spans_pair = [(31, 35), (40, 47), (49, 60)]
encoding = tokenizer(
sentence,
sentence_pair,
entities=entities,
entities_pair=entities_pair,
entity_spans=spans,
entity_spans_pair=spans_pair,
return_token_type_ids=True,
padding="max_length",
max_length=40,
max_entity_length=16,
return_tensors="pt",
)
# test words
self.assertEqual(encoding["input_ids"].shape, (1, 40))
self.assertEqual(encoding["attention_mask"].shape, (1, 40))
self.assertEqual(encoding["token_type_ids"].shape, (1, 40))
# test entities
self.assertEqual(encoding["entity_ids"].shape, (1, 16))
self.assertEqual(encoding["entity_attention_mask"].shape, (1, 16))
self.assertEqual(encoding["entity_token_type_ids"].shape, (1, 16))
self.assertEqual(encoding["entity_position_ids"].shape, (1, 16, tokenizer.max_mention_length))
def test_entity_classification_no_padding_or_truncation(self):
tokenizer = self.entity_classification_tokenizer
sentence = "Japanese is an East Asian language spoken by about 128 million people, primarily in Japan."
span = (15, 34)
encoding = tokenizer(sentence, entity_spans=[span], return_token_type_ids=True)
# test words
self.assertEqual(len(encoding["input_ids"]), 23)
self.assertEqual(len(encoding["attention_mask"]), 23)
self.assertEqual(len(encoding["token_type_ids"]), 23)
self.assertEqual(
tokenizer.decode(encoding["input_ids"], spaces_between_special_tokens=False),
"<s> Japanese is an<ent>East Asian language<ent>spoken by about 128 million people, primarily in"
" Japan.</s>",
)
self.assertEqual(
tokenizer.decode(encoding["input_ids"][4:9], spaces_between_special_tokens=False),
"<ent>East Asian language<ent>",
)
# test entities
mask_id = tokenizer.entity_vocab["[MASK]"]
self.assertEqual(encoding["entity_ids"], [mask_id])
self.assertEqual(encoding["entity_attention_mask"], [1])
self.assertEqual(encoding["entity_token_type_ids"], [0])
# fmt: off
self.assertEqual(
encoding["entity_position_ids"],
[[4, 5, 6, 7, 8, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]]
)
# fmt: on
def test_entity_classification_padding_pytorch_tensors(self):
tokenizer = self.entity_classification_tokenizer
sentence = "Japanese is an East Asian language spoken by about 128 million people, primarily in Japan."
span = (15, 34)
encoding = tokenizer(
sentence, entity_spans=[span], return_token_type_ids=True, padding="max_length", return_tensors="pt"
)
# test words
self.assertEqual(encoding["input_ids"].shape, (1, 512))
self.assertEqual(encoding["attention_mask"].shape, (1, 512))
self.assertEqual(encoding["token_type_ids"].shape, (1, 512))
# test entities
self.assertEqual(encoding["entity_ids"].shape, (1, 1))
self.assertEqual(encoding["entity_attention_mask"].shape, (1, 1))
self.assertEqual(encoding["entity_token_type_ids"].shape, (1, 1))
self.assertEqual(
encoding["entity_position_ids"].shape, (1, tokenizer.max_entity_length, tokenizer.max_mention_length)
)
def test_entity_pair_classification_no_padding_or_truncation(self):
tokenizer = self.entity_pair_tokenizer
sentence = "Japanese is an East Asian language spoken by about 128 million people, primarily in Japan."
# head and tail information
spans = [(0, 8), (84, 89)]
encoding = tokenizer(sentence, entity_spans=spans, return_token_type_ids=True)
self.assertEqual(
tokenizer.decode(encoding["input_ids"], spaces_between_special_tokens=False),
"<s><ent>Japanese<ent>is an East Asian language spoken by about 128 million people, primarily"
" in<ent2>Japan<ent2>.</s>",
)
self.assertEqual(
tokenizer.decode(encoding["input_ids"][1:4], spaces_between_special_tokens=False),
"<ent>Japanese<ent>",
)
self.assertEqual(
tokenizer.decode(encoding["input_ids"][20:23], spaces_between_special_tokens=False), "<ent2>Japan<ent2>"
)
mask_id = tokenizer.entity_vocab["[MASK]"]
mask2_id = tokenizer.entity_vocab["[MASK2]"]
self.assertEqual(encoding["entity_ids"], [mask_id, mask2_id])
self.assertEqual(encoding["entity_attention_mask"], [1, 1])
self.assertEqual(encoding["entity_token_type_ids"], [0, 0])
# fmt: off
self.assertEqual(
encoding["entity_position_ids"],
[
[1, 2, 3, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
[20, 21, 22, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]
]
)
# fmt: on
def test_entity_pair_classification_padding_pytorch_tensors(self):
tokenizer = self.entity_pair_tokenizer
sentence = "Japanese is an East Asian language spoken by about 128 million people, primarily in Japan."
# head and tail information
spans = [(0, 8), (84, 89)]
encoding = tokenizer(
sentence,
entity_spans=spans,
return_token_type_ids=True,
padding="max_length",
max_length=30,
return_tensors="pt",
)
# test words
self.assertEqual(encoding["input_ids"].shape, (1, 30))
self.assertEqual(encoding["attention_mask"].shape, (1, 30))
self.assertEqual(encoding["token_type_ids"].shape, (1, 30))
# test entities
self.assertEqual(encoding["entity_ids"].shape, (1, 2))
self.assertEqual(encoding["entity_attention_mask"].shape, (1, 2))
self.assertEqual(encoding["entity_token_type_ids"].shape, (1, 2))
self.assertEqual(
encoding["entity_position_ids"].shape, (1, tokenizer.max_entity_length, tokenizer.max_mention_length)
)
def test_entity_span_classification_no_padding_or_truncation(self):
tokenizer = self.entity_span_tokenizer
sentence = "Japanese is an East Asian language spoken by about 128 million people, primarily in Japan."
spans = [(0, 8), (15, 34), (84, 89)]
encoding = tokenizer(sentence, entity_spans=spans, return_token_type_ids=True)
self.assertEqual(
tokenizer.decode(encoding["input_ids"], spaces_between_special_tokens=False),
"<s> Japanese is an East Asian language spoken by about 128 million people, primarily in Japan.</s>",
)
mask_id = tokenizer.entity_vocab["[MASK]"]
self.assertEqual(encoding["entity_ids"], [mask_id, mask_id, mask_id])
self.assertEqual(encoding["entity_attention_mask"], [1, 1, 1])
self.assertEqual(encoding["entity_token_type_ids"], [0, 0, 0])
# fmt: off
self.assertEqual(
encoding["entity_position_ids"],
[
[1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
[4, 5, 6, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
[18, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]]
)
# fmt: on
self.assertEqual(encoding["entity_start_positions"], [1, 4, 18])
self.assertEqual(encoding["entity_end_positions"], [1, 6, 18])
def test_entity_span_classification_padding_pytorch_tensors(self):
tokenizer = self.entity_span_tokenizer
sentence = "Japanese is an East Asian language spoken by about 128 million people, primarily in Japan."
spans = [(0, 8), (15, 34), (84, 89)]
encoding = tokenizer(
sentence,
entity_spans=spans,
return_token_type_ids=True,
padding="max_length",
max_length=30,
max_entity_length=16,
return_tensors="pt",
)
# test words
self.assertEqual(encoding["input_ids"].shape, (1, 30))
self.assertEqual(encoding["attention_mask"].shape, (1, 30))
self.assertEqual(encoding["token_type_ids"].shape, (1, 30))
# test entities
self.assertEqual(encoding["entity_ids"].shape, (1, 16))
self.assertEqual(encoding["entity_attention_mask"].shape, (1, 16))
self.assertEqual(encoding["entity_token_type_ids"].shape, (1, 16))
self.assertEqual(encoding["entity_position_ids"].shape, (1, 16, tokenizer.max_mention_length))
self.assertEqual(encoding["entity_start_positions"].shape, (1, 16))
self.assertEqual(encoding["entity_end_positions"].shape, (1, 16))
| transformers/tests/models/mluke/test_tokenization_mluke.py/0 | {
"file_path": "transformers/tests/models/mluke/test_tokenization_mluke.py",
"repo_id": "transformers",
"token_count": 14711
} | 375 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch MobileViTV2 model. """
import unittest
from transformers import MobileViTV2Config
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MobileViTV2ForImageClassification, MobileViTV2ForSemanticSegmentation, MobileViTV2Model
from transformers.models.mobilevitv2.modeling_mobilevitv2 import (
MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
make_divisible,
)
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class MobileViTV2ConfigTester(ConfigTester):
def create_and_test_config_common_properties(self):
config = self.config_class(**self.inputs_dict)
self.parent.assertTrue(hasattr(config, "width_multiplier"))
class MobileViTV2ModelTester:
def __init__(
self,
parent,
batch_size=13,
image_size=64,
patch_size=2,
num_channels=3,
hidden_act="swish",
conv_kernel_size=3,
output_stride=32,
classifier_dropout_prob=0.1,
initializer_range=0.02,
is_training=True,
use_labels=True,
num_labels=10,
scope=None,
width_multiplier=0.25,
ffn_dropout=0.0,
attn_dropout=0.0,
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.last_hidden_size = make_divisible(512 * width_multiplier, divisor=8)
self.hidden_act = hidden_act
self.conv_kernel_size = conv_kernel_size
self.output_stride = output_stride
self.classifier_dropout_prob = classifier_dropout_prob
self.use_labels = use_labels
self.is_training = is_training
self.num_labels = num_labels
self.initializer_range = initializer_range
self.scope = scope
self.width_multiplier = width_multiplier
self.ffn_dropout_prob = ffn_dropout
self.attn_dropout_prob = attn_dropout
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
labels = None
pixel_labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size], self.num_labels)
pixel_labels = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels)
config = self.get_config()
return config, pixel_values, labels, pixel_labels
def get_config(self):
return MobileViTV2Config(
image_size=self.image_size,
patch_size=self.patch_size,
num_channels=self.num_channels,
hidden_act=self.hidden_act,
conv_kernel_size=self.conv_kernel_size,
output_stride=self.output_stride,
classifier_dropout_prob=self.classifier_dropout_prob,
initializer_range=self.initializer_range,
width_multiplier=self.width_multiplier,
ffn_dropout=self.ffn_dropout_prob,
attn_dropout=self.attn_dropout_prob,
base_attn_unit_dims=[16, 24, 32],
n_attn_blocks=[1, 1, 2],
aspp_out_channels=32,
)
def create_and_check_model(self, config, pixel_values, labels, pixel_labels):
model = MobileViTV2Model(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
self.parent.assertEqual(
result.last_hidden_state.shape,
(
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
),
)
def create_and_check_for_image_classification(self, config, pixel_values, labels, pixel_labels):
config.num_labels = self.num_labels
model = MobileViTV2ForImageClassification(config)
model.to(torch_device)
model.eval()
result = model(pixel_values, labels=labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_semantic_segmentation(self, config, pixel_values, labels, pixel_labels):
config.num_labels = self.num_labels
model = MobileViTV2ForSemanticSegmentation(config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
self.parent.assertEqual(
result.logits.shape,
(
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
),
)
result = model(pixel_values, labels=pixel_labels)
self.parent.assertEqual(
result.logits.shape,
(
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
),
)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, labels, pixel_labels = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class MobileViTV2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
"""
Here we also overwrite some of the tests of test_modeling_common.py, as MobileViTV2 does not use input_ids, inputs_embeds,
attention_mask and seq_length.
"""
all_model_classes = (
(MobileViTV2Model, MobileViTV2ForImageClassification, MobileViTV2ForSemanticSegmentation)
if is_torch_available()
else ()
)
pipeline_model_mapping = (
{
"image-feature-extraction": MobileViTV2Model,
"image-classification": MobileViTV2ForImageClassification,
"image-segmentation": MobileViTV2ForSemanticSegmentation,
}
if is_torch_available()
else {}
)
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
has_attentions = False
def setUp(self):
self.model_tester = MobileViTV2ModelTester(self)
self.config_tester = MobileViTV2ConfigTester(self, config_class=MobileViTV2Config, has_text_modality=False)
def test_config(self):
self.config_tester.run_common_tests()
@unittest.skip(reason="MobileViTV2 does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="MobileViTV2 does not support input and output embeddings")
def test_model_common_attributes(self):
pass
@unittest.skip(reason="MobileViTV2 does not output attentions")
def test_attention_outputs(self):
pass
@require_torch_multi_gpu
@unittest.skip(reason="Got `CUDA error: misaligned address` for tests after this one being run.")
def test_multi_gpu_data_parallel_forward(self):
pass
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.hidden_states
expected_num_stages = 5
self.assertEqual(len(hidden_states), expected_num_stages)
# MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
# with the width and height being successively divided by 2.
divisor = 2
for i in range(len(hidden_states)):
self.assertListEqual(
list(hidden_states[i].shape[-2:]),
[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor],
)
divisor *= 2
self.assertEqual(self.model_tester.output_stride, divisor // 2)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
def test_for_image_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
def test_for_semantic_segmentation(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = MobileViTV2Model.from_pretrained(model_name)
self.assertIsNotNone(model)
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_torch
@require_vision
class MobileViTV2ModelIntegrationTest(unittest.TestCase):
@cached_property
def default_image_processor(self):
return (
MobileViTImageProcessor.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256")
if is_vision_available()
else None
)
@slow
def test_inference_image_classification_head(self):
model = MobileViTV2ForImageClassification.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256").to(
torch_device
)
image_processor = self.default_image_processor
image = prepare_img()
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
# verify the logits
expected_shape = torch.Size((1, 1000))
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]).to(torch_device)
self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))
@slow
def test_inference_semantic_segmentation(self):
model = MobileViTV2ForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")
model = model.to(torch_device)
image_processor = MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")
image = prepare_img()
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
# verify the logits
expected_shape = torch.Size((1, 21, 32, 32))
self.assertEqual(logits.shape, expected_shape)
expected_slice = torch.tensor(
[
[[7.0863, 7.1525, 6.8201], [6.6931, 6.8770, 6.8933], [6.2978, 7.0366, 6.9636]],
[[-3.7134, -3.6712, -3.6675], [-3.5825, -3.3549, -3.4777], [-3.3435, -3.3979, -3.2857]],
[[-2.9329, -2.8003, -2.7369], [-3.0564, -2.4780, -2.0207], [-2.6889, -1.9298, -1.7640]],
],
device=torch_device,
)
self.assertTrue(torch.allclose(logits[0, :3, :3, :3], expected_slice, atol=1e-4))
@slow
def test_post_processing_semantic_segmentation(self):
model = MobileViTV2ForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")
model = model.to(torch_device)
image_processor = MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")
image = prepare_img()
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
outputs.logits = outputs.logits.detach().cpu()
segmentation = image_processor.post_process_semantic_segmentation(outputs=outputs, target_sizes=[(50, 60)])
expected_shape = torch.Size((50, 60))
self.assertEqual(segmentation[0].shape, expected_shape)
segmentation = image_processor.post_process_semantic_segmentation(outputs=outputs)
expected_shape = torch.Size((32, 32))
self.assertEqual(segmentation[0].shape, expected_shape)
| transformers/tests/models/mobilevitv2/test_modeling_mobilevitv2.py/0 | {
"file_path": "transformers/tests/models/mobilevitv2/test_modeling_mobilevitv2.py",
"repo_id": "transformers",
"token_count": 6229
} | 376 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import NougatImageProcessor
class NougatImageProcessingTester(unittest.TestCase):
def __init__(
self,
parent,
batch_size=7,
num_channels=3,
image_size=18,
min_resolution=30,
max_resolution=400,
do_crop_margin=True,
do_resize=True,
size=None,
do_thumbnail=True,
do_align_long_axis: bool = False,
do_pad=True,
do_normalize: bool = True,
image_mean=[0.5, 0.5, 0.5],
image_std=[0.5, 0.5, 0.5],
):
size = size if size is not None else {"height": 20, "width": 20}
self.parent = parent
self.batch_size = batch_size
self.num_channels = num_channels
self.image_size = image_size
self.min_resolution = min_resolution
self.max_resolution = max_resolution
self.do_crop_margin = do_crop_margin
self.do_resize = do_resize
self.size = size
self.do_thumbnail = do_thumbnail
self.do_align_long_axis = do_align_long_axis
self.do_pad = do_pad
self.do_normalize = do_normalize
self.image_mean = image_mean
self.image_std = image_std
def prepare_image_processor_dict(self):
return {
"do_crop_margin": self.do_crop_margin,
"do_resize": self.do_resize,
"size": self.size,
"do_thumbnail": self.do_thumbnail,
"do_align_long_axis": self.do_align_long_axis,
"do_pad": self.do_pad,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
}
def expected_output_image_shape(self, images):
return self.num_channels, self.size["height"], self.size["width"]
def prepare_dummy_image(self):
filepath = hf_hub_download(
repo_id="hf-internal-testing/fixtures_docvqa", filename="nougat_pdf.png", repo_type="dataset"
)
image = Image.open(filepath).convert("RGB")
return image
def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
return prepare_image_inputs(
batch_size=self.batch_size,
num_channels=self.num_channels,
min_resolution=self.min_resolution,
max_resolution=self.max_resolution,
equal_resolution=equal_resolution,
numpify=numpify,
torchify=torchify,
)
@require_torch
@require_vision
class NougatImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = NougatImageProcessor if is_vision_available() else None
def setUp(self):
self.image_processor_tester = NougatImageProcessingTester(self)
@property
def image_processor_dict(self):
return self.image_processor_tester.prepare_image_processor_dict()
@cached_property
def image_processor(self):
return self.image_processing_class(**self.image_processor_dict)
def test_image_processor_properties(self):
image_processing = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(image_processing, "do_resize"))
self.assertTrue(hasattr(image_processing, "size"))
self.assertTrue(hasattr(image_processing, "do_normalize"))
self.assertTrue(hasattr(image_processing, "image_mean"))
self.assertTrue(hasattr(image_processing, "image_std"))
def test_image_processor_from_dict_with_kwargs(self):
image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
self.assertEqual(image_processor.size, {"height": 20, "width": 20})
image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42)
self.assertEqual(image_processor.size, {"height": 42, "width": 42})
def test_expected_output(self):
dummy_image = self.image_processor_tester.prepare_dummy_image()
image_processor = self.image_processor
inputs = image_processor(dummy_image, return_tensors="pt")
self.assertTrue(torch.allclose(inputs["pixel_values"].mean(), torch.tensor(0.4906), atol=1e-3, rtol=1e-3))
def test_crop_margin_all_white(self):
image = np.uint8(np.ones((100, 100, 3)) * 255)
image_processor = self.image_processor
cropped_image = image_processor.crop_margin(image)
self.assertTrue(np.array_equal(image, cropped_image))
def test_crop_margin_centered_black_square(self):
image = np.ones((100, 100, 3), dtype=np.uint8) * 255
image[45:55, 45:55, :] = 0
image_processor = self.image_processor
cropped_image = image_processor.crop_margin(image)
expected_cropped = image[45:55, 45:55, :]
self.assertTrue(np.array_equal(expected_cropped, cropped_image))
def test_align_long_axis_no_rotation(self):
image = np.uint8(np.ones((100, 200, 3)) * 255)
image_processor = self.image_processor
size = {"height": 200, "width": 300}
aligned_image = image_processor.align_long_axis(image, size)
self.assertEqual(image.shape, aligned_image.shape)
def test_align_long_axis_with_rotation(self):
image = np.uint8(np.ones((200, 100, 3)) * 255)
image_processor = self.image_processor
size = {"height": 300, "width": 200}
aligned_image = image_processor.align_long_axis(image, size)
self.assertEqual((200, 100, 3), aligned_image.shape)
def test_align_long_axis_data_format(self):
image = np.uint8(np.ones((100, 200, 3)) * 255)
data_format = "channels_first"
size = {"height": 200, "width": 300}
image_processor = self.image_processor
aligned_image = image_processor.align_long_axis(image, size, data_format=data_format)
self.assertEqual((3, 100, 200), aligned_image.shape)
def prepare_dummy_np_image(self):
filepath = hf_hub_download(
repo_id="hf-internal-testing/fixtures_docvqa", filename="nougat_pdf.png", repo_type="dataset"
)
image = Image.open(filepath).convert("RGB")
return np.array(image)
def test_crop_margin_equality_cv2_python(self):
image = self.prepare_dummy_np_image()
image_processor = self.image_processor
image_cropped_python = image_processor.crop_margin(image)
self.assertEqual(image_cropped_python.shape, (850, 685, 3))
self.assertEqual(image_cropped_python.mean(), 237.43881150708458)
| transformers/tests/models/nougat/test_image_processing_nougat.py/0 | {
"file_path": "transformers/tests/models/nougat/test_image_processing_nougat.py",
"repo_id": "transformers",
"token_count": 3133
} | 377 |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the TensorFlow SegFormer model. """
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import SegformerConfig
from transformers.file_utils import is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFSegformerForImageClassification, TFSegformerForSemanticSegmentation, TFSegformerModel
from transformers.models.segformer.modeling_tf_segformer import TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import SegformerImageProcessor
class TFSegformerConfigTester(ConfigTester):
def create_and_test_config_common_properties(self):
config = self.config_class(**self.inputs_dict)
self.parent.assertTrue(hasattr(config, "hidden_sizes"))
self.parent.assertTrue(hasattr(config, "num_attention_heads"))
self.parent.assertTrue(hasattr(config, "num_encoder_blocks"))
class TFSegformerModelTester:
def __init__(
self,
parent,
batch_size=13,
image_size=64,
num_channels=3,
num_encoder_blocks=4,
depths=[1, 1, 1, 1],
sr_ratios=[8, 4, 2, 1],
hidden_sizes=[8, 8, 16, 16],
downsampling_rates=[1, 4, 8, 16],
num_attention_heads=[1, 1, 2, 2],
is_training=True,
use_labels=True,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
initializer_range=0.02,
num_labels=3,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.num_channels = num_channels
self.num_encoder_blocks = num_encoder_blocks
self.sr_ratios = sr_ratios
self.depths = depths
self.hidden_sizes = hidden_sizes
self.downsampling_rates = downsampling_rates
self.num_attention_heads = num_attention_heads
self.is_training = is_training
self.use_labels = use_labels
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.initializer_range = initializer_range
self.num_labels = num_labels
self.scope = scope
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels)
config = self.get_config()
return config, pixel_values, labels
def get_config(self):
return SegformerConfig(
image_size=self.image_size,
num_channels=self.num_channels,
num_encoder_blocks=self.num_encoder_blocks,
depths=self.depths,
hidden_sizes=self.hidden_sizes,
num_attention_heads=self.num_attention_heads,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
initializer_range=self.initializer_range,
num_labels=self.num_labels,
)
def create_and_check_model(self, config, pixel_values, labels):
model = TFSegformerModel(config=config)
result = model(pixel_values, training=False)
expected_height = expected_width = self.image_size // (self.downsampling_rates[-1] * 2)
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)
)
def create_and_check_for_image_segmentation(self, config, pixel_values, labels):
config.num_labels = self.num_labels
model = TFSegformerForSemanticSegmentation(config)
result = model(pixel_values, training=False)
self.parent.assertEqual(
result.logits.shape, (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)
)
result = model(pixel_values, labels=labels, training=False)
self.parent.assertEqual(
result.logits.shape, (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)
)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, labels = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
def prepare_config_and_inputs_for_keras_fit(self, for_segmentation: bool = False):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, seg_labels = config_and_inputs
if for_segmentation:
inputs_dict = {"pixel_values": pixel_values, "labels": seg_labels}
else:
inputs_dict = {"pixel_values": pixel_values, "labels": tf.zeros((self.batch_size))}
return config, inputs_dict
@require_tf
class TFSegformerModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(TFSegformerModel, TFSegformerForImageClassification, TFSegformerForSemanticSegmentation)
if is_tf_available()
else ()
)
pipeline_model_mapping = (
{"feature-extraction": TFSegformerModel, "image-classification": TFSegformerForImageClassification}
if is_tf_available()
else {}
)
test_head_masking = False
test_onnx = False
test_pruning = False
test_resize_embeddings = False
def setUp(self):
self.model_tester = TFSegformerModelTester(self)
self.config_tester = TFSegformerConfigTester(self, config_class=SegformerConfig, has_text_modality=False)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
@unittest.skip("SegFormer does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip("SegFormer does not have get_input_embeddings method and get_output_embeddings methods")
def test_model_common_attributes(self):
pass
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.call)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
expected_num_attentions = sum(self.model_tester.depths)
self.assertEqual(len(attentions), expected_num_attentions)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
self.assertEqual(len(attentions), expected_num_attentions)
# verify the first attentions (first block, first layer)
expected_seq_len = (self.model_tester.image_size // 4) ** 2
expected_reduced_seq_len = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len],
)
# verify the last attentions (last block, last layer)
expected_seq_len = (self.model_tester.image_size // 32) ** 2
expected_reduced_seq_len = (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
self.assertListEqual(
list(attentions[-1].shape[-3:]),
[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len],
)
out_len = len(outputs)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
self.assertEqual(out_len + 1, len(outputs))
self_attentions = outputs.attentions
self.assertEqual(len(self_attentions), expected_num_attentions)
# verify the first attentions (first block, first layer)
expected_seq_len = (self.model_tester.image_size // 4) ** 2
expected_reduced_seq_len = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len],
)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.hidden_states
expected_num_layers = self.model_tester.num_encoder_blocks
self.assertEqual(len(hidden_states), expected_num_layers)
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:]),
[
self.model_tester.hidden_sizes[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
def test_model_outputs_equivalence(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
tuple_output = model(tuple_inputs, return_dict=False, **additional_kwargs)
dict_output = model(dict_inputs, return_dict=True, **additional_kwargs).to_tuple()
def recursive_check(tuple_object, dict_object):
if isinstance(tuple_object, (List, Tuple)):
for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
recursive_check(tuple_iterable_value, dict_iterable_value)
elif tuple_object is None:
return
else:
self.assertTrue(
all(tf.equal(tuple_object, dict_object)),
msg=(
"Tuple and dict output are not equal. Difference:"
f" {tf.math.reduce_max(tf.abs(tuple_object - dict_object))}"
),
)
recursive_check(tuple_output, dict_output)
for model_class in self.all_model_classes:
model = model_class(config)
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs)
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
if self.has_attentions:
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True})
# todo: incorporate label support for semantic segmentation in `test_modeling_tf_common.py`.
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0,
reason="TF does not support backprop for grouped convolutions on CPU.",
)
def test_dataset_conversion(self):
super().test_dataset_conversion()
def check_keras_fit_results(self, val_loss1, val_loss2, atol=2e-1, rtol=2e-1):
self.assertTrue(np.allclose(val_loss1, val_loss2, atol=atol, rtol=rtol))
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0,
reason="TF does not support backprop for grouped convolutions on CPU.",
)
@slow
def test_keras_fit(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# Since `TFSegformerModel` cannot operate with the default `fit()` method.
if model_class.__name__ != "TFSegformerModel":
model = model_class(config)
if getattr(model, "hf_compute_loss", None):
super().test_keras_fit()
def test_loss_computation(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def apply(model):
for_segmentation = True if model_class.__name__ == "TFSegformerForSemanticSegmentation" else False
# The number of elements in the loss should be the same as the number of elements in the label
_, prepared_for_class = self.model_tester.prepare_config_and_inputs_for_keras_fit(
for_segmentation=for_segmentation
)
added_label = prepared_for_class[sorted(prepared_for_class.keys() - inputs_dict.keys(), reverse=True)[0]]
loss_size = tf.size(added_label)
# Test that model correctly compute the loss with kwargs
possible_input_names = {"input_ids", "pixel_values", "input_features"}
input_name = possible_input_names.intersection(set(prepared_for_class)).pop()
model_input = prepared_for_class.pop(input_name)
loss = model(model_input, **prepared_for_class)[0]
if model_class.__name__ == "TFSegformerForSemanticSegmentation":
# Semantic segmentation loss is computed similarly as
# https://github.com/huggingface/transformers/blob/main/src/transformers/modeling_tf_utils.py#L210.
self.assertEqual(loss.shape, (1,))
else:
self.assertEqual(loss.shape, [loss_size])
# Test that model correctly compute the loss with a dict
_, prepared_for_class = self.model_tester.prepare_config_and_inputs_for_keras_fit(
for_segmentation=for_segmentation
)
loss = model(**prepared_for_class)[0]
if model_class.__name__ == "TFSegformerForSemanticSegmentation":
self.assertEqual(loss.shape, (1,))
else:
self.assertEqual(loss.shape, [loss_size])
# Test that model correctly compute the loss with a tuple
label_keys = prepared_for_class.keys() - inputs_dict.keys()
signature = inspect.signature(model.call).parameters
signature_names = list(signature.keys())
# Create a dictionary holding the location of the tensors in the tuple
tuple_index_mapping = {0: input_name}
for label_key in label_keys:
label_key_index = signature_names.index(label_key)
tuple_index_mapping[label_key_index] = label_key
sorted_tuple_index_mapping = sorted(tuple_index_mapping.items())
# Initialize a list with their default values, update the values and convert to a tuple
list_input = []
for name in signature_names:
if name != "kwargs":
list_input.append(signature[name].default)
for index, value in sorted_tuple_index_mapping:
list_input[index] = prepared_for_class[value]
tuple_input = tuple(list_input)
# Send to model
loss = model(tuple_input[:-1])[0]
if model_class.__name__ == "TFSegformerForSemanticSegmentation":
self.assertEqual(loss.shape, (1,))
else:
self.assertEqual(loss.shape, [loss_size])
for model_class in self.all_model_classes:
# Since `TFSegformerModel` won't have labels against which we
# could compute loss.
if model_class.__name__ != "TFSegformerModel":
model = model_class(config)
apply(model)
def check_pt_tf_outputs(self, tf_outputs, pt_outputs, model_class, tol=2e-4, name="outputs", attributes=None):
# We override with a slightly higher tol value, as semseg models tend to diverge a bit more
super().check_pt_tf_outputs(tf_outputs, pt_outputs, model_class, tol, name, attributes)
@slow
def test_model_from_pretrained(self):
for model_name in TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = TFSegformerModel.from_pretrained(model_name)
self.assertIsNotNone(model)
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_tf
class TFSegformerModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_image_segmentation_ade(self):
# only resize + normalize
image_processor = SegformerImageProcessor(
image_scale=(512, 512), keep_ratio=False, align=False, do_random_crop=False
)
model = TFSegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512")
image = prepare_img()
encoded_inputs = image_processor(images=image, return_tensors="tf")
pixel_values = encoded_inputs.pixel_values
outputs = model(pixel_values, training=False)
expected_shape = tf.TensorShape((1, model.config.num_labels, 128, 128))
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = tf.constant(
[
[[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
[[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
[[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
]
)
tf.debugging.assert_near(outputs.logits[0, :3, :3, :3], expected_slice, atol=1e-4)
@slow
def test_inference_image_segmentation_city(self):
# only resize + normalize
image_processor = SegformerImageProcessor(
image_scale=(512, 512), keep_ratio=False, align=False, do_random_crop=False
)
model = TFSegformerForSemanticSegmentation.from_pretrained(
"nvidia/segformer-b1-finetuned-cityscapes-1024-1024"
)
image = prepare_img()
encoded_inputs = image_processor(images=image, return_tensors="tf")
pixel_values = encoded_inputs.pixel_values
outputs = model(pixel_values, training=False)
expected_shape = tf.TensorShape((1, model.config.num_labels, 128, 128))
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = tf.constant(
[
[[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
[[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
[[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
]
)
tf.debugging.assert_near(outputs.logits[0, :3, :3, :3], expected_slice, atol=1e-1)
| transformers/tests/models/segformer/test_modeling_tf_segformer.py/0 | {
"file_path": "transformers/tests/models/segformer/test_modeling_tf_segformer.py",
"repo_id": "transformers",
"token_count": 9892
} | 378 |
# coding=utf-8
# Copyright 2021 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import Speech2TextFeatureExtractor
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
global_rng = random.Random()
# Copied from tests.models.whisper.test_feature_extraction_whisper.floats_list
def floats_list(shape, scale=1.0, rng=None, name=None):
"""Creates a random float32 tensor"""
if rng is None:
rng = global_rng
values = []
for batch_idx in range(shape[0]):
values.append([])
for _ in range(shape[1]):
values[-1].append(rng.random() * scale)
return values
@require_torch
@require_torchaudio
class Speech2TextFeatureExtractionTester(unittest.TestCase):
def __init__(
self,
parent,
batch_size=7,
min_seq_length=400,
max_seq_length=2000,
feature_size=24,
num_mel_bins=24,
padding_value=0.0,
sampling_rate=16_000,
return_attention_mask=True,
do_normalize=True,
):
self.parent = parent
self.batch_size = batch_size
self.min_seq_length = min_seq_length
self.max_seq_length = max_seq_length
self.seq_length_diff = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
self.feature_size = feature_size
self.num_mel_bins = num_mel_bins
self.padding_value = padding_value
self.sampling_rate = sampling_rate
self.return_attention_mask = return_attention_mask
self.do_normalize = do_normalize
def prepare_feat_extract_dict(self):
return {
"feature_size": self.feature_size,
"num_mel_bins": self.num_mel_bins,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def prepare_inputs_for_common(self, equal_length=False, numpify=False):
def _flatten(list_of_lists):
return list(itertools.chain(*list_of_lists))
if equal_length:
speech_inputs = [floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)]
else:
# make sure that inputs increase in size
speech_inputs = [
floats_list((x, self.feature_size))
for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff)
]
if numpify:
speech_inputs = [np.asarray(x) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class Speech2TextFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):
feature_extraction_class = Speech2TextFeatureExtractor
def setUp(self):
self.feat_extract_tester = Speech2TextFeatureExtractionTester(self)
def _check_zero_mean_unit_variance(self, input_vector):
self.assertTrue(np.all(np.mean(input_vector, axis=0) < 1e-3))
self.assertTrue(np.all(np.abs(np.var(input_vector, axis=0) - 1) < 1e-3))
def test_call(self):
# Tests that all call wrap to encode_plus and batch_encode_plus
feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
# create three inputs of length 800, 1000, and 1200
speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
np_speech_inputs = [np.asarray(speech_input) for speech_input in speech_inputs]
# Test feature size
input_features = feature_extractor(np_speech_inputs, padding=True, return_tensors="np").input_features
self.assertTrue(input_features.ndim == 3)
self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size)
# Test not batched input
encoded_sequences_1 = feature_extractor(speech_inputs[0], return_tensors="np").input_features
encoded_sequences_2 = feature_extractor(np_speech_inputs[0], return_tensors="np").input_features
self.assertTrue(np.allclose(encoded_sequences_1, encoded_sequences_2, atol=1e-3))
# Test batched
encoded_sequences_1 = feature_extractor(speech_inputs, return_tensors="np").input_features
encoded_sequences_2 = feature_extractor(np_speech_inputs, return_tensors="np").input_features
for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
# Test 2-D numpy arrays are batched.
speech_inputs = [floats_list((1, x))[0] for x in (800, 800, 800)]
np_speech_inputs = np.asarray(speech_inputs)
encoded_sequences_1 = feature_extractor(speech_inputs, return_tensors="np").input_features
encoded_sequences_2 = feature_extractor(np_speech_inputs, return_tensors="np").input_features
for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
def test_cepstral_mean_and_variance_normalization(self):
feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
paddings = ["longest", "max_length", "do_not_pad"]
max_lengths = [None, 16, None]
for max_length, padding in zip(max_lengths, paddings):
inputs = feature_extractor(
speech_inputs, padding=padding, max_length=max_length, return_attention_mask=True
)
input_features = inputs.input_features
attention_mask = inputs.attention_mask
fbank_feat_lengths = [np.sum(x) for x in attention_mask]
self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]])
self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]])
self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]])
def test_cepstral_mean_and_variance_normalization_np(self):
feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
paddings = ["longest", "max_length", "do_not_pad"]
max_lengths = [None, 16, None]
for max_length, padding in zip(max_lengths, paddings):
inputs = feature_extractor(
speech_inputs, max_length=max_length, padding=padding, return_tensors="np", return_attention_mask=True
)
input_features = inputs.input_features
attention_mask = inputs.attention_mask
fbank_feat_lengths = [np.sum(x) for x in attention_mask]
self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]])
self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6)
self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]])
self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6)
self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]])
def test_cepstral_mean_and_variance_normalization_trunc_max_length(self):
feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
inputs = feature_extractor(
speech_inputs,
padding="max_length",
max_length=4,
truncation=True,
return_tensors="np",
return_attention_mask=True,
)
input_features = inputs.input_features
attention_mask = inputs.attention_mask
fbank_feat_lengths = np.sum(attention_mask == 1, axis=1)
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]])
self._check_zero_mean_unit_variance(input_features[1])
self._check_zero_mean_unit_variance(input_features[2])
def test_cepstral_mean_and_variance_normalization_trunc_longest(self):
feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
inputs = feature_extractor(
speech_inputs,
padding="longest",
max_length=4,
truncation=True,
return_tensors="np",
return_attention_mask=True,
)
input_features = inputs.input_features
attention_mask = inputs.attention_mask
fbank_feat_lengths = np.sum(attention_mask == 1, axis=1)
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]])
self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]])
self._check_zero_mean_unit_variance(input_features[2])
# make sure that if max_length < longest -> then pad to max_length
self.assertEqual(input_features.shape, (3, 4, 24))
speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
inputs = feature_extractor(
speech_inputs,
padding="longest",
max_length=16,
truncation=True,
return_tensors="np",
return_attention_mask=True,
)
input_features = inputs.input_features
attention_mask = inputs.attention_mask
fbank_feat_lengths = np.sum(attention_mask == 1, axis=1)
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]])
self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]])
self._check_zero_mean_unit_variance(input_features[2])
# make sure that if max_length < longest -> then pad to max_length
self.assertEqual(input_features.shape, (3, 6, 24))
def test_double_precision_pad(self):
import torch
feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
np_speech_inputs = np.random.rand(100, 32).astype(np.float64)
py_speech_inputs = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
np_processed = feature_extractor.pad([{"input_features": inputs}], return_tensors="np")
self.assertTrue(np_processed.input_features.dtype == np.float32)
pt_processed = feature_extractor.pad([{"input_features": inputs}], return_tensors="pt")
self.assertTrue(pt_processed.input_features.dtype == torch.float32)
def _load_datasamples(self, num_samples):
from datasets import load_dataset
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
# automatic decoding with librispeech
speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
def test_integration(self):
# fmt: off
expected = np.array([
-1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
-1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
-1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
])
# fmt: on
input_speech = self._load_datasamples(1)
feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
input_features = feature_extractor(input_speech, return_tensors="pt").input_features
self.assertEquals(input_features.shape, (1, 584, 24))
self.assertTrue(np.allclose(input_features[0, 0, :30], expected, atol=1e-4))
def test_feat_extract_from_and_save_pretrained(self):
feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
saved_file = feat_extract_first.save_pretrained(tmpdirname)[0]
check_json_file_has_correct_format(saved_file)
feat_extract_second = self.feature_extraction_class.from_pretrained(tmpdirname)
dict_first = feat_extract_first.to_dict()
dict_second = feat_extract_second.to_dict()
self.assertDictEqual(dict_first, dict_second)
def test_feat_extract_to_json_file(self):
feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
json_file_path = os.path.join(tmpdirname, "feat_extract.json")
feat_extract_first.to_json_file(json_file_path)
feat_extract_second = self.feature_extraction_class.from_json_file(json_file_path)
dict_first = feat_extract_first.to_dict()
dict_second = feat_extract_second.to_dict()
self.assertEqual(dict_first, dict_second)
# exact same tests than before, except that we simulate that torchaudio is not available
@require_torch
@unittest.mock.patch(
"transformers.models.speech_to_text.feature_extraction_speech_to_text.is_speech_available", lambda: False
)
class Speech2TextFeatureExtractionWithoutTorchaudioTest(Speech2TextFeatureExtractionTest):
def test_using_audio_utils(self):
# Tests that it uses audio_utils instead of torchaudio
feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
self.assertTrue(hasattr(feat_extract, "window"))
self.assertTrue(hasattr(feat_extract, "mel_filters"))
from transformers.models.speech_to_text.feature_extraction_speech_to_text import is_speech_available
self.assertFalse(is_speech_available())
| transformers/tests/models/speech_to_text/test_feature_extraction_speech_to_text.py/0 | {
"file_path": "transformers/tests/models/speech_to_text/test_feature_extraction_speech_to_text.py",
"repo_id": "transformers",
"token_count": 6315
} | 379 |
# coding=utf-8
# Copyright 2020 The SqueezeBert authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
)
class SqueezeBertModelTester(object):
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=False,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=64,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
q_groups=2,
k_groups=2,
v_groups=2,
post_attention_groups=2,
intermediate_groups=4,
output_groups=1,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
self.q_groups = q_groups
self.k_groups = k_groups
self.v_groups = v_groups
self.post_attention_groups = post_attention_groups
self.intermediate_groups = intermediate_groups
self.output_groups = output_groups
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
return SqueezeBertConfig(
embedding_size=self.hidden_size,
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
attention_probs_dropout_prob=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
initializer_range=self.initializer_range,
q_groups=self.q_groups,
k_groups=self.k_groups,
v_groups=self.v_groups,
post_attention_groups=self.post_attention_groups,
intermediate_groups=self.intermediate_groups,
output_groups=self.output_groups,
)
def create_and_check_squeezebert_model(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = SqueezeBertModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, input_mask)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_squeezebert_for_masked_lm(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = SqueezeBertForMaskedLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_squeezebert_for_question_answering(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = SqueezeBertForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids, attention_mask=input_mask, start_positions=sequence_labels, end_positions=sequence_labels
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def create_and_check_squeezebert_for_sequence_classification(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = SqueezeBertForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_squeezebert_for_token_classification(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = SqueezeBertForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_squeezebert_for_multiple_choice(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_choices = self.num_choices
model = SqueezeBertForMultipleChoice(config=config)
model.to(torch_device)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
result = model(
multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
labels=choice_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids, input_mask, sequence_labels, token_labels, choice_labels) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class SqueezeBertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
SqueezeBertModel,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
)
if is_torch_available()
else None
)
pipeline_model_mapping = (
{
"feature-extraction": SqueezeBertModel,
"fill-mask": SqueezeBertForMaskedLM,
"question-answering": SqueezeBertForQuestionAnswering,
"text-classification": SqueezeBertForSequenceClassification,
"token-classification": SqueezeBertForTokenClassification,
"zero-shot": SqueezeBertForSequenceClassification,
}
if is_torch_available()
else {}
)
test_pruning = False
test_resize_embeddings = True
test_head_masking = False
def setUp(self):
self.model_tester = SqueezeBertModelTester(self)
self.config_tester = ConfigTester(self, config_class=SqueezeBertConfig, dim=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_squeezebert_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_model(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_masked_lm(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_question_answering(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_sequence_classification(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_token_classification(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_multiple_choice(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = SqueezeBertModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@require_sentencepiece
@require_tokenizers
@require_torch
class SqueezeBertModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_classification_head(self):
model = SqueezeBertForSequenceClassification.from_pretrained("squeezebert/squeezebert-mnli")
input_ids = torch.tensor([[1, 29414, 232, 328, 740, 1140, 12695, 69, 13, 1588, 2]])
output = model(input_ids)[0]
expected_shape = torch.Size((1, 3))
self.assertEqual(output.shape, expected_shape)
expected_tensor = torch.tensor([[0.6401, -0.0349, -0.6041]])
self.assertTrue(torch.allclose(output, expected_tensor, atol=1e-4))
| transformers/tests/models/squeezebert/test_modeling_squeezebert.py/0 | {
"file_path": "transformers/tests/models/squeezebert/test_modeling_squeezebert.py",
"repo_id": "transformers",
"token_count": 5314
} | 380 |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch Swin2SR model. """
import unittest
from transformers import Swin2SRConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import Swin2SRForImageSuperResolution, Swin2SRModel
from transformers.models.swin2sr.modeling_swin2sr import SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import Swin2SRImageProcessor
class Swin2SRModelTester:
def __init__(
self,
parent,
batch_size=13,
image_size=32,
patch_size=1,
num_channels=3,
num_channels_out=1,
embed_dim=16,
depths=[1, 2, 1],
num_heads=[2, 2, 4],
window_size=2,
mlp_ratio=2.0,
qkv_bias=True,
hidden_dropout_prob=0.0,
attention_probs_dropout_prob=0.0,
drop_path_rate=0.1,
hidden_act="gelu",
use_absolute_embeddings=False,
patch_norm=True,
initializer_range=0.02,
layer_norm_eps=1e-5,
is_training=True,
scope=None,
use_labels=False,
upscale=2,
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.num_channels_out = num_channels_out
self.embed_dim = embed_dim
self.depths = depths
self.num_heads = num_heads
self.window_size = window_size
self.mlp_ratio = mlp_ratio
self.qkv_bias = qkv_bias
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.drop_path_rate = drop_path_rate
self.hidden_act = hidden_act
self.use_absolute_embeddings = use_absolute_embeddings
self.patch_norm = patch_norm
self.layer_norm_eps = layer_norm_eps
self.initializer_range = initializer_range
self.is_training = is_training
self.scope = scope
self.use_labels = use_labels
self.upscale = upscale
# here we set some attributes to make tests pass
self.num_hidden_layers = len(depths)
self.hidden_size = embed_dim
self.seq_length = (image_size // patch_size) ** 2
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
config = self.get_config()
return config, pixel_values, labels
def get_config(self):
return Swin2SRConfig(
image_size=self.image_size,
patch_size=self.patch_size,
num_channels=self.num_channels,
num_channels_out=self.num_channels_out,
embed_dim=self.embed_dim,
depths=self.depths,
num_heads=self.num_heads,
window_size=self.window_size,
mlp_ratio=self.mlp_ratio,
qkv_bias=self.qkv_bias,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
drop_path_rate=self.drop_path_rate,
hidden_act=self.hidden_act,
use_absolute_embeddings=self.use_absolute_embeddings,
path_norm=self.patch_norm,
layer_norm_eps=self.layer_norm_eps,
initializer_range=self.initializer_range,
upscale=self.upscale,
)
def create_and_check_model(self, config, pixel_values, labels):
model = Swin2SRModel(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.embed_dim, self.image_size, self.image_size)
)
def create_and_check_for_image_super_resolution(self, config, pixel_values, labels):
model = Swin2SRForImageSuperResolution(config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
expected_image_size = self.image_size * self.upscale
self.parent.assertEqual(
result.reconstruction.shape,
(self.batch_size, self.num_channels_out, expected_image_size, expected_image_size),
)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, labels = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class Swin2SRModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (Swin2SRModel, Swin2SRForImageSuperResolution) if is_torch_available() else ()
pipeline_model_mapping = (
{"image-feature-extraction": Swin2SRModel, "image-to-image": Swin2SRForImageSuperResolution}
if is_torch_available()
else {}
)
fx_compatible = False
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
test_torchscript = False
def setUp(self):
self.model_tester = Swin2SRModelTester(self)
self.config_tester = ConfigTester(self, config_class=Swin2SRConfig, embed_dim=37)
def test_config(self):
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_for_image_super_resolution(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_super_resolution(*config_and_inputs)
# TODO: check if this works again for PyTorch 2.x.y
@unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0.")
def test_multi_gpu_data_parallel_forward(self):
pass
@unittest.skip(reason="Swin2SR does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="Swin2SR does not support training yet")
def test_training(self):
pass
@unittest.skip(reason="Swin2SR does not support training yet")
def test_training_gradient_checkpointing(self):
pass
@unittest.skip(
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
)
def test_training_gradient_checkpointing_use_reentrant(self):
pass
@unittest.skip(
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
)
def test_training_gradient_checkpointing_use_reentrant_false(self):
pass
def test_model_common_attributes(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
x = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(x, nn.Linear))
@slow
def test_model_from_pretrained(self):
for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = Swin2SRModel.from_pretrained(model_name)
self.assertIsNotNone(model)
# overwriting because of `logit_scale` parameter
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
if "logit_scale" in name:
continue
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
expected_num_attentions = len(self.model_tester.depths)
self.assertEqual(len(attentions), expected_num_attentions)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
window_size_squared = config.window_size**2
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
self.assertEqual(len(attentions), expected_num_attentions)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_heads[0], window_size_squared, window_size_squared],
)
out_len = len(outputs)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
self.assertEqual(out_len + 1, len(outputs))
self_attentions = outputs.attentions
self.assertEqual(len(self_attentions), expected_num_attentions)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_heads[0], window_size_squared, window_size_squared],
)
@require_vision
@require_torch
@slow
class Swin2SRModelIntegrationTest(unittest.TestCase):
def test_inference_image_super_resolution_head(self):
processor = Swin2SRImageProcessor()
model = Swin2SRForImageSuperResolution.from_pretrained("caidas/swin2SR-classical-sr-x2-64").to(torch_device)
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
inputs = processor(images=image, return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
# verify the logits
expected_shape = torch.Size([1, 3, 976, 1296])
self.assertEqual(outputs.reconstruction.shape, expected_shape)
expected_slice = torch.tensor(
[[0.5458, 0.5546, 0.5638], [0.5526, 0.5565, 0.5651], [0.5396, 0.5426, 0.5621]]
).to(torch_device)
self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3], expected_slice, atol=1e-4))
| transformers/tests/models/swin2sr/test_modeling_swin2sr.py/0 | {
"file_path": "transformers/tests/models/swin2sr/test_modeling_swin2sr.py",
"repo_id": "transformers",
"token_count": 5792
} | 381 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import shutil
import tempfile
import unittest
import pytest
from transformers import WhisperTokenizer, is_speech_available
from transformers.testing_utils import require_sentencepiece, require_torch, require_torchaudio
from .test_feature_extraction_whisper import floats_list
if is_speech_available():
from transformers import WhisperFeatureExtractor, WhisperProcessor
TRANSCRIBE = 50358
NOTIMESTAMPS = 50362
@require_torch
@require_torchaudio
@require_sentencepiece
class WhisperProcessorTest(unittest.TestCase):
def setUp(self):
self.checkpoint = "openai/whisper-small.en"
self.tmpdirname = tempfile.mkdtemp()
def get_tokenizer(self, **kwargs):
return WhisperTokenizer.from_pretrained(self.checkpoint, **kwargs)
def get_feature_extractor(self, **kwargs):
return WhisperFeatureExtractor.from_pretrained(self.checkpoint, **kwargs)
def tearDown(self):
shutil.rmtree(self.tmpdirname)
def test_save_load_pretrained_default(self):
tokenizer = self.get_tokenizer()
feature_extractor = self.get_feature_extractor()
processor = WhisperProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
processor.save_pretrained(self.tmpdirname)
processor = WhisperProcessor.from_pretrained(self.tmpdirname)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab())
self.assertIsInstance(processor.tokenizer, WhisperTokenizer)
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string())
self.assertIsInstance(processor.feature_extractor, WhisperFeatureExtractor)
def test_save_load_pretrained_additional_features(self):
processor = WhisperProcessor(tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor())
processor.save_pretrained(self.tmpdirname)
tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
feature_extractor_add_kwargs = self.get_feature_extractor(do_normalize=False, padding_value=1.0)
processor = WhisperProcessor.from_pretrained(
self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
self.assertIsInstance(processor.tokenizer, WhisperTokenizer)
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string())
self.assertIsInstance(processor.feature_extractor, WhisperFeatureExtractor)
def test_feature_extractor(self):
feature_extractor = self.get_feature_extractor()
tokenizer = self.get_tokenizer()
processor = WhisperProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
raw_speech = floats_list((3, 1000))
input_feat_extract = feature_extractor(raw_speech, return_tensors="np")
input_processor = processor(raw_speech, return_tensors="np")
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
def test_tokenizer(self):
feature_extractor = self.get_feature_extractor()
tokenizer = self.get_tokenizer()
processor = WhisperProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
input_str = "This is a test string"
encoded_processor = processor(text=input_str)
encoded_tok = tokenizer(input_str)
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key], encoded_processor[key])
def test_tokenizer_decode(self):
feature_extractor = self.get_feature_extractor()
tokenizer = self.get_tokenizer()
processor = WhisperProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
decoded_processor = processor.batch_decode(predicted_ids)
decoded_tok = tokenizer.batch_decode(predicted_ids)
self.assertListEqual(decoded_tok, decoded_processor)
def test_model_input_names(self):
feature_extractor = self.get_feature_extractor()
tokenizer = self.get_tokenizer()
processor = WhisperProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
self.assertListEqual(
processor.model_input_names,
feature_extractor.model_input_names,
msg="`processor` and `feature_extractor` model input names do not match",
)
def test_get_decoder_prompt_ids(self):
feature_extractor = self.get_feature_extractor()
tokenizer = self.get_tokenizer()
processor = WhisperProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
forced_decoder_ids = processor.get_decoder_prompt_ids(task="transcribe", no_timestamps=True)
self.assertIsInstance(forced_decoder_ids, list)
for ids in forced_decoder_ids:
self.assertIsInstance(ids, (list, tuple))
expected_ids = [TRANSCRIBE, NOTIMESTAMPS]
self.assertListEqual([ids[-1] for ids in forced_decoder_ids], expected_ids)
def test_get_prompt_ids(self):
processor = WhisperProcessor(tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor())
prompt_ids = processor.get_prompt_ids("Mr. Quilter")
decoded_prompt = processor.tokenizer.decode(prompt_ids)
self.assertListEqual(prompt_ids.tolist(), [50360, 1770, 13, 2264, 346, 353])
self.assertEqual(decoded_prompt, "<|startofprev|> Mr. Quilter")
def test_empty_get_prompt_ids(self):
processor = WhisperProcessor(tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor())
prompt_ids = processor.get_prompt_ids("")
decoded_prompt = processor.tokenizer.decode(prompt_ids)
self.assertListEqual(prompt_ids.tolist(), [50360, 220])
self.assertEqual(decoded_prompt, "<|startofprev|> ")
def test_get_prompt_ids_with_special_tokens(self):
processor = WhisperProcessor(tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor())
def _test_prompt_error_raised_helper(prompt, special_token):
with pytest.raises(ValueError) as excinfo:
processor.get_prompt_ids(prompt)
expected = f"Encountered text in the prompt corresponding to disallowed special token: {special_token}."
self.assertEqual(expected, str(excinfo.value))
_test_prompt_error_raised_helper("<|startofprev|> test", "<|startofprev|>")
_test_prompt_error_raised_helper("test <|notimestamps|>", "<|notimestamps|>")
_test_prompt_error_raised_helper("test <|zh|> test <|transcribe|>", "<|zh|>")
| transformers/tests/models/whisper/test_processor_whisper.py/0 | {
"file_path": "transformers/tests/models/whisper/test_processor_whisper.py",
"repo_id": "transformers",
"token_count": 2869
} | 382 |
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
import pytest
from transformers import (
MODEL_MAPPING,
TF_MODEL_MAPPING,
TOKENIZER_MAPPING,
ImageFeatureExtractionPipeline,
is_tf_available,
is_torch_available,
is_vision_available,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
if is_vision_available():
from PIL import Image
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@is_pipeline_test
class ImageFeatureExtractionPipelineTests(unittest.TestCase):
model_mapping = MODEL_MAPPING
tf_model_mapping = TF_MODEL_MAPPING
@require_torch
def test_small_model_pt(self):
feature_extractor = pipeline(
task="image-feature-extraction", model="hf-internal-testing/tiny-random-vit", framework="pt"
)
img = prepare_img()
outputs = feature_extractor(img)
self.assertEqual(
nested_simplify(outputs[0][0]),
[-1.417, -0.392, -1.264, -1.196, 1.648, 0.885, 0.56, -0.606, -1.175, 0.823, 1.912, 0.081, -0.053, 1.119, -0.062, -1.757, -0.571, 0.075, 0.959, 0.118, 1.201, -0.672, -0.498, 0.364, 0.937, -1.623, 0.228, 0.19, 1.697, -1.115, 0.583, -0.981]) # fmt: skip
@require_torch
def test_small_model_w_pooler_pt(self):
feature_extractor = pipeline(
task="image-feature-extraction", model="hf-internal-testing/tiny-random-vit-w-pooler", framework="pt"
)
img = prepare_img()
outputs = feature_extractor(img, pool=True)
self.assertEqual(
nested_simplify(outputs[0]),
[-0.056, 0.083, 0.021, 0.038, 0.242, -0.279, -0.033, -0.003, 0.200, -0.192, 0.045, -0.095, -0.077, 0.017, -0.058, -0.063, -0.029, -0.204, 0.014, 0.042, 0.305, -0.205, -0.099, 0.146, -0.287, 0.020, 0.168, -0.052, 0.046, 0.048, -0.156, 0.093]) # fmt: skip
@require_tf
def test_small_model_tf(self):
feature_extractor = pipeline(
task="image-feature-extraction", model="hf-internal-testing/tiny-random-vit-w-pooler", framework="tf"
)
img = prepare_img()
outputs = feature_extractor(img)
self.assertEqual(
nested_simplify(outputs[0][0]),
[-1.417, -0.392, -1.264, -1.196, 1.648, 0.885, 0.56, -0.606, -1.175, 0.823, 1.912, 0.081, -0.053, 1.119, -0.062, -1.757, -0.571, 0.075, 0.959, 0.118, 1.201, -0.672, -0.498, 0.364, 0.937, -1.623, 0.228, 0.19, 1.697, -1.115, 0.583, -0.981]) # fmt: skip
@require_tf
def test_small_model_w_pooler_tf(self):
feature_extractor = pipeline(
task="image-feature-extraction", model="hf-internal-testing/tiny-random-vit-w-pooler", framework="tf"
)
img = prepare_img()
outputs = feature_extractor(img, pool=True)
self.assertEqual(
nested_simplify(outputs[0]),
[-0.056, 0.083, 0.021, 0.038, 0.242, -0.279, -0.033, -0.003, 0.200, -0.192, 0.045, -0.095, -0.077, 0.017, -0.058, -0.063, -0.029, -0.204, 0.014, 0.042, 0.305, -0.205, -0.099, 0.146, -0.287, 0.020, 0.168, -0.052, 0.046, 0.048, -0.156, 0.093]) # fmt: skip
@require_torch
def test_image_processing_small_model_pt(self):
feature_extractor = pipeline(
task="image-feature-extraction", model="hf-internal-testing/tiny-random-vit", framework="pt"
)
# test with image processor parameters
image_processor_kwargs = {"size": {"height": 300, "width": 300}}
img = prepare_img()
with pytest.raises(ValueError):
# Image doesn't match model input size
feature_extractor(img, image_processor_kwargs=image_processor_kwargs)
image_processor_kwargs = {"image_mean": [0, 0, 0], "image_std": [1, 1, 1]}
img = prepare_img()
outputs = feature_extractor(img, image_processor_kwargs=image_processor_kwargs)
self.assertEqual(np.squeeze(outputs).shape, (226, 32))
# Test pooling option
outputs = feature_extractor(img, pool=True)
self.assertEqual(np.squeeze(outputs).shape, (32,))
@require_tf
def test_image_processing_small_model_tf(self):
feature_extractor = pipeline(
task="image-feature-extraction", model="hf-internal-testing/tiny-random-vit", framework="tf"
)
# test with image processor parameters
image_processor_kwargs = {"size": {"height": 300, "width": 300}}
img = prepare_img()
with pytest.raises(ValueError):
# Image doesn't match model input size
feature_extractor(img, image_processor_kwargs=image_processor_kwargs)
image_processor_kwargs = {"image_mean": [0, 0, 0], "image_std": [1, 1, 1]}
img = prepare_img()
outputs = feature_extractor(img, image_processor_kwargs=image_processor_kwargs)
self.assertEqual(np.squeeze(outputs).shape, (226, 32))
# Test pooling option
outputs = feature_extractor(img, pool=True)
self.assertEqual(np.squeeze(outputs).shape, (32,))
@require_torch
def test_return_tensors_pt(self):
feature_extractor = pipeline(
task="image-feature-extraction", model="hf-internal-testing/tiny-random-vit", framework="pt"
)
img = prepare_img()
outputs = feature_extractor(img, return_tensors=True)
self.assertTrue(torch.is_tensor(outputs))
@require_tf
def test_return_tensors_tf(self):
feature_extractor = pipeline(
task="image-feature-extraction", model="hf-internal-testing/tiny-random-vit", framework="tf"
)
img = prepare_img()
outputs = feature_extractor(img, return_tensors=True)
self.assertTrue(tf.is_tensor(outputs))
def get_test_pipeline(self, model, tokenizer, processor):
if processor is None:
self.skipTest("No image processor")
elif type(model.config) in TOKENIZER_MAPPING:
self.skipTest("This is a bimodal model, we need to find a more consistent way to switch on those models.")
elif model.config.is_encoder_decoder:
self.skipTest(
"""encoder_decoder models are trickier for this pipeline.
Do we want encoder + decoder inputs to get some featues?
Do we want encoder only features ?
For now ignore those.
"""
)
feature_extractor = ImageFeatureExtractionPipeline(model=model, image_processor=processor)
img = prepare_img()
return feature_extractor, [img, img]
def run_pipeline_test(self, feature_extractor, examples):
imgs = examples
outputs = feature_extractor(imgs[0])
self.assertEqual(len(outputs), 1)
outputs = feature_extractor(imgs)
self.assertEqual(len(outputs), 2)
| transformers/tests/pipelines/test_pipelines_image_feature_extraction.py/0 | {
"file_path": "transformers/tests/pipelines/test_pipelines_image_feature_extraction.py",
"repo_id": "transformers",
"token_count": 3416
} | 383 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
require_tf,
require_torch,
require_torch_accelerator,
require_vision,
slow,
torch_device,
)
from .test_pipelines_common import ANY
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
else:
class Image:
@staticmethod
def open(*args, **kwargs):
pass
@is_pipeline_test
@require_torch
@require_vision
class VisualQuestionAnsweringPipelineTests(unittest.TestCase):
model_mapping = MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
def get_test_pipeline(self, model, tokenizer, processor):
vqa_pipeline = pipeline("visual-question-answering", model="hf-internal-testing/tiny-vilt-random-vqa")
examples = [
{
"image": Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
"question": "How many cats are there?",
},
{
"image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
"question": "How many cats are there?",
},
]
return vqa_pipeline, examples
def run_pipeline_test(self, vqa_pipeline, examples):
outputs = vqa_pipeline(examples, top_k=1)
self.assertEqual(
outputs,
[
[{"score": ANY(float), "answer": ANY(str)}],
[{"score": ANY(float), "answer": ANY(str)}],
],
)
@require_torch
def test_small_model_pt(self):
vqa_pipeline = pipeline("visual-question-answering", model="hf-internal-testing/tiny-vilt-random-vqa")
image = "./tests/fixtures/tests_samples/COCO/000000039769.png"
question = "How many cats are there?"
outputs = vqa_pipeline(image=image, question="How many cats are there?", top_k=2)
self.assertEqual(
outputs, [{"score": ANY(float), "answer": ANY(str)}, {"score": ANY(float), "answer": ANY(str)}]
)
outputs = vqa_pipeline({"image": image, "question": question}, top_k=2)
self.assertEqual(
outputs, [{"score": ANY(float), "answer": ANY(str)}, {"score": ANY(float), "answer": ANY(str)}]
)
@require_torch
@require_torch_accelerator
def test_small_model_pt_blip2(self):
vqa_pipeline = pipeline(
"visual-question-answering", model="hf-internal-testing/tiny-random-Blip2ForConditionalGeneration"
)
image = "./tests/fixtures/tests_samples/COCO/000000039769.png"
question = "How many cats are there?"
outputs = vqa_pipeline(image=image, question=question)
self.assertEqual(outputs, [{"answer": ANY(str)}])
outputs = vqa_pipeline({"image": image, "question": question})
self.assertEqual(outputs, [{"answer": ANY(str)}])
outputs = vqa_pipeline([{"image": image, "question": question}, {"image": image, "question": question}])
self.assertEqual(outputs, [[{"answer": ANY(str)}]] * 2)
vqa_pipeline = pipeline(
"visual-question-answering",
model="hf-internal-testing/tiny-random-Blip2ForConditionalGeneration",
model_kwargs={"torch_dtype": torch.float16},
device=torch_device,
)
self.assertEqual(vqa_pipeline.model.device, torch.device("{}:0".format(torch_device)))
self.assertEqual(vqa_pipeline.model.language_model.dtype, torch.float16)
self.assertEqual(vqa_pipeline.model.vision_model.dtype, torch.float16)
outputs = vqa_pipeline(image=image, question=question)
self.assertEqual(outputs, [{"answer": ANY(str)}])
@slow
@require_torch
def test_large_model_pt(self):
vqa_pipeline = pipeline("visual-question-answering", model="dandelin/vilt-b32-finetuned-vqa")
image = "./tests/fixtures/tests_samples/COCO/000000039769.png"
question = "How many cats are there?"
outputs = vqa_pipeline(image=image, question=question, top_k=2)
self.assertEqual(
nested_simplify(outputs, decimals=4), [{"score": 0.8799, "answer": "2"}, {"score": 0.296, "answer": "1"}]
)
outputs = vqa_pipeline({"image": image, "question": question}, top_k=2)
self.assertEqual(
nested_simplify(outputs, decimals=4), [{"score": 0.8799, "answer": "2"}, {"score": 0.296, "answer": "1"}]
)
outputs = vqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}], top_k=2
)
self.assertEqual(
nested_simplify(outputs, decimals=4),
[[{"score": 0.8799, "answer": "2"}, {"score": 0.296, "answer": "1"}]] * 2,
)
@slow
@require_torch
@require_torch_accelerator
def test_large_model_pt_blip2(self):
vqa_pipeline = pipeline(
"visual-question-answering",
model="Salesforce/blip2-opt-2.7b",
model_kwargs={"torch_dtype": torch.float16},
device=torch_device,
)
self.assertEqual(vqa_pipeline.model.device, torch.device("{}:0".format(torch_device)))
self.assertEqual(vqa_pipeline.model.language_model.dtype, torch.float16)
image = "./tests/fixtures/tests_samples/COCO/000000039769.png"
question = "Question: how many cats are there? Answer:"
outputs = vqa_pipeline(image=image, question=question)
self.assertEqual(outputs, [{"answer": "two"}])
outputs = vqa_pipeline({"image": image, "question": question})
self.assertEqual(outputs, [{"answer": "two"}])
outputs = vqa_pipeline([{"image": image, "question": question}, {"image": image, "question": question}])
self.assertEqual(outputs, [[{"answer": "two"}]] * 2)
@require_tf
@unittest.skip("Visual question answering not implemented in TF")
def test_small_model_tf(self):
pass
| transformers/tests/pipelines/test_pipelines_visual_question_answering.py/0 | {
"file_path": "transformers/tests/pipelines/test_pipelines_visual_question_answering.py",
"repo_id": "transformers",
"token_count": 2897
} | 384 |
# coding=utf-8
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import tempfile
import unittest
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, QuantoConfig
from transformers.testing_utils import require_accelerate, require_quanto, require_torch_gpu, slow
from transformers.utils import is_accelerate_available, is_quanto_available, is_torch_available
if is_torch_available():
import torch
if is_accelerate_available():
from accelerate import init_empty_weights
if is_quanto_available():
from quanto import QLayerNorm, QLinear
from transformers.integrations.quanto import replace_with_quanto_layers
class QuantoConfigTest(unittest.TestCase):
def test_attributes(self):
pass
@require_quanto
@require_accelerate
class QuantoTestIntegration(unittest.TestCase):
model_id = "facebook/opt-350m"
def setUp(self):
config = AutoConfig.from_pretrained(self.model_id)
with init_empty_weights():
self.model = AutoModelForCausalLM.from_config(config)
self.nb_linear = 0
self.nb_layernorm = 0
for module in self.model.modules():
if isinstance(module, torch.nn.Linear):
self.nb_linear += 1
elif isinstance(module, torch.nn.LayerNorm):
self.nb_layernorm += 1
def test_weight_only_quantization_conversion(self):
"""
Simple test that checks if the quantized model has been converted properly when using weight only quantization
"""
# Try with weight only quantization
quantization_config = QuantoConfig(weights="int8", activations=None)
self.model, _ = replace_with_quanto_layers(self.model, quantization_config=quantization_config)
nb_qlinear = 0
for module in self.model.modules():
if isinstance(module, QLinear):
nb_qlinear += 1
self.assertEqual(self.nb_linear, nb_qlinear)
def test_weight_and_activation_quantization_conversion(self):
"""
Simple test that checks if the quantized model has been converted properly when using weight + activation quantization
"""
# Try with weight + activation quantization
quantization_config = QuantoConfig(weights="int8", activations="int8")
self.model, _ = replace_with_quanto_layers(self.model, quantization_config=quantization_config)
nb_qlinear = 0
nb_qlayernorm = 0
for module in self.model.modules():
if isinstance(module, QLinear):
nb_qlinear += 1
if isinstance(module, QLayerNorm):
nb_qlayernorm += 1
self.assertEqual(self.nb_linear, nb_qlinear)
self.assertEqual(self.nb_layernorm, nb_qlayernorm)
def test_conversion_with_modules_to_not_convert(self):
"""
Simple test that checks if the quantized model has been converted properly when specifying modules_to_not_convert argument
"""
# Try with weight + activatioin quantization
quantization_config = QuantoConfig(weights="int8", activations="int8")
self.model, _ = replace_with_quanto_layers(
self.model, quantization_config=quantization_config, modules_to_not_convert=["lm_head"]
)
nb_qlinear = 0
nb_qlayernorm = 0
for module in self.model.modules():
if isinstance(module, QLinear):
nb_qlinear += 1
if isinstance(module, QLayerNorm):
nb_qlayernorm += 1
self.assertEqual(self.nb_linear - 1, nb_qlinear)
@slow
@require_torch_gpu
@require_quanto
@require_accelerate
class QuantoQuantizationTest(unittest.TestCase):
"""
Test 8-bit weights only quantization
"""
model_name = "bigscience/bloom-560m"
weights = "int8"
activations = None
device_map = "cpu"
input_text = "Hello my name is"
EXPECTED_OUTPUTS = "Hello my name is John, I am a professional photographer and I"
def setUp(self):
"""
Setup quantized model
"""
quantization_config = QuantoConfig(
weights=self.weights,
activations=self.activations,
)
self.quantized_model = AutoModelForCausalLM.from_pretrained(
self.model_name,
device_map=self.device_map,
quantization_config=quantization_config,
torch_dtype=torch.float32,
)
self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
self.have_accelerate_hooks = (
getattr(self.quantized_model, "hf_device_map", False) and len(self.quantized_model.hf_device_map) > 1
)
def check_inference_correctness(self, model, device):
r"""
Test the generation quality of the quantized model and see that we are matching the expected output.
Given that we are operating on small numbers + the testing model is relatively small, we might not get
the same output across GPUs. So we'll generate few tokens (5-10) and check their output.
"""
if not self.have_accelerate_hooks:
model.to(device)
encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
output_sequences = model.generate(input_ids=encoded_input["input_ids"].to(device), max_new_tokens=10)
self.assertIn(self.tokenizer.decode(output_sequences[0], skip_special_tokens=True), self.EXPECTED_OUTPUTS)
def test_generate_quality_cpu(self):
"""
Simple test to check the quality of the model on cpu by comparing the generated tokens with the expected tokens
"""
self.check_inference_correctness(self.quantized_model, "cpu")
def test_generate_quality_cuda(self):
"""
Simple test to check the quality of the model on cuda by comparing the generated tokens with the expected tokens
"""
self.check_inference_correctness(self.quantized_model, "cuda")
def test_quantized_model_layers(self):
from quanto import QBitsTensor, QModuleMixin, QTensor
"""
Suite of simple test to check if the layers are quantized and are working properly
"""
# Test the type of the quantized layer
self.assertTrue(isinstance(self.quantized_model.transformer.h[0].self_attention.query_key_value, QModuleMixin))
self.assertTrue(
isinstance(self.quantized_model.transformer.h[0].self_attention.query_key_value.weight, QTensor)
)
if self.weights == "int4":
self.assertTrue(
isinstance(self.quantized_model.transformer.h[0].self_attention.query_key_value.weight, QBitsTensor)
)
# check that the lm_head was indeed not quantized, just like bnb
self.assertTrue(
isinstance(self.quantized_model.lm_head, torch.nn.Linear)
and not isinstance(self.quantized_model.lm_head, QModuleMixin)
)
if self.device_map in ["cpu", "cuda"]:
self.assertEqual(
self.quantized_model.transformer.h[0].self_attention.query_key_value.weight._data.device.type,
self.device_map,
)
self.quantized_model.to(0)
self.assertEqual(
self.quantized_model.transformer.h[0].self_attention.query_key_value.weight._data.device.type, "cuda"
)
def test_serialization_bin(self):
"""
Test the serialization, the loading and the inference of the quantized weights
"""
with tempfile.TemporaryDirectory() as tmpdirname:
with self.assertRaises(ValueError) as e:
self.quantized_model.save_pretrained(tmpdirname, safe_serialization=False)
self.assertIn("The model is quantized with quanto and is not serializable", str(e.exception))
# TODO: replace by the following when it works
# quantized_model_from_saved = AutoModelForCausalLM.from_pretrained(
# tmpdirname, torch_dtype=torch.float32, device_map="cpu"
# )
# self.check_inference_correctness(quantized_model_from_saved, device="cuda")
def test_serialization_safetensors(self):
"""
Test the serialization, the loading and the inference of the quantized weights
"""
with tempfile.TemporaryDirectory() as tmpdirname:
with self.assertRaises(ValueError) as e:
self.quantized_model.save_pretrained(tmpdirname)
self.assertIn("The model is quantized with quanto and is not serializable", str(e.exception))
# quantized_model_from_saved = AutoModelForCausalLM.from_pretrained(
# tmpdirname, torch_dtype=torch.float32, device_map="cpu"
# )
# self.check_inference_correctness(quantized_model_from_saved, device="cuda")
def check_same_model(self, model1, model2):
d0 = dict(model1.named_parameters())
d1 = dict(model2.named_parameters())
self.assertTrue(d0.keys() == d1.keys())
for k in d0.keys():
self.assertTrue(d0[k].shape == d1[k].shape)
self.assertTrue(d0[k].device.type == d1[k].device.type)
self.assertTrue(d0[k].device == d1[k].device)
self.assertTrue(d0[k].dtype == d1[k].dtype)
self.assertTrue(torch.equal(d0[k], d1[k].to(d0[k].device)))
def test_compare_with_quanto(self):
from quanto import freeze, qint4, qint8, quantize
w_mapping = {"int8": qint8, "int4": qint4}
model = AutoModelForCausalLM.from_pretrained(
self.model_name,
device_map=self.device_map,
torch_dtype=torch.float32,
)
# we do not quantize the lm_head since we don't do that in transformers
quantize(model.transformer, weights=w_mapping[self.weights])
freeze(model.transformer)
self.check_same_model(model, self.quantized_model)
self.check_inference_correctness(model, device="cuda")
@unittest.skip
def test_load_from_quanto_saved(self):
from quanto import freeze, qint4, qint8, quantize
from transformers import QuantoConfig
w_mapping = {"int8": qint8, "int4": qint4}
model = AutoModelForCausalLM.from_pretrained(
self.model_name,
device_map=self.device_map,
torch_dtype=torch.float32,
)
# we do not quantize the lm_head since we don't do that in transformers
quantize(model.transformer, weights=w_mapping[self.weights])
freeze(model.transformer)
with tempfile.TemporaryDirectory() as tmpdirname:
model.config.quantization_config = QuantoConfig(
weights=self.weights, activations=self.activations, modules_to_not_convert=["lm_head"]
)
model.save_pretrained(tmpdirname, safe_serialization=False)
quantized_model_from_saved = AutoModelForCausalLM.from_pretrained(
tmpdirname,
device_map=self.device_map,
torch_dtype=torch.float32,
)
self.check_same_model(model, quantized_model_from_saved)
self.check_inference_correctness(quantized_model_from_saved, device="cuda")
class QuantoQuantizationOffloadTest(QuantoQuantizationTest):
device_map = {
"transformer.word_embeddings": 0,
"transformer.word_embeddings_layernorm": 0,
"transformer.ln_f": 0,
"transformer.h.0": 0,
"transformer.h.1": 0,
"transformer.h.2": 0,
"transformer.h.3": 0,
"transformer.h.4": 0,
"transformer.h.5": 0,
"transformer.h.6": 0,
"transformer.h.7": 0,
"transformer.h.8": 0,
"transformer.h.9": 0,
"transformer.h.10": 0,
"transformer.h.11": 0,
"transformer.h.12": 0,
"transformer.h.13": 0,
"transformer.h.14": 0,
"transformer.h.15": 0,
"transformer.h.16": 0,
"transformer.h.17": 0,
"transformer.h.18": 0,
"transformer.h.19": 0,
"transformer.h.20": 0,
"transformer.h.21": 0,
"transformer.h.22": "cpu",
"transformer.h.23": "disk",
"lm_head": 0,
}
# the execution device is a gpu
def test_generate_quality_cpu(self):
pass
# we can't save offloaded values
def test_serialization_bin(self):
pass
def test_serialization_safetensors(self):
pass
def test_compare_with_quanto(self):
pass
def test_load_from_quanto_saved(self):
pass
def test_check_offload_quantized(self):
"""
We check that we have unquantized value in the cpu and in the disk
"""
import quanto
cpu_weights = self.quantized_model.transformer.h[22].self_attention.query_key_value._hf_hook.weights_map[
"weight"
]
disk_weights = self.quantized_model.transformer.h[23].self_attention.query_key_value._hf_hook.weights_map[
"weight"
]
self.assertTrue(isinstance(cpu_weights, torch.Tensor) and not isinstance(cpu_weights, quanto.QTensor))
self.assertTrue(isinstance(disk_weights, torch.Tensor) and not isinstance(disk_weights, quanto.QTensor))
if self.weights == "int4":
self.assertTrue(isinstance(cpu_weights, torch.Tensor) and not isinstance(disk_weights, quanto.QBitsTensor))
self.assertTrue(
isinstance(disk_weights, torch.Tensor) and not isinstance(disk_weights, quanto.QBitsTensor)
)
@unittest.skip("Skipping test class because serialization is not supported yet")
class QuantoQuantizationSerializationTest(QuantoQuantizationTest):
"""
Perform the same tests as in QuantoQuantizationTest but with a serialized model.
"""
def setUp(self):
"""
Setup quantized model
"""
quantization_config = QuantoConfig(
weights=self.weights,
activations=self.activations,
)
quantized_model = AutoModelForCausalLM.from_pretrained(
self.model_name,
device_map=self.device_map,
quantization_config=quantization_config,
torch_dtype=torch.float32,
)
with tempfile.TemporaryDirectory() as tmpdirname:
quantized_model.save_pretrained(tmpdirname, safe_serialization=False)
self.quantized_model = AutoModelForCausalLM.from_pretrained(
tmpdirname, torch_dtype=torch.float32, device_map=self.device_map
)
self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
self.have_accelerate_hooks = (
getattr(self.quantized_model, "hf_device_map", False) and len(self.quantized_model.hf_device_map) > 1
)
@unittest.skip("Skipping test class because serialization is not supported yet")
class QuantoQuantizationSerializationCudaTest(QuantoQuantizationTest):
"""
Perform the same tests as in QuantoQuantizationTest but with model on cuda
"""
device_map = "cuda:0"
class QuantoQuantizationQBitsTensorTest(QuantoQuantizationTest):
EXPECTED_OUTPUTS = "Hello my name is John, I am a young man from the Philippines"
weights = "int4"
class QuantoQuantizationQBitsTensorOffloadTest(QuantoQuantizationOffloadTest):
EXPECTED_OUTPUTS = "Hello my name is John, I am a young man from the Philippines"
weights = "int4"
@unittest.skip("Skipping test class because serialization is not supported yet")
class QuantoQuantizationQBitsTensorSerializationTest(QuantoQuantizationSerializationTest):
EXPECTED_OUTPUTS = "Hello my name is John, I am a young man from the Philippines"
weights = "int4"
@require_torch_gpu
class QuantoQuantizationActivationTest(unittest.TestCase):
def test_quantize_activation(self):
quantization_config = QuantoConfig(
weights="int8",
activations="int8",
)
with self.assertRaises(ValueError) as e:
AutoModelForCausalLM.from_pretrained("bigscience/bloom-560m", quantization_config=quantization_config)
self.assertIn("We don't support quantizing the activations with transformers library", str(e.exception))
| transformers/tests/quantization/quanto_integration/test_quanto.py/0 | {
"file_path": "transformers/tests/quantization/quanto_integration/test_quanto.py",
"repo_id": "transformers",
"token_count": 7115
} | 385 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv("TEST_SAGEMAKER", "False")) is not True,
reason="Skipping test because should only be run when releasing minor transformers version",
)
@pytest.mark.usefixtures("sm_env")
@parameterized_class(
[
{
"framework": "pytorch",
"script": "run_glue_model_parallelism.py",
"model_name_or_path": "FacebookAI/roberta-large",
"instance_type": "ml.p3dn.24xlarge",
"results": {"train_runtime": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
},
{
"framework": "pytorch",
"script": "run_glue.py",
"model_name_or_path": "FacebookAI/roberta-large",
"instance_type": "ml.p3dn.24xlarge",
"results": {"train_runtime": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
},
]
)
class MultiNodeTest(unittest.TestCase):
def setUp(self):
if self.framework == "pytorch":
subprocess.run(
f"cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py".split(),
encoding="utf-8",
check=True,
)
assert hasattr(self, "env")
def create_estimator(self, instance_count):
# configuration for running training on smdistributed Model Parallel
mpi_options = {
"enabled": True,
"processes_per_host": 8,
}
smp_options = {
"enabled": True,
"parameters": {
"microbatches": 4,
"placement_strategy": "spread",
"pipeline": "interleaved",
"optimize": "speed",
"partitions": 4,
"ddp": True,
},
}
distribution = {"smdistributed": {"modelparallel": smp_options}, "mpi": mpi_options}
name_extension = "trainer" if self.script == "run_glue.py" else "smtrainer"
# creates estimator
return HuggingFace(
entry_point=self.script,
source_dir=self.env.test_path,
role=self.env.role,
image_uri=self.env.image_uri,
base_job_name=f"{self.env.base_job_name}-{instance_count}-smp-{name_extension}",
instance_count=instance_count,
instance_type=self.instance_type,
debugger_hook_config=False,
hyperparameters={
**self.env.hyperparameters,
"model_name_or_path": self.model_name_or_path,
"max_steps": 500,
},
metric_definitions=self.env.metric_definitions,
distribution=distribution,
py_version="py36",
)
def save_results_as_csv(self, job_name):
TrainingJobAnalytics(job_name).export_csv(f"{self.env.test_path}/{job_name}_metrics.csv")
# @parameterized.expand([(2,), (4,),])
@parameterized.expand([(1,)])
def test_scripz(self, instance_count):
# create estimator
estimator = self.create_estimator(instance_count)
# run training
estimator.fit()
# result dataframe
result_metrics_df = TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()
# extract kpis
eval_accuracy = list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"])
eval_loss = list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"])
# get train time from SageMaker job, this includes starting, preprocessing, stopping
train_runtime = (
Session().describe_training_job(estimator.latest_training_job.name).get("TrainingTimeInSeconds", 999999)
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy)
assert all(t <= self.results["eval_loss"] for t in eval_loss)
# dump tests result into json file to share in PR
with open(f"{estimator.latest_training_job.name}.json", "w") as outfile:
json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss}, outfile)
| transformers/tests/sagemaker/test_multi_node_model_parallel.py/0 | {
"file_path": "transformers/tests/sagemaker/test_multi_node_model_parallel.py",
"repo_id": "transformers",
"token_count": 2103
} | 386 |
# coding=utf-8
# Copyright 2019 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import gc
import glob
import json
import os
import os.path
import sys
import tempfile
import unittest
import unittest.mock as mock
import uuid
from pathlib import Path
import requests
from huggingface_hub import HfApi, HfFolder, delete_repo
from pytest import mark
from requests.exceptions import HTTPError
from transformers import (
AutoConfig,
AutoModel,
AutoModelForSequenceClassification,
OwlViTForObjectDetection,
PretrainedConfig,
is_torch_available,
logging,
)
from transformers.testing_utils import (
TOKEN,
USER,
CaptureLogger,
LoggingLevel,
TestCasePlus,
is_staging_test,
require_accelerate,
require_flax,
require_safetensors,
require_tf,
require_torch,
require_torch_accelerator,
require_torch_gpu,
require_torch_multi_accelerator,
require_usr_bin_time,
slow,
torch_device,
)
from transformers.utils import (
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from transformers.utils.import_utils import (
is_flash_attn_2_available,
is_flax_available,
is_tf_available,
is_torch_sdpa_available,
is_torchdynamo_available,
)
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig, NoSuperInitConfig # noqa E402
if is_torch_available():
import torch
from safetensors.torch import save_file as safe_save_file
from test_module.custom_modeling import CustomModel, NoSuperInitModel
from torch import nn
from transformers import (
BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
AutoModelForCausalLM,
AutoTokenizer,
BertConfig,
BertModel,
CLIPTextModel,
PreTrainedModel,
T5Config,
T5ForConditionalGeneration,
)
from transformers.modeling_attn_mask_utils import (
AttentionMaskConverter,
_create_4d_causal_attention_mask,
_prepare_4d_attention_mask,
_prepare_4d_causal_attention_mask,
)
from transformers.modeling_utils import shard_checkpoint
# Fake pretrained models for tests
class BaseModel(PreTrainedModel):
base_model_prefix = "base"
config_class = PretrainedConfig
def __init__(self, config):
super().__init__(config)
self.linear = nn.Linear(5, 5)
self.linear_2 = nn.Linear(5, 5)
def forward(self, x):
return self.linear_2(self.linear(x))
class BaseModelWithTiedWeights(PreTrainedModel):
config_class = PretrainedConfig
def __init__(self, config):
super().__init__(config)
self.linear = nn.Linear(5, 5)
self.linear_2 = nn.Linear(5, 5)
def forward(self, x):
return self.linear_2(self.linear(x))
def tie_weights(self):
self.linear_2.weight = self.linear.weight
class ModelWithHead(PreTrainedModel):
base_model_prefix = "base"
config_class = PretrainedConfig
def _init_weights(self, module):
pass
def __init__(self, config):
super().__init__(config)
self.base = BaseModel(config)
# linear is a common name between Base and Head on purpose.
self.linear = nn.Linear(5, 5)
self.linear2 = nn.Linear(5, 5)
def forward(self, x):
return self.linear2(self.linear(self.base(x)))
class ModelWithHeadAndTiedWeights(PreTrainedModel):
base_model_prefix = "base"
config_class = PretrainedConfig
def _init_weights(self, module):
pass
def __init__(self, config):
super().__init__(config)
self.base = BaseModel(config)
self.decoder = nn.Linear(5, 5)
def forward(self, x):
return self.decoder(self.base(x))
def tie_weights(self):
self.decoder.weight = self.base.linear.weight
class Prepare4dCausalAttentionMaskModel(nn.Module):
def forward(self, inputs_embeds):
batch_size, seq_length, _ = inputs_embeds.shape
past_key_values_length = 4
attention_mask = _prepare_4d_causal_attention_mask(
None, (batch_size, seq_length), inputs_embeds, past_key_values_length
)
return attention_mask
class Create4dCausalAttentionMaskModel(nn.Module):
def forward(self, inputs_embeds):
batch_size, seq_length, _ = inputs_embeds.shape
past_key_values_length = 4
attention_mask = _create_4d_causal_attention_mask(
(batch_size, seq_length),
dtype=inputs_embeds.dtype,
device=inputs_embeds.device,
past_key_values_length=past_key_values_length,
)
return attention_mask
class Prepare4dAttentionMaskModel(nn.Module):
def forward(self, mask, inputs_embeds):
attention_mask = _prepare_4d_attention_mask(mask, dtype=inputs_embeds.dtype)
return attention_mask
if is_flax_available():
from transformers import FlaxBertModel
if is_tf_available():
from transformers import TFBertModel
TINY_T5 = "patrickvonplaten/t5-tiny-random"
TINY_BERT_FOR_TOKEN_CLASSIFICATION = "hf-internal-testing/tiny-bert-for-token-classification"
TINY_MISTRAL = "hf-internal-testing/tiny-random-MistralForCausalLM"
def check_models_equal(model1, model2):
models_are_equal = True
for model1_p, model2_p in zip(model1.parameters(), model2.parameters()):
if model1_p.data.ne(model2_p.data).sum() > 0:
models_are_equal = False
return models_are_equal
@require_torch
class ModelUtilsTest(TestCasePlus):
@slow
def test_model_from_pretrained(self):
for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = BertConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, PretrainedConfig)
model = BertModel.from_pretrained(model_name)
model, loading_info = BertModel.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, PreTrainedModel)
self.assertEqual(len(loading_info["missing_keys"]), 0)
self.assertEqual(len(loading_info["unexpected_keys"]), 8)
self.assertEqual(len(loading_info["mismatched_keys"]), 0)
self.assertEqual(len(loading_info["error_msgs"]), 0)
config = BertConfig.from_pretrained(model_name, output_attentions=True, output_hidden_states=True)
# Not sure this is the intended behavior. TODO fix Lysandre & Thom
config.name_or_path = model_name
model = BertModel.from_pretrained(model_name, output_attentions=True, output_hidden_states=True)
self.assertEqual(model.config.output_hidden_states, True)
self.assertEqual(model.config, config)
def test_model_from_pretrained_subfolder(self):
config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert")
model = BertModel(config)
subfolder = "bert"
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(tmp_dir, subfolder))
with self.assertRaises(OSError):
_ = BertModel.from_pretrained(tmp_dir)
model_loaded = BertModel.from_pretrained(tmp_dir, subfolder=subfolder)
self.assertTrue(check_models_equal(model, model_loaded))
def test_model_from_pretrained_subfolder_sharded(self):
config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert")
model = BertModel(config)
subfolder = "bert"
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(tmp_dir, subfolder), max_shard_size="10KB")
with self.assertRaises(OSError):
_ = BertModel.from_pretrained(tmp_dir)
model_loaded = BertModel.from_pretrained(tmp_dir, subfolder=subfolder)
self.assertTrue(check_models_equal(model, model_loaded))
def test_model_from_pretrained_hub_subfolder(self):
subfolder = "bert"
model_id = "hf-internal-testing/tiny-random-bert-subfolder"
with self.assertRaises(OSError):
_ = BertModel.from_pretrained(model_id)
model = BertModel.from_pretrained(model_id, subfolder=subfolder)
self.assertIsNotNone(model)
def test_model_from_pretrained_hub_subfolder_sharded(self):
subfolder = "bert"
model_id = "hf-internal-testing/tiny-random-bert-sharded-subfolder"
with self.assertRaises(OSError):
_ = BertModel.from_pretrained(model_id)
model = BertModel.from_pretrained(model_id, subfolder=subfolder)
self.assertIsNotNone(model)
def test_model_from_pretrained_with_different_pretrained_model_name(self):
model = T5ForConditionalGeneration.from_pretrained(TINY_T5)
self.assertIsNotNone(model)
logger = logging.get_logger("transformers.configuration_utils")
with LoggingLevel(logging.WARNING):
with CaptureLogger(logger) as cl:
BertModel.from_pretrained(TINY_T5)
self.assertTrue("You are using a model of type t5 to instantiate a model of type bert" in cl.out)
@require_accelerate
def test_model_from_pretrained_with_none_quantization_config(self):
# Needs a device_map for to enter the low_cpu_mem branch. We also load AutoModelForSequenceClassification
# deliberately to enter the missing keys branch.
model = AutoModelForSequenceClassification.from_pretrained(
TINY_MISTRAL, device_map="auto", quantization_config=None
)
self.assertIsNotNone(model)
def test_model_from_config_torch_dtype(self):
# test that the model can be instantiated with dtype of user's choice - as long as it's a
# float dtype. To make it happen config.torch_dtype needs to be set before instantiating the
# model from the config object.
config = T5Config.from_pretrained(TINY_T5)
model = AutoModel.from_config(config)
# XXX: isn't supported
# model = T5ForConditionalGeneration.from_config(config)
self.assertEqual(model.dtype, torch.float32)
model = AutoModel.from_config(config, torch_dtype=torch.float16)
self.assertEqual(model.dtype, torch.float16)
# torch.set_default_dtype() supports only float dtypes, so will fail with non-float type
with self.assertRaises(ValueError):
model = AutoModel.from_config(config, torch_dtype=torch.int64)
def test_model_from_pretrained_torch_dtype(self):
# test that the model can be instantiated with dtype of either
# 1. explicit from_pretrained's torch_dtype argument
# 2. via autodiscovery by looking at model weights (torch_dtype="auto")
# so if a model.half() was saved, we want it to be instantiated as such.
#
# test an explicit model class, but also AutoModel separately as the latter goes through a different code path
model_path = self.get_auto_remove_tmp_dir()
# baseline - we know TINY_T5 is fp32 model
model = T5ForConditionalGeneration.from_pretrained(TINY_T5)
self.assertEqual(model.dtype, torch.float32)
def remove_torch_dtype(model_path):
file = f"{model_path}/config.json"
with open(file, "r", encoding="utf-8") as f:
s = json.load(f)
s.pop("torch_dtype")
with open(file, "w", encoding="utf-8") as f:
json.dump(s, f)
# test the default fp32 save_pretrained => from_pretrained cycle
model.save_pretrained(model_path)
model = T5ForConditionalGeneration.from_pretrained(model_path)
self.assertEqual(model.dtype, torch.float32)
# 1. test torch_dtype="auto" via `config.torch_dtype`
model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto")
self.assertEqual(model.dtype, torch.float32)
# 2. test torch_dtype="auto" via auto-derivation
# now remove the torch_dtype entry from config.json and try "auto" again which should
# perform auto-derivation from weights
remove_torch_dtype(model_path)
model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto")
self.assertEqual(model.dtype, torch.float32)
# test forced loading in fp16 (even though the weights are in fp32)
model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype=torch.float16)
self.assertEqual(model.dtype, torch.float16)
# test fp16 save_pretrained, loaded with auto-detection
model = model.half()
model.save_pretrained(model_path)
# 1. test torch_dtype="auto" via `config.torch_dtype`
model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto")
self.assertEqual(model.config.torch_dtype, torch.float16)
self.assertEqual(model.dtype, torch.float16)
# tests `config.torch_dtype` saving
with open(f"{model_path}/config.json") as f:
config_dict = json.load(f)
self.assertEqual(config_dict["torch_dtype"], "float16")
# 2. test torch_dtype="auto" via auto-derivation
# now same with using config info
remove_torch_dtype(model_path)
model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto")
self.assertEqual(model.dtype, torch.float16)
# 3. now retest that AutoModel behaves the same wrt torch_dtype="auto" as T5ForConditionalGeneration
model = AutoModel.from_pretrained(model_path, torch_dtype="auto")
self.assertEqual(model.dtype, torch.float16)
# test fp16 save_pretrained, loaded with the explicit fp16
model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype=torch.float16)
self.assertEqual(model.dtype, torch.float16)
# test AutoModel separately as it goes through a different path
# test auto-detection - as currently TINY_T5 doesn't have torch_dtype entry
model = AutoModel.from_pretrained(TINY_T5, torch_dtype="auto")
# test that the config object didn't get polluted with torch_dtype="auto"
# there was a bug that after this call we ended up with config.torch_dtype=="auto"
self.assertNotEqual(model.config.torch_dtype, "auto")
# now test the outcome
self.assertEqual(model.dtype, torch.float32)
model = AutoModel.from_pretrained(TINY_T5, torch_dtype=torch.float16)
self.assertEqual(model.dtype, torch.float16)
# test model whose first param is not of a floating type, but int
model = AutoModel.from_pretrained(TINY_BERT_FOR_TOKEN_CLASSIFICATION, torch_dtype="auto")
self.assertEqual(model.dtype, torch.float32)
def test_no_super_init_config_and_model(self):
config = NoSuperInitConfig(attribute=32)
model = NoSuperInitModel(config)
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir)
new_model = NoSuperInitModel.from_pretrained(tmp_dir)
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
def test_shard_checkpoint(self):
# This is the model we will use, total size 340,000 bytes.
model = torch.nn.Sequential(
torch.nn.Linear(100, 200, bias=False), # size 80,000
torch.nn.Linear(200, 200, bias=False), # size 160,000
torch.nn.Linear(200, 100, bias=False), # size 80,000
torch.nn.Linear(100, 50, bias=False), # size 20,000
)
state_dict = model.state_dict()
with self.subTest("No shard when max size is bigger than model size"):
shards, index = shard_checkpoint(state_dict)
self.assertIsNone(index)
self.assertDictEqual(shards, {WEIGHTS_NAME: state_dict})
with self.subTest("Test sharding, no weights bigger than max size"):
shards, index = shard_checkpoint(state_dict, max_shard_size="300kB")
# Split is first two layers then last two.
self.assertDictEqual(
index,
{
"metadata": {"total_size": 340000},
"weight_map": {
"0.weight": "pytorch_model-00001-of-00002.bin",
"1.weight": "pytorch_model-00001-of-00002.bin",
"2.weight": "pytorch_model-00002-of-00002.bin",
"3.weight": "pytorch_model-00002-of-00002.bin",
},
},
)
shard1 = {"0.weight": state_dict["0.weight"], "1.weight": state_dict["1.weight"]}
shard2 = {"2.weight": state_dict["2.weight"], "3.weight": state_dict["3.weight"]}
self.assertDictEqual(
shards, {"pytorch_model-00001-of-00002.bin": shard1, "pytorch_model-00002-of-00002.bin": shard2}
)
with self.subTest("Test sharding with weights bigger than max size"):
shards, index = shard_checkpoint(state_dict, max_shard_size="100kB")
# Split is first layer, second layer then last 2.
self.assertDictEqual(
index,
{
"metadata": {"total_size": 340000},
"weight_map": {
"0.weight": "pytorch_model-00001-of-00003.bin",
"1.weight": "pytorch_model-00002-of-00003.bin",
"2.weight": "pytorch_model-00003-of-00003.bin",
"3.weight": "pytorch_model-00003-of-00003.bin",
},
},
)
shard1 = {"0.weight": state_dict["0.weight"]}
shard2 = {"1.weight": state_dict["1.weight"]}
shard3 = {"2.weight": state_dict["2.weight"], "3.weight": state_dict["3.weight"]}
self.assertDictEqual(
shards,
{
"pytorch_model-00001-of-00003.bin": shard1,
"pytorch_model-00002-of-00003.bin": shard2,
"pytorch_model-00003-of-00003.bin": shard3,
},
)
def test_checkpoint_sharding_local_bin(self):
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
# We use the same folder for various sizes to make sure a new save erases the old checkpoint.
for max_size in ["50kB", "50kiB", "100kB", "100kiB", "200kB", "200kiB"]:
model.save_pretrained(tmp_dir, max_shard_size=max_size, safe_serialization=False)
# Get each shard file and its size
shard_to_size = {}
for shard in os.listdir(tmp_dir):
if shard.endswith(".bin"):
shard_file = os.path.join(tmp_dir, shard)
shard_to_size[shard_file] = os.path.getsize(shard_file)
index_file = os.path.join(tmp_dir, WEIGHTS_INDEX_NAME)
# Check there is an index but no regular weight file
self.assertTrue(os.path.isfile(index_file))
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_NAME)))
# Check a file is bigger than max_size only when it has a single weight
for shard_file, size in shard_to_size.items():
if max_size.endswith("kiB"):
max_size_int = int(max_size[:-3]) * 2**10
else:
max_size_int = int(max_size[:-2]) * 10**3
# Note: pickle adds some junk so the weight of the file can end up being slightly bigger than
# the size asked for (since we count parameters)
if size >= max_size_int + 50000:
state_dict = torch.load(shard_file)
self.assertEqual(len(state_dict), 1)
# Check the index and the shard files found match
with open(index_file, "r", encoding="utf-8") as f:
index = json.loads(f.read())
all_shards = set(index["weight_map"].values())
shards_found = {f for f in os.listdir(tmp_dir) if f.endswith(".bin")}
self.assertSetEqual(all_shards, shards_found)
# Finally, check the model can be reloaded
new_model = BertModel.from_pretrained(tmp_dir)
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
def test_checkpoint_sharding_from_hub(self):
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-sharded")
# the model above is the same as the model below, just a sharded version.
ref_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
for p1, p2 in zip(model.parameters(), ref_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
def test_checkpoint_variant_local_bin(self):
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, variant="v2", safe_serialization=False)
weights_name = ".".join(WEIGHTS_NAME.split(".")[:-1] + ["v2"] + ["bin"])
weights_file = os.path.join(tmp_dir, weights_name)
self.assertTrue(os.path.isfile(weights_file))
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_NAME)))
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained(tmp_dir)
new_model = BertModel.from_pretrained(tmp_dir, variant="v2")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
def test_checkpoint_variant_local_sharded_bin(self):
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, variant="v2", max_shard_size="50kB", safe_serialization=False)
weights_index_name = ".".join(WEIGHTS_INDEX_NAME.split(".")[:-1] + ["v2"] + ["json"])
weights_index_file = os.path.join(tmp_dir, weights_index_name)
self.assertTrue(os.path.isfile(weights_index_file))
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_INDEX_NAME)))
for i in range(1, 5):
weights_name = ".".join(WEIGHTS_NAME.split(".")[:-1] + [f"v2-0000{i}-of-00005"] + ["bin"])
weights_name_file = os.path.join(tmp_dir, weights_name)
self.assertTrue(os.path.isfile(weights_name_file))
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained(tmp_dir)
new_model = BertModel.from_pretrained(tmp_dir, variant="v2")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
@require_safetensors
def test_checkpoint_variant_local_safe(self):
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, variant="v2", safe_serialization=True)
weights_name = ".".join(SAFE_WEIGHTS_NAME.split(".")[:-1] + ["v2"] + ["safetensors"])
weights_file = os.path.join(tmp_dir, weights_name)
self.assertTrue(os.path.isfile(weights_file))
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_NAME)))
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained(tmp_dir)
new_model = BertModel.from_pretrained(tmp_dir, variant="v2")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
@require_safetensors
def test_checkpoint_variant_local_sharded_safe(self):
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, variant="v2", max_shard_size="50kB", safe_serialization=True)
weights_index_name = ".".join(SAFE_WEIGHTS_INDEX_NAME.split(".")[:-1] + ["v2"] + ["json"])
weights_index_file = os.path.join(tmp_dir, weights_index_name)
self.assertTrue(os.path.isfile(weights_index_file))
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_INDEX_NAME)))
for i in range(1, 5):
weights_name = ".".join(SAFE_WEIGHTS_NAME.split(".")[:-1] + [f"v2-0000{i}-of-00005"] + ["safetensors"])
weights_name_file = os.path.join(tmp_dir, weights_name)
self.assertTrue(os.path.isfile(weights_name_file))
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained(tmp_dir)
new_model = BertModel.from_pretrained(tmp_dir, variant="v2")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
def test_checkpoint_variant_hub(self):
with tempfile.TemporaryDirectory() as tmp_dir:
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-variant", cache_dir=tmp_dir)
model = BertModel.from_pretrained(
"hf-internal-testing/tiny-random-bert-variant", cache_dir=tmp_dir, variant="v2"
)
self.assertIsNotNone(model)
def test_checkpoint_variant_hub_sharded(self):
with tempfile.TemporaryDirectory() as tmp_dir:
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained(
"hf-internal-testing/tiny-random-bert-variant-sharded", cache_dir=tmp_dir
)
model = BertModel.from_pretrained(
"hf-internal-testing/tiny-random-bert-variant-sharded", cache_dir=tmp_dir, variant="v2"
)
self.assertIsNotNone(model)
@require_safetensors
def test_checkpoint_variant_hub_safe(self):
with tempfile.TemporaryDirectory() as tmp_dir:
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-variant-safe", cache_dir=tmp_dir)
model = BertModel.from_pretrained(
"hf-internal-testing/tiny-random-bert-variant-safe", cache_dir=tmp_dir, variant="v2"
)
self.assertIsNotNone(model)
@require_safetensors
def test_checkpoint_variant_hub_sharded_safe(self):
with tempfile.TemporaryDirectory() as tmp_dir:
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained(
"hf-internal-testing/tiny-random-bert-variant-sharded-safe", cache_dir=tmp_dir
)
model = BertModel.from_pretrained(
"hf-internal-testing/tiny-random-bert-variant-sharded-safe", cache_dir=tmp_dir, variant="v2"
)
self.assertIsNotNone(model)
def test_checkpoint_variant_save_load_bin(self):
with tempfile.TemporaryDirectory() as tmp_dir:
model = BertModel.from_pretrained(
"hf-internal-testing/tiny-random-bert-variant", cache_dir=tmp_dir, variant="v2"
)
weights_name = ".".join(WEIGHTS_NAME.split(".")[:-1] + ["v2"] + ["bin"])
model.save_pretrained(tmp_dir, variant="v2", safe_serialization=False)
# saving will create a variant checkpoint
self.assertTrue(os.path.isfile(os.path.join(tmp_dir, weights_name)))
model.save_pretrained(tmp_dir, safe_serialization=False)
# saving shouldn't delete variant checkpoints
weights_name = ".".join(WEIGHTS_NAME.split(".")[:-1] + ["v2"] + ["bin"])
self.assertTrue(os.path.isfile(os.path.join(tmp_dir, weights_name)))
# there should be a normal checkpoint
self.assertTrue(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_NAME)))
self.assertIsNotNone(model)
@require_accelerate
@mark.accelerate_tests
def test_from_pretrained_low_cpu_mem_usage_functional(self):
# test that we can use `from_pretrained(..., low_cpu_mem_usage=True)` with normal and
# sharded models
mnames = [
"hf-internal-testing/tiny-random-bert-sharded",
"hf-internal-testing/tiny-random-bert",
]
for mname in mnames:
_ = BertModel.from_pretrained(mname, low_cpu_mem_usage=True)
@require_usr_bin_time
@require_accelerate
@mark.accelerate_tests
def test_from_pretrained_low_cpu_mem_usage_measured(self):
# test that `from_pretrained(..., low_cpu_mem_usage=True)` uses less cpu memory than default
mname = "google-bert/bert-base-cased"
preamble = "from transformers import AutoModel"
one_liner_str = f'{preamble}; AutoModel.from_pretrained("{mname}", low_cpu_mem_usage=False)'
max_rss_normal = self.python_one_liner_max_rss(one_liner_str)
# print(f"{max_rss_normal=}")
one_liner_str = f'{preamble}; AutoModel.from_pretrained("{mname}", low_cpu_mem_usage=True)'
max_rss_low_mem = self.python_one_liner_max_rss(one_liner_str)
# print(f"{max_rss_low_mem=}")
diff_bytes = max_rss_normal - max_rss_low_mem
diff_percent = diff_bytes / max_rss_low_mem
# print(f"{diff_bytes=}, {diff_percent=}")
# ideally we would compare that the diff is close to ~1x checkpoint size in bytes, but
# measuring cpu memory on linux is very tricky and inconsistent, so instead let's check that
# it's at least 15% less cpu memory consumed
self.assertGreater(
diff_percent,
0.15,
"should use less CPU memory for low_cpu_mem_usage=True, "
f"but got max_rss_normal={max_rss_normal} and max_rss_low_mem={max_rss_low_mem}",
)
# if you want to compare things manually, let's first look at the size of the model in bytes
# model = BertModel.from_pretrained(mname, low_cpu_mem_usage=False)
# total_numel = sum(dict((p.data_ptr(), p.numel()) for p in model.parameters()).values())
# total_bytes = total_numel * 4 # 420MB
# Now the diff_bytes should be very close to total_bytes, but the reports are inconsistent.
# The easiest way to test this is to switch the model and torch.load to do all the work on
# gpu - that way one can measure exactly the total and peak memory used. Perhaps once we add
# functionality to load models directly on gpu, this test can be rewritten to use torch's
# cuda memory tracking and then we should be able to do a much more precise test.
@require_accelerate
@mark.accelerate_tests
@require_torch_multi_accelerator
@slow
def test_model_parallelism_gpt2(self):
device_map = {"transformer.wte": 0, "transformer.wpe": 0, "lm_head": 0, "transformer.ln_f": 1}
for i in range(12):
device_map[f"transformer.h.{i}"] = 0 if i <= 5 else 1
model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2", device_map=device_map)
tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
inputs = tokenizer("Hello, my name is", return_tensors="pt")
output = model.generate(inputs["input_ids"].to(0))
text_output = tokenizer.decode(output[0].tolist())
self.assertEqual(text_output, "Hello, my name is John. I'm a writer, and I'm a writer. I'm")
@require_accelerate
@mark.accelerate_tests
@require_torch_gpu
def test_from_pretrained_disk_offload_task_model(self):
model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-gpt2")
device_map = {
"transformer.wte": 0,
"transformer.wpe": 0,
"transformer.h.0": "cpu",
"transformer.h.1": "cpu",
"transformer.h.2": "cpu",
"transformer.h.3": "disk",
"transformer.h.4": "disk",
"transformer.ln_f": 0,
"lm_head": 0,
}
with tempfile.TemporaryDirectory() as tmp_dir:
inputs = torch.tensor([[1, 2, 3]]).to(0)
model.save_pretrained(tmp_dir)
new_model = AutoModelForCausalLM.from_pretrained(tmp_dir).to(0)
outputs1 = new_model.to(0)(inputs)
offload_folder = os.path.join(tmp_dir, "offload")
new_model_with_offload = AutoModelForCausalLM.from_pretrained(
tmp_dir, device_map=device_map, offload_folder=offload_folder
)
outputs2 = new_model_with_offload(inputs)
self.assertTrue(torch.allclose(outputs1.logits.cpu(), outputs2.logits.cpu()))
# With state dict temp offload
offload_folder = os.path.join(tmp_dir, "offload")
new_model_with_offload = AutoModelForCausalLM.from_pretrained(
tmp_dir,
device_map=device_map,
offload_folder=offload_folder,
offload_state_dict=True,
)
outputs2 = new_model_with_offload(inputs)
self.assertTrue(torch.allclose(outputs1.logits.cpu(), outputs2.logits.cpu()))
@require_accelerate
@mark.accelerate_tests
@require_torch_gpu
def test_from_pretrained_disk_offload_derived_to_base_model(self):
derived_model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2")
device_map = {
"wte": 0,
"wpe": 0,
"h.0": "cpu",
"h.1": "cpu",
"h.2": "cpu",
"h.3": "disk",
"h.4": "disk",
"ln_f": 0,
}
with tempfile.TemporaryDirectory() as tmp_dir:
inputs = torch.tensor([[1, 2, 3]]).to(0)
derived_model.save_pretrained(tmp_dir, use_safetensors=True)
base_model = AutoModel.from_pretrained(tmp_dir)
outputs1 = base_model.to(0)(inputs)
# with disk offload
offload_folder = os.path.join(tmp_dir, "offload")
base_model_with_offload = AutoModel.from_pretrained(
tmp_dir, device_map=device_map, offload_folder=offload_folder
)
outputs2 = base_model_with_offload(inputs)
self.assertTrue(torch.allclose(outputs1[0].cpu(), outputs2[0].cpu()))
# With state dict temp offload
new_model_with_offload = AutoModel.from_pretrained(
tmp_dir,
device_map=device_map,
offload_folder=offload_folder,
offload_state_dict=True,
)
outputs2 = new_model_with_offload(inputs)
self.assertTrue(torch.allclose(outputs1[0].cpu(), outputs2[0].cpu()))
@slow
@require_torch
def test_from_pretrained_non_contiguous_checkpoint(self):
# See: https://github.com/huggingface/transformers/pull/28414
# Tiny models on the Hub have contiguous weights, contrarily to google/owlvit
model = OwlViTForObjectDetection.from_pretrained("fxmarty/owlvit-tiny-non-contiguous-weight")
self.assertTrue(model.owlvit.visual_projection.weight.is_contiguous())
model = OwlViTForObjectDetection.from_pretrained(
"fxmarty/owlvit-tiny-non-contiguous-weight", device_map="auto"
)
self.assertTrue(model.owlvit.visual_projection.weight.is_contiguous())
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, safe_serialization=False)
model.save_pretrained(tmp_dir, safe_serialization=True)
def test_cached_files_are_used_when_internet_is_down(self):
# A mock response for an HTTP head request to emulate server down
response_mock = mock.Mock()
response_mock.status_code = 500
response_mock.headers = {}
response_mock.raise_for_status.side_effect = HTTPError
response_mock.json.return_value = {}
# Download this model to make sure it's in the cache.
_ = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.Session.request", return_value=response_mock) as mock_head:
_ = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
# This check we did call the fake head request
mock_head.assert_called()
@require_safetensors
def test_use_safetensors(self):
# Should not raise anymore
AutoModel.from_pretrained("hf-internal-testing/tiny-random-RobertaModel", use_safetensors=True)
# test that error if only safetensors is available
with self.assertRaises(OSError) as env_error:
BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-safetensors", use_safetensors=False)
self.assertTrue("does not appear to have a file named pytorch_model.bin" in str(env_error.exception))
# test that only safetensors if both available and use_safetensors=False
with tempfile.TemporaryDirectory() as tmp_dir:
CLIPTextModel.from_pretrained(
"hf-internal-testing/diffusers-stable-diffusion-tiny-all",
subfolder="text_encoder",
use_safetensors=False,
cache_dir=tmp_dir,
)
all_downloaded_files = glob.glob(os.path.join(tmp_dir, "*", "snapshots", "*", "*", "*"))
self.assertTrue(any(f.endswith("bin") for f in all_downloaded_files))
self.assertFalse(any(f.endswith("safetensors") for f in all_downloaded_files))
# test that no safetensors if both available and use_safetensors=True
with tempfile.TemporaryDirectory() as tmp_dir:
CLIPTextModel.from_pretrained(
"hf-internal-testing/diffusers-stable-diffusion-tiny-all",
subfolder="text_encoder",
use_safetensors=True,
cache_dir=tmp_dir,
)
all_downloaded_files = glob.glob(os.path.join(tmp_dir, "*", "snapshots", "*", "*", "*"))
self.assertTrue(any(f.endswith("safetensors") for f in all_downloaded_files))
self.assertFalse(any(f.endswith("bin") for f in all_downloaded_files))
@require_safetensors
def test_safetensors_save_and_load(self):
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, safe_serialization=True)
# No pytorch_model.bin file, only a model.safetensors
self.assertTrue(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_NAME)))
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_NAME)))
new_model = BertModel.from_pretrained(tmp_dir)
# Check models are equal
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
@require_safetensors
def test_safetensors_load_from_hub(self):
safetensors_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-safetensors")
pytorch_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
# Check models are equal
for p1, p2 in zip(safetensors_model.parameters(), pytorch_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
@require_safetensors
def test_safetensors_save_and_load_sharded(self):
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, safe_serialization=True, max_shard_size="100kB")
# No pytorch_model.bin index file, only a model.safetensors index
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_INDEX_NAME)))
self.assertTrue(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_INDEX_NAME)))
# No regular weights file
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_NAME)))
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_NAME)))
new_model = BertModel.from_pretrained(tmp_dir)
# Check models are equal
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
@require_safetensors
def test_safetensors_load_from_hub_sharded(self):
safetensors_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-sharded-safetensors")
pytorch_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-sharded")
# Check models are equal
for p1, p2 in zip(safetensors_model.parameters(), pytorch_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
def test_base_model_to_head_model_load(self):
base_model = BaseModel(PretrainedConfig())
with tempfile.TemporaryDirectory() as tmp_dir:
base_model.save_pretrained(tmp_dir, safe_serialization=False)
# Can load a base model in a model with head
model = ModelWithHead.from_pretrained(tmp_dir)
for p1, p2 in zip(model.base.parameters(), base_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
# It doesn't work if the state dict has a mix of keys of the head and base without prefix though.
base_state_dict = base_model.state_dict()
head_state_dict = model.state_dict()
base_state_dict["linear2.weight"] = head_state_dict["linear2.weight"]
base_state_dict["linear2.bias"] = head_state_dict["linear2.bias"]
safe_save_file(base_state_dict, os.path.join(tmp_dir, SAFE_WEIGHTS_NAME), metadata={"format": "pt"})
with self.assertRaisesRegex(
ValueError, "The state dictionary of the model you are trying to load is corrupted."
):
_ = ModelWithHead.from_pretrained(tmp_dir)
def test_tied_weights_reload(self):
# Base
model = BaseModelWithTiedWeights(PretrainedConfig())
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir)
new_model = BaseModelWithTiedWeights.from_pretrained(tmp_dir)
self.assertIs(new_model.linear.weight, new_model.linear_2.weight)
state_dict = model.state_dict()
# Remove tied weight from state_dict -> model should load with no complain of missing keys
del state_dict["linear_2.weight"]
torch.save(state_dict, os.path.join(tmp_dir, WEIGHTS_NAME))
new_model, load_info = BaseModelWithTiedWeights.from_pretrained(tmp_dir, output_loading_info=True)
self.assertListEqual(load_info["missing_keys"], [])
self.assertIs(new_model.linear.weight, new_model.linear_2.weight)
# With head
model.save_pretrained(tmp_dir)
new_model, load_info = ModelWithHeadAndTiedWeights.from_pretrained(tmp_dir, output_loading_info=True)
self.assertIs(new_model.base.linear.weight, new_model.decoder.weight)
# Should only complain about the missing bias
self.assertListEqual(load_info["missing_keys"], ["decoder.bias"])
def test_unexpected_keys_warnings(self):
model = ModelWithHead(PretrainedConfig())
logger = logging.get_logger("transformers.modeling_utils")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir)
# Loading the model with a new class, we don't get a warning for unexpected weights, just an info
with LoggingLevel(logging.WARNING):
with CaptureLogger(logger) as cl:
_, loading_info = BaseModel.from_pretrained(tmp_dir, output_loading_info=True)
self.assertNotIn("were not used when initializing ModelWithHead", cl.out)
self.assertEqual(
set(loading_info["unexpected_keys"]),
{"linear.weight", "linear.bias", "linear2.weight", "linear2.bias"},
)
# Loading the model with the same class, we do get a warning for unexpected weights
state_dict = model.state_dict()
state_dict["added_key"] = copy.deepcopy(state_dict["linear.weight"])
safe_save_file(state_dict, os.path.join(tmp_dir, SAFE_WEIGHTS_NAME), metadata={"format": "pt"})
with LoggingLevel(logging.WARNING):
with CaptureLogger(logger) as cl:
_, loading_info = ModelWithHead.from_pretrained(tmp_dir, output_loading_info=True)
self.assertIn("were not used when initializing ModelWithHead: ['added_key']", cl.out)
self.assertEqual(loading_info["unexpected_keys"], ["added_key"])
def test_warn_if_padding_and_no_attention_mask(self):
logger = logging.get_logger("transformers.modeling_utils")
with self.subTest("Ensure no warnings when pad_token_id is None."):
logger.warning_once.cache_clear()
with LoggingLevel(logging.WARNING):
with CaptureLogger(logger) as cl:
config_no_pad_token = PretrainedConfig()
config_no_pad_token.pad_token_id = None
model = ModelWithHead(config_no_pad_token)
input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 0, 0]])
model.warn_if_padding_and_no_attention_mask(input_ids, attention_mask=None)
self.assertNotIn("We strongly recommend passing in an `attention_mask`", cl.out)
with self.subTest("Ensure no warnings when there is an attention_mask."):
logger.warning_once.cache_clear()
with LoggingLevel(logging.WARNING):
with CaptureLogger(logger) as cl:
config = PretrainedConfig()
config.pad_token_id = 0
model = ModelWithHead(config)
input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 0, 0]])
attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0]])
model.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
self.assertNotIn("We strongly recommend passing in an `attention_mask`", cl.out)
with self.subTest("Ensure no warnings when there are no pad_token_ids in the input_ids."):
logger.warning_once.cache_clear()
with LoggingLevel(logging.WARNING):
with CaptureLogger(logger) as cl:
config = PretrainedConfig()
config.pad_token_id = 0
model = ModelWithHead(config)
input_ids = torch.tensor([[1, 345, 232, 328, 740, 140, 1695, 69, 6078, 2341, 25]])
model.warn_if_padding_and_no_attention_mask(input_ids, attention_mask=None)
self.assertNotIn("We strongly recommend passing in an `attention_mask`", cl.out)
with self.subTest("Ensure a warning is shown when the input_ids start with a pad_token_id."):
logger.warning_once.cache_clear()
with LoggingLevel(logging.WARNING):
with CaptureLogger(logger) as cl:
config = PretrainedConfig()
config.pad_token_id = 0
model = ModelWithHead(config)
input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 432, 5232]])
model.warn_if_padding_and_no_attention_mask(input_ids, attention_mask=None)
self.assertIn("We strongly recommend passing in an `attention_mask`", cl.out)
with self.subTest("Ensure a warning is shown when the input_ids end with a pad_token_id."):
logger.warning_once.cache_clear()
with LoggingLevel(logging.WARNING):
with CaptureLogger(logger) as cl:
config = PretrainedConfig()
config.pad_token_id = 0
model = ModelWithHead(config)
input_ids = torch.tensor([[432, 345, 232, 328, 740, 140, 1695, 69, 6078, 0, 0]])
model.warn_if_padding_and_no_attention_mask(input_ids, attention_mask=None)
self.assertIn("We strongly recommend passing in an `attention_mask`", cl.out)
with self.subTest("Ensure that the warning is shown at most once."):
logger.warning_once.cache_clear()
with LoggingLevel(logging.WARNING):
with CaptureLogger(logger) as cl:
config = PretrainedConfig()
config.pad_token_id = 0
model = ModelWithHead(config)
input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 0, 0]])
model.warn_if_padding_and_no_attention_mask(input_ids, attention_mask=None)
model.warn_if_padding_and_no_attention_mask(input_ids, attention_mask=None)
self.assertEqual(cl.out.count("We strongly recommend passing in an `attention_mask`"), 1)
with self.subTest("Ensure a different warning is shown when the pad_token_id is equal to the bos_token_id."):
logger.warning_once.cache_clear()
with LoggingLevel(logging.WARNING):
with CaptureLogger(logger) as cl:
config = PretrainedConfig()
config.pad_token_id = 0
config.bos_token_id = config.pad_token_id
model = ModelWithHead(config)
input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 0, 0]])
model.warn_if_padding_and_no_attention_mask(input_ids, attention_mask=None)
self.assertIn("You may ignore this warning if your `pad_token_id`", cl.out)
if not is_torchdynamo_available():
return
with self.subTest("Ensure that the warning code is skipped when compiling with torchdynamo."):
logger.warning_once.cache_clear()
from torch._dynamo import config, testing
config = PretrainedConfig()
config.pad_token_id = 0
model = ModelWithHead(config)
input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 432, 5232]])
def f(input_ids):
model.warn_if_padding_and_no_attention_mask(input_ids, attention_mask=None)
compile_counter = testing.CompileCounter()
opt_fn = torch.compile(f, dynamic=True, backend=compile_counter)
opt_fn(input_ids)
self.assertEqual(compile_counter.frame_count, 0)
@require_torch_accelerator
@slow
def test_pretrained_low_mem_new_config(self):
# Checking for 1 model(the same one which was described in the issue) .
model_ids = ["openai-community/gpt2"]
for model_id in model_ids:
model_config = AutoConfig.from_pretrained(pretrained_model_name_or_path=model_id)
model_config.n_layer = 48
model_config.n_head = 25
model_config.n_embd = 1600
model = AutoModelForCausalLM.from_pretrained(
pretrained_model_name_or_path=model_id,
config=model_config,
ignore_mismatched_sizes=True,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
)
model_ref = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path=model_id)
self.assertEqual(model.__class__.__name__, model_ref.__class__.__name__)
def test_generation_config_is_loaded_with_model(self):
# Note: `joaogante/tiny-random-gpt2-with-generation-config` has a `generation_config.json` containing a dummy
# `transformers_version` field set to `foo`. If loading the file fails, this test also fails.
# 1. Load without further parameters
model = AutoModelForCausalLM.from_pretrained(
"joaogante/tiny-random-gpt2-with-generation-config", use_safetensors=False
)
self.assertEqual(model.generation_config.transformers_version, "foo")
# 2. Load with `device_map`
model = AutoModelForCausalLM.from_pretrained(
"joaogante/tiny-random-gpt2-with-generation-config", device_map="auto", use_safetensors=False
)
self.assertEqual(model.generation_config.transformers_version, "foo")
@require_safetensors
def test_safetensors_torch_from_torch(self):
model = BertModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, safe_serialization=True)
new_model = BertModel.from_pretrained(tmp_dir)
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
@require_safetensors
@require_flax
def test_safetensors_torch_from_flax(self):
hub_model = BertModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only")
model = FlaxBertModel.from_pretrained("hf-internal-testing/tiny-bert-flax-only")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, safe_serialization=True)
new_model = BertModel.from_pretrained(tmp_dir)
for p1, p2 in zip(hub_model.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
@require_tf
@require_safetensors
def test_safetensors_torch_from_tf(self):
hub_model = BertModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only")
model = TFBertModel.from_pretrained("hf-internal-testing/tiny-bert-tf-only")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, safe_serialization=True)
new_model = BertModel.from_pretrained(tmp_dir)
for p1, p2 in zip(hub_model.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
@require_safetensors
def test_safetensors_torch_from_torch_sharded(self):
model = BertModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, safe_serialization=True, max_shard_size="100kB")
new_model = BertModel.from_pretrained(tmp_dir)
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
def test_modifying_model_config_causes_warning_saving_generation_config(self):
model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
model.config.top_k = 1
with tempfile.TemporaryDirectory() as tmp_dir:
with self.assertLogs("transformers.modeling_utils", level="WARNING") as logs:
model.save_pretrained(tmp_dir)
self.assertEqual(len(logs.output), 1)
self.assertIn("Your generation config was originally created from the model config", logs.output[0])
@require_safetensors
def test_model_from_pretrained_from_mlx(self):
from safetensors import safe_open
model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-mistral-mlx")
self.assertIsNotNone(model)
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, safe_serialization=True)
with safe_open(os.path.join(tmp_dir, "model.safetensors"), framework="pt") as f:
metadata = f.metadata()
self.assertEqual(metadata.get("format"), "pt")
new_model = AutoModelForCausalLM.from_pretrained(tmp_dir)
input_ids = torch.randint(100, 1000, (1, 10))
with torch.no_grad():
outputs = model(input_ids)
outputs_from_saved = new_model(input_ids)
self.assertTrue(torch.allclose(outputs_from_saved["logits"], outputs["logits"]))
@slow
@require_torch
class ModelOnTheFlyConversionTester(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.user = "huggingface-hub-ci"
cls.token = os.getenv("HUGGINGFACE_PRODUCTION_USER_TOKEN", None)
if cls.token is None:
raise ValueError("Cannot run tests as secret isn't setup.")
cls.api = HfApi(token=cls.token)
def setUp(self) -> None:
self.repo_name = f"{self.user}/test-model-on-the-fly-{uuid.uuid4()}"
def tearDown(self) -> None:
self.api.delete_repo(self.repo_name)
def test_safetensors_on_the_fly_conversion(self):
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
initial_model = BertModel(config)
initial_model.push_to_hub(self.repo_name, token=self.token, safe_serialization=False)
converted_model = BertModel.from_pretrained(self.repo_name, use_safetensors=True)
with self.subTest("Initial and converted models are equal"):
for p1, p2 in zip(initial_model.parameters(), converted_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
with self.subTest("PR was open with the safetensors account"):
discussions = self.api.get_repo_discussions(self.repo_name)
discussion = next(discussions)
self.assertEqual(discussion.author, "SFconvertbot")
self.assertEqual(discussion.title, "Adding `safetensors` variant of this model")
def test_safetensors_on_the_fly_conversion_private(self):
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
initial_model = BertModel(config)
initial_model.push_to_hub(self.repo_name, token=self.token, safe_serialization=False, private=True)
converted_model = BertModel.from_pretrained(self.repo_name, use_safetensors=True, token=self.token)
with self.subTest("Initial and converted models are equal"):
for p1, p2 in zip(initial_model.parameters(), converted_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
with self.subTest("PR was open with the safetensors account"):
discussions = self.api.get_repo_discussions(self.repo_name, token=self.token)
discussion = next(discussions)
self.assertEqual(discussion.author, self.user)
self.assertEqual(discussion.title, "Adding `safetensors` variant of this model")
def test_safetensors_on_the_fly_conversion_gated(self):
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
initial_model = BertModel(config)
initial_model.push_to_hub(self.repo_name, token=self.token, safe_serialization=False)
headers = {"Authorization": f"Bearer {self.token}"}
requests.put(
f"https://huggingface.co/api/models/{self.repo_name}/settings", json={"gated": "auto"}, headers=headers
)
converted_model = BertModel.from_pretrained(self.repo_name, use_safetensors=True, token=self.token)
with self.subTest("Initial and converted models are equal"):
for p1, p2 in zip(initial_model.parameters(), converted_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
with self.subTest("PR was open with the safetensors account"):
discussions = self.api.get_repo_discussions(self.repo_name)
discussion = next(discussions)
self.assertEqual(discussion.author, "SFconvertbot")
self.assertEqual(discussion.title, "Adding `safetensors` variant of this model")
def test_safetensors_on_the_fly_sharded_conversion(self):
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
initial_model = BertModel(config)
initial_model.push_to_hub(self.repo_name, token=self.token, safe_serialization=False, max_shard_size="200kb")
converted_model = BertModel.from_pretrained(self.repo_name, use_safetensors=True)
with self.subTest("Initial and converted models are equal"):
for p1, p2 in zip(initial_model.parameters(), converted_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
with self.subTest("PR was open with the safetensors account"):
discussions = self.api.get_repo_discussions(self.repo_name)
discussion = next(discussions)
self.assertEqual(discussion.author, "SFconvertbot")
self.assertEqual(discussion.title, "Adding `safetensors` variant of this model")
def test_safetensors_on_the_fly_sharded_conversion_private(self):
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
initial_model = BertModel(config)
initial_model.push_to_hub(
self.repo_name, token=self.token, safe_serialization=False, max_shard_size="200kb", private=True
)
converted_model = BertModel.from_pretrained(self.repo_name, use_safetensors=True, token=self.token)
with self.subTest("Initial and converted models are equal"):
for p1, p2 in zip(initial_model.parameters(), converted_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
with self.subTest("PR was open with the safetensors account"):
discussions = self.api.get_repo_discussions(self.repo_name)
discussion = next(discussions)
self.assertEqual(discussion.author, self.user)
self.assertEqual(discussion.title, "Adding `safetensors` variant of this model")
def test_safetensors_on_the_fly_sharded_conversion_gated(self):
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
initial_model = BertModel(config)
initial_model.push_to_hub(self.repo_name, token=self.token, max_shard_size="200kb", safe_serialization=False)
headers = {"Authorization": f"Bearer {self.token}"}
requests.put(
f"https://huggingface.co/api/models/{self.repo_name}/settings", json={"gated": "auto"}, headers=headers
)
converted_model = BertModel.from_pretrained(self.repo_name, use_safetensors=True, token=self.token)
with self.subTest("Initial and converted models are equal"):
for p1, p2 in zip(initial_model.parameters(), converted_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
with self.subTest("PR was open with the safetensors account"):
discussions = self.api.get_repo_discussions(self.repo_name)
discussion = next(discussions)
self.assertEqual(discussion.author, "SFconvertbot")
self.assertEqual(discussion.title, "Adding `safetensors` variant of this model")
@unittest.skip("Edge case, should work once the Space is updated`")
def test_safetensors_on_the_fly_wrong_user_opened_pr(self):
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
initial_model = BertModel(config)
initial_model.push_to_hub(self.repo_name, token=self.token, safe_serialization=False, private=True)
BertModel.from_pretrained(self.repo_name, use_safetensors=True, token=self.token)
# This should have opened a PR with the user's account
with self.subTest("PR was open with the safetensors account"):
discussions = self.api.get_repo_discussions(self.repo_name)
discussion = next(discussions)
self.assertEqual(discussion.author, self.user)
self.assertEqual(discussion.title, "Adding `safetensors` variant of this model")
# We now switch the repo visibility to public
self.api.update_repo_visibility(self.repo_name, private=False)
# We once again call from_pretrained, which should call the bot to open a PR
BertModel.from_pretrained(self.repo_name, use_safetensors=True, token=self.token)
with self.subTest("PR was open with the safetensors account"):
discussions = self.api.get_repo_discussions(self.repo_name)
bot_opened_pr = None
bot_opened_pr_title = None
for discussion in discussions:
if discussion.author == "SFconvertBot":
bot_opened_pr = True
bot_opened_pr_title = discussion.title
self.assertTrue(bot_opened_pr)
self.assertEqual(bot_opened_pr_title, "Adding `safetensors` variant of this model")
def test_safetensors_on_the_fly_specific_revision(self):
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
initial_model = BertModel(config)
# Push a model on `main`
initial_model.push_to_hub(self.repo_name, token=self.token, safe_serialization=False)
# Push a model on a given revision
initial_model.push_to_hub(self.repo_name, token=self.token, safe_serialization=False, revision="new-branch")
# Try to convert the model on that revision should raise
with self.assertRaises(EnvironmentError):
BertModel.from_pretrained(self.repo_name, use_safetensors=True, token=self.token, revision="new-branch")
@require_torch
@is_staging_test
class ModelPushToHubTester(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls._token = TOKEN
HfFolder.save_token(TOKEN)
@classmethod
def tearDownClass(cls):
try:
delete_repo(token=cls._token, repo_id="test-model")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="valid_org/test-model-org")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="test-dynamic-model")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="test-dynamic-model-with-tags")
except HTTPError:
pass
@unittest.skip("This test is flaky")
def test_push_to_hub(self):
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
model = BertModel(config)
model.push_to_hub("test-model", token=self._token)
new_model = BertModel.from_pretrained(f"{USER}/test-model")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
# Reset repo
delete_repo(token=self._token, repo_id="test-model")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, repo_id="test-model", push_to_hub=True, token=self._token)
new_model = BertModel.from_pretrained(f"{USER}/test-model")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
def test_push_to_hub_with_description(self):
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
model = BertModel(config)
COMMIT_DESCRIPTION = """
The commit description supports markdown synthax see:
```python
>>> form transformers import AutoConfig
>>> config = AutoConfig.from_pretrained("google-bert/bert-base-uncased")
```
"""
commit_details = model.push_to_hub(
"test-model", use_auth_token=self._token, create_pr=True, commit_description=COMMIT_DESCRIPTION
)
self.assertEqual(commit_details.commit_description, COMMIT_DESCRIPTION)
@unittest.skip("This test is flaky")
def test_push_to_hub_in_organization(self):
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
model = BertModel(config)
model.push_to_hub("valid_org/test-model-org", token=self._token)
new_model = BertModel.from_pretrained("valid_org/test-model-org")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
# Reset repo
delete_repo(token=self._token, repo_id="valid_org/test-model-org")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, push_to_hub=True, token=self._token, repo_id="valid_org/test-model-org")
new_model = BertModel.from_pretrained("valid_org/test-model-org")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
def test_push_to_hub_dynamic_model(self):
CustomConfig.register_for_auto_class()
CustomModel.register_for_auto_class()
config = CustomConfig(hidden_size=32)
model = CustomModel(config)
model.push_to_hub("test-dynamic-model", token=self._token)
# checks
self.assertDictEqual(
config.auto_map,
{"AutoConfig": "custom_configuration.CustomConfig", "AutoModel": "custom_modeling.CustomModel"},
)
new_model = AutoModel.from_pretrained(f"{USER}/test-dynamic-model", trust_remote_code=True)
# Can't make an isinstance check because the new_model is from the CustomModel class of a dynamic module
self.assertEqual(new_model.__class__.__name__, "CustomModel")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
config = AutoConfig.from_pretrained(f"{USER}/test-dynamic-model", trust_remote_code=True)
new_model = AutoModel.from_config(config, trust_remote_code=True)
self.assertEqual(new_model.__class__.__name__, "CustomModel")
def test_push_to_hub_with_tags(self):
from huggingface_hub import ModelCard
new_tags = ["tag-1", "tag-2"]
CustomConfig.register_for_auto_class()
CustomModel.register_for_auto_class()
config = CustomConfig(hidden_size=32)
model = CustomModel(config)
self.assertTrue(model.model_tags is None)
model.add_model_tags(new_tags)
self.assertTrue(model.model_tags == new_tags)
model.push_to_hub("test-dynamic-model-with-tags", token=self._token)
loaded_model_card = ModelCard.load(f"{USER}/test-dynamic-model-with-tags")
self.assertEqual(loaded_model_card.data.tags, new_tags)
@require_torch
class AttentionMaskTester(unittest.TestCase):
def check_non_causal(self, bsz, q_len, kv_len, mask_2d, mask_4d):
mask_indices = (mask_2d != 1)[:, None].broadcast_to((bsz, q_len, kv_len))
mask_4d_values = mask_4d[:, 0][mask_indices]
is_inf = mask_4d_values == -float("inf")
is_min = mask_4d_values == torch.finfo(mask_4d.dtype).min
assert torch.logical_or(is_inf, is_min).all()
def check_to_4d(self, mask_converter, q_len, kv_len, additional_mask=None, bsz=3):
mask_2d = torch.ones((bsz, kv_len), device=torch_device, dtype=torch.long)
if additional_mask is not None:
for bsz_idx, seq_idx in additional_mask:
mask_2d[bsz_idx, seq_idx] = 0
mask_4d = mask_converter.to_4d(mask_2d, query_length=q_len, key_value_length=kv_len, dtype=torch.float32)
assert mask_4d.shape == (bsz, 1, q_len, kv_len)
# make sure there are no overflows
assert mask_4d.min() != float("-inf")
context = mask_converter.sliding_window
if mask_converter.is_causal and context is None:
# k * (k+1) / 2 tokens are masked in triangualar masks
num_tokens_masked = bsz * (q_len * (q_len - 1) // 2)
if 0 not in mask_2d:
assert (mask_4d != 0).sum().cpu().item() == num_tokens_masked
if 0 in mask_2d:
# at least causal mask + maybe more
assert (mask_4d != 0).sum().cpu().item() >= num_tokens_masked
self.check_non_causal(bsz, q_len, kv_len, mask_2d, mask_4d)
elif not mask_converter.is_causal and context is None:
if 0 not in mask_2d:
assert (mask_4d != 0).sum().cpu().item() == 0
if 0 in mask_2d:
self.check_non_causal(bsz, q_len, kv_len, mask_2d, mask_4d)
elif mask_converter.is_causal and context is not None:
# k * (k+1) / 2 tokens are masked in triangualar masks
num_tokens_masked = (q_len * (q_len - 1) // 2) + self.compute_num_context_mask(kv_len, context, q_len)
num_tokens_masked = bsz * num_tokens_masked
if 0 not in mask_2d:
assert (mask_4d != 0).sum().cpu().item() == num_tokens_masked
if 0 in mask_2d:
# at least causal mask + maybe more
assert (mask_4d != 0).sum().cpu().item() >= num_tokens_masked
self.check_non_causal(bsz, q_len, kv_len, mask_2d, mask_4d)
def check_to_causal(self, mask_converter, q_len, kv_len, bsz=3):
mask_4d = mask_converter.to_causal_4d(
bsz, query_length=q_len, key_value_length=kv_len, device=torch_device, dtype=torch.float32
)
if q_len == 1 and mask_converter.sliding_window is None:
# no causal mask if q_len is 1
assert mask_4d is None
return
context = mask_converter.sliding_window
if mask_converter.is_causal and context is None:
# k * (k+1) / 2 tokens are masked in triangualar masks
num_tokens_masked = bsz * (q_len * (q_len - 1) // 2)
assert (mask_4d != 0).sum().cpu().item() == num_tokens_masked
elif not mask_converter.is_causal and context is None:
assert (mask_4d != 0).sum().cpu().item() == 0
elif mask_converter.is_causal and context is not None:
# k * (k+1) / 2 tokens are masked in triangualar masks
num_tokens_masked = (q_len * (q_len - 1) // 2) + self.compute_num_context_mask(kv_len, context, q_len)
num_tokens_masked = bsz * num_tokens_masked
assert (mask_4d != 0).sum().cpu().item() == num_tokens_masked
def compute_num_context_mask(self, kv_len, context, q_len):
# This function computes the # of attention tokens that are added for
# the sliding window
c_mask_len = kv_len - context - 1
num_mask_triangle = c_mask_len * (c_mask_len + 1) // 2
cut_mask_len = max(c_mask_len - q_len, 0)
num_cut_mask = cut_mask_len * (cut_mask_len + 1) // 2
return num_mask_triangle - num_cut_mask
def test_2d_to_4d_causal(self):
mask_converter = AttentionMaskConverter(is_causal=True)
# auto-regressive use case
self.check_to_4d(mask_converter, q_len=1, kv_len=7)
# special auto-regressive case
self.check_to_4d(mask_converter, q_len=3, kv_len=7)
# non auto-regressive case
self.check_to_4d(mask_converter, q_len=7, kv_len=7)
# same with extra attention masks
self.check_to_4d(mask_converter, q_len=1, kv_len=7, additional_mask=[(0, 2), (1, 3), (2, 0)])
self.check_to_4d(mask_converter, q_len=3, kv_len=7, additional_mask=[(0, 2), (1, 3), (2, 0)])
self.check_to_4d(mask_converter, q_len=7, kv_len=7, additional_mask=[(0, 2), (1, 3), (2, 0)])
# check that the mask does not overflow on causal masked tokens
self.check_to_4d(mask_converter, q_len=7, kv_len=7, additional_mask=[(0, 0), (1, 0), (1, 1)])
def test_2d_to_4d(self):
mask_converter = AttentionMaskConverter(is_causal=False)
# non auto-regressive case
self.check_to_4d(mask_converter, q_len=7, kv_len=7)
# same with extra attention masks
self.check_to_4d(mask_converter, q_len=7, kv_len=7, additional_mask=[(0, 2), (1, 3), (2, 0)])
def test_2d_to_4d_causal_sliding(self):
mask_converter = AttentionMaskConverter(is_causal=True, sliding_window=5)
# auto-regressive use case
self.check_to_4d(mask_converter, q_len=1, kv_len=7)
# special auto-regressive case
self.check_to_4d(mask_converter, q_len=3, kv_len=7)
# non auto-regressive case
self.check_to_4d(mask_converter, q_len=7, kv_len=7)
# same with extra attention masks
self.check_to_4d(mask_converter, q_len=1, kv_len=7, additional_mask=[(0, 2), (1, 3), (2, 0)])
self.check_to_4d(mask_converter, q_len=3, kv_len=7, additional_mask=[(0, 2), (1, 3), (2, 0)])
self.check_to_4d(mask_converter, q_len=7, kv_len=7, additional_mask=[(0, 2), (1, 3), (2, 0)])
def test_causal_mask(self):
mask_converter = AttentionMaskConverter(is_causal=True)
# auto-regressive use case
self.check_to_causal(mask_converter, q_len=1, kv_len=7)
# special auto-regressive case
self.check_to_causal(mask_converter, q_len=3, kv_len=7)
# non auto-regressive case
self.check_to_causal(mask_converter, q_len=7, kv_len=7)
def test_causal_mask_sliding(self):
mask_converter = AttentionMaskConverter(is_causal=True, sliding_window=3)
# auto-regressive use case
self.check_to_causal(mask_converter, q_len=1, kv_len=7)
# special auto-regressive case
self.check_to_causal(mask_converter, q_len=3, kv_len=7)
# non auto-regressive case
self.check_to_causal(mask_converter, q_len=7, kv_len=7)
def test_torch_compile_fullgraph(self):
model = Prepare4dCausalAttentionMaskModel()
inputs_embeds = torch.rand([1, 3, 32])
res_non_compiled = model(inputs_embeds)
compiled_model = torch.compile(model, fullgraph=True)
res_compiled = compiled_model(inputs_embeds)
self.assertTrue(torch.equal(res_non_compiled, res_compiled))
model = Create4dCausalAttentionMaskModel()
inputs_embeds = torch.rand(2, 4, 16)
res_non_compiled = model(inputs_embeds)
compiled_model = torch.compile(model, fullgraph=True)
res_compiled = compiled_model(inputs_embeds)
self.assertTrue(torch.equal(res_non_compiled, res_compiled))
model = Prepare4dAttentionMaskModel()
mask = torch.ones(2, 4)
mask[0, :2] = 0
inputs_embeds = torch.rand(2, 4, 16)
res_non_compiled = model(mask, inputs_embeds)
compiled_model = torch.compile(model, fullgraph=True)
res_compiled = compiled_model(mask, inputs_embeds)
self.assertTrue(torch.equal(res_non_compiled, res_compiled))
@require_torch
@slow
def test_unmask_unattended_left_padding(self):
attention_mask = torch.Tensor([[0, 0, 1], [1, 1, 1], [0, 1, 1]]).to(torch.int64)
expanded_mask = torch.Tensor(
[
[[[0, 0, 0], [0, 0, 0], [0, 0, 1]]],
[[[1, 0, 0], [1, 1, 0], [1, 1, 1]]],
[[[0, 0, 0], [0, 1, 0], [0, 1, 1]]],
]
).to(torch.int64)
reference_output = torch.Tensor(
[
[[[1, 1, 1], [1, 1, 1], [0, 0, 1]]],
[[[1, 0, 0], [1, 1, 0], [1, 1, 1]]],
[[[1, 1, 1], [0, 1, 0], [0, 1, 1]]],
]
).to(torch.int64)
result = AttentionMaskConverter._unmask_unattended(expanded_mask, attention_mask, unmasked_value=1)
self.assertTrue(torch.equal(result, reference_output))
attention_mask = torch.Tensor([[0, 0, 1, 1, 1], [1, 1, 1, 1, 1], [0, 1, 1, 1, 1]]).to(torch.int64)
attn_mask_converter = AttentionMaskConverter(is_causal=True)
past_key_values_length = 0
key_value_length = attention_mask.shape[-1] + past_key_values_length
expanded_mask = attn_mask_converter.to_4d(
attention_mask, attention_mask.shape[-1], key_value_length=key_value_length, dtype=torch.float32
)
result = AttentionMaskConverter._unmask_unattended(expanded_mask, attention_mask, unmasked_value=0)
min_inf = torch.finfo(torch.float32).min
reference_output = torch.Tensor(
[
[
[
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[min_inf, min_inf, 0, min_inf, min_inf],
[min_inf, min_inf, 0, 0, min_inf],
[min_inf, min_inf, 0, 0, 0],
]
],
[
[
[0, min_inf, min_inf, min_inf, min_inf],
[0, 0, min_inf, min_inf, min_inf],
[0, 0, 0, min_inf, min_inf],
[0, 0, 0, 0, min_inf],
[0, 0, 0, 0, 0],
]
],
[
[
[0, 0, 0, 0, 0],
[min_inf, 0, min_inf, min_inf, min_inf],
[min_inf, 0, 0, min_inf, min_inf],
[min_inf, 0, 0, 0, min_inf],
[min_inf, 0, 0, 0, 0],
]
],
]
)
self.assertTrue(torch.equal(reference_output, result))
@require_torch
@slow
def test_unmask_unattended_right_padding(self):
attention_mask = torch.Tensor([[1, 1, 1, 0], [1, 1, 1, 1], [1, 1, 0, 0]]).to(torch.int64)
attn_mask_converter = AttentionMaskConverter(is_causal=True)
past_key_values_length = 0
key_value_length = attention_mask.shape[-1] + past_key_values_length
expanded_mask = attn_mask_converter.to_4d(
attention_mask, attention_mask.shape[-1], key_value_length=key_value_length, dtype=torch.float32
)
result = AttentionMaskConverter._unmask_unattended(expanded_mask, attention_mask, unmasked_value=0)
self.assertTrue(torch.equal(expanded_mask, result))
@require_torch
@slow
def test_unmask_unattended_random_mask(self):
attention_mask = torch.Tensor([[1, 0, 1, 0], [1, 0, 1, 1], [1, 1, 0, 1]]).to(torch.int64)
attn_mask_converter = AttentionMaskConverter(is_causal=True)
past_key_values_length = 0
key_value_length = attention_mask.shape[-1] + past_key_values_length
expanded_mask = attn_mask_converter.to_4d(
attention_mask, attention_mask.shape[-1], key_value_length=key_value_length, dtype=torch.float32
)
result = AttentionMaskConverter._unmask_unattended(expanded_mask, attention_mask, unmasked_value=0)
self.assertTrue(torch.equal(expanded_mask, result))
@require_torch
class TestAttentionImplementation(unittest.TestCase):
def test_error_no_sdpa_available(self):
with self.assertRaises(ValueError) as cm:
_ = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-MCTCTModel", attn_implementation="sdpa")
self.assertTrue(
"does not support an attention implementation through torch.nn.functional.scaled_dot_product_attention"
in str(cm.exception)
)
_ = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-MCTCTModel")
def test_error_no_flash_available(self):
with self.assertRaises(ValueError) as cm:
_ = AutoModel.from_pretrained(
"hf-tiny-model-private/tiny-random-MCTCTModel", attn_implementation="flash_attention_2"
)
self.assertTrue("does not support Flash Attention 2.0" in str(cm.exception))
def test_error_no_flash_available_with_config(self):
with self.assertRaises(ValueError) as cm:
config = AutoConfig.from_pretrained("hf-tiny-model-private/tiny-random-MCTCTModel")
_ = AutoModel.from_pretrained(
"hf-tiny-model-private/tiny-random-MCTCTModel", config=config, attn_implementation="flash_attention_2"
)
self.assertTrue("does not support Flash Attention 2.0" in str(cm.exception))
def test_error_wrong_attn_implementation(self):
with self.assertRaises(ValueError) as cm:
_ = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-MCTCTModel", attn_implementation="foo")
self.assertTrue('The only possible arguments are `attn_implementation="eager"' in str(cm.exception))
def test_not_available_flash(self):
if is_flash_attn_2_available():
self.skipTest("Please uninstall flash-attn package to run test_not_available_flash")
with self.assertRaises(ImportError) as cm:
_ = AutoModel.from_pretrained(
"hf-internal-testing/tiny-random-GPTBigCodeModel", attn_implementation="flash_attention_2"
)
self.assertTrue("the package flash_attn seems to be not installed" in str(cm.exception))
def test_not_available_flash_with_config(self):
if is_flash_attn_2_available():
self.skipTest("Please uninstall flash-attn package to run test_not_available_flash")
config = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-GPTBigCodeModel")
with self.assertRaises(ImportError) as cm:
_ = AutoModel.from_pretrained(
"hf-internal-testing/tiny-random-GPTBigCodeModel",
config=config,
attn_implementation="flash_attention_2",
)
self.assertTrue("the package flash_attn seems to be not installed" in str(cm.exception))
def test_not_available_sdpa(self):
if is_torch_sdpa_available():
self.skipTest("This test requires torch<=2.0")
with self.assertRaises(ImportError) as cm:
_ = AutoModel.from_pretrained(
"hf-internal-testing/tiny-random-GPTBigCodeModel", attn_implementation="sdpa"
)
self.assertTrue("PyTorch SDPA requirements in Transformers are not met" in str(cm.exception))
@require_torch_gpu
class Mask4DTestBase(unittest.TestCase):
def tearDown(self):
gc.collect()
torch.cuda.empty_cache()
def get_test_data(self):
texts = ["the cat sat", "the cat had", "the cat is"]
encoded = [self.tokenizer.encode(t) for t in texts]
input_0 = torch.tensor(encoded, device=torch_device)
# tensor([[ 1, 278, 6635, 3290],
# [ 1, 278, 6635, 750],
# [ 1, 278, 6635, 338]], device='cuda:0')
position_ids_0 = torch.tensor([[0, 1, 2, 3]] * 3, device=torch_device, dtype=torch.int64)
# Combining common prefix with the unique ending tokens:
input_1 = torch.cat([input_0[0][:-1], input_0[:, -1]]).unsqueeze(0)
# tensor([[ 1, 278, 6635, 3290, 750, 338]], device='cuda:0')
# Creating a 4D mask where each of the last 3 tokens do not attend to each other.
mask_1 = torch.tensor(
[
[
[
[1, 0, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0],
[1, 1, 1, 1, 0, 0],
[1, 1, 1, 0, 1, 0],
[1, 1, 1, 0, 0, 1],
]
]
],
device="cuda:0",
dtype=torch.int64,
)
# Creating a position_ids tensor. note the repeating figures in the end.
position_ids_1 = torch.tensor([[0, 1, 2, 3, 3, 3]], device=torch_device, dtype=torch.int64)
return input_0, position_ids_0, input_1, mask_1, position_ids_1
@require_torch_gpu
class Mask4DTestFP32(Mask4DTestBase):
def setUp(self):
model_name = "JackFram/llama-68m" # small Llama-like model from FlexFlow
self.model_dtype = torch.float32
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=self.model_dtype).to(torch_device)
def test_attention(self):
"""comparing outputs of attention layer"""
# Input 0: one row per sentence; Input 1: same data, but stacked into a single row with custom attention
input_0, position_ids_0, input_1, mask_1, position_ids_1 = self.get_test_data()
causal_mask_1 = (1 - mask_1).to(self.model_dtype) * torch.finfo(self.model_dtype).min
hid_0 = self.model.model.embed_tokens(input_0)
outs_0 = self.model.model.layers[0].self_attn.forward(hid_0, position_ids=position_ids_0)[0]
# outs_0.shape == torch.Size([3, 4, 768])
hid_1 = self.model.model.embed_tokens(input_1)
outs_1 = self.model.model.layers[0].self_attn.forward(
hid_1, attention_mask=causal_mask_1, position_ids=position_ids_1
)[0]
# outs_1.shape == torch.Size([1, 6, 768])
outs_0_last_tokens = outs_0[:, -1, :] # last tokens in each batch line
outs_1_last_tokens = outs_1[0, -3:, :] # last three tokens
torch.testing.assert_close(outs_0_last_tokens, outs_1_last_tokens)
def test_causal_model_logits(self):
"""comparing logits outputs of whole inner model"""
# Input 0: one row per sentence; Input 1: same data, but stacked into a single row with custom attention
input_0, position_ids_0, input_1, mask_1, position_ids_1 = self.get_test_data()
logits_0 = self.model.forward(input_0, position_ids=position_ids_0).logits
logits_1 = self.model.forward(input_1, attention_mask=mask_1.bool(), position_ids=position_ids_1).logits
logits_0_last_tokens = logits_0[:, -1, :] # last tokens in each batch line
logits_1_last_tokens = logits_1[0, -3:, :] # last three tokens
torch.testing.assert_close(logits_0_last_tokens, logits_1_last_tokens)
@require_torch_gpu
class Mask4DTestFP16(Mask4DTestBase):
test_attention = Mask4DTestFP32.test_attention
def setUp(self):
model_name = "JackFram/llama-68m" # small Llama-like model from FlexFlow
self.model_dtype = torch.float16
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=self.model_dtype).to(torch_device)
def test_causal_model_logits(self):
"""comparing logits outputs of whole inner model"""
# Input 0: one row per sentence; Input 1: same data, but stacked into a single row with custom attention
input_0, position_ids_0, input_1, mask_1, position_ids_1 = self.get_test_data()
logits_0 = self.model.forward(input_0, position_ids=position_ids_0).logits
logits_1 = self.model.forward(input_1, attention_mask=mask_1.bool(), position_ids=position_ids_1).logits
logits_0_last_tokens = logits_0[:, -1, :] # last tokens in each batch line
logits_1_last_tokens = logits_1[0, -3:, :] # last three tokens
indices_0 = logits_0_last_tokens.sort(descending=True).indices
indices_1 = logits_1_last_tokens.sort(descending=True).indices
# checking logits, but note relaxed tolerances for FP16
torch.testing.assert_close(logits_0_last_tokens, logits_1_last_tokens, atol=0.02, rtol=0.001)
# checking tokens order for the top tokens
for token_ids_0, token_ids_1 in zip(indices_0, indices_1):
self.assertTrue(torch.equal(token_ids_0[:128], token_ids_1[:128]))
@slow
@require_torch_gpu
class Mask4DTestHard(unittest.TestCase):
def tearDown(self):
gc.collect()
torch.cuda.empty_cache()
def setUp(self):
model_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
self.model_dtype = torch.float32
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=self.model_dtype).to(torch_device)
def get_test_data(self):
template = "my favorite {}"
items = ("pet is a", "artist plays a", "name is L") # same number of tokens in each item
batch_0 = [template.format(x) for x in items] # 3 separate lines
batch_1 = template.format(" ".join(items)) # 1 line with options concatenated
input_0 = self.tokenizer(batch_0, return_tensors="pt").input_ids.to(torch_device)
input_1 = self.tokenizer(batch_1, return_tensors="pt").input_ids.to(torch_device)
mask_1 = torch.tensor(
[
[
[
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0],
[1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1],
]
]
],
device=torch_device,
dtype=torch.int64,
)
position_ids_0 = torch.arange(input_0.shape[1]).tile(input_0.shape[0], 1).to(torch_device)
# equivalent: position_ids_1 = torch.tensor([[0, 1, 2, 3, 4, 5, 3, 4, 5, 3, 4, 5]]).to(device)
position_ids_1 = (mask_1.sum(dim=-1) - 1).reshape(1, -1) # same but nicer
return input_0, position_ids_0, input_1, mask_1, position_ids_1
def test_stacked_causal_mask(self):
# Input 0: one row per sentence; Input 1: same data, but stacked into a single row with custom attention
input_0, position_ids_0, input_1, mask_1, position_ids_1 = self.get_test_data()
# regular batch
logits_0 = self.model.forward(input_0, position_ids=position_ids_0).logits
logits_0_last = logits_0[:, -1, :] # last tokens in each batch line
decoded_0 = [self.tokenizer.decode(t) for t in logits_0_last.argmax(dim=-1)]
# single forward run with 4D custom mask
logits_1 = self.model.forward(input_1, attention_mask=mask_1.bool(), position_ids=position_ids_1).logits
logits_1_last = logits_1[0, torch.where(position_ids_1 == position_ids_1.max())[1], :] # last three tokens
decoded_1 = [self.tokenizer.decode(t) for t in logits_1_last.argmax(dim=-1)]
self.assertEqual(decoded_0, decoded_1)
def test_partial_stacked_causal_mask(self):
# Same as the test above, but the input is passed in two groups. It tests that we can pass partial 4D attention
# masks
# Input 0: one row per sentence; Input 1: same data, but stacked into a single row with custom attention
input_0, position_ids_0, input_1, mask_1, position_ids_1 = self.get_test_data()
# regular batch
logits_0 = self.model.forward(input_0, position_ids=position_ids_0).logits
logits_0_last = logits_0[:, -1, :] # last tokens in each batch line
decoded_0 = [self.tokenizer.decode(t) for t in logits_0_last.argmax(dim=-1)]
# 2 forward runs with custom 4D masks
part_a = 3 # split point
input_1a = input_1[:, :part_a]
position_ids_1a = position_ids_1[:, :part_a]
mask_1a = mask_1[:, :, :part_a, :part_a]
outs_1a = self.model.forward(input_1a, attention_mask=mask_1a.bool(), position_ids=position_ids_1a)
past_key_values_a = outs_1a["past_key_values"]
input_1b = input_1[:, part_a:]
position_ids_1b = position_ids_1[:, part_a:]
mask_1b = mask_1[:, :, part_a:, :]
outs_1b = self.model.forward(
input_1b, attention_mask=mask_1b.bool(), position_ids=position_ids_1b, past_key_values=past_key_values_a
)
decoded_1b = [
self.tokenizer.decode(t)
for t in outs_1b.logits.argmax(-1)[0, torch.where(position_ids_1 == position_ids_1.max())[1] - part_a]
]
self.assertEqual(decoded_0, decoded_1b)
| transformers/tests/test_modeling_utils.py/0 | {
"file_path": "transformers/tests/test_modeling_utils.py",
"repo_id": "transformers",
"token_count": 45092
} | 387 |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class TestActivations(unittest.TestCase):
def test_gelu_versions(self):
x = torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100])
torch_builtin = get_activation("gelu")
self.assertTrue(torch.allclose(gelu_python(x), torch_builtin(x)))
self.assertFalse(torch.allclose(gelu_python(x), gelu_new(x)))
def test_gelu_10(self):
x = torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100])
torch_builtin = get_activation("gelu")
gelu10 = get_activation("gelu_10")
y_gelu = torch_builtin(x)
y_gelu_10 = gelu10(x)
clipped_mask = torch.where(y_gelu_10 < 10.0, 1, 0)
self.assertTrue(torch.max(y_gelu_10).item() == 10.0)
self.assertTrue(torch.allclose(y_gelu * clipped_mask, y_gelu_10 * clipped_mask))
def test_get_activation(self):
get_activation("gelu")
get_activation("gelu_10")
get_activation("gelu_fast")
get_activation("gelu_new")
get_activation("gelu_python")
get_activation("gelu_pytorch_tanh")
get_activation("linear")
get_activation("mish")
get_activation("quick_gelu")
get_activation("relu")
get_activation("sigmoid")
get_activation("silu")
get_activation("swish")
get_activation("tanh")
with self.assertRaises(KeyError):
get_activation("bogus")
with self.assertRaises(KeyError):
get_activation(None)
def test_activations_are_distinct_objects(self):
act1 = get_activation("gelu")
act1.a = 1
act2 = get_activation("gelu")
self.assertEqual(act1.a, 1)
with self.assertRaises(AttributeError):
_ = act2.a
| transformers/tests/utils/test_activations.py/0 | {
"file_path": "transformers/tests/utils/test_activations.py",
"repo_id": "transformers",
"token_count": 1061
} | 388 |
# coding=utf-8
# Copyright 2019 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import tempfile
import unittest
from transformers.modelcard import ModelCard
class ModelCardTester(unittest.TestCase):
def setUp(self):
self.inputs_dict = {
"model_details": {
"Organization": "testing",
"Model date": "today",
"Model version": "v2.1, Developed by Test Corp in 2019.",
"Architecture": "Convolutional Neural Network.",
},
"metrics": "BLEU and ROUGE-1",
"evaluation_data": {
"Datasets": {"BLEU": "My-great-dataset-v1", "ROUGE-1": "My-short-dataset-v2.1"},
"Preprocessing": "See details on https://arxiv.org/pdf/1810.03993.pdf",
},
"training_data": {
"Dataset": "English Wikipedia dump dated 2018-12-01",
"Preprocessing": (
"Using SentencePiece vocabulary of size 52k tokens. See details on"
" https://arxiv.org/pdf/1810.03993.pdf"
),
},
"quantitative_analyses": {"BLEU": 55.1, "ROUGE-1": 76},
}
def test_model_card_common_properties(self):
modelcard = ModelCard.from_dict(self.inputs_dict)
self.assertTrue(hasattr(modelcard, "model_details"))
self.assertTrue(hasattr(modelcard, "intended_use"))
self.assertTrue(hasattr(modelcard, "factors"))
self.assertTrue(hasattr(modelcard, "metrics"))
self.assertTrue(hasattr(modelcard, "evaluation_data"))
self.assertTrue(hasattr(modelcard, "training_data"))
self.assertTrue(hasattr(modelcard, "quantitative_analyses"))
self.assertTrue(hasattr(modelcard, "ethical_considerations"))
self.assertTrue(hasattr(modelcard, "caveats_and_recommendations"))
def test_model_card_to_json_string(self):
modelcard = ModelCard.from_dict(self.inputs_dict)
obj = json.loads(modelcard.to_json_string())
for key, value in self.inputs_dict.items():
self.assertEqual(obj[key], value)
def test_model_card_to_json_file(self):
model_card_first = ModelCard.from_dict(self.inputs_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
filename = os.path.join(tmpdirname, "modelcard.json")
model_card_first.to_json_file(filename)
model_card_second = ModelCard.from_json_file(filename)
self.assertEqual(model_card_second.to_dict(), model_card_first.to_dict())
def test_model_card_from_and_save_pretrained(self):
model_card_first = ModelCard.from_dict(self.inputs_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
model_card_first.save_pretrained(tmpdirname)
model_card_second = ModelCard.from_pretrained(tmpdirname)
self.assertEqual(model_card_second.to_dict(), model_card_first.to_dict())
| transformers/tests/utils/test_model_card.py/0 | {
"file_path": "transformers/tests/utils/test_model_card.py",
"repo_id": "transformers",
"token_count": 1475
} | 389 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(".")
r"""
The argument `test_file` in this file refers to a model test file. This should be a string of the from
`tests/models/*/test_modeling_*.py`.
"""
def get_module_path(test_file):
"""Return the module path of a model test file."""
components = test_file.split(os.path.sep)
if components[0:2] != ["tests", "models"]:
raise ValueError(
"`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got "
f"{test_file} instead."
)
test_fn = components[-1]
if not test_fn.endswith("py"):
raise ValueError(f"`test_file` should be a python file. Got {test_fn} instead.")
if not test_fn.startswith("test_modeling_"):
raise ValueError(
f"`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead."
)
components = components[:-1] + [test_fn.replace(".py", "")]
test_module_path = ".".join(components)
return test_module_path
def get_test_module(test_file):
"""Get the module of a model test file."""
test_module_path = get_module_path(test_file)
test_module = importlib.import_module(test_module_path)
return test_module
def get_tester_classes(test_file):
"""Get all classes in a model test file whose names ends with `ModelTester`."""
tester_classes = []
test_module = get_test_module(test_file)
for attr in dir(test_module):
if attr.endswith("ModelTester"):
tester_classes.append(getattr(test_module, attr))
# sort with class names
return sorted(tester_classes, key=lambda x: x.__name__)
def get_test_classes(test_file):
"""Get all [test] classes in a model test file with attribute `all_model_classes` that are non-empty.
These are usually the (model) test classes containing the (non-slow) tests to run and are subclasses of one of the
classes `ModelTesterMixin`, `TFModelTesterMixin` or `FlaxModelTesterMixin`, as well as a subclass of
`unittest.TestCase`. Exceptions include `RagTestMixin` (and its subclasses).
"""
test_classes = []
test_module = get_test_module(test_file)
for attr in dir(test_module):
attr_value = getattr(test_module, attr)
# (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
# `all_model_classes` is not empty (which also excludes other special classes).
model_classes = getattr(attr_value, "all_model_classes", [])
if len(model_classes) > 0:
test_classes.append(attr_value)
# sort with class names
return sorted(test_classes, key=lambda x: x.__name__)
def get_model_classes(test_file):
"""Get all model classes that appear in `all_model_classes` attributes in a model test file."""
test_classes = get_test_classes(test_file)
model_classes = set()
for test_class in test_classes:
model_classes.update(test_class.all_model_classes)
# sort with class names
return sorted(model_classes, key=lambda x: x.__name__)
def get_model_tester_from_test_class(test_class):
"""Get the model tester class of a model test class."""
test = test_class()
if hasattr(test, "setUp"):
test.setUp()
model_tester = None
if hasattr(test, "model_tester"):
# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
if test.model_tester is not None:
model_tester = test.model_tester.__class__
return model_tester
def get_test_classes_for_model(test_file, model_class):
"""Get all [test] classes in `test_file` that have `model_class` in their `all_model_classes`."""
test_classes = get_test_classes(test_file)
target_test_classes = []
for test_class in test_classes:
if model_class in test_class.all_model_classes:
target_test_classes.append(test_class)
# sort with class names
return sorted(target_test_classes, key=lambda x: x.__name__)
def get_tester_classes_for_model(test_file, model_class):
"""Get all model tester classes in `test_file` that are associated to `model_class`."""
test_classes = get_test_classes_for_model(test_file, model_class)
tester_classes = []
for test_class in test_classes:
tester_class = get_model_tester_from_test_class(test_class)
if tester_class is not None:
tester_classes.append(tester_class)
# sort with class names
return sorted(tester_classes, key=lambda x: x.__name__)
def get_test_to_tester_mapping(test_file):
"""Get a mapping from [test] classes to model tester classes in `test_file`.
This uses `get_test_classes` which may return classes that are NOT subclasses of `unittest.TestCase`.
"""
test_classes = get_test_classes(test_file)
test_tester_mapping = {test_class: get_model_tester_from_test_class(test_class) for test_class in test_classes}
return test_tester_mapping
def get_model_to_test_mapping(test_file):
"""Get a mapping from model classes to test classes in `test_file`."""
model_classes = get_model_classes(test_file)
model_test_mapping = {
model_class: get_test_classes_for_model(test_file, model_class) for model_class in model_classes
}
return model_test_mapping
def get_model_to_tester_mapping(test_file):
"""Get a mapping from model classes to model tester classes in `test_file`."""
model_classes = get_model_classes(test_file)
model_to_tester_mapping = {
model_class: get_tester_classes_for_model(test_file, model_class) for model_class in model_classes
}
return model_to_tester_mapping
def to_json(o):
"""Make the information succinct and easy to read.
Avoid the full class representation like `<class 'transformers.models.bert.modeling_bert.BertForMaskedLM'>` when
displaying the results. Instead, we use class name (`BertForMaskedLM`) for the readability.
"""
if isinstance(o, str):
return o
elif isinstance(o, type):
return o.__name__
elif isinstance(o, (list, tuple)):
return [to_json(x) for x in o]
elif isinstance(o, dict):
return {to_json(k): to_json(v) for k, v in o.items()}
else:
return o
| transformers/utils/get_test_info.py/0 | {
"file_path": "transformers/utils/get_test_info.py",
"repo_id": "transformers",
"token_count": 2577
} | 390 |
from transformers import ProcessorMixin
class CustomProcessor(ProcessorMixin):
feature_extractor_class = "AutoFeatureExtractor"
tokenizer_class = "AutoTokenizer"
| transformers/utils/test_module/custom_processing.py/0 | {
"file_path": "transformers/utils/test_module/custom_processing.py",
"repo_id": "transformers",
"token_count": 51
} | 391 |
include settings.ini
include LICENSE
include CONTRIBUTING.md
include README.md
recursive-exclude * __pycache__ | trl/MANIFEST.in/0 | {
"file_path": "trl/MANIFEST.in",
"repo_id": "trl",
"token_count": 33
} | 392 |
#!/bin/bash
# This script runs an SFT example end-to-end on a tiny model using different possible configurations
# but defaults to QLoRA + PEFT
OUTPUT_DIR="test_dpo/"
MODEL_NAME="HuggingFaceM4/tiny-random-LlamaForCausalLM"
DATASET_NAME="trl-internal-testing/hh-rlhf-trl-style"
MAX_STEPS=5
BATCH_SIZE=2
SEQ_LEN=128
# Handle extra arguments in case one passes accelerate configs.
EXTRA_ACCELERATE_ARGS=""
EXTRA_TRAINING_ARGS="""--use_peft \
--load_in_4bit
"""
# This is a hack to get the number of available GPUs
NUM_GPUS=2
if [[ "${TRL_ACCELERATE_CONFIG}" == "" ]]; then
EXTRA_ACCELERATE_ARGS=""
else
EXTRA_ACCELERATE_ARGS="--config_file $TRL_ACCELERATE_CONFIG"
# For DeepSpeed configs we need to set the `--fp16` flag to comply with our configs exposed
# on `examples/accelerate_configs` and our runners do not support bf16 mixed precision training.
if [[ $TRL_ACCELERATE_CONFIG == *"deepspeed"* ]]; then
EXTRA_TRAINING_ARGS="--fp16"
else
echo "Keeping QLoRA + PEFT"
fi
fi
CMD="""
accelerate launch $EXTRA_ACCELERATE_ARGS \
--num_processes $NUM_GPUS \
--mixed_precision 'fp16' \
`pwd`/examples/scripts/dpo.py \
--model_name_or_path $MODEL_NAME \
--dataset_name $DATASET_NAME \
--output_dir $OUTPUT_DIR \
--max_steps $MAX_STEPS \
--per_device_train_batch_size $BATCH_SIZE \
--max_length $SEQ_LEN \
$EXTRA_TRAINING_ARGS
"""
echo "Starting program..."
{ # try
echo $CMD
eval "$CMD"
} || { # catch
# save log for exception
echo "Operation Failed!"
exit 1
}
exit 0 | trl/commands/run_dpo.sh/0 | {
"file_path": "trl/commands/run_dpo.sh",
"repo_id": "trl",
"token_count": 639
} | 393 |
# KTO Trainer
TRL supports the Kahneman-Tversky Optimization (KTO) Trainer for aligning language models with binary feedback data (e.g., upvote/downvote), as described in the [paper](https://arxiv.org/abs/2402.01306) by Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff, Dan Jurafsky, and Douwe Kiela.
For a full example have a look at [`examples/scripts/kto.py`].
Depending on how good your base model is, you may or may not need to do SFT before KTO.
This is different from standard RLHF and DPO, which always require SFT.
## Expected dataset format
The KTO trainer expects a very specific format for the dataset as it does not require pairwise preferences. Since the model will be trained to directly optimize examples that consist of a prompt, model completion, and a label to indicate whether the completion is "good" or "bad", we expect a dataset with the following columns:
- `prompt`
- `completion`
- `label`
for example:
```
kto_dataset_dict = {
"prompt": [
"Hey, hello",
"How are you",
"What is your name?",
"What is your name?",
"Which is the best programming language?",
"Which is the best programming language?",
"Which is the best programming language?",
],
"completion": [
"hi nice to meet you",
"leave me alone",
"I don't have a name",
"My name is Mary",
"Python",
"C++",
"Java",
],
"label": [
True,
False,
False,
True,
True,
False,
False,
],
}
```
where the `prompt` contains the context inputs, `completion` contains the corresponding responses and `label` contains the corresponding flag that indicates if the generated completion is desired (`True`) or undesired (`False`).
A prompt can have multiple responses and this is reflected in the entries being repeated in the dictionary's value arrays.
## Expected model format
The KTO trainer expects a model of `AutoModelForCausalLM`, compared to PPO that expects `AutoModelForCausalLMWithValueHead` for the value function.
## Using the `KTOTrainer`
For a detailed example have a look at the `examples/scripts/kto.py` script. At a high level we need to initialize the `KTOTrainer` with a `model` we wish to train and a reference `ref_model` which we will use to calculate the implicit rewards of the preferred and rejected response.
The `beta` refers to the hyperparameter of the implicit reward, and the dataset contains the 3 entries listed above. Note that the `model` and `ref_model` need to have the same architecture (ie decoder only or encoder-decoder).
The `desirable_weight` and `undesirable_weight` refer to the weights placed on the losses for desirable/positive and undesirable/negative examples.
By default, they are both 1. However, if you have more of one or the other, then you should upweight the less common type such that the ratio of (`desirable_weight` * number of positives) to (`undesirable_weight` * number of negatives) is in the range 1:1 to 4:3.
```py
training_args = KTOConfig(
beta=0.1,
desirable_weight=1.0,
undesirable_weight=1.0,
)
kto_trainer = KTOTrainer(
model,
model_ref,
args=training_args,
train_dataset=train_dataset,
tokenizer=tokenizer,
)
```
After this one can then call:
```py
kto_trainer.train()
```
## KTOTrainer
[[autodoc]] KTOTrainer
## KTOConfig
[[autodoc]] KTOConfig | trl/docs/source/kto_trainer.mdx/0 | {
"file_path": "trl/docs/source/kto_trainer.mdx",
"repo_id": "trl",
"token_count": 1131
} | 394 |
# Examples
Please check out https://huggingface.co/docs/trl/example_overview for documentation on our examples. | trl/examples/README.md/0 | {
"file_path": "trl/examples/README.md",
"repo_id": "trl",
"token_count": 30
} | 395 |
from dataclasses import dataclass, field
from typing import Optional
import torch
from peft import PeftConfig, PeftModel
from transformers import AutoModelForCausalLM, AutoModelForSequenceClassification, AutoTokenizer, HfArgumentParser
@dataclass
class ScriptArguments:
"""
The input names representing the Adapter and Base model fine-tuned with PEFT, and the output name representing the
merged model.
"""
adapter_model_name: Optional[str] = field(default=None, metadata={"help": "the adapter name"})
base_model_name: Optional[str] = field(default=None, metadata={"help": "the base model name"})
output_name: Optional[str] = field(default=None, metadata={"help": "the merged model name"})
parser = HfArgumentParser(ScriptArguments)
script_args = parser.parse_args_into_dataclasses()[0]
assert script_args.adapter_model_name is not None, "please provide the name of the Adapter you would like to merge"
assert script_args.base_model_name is not None, "please provide the name of the Base model"
assert script_args.output_name is not None, "please provide the output name of the merged model"
peft_config = PeftConfig.from_pretrained(script_args.adapter_model_name)
if peft_config.task_type == "SEQ_CLS":
# The sequence classification task is used for the reward model in PPO
model = AutoModelForSequenceClassification.from_pretrained(
script_args.base_model_name, num_labels=1, torch_dtype=torch.bfloat16
)
else:
model = AutoModelForCausalLM.from_pretrained(
script_args.base_model_name, return_dict=True, torch_dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained(script_args.base_model_name)
# Load the PEFT model
model = PeftModel.from_pretrained(model, script_args.adapter_model_name)
model.eval()
model = model.merge_and_unload()
model.save_pretrained(f"{script_args.output_name}")
tokenizer.save_pretrained(f"{script_args.output_name}")
model.push_to_hub(f"{script_args.output_name}", use_temp_dir=False)
| trl/examples/research_projects/stack_llama/scripts/merge_peft_adapter.py/0 | {
"file_path": "trl/examples/research_projects/stack_llama/scripts/merge_peft_adapter.py",
"repo_id": "trl",
"token_count": 657
} | 396 |
# Copyright 2023 metric-space, The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
python examples/scripts/ddpo.py \
--num_epochs=200 \
--train_gradient_accumulation_steps=1 \
--sample_num_steps=50 \
--sample_batch_size=6 \
--train_batch_size=3 \
--sample_num_batches_per_epoch=4 \
--per_prompt_stat_tracking=True \
--per_prompt_stat_tracking_buffer_size=32 \
--tracker_project_name="stable_diffusion_training" \
--log_with="wandb"
"""
import os
from dataclasses import dataclass, field
import numpy as np
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from huggingface_hub.utils import EntryNotFoundError
from transformers import CLIPModel, CLIPProcessor, HfArgumentParser
from trl import DDPOConfig, DDPOTrainer, DefaultDDPOStableDiffusionPipeline
from trl.import_utils import is_npu_available, is_xpu_available
@dataclass
class ScriptArguments:
pretrained_model: str = field(
default="runwayml/stable-diffusion-v1-5", metadata={"help": "the pretrained model to use"}
)
pretrained_revision: str = field(default="main", metadata={"help": "the pretrained model revision to use"})
hf_hub_model_id: str = field(
default="ddpo-finetuned-stable-diffusion", metadata={"help": "HuggingFace repo to save model weights to"}
)
hf_hub_aesthetic_model_id: str = field(
default="trl-lib/ddpo-aesthetic-predictor",
metadata={"help": "HuggingFace model ID for aesthetic scorer model weights"},
)
hf_hub_aesthetic_model_filename: str = field(
default="aesthetic-model.pth",
metadata={"help": "HuggingFace model filename for aesthetic scorer model weights"},
)
use_lora: bool = field(default=True, metadata={"help": "Whether to use LoRA."})
class MLP(nn.Module):
def __init__(self):
super().__init__()
self.layers = nn.Sequential(
nn.Linear(768, 1024),
nn.Dropout(0.2),
nn.Linear(1024, 128),
nn.Dropout(0.2),
nn.Linear(128, 64),
nn.Dropout(0.1),
nn.Linear(64, 16),
nn.Linear(16, 1),
)
@torch.no_grad()
def forward(self, embed):
return self.layers(embed)
class AestheticScorer(torch.nn.Module):
"""
This model attempts to predict the aesthetic score of an image. The aesthetic score
is a numerical approximation of how much a specific image is liked by humans on average.
This is from https://github.com/christophschuhmann/improved-aesthetic-predictor
"""
def __init__(self, *, dtype, model_id, model_filename):
super().__init__()
self.clip = CLIPModel.from_pretrained("openai/clip-vit-large-patch14")
self.processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")
self.mlp = MLP()
try:
cached_path = hf_hub_download(model_id, model_filename)
except EntryNotFoundError:
cached_path = os.path.join(model_id, model_filename)
state_dict = torch.load(cached_path, map_location=torch.device("cpu"))
self.mlp.load_state_dict(state_dict)
self.dtype = dtype
self.eval()
@torch.no_grad()
def __call__(self, images):
device = next(self.parameters()).device
inputs = self.processor(images=images, return_tensors="pt")
inputs = {k: v.to(self.dtype).to(device) for k, v in inputs.items()}
embed = self.clip.get_image_features(**inputs)
# normalize embedding
embed = embed / torch.linalg.vector_norm(embed, dim=-1, keepdim=True)
return self.mlp(embed).squeeze(1)
def aesthetic_scorer(hub_model_id, model_filename):
scorer = AestheticScorer(
model_id=hub_model_id,
model_filename=model_filename,
dtype=torch.float32,
)
if is_npu_available():
scorer = scorer.npu()
elif is_xpu_available():
scorer = scorer.xpu()
else:
scorer = scorer.cuda()
def _fn(images, prompts, metadata):
images = (images * 255).round().clamp(0, 255).to(torch.uint8)
scores = scorer(images)
return scores, {}
return _fn
# list of example prompts to feed stable diffusion
animals = [
"cat",
"dog",
"horse",
"monkey",
"rabbit",
"zebra",
"spider",
"bird",
"sheep",
"deer",
"cow",
"goat",
"lion",
"frog",
"chicken",
"duck",
"goose",
"bee",
"pig",
"turkey",
"fly",
"llama",
"camel",
"bat",
"gorilla",
"hedgehog",
"kangaroo",
]
def prompt_fn():
return np.random.choice(animals), {}
def image_outputs_logger(image_data, global_step, accelerate_logger):
# For the sake of this example, we will only log the last batch of images
# and associated data
result = {}
images, prompts, _, rewards, _ = image_data[-1]
for i, image in enumerate(images):
prompt = prompts[i]
reward = rewards[i].item()
result[f"{prompt:.25} | {reward:.2f}"] = image.unsqueeze(0).float()
accelerate_logger.log_images(
result,
step=global_step,
)
if __name__ == "__main__":
parser = HfArgumentParser((ScriptArguments, DDPOConfig))
args, ddpo_config = parser.parse_args_into_dataclasses()
ddpo_config.project_kwargs = {
"logging_dir": "./logs",
"automatic_checkpoint_naming": True,
"total_limit": 5,
"project_dir": "./save",
}
pipeline = DefaultDDPOStableDiffusionPipeline(
args.pretrained_model, pretrained_model_revision=args.pretrained_revision, use_lora=args.use_lora
)
trainer = DDPOTrainer(
ddpo_config,
aesthetic_scorer(args.hf_hub_aesthetic_model_id, args.hf_hub_aesthetic_model_filename),
prompt_fn,
pipeline,
image_samples_hook=image_outputs_logger,
)
trainer.train()
trainer.push_to_hub(args.hf_hub_model_id)
| trl/examples/scripts/ddpo.py/0 | {
"file_path": "trl/examples/scripts/ddpo.py",
"repo_id": "trl",
"token_count": 2705
} | 397 |
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import itertools
import tempfile
import unittest
import torch
from accelerate.utils.memory import release_memory
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TrainingArguments
from trl import DPOTrainer, is_peft_available
from ..testing_utils import require_bitsandbytes, require_peft, require_torch_gpu
from .testing_constants import DPO_LOSS_TYPES, DPO_PRECOMPUTE_LOGITS, GRADIENT_CHECKPOINTING_KWARGS, MODELS_TO_TEST
if is_peft_available():
from peft import LoraConfig, PeftModel
@require_torch_gpu
class DPOTrainerSlowTester(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.dataset = load_dataset("trl-internal-testing/mlabonne-chatml-dpo-pairs-copy", split="train[:10%]")
cls.peft_config = LoraConfig(
lora_alpha=16,
lora_dropout=0.1,
r=8,
bias="none",
task_type="CAUSAL_LM",
)
cls.max_length = 128
def tearDown(self):
gc.collect()
torch.cuda.empty_cache()
gc.collect()
@parameterized.expand(list(itertools.product(MODELS_TO_TEST, DPO_LOSS_TYPES, DPO_PRECOMPUTE_LOGITS)))
def test_dpo_bare_model(self, model_id, loss_type, pre_compute_logits):
"""
A test that tests the simple usage of `DPOTrainer` using a bare model in full precision.
"""
model = AutoModelForCausalLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = TrainingArguments(
output_dir=tmp_dir,
per_device_train_batch_size=2,
max_steps=2,
remove_unused_columns=False,
gradient_accumulation_steps=2,
learning_rate=9e-1,
evaluation_strategy="steps",
fp16=True,
logging_strategy="no",
report_to="none",
)
# dpo train lora model
trainer = DPOTrainer(
model=model,
ref_model=None,
beta=0.1,
args=training_args,
tokenizer=tokenizer,
train_dataset=self.dataset,
eval_dataset=self.dataset,
loss_type=loss_type,
precompute_ref_log_probs=pre_compute_logits,
max_length=self.max_length,
)
# train the model
trainer.train()
# save trained model or adapter
trainer.save_model()
release_memory(model, trainer)
@parameterized.expand(
list(
itertools.product(
MODELS_TO_TEST,
DPO_LOSS_TYPES,
DPO_PRECOMPUTE_LOGITS,
GRADIENT_CHECKPOINTING_KWARGS,
)
)
)
@require_peft
def test_dpo_peft_model(self, model_id, loss_type, pre_compute_logits, gradient_checkpointing_kwargs):
"""
A test that tests the simple usage of `DPOTrainer` using a peft model in full precision + different scenarios of gradient checkpointing.
"""
model = AutoModelForCausalLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = TrainingArguments(
output_dir=tmp_dir,
per_device_train_batch_size=2,
max_steps=2,
remove_unused_columns=False,
gradient_accumulation_steps=2,
learning_rate=9e-1,
evaluation_strategy="steps",
fp16=True,
logging_strategy="no",
report_to="none",
gradient_checkpointing=True,
gradient_checkpointing_kwargs=gradient_checkpointing_kwargs,
)
# dpo train lora model
trainer = DPOTrainer(
model=model,
ref_model=None,
beta=0.1,
args=training_args,
tokenizer=tokenizer,
train_dataset=self.dataset,
eval_dataset=self.dataset,
generate_during_eval=False,
loss_type=loss_type,
precompute_ref_log_probs=pre_compute_logits,
peft_config=self.peft_config,
max_length=self.max_length,
)
assert isinstance(trainer.model, PeftModel)
assert trainer.ref_model is None
# train the model
trainer.train()
# save trained model or adapter
trainer.save_model()
release_memory(model, trainer)
@parameterized.expand(
list(
itertools.product(
MODELS_TO_TEST,
DPO_LOSS_TYPES,
DPO_PRECOMPUTE_LOGITS,
GRADIENT_CHECKPOINTING_KWARGS,
)
)
)
@require_bitsandbytes
@require_peft
def test_dpo_peft_model_qlora(self, model_id, loss_type, pre_compute_logits, gradient_checkpointing_kwargs):
"""
A test that tests the simple usage of `DPOTrainer` using QLoRA + different scenarios of gradient checkpointing.
"""
quantization_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.float16)
model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=quantization_config)
tokenizer = AutoTokenizer.from_pretrained(model_id)
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = TrainingArguments(
output_dir=tmp_dir,
per_device_train_batch_size=2,
max_steps=2,
remove_unused_columns=False,
gradient_accumulation_steps=2,
learning_rate=9e-1,
evaluation_strategy="steps",
fp16=True,
logging_strategy="no",
report_to="none",
gradient_checkpointing=True,
gradient_checkpointing_kwargs=gradient_checkpointing_kwargs,
)
# dpo train lora model
trainer = DPOTrainer(
model=model,
ref_model=None,
beta=0.1,
args=training_args,
tokenizer=tokenizer,
train_dataset=self.dataset,
eval_dataset=self.dataset,
generate_during_eval=False,
loss_type=loss_type,
precompute_ref_log_probs=pre_compute_logits,
peft_config=self.peft_config,
max_length=self.max_length,
)
assert isinstance(trainer.model, PeftModel)
assert trainer.ref_model is None
# train the model
trainer.train()
# save trained model or adapter
trainer.save_model()
release_memory(model, trainer)
| trl/tests/slow/test_dpo_slow.py/0 | {
"file_path": "trl/tests/slow/test_dpo_slow.py",
"repo_id": "trl",
"token_count": 3884
} | 398 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import tempfile
import unittest
import torch
from pytest import mark
from transformers import AutoModelForCausalLM
from trl import AutoModelForCausalLMWithValueHead, is_peft_available
if is_peft_available():
from peft import LoraConfig, get_peft_model
from .testing_utils import require_bitsandbytes, require_peft
@require_peft
@mark.peft_test
class PeftModelTester(unittest.TestCase):
def setUp(self):
self.causal_lm_model_id = "trl-internal-testing/tiny-random-GPTNeoXForCausalLM"
self.lora_config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
)
def test_create_peft_model(self):
r"""
Simply creates a peft model and checks that it can be loaded.
"""
causal_lm_model = AutoModelForCausalLM.from_pretrained(self.causal_lm_model_id)
pretrained_model = get_peft_model(causal_lm_model, self.lora_config)
_ = AutoModelForCausalLMWithValueHead.from_pretrained(pretrained_model)
def test_peft_requires_grad(self):
r"""
Check that the value head of the returned model has requires_grad=True.
"""
causal_lm_model = AutoModelForCausalLM.from_pretrained(self.causal_lm_model_id)
pretrained_model = get_peft_model(causal_lm_model, self.lora_config)
model = AutoModelForCausalLMWithValueHead.from_pretrained(pretrained_model)
# Check that the value head has requires_grad=True
assert model.v_head.summary.weight.requires_grad
def test_check_peft_model_nb_trainable_params(self):
r"""
Check that the number of trainable parameters is correct.
"""
causal_lm_model = AutoModelForCausalLM.from_pretrained(self.causal_lm_model_id)
pretrained_model = get_peft_model(causal_lm_model, self.lora_config)
model = AutoModelForCausalLMWithValueHead.from_pretrained(pretrained_model)
# Check that the number of trainable parameters is correct
nb_trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
assert nb_trainable_params == 10273
# Check that the number of trainable param for the non-peft model is correct
non_peft_model = AutoModelForCausalLMWithValueHead.from_pretrained(self.causal_lm_model_id)
nb_trainable_params = sum(p.numel() for p in non_peft_model.parameters() if p.requires_grad)
assert nb_trainable_params == 99578
def test_create_peft_model_from_config(self):
r"""
Simply creates a peft model and checks that it can be loaded.
"""
trl_model = AutoModelForCausalLMWithValueHead.from_pretrained(
self.causal_lm_model_id, peft_config=self.lora_config
)
# Check that the number of trainable parameters is correct
nb_trainable_params = sum(p.numel() for p in trl_model.parameters() if p.requires_grad)
assert nb_trainable_params == 10273
causal_lm_model = AutoModelForCausalLM.from_pretrained(self.causal_lm_model_id)
trl_model = AutoModelForCausalLMWithValueHead.from_pretrained(causal_lm_model, peft_config=self.lora_config)
# Check that the number of trainable parameters is correct
nb_trainable_params = sum(p.numel() for p in trl_model.parameters() if p.requires_grad)
assert nb_trainable_params == 10273
@require_bitsandbytes
def test_create_bnb_peft_model_from_config(self):
r"""
Simply creates a peft model and checks that it can be loaded.
"""
from bitsandbytes.nn import Linear8bitLt
trl_model = AutoModelForCausalLMWithValueHead.from_pretrained(
self.causal_lm_model_id, peft_config=self.lora_config, load_in_8bit=True
)
# Check that the number of trainable parameters is correct
nb_trainable_params = sum(p.numel() for p in trl_model.parameters() if p.requires_grad)
assert nb_trainable_params == 10273
assert trl_model.pretrained_model.model.gpt_neox.layers[0].mlp.dense_h_to_4h.__class__ == Linear8bitLt
causal_lm_model = AutoModelForCausalLM.from_pretrained(
self.causal_lm_model_id, load_in_8bit=True, device_map="auto"
)
trl_model = AutoModelForCausalLMWithValueHead.from_pretrained(causal_lm_model, peft_config=self.lora_config)
# Check that the number of trainable parameters is correct
nb_trainable_params = sum(p.numel() for p in trl_model.parameters() if p.requires_grad)
assert nb_trainable_params == 10273
assert trl_model.pretrained_model.model.gpt_neox.layers[0].mlp.dense_h_to_4h.__class__ == Linear8bitLt
def test_save_pretrained_peft(self):
r"""
Check that the model can be saved and loaded properly.
"""
causal_lm_model = AutoModelForCausalLM.from_pretrained(self.causal_lm_model_id)
pretrained_model = get_peft_model(causal_lm_model, self.lora_config)
model = AutoModelForCausalLMWithValueHead.from_pretrained(pretrained_model)
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir)
# check that the files `adapter_model.safetensors` and `adapter_config.json` are in the directory
assert os.path.isfile(
f"{tmp_dir}/adapter_model.safetensors"
), f"{tmp_dir}/adapter_model.safetensors does not exist"
assert os.path.exists(f"{tmp_dir}/adapter_config.json"), f"{tmp_dir}/adapter_config.json does not exist"
# check also for `pytorch_model.bin` and make sure it only contains `v_head` weights
assert os.path.exists(f"{tmp_dir}/pytorch_model.bin"), f"{tmp_dir}/pytorch_model.bin does not exist"
maybe_v_head = torch.load(f"{tmp_dir}/pytorch_model.bin")
# check that only keys that starts with `v_head` are in the dict
assert all(
k.startswith("v_head") for k in maybe_v_head.keys()
), f"keys in {tmp_dir}/pytorch_model.bin do not start with `v_head`"
model_from_pretrained = AutoModelForCausalLMWithValueHead.from_pretrained(tmp_dir)
# check all the weights are the same
for p1, p2 in zip(model.named_parameters(), model_from_pretrained.named_parameters()):
assert torch.allclose(p1[1], p2[1]), f"{p1[0]} != {p2[0]}"
def test_load_pretrained_peft(self):
r"""
Check that the model saved with peft class interface can be loaded properly.
"""
causal_lm_model = AutoModelForCausalLM.from_pretrained(self.causal_lm_model_id)
pretrained_model = get_peft_model(causal_lm_model, self.lora_config)
model = AutoModelForCausalLMWithValueHead.from_pretrained(pretrained_model)
with tempfile.TemporaryDirectory() as tmp_dir:
pretrained_model.save_pretrained(tmp_dir)
model_from_pretrained = AutoModelForCausalLMWithValueHead.from_pretrained(tmp_dir)
# check that the files `adapter_model.safetensors` and `adapter_config.json` are in the directory
assert os.path.isfile(
f"{tmp_dir}/adapter_model.safetensors"
), f"{tmp_dir}/adapter_model.safetensors does not exist"
assert os.path.exists(f"{tmp_dir}/adapter_config.json"), f"{tmp_dir}/adapter_config.json does not exist"
# check all the weights are the same
for p1, p2 in zip(model.named_parameters(), model_from_pretrained.named_parameters()):
if p1[0] not in ["v_head.summary.weight", "v_head.summary.bias"]:
assert torch.allclose(p1[1], p2[1]), f"{p1[0]} != {p2[0]}"
def test_continue_training_peft_model(self):
r"""
Load peft and checks that it can continue training.
"""
causal_lm_model = AutoModelForCausalLM.from_pretrained(self.causal_lm_model_id)
pretrained_model = get_peft_model(causal_lm_model, self.lora_config)
with tempfile.TemporaryDirectory() as tmp_dir:
pretrained_model.save_pretrained(tmp_dir)
# set is_trainable to True
model = AutoModelForCausalLMWithValueHead.from_pretrained(tmp_dir, is_trainable=True)
# Check that the number of trainable parameters is correct
nb_trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
assert nb_trainable_params == 10273
| trl/tests/test_peft_models.py/0 | {
"file_path": "trl/tests/test_peft_models.py",
"repo_id": "trl",
"token_count": 3832
} | 399 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import importlib
import os
import sys
from importlib.util import find_spec
from itertools import chain
from types import ModuleType
from typing import Any
if sys.version_info < (3, 8):
_is_python_greater_3_8 = False
else:
_is_python_greater_3_8 = True
def is_peft_available() -> bool:
return find_spec("peft") is not None
def is_unsloth_available() -> bool:
return find_spec("unsloth") is not None
def is_accelerate_greater_20_0() -> bool:
if _is_python_greater_3_8:
from importlib.metadata import version
accelerate_version = version("accelerate")
else:
import pkg_resources
accelerate_version = pkg_resources.get_distribution("accelerate").version
return accelerate_version >= "0.20.0"
def is_transformers_greater_than(current_version: str) -> bool:
if _is_python_greater_3_8:
from importlib.metadata import version
_transformers_version = version("transformers")
else:
import pkg_resources
_transformers_version = pkg_resources.get_distribution("transformers").version
return _transformers_version > current_version
def is_torch_greater_2_0() -> bool:
if _is_python_greater_3_8:
from importlib.metadata import version
torch_version = version("torch")
else:
import pkg_resources
torch_version = pkg_resources.get_distribution("torch").version
return torch_version >= "2.0"
def is_diffusers_available() -> bool:
return find_spec("diffusers") is not None
def is_bitsandbytes_available() -> bool:
import torch
# bnb can be imported without GPU but is not usable.
return find_spec("bitsandbytes") is not None and torch.cuda.is_available()
def is_torchvision_available() -> bool:
return find_spec("torchvision") is not None
def is_rich_available() -> bool:
return find_spec("rich") is not None
def is_wandb_available() -> bool:
return find_spec("wandb") is not None
def is_xpu_available() -> bool:
if is_accelerate_greater_20_0():
import accelerate
return accelerate.utils.is_xpu_available()
else:
if find_spec("intel_extension_for_pytorch") is None:
return False
try:
import torch
return hasattr(torch, "xpu") and torch.xpu.is_available()
except RuntimeError:
return False
def is_npu_available() -> bool:
"""Checks if `torch_npu` is installed and potentially if a NPU is in the environment"""
if find_spec("torch") is None or find_spec("torch_npu") is None:
return False
import torch
import torch_npu # noqa: F401
return hasattr(torch, "npu") and torch.npu.is_available()
class _LazyModule(ModuleType):
"""
Module class that surfaces all objects but only performs associated imports when the objects are requested.
"""
# Very heavily inspired by optuna.integration._IntegrationModule
# https://github.com/optuna/optuna/blob/master/optuna/integration/__init__.py
def __init__(self, name, module_file, import_structure, module_spec=None, extra_objects=None):
super().__init__(name)
self._modules = set(import_structure.keys())
self._class_to_module = {}
for key, values in import_structure.items():
for value in values:
self._class_to_module[value] = key
# Needed for autocompletion in an IDE
self.__all__ = list(import_structure.keys()) + list(chain(*import_structure.values()))
self.__file__ = module_file
self.__spec__ = module_spec
self.__path__ = [os.path.dirname(module_file)]
self._objects = {} if extra_objects is None else extra_objects
self._name = name
self._import_structure = import_structure
# Needed for autocompletion in an IDE
def __dir__(self):
result = super().__dir__()
# The elements of self.__all__ that are submodules may or may not be in the dir already, depending on whether
# they have been accessed or not. So we only add the elements of self.__all__ that are not already in the dir.
for attr in self.__all__:
if attr not in result:
result.append(attr)
return result
def __getattr__(self, name: str) -> Any:
if name in self._objects:
return self._objects[name]
if name in self._modules:
value = self._get_module(name)
elif name in self._class_to_module.keys():
module = self._get_module(self._class_to_module[name])
value = getattr(module, name)
else:
raise AttributeError(f"module {self.__name__} has no attribute {name}")
setattr(self, name, value)
return value
def _get_module(self, module_name: str):
try:
return importlib.import_module("." + module_name, self.__name__)
except Exception as e:
raise RuntimeError(
f"Failed to import {self.__name__}.{module_name} because of the following error (look up to see its"
f" traceback):\n{e}"
) from e
def __reduce__(self):
return (self.__class__, (self._name, self.__file__, self._import_structure))
class OptionalDependencyNotAvailable(BaseException):
"""Internally used error class for signalling an optional dependency was not found."""
| trl/trl/import_utils.py/0 | {
"file_path": "trl/trl/import_utils.py",
"repo_id": "trl",
"token_count": 2272
} | 400 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import sys
import warnings
from dataclasses import dataclass, field
from typing import Literal, Optional
import numpy as np
import tyro
from typing_extensions import Annotated
from trl.trainer.utils import exact_div
from ..core import flatten_dict
from ..import_utils import is_wandb_available
JSONDict = Annotated[Optional[dict], tyro.conf.arg(metavar="JSON", constructor=json.loads)]
@dataclass
class PPOConfig:
"""
Configuration class for PPOTrainer
"""
# common parameters
exp_name: str = os.path.basename(sys.argv[0])[: -len(".py")]
"""the name of this experiment (by default is the file name without the extension name)"""
seed: int = 0
"""Seed value for random generations"""
log_with: Optional[Literal["wandb", "tensorboard"]] = None
"""Log with either 'wandb' or 'tensorboard', check https://huggingface.co/docs/accelerate/usage_guides/tracking for more details"""
task_name: Optional[str] = None
"""Name of task to use - used only for tracking purposes"""
model_name: Optional[str] = "gpt2"
"""Name of model to use - used only for tracking purposes"""
query_dataset: Optional[str] = "imdb"
"""Name of dataset to query - used only for tracking purposes"""
reward_model: Optional[str] = "sentiment-analysis:lvwerra/distilbert-imdb"
"""The reward model to use - used only for tracking purposes"""
remove_unused_columns: bool = True
"""Remove unused columns from the dataset if `datasets.Dataset` is used"""
tracker_kwargs: JSONDict = field(default_factory=dict)
"""Keyword arguments for the tracker (e.g. python ppo.py --tracker_kwargs='{"wandb": {"entity": "my_wandb_entity", "name": "my_exp_name"}}'"""
accelerator_kwargs: JSONDict = field(default_factory=dict)
"""Keyword arguments for the accelerator"""
project_kwargs: JSONDict = field(default_factory=dict)
"""Keyword arguments for the accelerator project config (e.g. `logging_dir`)"""
tracker_project_name: str = "trl"
"""Name of project to use for tracking"""
push_to_hub_if_best_kwargs: JSONDict = field(default_factory=dict)
"""Keyword arguments for pushing model to the hub during training (e.g. repo_id)"""
# hyperparameters
steps: int = 20000
"""Number of training steps"""
learning_rate: float = 1.41e-5
"""Adam learning rate"""
adap_kl_ctrl: bool = True
"""Use adaptive KL control, otherwise linear"""
init_kl_coef: Optional[float] = 0.2
"""Initial KL penalty coefficient (used for adaptive and linear control)"""
kl_penalty: Literal["kl", "abs", "mse", "full"] = "kl"
"""kl penalty options: 'kl': model_logp - ref_logp, 'abs': abs(kl), 'mse': mean squared error mse(kl) and 'full': the actual kl for all tokens in the distribution"""
target: Optional[float] = 6
"""Target KL value for adaptive KL control"""
horizon: Optional[float] = 10000
"""Horizon for adaptive KL control"""
gamma: float = 1
"""Gamma parameter for advantage calculation"""
lam: float = 0.95
"""Lambda parameter for advantage calculation"""
cliprange: float = 0.2
"""Range for clipping in PPO policy gradient loss"""
cliprange_value: float = 0.2
"""Range for clipping values in loss calculation"""
vf_coef: float = 0.1
"""Scaling factor for value loss"""
batch_size: int = 128
"""Number of samples per optimisation step"""
forward_batch_size: Optional[int] = None
"""DEPRECATED: use `mini_batch_size` instead, which does the same thing."""
mini_batch_size: int = 128
"""Number of samples optimized in each mini batch"""
gradient_accumulation_steps: int = 1
"""The number of gradient accumulation steps"""
world_size: tyro.conf.Suppress[int] = None
"""The world size for distributed training"""
ppo_epochs: int = 4
"""Number of optimisation epochs per batch of samples"""
max_grad_norm: Optional[float] = None
"""Maximum gradient norm for gradient clipping"""
optimize_cuda_cache: Optional[bool] = None
"""DEPRECATED: use `optimize_device_cache` instead, which does the same thing."""
optimize_device_cache: Optional[bool] = False
"""Optimize device cache for slightly more memory-efficient training"""
early_stopping: bool = False
"""Whether to stop the PPO optimization loop early is the KL too high"""
target_kl: float = 1
"""Stop early if we exceed this value by over 50%"""
compare_steps: int = 1
"""Number of steps between comparison of the current reward with the best seen so far"""
ratio_threshold: float = 10.0
"""Skip mini-batches with high PPO ratios that can cause loss spikes"""
use_score_scaling: bool = False
"""Use score scaling"""
use_score_norm: bool = False
"""Use score normalization. Only applicable if use_score_scaling is True"""
score_clip: Optional[float] = None
"""Score clipping"""
whiten_rewards: bool = False
"""Whiten the rewards before compute advantages"""
# computed hyperparameters at runtime; we use `tyro.conf.Suppress` to hide them from the help text
is_encoder_decoder: Optional[tyro.conf.Suppress[bool]] = None
"""TO BE FILLED In RUNTIME: Whether the model is an encoder-decoder model"""
is_peft_model: Optional[tyro.conf.Suppress[bool]] = None
"""TO BE FILLED In RUNTIME: Whether the model is a PEFT model"""
backward_batch_size: tyro.conf.Suppress[int] = None
"""TO BE FILLED In RUNTIME: Number of samples optimized in an `optimizer.step()` call"""
global_backward_batch_size: tyro.conf.Suppress[int] = None
"""TO BE FILLED In RUNTIME: the effective `backward_batch_size` across all processes"""
global_batch_size: tyro.conf.Suppress[int] = None
"""TO BE FILLED In RUNTIME: the effective `batch_size` across all processes"""
if optimize_cuda_cache is not None:
warnings.warn(
"The `optimize_cuda_cache` argument will be deprecated soon, please use `optimize_device_cache` instead."
)
optimize_device_cache = optimize_cuda_cache
else:
optimize_device_cache = False
def __post_init__(self):
if self.forward_batch_size is not None:
warnings.warn(
"Note that using `forward_batch_size` is deprecated, use `mini_batch_size` instead. By setting it you overwrite `mini_batch_size` which affects both the batch size during forward passes and also the mini batch size for PPO optimization."
)
self.mini_batch_size = self.forward_batch_size
self.backward_batch_size = self.mini_batch_size * self.gradient_accumulation_steps
exact_div(
self.batch_size,
self.backward_batch_size,
"`batch_size`",
"`mini_batch_size * gradient_accumulation_steps`",
"`batch_size` must be a multiple of `mini_batch_size * gradient_accumulation_steps`",
)
# check if wandb is installed
if self.log_with == "wandb":
# raise error if wandb is not installed
if not is_wandb_available():
raise ImportError(
"Please install wandb to use wandb logging. You can do this by running `pip install wandb`."
)
self.total_ppo_epochs = int(np.ceil(self.steps / self.batch_size))
assert self.kl_penalty in ["kl", "abs", "mse", "full"]
def to_dict(self):
output_dict = {}
for key, value in self.__dict__.items():
output_dict[key] = value
return flatten_dict(output_dict)
| trl/trl/trainer/ppo_config.py/0 | {
"file_path": "trl/trl/trainer/ppo_config.py",
"repo_id": "trl",
"token_count": 2849
} | 401 |
.PHONY: quality style test docs utils
check_dirs := .
# Check that source code meets quality standards
extra_quality_checks:
python utils/check_copies.py
python utils/check_dummies.py
python utils/check_repo.py
doc-builder style src/accelerate docs/source --max_len 119
# this target runs checks on all files
quality:
ruff check $(check_dirs)
ruff format --check $(check_dirs)
doc-builder style src/accelerate docs/source --max_len 119 --check_only
# Format source code automatically and check is there are any problems left that need manual fixing
style:
ruff check $(check_dirs) --fix
ruff format $(check_dirs)
doc-builder style src/accelerate docs/source --max_len 119
# Run tests for the library
test_big_modeling:
python -m pytest -s -v ./tests/test_big_modeling.py ./tests/test_modeling_utils.py $(if $(IS_GITHUB_CI),--report-log "$(PYTORCH_VERSION)_big_modeling.log",)
test_core:
python -m pytest -s -v ./tests/ --ignore=./tests/test_examples.py --ignore=./tests/deepspeed --ignore=./tests/test_big_modeling.py \
--ignore=./tests/fsdp --ignore=./tests/test_cli.py $(if $(IS_GITHUB_CI),--report-log "$(PYTORCH_VERSION)_core.log",)
test_cli:
python -m pytest -s -v ./tests/test_cli.py $(if $(IS_GITHUB_CI),--report-log "$(PYTORCH_VERSION)_cli.log",)
test_deepspeed:
python -m pytest -s -v ./tests/deepspeed $(if $(IS_GITHUB_CI),--report-log "$(PYTORCH_VERSION)_deepspeed.log",)
test_fsdp:
python -m pytest -s -v ./tests/fsdp $(if $(IS_GITHUB_CI),--report-log "$(PYTORCH_VERSION)_fsdp.log",)
# Since the new version of pytest will *change* how things are collected, we need `deepspeed` to
# run after test_core and test_cli
test:
$(MAKE) test_core
$(MAKE) test_cli
$(MAKE) test_big_modeling
$(MAKE) test_deepspeed
$(MAKE) test_fsdp
test_examples:
python -m pytest -s -v ./tests/test_examples.py $(if $(IS_GITHUB_CI),--report-log "$(PYTORCH_VERSION)_examples.log",)
# Broken down example tests for the CI runners
test_integrations:
python -m pytest -s -v ./tests/deepspeed ./tests/fsdp $(if $(IS_GITHUB_CI),--report-log "$(PYTORCH_VERSION)_integrations.log",)
test_example_differences:
python -m pytest -s -v ./tests/test_examples.py::ExampleDifferenceTests $(if $(IS_GITHUB_CI),--report-log "$(PYTORCH_VERSION)_example_diff.log",)
test_checkpoint_epoch:
python -m pytest -s -v ./tests/test_examples.py::FeatureExamplesTests -k "by_epoch" $(if $(IS_GITHUB_CI),--report-log "$(PYTORCH_VERSION)_checkpoint_epoch.log",)
test_checkpoint_step:
python -m pytest -s -v ./tests/test_examples.py::FeatureExamplesTests -k "by_step" $(if $(IS_GITHUB_CI),--report-log "$(PYTORCH_VERSION)_checkpoint_step.log",)
# Same as test but used to install only the base dependencies
test_prod:
$(MAKE) test_core
test_rest:
python -m pytest -s -v ./tests/test_examples.py::FeatureExamplesTests -k "not by_step and not by_epoch" $(if $(IS_GITHUB_CI),--report-log "$(PYTORCH_VERSION)_rest.log",)
| accelerate/Makefile/0 | {
"file_path": "accelerate/Makefile",
"repo_id": "accelerate",
"token_count": 1111
} | 0 |
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# Learning how to incorporate 🤗 Accelerate features quickly!
Please use the interactive tool below to help you get started with learning about a particular
feature of 🤗 Accelerate and how to utilize it! It will provide you with a code diff, an explanation
towards what is going on, as well as provide you with some useful links to explore more within
the documentation!
Most code examples start from the following python code before integrating 🤗 Accelerate in some way:
```python
for batch in dataloader:
optimizer.zero_grad()
inputs, targets = batch
inputs = inputs.to(device)
targets = targets.to(device)
outputs = model(inputs)
loss = loss_function(outputs, targets)
loss.backward()
optimizer.step()
scheduler.step()
```
<div class="block dark:hidden">
<iframe
src="https://hf-accelerate-accelerate-examples.hf.space?__theme=light"
width="850"
height="1600"
></iframe>
</div>
<div class="hidden dark:block">
<iframe
src="https://hf-accelerate-accelerate-examples.hf.space?__theme=dark"
width="850"
height="1600"
></iframe>
</div>
| accelerate/docs/source/usage_guides/explore.md/0 | {
"file_path": "accelerate/docs/source/usage_guides/explore.md",
"repo_id": "accelerate",
"token_count": 581
} | 1 |
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
MAX_GPU_BATCH_SIZE = 16
EVAL_BATCH_SIZE = 32
def get_dataloaders(accelerator: Accelerator, batch_size: int = 16):
"""
Creates a set of `DataLoader`s for the `glue` dataset,
using "bert-base-cased" as the tokenizer.
Args:
accelerator (`Accelerator`):
An `Accelerator` object
batch_size (`int`, *optional*):
The batch size for the train and validation DataLoaders.
"""
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
datasets = load_dataset("glue", "mrpc")
def tokenize_function(examples):
# max_length=None => use the model max length (it's actually the default)
outputs = tokenizer(examples["sentence1"], examples["sentence2"], truncation=True, max_length=None)
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
remove_columns=["idx", "sentence1", "sentence2"],
)
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
tokenized_datasets = tokenized_datasets.rename_column("label", "labels")
def collate_fn(examples):
# On TPU it's best to pad everything to the same length or training will be very slow.
max_length = 128 if accelerator.distributed_type == DistributedType.XLA else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
pad_to_multiple_of = 16
elif accelerator.mixed_precision != "no":
pad_to_multiple_of = 8
else:
pad_to_multiple_of = None
return tokenizer.pad(
examples,
padding="longest",
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors="pt",
)
# Instantiate dataloaders.
train_dataloader = DataLoader(
tokenized_datasets["train"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size
)
eval_dataloader = DataLoader(
tokenized_datasets["validation"], shuffle=False, collate_fn=collate_fn, batch_size=EVAL_BATCH_SIZE
)
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
get_dataloaders = mocked_dataloaders # noqa: F811
def training_function(config, args):
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
config["num_epochs"] = 2
# Initialize accelerator
accelerator = Accelerator(cpu=args.cpu, mixed_precision=args.mixed_precision)
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lr = config["lr"]
num_epochs = int(config["num_epochs"])
seed = int(config["seed"])
batch_size = int(config["batch_size"])
# New Code #
# Parse out whether we are saving every epoch or after a certain number of batches
if hasattr(args.checkpointing_steps, "isdigit"):
if args.checkpointing_steps == "epoch":
checkpointing_steps = args.checkpointing_steps
elif args.checkpointing_steps.isdigit():
checkpointing_steps = int(args.checkpointing_steps)
else:
raise ValueError(
f"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."
)
else:
checkpointing_steps = None
set_seed(seed)
train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)
metric = evaluate.load("glue", "mrpc")
# If the batch size is too big we use gradient accumulation
gradient_accumulation_steps = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.XLA:
gradient_accumulation_steps = batch_size // MAX_GPU_BATCH_SIZE
batch_size = MAX_GPU_BATCH_SIZE
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", return_dict=True)
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
model = model.to(accelerator.device)
# Instantiate optimizer
optimizer = AdamW(params=model.parameters(), lr=lr)
# Instantiate scheduler
lr_scheduler = get_linear_schedule_with_warmup(
optimizer=optimizer,
num_warmup_steps=100,
num_training_steps=(len(train_dataloader) * num_epochs) // gradient_accumulation_steps,
)
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(
model, optimizer, train_dataloader, eval_dataloader, lr_scheduler
)
# New Code #
# We need to keep track of how many total steps we have iterated over
overall_step = 0
# We also need to keep track of the stating epoch so files are named properly
starting_epoch = 0
# We need to load the checkpoint back in before training here with `load_state`
# The total number of epochs is adjusted based on where the state is being loaded from,
# as we assume continuation of the same training script
if args.resume_from_checkpoint:
if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}")
accelerator.load_state(args.resume_from_checkpoint)
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the most recent checkpoint
dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()]
dirs.sort(key=os.path.getctime)
path = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last
# Extract `epoch_{i}` or `step_{i}`
training_difference = os.path.splitext(path)[0]
if "epoch" in training_difference:
starting_epoch = int(training_difference.replace("epoch_", "")) + 1
resume_step = None
else:
resume_step = int(training_difference.replace("step_", ""))
starting_epoch = resume_step // len(train_dataloader)
resume_step -= starting_epoch * len(train_dataloader)
# Now we train the model
for epoch in range(starting_epoch, num_epochs):
model.train()
# New Code #
if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
# We need to skip steps until we reach the resumed step
active_dataloader = accelerator.skip_first_batches(train_dataloader, resume_step)
overall_step += resume_step
else:
# After the first iteration though, we need to go back to the original dataloader
active_dataloader = train_dataloader
for step, batch in enumerate(active_dataloader):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device)
outputs = model(**batch)
loss = outputs.loss
loss = loss / gradient_accumulation_steps
accelerator.backward(loss)
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# New Code #
overall_step += 1
# New Code #
# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
# These are saved to folders named `step_{overall_step}`
# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
# If mixed precision was used, will also save a "scalar.bin" file
if isinstance(checkpointing_steps, int):
output_dir = f"step_{overall_step}"
if overall_step % checkpointing_steps == 0:
if args.output_dir is not None:
output_dir = os.path.join(args.output_dir, output_dir)
accelerator.save_state(output_dir)
model.eval()
for step, batch in enumerate(eval_dataloader):
# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
batch.to(accelerator.device)
with torch.no_grad():
outputs = model(**batch)
predictions = outputs.logits.argmax(dim=-1)
predictions, references = accelerator.gather_for_metrics((predictions, batch["labels"]))
metric.add_batch(
predictions=predictions,
references=references,
)
eval_metric = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f"epoch {epoch}:", eval_metric)
# New Code #
# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
# These are saved to folders named `epoch_{epoch}`
# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
# If mixed precision was used, will also save a "scalar.bin" file
if checkpointing_steps == "epoch":
output_dir = f"epoch_{epoch}"
if args.output_dir is not None:
output_dir = os.path.join(args.output_dir, output_dir)
accelerator.save_state(output_dir)
def main():
parser = argparse.ArgumentParser(description="Simple example of training script.")
parser.add_argument(
"--mixed_precision",
type=str,
default=None,
choices=["no", "fp16", "bf16", "fp8"],
help="Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU.",
)
parser.add_argument("--cpu", action="store_true", help="If passed, will train on the CPU.")
parser.add_argument(
"--checkpointing_steps",
type=str,
default=None,
help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",
)
parser.add_argument(
"--output_dir",
type=str,
default=".",
help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory.",
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help="If the training should continue from a checkpoint folder.",
)
args = parser.parse_args()
config = {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
training_function(config, args)
if __name__ == "__main__":
main()
| accelerate/examples/by_feature/checkpointing.py/0 | {
"file_path": "accelerate/examples/by_feature/checkpointing.py",
"repo_id": "accelerate",
"token_count": 5198
} | 2 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from manim import *
class Stage3(Scene):
def construct(self):
mem = Rectangle(height=0.5,width=0.5)
meta_mem = Rectangle(height=0.25,width=0.25)
fill = Rectangle(height=0.46,width=0.46).set_stroke(width=0)
cpu_left_col_base = [mem.copy() for i in range(6)]
cpu_right_col_base = [mem.copy() for i in range(6)]
cpu_left_col = VGroup(*cpu_left_col_base).arrange(UP, buff=0)
cpu_right_col = VGroup(*cpu_right_col_base).arrange(UP, buff=0)
cpu_rects = VGroup(cpu_left_col,cpu_right_col).arrange(RIGHT, buff=0)
cpu_text = Text("CPU", font_size=24)
cpu = Group(cpu_rects,cpu_text).arrange(DOWN, buff=0.5, aligned_edge=DOWN)
cpu.move_to([-2.5,-.5,0])
self.add(cpu)
gpu_base = [mem.copy() for i in range(4)]
gpu_rect = VGroup(*gpu_base).arrange(UP,buff=0)
gpu_text = Text("GPU", font_size=24)
gpu = Group(gpu_rect,gpu_text).arrange(DOWN, buff=0.5, aligned_edge=DOWN)
gpu.move_to([-1,-1,0])
self.add(gpu)
model_base = [mem.copy() for i in range(6)]
model_rect = VGroup(*model_base).arrange(RIGHT,buff=0)
model_text = Text("Model", font_size=24)
model = Group(model_rect,model_text).arrange(DOWN, buff=0.5, aligned_edge=DOWN)
model.move_to([3, -1., 0])
self.add(model)
model_arr = []
model_cpu_arr = []
model_meta_arr = []
for i,rect in enumerate(model_base):
rect.set_stroke(YELLOW)
cpu_target = Rectangle(height=0.46/4,width=0.46/3).set_stroke(width=0.).set_fill(YELLOW, opacity=0.7)
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN+LEFT), buff=0.02, direction=UP)
cpu_target.set_x(cpu_target.get_x()+0.1)
elif i == 3:
cpu_target.next_to(model_cpu_arr[0], direction=UP, buff=0.)
else:
cpu_target.next_to(model_cpu_arr[i-1], direction=RIGHT, buff=0.)
self.add(cpu_target)
model_cpu_arr.append(cpu_target)
self.add(*model_arr, *model_cpu_arr, *model_meta_arr)
checkpoint_base = [mem.copy() for i in range(6)]
checkpoint_rect = VGroup(*checkpoint_base).arrange(RIGHT,buff=0)
checkpoint_text = Text("Loaded Checkpoint", font_size=24)
checkpoint = Group(checkpoint_rect,checkpoint_text).arrange(DOWN, buff=0.5, aligned_edge=DOWN)
checkpoint.move_to([3, .5, 0])
self.add(checkpoint)
ckpt_arr = []
ckpt_cpu_arr = []
for i,rect in enumerate(checkpoint_base):
target = fill.copy().set_fill(BLUE, opacity=0.7)
target.move_to(rect)
ckpt_arr.append(target)
cpu_target = target.copy()
if i < 5:
cpu_target.move_to(cpu_left_col_base[i+1])
else:
cpu_target.move_to(cpu_right_col_base[i-5])
ckpt_cpu_arr.append(cpu_target)
self.add(*ckpt_arr, *ckpt_cpu_arr)
key = Square(side_length=2.2)
key.move_to([-5, 2, 0])
key_text = MarkupText(
f"<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model",
font_size=18,
)
key_text.move_to([-5, 2.4, 0])
self.add(key_text, key)
blue_text = MarkupText(
f"<span fgcolor='{BLUE}'>●</span> Checkpoint",
font_size=18,
)
blue_text.next_to(key_text, DOWN*2.4, aligned_edge=key_text.get_left())
self.add(blue_text)
step_3 = MarkupText(
f'Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.',
font_size=24
)
step_3.move_to([2, 2, 0])
disk_left_col_base = [meta_mem.copy() for i in range(6)]
disk_right_col_base = [meta_mem.copy() for i in range(6)]
disk_left_col = VGroup(*disk_left_col_base).arrange(UP, buff=0)
disk_right_col = VGroup(*disk_right_col_base).arrange(UP, buff=0)
disk_rects = VGroup(disk_left_col,disk_right_col).arrange(RIGHT, buff=0)
disk_text = Text("Disk", font_size=24)
disk = Group(disk_rects,disk_text).arrange(DOWN, buff=0.5, aligned_edge=DOWN)
disk.move_to([-4.,-1.25,0])
self.play(
Write(step_3, run_time=3),
Write(disk_text, run_time=1),
Create(disk_rects, run_time=1)
)
animations = []
for i,rect in enumerate(ckpt_cpu_arr):
target = rect.copy()
target.generate_target()
target.target.move_to(disk_left_col_base[i]).scale(0.5)
animations.append(MoveToTarget(target, run_time=1.5))
self.play(*animations)
self.play(FadeOut(step_3))
step_4 = MarkupText(
f'Then, the checkpoint is removed from memory\nthrough garbage collection.',
font_size=24
)
step_4.move_to([2, 2, 0])
self.play(
Write(step_4, run_time=3)
)
self.play(
FadeOut(checkpoint_rect, checkpoint_text, *ckpt_arr, *ckpt_cpu_arr),
)
self.wait() | accelerate/manim_animations/big_model_inference/stage_3.py/0 | {
"file_path": "accelerate/manim_animations/big_model_inference/stage_3.py",
"repo_id": "accelerate",
"token_count": 2891
} | 3 |
#!/usr/bin/env python
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from pathlib import Path
import torch
from ...utils import is_mlu_available, is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter
description = "Create a default config file for Accelerate with only a few flags set."
def write_basic_config(mixed_precision="no", save_location: str = default_json_config_file, use_xpu: bool = False):
"""
Creates and saves a basic cluster config to be used on a local machine with potentially multiple GPUs. Will also
set CPU if it is a CPU-only machine.
Args:
mixed_precision (`str`, *optional*, defaults to "no"):
Mixed Precision to use. Should be one of "no", "fp16", or "bf16"
save_location (`str`, *optional*, defaults to `default_json_config_file`):
Optional custom save location. Should be passed to `--config_file` when using `accelerate launch`. Default
location is inside the huggingface cache folder (`~/.cache/huggingface`) but can be overriden by setting
the `HF_HOME` environmental variable, followed by `accelerate/default_config.yaml`.
use_xpu (`bool`, *optional*, defaults to `False`):
Whether to use XPU if available.
"""
path = Path(save_location)
path.parent.mkdir(parents=True, exist_ok=True)
if path.exists():
print(
f"Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`."
)
return False
mixed_precision = mixed_precision.lower()
if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
raise ValueError(
f"`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}"
)
config = {
"compute_environment": "LOCAL_MACHINE",
"mixed_precision": mixed_precision,
}
if is_mlu_available():
num_mlus = torch.mlu.device_count()
config["num_processes"] = num_mlus
config["use_cpu"] = False
if num_mlus > 1:
config["distributed_type"] = "MULTI_MLU"
else:
config["distributed_type"] = "NO"
elif torch.cuda.is_available():
num_gpus = torch.cuda.device_count()
config["num_processes"] = num_gpus
config["use_cpu"] = False
if num_gpus > 1:
config["distributed_type"] = "MULTI_GPU"
else:
config["distributed_type"] = "NO"
elif is_xpu_available() and use_xpu:
num_xpus = torch.xpu.device_count()
config["num_processes"] = num_xpus
config["use_cpu"] = False
if num_xpus > 1:
config["distributed_type"] = "MULTI_XPU"
else:
config["distributed_type"] = "NO"
elif is_npu_available():
num_npus = torch.npu.device_count()
config["num_processes"] = num_npus
config["use_cpu"] = False
if num_npus > 1:
config["distributed_type"] = "MULTI_NPU"
else:
config["distributed_type"] = "NO"
else:
num_xpus = 0
config["use_cpu"] = True
config["num_processes"] = 1
config["distributed_type"] = "NO"
config["debug"] = False
config = ClusterConfig(**config)
config.to_json_file(path)
return path
def default_command_parser(parser, parents):
parser = parser.add_parser("default", parents=parents, help=description, formatter_class=SubcommandHelpFormatter)
parser.add_argument(
"--config_file",
default=default_json_config_file,
help=(
"The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
"location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
"such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
"with 'huggingface'."
),
dest="save_location",
)
parser.add_argument(
"--mixed_precision",
choices=["no", "fp16", "bf16"],
type=str,
help="Whether or not to use mixed precision training. "
"Choose between FP16 and BF16 (bfloat16) training. "
"BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.",
default="no",
)
parser.set_defaults(func=default_config_command)
return parser
def default_config_command(args):
config_file = write_basic_config(args.mixed_precision, args.save_location)
if config_file:
print(f"accelerate configuration saved at {config_file}")
| accelerate/src/accelerate/commands/config/default.py/0 | {
"file_path": "accelerate/src/accelerate/commands/config/default.py",
"repo_id": "accelerate",
"token_count": 2110
} | 4 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import functools
from typing import Dict, List, Mapping, Optional, Union
import torch
import torch.nn as nn
from .state import PartialState
from .utils import (
PrefixedDataset,
find_device,
named_module_tensors,
send_to_device,
set_module_tensor_to_device,
)
from .utils.modeling import get_non_persistent_buffers
from .utils.other import recursive_getattr
class ModelHook:
"""
A hook that contains callbacks to be executed just before and after the forward method of a model. The difference
with PyTorch existing hooks is that they get passed along the kwargs.
Class attribute:
- **no_grad** (`bool`, *optional*, defaults to `False`) -- Whether or not to execute the actual forward pass under
the `torch.no_grad()` context manager.
"""
no_grad = False
def init_hook(self, module):
"""
To be executed when the hook is attached to the module.
Args:
module (`torch.nn.Module`): The module attached to this hook.
"""
return module
def pre_forward(self, module, *args, **kwargs):
"""
To be executed just before the forward method of the model.
Args:
module (`torch.nn.Module`): The module whose forward pass will be executed just after this event.
args (`Tuple[Any]`): The positional arguments passed to the module.
kwargs (`Dict[Str, Any]`): The keyword arguments passed to the module.
Returns:
`Tuple[Tuple[Any], Dict[Str, Any]]`: A tuple with the treated `args` and `kwargs`.
"""
return args, kwargs
def post_forward(self, module, output):
"""
To be executed just after the forward method of the model.
Args:
module (`torch.nn.Module`): The module whose forward pass been executed just before this event.
output (`Any`): The output of the module.
Returns:
`Any`: The processed `output`.
"""
return output
def detach_hook(self, module):
"""
To be executed when the hook is detached from a module.
Args:
module (`torch.nn.Module`): The module detached from this hook.
"""
return module
class SequentialHook(ModelHook):
"""
A hook that can contain several hooks and iterates through them at each event.
"""
def __init__(self, *hooks):
self.hooks = hooks
def init_hook(self, module):
for hook in self.hooks:
module = hook.init_hook(module)
return module
def pre_forward(self, module, *args, **kwargs):
for hook in self.hooks:
args, kwargs = hook.pre_forward(module, *args, **kwargs)
return args, kwargs
def post_forward(self, module, output):
for hook in self.hooks:
output = hook.post_forward(module, output)
return output
def detach_hook(self, module):
for hook in self.hooks:
module = hook.detach_hook(module)
return module
def add_hook_to_module(module: nn.Module, hook: ModelHook, append: bool = False):
"""
Adds a hook to a given module. This will rewrite the `forward` method of the module to include the hook, to remove
this behavior and restore the original `forward` method, use `remove_hook_from_module`.
<Tip warning={true}>
If the module already contains a hook, this will replace it with the new hook passed by default. To chain two hooks
together, pass `append=True`, so it chains the current and new hook into an instance of the `SequentialHook` class.
</Tip>
Args:
module (`torch.nn.Module`):
The module to attach a hook to.
hook (`ModelHook`):
The hook to attach.
append (`bool`, *optional*, defaults to `False`):
Whether the hook should be chained with an existing one (if module already contains a hook) or not.
Returns:
`torch.nn.Module`: The same module, with the hook attached (the module is modified in place, so the result can
be discarded).
"""
if append and (getattr(module, "_hf_hook", None) is not None):
old_hook = module._hf_hook
remove_hook_from_module(module)
hook = SequentialHook(old_hook, hook)
if hasattr(module, "_hf_hook") and hasattr(module, "_old_forward"):
# If we already put some hook on this module, we replace it with the new one.
old_forward = module._old_forward
else:
old_forward = module.forward
module._old_forward = old_forward
module = hook.init_hook(module)
module._hf_hook = hook
def new_forward(module, *args, **kwargs):
args, kwargs = module._hf_hook.pre_forward(module, *args, **kwargs)
if module._hf_hook.no_grad:
with torch.no_grad():
output = module._old_forward(*args, **kwargs)
else:
output = module._old_forward(*args, **kwargs)
return module._hf_hook.post_forward(module, output)
# Overriding a GraphModuleImpl forward freezes the forward call and later modifications on the graph will fail.
# Reference: https://pytorch.slack.com/archives/C3PDTEV8E/p1705929610405409
if "GraphModuleImpl" in str(type(module)):
module.__class__.forward = functools.update_wrapper(functools.partial(new_forward, module), old_forward)
else:
module.forward = functools.update_wrapper(functools.partial(new_forward, module), old_forward)
return module
def remove_hook_from_module(module: nn.Module, recurse=False):
"""
Removes any hook attached to a module via `add_hook_to_module`.
Args:
module (`torch.nn.Module`): The module to attach a hook to.
recurse (`bool`, **optional**): Whether to remove the hooks recursively
Returns:
`torch.nn.Module`: The same module, with the hook detached (the module is modified in place, so the result can
be discarded).
"""
if hasattr(module, "_hf_hook"):
module._hf_hook.detach_hook(module)
delattr(module, "_hf_hook")
if hasattr(module, "_old_forward"):
# Overriding a GraphModuleImpl forward freezes the forward call and later modifications on the graph will fail.
# Reference: https://pytorch.slack.com/archives/C3PDTEV8E/p1705929610405409
if "GraphModuleImpl" in str(type(module)):
module.__class__.forward = module._old_forward
else:
module.forward = module._old_forward
delattr(module, "_old_forward")
if recurse:
for child in module.children():
remove_hook_from_module(child, recurse)
return module
class AlignDevicesHook(ModelHook):
"""
A generic `ModelHook` that ensures inputs and model weights are on the same device for the forward pass of the
associated module, potentially offloading the weights after the forward pass.
Args:
execution_device (`torch.device`, *optional*):
The device on which inputs and model weights should be placed before the forward pass.
offload (`bool`, *optional*, defaults to `False`):
Whether or not the weights should be offloaded after the forward pass.
io_same_device (`bool`, *optional*, defaults to `False`):
Whether or not the output should be placed on the same device as the input was.
weights_map (`Mapping[str, torch.Tensor]`, *optional*):
When the model weights are offloaded, a (potentially lazy) map from param names to the tensor values.
offload_buffers (`bool`, *optional*, defaults to `False`):
Whether or not to include the associated module's buffers when offloading.
place_submodules (`bool`, *optional*, defaults to `False`):
Whether to place the submodules on `execution_device` during the `init_hook` event.
"""
def __init__(
self,
execution_device: Optional[Union[int, str, torch.device]] = None,
offload: bool = False,
io_same_device: bool = False,
weights_map: Optional[Mapping] = None,
offload_buffers: bool = False,
place_submodules: bool = False,
skip_keys: Optional[Union[str, List[str]]] = None,
tied_params_map: Optional[Dict[int, Dict[torch.device, torch.Tensor]]] = None,
):
self.execution_device = execution_device
self.offload = offload
self.io_same_device = io_same_device
self.weights_map = weights_map
self.offload_buffers = offload_buffers
self.place_submodules = place_submodules
self.skip_keys = skip_keys
# Will contain the input device when `io_same_device=True`.
self.input_device = None
self.param_original_devices = {}
self.buffer_original_devices = {}
self.tied_params_names = set()
# The hook pre_forward/post_forward need to have knowledge of this dictionary, as with offloading we want to avoid duplicating memory
# for tied weights already loaded on the target execution device.
self.tied_params_map = tied_params_map
def __repr__(self):
return (
f"AlignDevicesHook(execution_device={self.execution_device}, offload={self.offload}, "
f"io_same_device={self.io_same_device}, offload_buffers={self.offload_buffers}, "
f"place_submodules={self.place_submodules}, skip_keys={repr(self.skip_keys)})"
)
def init_hook(self, module):
# In case the AlignDevicesHook is on meta device, ignore tied weights as data_ptr() is then always zero.
if self.execution_device == "meta" or self.execution_device == torch.device("meta"):
self.tied_params_map = None
if not self.offload and self.execution_device is not None:
for name, _ in named_module_tensors(module, recurse=self.place_submodules):
set_module_tensor_to_device(module, name, self.execution_device, tied_params_map=self.tied_params_map)
elif self.offload:
self.original_devices = {
name: param.device for name, param in named_module_tensors(module, recurse=self.place_submodules)
}
if self.weights_map is None:
self.weights_map = {
name: param.to("cpu")
for name, param in named_module_tensors(
module, include_buffers=self.offload_buffers, recurse=self.place_submodules
)
}
for name, _ in named_module_tensors(
module, include_buffers=self.offload_buffers, recurse=self.place_submodules, remove_non_persistent=True
):
# When using disk offloading, we can not rely on `weights_map[name].data_ptr()` as the reference pointer,
# as we have no guarantee that safetensors' `file.get_tensor()` will always give the same pointer.
# As we have no reliable way to track the shared data pointer of tied weights in this case, we use tied_params_names: List[str]
# to add on the fly pointers to `tied_params_map` in the pre_forward call.
if (
self.tied_params_map is not None
and recursive_getattr(module, name).data_ptr() in self.tied_params_map
):
self.tied_params_names.add(name)
set_module_tensor_to_device(module, name, "meta")
if not self.offload_buffers and self.execution_device is not None:
for name, _ in module.named_buffers(recurse=self.place_submodules):
set_module_tensor_to_device(
module, name, self.execution_device, tied_params_map=self.tied_params_map
)
elif self.offload_buffers and self.execution_device is not None:
for name in get_non_persistent_buffers(module, recurse=self.place_submodules):
set_module_tensor_to_device(
module, name, self.execution_device, tied_params_map=self.tied_params_map
)
return module
def pre_forward(self, module, *args, **kwargs):
if self.io_same_device:
self.input_device = find_device([args, kwargs])
if self.offload:
self.tied_pointers_to_remove = set()
for name, _ in named_module_tensors(
module,
include_buffers=self.offload_buffers,
recurse=self.place_submodules,
remove_non_persistent=True,
):
fp16_statistics = None
value = self.weights_map[name]
if "weight" in name and name.replace("weight", "SCB") in self.weights_map.keys():
if value.dtype == torch.int8:
fp16_statistics = self.weights_map[name.replace("weight", "SCB")]
# In case we are using offloading with tied weights, we need to keep track of the offloaded weights
# that are loaded on device at this point, as we will need to remove them as well from the dictionary
# self.tied_params_map in order to allow to free memory.
if name in self.tied_params_names and value.data_ptr() not in self.tied_params_map:
self.tied_params_map[value.data_ptr()] = {}
if (
value is not None
and self.tied_params_map is not None
and value.data_ptr() in self.tied_params_map
and self.execution_device not in self.tied_params_map[value.data_ptr()]
):
self.tied_pointers_to_remove.add((value.data_ptr(), self.execution_device))
set_module_tensor_to_device(
module,
name,
self.execution_device,
value=value,
fp16_statistics=fp16_statistics,
tied_params_map=self.tied_params_map,
)
return send_to_device(args, self.execution_device), send_to_device(
kwargs, self.execution_device, skip_keys=self.skip_keys
)
def post_forward(self, module, output):
if self.offload:
for name, _ in named_module_tensors(
module,
include_buffers=self.offload_buffers,
recurse=self.place_submodules,
remove_non_persistent=True,
):
set_module_tensor_to_device(module, name, "meta")
if type(module).__name__ == "Linear8bitLt":
module.state.SCB = None
module.state.CxB = None
# We may have loaded tied weights into self.tied_params_map (avoiding to load them several times in e.g. submodules): remove them from
# this dictionary to allow the garbage collector to do its job.
for value_pointer, device in self.tied_pointers_to_remove:
del self.tied_params_map[value_pointer][device]
self.tied_pointers_to_remove = set()
if self.io_same_device and self.input_device is not None:
output = send_to_device(output, self.input_device, skip_keys=self.skip_keys)
return output
def detach_hook(self, module):
if self.offload:
for name, device in self.original_devices.items():
if device != torch.device("meta"):
set_module_tensor_to_device(module, name, device, value=self.weights_map.get(name, None))
return module
def attach_execution_device_hook(
module: torch.nn.Module,
execution_device: Union[int, str, torch.device],
skip_keys: Optional[Union[str, List[str]]] = None,
preload_module_classes: Optional[List[str]] = None,
tied_params_map: Optional[Dict[int, Dict[torch.device, torch.Tensor]]] = None,
):
"""
Recursively attaches `AlignDevicesHook` to all submodules of a given model to make sure they have the right
execution device
Args:
module (`torch.nn.Module`):
The module where we want to attach the hooks.
execution_device (`int`, `str` or `torch.device`):
The device on which inputs and model weights should be placed before the forward pass.
skip_keys (`str` or `List[str]`, *optional*):
A list of keys to ignore when moving inputs or outputs between devices.
preload_module_classes (`List[str]`, *optional*):
A list of classes whose instances should load all their weights (even in the submodules) at the beginning
of the forward. This should only be used for classes that have submodules which are registered but not
called directly during the forward, for instance if a `dense` linear layer is registered, but at forward,
`dense.weight` and `dense.bias` are used in some operations instead of calling `dense` directly.
tied_params_map (Optional[Dict[int, Dict[torch.device, torch.Tensor]]], *optional*, defaults to `None`):
A map of data pointers to dictionaries of devices to already dispatched tied weights. For a given execution
device, this parameter is useful to reuse the first available pointer of a shared weight for all others,
instead of duplicating memory.
"""
if not hasattr(module, "_hf_hook") and len(module.state_dict()) > 0:
add_hook_to_module(
module,
AlignDevicesHook(execution_device, skip_keys=skip_keys, tied_params_map=tied_params_map),
)
# Break the recursion if we get to a preload module.
if preload_module_classes is not None and module.__class__.__name__ in preload_module_classes:
return
for child in module.children():
attach_execution_device_hook(child, execution_device, tied_params_map=tied_params_map)
def attach_align_device_hook(
module: torch.nn.Module,
execution_device: Optional[torch.device] = None,
offload: bool = False,
weights_map: Optional[Mapping] = None,
offload_buffers: bool = False,
module_name: str = "",
skip_keys: Optional[Union[str, List[str]]] = None,
preload_module_classes: Optional[List[str]] = None,
tied_params_map: Optional[Dict[int, Dict[torch.device, torch.Tensor]]] = None,
):
"""
Recursively attaches `AlignDevicesHook` to all submodules of a given model that have direct parameters and/or
buffers.
Args:
module (`torch.nn.Module`):
The module where we want to attach the hooks.
execution_device (`torch.device`, *optional*):
The device on which inputs and model weights should be placed before the forward pass.
offload (`bool`, *optional*, defaults to `False`):
Whether or not the weights should be offloaded after the forward pass.
weights_map (`Mapping[str, torch.Tensor]`, *optional*):
When the model weights are offloaded, a (potentially lazy) map from param names to the tensor values.
offload_buffers (`bool`, *optional*, defaults to `False`):
Whether or not to include the associated module's buffers when offloading.
module_name (`str`, *optional*, defaults to `""`):
The name of the module.
skip_keys (`str` or `List[str]`, *optional*):
A list of keys to ignore when moving inputs or outputs between devices.
preload_module_classes (`List[str]`, *optional*):
A list of classes whose instances should load all their weights (even in the submodules) at the beginning
of the forward. This should only be used for classes that have submodules which are registered but not
called directly during the forward, for instance if a `dense` linear layer is registered, but at forward,
`dense.weight` and `dense.bias` are used in some operations instead of calling `dense` directly.
tied_params_map (Optional[Dict[int, Dict[torch.device, torch.Tensor]]], *optional*, defaults to `None`):
A map of data pointers to dictionaries of devices to already dispatched tied weights. For a given execution
device, this parameter is useful to reuse the first available pointer of a shared weight for all others,
instead of duplicating memory.
"""
# Attach the hook on this module if it has any direct tensor.
directs = named_module_tensors(module)
full_offload = (
offload and preload_module_classes is not None and module.__class__.__name__ in preload_module_classes
)
if len(list(directs)) > 0 or full_offload:
if weights_map is not None:
prefix = f"{module_name}." if len(module_name) > 0 else ""
prefixed_weights_map = PrefixedDataset(weights_map, prefix)
else:
prefixed_weights_map = None
hook = AlignDevicesHook(
execution_device=execution_device,
offload=offload,
weights_map=prefixed_weights_map,
offload_buffers=offload_buffers,
place_submodules=full_offload,
skip_keys=skip_keys,
tied_params_map=tied_params_map,
)
add_hook_to_module(module, hook, append=True)
# We stop the recursion in case we hit the full offload.
if full_offload:
return
# Recurse on all children of the module.
for child_name, child in module.named_children():
child_name = f"{module_name}.{child_name}" if len(module_name) > 0 else child_name
attach_align_device_hook(
child,
execution_device=execution_device,
offload=offload,
weights_map=weights_map,
offload_buffers=offload_buffers,
module_name=child_name,
preload_module_classes=preload_module_classes,
skip_keys=skip_keys,
tied_params_map=tied_params_map,
)
def remove_hook_from_submodules(module: nn.Module):
"""
Recursively removes all hooks attached on the submodules of a given model.
Args:
module (`torch.nn.Module`): The module on which to remove all hooks.
"""
remove_hook_from_module(module)
for child in module.children():
remove_hook_from_submodules(child)
def attach_align_device_hook_on_blocks(
module: nn.Module,
execution_device: Optional[Union[torch.device, Dict[str, torch.device]]] = None,
offload: Union[bool, Dict[str, bool]] = False,
weights_map: Mapping = None,
offload_buffers: bool = False,
module_name: str = "",
skip_keys: Optional[Union[str, List[str]]] = None,
preload_module_classes: Optional[List[str]] = None,
tied_params_map: Optional[Dict[int, Dict[torch.device, torch.Tensor]]] = None,
):
"""
Attaches `AlignDevicesHook` to all blocks of a given model as needed.
Args:
module (`torch.nn.Module`):
The module where we want to attach the hooks.
execution_device (`torch.device` or `Dict[str, torch.device]`, *optional*):
The device on which inputs and model weights should be placed before the forward pass. It can be one device
for the whole module, or a dictionary mapping module name to device.
offload (`bool`, *optional*, defaults to `False`):
Whether or not the weights should be offloaded after the forward pass. It can be one boolean for the whole
module, or a dictionary mapping module name to boolean.
weights_map (`Mapping[str, torch.Tensor]`, *optional*):
When the model weights are offloaded, a (potentially lazy) map from param names to the tensor values.
offload_buffers (`bool`, *optional*, defaults to `False`):
Whether or not to include the associated module's buffers when offloading.
module_name (`str`, *optional*, defaults to `""`):
The name of the module.
skip_keys (`str` or `List[str]`, *optional*):
A list of keys to ignore when moving inputs or outputs between devices.
preload_module_classes (`List[str]`, *optional*):
A list of classes whose instances should load all their weights (even in the submodules) at the beginning
of the forward. This should only be used for classes that have submodules which are registered but not
called directly during the forward, for instance if a `dense` linear layer is registered, but at forward,
`dense.weight` and `dense.bias` are used in some operations instead of calling `dense` directly.
tied_params_map (Optional[Dict[int, Dict[torch.device, torch.Tensor]]], *optional*, defaults to `None`):
A map of data pointers to dictionaries of devices to already dispatched tied weights. For a given execution
device, this parameter is useful to reuse the first available pointer of a shared weight for all others,
instead of duplicating memory.
"""
# If one device and one offload, we've got one hook.
if not isinstance(execution_device, Mapping) and not isinstance(offload, dict):
if not offload:
hook = AlignDevicesHook(
execution_device=execution_device,
io_same_device=True,
skip_keys=skip_keys,
place_submodules=True,
tied_params_map=tied_params_map,
)
add_hook_to_module(module, hook)
else:
attach_align_device_hook(
module,
execution_device=execution_device,
offload=True,
weights_map=weights_map,
offload_buffers=offload_buffers,
module_name=module_name,
skip_keys=skip_keys,
tied_params_map=tied_params_map,
)
return
if not isinstance(execution_device, Mapping):
execution_device = {key: execution_device for key in offload.keys()}
if not isinstance(offload, Mapping):
offload = {key: offload for key in execution_device.keys()}
if module_name in execution_device and module_name in offload and not offload[module_name]:
hook = AlignDevicesHook(
execution_device=execution_device[module_name],
offload_buffers=offload_buffers,
io_same_device=(module_name == ""),
place_submodules=True,
skip_keys=skip_keys,
tied_params_map=tied_params_map,
)
add_hook_to_module(module, hook)
attach_execution_device_hook(module, execution_device[module_name], tied_params_map=tied_params_map)
elif module_name in execution_device and module_name in offload:
attach_align_device_hook(
module,
execution_device=execution_device[module_name],
offload=True,
weights_map=weights_map,
offload_buffers=offload_buffers,
module_name=module_name,
skip_keys=skip_keys,
preload_module_classes=preload_module_classes,
tied_params_map=tied_params_map,
)
if not hasattr(module, "_hf_hook"):
hook = AlignDevicesHook(
execution_device=execution_device[module_name],
io_same_device=(module_name == ""),
skip_keys=skip_keys,
tied_params_map=tied_params_map,
)
add_hook_to_module(module, hook)
attach_execution_device_hook(
module,
execution_device[module_name],
preload_module_classes=preload_module_classes,
skip_keys=skip_keys,
tied_params_map=tied_params_map,
)
elif module_name == "":
hook = AlignDevicesHook(
execution_device=execution_device.get(""),
io_same_device=True,
skip_keys=skip_keys,
tied_params_map=tied_params_map,
)
add_hook_to_module(module, hook)
for child_name, child in module.named_children():
child_name = f"{module_name}.{child_name}" if len(module_name) > 0 else child_name
attach_align_device_hook_on_blocks(
child,
execution_device=execution_device,
offload=offload,
weights_map=weights_map,
offload_buffers=offload_buffers,
module_name=child_name,
preload_module_classes=preload_module_classes,
skip_keys=skip_keys,
tied_params_map=tied_params_map,
)
class CpuOffload(ModelHook):
"""
Offloads a model on the CPU until its forward pass is called. The model will not be offloaded back to the CPU after
the forward, the user needs to call the `init_hook` method again for this.
Args:
execution_device(`str`, `int` or `torch.device`, *optional*):
The device on which the model should be executed. Will default to the MPS device if it's available, then
GPU 0 if there is a GPU, and finally to the CPU.
prev_module_hook (`UserCpuOffloadHook`, *optional*):
The hook sent back by [`cpu_offload_with_hook`] for a previous model in the pipeline you are running. If
passed, its offload method will be called just before the forward of the model to which this hook is
attached.
"""
def __init__(
self,
execution_device: Optional[Union[str, int, torch.device]] = None,
prev_module_hook: Optional["UserCpuOffloadHook"] = None,
):
self.prev_module_hook = prev_module_hook
self.execution_device = execution_device if execution_device is not None else PartialState().default_device
def init_hook(self, module):
return module.to("cpu")
def pre_forward(self, module, *args, **kwargs):
if self.prev_module_hook is not None:
self.prev_module_hook.offload()
module.to(self.execution_device)
return send_to_device(args, self.execution_device), send_to_device(kwargs, self.execution_device)
class UserCpuOffloadHook:
"""
A simple hook grouping a model and a `ModelHook`, which provides easy APIs for to call the init method of the hook
or remove it entirely.
"""
def __init__(self, model, hook):
self.model = model
self.hook = hook
def offload(self):
self.hook.init_hook(self.model)
def remove(self):
remove_hook_from_module(self.model)
| accelerate/src/accelerate/hooks.py/0 | {
"file_path": "accelerate/src/accelerate/hooks.py",
"repo_id": "accelerate",
"token_count": 12920
} | 5 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import os
import platform
import subprocess
import sys
from shutil import which
from typing import List
import torch
from packaging.version import parse
logger = logging.getLogger(__name__)
def convert_dict_to_env_variables(current_env: dict):
"""
Verifies that all keys and values in `current_env` do not contain illegal keys or values, and returns a list of
strings as the result.
Example:
```python
>>> from accelerate.utils.environment import verify_env
>>> env = {"ACCELERATE_DEBUG_MODE": "1", "BAD_ENV_NAME": "<mything", "OTHER_ENV": "2"}
>>> valid_env_items = verify_env(env)
>>> print(valid_env_items)
["ACCELERATE_DEBUG_MODE=1\n", "OTHER_ENV=2\n"]
```
"""
forbidden_chars = [";", "\n", "<", ">", " "]
valid_env_items = []
for key, value in current_env.items():
if all(char not in (key + value) for char in forbidden_chars) and len(key) >= 1 and len(value) >= 1:
valid_env_items.append(f"{key}={value}\n")
else:
logger.warning(f"WARNING: Skipping {key}={value} as it contains forbidden characters or missing values.")
return valid_env_items
def str_to_bool(value) -> int:
"""
Converts a string representation of truth to `True` (1) or `False` (0).
True values are `y`, `yes`, `t`, `true`, `on`, and `1`; False value are `n`, `no`, `f`, `false`, `off`, and `0`;
"""
value = value.lower()
if value in ("y", "yes", "t", "true", "on", "1"):
return 1
elif value in ("n", "no", "f", "false", "off", "0"):
return 0
else:
raise ValueError(f"invalid truth value {value}")
def get_int_from_env(env_keys, default):
"""Returns the first positive env value found in the `env_keys` list or the default."""
for e in env_keys:
val = int(os.environ.get(e, -1))
if val >= 0:
return val
return default
def parse_flag_from_env(key, default=False):
"""Returns truthy value for `key` from the env if available else the default."""
value = os.environ.get(key, str(default))
return str_to_bool(value) == 1 # As its name indicates `str_to_bool` actually returns an int...
def parse_choice_from_env(key, default="no"):
value = os.environ.get(key, str(default))
return value
def are_libraries_initialized(*library_names: str) -> List[str]:
"""
Checks if any of `library_names` are imported in the environment. Will return any names that are.
"""
return [lib_name for lib_name in library_names if lib_name in sys.modules.keys()]
def _nvidia_smi():
"""
Returns the right nvidia-smi command based on the system.
"""
if platform.system() == "Windows":
# If platform is Windows and nvidia-smi can't be found in path
# try from systemd drive with default installation path
command = which("nvidia-smi")
if command is None:
command = "%s\\Program Files\\NVIDIA Corporation\\NVSMI\\nvidia-smi.exe" % os.environ["systemdrive"]
else:
command = "nvidia-smi"
return command
def get_gpu_info():
"""
Gets GPU count and names using `nvidia-smi` instead of torch to not initialize CUDA.
Largely based on the `gputil` library.
"""
# Returns as list of `n` GPUs and their names
output = subprocess.check_output(
[_nvidia_smi(), "--query-gpu=count,name", "--format=csv,noheader"], universal_newlines=True
)
output = output.strip()
gpus = output.split(os.linesep)
# Get names from output
gpu_count = len(gpus)
gpu_names = [gpu.split(",")[1].strip() for gpu in gpus]
return gpu_names, gpu_count
def get_driver_version():
"""
Returns the driver version
In the case of multiple GPUs, will return the first.
"""
output = subprocess.check_output(
[_nvidia_smi(), "--query-gpu=driver_version", "--format=csv,noheader"], universal_newlines=True
)
output = output.strip()
return output.split(os.linesep)[0]
def check_cuda_p2p_ib_support():
"""
Checks if the devices being used have issues with P2P and IB communications, namely any consumer GPU hardware after
the 3090.
Noteably uses `nvidia-smi` instead of torch to not initialize CUDA.
"""
try:
device_names, device_count = get_gpu_info()
# As new consumer GPUs get released, add them to `unsupported_devices``
unsupported_devices = {"RTX 40"}
if device_count > 1:
if any(
unsupported_device in device_name
for device_name in device_names
for unsupported_device in unsupported_devices
):
# Check if they have the right driver version
acceptable_driver_version = "550.40.07"
current_driver_version = get_driver_version()
if parse(current_driver_version) < parse(acceptable_driver_version):
return False
return True
except Exception:
pass
return True
def check_fp8_capability():
"""
Checks if all the current GPUs available support FP8.
Notably must initialize `torch.cuda` to check.
"""
cuda_device_capacity = torch.cuda.get_device_capability()
return cuda_device_capacity >= (8, 9)
| accelerate/src/accelerate/utils/environment.py/0 | {
"file_path": "accelerate/src/accelerate/utils/environment.py",
"repo_id": "accelerate",
"token_count": 2221
} | 6 |
{
"fp16": {
"enabled": "auto",
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
},
"bf16": {
"enabled": "auto"
},
"optimizer": {
"type": "AdamW",
"params": {
"lr": "auto",
"weight_decay": "auto",
"torch_adam": true,
"adam_w_mode": true
}
},
"scheduler": {
"type": "WarmupLR",
"params": {
"warmup_min_lr": "auto",
"warmup_max_lr": "auto",
"warmup_num_steps": "auto"
}
},
"zero_optimization": {
"stage": 2,
"offload_optimizer": {
"device": "cpu",
"pin_memory": true
},
"allgather_partitions": true,
"allgather_bucket_size": 2e8,
"overlap_comm": true,
"reduce_scatter": true,
"reduce_bucket_size": "auto",
"contiguous_gradients": true
},
"gradient_accumulation_steps": 1,
"gradient_clipping": "auto",
"steps_per_print": 2000,
"train_batch_size": "auto",
"train_micro_batch_size_per_gpu": "auto",
"wall_clock_breakdown": false
} | accelerate/tests/deepspeed/ds_config_zero2.json/0 | {
"file_path": "accelerate/tests/deepspeed/ds_config_zero2.json",
"repo_id": "accelerate",
"token_count": 680
} | 7 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import (
DEFAULT_LAUNCH_COMMAND,
device_count,
execute_subprocess_async,
path_in_accelerate_package,
require_cpu,
require_multi_device,
require_non_cpu,
test_sync,
)
from accelerate.utils import patch_environment
class SyncScheduler(unittest.TestCase):
test_file_path = path_in_accelerate_package("test_utils", "scripts", "test_sync.py")
@require_cpu
def test_gradient_sync_cpu_noop(self):
debug_launcher(test_sync.main, num_processes=1)
@require_cpu
def test_gradient_sync_cpu_multi(self):
debug_launcher(test_sync.main)
@require_non_cpu
def test_gradient_sync_gpu(self):
test_sync.main()
@require_multi_device
def test_gradient_sync_gpu_multi(self):
print(f"Found {device_count} devices.")
cmd = DEFAULT_LAUNCH_COMMAND + [self.test_file_path]
with patch_environment(omp_num_threads=1):
execute_subprocess_async(cmd)
| accelerate/tests/test_grad_sync.py/0 | {
"file_path": "accelerate/tests/test_grad_sync.py",
"repo_id": "accelerate",
"token_count": 579
} | 8 |
# Model arguments
model_name_or_path: alignment-handbook/zephyr-7b-sft-qlora
torch_dtype: bfloat16
# LoRA arguments
use_peft: true
load_in_4bit: true
lora_r: 128
lora_alpha: 128
lora_dropout: 0.05
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
- gate_proj
- up_proj
- down_proj
# Data training arguments
dataset_mixer:
HuggingFaceH4/ultrafeedback_binarized: 1.0
dataset_splits:
- train_prefs
- test_prefs
preprocessing_num_workers: 12
# DPOTrainer arguments
bf16: true
beta: 0.01
do_eval: true
evaluation_strategy: steps
eval_steps: 100
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
hub_model_id: zephyr-7b-dpo-qlora
learning_rate: 5.0e-6
log_level: info
logging_steps: 10
lr_scheduler_type: cosine
max_length: 1024
max_prompt_length: 512
num_train_epochs: 1
optim: paged_adamw_32bit
output_dir: data/zephyr-7b-dpo-qlora # It is handy to append `hub_model_revision` to keep track of your local experiments
per_device_train_batch_size: 4
per_device_eval_batch_size: 8
push_to_hub: true
save_strategy: "steps"
save_steps: 100
save_total_limit: 1
seed: 42
warmup_ratio: 0.1 | alignment-handbook/recipes/zephyr-7b-beta/dpo/config_qlora.yaml/0 | {
"file_path": "alignment-handbook/recipes/zephyr-7b-beta/dpo/config_qlora.yaml",
"repo_id": "alignment-handbook",
"token_count": 478
} | 9 |
# coding=utf-8
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from pathlib import Path
from typing import Dict
import torch
from transformers import AutoTokenizer, BitsAndBytesConfig, PreTrainedTokenizer
from transformers.trainer_utils import get_last_checkpoint
from accelerate import Accelerator
from huggingface_hub import list_repo_files
from huggingface_hub.utils._errors import RepositoryNotFoundError
from huggingface_hub.utils._validators import HFValidationError
from peft import LoraConfig, PeftConfig
from .configs import DataArguments, DPOConfig, ModelArguments, SFTConfig
from .data import DEFAULT_CHAT_TEMPLATE
def get_current_device() -> int:
"""Get the current device. For GPU we return the local process index to enable multiple GPU training."""
return Accelerator().local_process_index if torch.cuda.is_available() else "cpu"
def get_kbit_device_map() -> Dict[str, int] | None:
"""Useful for running inference with quantized models by setting `device_map=get_peft_device_map()`"""
return {"": get_current_device()} if torch.cuda.is_available() else None
def get_quantization_config(model_args: ModelArguments) -> BitsAndBytesConfig | None:
if model_args.load_in_4bit:
compute_dtype = torch.float16
if model_args.torch_dtype not in {"auto", None}:
compute_dtype = getattr(torch, model_args.torch_dtype)
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=compute_dtype,
bnb_4bit_quant_type=model_args.bnb_4bit_quant_type,
bnb_4bit_use_double_quant=model_args.use_bnb_nested_quant,
)
elif model_args.load_in_8bit:
quantization_config = BitsAndBytesConfig(
load_in_8bit=True,
)
else:
quantization_config = None
return quantization_config
def get_tokenizer(
model_args: ModelArguments, data_args: DataArguments, auto_set_chat_template: bool = True
) -> PreTrainedTokenizer:
"""Get the tokenizer for the model."""
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name_or_path
if model_args.tokenizer_name_or_path is None
else model_args.tokenizer_name_or_path,
revision=model_args.model_revision,
)
if tokenizer.pad_token_id is None:
tokenizer.pad_token_id = tokenizer.eos_token_id
if data_args.truncation_side is not None:
tokenizer.truncation_side = data_args.truncation_side
# Set reasonable default for models without max length
if tokenizer.model_max_length > 100_000:
tokenizer.model_max_length = 2048
if data_args.chat_template is not None:
tokenizer.chat_template = data_args.chat_template
elif auto_set_chat_template and tokenizer.chat_template is None and tokenizer.default_chat_template is None:
tokenizer.chat_template = DEFAULT_CHAT_TEMPLATE
return tokenizer
def get_peft_config(model_args: ModelArguments) -> PeftConfig | None:
if model_args.use_peft is False:
return None
peft_config = LoraConfig(
r=model_args.lora_r,
lora_alpha=model_args.lora_alpha,
lora_dropout=model_args.lora_dropout,
bias="none",
task_type="CAUSAL_LM",
target_modules=model_args.lora_target_modules,
modules_to_save=model_args.lora_modules_to_save,
)
return peft_config
def is_adapter_model(model_name_or_path: str, revision: str = "main") -> bool:
try:
# Try first if model on a Hub repo
repo_files = list_repo_files(model_name_or_path, revision=revision)
except (HFValidationError, RepositoryNotFoundError):
# If not, check local repo
repo_files = os.listdir(model_name_or_path)
return "adapter_model.safetensors" in repo_files or "adapter_model.bin" in repo_files
def get_checkpoint(training_args: SFTConfig | DPOConfig) -> Path | None:
last_checkpoint = None
if os.path.isdir(training_args.output_dir):
last_checkpoint = get_last_checkpoint(training_args.output_dir)
return last_checkpoint
| alignment-handbook/src/alignment/model_utils.py/0 | {
"file_path": "alignment-handbook/src/alignment/model_utils.py",
"repo_id": "alignment-handbook",
"token_count": 1718
} | 10 |
[workspace]
members = [
"candle-core",
"candle-datasets",
"candle-examples",
"candle-book",
"candle-nn",
"candle-pyo3",
"candle-transformers",
"candle-wasm-examples/*",
"candle-wasm-tests",
]
exclude = [
"candle-flash-attn",
"candle-kernels",
"candle-metal-kernels",
"candle-onnx",
]
resolver = "2"
[workspace.package]
version = "0.4.2"
edition = "2021"
description = "Minimalist ML framework."
repository = "https://github.com/huggingface/candle"
keywords = ["blas", "tensor", "machine-learning"]
categories = ["science"]
license = "MIT OR Apache-2.0"
[workspace.dependencies]
accelerate-src = { version = "0.3.2" }
anyhow = { version = "1", features = ["backtrace"] }
byteorder = "1.4.3"
candle = { path = "./candle-core", package = "candle-core", version = "0.4.2" }
candle-datasets = { path = "./candle-datasets", version = "0.4.2" }
candle-flash-attn = { path = "./candle-flash-attn", version = "0.4.2" }
candle-kernels = { path = "./candle-kernels", version = "0.4.2" }
candle-metal-kernels = { path = "./candle-metal-kernels", version = "0.4.2" }
candle-nn = { path = "./candle-nn", version = "0.4.2" }
candle-onnx = { path = "./candle-onnx", version = "0.4.2" }
candle-transformers = { path = "./candle-transformers", version = "0.4.2" }
clap = { version = "4.2.4", features = ["derive"] }
criterion = { version = "0.5.1", default-features=false }
cudarc = { version = "0.10.0", features = ["f16"] }
fancy-regex = "0.13.0"
gemm = { version = "0.17.0", features = ["wasm-simd128-enable"] }
hf-hub = "0.3.0"
half = { version = "2.3.1", features = ["num-traits", "use-intrinsics", "rand_distr"] }
image = { version = "0.24.7", default-features = false, features = ["jpeg", "png"] }
imageproc = { version = "0.23.0", default-features = false }
intel-mkl-src = { version = "0.8.1", features = ["mkl-static-lp64-iomp"] }
libc = { version = "0.2.147" }
log = "0.4"
memmap2 = { version = "0.9.3", features = ["stable_deref_trait"] }
num_cpus = "1.15.0"
num-traits = "0.2.15"
parquet = { version = "50.0.0" }
rand = "0.8.5"
rand_distr = "0.4.3"
rayon = "1.7.0"
rusttype = { version = "0.9", default-features = false }
safetensors = "0.4.1"
serde = { version = "1.0.171", features = ["derive"] }
serde_plain = "1.0.2"
serde_json = "1.0.99"
thiserror = "1"
tokenizers = { version = "0.15.0", default-features = false }
tracing = "0.1.37"
tracing-chrome = "0.7.1"
tracing-subscriber = "0.3.7"
wav = "1.0.0"
yoke = { version = "0.7.2", features = ["derive"] }
zip = { version = "0.6.6", default-features = false }
metal = { version = "0.27.0", features = ["mps"]}
[profile.release-with-debug]
inherits = "release"
debug = true
| candle/Cargo.toml/0 | {
"file_path": "candle/Cargo.toml",
"repo_id": "candle",
"token_count": 1156
} | 11 |
# Advanced Cuda usage
| candle/candle-book/src/cuda/README.md/0 | {
"file_path": "candle/candle-book/src/cuda/README.md",
"repo_id": "candle",
"token_count": 6
} | 12 |
# Serialization
| candle/candle-book/src/training/serialization.md/0 | {
"file_path": "candle/candle-book/src/training/serialization.md",
"repo_id": "candle",
"token_count": 4
} | 13 |
#![allow(dead_code)]
use libc::{c_char, c_double, c_float, c_int, c_long, c_ulong};
mod ffi {
use super::*;
extern "C" {
// It would be nice to be able to switch to the NEWLAPACK version of the function but this
// seems to trigger some link error. Available function names can be seen here:
// /Library/Developer/CommandLineTools/SDKs/MacOSX13.3.sdk/System/Library/Frameworks/Accelerate.framework/Versions/A/Accelerate.tbd
#[link_name = "sgemm_"]
pub fn sgemm_ffi(
transa: *const c_char,
transb: *const c_char,
m: *const c_int,
n: *const c_int,
k: *const c_int,
alpha: *const c_float,
a: *const c_float,
lda: *const c_int,
b: *const c_float,
ldb: *const c_int,
beta: *const c_float,
c: *mut c_float,
ldc: *const c_int,
);
#[link_name = "dgemm_"]
pub fn dgemm_ffi(
transa: *const c_char,
transb: *const c_char,
m: *const c_int,
n: *const c_int,
k: *const c_int,
alpha: *const c_double,
a: *const c_double,
lda: *const c_int,
b: *const c_double,
ldb: *const c_int,
beta: *const c_double,
c: *mut c_double,
ldc: *const c_int,
);
pub fn vvexpf(dst: *mut c_float, src: *const c_float, len: *const c_int);
pub fn vvexp(dst: *mut c_double, src: *const c_double, len: *const c_int);
pub fn vvsqrtf(dst: *mut c_float, src: *const c_float, len: *const c_int);
pub fn vvsqrt(dst: *mut c_double, src: *const c_double, len: *const c_int);
pub fn vvsinf(dst: *mut c_float, src: *const c_float, len: *const c_int);
pub fn vvsin(dst: *mut c_double, src: *const c_double, len: *const c_int);
pub fn vvcosf(dst: *mut c_float, src: *const c_float, len: *const c_int);
pub fn vvcos(dst: *mut c_double, src: *const c_double, len: *const c_int);
pub fn vvlogf(dst: *mut c_float, src: *const c_float, len: *const c_int);
pub fn vvlog(dst: *mut c_double, src: *const c_double, len: *const c_int);
pub fn vvtanhf(dst: *mut c_float, src: *const c_float, len: *const c_int);
pub fn vvtanh(dst: *mut c_double, src: *const c_double, len: *const c_int);
pub fn vDSP_vaddD(
_: *const c_double,
_: c_long,
_: *const c_double,
_: c_long,
_: *mut c_double,
_: c_long,
_: c_ulong,
);
pub fn vDSP_vadd(
_: *const c_float,
_: c_long,
_: *const c_float,
_: c_long,
_: *mut c_float,
_: c_long,
_: c_ulong,
);
pub fn vDSP_vsubD(
_: *const c_double,
_: c_long,
_: *const c_double,
_: c_long,
_: *mut c_double,
_: c_long,
_: c_ulong,
);
pub fn vDSP_vsub(
_: *const c_float,
_: c_long,
_: *const c_float,
_: c_long,
_: *mut c_float,
_: c_long,
_: c_ulong,
);
pub fn vDSP_vmulD(
_: *const c_double,
_: c_long,
_: *const c_double,
_: c_long,
_: *mut c_double,
_: c_long,
_: c_ulong,
);
pub fn vDSP_vmul(
_: *const c_float,
_: c_long,
_: *const c_float,
_: c_long,
_: *mut c_float,
_: c_long,
_: c_ulong,
);
pub fn vDSP_vdivD(
_: *const c_double,
_: c_long,
_: *const c_double,
_: c_long,
_: *mut c_double,
_: c_long,
_: c_ulong,
);
pub fn vDSP_vdiv(
_: *const c_float,
_: c_long,
_: *const c_float,
_: c_long,
_: *mut c_float,
_: c_long,
_: c_ulong,
);
pub fn vDSP_vminD(
_: *const c_double,
_: c_long,
_: *const c_double,
_: c_long,
_: *mut c_double,
_: c_long,
_: c_ulong,
);
pub fn vDSP_vmin(
_: *const c_float,
_: c_long,
_: *const c_float,
_: c_long,
_: *mut c_float,
_: c_long,
_: c_ulong,
);
pub fn vDSP_vmaxD(
_: *const c_double,
_: c_long,
_: *const c_double,
_: c_long,
_: *mut c_double,
_: c_long,
_: c_ulong,
);
pub fn vDSP_vmax(
_: *const c_float,
_: c_long,
_: *const c_float,
_: c_long,
_: *mut c_float,
_: c_long,
_: c_ulong,
);
}
}
#[allow(clippy::too_many_arguments)]
#[inline]
pub unsafe fn sgemm(
transa: u8,
transb: u8,
m: i32,
n: i32,
k: i32,
alpha: f32,
a: &[f32],
lda: i32,
b: &[f32],
ldb: i32,
beta: f32,
c: &mut [f32],
ldc: i32,
) {
ffi::sgemm_ffi(
&(transa as c_char),
&(transb as c_char),
&m,
&n,
&k,
&alpha,
a.as_ptr(),
&lda,
b.as_ptr(),
&ldb,
&beta,
c.as_mut_ptr(),
&ldc,
)
}
#[allow(clippy::too_many_arguments)]
#[inline]
pub unsafe fn dgemm(
transa: u8,
transb: u8,
m: i32,
n: i32,
k: i32,
alpha: f64,
a: &[f64],
lda: i32,
b: &[f64],
ldb: i32,
beta: f64,
c: &mut [f64],
ldc: i32,
) {
ffi::dgemm_ffi(
&(transa as c_char),
&(transb as c_char),
&m,
&n,
&k,
&alpha,
a.as_ptr(),
&lda,
b.as_ptr(),
&ldb,
&beta,
c.as_mut_ptr(),
&ldc,
)
}
#[inline]
pub fn vs_exp(a: &[f32], y: &mut [f32]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vvexpf(y.as_mut_ptr(), a.as_ptr(), &(a_len as i32)) }
}
#[inline]
pub fn vd_exp(a: &[f64], y: &mut [f64]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vvexp(y.as_mut_ptr(), a.as_ptr(), &(a_len as i32)) }
}
#[inline]
pub fn vs_sqrt(a: &[f32], y: &mut [f32]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vvsqrtf(y.as_mut_ptr(), a.as_ptr(), &(a_len as i32)) }
}
#[inline]
pub fn vd_sqrt(a: &[f64], y: &mut [f64]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vvsqrt(y.as_mut_ptr(), a.as_ptr(), &(a_len as i32)) }
}
#[inline]
pub fn vs_sin(a: &[f32], y: &mut [f32]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vvsinf(y.as_mut_ptr(), a.as_ptr(), &(a_len as i32)) }
}
#[inline]
pub fn vd_sin(a: &[f64], y: &mut [f64]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vvsin(y.as_mut_ptr(), a.as_ptr(), &(a_len as i32)) }
}
#[inline]
pub fn vs_cos(a: &[f32], y: &mut [f32]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vvcosf(y.as_mut_ptr(), a.as_ptr(), &(a_len as i32)) }
}
#[inline]
pub fn vd_cos(a: &[f64], y: &mut [f64]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vvcos(y.as_mut_ptr(), a.as_ptr(), &(a_len as i32)) }
}
#[inline]
pub fn vs_tanh(a: &[f32], y: &mut [f32]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vvtanhf(y.as_mut_ptr(), a.as_ptr(), &(a_len as i32)) }
}
#[inline]
pub fn vd_tanh(a: &[f64], y: &mut [f64]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vvtanh(y.as_mut_ptr(), a.as_ptr(), &(a_len as i32)) }
}
#[inline]
pub fn vs_ln(a: &[f32], y: &mut [f32]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vvlogf(y.as_mut_ptr(), a.as_ptr(), &(a_len as i32)) }
}
#[inline]
pub fn vd_ln(a: &[f64], y: &mut [f64]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vvlog(y.as_mut_ptr(), a.as_ptr(), &(a_len as i32)) }
}
#[inline]
pub fn vs_sqr(a: &[f32], y: &mut [f32]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
y.iter_mut().zip(a.iter()).for_each(|(y, a)| *y = *a * *a)
}
#[inline]
pub fn vd_sqr(a: &[f64], y: &mut [f64]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
y.iter_mut().zip(a.iter()).for_each(|(y, a)| *y = *a * *a)
}
#[inline]
pub fn vs_tanh_inplace(y: &mut [f32]) {
unsafe { ffi::vvtanhf(y.as_mut_ptr(), y.as_ptr(), &(y.len() as i32)) }
}
#[inline]
pub fn vd_tanh_inplace(y: &mut [f64]) {
unsafe { ffi::vvtanh(y.as_mut_ptr(), y.as_ptr(), &(y.len() as i32)) }
}
#[inline]
pub fn vs_exp_inplace(y: &mut [f32]) {
unsafe { ffi::vvexpf(y.as_mut_ptr(), y.as_ptr(), &(y.len() as i32)) }
}
#[inline]
pub fn vd_exp_inplace(y: &mut [f64]) {
unsafe { ffi::vvexp(y.as_mut_ptr(), y.as_ptr(), &(y.len() as i32)) }
}
#[inline]
pub fn vs_gelu(vs: &[f32], ys: &mut [f32]) {
for (&v, y) in vs.iter().zip(ys.iter_mut()) {
*y = (2.0f32 / std::f32::consts::PI).sqrt() * v * (1.0 + 0.044715 * v * v)
}
vs_tanh_inplace(ys);
for (&v, y) in vs.iter().zip(ys.iter_mut()) {
*y = 0.5 * v * (1.0 + *y)
}
}
#[inline]
pub fn vd_gelu(vs: &[f64], ys: &mut [f64]) {
for (&v, y) in vs.iter().zip(ys.iter_mut()) {
*y = (2.0f64 / std::f64::consts::PI).sqrt() * v * (1.0 + 0.044715 * v * v)
}
vd_tanh_inplace(ys);
for (&v, y) in vs.iter().zip(ys.iter_mut()) {
*y = 0.5 * v * (1.0 + *y)
}
}
#[inline]
pub fn vs_silu(vs: &[f32], ys: &mut [f32]) {
for (&v, y) in vs.iter().zip(ys.iter_mut()) {
*y = -v
}
vs_exp_inplace(ys);
for (&v, y) in vs.iter().zip(ys.iter_mut()) {
*y = v / (1.0 + *y)
}
}
#[inline]
pub fn vd_silu(vs: &[f64], ys: &mut [f64]) {
for (&v, y) in vs.iter().zip(ys.iter_mut()) {
*y = -v
}
vd_exp_inplace(ys);
for (&v, y) in vs.iter().zip(ys.iter_mut()) {
*y = v / (1.0 + *y)
}
}
macro_rules! binary_op {
($fn_name:ident, $ty:ty, $accelerate_name:ident) => {
#[inline]
pub fn $fn_name(a: &[$ty], b: &[$ty], y: &mut [$ty]) {
let a_len = a.len();
let b_len = b.len();
let y_len = y.len();
if a_len != y_len || b_len != y_len {
panic!(
"{} a,b,y len mismatch {a_len} {b_len} {y_len}",
stringify!($fn_name)
);
}
unsafe {
// Weird quirk of accelerate, the rhs comes before the lhs.
ffi::$accelerate_name(
b.as_ptr(),
1,
a.as_ptr(),
1,
y.as_mut_ptr(),
1,
a_len as u64,
)
}
}
};
}
binary_op!(vs_add, f32, vDSP_vadd);
binary_op!(vd_add, f64, vDSP_vaddD);
binary_op!(vs_sub, f32, vDSP_vsub);
binary_op!(vd_sub, f64, vDSP_vsubD);
binary_op!(vs_mul, f32, vDSP_vmul);
binary_op!(vd_mul, f64, vDSP_vmulD);
binary_op!(vs_div, f32, vDSP_vdiv);
binary_op!(vd_div, f64, vDSP_vdivD);
binary_op!(vs_max, f32, vDSP_vmax);
binary_op!(vd_max, f64, vDSP_vmaxD);
binary_op!(vs_min, f32, vDSP_vmin);
binary_op!(vd_min, f64, vDSP_vminD);
| candle/candle-core/src/accelerate.rs/0 | {
"file_path": "candle/candle-core/src/accelerate.rs",
"repo_id": "candle",
"token_count": 7639
} | 14 |
//! Types for elements that can be stored and manipulated using tensors.
#![allow(clippy::redundant_closure_call)]
use crate::backend::BackendStorage;
use crate::{CpuStorage, Error, Result};
/// The different types of elements allowed in tensors.
#[derive(Debug, Copy, Clone, PartialEq, Eq, Hash)]
pub enum DType {
// Unsigned 8 bits integer.
U8,
// Unsigned 32 bits integer.
U32,
// Signed 64 bits integer.
I64,
// Brain floating-point using half precision (16 bits).
BF16,
// Floating-point using half precision (16 bits).
F16,
// Floating-point using single precision (32 bits).
F32,
// Floating-point using double precision (64 bits).
F64,
}
#[derive(Debug, PartialEq, Eq)]
pub struct DTypeParseError(String);
impl std::fmt::Display for DTypeParseError {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
write!(f, "cannot parse '{}' as a dtype", self.0)
}
}
impl std::error::Error for DTypeParseError {}
impl std::str::FromStr for DType {
type Err = DTypeParseError;
fn from_str(s: &str) -> std::result::Result<Self, Self::Err> {
match s {
"u8" => Ok(Self::U8),
"u32" => Ok(Self::U32),
"i64" => Ok(Self::I64),
"bf16" => Ok(Self::BF16),
"f16" => Ok(Self::F16),
"f32" => Ok(Self::F32),
"f64" => Ok(Self::F64),
_ => Err(DTypeParseError(s.to_string())),
}
}
}
impl DType {
/// String representation for dtypes.
pub fn as_str(&self) -> &'static str {
match self {
Self::U8 => "u8",
Self::U32 => "u32",
Self::I64 => "i64",
Self::BF16 => "bf16",
Self::F16 => "f16",
Self::F32 => "f32",
Self::F64 => "f64",
}
}
/// The size used by each element in bytes, i.e. 1 for `U8`, 4 for `F32`.
pub fn size_in_bytes(&self) -> usize {
match self {
Self::U8 => 1,
Self::U32 => 4,
Self::I64 => 8,
Self::BF16 => 2,
Self::F16 => 2,
Self::F32 => 4,
Self::F64 => 8,
}
}
pub fn is_int(&self) -> bool {
match self {
Self::U8 | Self::U32 | Self::I64 => true,
Self::BF16 | Self::F16 | Self::F32 | Self::F64 => false,
}
}
pub fn is_float(&self) -> bool {
match self {
Self::U8 | Self::U32 | Self::I64 => false,
Self::BF16 | Self::F16 | Self::F32 | Self::F64 => true,
}
}
}
pub trait WithDType:
Sized
+ Copy
+ num_traits::NumAssign
+ std::cmp::PartialOrd
+ std::fmt::Display
+ 'static
+ Send
+ Sync
+ crate::cpu::kernels::VecOps
{
const DTYPE: DType;
fn from_f64(v: f64) -> Self;
fn to_f64(self) -> f64;
fn to_cpu_storage_owned(data: Vec<Self>) -> CpuStorage;
fn to_cpu_storage(data: &[Self]) -> CpuStorage {
Self::to_cpu_storage_owned(data.to_vec())
}
fn cpu_storage_as_slice(s: &CpuStorage) -> Result<&[Self]>;
fn cpu_storage_data(s: CpuStorage) -> Result<Vec<Self>>;
}
macro_rules! with_dtype {
($ty:ty, $dtype:ident, $from_f64:expr, $to_f64:expr) => {
impl WithDType for $ty {
const DTYPE: DType = DType::$dtype;
fn from_f64(v: f64) -> Self {
$from_f64(v)
}
fn to_f64(self) -> f64 {
$to_f64(self)
}
fn to_cpu_storage_owned(data: Vec<Self>) -> CpuStorage {
CpuStorage::$dtype(data)
}
fn cpu_storage_data(s: CpuStorage) -> Result<Vec<Self>> {
match s {
CpuStorage::$dtype(data) => Ok(data),
_ => Err(Error::UnexpectedDType {
expected: DType::$dtype,
got: s.dtype(),
msg: "unexpected dtype",
}
.bt()),
}
}
fn cpu_storage_as_slice(s: &CpuStorage) -> Result<&[Self]> {
match s {
CpuStorage::$dtype(data) => Ok(data),
_ => Err(Error::UnexpectedDType {
expected: DType::$dtype,
got: s.dtype(),
msg: "unexpected dtype",
}
.bt()),
}
}
}
};
}
use half::{bf16, f16};
with_dtype!(u8, U8, |v: f64| v as u8, |v: u8| v as f64);
with_dtype!(u32, U32, |v: f64| v as u32, |v: u32| v as f64);
with_dtype!(i64, I64, |v: f64| v as i64, |v: i64| v as f64);
with_dtype!(f16, F16, f16::from_f64, f16::to_f64);
with_dtype!(bf16, BF16, bf16::from_f64, bf16::to_f64);
with_dtype!(f32, F32, |v: f64| v as f32, |v: f32| v as f64);
with_dtype!(f64, F64, |v: f64| v, |v: f64| v);
pub trait IntDType: WithDType {
fn is_true(&self) -> bool;
fn as_usize(&self) -> usize;
}
impl IntDType for i64 {
fn is_true(&self) -> bool {
*self != 0
}
fn as_usize(&self) -> usize {
*self as usize
}
}
impl IntDType for u32 {
fn is_true(&self) -> bool {
*self != 0
}
fn as_usize(&self) -> usize {
*self as usize
}
}
impl IntDType for u8 {
fn is_true(&self) -> bool {
*self != 0
}
fn as_usize(&self) -> usize {
*self as usize
}
}
pub trait FloatDType: WithDType {}
impl FloatDType for f16 {}
impl FloatDType for bf16 {}
impl FloatDType for f32 {}
impl FloatDType for f64 {}
| candle/candle-core/src/dtype.rs/0 | {
"file_path": "candle/candle-core/src/dtype.rs",
"repo_id": "candle",
"token_count": 2984
} | 15 |
//! Support for the GGML file format.
use super::{k_quants, GgmlDType, QStorage};
use crate::{Device, Result};
use byteorder::{LittleEndian, ReadBytesExt};
use std::collections::HashMap;
// https://github.com/ggerganov/llama.cpp/blob/468ea24fb4633a0d681f7ac84089566c1c6190cb/llama.h#L37
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
enum Magic {
Ggjt,
Ggla,
Ggmf,
Ggml,
Ggsn,
}
impl TryFrom<u32> for Magic {
type Error = crate::Error;
fn try_from(value: u32) -> Result<Self> {
let magic = match value {
0x67676a74 => Self::Ggjt,
0x67676c61 => Self::Ggla,
0x67676d66 => Self::Ggmf,
0x67676d6c => Self::Ggml,
0x6767736e => Self::Ggsn,
_ => crate::bail!("unknown magic {value:08x}"),
};
Ok(magic)
}
}
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum VersionedMagic {
GgmlUnversioned,
GgmfV1,
GgjtV1,
GgjtV2,
GgjtV3,
}
impl VersionedMagic {
fn read<R: std::io::Read>(reader: &mut R) -> Result<Self> {
let magic = reader.read_u32::<LittleEndian>()?;
let magic = Magic::try_from(magic)?;
if magic == Magic::Ggml {
return Ok(Self::GgmlUnversioned);
}
let version = reader.read_u32::<LittleEndian>()?;
let versioned_magic = match (magic, version) {
(Magic::Ggmf, 1) => Self::GgmfV1,
(Magic::Ggjt, 1) => Self::GgjtV1,
(Magic::Ggjt, 2) => Self::GgjtV2,
(Magic::Ggjt, 3) => Self::GgjtV3,
_ => crate::bail!("ggml: unsupported magic/version {magic:?}/{version}"),
};
Ok(versioned_magic)
}
fn align32(&self) -> bool {
match self {
Self::GgmlUnversioned | Self::GgmfV1 => false,
Self::GgjtV1 | Self::GgjtV2 | Self::GgjtV3 => true,
}
}
}
#[derive(Debug, Clone, PartialEq, Eq)]
pub struct HParams {
pub n_vocab: u32,
pub n_embd: u32,
pub n_mult: u32,
pub n_head: u32,
pub n_layer: u32,
pub n_rot: u32,
pub ftype: u32,
}
impl HParams {
fn read<R: std::io::Read>(reader: &mut R) -> Result<Self> {
let n_vocab = reader.read_u32::<LittleEndian>()?;
let n_embd = reader.read_u32::<LittleEndian>()?;
let n_mult = reader.read_u32::<LittleEndian>()?;
let n_head = reader.read_u32::<LittleEndian>()?;
let n_layer = reader.read_u32::<LittleEndian>()?;
let n_rot = reader.read_u32::<LittleEndian>()?;
let ftype = reader.read_u32::<LittleEndian>()?;
Ok(Self {
n_vocab,
n_embd,
n_mult,
n_head,
n_layer,
n_rot,
ftype,
})
}
}
#[derive(Debug, Clone, PartialEq)]
pub struct Vocab {
pub token_score_pairs: Vec<(Vec<u8>, f32)>,
}
impl Vocab {
fn read<R: std::io::Read>(reader: &mut R, n_vocab: usize) -> Result<Self> {
// https://github.com/ggerganov/llama.cpp/blob/468ea24fb4633a0d681f7ac84089566c1c6190cb/llama.cpp#L556
let mut token_score_pairs = Vec::with_capacity(n_vocab);
for _index in 0..n_vocab {
let len = reader.read_u32::<LittleEndian>()? as usize;
let mut word = vec![0u8; len];
reader.read_exact(&mut word)?;
let score = reader.read_f32::<LittleEndian>()?;
token_score_pairs.push((word, score))
}
Ok(Self { token_score_pairs })
}
}
fn from_raw_data<T: super::GgmlType + Send + Sync + 'static>(
raw_data: &[u8],
size_in_bytes: usize,
dims: Vec<usize>,
device: &Device,
) -> Result<super::QTensor> {
let raw_data_ptr = raw_data.as_ptr();
let n_blocks = size_in_bytes / std::mem::size_of::<T>();
let data = unsafe { std::slice::from_raw_parts(raw_data_ptr as *const T, n_blocks) };
let data: QStorage = match device {
Device::Cpu => QStorage::Cpu(Box::new(data.to_vec())),
Device::Metal(metal) => super::metal::load_quantized(metal, data)?,
Device::Cuda(cuda) => super::cuda::load_quantized(cuda, data)?,
};
super::QTensor::new(data, dims)
}
/// Creates a [Tensor] from a raw GGML tensor.
pub fn qtensor_from_ggml(
ggml_dtype: GgmlDType,
raw_data: &[u8],
dims: Vec<usize>,
device: &Device,
) -> Result<super::QTensor> {
let tensor_elems = dims.iter().product::<usize>();
let block_size = ggml_dtype.block_size();
if tensor_elems % block_size != 0 {
crate::bail!(
"the number of elements {tensor_elems} is not divisible by the block size {block_size}"
)
}
let size_in_bytes = tensor_elems / block_size * ggml_dtype.type_size();
match ggml_dtype {
GgmlDType::F32 => from_raw_data::<f32>(raw_data, size_in_bytes, dims, device),
GgmlDType::F16 => from_raw_data::<half::f16>(raw_data, size_in_bytes, dims, device),
GgmlDType::Q4_0 => {
from_raw_data::<k_quants::BlockQ4_0>(raw_data, size_in_bytes, dims, device)
}
GgmlDType::Q4_1 => {
from_raw_data::<k_quants::BlockQ4_1>(raw_data, size_in_bytes, dims, device)
}
GgmlDType::Q5_0 => {
from_raw_data::<k_quants::BlockQ5_0>(raw_data, size_in_bytes, dims, device)
}
GgmlDType::Q5_1 => {
from_raw_data::<k_quants::BlockQ5_1>(raw_data, size_in_bytes, dims, device)
}
GgmlDType::Q8_0 => {
from_raw_data::<k_quants::BlockQ8_0>(raw_data, size_in_bytes, dims, device)
}
GgmlDType::Q2K => {
from_raw_data::<k_quants::BlockQ2K>(raw_data, size_in_bytes, dims, device)
}
GgmlDType::Q3K => {
from_raw_data::<k_quants::BlockQ3K>(raw_data, size_in_bytes, dims, device)
}
GgmlDType::Q4K => {
from_raw_data::<k_quants::BlockQ4K>(raw_data, size_in_bytes, dims, device)
}
GgmlDType::Q5K => {
from_raw_data::<k_quants::BlockQ5K>(raw_data, size_in_bytes, dims, device)
}
GgmlDType::Q6K => {
from_raw_data::<k_quants::BlockQ6K>(raw_data, size_in_bytes, dims, device)
}
_ => crate::bail!("quantized type {ggml_dtype:?} is not supported yet"),
}
}
fn read_one_tensor<R: std::io::Seek + std::io::Read>(
reader: &mut R,
magic: VersionedMagic,
device: &Device,
) -> Result<(String, super::QTensor)> {
let n_dims = reader.read_u32::<LittleEndian>()?;
let name_len = reader.read_u32::<LittleEndian>()?;
let ggml_dtype = reader.read_u32::<LittleEndian>()?;
let ggml_dtype = GgmlDType::from_u32(ggml_dtype)?;
let mut dims = vec![0u32; n_dims as usize];
reader.read_u32_into::<LittleEndian>(&mut dims)?;
// The dimensions are stored in reverse order, see for example:
// https://github.com/ggerganov/llama.cpp/blob/b5ffb2849d23afe73647f68eec7b68187af09be6/convert.py#L969
dims.reverse();
let mut name = vec![0u8; name_len as usize];
reader.read_exact(&mut name)?;
let name = String::from_utf8_lossy(&name).into_owned();
if magic.align32() {
let pos = reader.stream_position()?;
reader.seek(std::io::SeekFrom::Current(((32 - pos % 32) % 32) as i64))?;
}
let dims = dims.iter().map(|&u| u as usize).collect::<Vec<_>>();
let tensor_elems = dims.iter().product::<usize>();
let size_in_bytes = tensor_elems * ggml_dtype.type_size() / ggml_dtype.block_size();
// TODO: Mmap version to avoid copying the data around?
let mut raw_data = vec![0u8; size_in_bytes];
reader.read_exact(&mut raw_data)?;
match qtensor_from_ggml(ggml_dtype, &raw_data, dims, device) {
Ok(tensor) => Ok((name, tensor)),
Err(e) => crate::bail!("Error creating tensor {name}: {e}"),
}
}
pub struct Content {
pub magic: VersionedMagic,
pub hparams: HParams,
pub vocab: Vocab,
pub tensors: HashMap<String, super::QTensor>,
pub device: Device,
}
impl Content {
pub fn read<R: std::io::Seek + std::io::Read>(
reader: &mut R,
device: &Device,
) -> Result<Content> {
// https://github.com/ggerganov/llama.cpp/blob/468ea24fb4633a0d681f7ac84089566c1c6190cb/llama.cpp#L505
let last_position = reader.seek(std::io::SeekFrom::End(0))?;
reader.seek(std::io::SeekFrom::Start(0))?;
let magic = VersionedMagic::read(reader)?;
let hparams = HParams::read(reader)?;
let vocab = Vocab::read(reader, hparams.n_vocab as usize)?;
let mut tensors = HashMap::new();
while reader.stream_position()? != last_position {
let (name, tensor) = read_one_tensor(reader, magic, device)?;
tensors.insert(name, tensor);
}
let device = device.clone();
Ok(Self {
magic,
hparams,
vocab,
tensors,
device,
})
}
pub fn remove(&mut self, name: &str) -> Result<super::QTensor> {
match self.tensors.remove(name) {
None => crate::bail!("cannot find tensor with name '{name}'"),
Some(tensor) => Ok(tensor),
}
}
}
| candle/candle-core/src/quantized/ggml_file.rs/0 | {
"file_path": "candle/candle-core/src/quantized/ggml_file.rs",
"repo_id": "candle",
"token_count": 4586
} | 16 |
use std::str::FromStr;
pub fn get_num_threads() -> usize {
// Respond to the same environment variable as rayon.
match std::env::var("RAYON_NUM_THREADS")
.ok()
.and_then(|s| usize::from_str(&s).ok())
{
Some(x) if x > 0 => x,
Some(_) | None => num_cpus::get(),
}
}
pub fn has_accelerate() -> bool {
cfg!(feature = "accelerate")
}
pub fn has_mkl() -> bool {
cfg!(feature = "mkl")
}
pub fn cuda_is_available() -> bool {
cfg!(feature = "cuda")
}
pub fn metal_is_available() -> bool {
cfg!(feature = "metal")
}
pub fn with_avx() -> bool {
cfg!(target_feature = "avx")
}
pub fn with_neon() -> bool {
cfg!(target_feature = "neon")
}
pub fn with_simd128() -> bool {
cfg!(target_feature = "simd128")
}
pub fn with_f16c() -> bool {
cfg!(target_feature = "f16c")
}
| candle/candle-core/src/utils.rs/0 | {
"file_path": "candle/candle-core/src/utils.rs",
"repo_id": "candle",
"token_count": 389
} | 17 |
#![allow(unused)]
use anyhow::{Context, Result};
use std::io::Write;
use std::path::PathBuf;
struct KernelDirectories {
kernel_glob: &'static str,
rust_target: &'static str,
include_dirs: &'static [&'static str],
}
const KERNEL_DIRS: [KernelDirectories; 1] = [KernelDirectories {
kernel_glob: "examples/custom-ops/kernels/*.cu",
rust_target: "examples/custom-ops/cuda_kernels.rs",
include_dirs: &[],
}];
fn main() -> Result<()> {
println!("cargo:rerun-if-changed=build.rs");
#[cfg(feature = "cuda")]
{
for kdir in KERNEL_DIRS.iter() {
let builder = bindgen_cuda::Builder::default().kernel_paths_glob(kdir.kernel_glob);
println!("cargo:info={builder:?}");
let bindings = builder.build_ptx().unwrap();
bindings.write(kdir.rust_target).unwrap()
}
}
Ok(())
}
| candle/candle-examples/build.rs/0 | {
"file_path": "candle/candle-examples/build.rs",
"repo_id": "candle",
"token_count": 391
} | 18 |
// An implementation of LLaMA https://github.com/facebookresearch/llama
//
// This is based on nanoGPT in a similar way to:
// https://github.com/Lightning-AI/lit-llama/blob/main/lit_llama/model.py
//
// The tokenizer config can be retrieved from:
// https://huggingface.co/hf-internal-testing/llama-tokenizer/raw/main/tokenizer.json
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
use anyhow::{bail, Error as E, Result};
use clap::{Parser, ValueEnum};
use candle::{DType, Tensor};
use candle_nn::VarBuilder;
use candle_transformers::generation::LogitsProcessor;
use hf_hub::{api::sync::Api, Repo, RepoType};
use std::io::Write;
use candle_transformers::models::llama as model;
use model::{Llama, LlamaConfig};
const EOS_TOKEN: &str = "</s>";
const DEFAULT_PROMPT: &str = "My favorite theorem is ";
#[derive(Clone, Debug, Copy, PartialEq, Eq, ValueEnum)]
enum Which {
V1,
V2,
#[value(name = "solar-10.7b")]
Solar10_7B,
#[value(name = "tiny-llama-1.1b-chat")]
TinyLlama1_1BChat,
}
#[derive(Parser, Debug)]
#[command(author, version, about, long_about = None)]
struct Args {
/// Run on CPU rather than on GPU.
#[arg(long)]
cpu: bool,
/// The temperature used to generate samples.
#[arg(long)]
temperature: Option<f64>,
/// Nucleus sampling probability cutoff.
#[arg(long)]
top_p: Option<f64>,
/// The seed to use when generating random samples.
#[arg(long, default_value_t = 299792458)]
seed: u64,
/// The length of the sample to generate (in tokens).
#[arg(long, default_value_t = 10000)]
sample_len: usize,
/// Disable the key-value cache.
#[arg(long)]
no_kv_cache: bool,
/// The initial prompt.
#[arg(long)]
prompt: Option<String>,
/// Use different dtype than f16
#[arg(long)]
dtype: Option<String>,
/// Enable tracing (generates a trace-timestamp.json file).
#[arg(long)]
tracing: bool,
#[arg(long)]
model_id: Option<String>,
#[arg(long)]
revision: Option<String>,
/// The model size to use.
#[arg(long, default_value = "v2")]
which: Which,
#[arg(long)]
use_flash_attn: bool,
/// Penalty to be applied for repeating tokens, 1. means no penalty.
#[arg(long, default_value_t = 1.0)]
repeat_penalty: f32,
/// The context size to consider for the repeat penalty.
#[arg(long, default_value_t = 64)]
repeat_last_n: usize,
}
fn main() -> Result<()> {
use tokenizers::Tokenizer;
use tracing_chrome::ChromeLayerBuilder;
use tracing_subscriber::prelude::*;
let args = Args::parse();
let _guard = if args.tracing {
let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
tracing_subscriber::registry().with(chrome_layer).init();
Some(guard)
} else {
None
};
let device = candle_examples::device(args.cpu)?;
let dtype = match args.dtype.as_deref() {
Some("f16") => DType::F16,
Some("bf16") => DType::BF16,
Some("f32") => DType::F32,
Some(dtype) => bail!("Unsupported dtype {dtype}"),
None => DType::F16,
};
let (llama, tokenizer_filename, mut cache) = {
let api = Api::new()?;
let model_id = args.model_id.unwrap_or_else(|| match args.which {
Which::V1 => "Narsil/amall-7b".to_string(),
Which::V2 => "meta-llama/Llama-2-7b-hf".to_string(),
Which::Solar10_7B => "upstage/SOLAR-10.7B-v1.0".to_string(),
Which::TinyLlama1_1BChat => "TinyLlama/TinyLlama-1.1B-Chat-v1.0".to_string(),
});
println!("loading the model weights from {model_id}");
let revision = args.revision.unwrap_or("main".to_string());
let api = api.repo(Repo::with_revision(model_id, RepoType::Model, revision));
let tokenizer_filename = api.get("tokenizer.json")?;
let config_filename = api.get("config.json")?;
let config: LlamaConfig = serde_json::from_slice(&std::fs::read(config_filename)?)?;
let config = config.into_config(args.use_flash_attn);
let filenames = match args.which {
Which::V1 | Which::V2 | Which::Solar10_7B => {
candle_examples::hub_load_safetensors(&api, "model.safetensors.index.json")?
}
Which::TinyLlama1_1BChat => vec![api.get("model.safetensors")?],
};
let cache = model::Cache::new(!args.no_kv_cache, dtype, &config, &device)?;
let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, dtype, &device)? };
(Llama::load(vb, &config)?, tokenizer_filename, cache)
};
let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
let eos_token_id = tokenizer.token_to_id(EOS_TOKEN);
let prompt = args.prompt.as_ref().map_or(DEFAULT_PROMPT, |p| p.as_str());
let mut tokens = tokenizer
.encode(prompt, true)
.map_err(E::msg)?
.get_ids()
.to_vec();
let mut tokenizer = candle_examples::token_output_stream::TokenOutputStream::new(tokenizer);
println!("starting the inference loop");
print!("{prompt}");
let mut logits_processor = LogitsProcessor::new(args.seed, args.temperature, args.top_p);
let start_gen = std::time::Instant::now();
let mut index_pos = 0;
let mut token_generated = 0;
for index in 0..args.sample_len {
let (context_size, context_index) = if cache.use_kv_cache && index > 0 {
(1, index_pos)
} else {
(tokens.len(), 0)
};
let ctxt = &tokens[tokens.len().saturating_sub(context_size)..];
let input = Tensor::new(ctxt, &device)?.unsqueeze(0)?;
let logits = llama.forward(&input, context_index, &mut cache)?;
let logits = logits.squeeze(0)?;
let logits = if args.repeat_penalty == 1. {
logits
} else {
let start_at = tokens.len().saturating_sub(args.repeat_last_n);
candle_transformers::utils::apply_repeat_penalty(
&logits,
args.repeat_penalty,
&tokens[start_at..],
)?
};
index_pos += ctxt.len();
let next_token = logits_processor.sample(&logits)?;
token_generated += 1;
tokens.push(next_token);
if Some(next_token) == eos_token_id {
break;
}
if let Some(t) = tokenizer.next_token(next_token)? {
print!("{t}");
std::io::stdout().flush()?;
}
}
if let Some(rest) = tokenizer.decode_rest().map_err(E::msg)? {
print!("{rest}");
}
let dt = start_gen.elapsed();
println!(
"\n\n{} tokens generated ({} token/s)\n",
token_generated,
token_generated as f64 / dt.as_secs_f64(),
);
Ok(())
}
| candle/candle-examples/examples/llama/main.rs/0 | {
"file_path": "candle/candle-examples/examples/llama/main.rs",
"repo_id": "candle",
"token_count": 3061
} | 19 |
# This script exports pre-trained model weights in the safetensors format.
import numpy as np
import torch
import torchvision
from safetensors import torch as stt
m = torchvision.models.resnet50(pretrained=True)
stt.save_file(m.state_dict(), 'resnet50.safetensors')
m = torchvision.models.resnet101(pretrained=True)
stt.save_file(m.state_dict(), 'resnet101.safetensors')
m = torchvision.models.resnet152(pretrained=True)
stt.save_file(m.state_dict(), 'resnet152.safetensors')
| candle/candle-examples/examples/resnet/export_models.py/0 | {
"file_path": "candle/candle-examples/examples/resnet/export_models.py",
"repo_id": "candle",
"token_count": 166
} | 20 |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::{Error as E, Result};
use clap::{Parser, ValueEnum};
use candle_transformers::models::quantized_stable_lm::Model as QStableLM;
use candle_transformers::models::stable_lm::{Config, Model as StableLM};
use candle::{DType, Device, Tensor};
use candle_examples::token_output_stream::TokenOutputStream;
use candle_nn::VarBuilder;
use candle_transformers::generation::LogitsProcessor;
use hf_hub::{api::sync::Api, Repo, RepoType};
use tokenizers::Tokenizer;
enum Model {
StableLM(StableLM),
Quantized(QStableLM),
}
struct TextGeneration {
model: Model,
device: Device,
tokenizer: TokenOutputStream,
logits_processor: LogitsProcessor,
repeat_penalty: f32,
repeat_last_n: usize,
}
impl TextGeneration {
#[allow(clippy::too_many_arguments)]
fn new(
model: Model,
tokenizer: Tokenizer,
seed: u64,
temp: Option<f64>,
top_p: Option<f64>,
repeat_penalty: f32,
repeat_last_n: usize,
device: &Device,
) -> Self {
let logits_processor = LogitsProcessor::new(seed, temp, top_p);
Self {
model,
tokenizer: TokenOutputStream::new(tokenizer),
logits_processor,
repeat_penalty,
repeat_last_n,
device: device.clone(),
}
}
fn run(&mut self, prompt: &str, sample_len: usize) -> Result<()> {
use std::io::Write;
self.tokenizer.clear();
let mut tokens = self
.tokenizer
.tokenizer()
.encode(prompt, true)
.map_err(E::msg)?
.get_ids()
.to_vec();
for &t in tokens.iter() {
if let Some(t) = self.tokenizer.next_token(t)? {
print!("{t}")
}
}
std::io::stdout().flush()?;
let mut generated_tokens = 0usize;
let eos_token = match self.tokenizer.get_token("<|endoftext|>") {
Some(token) => token,
None => anyhow::bail!("cannot find the <|endoftext|> token"),
};
let start_gen = std::time::Instant::now();
for index in 0..sample_len {
let context_size = if index > 0 { 1 } else { tokens.len() };
let start_pos = tokens.len().saturating_sub(context_size);
let ctxt = &tokens[start_pos..];
let input = Tensor::new(ctxt, &self.device)?.unsqueeze(0)?;
let logits = match &mut self.model {
Model::StableLM(m) => m.forward(&input, start_pos)?,
Model::Quantized(m) => m.forward(&input, start_pos)?,
};
let logits = logits.squeeze(0)?.squeeze(0)?.to_dtype(DType::F32)?;
let logits = if self.repeat_penalty == 1. {
logits
} else {
let start_at = tokens.len().saturating_sub(self.repeat_last_n);
candle_transformers::utils::apply_repeat_penalty(
&logits,
self.repeat_penalty,
&tokens[start_at..],
)?
};
let next_token = self.logits_processor.sample(&logits)?;
tokens.push(next_token);
generated_tokens += 1;
if next_token == eos_token {
break;
}
if let Some(t) = self.tokenizer.next_token(next_token)? {
print!("{t}");
std::io::stdout().flush()?;
}
}
let dt = start_gen.elapsed();
if let Some(rest) = self.tokenizer.decode_rest().map_err(E::msg)? {
print!("{rest}");
}
std::io::stdout().flush()?;
println!(
"\n{generated_tokens} tokens generated ({:.2} token/s)",
generated_tokens as f64 / dt.as_secs_f64(),
);
Ok(())
}
}
#[derive(Clone, Copy, Debug, ValueEnum, PartialEq, Eq)]
enum Which {
V1Orig,
V1,
V1Zephyr,
V2,
V2Zephyr,
Code,
}
#[derive(Parser, Debug)]
#[command(author, version, about, long_about = None)]
struct Args {
/// Run on CPU rather than on GPU.
#[arg(long)]
cpu: bool,
/// Enable tracing (generates a trace-timestamp.json file).
#[arg(long)]
tracing: bool,
#[arg(long)]
use_flash_attn: bool,
#[arg(long)]
prompt: String,
/// The temperature used to generate samples.
#[arg(long)]
temperature: Option<f64>,
/// Nucleus sampling probability cutoff.
#[arg(long)]
top_p: Option<f64>,
/// The seed to use when generating random samples.
#[arg(long, default_value_t = 299792458)]
seed: u64,
/// The length of the sample to generate (in tokens).
#[arg(long, short = 'n', default_value_t = 1000)]
sample_len: usize,
#[arg(long)]
model_id: Option<String>,
#[arg(long, default_value = "main")]
revision: String,
#[arg(long, default_value = "v2")]
which: Which,
#[arg(long)]
tokenizer_file: Option<String>,
#[arg(long)]
weight_files: Option<String>,
#[arg(long)]
quantized: bool,
/// Penalty to be applied for repeating tokens, 1. means no penalty.
#[arg(long, default_value_t = 1.1)]
repeat_penalty: f32,
/// The context size to consider for the repeat penalty.
#[arg(long, default_value_t = 64)]
repeat_last_n: usize,
}
fn main() -> Result<()> {
use tracing_chrome::ChromeLayerBuilder;
use tracing_subscriber::prelude::*;
let args = Args::parse();
let _guard = if args.tracing {
let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
tracing_subscriber::registry().with(chrome_layer).init();
Some(guard)
} else {
None
};
println!(
"avx: {}, neon: {}, simd128: {}, f16c: {}",
candle::utils::with_avx(),
candle::utils::with_neon(),
candle::utils::with_simd128(),
candle::utils::with_f16c()
);
println!(
"temp: {:.2} repeat-penalty: {:.2} repeat-last-n: {}",
args.temperature.unwrap_or(0.),
args.repeat_penalty,
args.repeat_last_n
);
let start = std::time::Instant::now();
let api = Api::new()?;
let model_id = match args.model_id {
Some(model_id) => model_id,
None => match args.which {
Which::V1Orig => "lmz/candle-stablelm-3b-4e1t".to_string(),
Which::V1 => "stabilityai/stablelm-3b-4e1t".to_string(),
Which::V1Zephyr => "stabilityai/stablelm-zephyr-3b".to_string(),
Which::Code => "stabilityai/stable-code-3b".to_string(),
Which::V2 => "stabilityai/stablelm-2-1_6b".to_string(),
Which::V2Zephyr => "stabilityai/stablelm-2-zephyr-1_6b".to_string(),
},
};
let repo = api.repo(Repo::with_revision(
model_id,
RepoType::Model,
args.revision,
));
let tokenizer_filename = match args.tokenizer_file {
Some(file) => std::path::PathBuf::from(file),
None => repo.get("tokenizer.json")?,
};
let filenames = match args.weight_files {
Some(files) => files
.split(',')
.map(std::path::PathBuf::from)
.collect::<Vec<_>>(),
None => match (args.which, args.quantized) {
(Which::V1Orig | Which::V1, true) => vec![repo.get("model-q4k.gguf")?],
(Which::V2, true) => {
let gguf = api
.model("lmz/candle-stablelm".to_string())
.get("stablelm-2-1_6b-q4k.gguf")?;
vec![gguf]
}
(Which::V2Zephyr, true) => {
let gguf = api
.model("lmz/candle-stablelm".to_string())
.get("stablelm-2-zephyr-1_6b-q4k.gguf")?;
vec![gguf]
}
(Which::V1Zephyr | Which::Code, true) => {
anyhow::bail!("Quantized {:?} variant not supported.", args.which)
}
(Which::V1Orig | Which::V1 | Which::V1Zephyr | Which::V2 | Which::V2Zephyr, false) => {
vec![repo.get("model.safetensors")?]
}
(Which::Code, false) => {
candle_examples::hub_load_safetensors(&repo, "model.safetensors.index.json")?
}
},
};
println!("retrieved the files in {:?}", start.elapsed());
let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
let start = std::time::Instant::now();
let config = match args.which {
Which::V1Orig => Config::stablelm_3b_4e1t(args.use_flash_attn),
Which::V1 | Which::V1Zephyr | Which::V2 | Which::V2Zephyr | Which::Code => {
let config_filename = repo.get("config.json")?;
let config = std::fs::read_to_string(config_filename)?;
let mut config: Config = serde_json::from_str(&config)?;
config.set_use_flash_attn(args.use_flash_attn);
config
}
};
let device = candle_examples::device(args.cpu)?;
let model = if args.quantized {
let filename = &filenames[0];
let vb =
candle_transformers::quantized_var_builder::VarBuilder::from_gguf(filename, &device)?;
let model = QStableLM::new(&config, vb)?;
Model::Quantized(model)
} else {
let dtype = if device.is_cuda() {
DType::BF16
} else {
DType::F32
};
let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, dtype, &device)? };
let model = StableLM::new(&config, vb)?;
Model::StableLM(model)
};
println!("loaded the model in {:?}", start.elapsed());
let mut pipeline = TextGeneration::new(
model,
tokenizer,
args.seed,
args.temperature,
args.top_p,
args.repeat_penalty,
args.repeat_last_n,
&device,
);
pipeline.run(&args.prompt, args.sample_len)?;
Ok(())
}
| candle/candle-examples/examples/stable-lm/main.rs/0 | {
"file_path": "candle/candle-examples/examples/stable-lm/main.rs",
"repo_id": "candle",
"token_count": 4969
} | 21 |
# Get the checkpoint from
# https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt
import torch
from safetensors.torch import save_file
data = torch.load("tiny.en.pt")
weights = {}
for k, v in data["model_state_dict"].items():
weights[k] = v.contiguous()
print(k, v.shape, v.dtype)
save_file(weights, "tiny.en.safetensors")
print(data["dims"])
| candle/candle-examples/examples/whisper/extract_weights.py/0 | {
"file_path": "candle/candle-examples/examples/whisper/extract_weights.py",
"repo_id": "candle",
"token_count": 183
} | 22 |
/******************************************************************************
* Copyright (c) 2023, Tri Dao.
******************************************************************************/
#pragma once
namespace flash {
////////////////////////////////////////////////////////////////////////////////////////////////////
template<bool Varlen=true>
struct BlockInfo {
template<typename Params>
__device__ BlockInfo(const Params ¶ms, const int bidb)
: sum_s_q(!Varlen || params.cu_seqlens_q == nullptr ? -1 : params.cu_seqlens_q[bidb])
, sum_s_k(!Varlen || params.cu_seqlens_k == nullptr || !params.is_seqlens_k_cumulative ? -1 : params.cu_seqlens_k[bidb])
, actual_seqlen_q(!Varlen || params.cu_seqlens_q == nullptr ? params.seqlen_q : params.cu_seqlens_q[bidb + 1] - sum_s_q)
// If is_seqlens_k_cumulative, then seqlen_k is cu_seqlens_k[bidb + 1] - cu_seqlens_k[bidb].
// Otherwise it's cu_seqlens_k[bidb], i.e., we use cu_seqlens_k to store the sequence lengths of K.
, seqlen_k_cache(!Varlen || params.cu_seqlens_k == nullptr ? params.seqlen_k : (params.is_seqlens_k_cumulative ? params.cu_seqlens_k[bidb + 1] - sum_s_k : params.cu_seqlens_k[bidb]))
, actual_seqlen_k(params.seqused_k ? params.seqused_k[bidb] : seqlen_k_cache + (params.knew_ptr == nullptr ? 0 : params.seqlen_knew))
{
}
template <typename index_t>
inline __device__ index_t q_offset(const index_t batch_stride, const index_t row_stride, const int bidb) const {
return sum_s_q == -1 ? bidb * batch_stride : uint32_t(sum_s_q) * row_stride;
}
template <typename index_t>
inline __device__ index_t k_offset(const index_t batch_stride, const index_t row_stride, const int bidb) const {
return sum_s_k == -1 ? bidb * batch_stride : uint32_t(sum_s_k) * row_stride;
}
const int sum_s_q;
const int sum_s_k;
const int actual_seqlen_q;
// We have to have seqlen_k_cache declared before actual_seqlen_k, otherwise actual_seqlen_k is set to 0.
const int seqlen_k_cache;
const int actual_seqlen_k;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace flash
| candle/candle-flash-attn/kernels/block_info.h/0 | {
"file_path": "candle/candle-flash-attn/kernels/block_info.h",
"repo_id": "candle",
"token_count": 851
} | 23 |
// Copyright (c) 2023, Tri Dao.
// Splitting the different head dimensions to different files to speed up compilation.
// This file is auto-generated. See "generate_kernels.py"
#include "flash_fwd_launch_template.h"
template<>
void run_mha_fwd_<cutlass::half_t, 64>(Flash_fwd_params ¶ms, cudaStream_t stream) {
run_mha_fwd_hdim64<cutlass::half_t>(params, stream);
}
| candle/candle-flash-attn/kernels/flash_fwd_hdim64_fp16_sm80.cu/0 | {
"file_path": "candle/candle-flash-attn/kernels/flash_fwd_hdim64_fp16_sm80.cu",
"repo_id": "candle",
"token_count": 135
} | 24 |
fn main() {
println!("cargo:rerun-if-changed=build.rs");
let builder = bindgen_cuda::Builder::default();
println!("cargo:info={builder:?}");
let bindings = builder.build_ptx().unwrap();
bindings.write("src/lib.rs").unwrap();
}
| candle/candle-kernels/build.rs/0 | {
"file_path": "candle/candle-kernels/build.rs",
"repo_id": "candle",
"token_count": 96
} | 25 |
# candle-metal-kernels
This crate contains Metal kernels used from candle. | candle/candle-metal-kernels/README.md/0 | {
"file_path": "candle/candle-metal-kernels/README.md",
"repo_id": "candle",
"token_count": 18
} | 26 |
use candle_metal_kernels::{call_cast_contiguous, Kernels};
use metal::objc::rc::autoreleasepool;
use metal::{Device, MTLResourceOptions};
use rand;
use std::any::type_name;
use std::time::Instant;
fn main() {
let device = Device::system_default().unwrap();
let kernels = Kernels::new();
let f32_1k = (0..1000).map(|_| rand::random::<f32>()).collect::<Vec<_>>();
let f32_10k = (0..10000)
.map(|_| rand::random::<f32>())
.collect::<Vec<_>>();
let f32_100k = (0..100000)
.map(|_| rand::random::<f32>())
.collect::<Vec<_>>();
let contiguous_kernels = ["cast_u32_f32"];
println!(
"{0: <5} | {1: <19} | {2: <6} | {3: <5} | {4: <11} | {5: <11}",
"dtype", "kernel", "size", "runs", "total time", "avg time"
);
// f32
run_cast_bench(&device, &kernels, &f32_1k, &contiguous_kernels);
run_cast_bench(&device, &kernels, &f32_10k, &contiguous_kernels);
run_cast_bench(&device, &kernels, &f32_100k, &contiguous_kernels);
}
fn run_cast_bench<T: Clone>(
device: &Device,
kernels: &Kernels,
v: &[T],
contiguous: &[&'static str],
) {
let command_queue = device.new_command_queue();
let options = MTLResourceOptions::StorageModeManaged;
let iterations = 1000;
let input = device.new_buffer_with_data(
v.as_ptr() as *const core::ffi::c_void,
core::mem::size_of_val(v) as u64,
options,
);
let mut output = device.new_buffer(core::mem::size_of_val(v) as u64, options);
// Contiguous
for kernel_name in contiguous {
let total_time = autoreleasepool(|| {
let command_buffer = command_queue.new_command_buffer();
let start = Instant::now();
for _ in 0..iterations {
call_cast_contiguous(
device,
&command_buffer,
kernels,
kernel_name,
v.len(),
&input,
&mut output,
)
.unwrap();
}
command_buffer.commit();
command_buffer.wait_until_completed();
start.elapsed()
});
println!(
"{0: <5} | {1: <19} | {2: <6} | {3: <5} | {4: <11?} | {5: <11?}",
type_name::<T>().split("::").last().unwrap(),
kernel_name.to_string(),
v.len(),
iterations,
total_time,
total_time / iterations
);
}
// Strided?
}
| candle/candle-metal-kernels/tmp/cast.rs/0 | {
"file_path": "candle/candle-metal-kernels/tmp/cast.rs",
"repo_id": "candle",
"token_count": 1299
} | 27 |
//! Linear layer
//!
//! This layer applies a linear transformation to the incoming data, `y = [email protected]() + b`.
//! The bias is optional. The `forward` method can be used to apply the layer, it supports input
//! with a batch dimension (so of shape `(b_sz, in_c)`) or without (of shape `(in_c,)`), the
//! output has shape `(b_sz, out_c)` and `(out_c,)` respectively.
//!
//! ```rust
//! use candle::{Tensor, Device::Cpu};
//! use candle_nn::{Linear, Module};
//! # fn main() -> candle::Result<()> {
//!
//! let w = Tensor::new(&[[1f32, 2.], [3., 4.], [5., 6.]], &Cpu)?;
//! let layer = Linear::new(w, None); // Use no bias.
//! let xs = Tensor::new(&[[10f32, 100.]], &Cpu)?;
//! let ys = layer.forward(&xs)?;
//! assert_eq!(ys.to_vec2::<f32>()?, &[[210.0, 430.0, 650.0]]);
//! # Ok(()) }
//! ```
use candle::{Result, Tensor};
#[derive(Clone, Debug)]
pub struct Linear {
weight: Tensor,
bias: Option<Tensor>,
}
impl Linear {
pub fn new(weight: Tensor, bias: Option<Tensor>) -> Self {
Self { weight, bias }
}
pub fn weight(&self) -> &Tensor {
&self.weight
}
pub fn bias(&self) -> Option<&Tensor> {
self.bias.as_ref()
}
}
impl super::Module for Linear {
fn forward(&self, x: &Tensor) -> candle::Result<Tensor> {
let w = match *x.dims() {
[b1, b2, _, _] => self.weight.broadcast_left((b1, b2))?.t()?,
[bsize, _, _] => self.weight.broadcast_left(bsize)?.t()?,
_ => self.weight.t()?,
};
let x = x.matmul(&w)?;
match &self.bias {
None => Ok(x),
Some(bias) => x.broadcast_add(bias),
}
}
}
/// Create or initialize a new linear layer.
///
/// This uses some default names for weights and biases, namely `"weight"` and `"bias"`.
pub fn linear(in_dim: usize, out_dim: usize, vb: crate::VarBuilder) -> Result<Linear> {
let init_ws = crate::init::DEFAULT_KAIMING_NORMAL;
let ws = vb.get_with_hints((out_dim, in_dim), "weight", init_ws)?;
let bound = 1. / (in_dim as f64).sqrt();
let init_bs = crate::Init::Uniform {
lo: -bound,
up: bound,
};
let bs = vb.get_with_hints(out_dim, "bias", init_bs)?;
Ok(Linear::new(ws, Some(bs)))
}
/// Create or initialize a new linear layer without biases.
pub fn linear_no_bias(in_dim: usize, out_dim: usize, vb: crate::VarBuilder) -> Result<Linear> {
let init_ws = crate::init::DEFAULT_KAIMING_NORMAL;
let ws = vb.get_with_hints((out_dim, in_dim), "weight", init_ws)?;
Ok(Linear::new(ws, None))
}
pub fn linear_b(
in_dim: usize,
out_dim: usize,
bias: bool,
vb: crate::VarBuilder,
) -> Result<Linear> {
if bias {
linear(in_dim, out_dim, vb)
} else {
linear_no_bias(in_dim, out_dim, vb)
}
}
| candle/candle-nn/src/linear.rs/0 | {
"file_path": "candle/candle-nn/src/linear.rs",
"repo_id": "candle",
"token_count": 1252
} | 28 |
[package]
name = "candle-onnx"
version = "0.4.2"
edition = "2021"
description = "ONNX support for Candle"
repository = "https://github.com/huggingface/candle"
keywords = ["blas", "tensor", "machine-learning"]
categories = ["science"]
license = "MIT OR Apache-2.0"
[dependencies]
candle = { path = "../candle-core", package = "candle-core", version = "0.4.2" }
candle-nn = { path = "../candle-nn", version = "0.4.2" }
prost = "0.12.1"
[build-dependencies]
prost-build = "0.12.1"
[dev-dependencies]
anyhow = { version = "1", features = ["backtrace"] }
clap = { version = "4.2.4", features = ["derive"] }
| candle/candle-onnx/Cargo.toml/0 | {
"file_path": "candle/candle-onnx/Cargo.toml",
"repo_id": "candle",
"token_count": 242
} | 29 |
# Generated content DO NOT EDIT
from .. import functional
avg_pool2d = functional.avg_pool2d
gelu = functional.gelu
max_pool2d = functional.max_pool2d
relu = functional.relu
silu = functional.silu
softmax = functional.softmax
tanh = functional.tanh
| candle/candle-pyo3/py_src/candle/functional/__init__.py/0 | {
"file_path": "candle/candle-pyo3/py_src/candle/functional/__init__.py",
"repo_id": "candle",
"token_count": 84
} | 30 |
[project]
name = 'candle-nn'
requires-python = '>=3.7'
authors = [
{name = 'The Candle Team'},
]
dynamic = [
'description',
'license',
'readme',
]
[project.urls]
Homepage = 'https://github.com/huggingface/candle'
Source = 'https://github.com/huggingface/candle'
[build-system]
requires = ["maturin>=1.0,<2.0"]
build-backend = "maturin"
[tool.maturin]
python-source = "py_src"
module-name = "candle.candle"
bindings = 'pyo3'
features = ["pyo3/extension-module"]
[tool.black]
line-length = 119
target-version = ['py35']
[project.optional-dependencies]
testing = ["pytest", "black==22.3"]
huggingface = ["transformers>=4.33.3", "huggingface-hub>=0.17.3"] | candle/candle-pyo3/pyproject.toml/0 | {
"file_path": "candle/candle-pyo3/pyproject.toml",
"repo_id": "candle",
"token_count": 285
} | 31 |
[package]
name = "candle-transformers"
version.workspace = true
edition.workspace = true
description.workspace = true
repository.workspace = true
keywords.workspace = true
categories.workspace = true
license.workspace = true
readme = "README.md"
[dependencies]
accelerate-src = { workspace = true, optional = true }
byteorder = { workspace = true }
candle = { workspace = true }
candle-flash-attn = { workspace = true, optional = true }
candle-nn = { workspace = true }
fancy-regex = { workspace = true }
intel-mkl-src = { workspace = true, optional = true }
num-traits = { workspace = true }
rand = { workspace = true }
rayon = { workspace = true }
serde = { workspace = true }
serde_json = { workspace = true }
serde_plain = { workspace = true }
tracing = { workspace = true }
[features]
default = []
accelerate = ["dep:accelerate-src", "candle/accelerate", "candle-nn/accelerate"]
cuda = ["candle/cuda", "candle-nn/cuda"]
flash-attn = ["cuda", "dep:candle-flash-attn"]
mkl = ["dep:intel-mkl-src", "candle/mkl", "candle-nn/mkl"]
metal = ["candle/metal", "candle-nn/metal"]
| candle/candle-transformers/Cargo.toml/0 | {
"file_path": "candle/candle-transformers/Cargo.toml",
"repo_id": "candle",
"token_count": 372
} | 32 |
use candle::{DType, Device, Result, Tensor, D};
use candle_nn::{embedding, linear_b as linear, Embedding, LayerNorm, Linear, Module, VarBuilder};
const MAX_SEQ_LEN: usize = 5000;
fn layer_norm(size: usize, eps: f64, vb: VarBuilder) -> Result<LayerNorm> {
let (weight, bias) = match (vb.get(size, "weight"), vb.get(size, "bias")) {
(Ok(weight), Ok(bias)) => (weight, bias),
(Err(err), _) | (_, Err(err)) => {
if let (Ok(weight), Ok(bias)) = (vb.get(size, "gamma"), vb.get(size, "beta")) {
(weight, bias)
} else {
return Err(err);
}
}
};
Ok(LayerNorm::new(weight, bias, eps))
}
// https://raw.githubusercontent.com/huggingface/transformers/030c863aaa0165e98352b61697430bf69bf33755/src/transformers/models/falcon/configuration_falcon.py
#[derive(Debug)]
pub struct Config {
pub vocab_size: usize,
pub hidden_size: usize,
pub num_hidden_layers: usize,
pub num_attention_heads: usize,
pub layer_norm_epsilon: f64,
pub initializer_range: f64,
pub use_cache: bool,
pub bos_token_id: u32,
pub eos_token_id: u32,
pub hidden_dropout: f64,
pub attention_dropout: f64,
pub n_head_kv: Option<usize>,
pub alibi: bool,
pub new_decoder_architecture: bool,
pub multi_query: bool,
pub parallel_attn: bool,
pub bias: bool,
}
impl Default for Config {
fn default() -> Self {
Self {
vocab_size: 65024,
hidden_size: 4544,
num_hidden_layers: 32,
num_attention_heads: 71,
layer_norm_epsilon: 1e-5,
initializer_range: 0.02,
use_cache: true,
bos_token_id: 11,
eos_token_id: 11,
hidden_dropout: 0.0,
attention_dropout: 0.0,
n_head_kv: None,
alibi: false,
new_decoder_architecture: false,
multi_query: true,
parallel_attn: true,
bias: false,
}
}
}
impl Config {
pub fn validate(&self) -> Result<()> {
if self.alibi {
candle::bail!("alibi is not supported");
}
if self.new_decoder_architecture {
candle::bail!("new_decoder_architecture is not supported");
}
if self.n_head_kv.is_some() {
candle::bail!("n_head_kv is not supported");
}
Ok(())
}
// https://huggingface.co/tiiuae/falcon-7b/blob/main/config.json
pub fn falcon7b() -> Self {
// This is currently on par with the defaults, the defaults come from the Python default
// arguments for the config initialization whereas the following come from the json config.
Self {
vocab_size: 65024,
hidden_size: 4544,
num_hidden_layers: 32,
num_attention_heads: 71,
layer_norm_epsilon: 1e-5,
initializer_range: 0.02,
use_cache: true,
bos_token_id: 11,
eos_token_id: 11,
hidden_dropout: 0.,
attention_dropout: 0.,
n_head_kv: None,
alibi: false,
new_decoder_architecture: false,
multi_query: true,
parallel_attn: true,
bias: false,
}
}
fn head_dim(&self) -> usize {
self.hidden_size / self.num_attention_heads
}
fn rotary(&self) -> bool {
!self.alibi
}
}
fn rotate_half(x: &Tensor) -> Result<Tensor> {
let l = x.dim(D::Minus1)?;
let x1 = x.narrow(D::Minus1, 0, l / 2)?;
let x2 = x.narrow(D::Minus1, l / 2, l - l / 2)?;
let x21 = Tensor::cat(&[&x2.neg()?, &x1], D::Minus1)?;
Ok(x21)
}
#[derive(Debug)]
struct FalconRotaryEmbedding {
inv_freq: Tensor,
cache: Option<(usize, Tensor, Tensor)>,
}
impl FalconRotaryEmbedding {
fn load(device: &Device, cfg: &Config) -> Result<Self> {
let head_dim = cfg.head_dim();
let inv_freq: Vec<_> = (0..head_dim)
.step_by(2)
.map(|i| 1f32 / 10000f32.powf(i as f32 / head_dim as f32))
.collect();
Ok(Self {
inv_freq: Tensor::new(inv_freq.as_slice(), device)?,
cache: None,
})
}
fn cos_sin(
&mut self,
seq_len: usize,
device: &Device,
dtype: DType,
) -> Result<(Tensor, Tensor)> {
match &self.cache {
Some((s, cos, sin)) if *s == seq_len => {
return Ok((cos.clone(), sin.clone()));
}
_ => {}
}
let t = Tensor::arange(0, seq_len as u32, device)?.to_dtype(dtype)?;
let inv_freq = self.inv_freq.to_dtype(dtype)?;
let freqs = t.unsqueeze(1)?.matmul(&inv_freq.unsqueeze(0)?)?;
let emb = Tensor::cat(&[&freqs, &freqs], D::Minus1)?;
let cos = emb.cos()?;
let sin = emb.sin()?;
self.cache = Some((seq_len, cos.clone(), sin.clone()));
Ok((cos, sin))
}
fn forward(
&mut self,
query: &Tensor,
key: &Tensor,
past_kv_len: usize,
) -> Result<(Tensor, Tensor)> {
let (_batch, seq_len, _head_dim) = query.dims3()?;
let (cos, sin) = self.cos_sin(MAX_SEQ_LEN, query.device(), query.dtype())?;
let cos = cos.narrow(0, past_kv_len, seq_len)?;
let sin = sin.narrow(0, past_kv_len, seq_len)?;
let qs = (query.broadcast_mul(&cos)? + &rotate_half(query)?.broadcast_mul(&sin)?)?;
let ks = (key.broadcast_mul(&cos)? + &rotate_half(key)?.broadcast_mul(&sin)?)?;
Ok((qs, ks))
}
}
fn masked_fill(on_false: &Tensor, mask: &Tensor, on_true: f32) -> Result<Tensor> {
let shape = mask.shape();
let on_true = Tensor::new(on_true, on_false.device())?.broadcast_as(shape.dims())?;
let m = mask.where_cond(&on_true, on_false)?;
Ok(m)
}
#[derive(Debug)]
struct FalconAttention {
query_key_value: Linear,
dense: Linear,
maybe_rotary: Option<FalconRotaryEmbedding>,
kv_cache: Option<(Tensor, Tensor)>,
inv_norm_factor: f64,
multi_query: bool,
use_cache: bool,
num_heads: usize,
head_dim: usize,
n_head_kv: usize,
}
impl FalconAttention {
fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
let maybe_rotary = if cfg.rotary() {
let rotary = FalconRotaryEmbedding::load(vb.device(), cfg)?;
Some(rotary)
} else {
None
};
let head_dim = cfg.head_dim();
let hidden_size = cfg.hidden_size;
let qkv_out_dim = if cfg.multi_query {
hidden_size + 2 * head_dim
} else {
3 * hidden_size
};
let query_key_value = linear(hidden_size, qkv_out_dim, cfg.bias, vb.pp("query_key_value"))?;
let dense = linear(hidden_size, hidden_size, cfg.bias, vb.pp("dense"))?;
Ok(Self {
query_key_value,
dense,
maybe_rotary,
kv_cache: None,
inv_norm_factor: 1. / (head_dim as f64).sqrt(),
multi_query: cfg.multi_query,
use_cache: cfg.use_cache,
num_heads: cfg.num_attention_heads,
n_head_kv: cfg.n_head_kv.unwrap_or(1),
head_dim,
})
}
fn split_heads(&self, fused_qkv: &Tensor) -> Result<(Tensor, Tensor, Tensor)> {
let (b_sz, seq_len, _) = fused_qkv.dims3()?;
if !self.multi_query {
let fused_qkv = fused_qkv.reshape((b_sz, seq_len, self.num_heads, 3, self.head_dim))?;
let q = fused_qkv.narrow(D::Minus2, 0, 1)?.squeeze(D::Minus2)?;
let k = fused_qkv.narrow(D::Minus2, 1, 1)?.squeeze(D::Minus2)?;
let v = fused_qkv.narrow(D::Minus2, 2, 1)?.squeeze(D::Minus2)?;
Ok((q, k, v))
} else {
let fused_qkv =
fused_qkv.reshape((b_sz, seq_len, self.num_heads + 2, self.head_dim))?;
let d = fused_qkv.dim(D::Minus2)?;
let q = fused_qkv.narrow(D::Minus2, 0, d - 2)?;
let k = fused_qkv.narrow(D::Minus2, d - 2, 1)?;
let v = fused_qkv.narrow(D::Minus2, d - 1, 1)?;
Ok((q, k, v))
}
}
fn forward(&mut self, x: &Tensor, mask: &Tensor, past_kv_len: usize) -> Result<Tensor> {
let fused_qkv = self.query_key_value.forward(x)?;
let head_dim = self.head_dim;
let (query, key, value) = self.split_heads(&fused_qkv)?;
let (b_sz, seq_len, _, _) = query.dims4()?;
let query = query
.transpose(1, 2)?
.reshape((b_sz * self.num_heads, seq_len, head_dim))?;
let key = key
.transpose(1, 2)?
.reshape((b_sz * self.n_head_kv, seq_len, head_dim))?;
let value = value
.transpose(1, 2)?
.reshape((b_sz * self.n_head_kv, seq_len, head_dim))?;
let (query, key) = if let Some(r) = &mut self.maybe_rotary {
r.forward(&query, &key, past_kv_len)?
} else {
(query, key)
};
let (mut key, mut value) = (key, value);
let mask = masked_fill(&mask.to_dtype(DType::F32)?, mask, -1e9)?.to_dtype(query.dtype())?;
if self.use_cache {
if let Some((cache_k, cache_v)) = &self.kv_cache {
// TODO: we could trim the tensors to MAX_SEQ_LEN so that this would work for
// arbitrarily large sizes.
key = Tensor::cat(&[cache_k, &key], 1)?.contiguous()?;
value = Tensor::cat(&[cache_v, &value], 1)?.contiguous()?;
}
self.kv_cache = Some((key.clone(), value.clone()))
}
let query = query.reshape((b_sz * self.num_heads, seq_len, head_dim))?;
let all_len = past_kv_len + seq_len;
let key = key.reshape((b_sz * self.n_head_kv, all_len, head_dim))?;
let value = value.reshape((b_sz * self.n_head_kv, all_len, head_dim))?;
let (key, value) = if self.n_head_kv == 1 {
(
key.broadcast_as((b_sz * self.num_heads, all_len, head_dim))?,
value.broadcast_as((b_sz * self.num_heads, all_len, head_dim))?,
)
} else {
(key, value)
};
// Only handle the case where alibi is None here, and non-flash attention.
let attention_scores = (query.matmul(&key.t()?)? * self.inv_norm_factor)?;
let attention_scores = candle_nn::ops::softmax(
&attention_scores
.broadcast_add(&mask.squeeze(1)?)?
.to_dtype(DType::F32)?,
D::Minus1,
)?
.to_dtype(x.dtype())?;
let attn_output = attention_scores
.matmul(&value)?
.reshape((b_sz, self.num_heads, seq_len, head_dim))?
.transpose(1, 2)?
.reshape((b_sz, seq_len, self.num_heads * head_dim))?;
let attn_output = self.dense.forward(&attn_output)?;
Ok(attn_output)
}
}
#[derive(Debug)]
struct FalconMlp {
dense_h_to_4h: Linear,
dense_4h_to_h: Linear,
}
impl FalconMlp {
fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
let h = cfg.hidden_size;
let b = cfg.bias;
let dense_h_to_4h = linear(h, 4 * h, b, vb.pp("dense_h_to_4h"))?;
let dense_4h_to_h = linear(4 * h, h, b, vb.pp("dense_4h_to_h"))?;
Ok(Self {
dense_h_to_4h,
dense_4h_to_h,
})
}
fn forward(&self, x: &Tensor) -> Result<Tensor> {
let x = self.dense_h_to_4h.forward(x)?.gelu()?;
let x = self.dense_4h_to_h.forward(&x)?;
Ok(x)
}
}
#[derive(Debug)]
struct FalconDecoderLayer {
inp_layernorm: LayerNorm,
self_attention: FalconAttention,
post_attention_layernorm: Option<LayerNorm>,
mlp: FalconMlp,
parallel_attn: bool,
}
impl FalconDecoderLayer {
fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
let mlp = FalconMlp::load(vb.pp("mlp"), cfg)?;
let inp_layernorm = layer_norm(
cfg.hidden_size,
cfg.layer_norm_epsilon,
vb.pp("input_layernorm"),
)?;
let self_attention = FalconAttention::load(vb.pp("self_attention"), cfg)?;
let post_attention_layernorm = if cfg.parallel_attn {
None
} else {
let ln = layer_norm(
cfg.hidden_size,
cfg.layer_norm_epsilon,
vb.pp("post_attention_layernorm"),
)?;
Some(ln)
};
Ok(Self {
inp_layernorm,
self_attention,
post_attention_layernorm,
mlp,
parallel_attn: cfg.parallel_attn,
})
}
fn forward(&mut self, x: &Tensor, mask: &Tensor, past_kv_len: usize) -> Result<Tensor> {
let residual = x.clone();
let ln_attn = self.inp_layernorm.forward(x)?;
let attn_output = self.self_attention.forward(&ln_attn, mask, past_kv_len)?;
let (residual, ln_mlp) = match &self.post_attention_layernorm {
None => (residual, ln_attn),
Some(pal) => {
// This should include some dropout.
let residual = (&attn_output + &residual)?;
let ln_mlp = pal.forward(&residual)?;
(residual, ln_mlp)
}
};
let mlp_output = self.mlp.forward(&ln_mlp)?;
let mlp_output = if self.parallel_attn {
(mlp_output + attn_output)?
} else {
mlp_output
};
let output = (mlp_output + residual)?;
Ok(output)
}
}
#[derive(Debug)]
pub struct Falcon {
word_embeddings: Embedding,
blocks: Vec<FalconDecoderLayer>,
ln_f: LayerNorm,
lm_head: Linear,
config: Config,
}
fn make_causal_mask(t: usize) -> Result<Tensor> {
let mask: Vec<_> = (0..t)
.flat_map(|i| (0..t).map(move |j| u8::from(j > i)))
.collect();
let mask = Tensor::from_slice(&mask, (t, t), &Device::Cpu)?;
Ok(mask)
}
fn prepare_attn_mask(b_sz: usize, seq_len: usize) -> Result<Tensor> {
// let mask = Tensor::ones((b_sz, seq_len), DType::U32, &Device::Cpu)?;
let mask = make_causal_mask(seq_len)?;
let mask = mask.broadcast_as((b_sz, 1, seq_len, seq_len))?;
Ok(mask)
}
impl Falcon {
pub fn config(&self) -> &Config {
&self.config
}
pub fn load(vb: VarBuilder, cfg: Config) -> Result<Self> {
let word_embeddings = embedding(
cfg.vocab_size,
cfg.hidden_size,
vb.pp("transformer.word_embeddings"),
)?;
let blocks = (0..cfg.num_hidden_layers)
.map(|i| FalconDecoderLayer::load(vb.pp(&format!("transformer.h.{i}")), &cfg))
.collect::<Result<Vec<_>>>()?;
let ln_f = layer_norm(
cfg.hidden_size,
cfg.layer_norm_epsilon,
vb.pp("transformer.ln_f"),
)?;
let lm_head = linear(cfg.hidden_size, cfg.vocab_size, false, vb.pp("lm_head"))?;
Ok(Self {
word_embeddings,
blocks,
ln_f,
lm_head,
config: cfg,
})
}
pub fn forward(&mut self, input_ids: &Tensor) -> Result<Tensor> {
let (b_sz, seq_len) = input_ids.dims2()?;
let mut hidden_state = self.word_embeddings.forward(input_ids)?;
let past_kv_len = match &self.blocks[0].self_attention.kv_cache {
Some((k, _)) => k.dim(1)?,
None => 0,
};
let causal_mask = prepare_attn_mask(b_sz, seq_len)?.to_device(input_ids.device())?;
for block in self.blocks.iter_mut() {
hidden_state = block.forward(&hidden_state, &causal_mask, past_kv_len)?;
}
let hidden_state = self.ln_f.forward(&hidden_state)?;
let hidden_state = hidden_state.narrow(1, seq_len - 1, 1)?;
let logits = self.lm_head.forward(&hidden_state)?.squeeze(1)?;
Ok(logits)
}
}
| candle/candle-transformers/src/models/falcon.rs/0 | {
"file_path": "candle/candle-transformers/src/models/falcon.rs",
"repo_id": "candle",
"token_count": 8446
} | 33 |
use crate::models::with_tracing::{layer_norm, linear, Embedding, LayerNorm, Linear};
/// Phi model.
/// https://huggingface.co/microsoft/phi-2
/// There is an alternative implementation of the phi model in mixformers.rs.
/// This corresponds to the model update made with the following commit:
/// https://huggingface.co/microsoft/phi-2/commit/cb2f4533604d8b67de604e7df03bfe6f3ca22869
use candle::{DType, Device, IndexOp, Module, Result, Tensor, D};
use candle_nn::{Activation, VarBuilder};
use serde::Deserialize;
// https://huggingface.co/microsoft/phi-2/blob/main/configuration_phi.py
#[derive(Debug, Clone, PartialEq, Deserialize)]
pub struct Config {
pub(crate) vocab_size: usize,
pub(crate) hidden_size: usize,
pub(crate) intermediate_size: usize,
pub(crate) num_hidden_layers: usize,
pub(crate) num_attention_heads: usize,
pub(crate) num_key_value_heads: Option<usize>,
pub(crate) hidden_act: Activation,
pub(crate) max_position_embeddings: usize,
pub(crate) layer_norm_eps: f64,
pub(crate) tie_word_embeddings: bool,
pub(crate) rope_theta: f32,
pub(crate) partial_rotary_factor: f64,
pub(crate) qk_layernorm: bool,
}
impl Config {
fn num_key_value_heads(&self) -> usize {
self.num_key_value_heads.unwrap_or(self.num_attention_heads)
}
fn head_dim(&self) -> usize {
self.hidden_size / self.num_attention_heads
}
}
#[derive(Debug, Clone)]
struct RotaryEmbedding {
dim: usize,
sin: Tensor,
cos: Tensor,
}
impl RotaryEmbedding {
fn new(cfg: &Config, dev: &Device) -> Result<Self> {
let dim = (cfg.partial_rotary_factor * cfg.head_dim() as f64) as usize;
let inv_freq: Vec<_> = (0..dim)
.step_by(2)
.map(|i| 1f32 / cfg.rope_theta.powf(i as f32 / dim as f32))
.collect();
let inv_freq_len = inv_freq.len();
let inv_freq = Tensor::from_vec(inv_freq, (1, inv_freq_len), dev)?;
let t = Tensor::arange(0u32, cfg.max_position_embeddings as u32, dev)?
.to_dtype(DType::F32)?
.reshape((cfg.max_position_embeddings, 1))?;
let freqs = t.matmul(&inv_freq)?;
let emb = Tensor::cat(&[&freqs, &freqs], D::Minus1)?;
Ok(Self {
dim,
sin: emb.sin()?,
cos: emb.cos()?,
})
}
fn apply_rotary_emb(&self, xs: &Tensor, seqlen_offset: usize) -> Result<Tensor> {
let (_b_size, _num_heads, seq_len, _headdim) = xs.dims4()?;
let xs_rot = xs.i((.., .., .., ..self.dim))?;
let xs_pass = xs.i((.., .., .., self.dim..))?;
let xs12 = xs_rot.chunk(2, D::Minus1)?;
let (xs1, xs2) = (&xs12[0], &xs12[1]);
let c = self.cos.narrow(0, seqlen_offset, seq_len)?;
let s = self.sin.narrow(0, seqlen_offset, seq_len)?;
let rotate_half = Tensor::cat(&[&xs2.neg()?, &xs1], D::Minus1)?;
let xs_rot = (xs_rot.broadcast_mul(&c)? + rotate_half.broadcast_mul(&s)?)?;
Tensor::cat(&[&xs_rot, &xs_pass], D::Minus1)
}
}
#[derive(Debug, Clone)]
#[allow(clippy::upper_case_acronyms)]
struct MLP {
fc1: Linear,
fc2: Linear,
act: Activation,
}
impl MLP {
fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
let fc1 = linear(cfg.hidden_size, cfg.intermediate_size, vb.pp("fc1"))?;
let fc2 = linear(cfg.intermediate_size, cfg.hidden_size, vb.pp("fc2"))?;
Ok(Self {
fc1,
fc2,
// This does not match the mixformers implementation where Gelu is used rather than
// GeluNew.
act: cfg.hidden_act,
})
}
}
impl Module for MLP {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
xs.apply(&self.fc1)?.apply(&self.act)?.apply(&self.fc2)
}
}
#[derive(Clone)]
struct Attention {
q_proj: Linear,
k_proj: Linear,
v_proj: Linear,
dense: Linear,
kv_cache: Option<(Tensor, Tensor)>,
q_layernorm: Option<LayerNorm>,
k_layernorm: Option<LayerNorm>,
rotary_emb: RotaryEmbedding,
softmax_scale: f64,
num_heads: usize,
num_kv_heads: usize,
head_dim: usize,
span: tracing::Span,
}
fn get_mask(size: usize, device: &Device) -> Result<Tensor> {
let mask: Vec<_> = (0..size)
.flat_map(|i| (0..size).map(move |j| u8::from(j > i)))
.collect();
Tensor::from_slice(&mask, (size, size), device)
}
fn masked_fill(on_false: &Tensor, mask: &Tensor, on_true: f32) -> Result<Tensor> {
let shape = mask.shape();
let on_true = Tensor::new(on_true, on_false.device())?.broadcast_as(shape.dims())?;
let m = mask.where_cond(&on_true, on_false)?;
Ok(m)
}
impl Attention {
fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
let num_heads = cfg.num_attention_heads;
let num_kv_heads = cfg.num_key_value_heads();
let head_dim = cfg.head_dim();
let q_proj = linear(cfg.hidden_size, num_heads * head_dim, vb.pp("q_proj"))?;
let k_proj = linear(cfg.hidden_size, num_kv_heads * head_dim, vb.pp("k_proj"))?;
let v_proj = linear(cfg.hidden_size, num_kv_heads * head_dim, vb.pp("v_proj"))?;
let dense = linear(num_heads * head_dim, cfg.hidden_size, vb.pp("dense"))?;
// Alternative rope scalings are not supported.
let rotary_emb = RotaryEmbedding::new(cfg, vb.device())?;
let (q_layernorm, k_layernorm) = if cfg.qk_layernorm {
let q_layernorm = layer_norm(head_dim, cfg.layer_norm_eps, vb.pp("q_layernorm"))?;
let k_layernorm = layer_norm(head_dim, cfg.layer_norm_eps, vb.pp("k_layernorm"))?;
(Some(q_layernorm), Some(k_layernorm))
} else {
(None, None)
};
let softmax_scale = 1f64 / (head_dim as f64).sqrt();
Ok(Self {
q_proj,
k_proj,
v_proj,
dense,
kv_cache: None,
q_layernorm,
k_layernorm,
rotary_emb,
softmax_scale,
num_heads,
num_kv_heads,
head_dim,
span: tracing::span!(tracing::Level::TRACE, "attention"),
})
}
fn repeat_kv(&self, xs: Tensor) -> Result<Tensor> {
let n_rep = self.num_heads / self.num_kv_heads;
if n_rep == 1 {
Ok(xs)
} else {
let (b_sz, num_kv_heads, seq_len, head_dim) = xs.dims4()?;
xs.unsqueeze(2)?
.expand((b_sz, num_kv_heads, n_rep, seq_len, head_dim))?
.reshape((b_sz, num_kv_heads * n_rep, seq_len, head_dim))
}
}
fn forward(&mut self, xs: &Tensor, mask: Option<&Tensor>) -> Result<Tensor> {
let _enter = self.span.enter();
let (b_size, seq_len, _n_embd) = xs.dims3()?;
let query_states = self.q_proj.forward(xs)?;
let key_states = self.k_proj.forward(xs)?;
let value_states = self.v_proj.forward(xs)?;
let query_states = match &self.q_layernorm {
None => query_states,
Some(ln) => query_states.apply(ln)?,
};
let key_states = match &self.k_layernorm {
None => key_states,
Some(ln) => key_states.apply(ln)?,
};
let query_states = query_states
.reshape((b_size, seq_len, self.num_heads, self.head_dim))?
.transpose(1, 2)?;
let key_states = key_states
.reshape((b_size, seq_len, self.num_kv_heads, self.head_dim))?
.transpose(1, 2)?;
let value_states = value_states
.reshape((b_size, seq_len, self.num_kv_heads, self.head_dim))?
.transpose(1, 2)?;
// Rotary embeddings.
let seqlen_offset = match &self.kv_cache {
None => 0,
Some((prev_k, _)) => prev_k.dim(2)?,
};
let query_states = self
.rotary_emb
.apply_rotary_emb(&query_states, seqlen_offset)?;
let key_states = self
.rotary_emb
.apply_rotary_emb(&key_states, seqlen_offset)?;
// KV cache.
let (key_states, value_states) = match &self.kv_cache {
None => (key_states, value_states),
Some((prev_k, prev_v)) => {
let k = Tensor::cat(&[prev_k, &key_states], 2)?;
let v = Tensor::cat(&[prev_v, &value_states], 2)?;
(k, v)
}
};
self.kv_cache = Some((key_states.clone(), value_states.clone()));
// Repeat kv.
let key_states = self.repeat_kv(key_states)?.contiguous()?;
let value_states = self.repeat_kv(value_states)?.contiguous()?;
let attn_weights = (query_states
.to_dtype(DType::F32)?
.contiguous()?
.matmul(&key_states.to_dtype(DType::F32)?.t()?)?
* self.softmax_scale)?;
let attn_weights = match mask {
None => attn_weights,
Some(mask) => masked_fill(
&attn_weights,
&mask.broadcast_left((b_size, self.num_heads))?,
f32::NEG_INFINITY,
)?,
};
let attn_weights =
candle_nn::ops::softmax_last_dim(&attn_weights)?.to_dtype(value_states.dtype())?;
let attn_output = attn_weights.matmul(&value_states)?;
let attn_output = attn_output
.transpose(1, 2)?
.reshape((b_size, seq_len, ()))?;
attn_output.apply(&self.dense)
}
fn clear_kv_cache(&mut self) {
self.kv_cache = None
}
}
#[derive(Clone)]
struct DecoderLayer {
self_attn: Attention,
mlp: MLP,
input_layernorm: LayerNorm,
span: tracing::Span,
}
impl DecoderLayer {
fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
let self_attn = Attention::new(cfg, vb.pp("self_attn"))?;
let mlp = MLP::new(cfg, vb.pp("mlp"))?;
let input_layernorm = layer_norm(
cfg.hidden_size,
cfg.layer_norm_eps,
vb.pp("input_layernorm"),
)?;
Ok(Self {
self_attn,
mlp,
input_layernorm,
span: tracing::span!(tracing::Level::TRACE, "block"),
})
}
fn forward(&mut self, xs: &Tensor, mask: Option<&Tensor>) -> Result<Tensor> {
let _enter = self.span.enter();
let residual = xs;
let xs = xs.apply(&self.input_layernorm)?;
let attn_outputs = self.self_attn.forward(&xs, mask)?;
let feed_forward_hidden_states = self.mlp.forward(&xs)?;
attn_outputs + feed_forward_hidden_states + residual
}
fn clear_kv_cache(&mut self) {
self.self_attn.clear_kv_cache()
}
}
#[derive(Clone)]
pub struct Model {
embed_tokens: Embedding,
layers: Vec<DecoderLayer>,
final_layernorm: LayerNorm,
lm_head: Linear,
span: tracing::Span,
}
impl Model {
pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
let vb_m = vb.pp("model");
let embed_tokens =
Embedding::new(cfg.vocab_size, cfg.hidden_size, vb_m.pp("embed_tokens"))?;
let final_layernorm = layer_norm(
cfg.hidden_size,
cfg.layer_norm_eps,
vb_m.pp("final_layernorm"),
)?;
let mut layers = Vec::with_capacity(cfg.num_hidden_layers);
let vb_m = vb_m.pp("layers");
for layer_idx in 0..cfg.num_hidden_layers {
let layer = DecoderLayer::new(cfg, vb_m.pp(layer_idx))?;
layers.push(layer)
}
let lm_head = linear(cfg.hidden_size, cfg.vocab_size, vb.pp("lm_head"))?;
Ok(Self {
embed_tokens,
layers,
final_layernorm,
lm_head,
span: tracing::span!(tracing::Level::TRACE, "model"),
})
}
pub fn forward(&mut self, xs: &Tensor) -> Result<Tensor> {
let _enter = self.span.enter();
let (_b_size, seq_len) = xs.dims2()?;
let mut xs = xs.apply(&self.embed_tokens)?;
let mask = if seq_len <= 1 {
None
} else {
Some(get_mask(seq_len, xs.device())?)
};
for layer in self.layers.iter_mut() {
xs = layer.forward(&xs, mask.as_ref())?;
}
xs.apply(&self.final_layernorm)?
.narrow(1, seq_len - 1, 1)?
.apply(&self.lm_head)?
.squeeze(1)
}
pub fn clear_kv_cache(&mut self) {
self.layers.iter_mut().for_each(|b| b.clear_kv_cache())
}
}
| candle/candle-transformers/src/models/phi.rs/0 | {
"file_path": "candle/candle-transformers/src/models/phi.rs",
"repo_id": "candle",
"token_count": 6392
} | 34 |
use super::with_tracing::{layer_norm, linear_no_bias as linear, LayerNorm, Linear};
use candle::{DType, Device, IndexOp, Result, Tensor};
use candle_nn::{embedding, Embedding, Module, VarBuilder};
use std::collections::{HashMap, HashSet};
fn default_num_attention_heads() -> usize {
64
}
// https://huggingface.co/RWKV/HF_v5-Eagle-7B/blob/main/configuration_rwkv5.py
#[derive(Debug, Clone, serde::Deserialize)]
pub struct Config {
pub vocab_size: usize,
pub hidden_size: usize,
pub num_hidden_layers: usize,
pub attention_hidden_size: usize,
#[serde(default = "default_num_attention_heads")]
pub num_attention_heads: usize,
pub head_size: usize,
pub intermediate_size: Option<usize>,
pub layer_norm_epsilon: f64,
pub rescale_every: usize,
}
pub struct StatePerLayer {
pub extract_key_value: Tensor,
pub linear_attention: Tensor,
pub feed_forward: Tensor,
}
pub struct State {
pub per_layer: Vec<StatePerLayer>,
pub pos: usize,
}
impl State {
pub fn new(batch_size: usize, cfg: &Config, dev: &Device) -> Result<Self> {
let mut per_layer = Vec::with_capacity(cfg.num_hidden_layers);
// Certainly a weird convention but taken from modeling_rwkv5.py
let num_attention_heads = cfg.hidden_size / cfg.num_attention_heads;
for _layer_idx in 0..cfg.num_hidden_layers {
let extract_key_value = Tensor::zeros((batch_size, cfg.hidden_size), DType::F32, dev)?;
let linear_attention = Tensor::zeros(
(
batch_size,
num_attention_heads,
cfg.hidden_size / num_attention_heads,
cfg.hidden_size / num_attention_heads,
),
DType::F32,
dev,
)?;
let feed_forward = Tensor::zeros((batch_size, cfg.hidden_size), DType::F32, dev)?;
per_layer.push(StatePerLayer {
extract_key_value,
linear_attention,
feed_forward,
});
}
Ok(Self { per_layer, pos: 0 })
}
}
#[derive(Debug, Clone)]
struct SelfAttention {
key: Linear,
receptance: Linear,
value: Linear,
gate: Linear,
output: Linear,
ln_x: candle_nn::GroupNorm,
time_mix_key: Tensor,
time_mix_value: Tensor,
time_mix_receptance: Tensor,
time_decay: Tensor,
time_faaaa: Tensor,
time_mix_gate: Tensor,
layer_id: usize,
n_attn_heads: usize,
}
impl SelfAttention {
pub fn new(layer_id: usize, cfg: &Config, vb: VarBuilder) -> Result<Self> {
let hidden_size = cfg.hidden_size;
let attn_hidden_size = cfg.attention_hidden_size;
let key = linear(hidden_size, attn_hidden_size, vb.pp("key"))?;
let receptance = linear(hidden_size, attn_hidden_size, vb.pp("receptance"))?;
let value = linear(hidden_size, attn_hidden_size, vb.pp("value"))?;
let gate = linear(hidden_size, attn_hidden_size, vb.pp("gate"))?;
let output = linear(attn_hidden_size, hidden_size, vb.pp("output"))?;
let ln_x = candle_nn::group_norm(
hidden_size / cfg.head_size,
hidden_size,
1e-5,
vb.pp("ln_x"),
)?;
let time_mix_key = vb.get((1, 1, cfg.hidden_size), "time_mix_key")?;
let time_mix_value = vb.get((1, 1, cfg.hidden_size), "time_mix_value")?;
let time_mix_receptance = vb.get((1, 1, cfg.hidden_size), "time_mix_receptance")?;
let n_attn_heads = cfg.hidden_size / cfg.head_size;
let time_decay = vb.get((n_attn_heads, cfg.head_size), "time_decay")?;
let time_faaaa = vb.get((n_attn_heads, cfg.head_size), "time_faaaa")?;
let time_mix_gate = vb.get((1, 1, cfg.hidden_size), "time_mix_gate")?;
Ok(Self {
key,
value,
receptance,
gate,
output,
ln_x,
time_mix_key,
time_mix_value,
time_mix_receptance,
time_decay,
time_faaaa,
time_mix_gate,
layer_id,
n_attn_heads,
})
}
pub fn forward(&self, xs: &Tensor, state: &mut State) -> Result<Tensor> {
let h = self.time_decay.dim(0)?;
let (b, t, s) = xs.dims3()?;
let s = s / h;
let (receptance, key, value, gate) = {
// extract key-value
let shifted = state.per_layer[self.layer_id].extract_key_value.clone();
let shifted = if shifted.rank() == 2 {
shifted.unsqueeze(1)?
} else {
shifted
};
let key = ((xs * &self.time_mix_key)? + &shifted * (1.0 - &self.time_mix_key)?)?;
let value = ((xs * &self.time_mix_value)? + &shifted * (1.0 - &self.time_mix_value)?)?;
let receptance = ((xs * &self.time_mix_receptance)?
+ &shifted * (1.0 - &self.time_mix_receptance)?)?;
let gate = ((xs * &self.time_mix_gate)? + &shifted * (1.0 - &self.time_mix_gate)?)?;
let key = self.key.forward(&key)?;
let value = self.value.forward(&value)?;
let receptance = self.receptance.forward(&receptance)?;
let gate = candle_nn::ops::silu(&self.gate.forward(&gate)?)?;
state.per_layer[self.layer_id].extract_key_value = xs.i((.., t - 1))?;
(receptance, key, value, gate)
};
// linear attention
let mut state_ = state.per_layer[self.layer_id].linear_attention.clone();
let key = key.reshape((b, t, h, s))?.permute((0, 2, 3, 1))?;
let value = value.reshape((b, t, h, s))?.transpose(1, 2)?;
let receptance = receptance.reshape((b, t, h, s))?.transpose(1, 2)?;
let time_decay = self
.time_decay
.exp()?
.neg()?
.exp()?
.reshape(((), 1, 1))?
.reshape((self.n_attn_heads, (), 1))?;
let time_faaaa =
self.time_faaaa
.reshape(((), 1, 1))?
.reshape((self.n_attn_heads, (), 1))?;
let mut out: Vec<Tensor> = Vec::with_capacity(t);
for t_ in 0..t {
let rt = receptance.i((.., .., t_..t_ + 1))?.contiguous()?;
let kt = key.i((.., .., .., t_..t_ + 1))?.contiguous()?;
let vt = value.i((.., .., t_..t_ + 1))?.contiguous()?;
let at = kt.matmul(&vt)?;
let rhs = (time_faaaa.broadcast_mul(&at)? + &state_)?;
let out_ = rt.matmul(&rhs)?.squeeze(2)?;
state_ = (&at + time_decay.broadcast_mul(&state_))?;
out.push(out_)
}
let out = Tensor::cat(&out, 1)?.reshape((b * t, h * s, 1))?;
let out = out.apply(&self.ln_x)?.reshape((b, t, h * s))?;
let out = (out * gate)?.apply(&self.output)?;
state.per_layer[self.layer_id].linear_attention = state_;
Ok(out)
}
}
#[derive(Debug, Clone)]
struct FeedForward {
time_mix_key: Tensor,
time_mix_receptance: Tensor,
key: Linear,
receptance: Linear,
value: Linear,
layer_id: usize,
}
impl FeedForward {
pub fn new(layer_id: usize, cfg: &Config, vb: VarBuilder) -> Result<Self> {
let int_size = cfg
.intermediate_size
.unwrap_or(((cfg.hidden_size as f64 * 3.5) as usize) / 32 * 32);
let key = linear(cfg.hidden_size, int_size, vb.pp("key"))?;
let receptance = linear(cfg.hidden_size, cfg.hidden_size, vb.pp("receptance"))?;
let value = linear(int_size, cfg.hidden_size, vb.pp("value"))?;
let time_mix_key = vb.get((1, 1, cfg.hidden_size), "time_mix_key")?;
let time_mix_receptance = vb.get((1, 1, cfg.hidden_size), "time_mix_receptance")?;
Ok(Self {
key,
receptance,
value,
time_mix_key,
time_mix_receptance,
layer_id,
})
}
pub fn forward(&self, xs: &Tensor, state: &mut State) -> Result<Tensor> {
let shifted = &state.per_layer[self.layer_id].feed_forward;
let key = (xs.broadcast_mul(&self.time_mix_key)?
+ shifted.broadcast_mul(&(1.0 - &self.time_mix_key)?)?)?;
let receptance = (xs.broadcast_mul(&self.time_mix_receptance)?
+ shifted.broadcast_mul(&(1.0 - &self.time_mix_receptance)?)?)?;
let key = key.apply(&self.key)?.relu()?.sqr()?;
let value = key.apply(&self.value)?;
let receptance = candle_nn::ops::sigmoid(&receptance.apply(&self.receptance)?)?;
state.per_layer[self.layer_id].feed_forward = xs.i((.., xs.dim(1)? - 1))?;
let xs = (receptance * value)?;
Ok(xs)
}
}
#[derive(Debug, Clone)]
struct Block {
pre_ln: Option<LayerNorm>,
ln1: LayerNorm,
ln2: LayerNorm,
attention: SelfAttention,
feed_forward: FeedForward,
}
impl Block {
pub fn new(layer_id: usize, cfg: &Config, vb: VarBuilder) -> Result<Self> {
let ln1 = layer_norm(cfg.hidden_size, cfg.layer_norm_epsilon, vb.pp("ln1"))?;
let ln2 = layer_norm(cfg.hidden_size, cfg.layer_norm_epsilon, vb.pp("ln2"))?;
let pre_ln = if layer_id == 0 {
let ln = layer_norm(cfg.hidden_size, cfg.layer_norm_epsilon, vb.pp("pre_ln"))?;
Some(ln)
} else {
None
};
let attention = SelfAttention::new(layer_id, cfg, vb.pp("attention"))?;
let feed_forward = FeedForward::new(layer_id, cfg, vb.pp("feed_forward"))?;
Ok(Self {
pre_ln,
ln1,
ln2,
attention,
feed_forward,
})
}
pub fn forward(&self, xs: &Tensor, state: &mut State) -> Result<Tensor> {
let xs = match self.pre_ln.as_ref() {
None => xs.clone(),
Some(pre_ln) => xs.apply(pre_ln)?,
};
let attention = self.attention.forward(&xs.apply(&self.ln1)?, state)?;
let xs = (xs + attention)?;
let feed_forward = self.feed_forward.forward(&xs.apply(&self.ln2)?, state)?;
let xs = (xs + feed_forward)?;
Ok(xs)
}
}
#[derive(Debug, Clone)]
pub struct Model {
embeddings: Embedding,
blocks: Vec<Block>,
ln_out: LayerNorm,
head: Linear,
rescale_every: usize,
layers_are_rescaled: bool,
}
impl Model {
pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
let vb_m = vb.pp("rwkv");
let embeddings = embedding(cfg.vocab_size, cfg.hidden_size, vb_m.pp("embeddings"))?;
let mut blocks = Vec::with_capacity(cfg.num_hidden_layers);
let vb_b = vb_m.pp("blocks");
for block_index in 0..cfg.num_hidden_layers {
let block = Block::new(block_index, cfg, vb_b.pp(block_index))?;
blocks.push(block)
}
let ln_out = layer_norm(cfg.hidden_size, 1e-5, vb_m.pp("ln_out"))?;
let head = linear(cfg.hidden_size, cfg.vocab_size, vb.pp("head"))?;
Ok(Self {
embeddings,
blocks,
ln_out,
head,
rescale_every: cfg.rescale_every,
layers_are_rescaled: false, // This seem to only happen for the f16/bf16 dtypes.
})
}
pub fn forward(&self, xs: &Tensor, state: &mut State) -> Result<Tensor> {
let (_b_size, _seq_len) = xs.dims2()?;
let mut xs = xs.apply(&self.embeddings)?;
for (block_idx, block) in self.blocks.iter().enumerate() {
xs = block.forward(&xs, state)?;
if self.layers_are_rescaled && (block_idx + 1) % self.rescale_every == 0 {
xs = (xs / 2.)?
}
}
let xs = xs.apply(&self.ln_out)?.apply(&self.head)?;
state.pos += 1;
Ok(xs)
}
}
type Bytes = Vec<u8>;
// https://github.com/BlinkDL/ChatRWKV/blob/095e812aef15a1f74107f6c39d13578a2412dc46/RWKV_v5_demo.py#L14
pub struct Tokenizer {
table: Vec<Vec<Vec<Bytes>>>,
good: Vec<HashSet<u8>>,
idx2token: HashMap<u32, Vec<u8>>,
token2idx: HashMap<Vec<u8>, u32>,
}
impl Tokenizer {
pub fn new<P: AsRef<std::path::Path>>(p: P) -> Result<Self> {
let file = std::fs::File::open(p)?;
let token2idx: HashMap<String, u32> =
serde_json::from_reader(file).map_err(candle::Error::wrap)?;
let token2idx = token2idx
.into_iter()
.map(|(key, value)| (key.into_bytes(), value))
.collect::<HashMap<_, _>>();
let idx2token = token2idx
.iter()
.map(|(key, value)| (*value, key.to_vec()))
.collect::<HashMap<_, _>>();
let max_idx = token2idx.values().copied().max().unwrap_or(0);
let mut table = vec![vec![vec![]; 256]; 256];
let mut good = vec![HashSet::new(); 256];
for idx in (0..(1 + max_idx)).rev() {
let s = match idx2token.get(&idx) {
None => continue,
Some(s) => s,
};
if s.len() >= 2 {
let (s0, s1) = (s[0], s[1]);
table[s0 as usize][s1 as usize].push(s.to_vec());
good[s0 as usize].insert(s1);
}
}
Ok(Self {
table,
good,
idx2token,
token2idx,
})
}
pub fn decode_bytes(&self, tokens: &[u32]) -> Vec<u8> {
let mut v = Vec::new();
for token_id in tokens.iter() {
if let Some(token) = self.idx2token.get(token_id) {
v.extend_from_slice(token.as_slice())
}
}
v
}
pub fn decode(&self, tokens: &[u32]) -> Result<String> {
let bytes = self.decode_bytes(tokens);
String::from_utf8(bytes).map_err(candle::Error::wrap)
}
pub fn encode_bytes(&self, bytes: &[u8]) -> Result<Vec<u32>> {
let mut tokens = Vec::new();
let mut i = 0;
while i < bytes.len() {
let mut s = vec![bytes[i]];
if i + 1 < bytes.len() && self.good[bytes[i] as usize].contains(&bytes[i + 1]) {
let table = &self.table[bytes[i] as usize][bytes[i + 1] as usize];
for table_elem in table.iter() {
if bytes[i..].starts_with(table_elem) {
s = table_elem.to_vec();
break;
}
}
}
i += s.len();
let token = match self.token2idx.get(&s) {
None => candle::bail!("unexpected token '{}' {s:?}", String::from_utf8_lossy(&s)),
Some(token) => *token,
};
tokens.push(token)
}
Ok(tokens)
}
pub fn encode(&self, str: &str) -> Result<Vec<u32>> {
self.encode_bytes(str.as_bytes())
}
}
| candle/candle-transformers/src/models/rwkv_v5.rs/0 | {
"file_path": "candle/candle-transformers/src/models/rwkv_v5.rs",
"repo_id": "candle",
"token_count": 7710
} | 35 |
pub mod attention;
pub mod clip;
pub mod ddim;
pub mod ddpm;
pub mod embeddings;
pub mod euler_ancestral_discrete;
pub mod resnet;
pub mod schedulers;
pub mod unet_2d;
pub mod unet_2d_blocks;
pub mod utils;
pub mod vae;
use std::sync::Arc;
use candle::{DType, Device, Result};
use candle_nn as nn;
use self::schedulers::{Scheduler, SchedulerConfig};
#[derive(Clone, Debug)]
pub struct StableDiffusionConfig {
pub width: usize,
pub height: usize,
pub clip: clip::Config,
pub clip2: Option<clip::Config>,
autoencoder: vae::AutoEncoderKLConfig,
unet: unet_2d::UNet2DConditionModelConfig,
scheduler: Arc<dyn SchedulerConfig>,
}
impl StableDiffusionConfig {
pub fn v1_5(
sliced_attention_size: Option<usize>,
height: Option<usize>,
width: Option<usize>,
) -> Self {
let bc = |out_channels, use_cross_attn, attention_head_dim| unet_2d::BlockConfig {
out_channels,
use_cross_attn,
attention_head_dim,
};
// https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/unet/config.json
let unet = unet_2d::UNet2DConditionModelConfig {
blocks: vec![
bc(320, Some(1), 8),
bc(640, Some(1), 8),
bc(1280, Some(1), 8),
bc(1280, None, 8),
],
center_input_sample: false,
cross_attention_dim: 768,
downsample_padding: 1,
flip_sin_to_cos: true,
freq_shift: 0.,
layers_per_block: 2,
mid_block_scale_factor: 1.,
norm_eps: 1e-5,
norm_num_groups: 32,
sliced_attention_size,
use_linear_projection: false,
};
let autoencoder = vae::AutoEncoderKLConfig {
block_out_channels: vec![128, 256, 512, 512],
layers_per_block: 2,
latent_channels: 4,
norm_num_groups: 32,
};
let height = if let Some(height) = height {
assert_eq!(height % 8, 0, "height has to be divisible by 8");
height
} else {
512
};
let width = if let Some(width) = width {
assert_eq!(width % 8, 0, "width has to be divisible by 8");
width
} else {
512
};
let scheduler = Arc::new(ddim::DDIMSchedulerConfig {
prediction_type: schedulers::PredictionType::Epsilon,
..Default::default()
});
StableDiffusionConfig {
width,
height,
clip: clip::Config::v1_5(),
clip2: None,
autoencoder,
scheduler,
unet,
}
}
fn v2_1_(
sliced_attention_size: Option<usize>,
height: Option<usize>,
width: Option<usize>,
prediction_type: schedulers::PredictionType,
) -> Self {
let bc = |out_channels, use_cross_attn, attention_head_dim| unet_2d::BlockConfig {
out_channels,
use_cross_attn,
attention_head_dim,
};
// https://huggingface.co/stabilityai/stable-diffusion-2-1/blob/main/unet/config.json
let unet = unet_2d::UNet2DConditionModelConfig {
blocks: vec![
bc(320, Some(1), 5),
bc(640, Some(1), 10),
bc(1280, Some(1), 20),
bc(1280, None, 20),
],
center_input_sample: false,
cross_attention_dim: 1024,
downsample_padding: 1,
flip_sin_to_cos: true,
freq_shift: 0.,
layers_per_block: 2,
mid_block_scale_factor: 1.,
norm_eps: 1e-5,
norm_num_groups: 32,
sliced_attention_size,
use_linear_projection: true,
};
// https://huggingface.co/stabilityai/stable-diffusion-2-1/blob/main/vae/config.json
let autoencoder = vae::AutoEncoderKLConfig {
block_out_channels: vec![128, 256, 512, 512],
layers_per_block: 2,
latent_channels: 4,
norm_num_groups: 32,
};
let scheduler = Arc::new(ddim::DDIMSchedulerConfig {
prediction_type,
..Default::default()
});
let height = if let Some(height) = height {
assert_eq!(height % 8, 0, "height has to be divisible by 8");
height
} else {
768
};
let width = if let Some(width) = width {
assert_eq!(width % 8, 0, "width has to be divisible by 8");
width
} else {
768
};
StableDiffusionConfig {
width,
height,
clip: clip::Config::v2_1(),
clip2: None,
autoencoder,
scheduler,
unet,
}
}
pub fn v2_1(
sliced_attention_size: Option<usize>,
height: Option<usize>,
width: Option<usize>,
) -> Self {
// https://huggingface.co/stabilityai/stable-diffusion-2-1/blob/main/scheduler/scheduler_config.json
Self::v2_1_(
sliced_attention_size,
height,
width,
schedulers::PredictionType::VPrediction,
)
}
fn sdxl_(
sliced_attention_size: Option<usize>,
height: Option<usize>,
width: Option<usize>,
prediction_type: schedulers::PredictionType,
) -> Self {
let bc = |out_channels, use_cross_attn, attention_head_dim| unet_2d::BlockConfig {
out_channels,
use_cross_attn,
attention_head_dim,
};
// https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/unet/config.json
let unet = unet_2d::UNet2DConditionModelConfig {
blocks: vec![
bc(320, None, 5),
bc(640, Some(2), 10),
bc(1280, Some(10), 20),
],
center_input_sample: false,
cross_attention_dim: 2048,
downsample_padding: 1,
flip_sin_to_cos: true,
freq_shift: 0.,
layers_per_block: 2,
mid_block_scale_factor: 1.,
norm_eps: 1e-5,
norm_num_groups: 32,
sliced_attention_size,
use_linear_projection: true,
};
// https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/vae/config.json
let autoencoder = vae::AutoEncoderKLConfig {
block_out_channels: vec![128, 256, 512, 512],
layers_per_block: 2,
latent_channels: 4,
norm_num_groups: 32,
};
let scheduler = Arc::new(ddim::DDIMSchedulerConfig {
prediction_type,
..Default::default()
});
let height = if let Some(height) = height {
assert_eq!(height % 8, 0, "height has to be divisible by 8");
height
} else {
1024
};
let width = if let Some(width) = width {
assert_eq!(width % 8, 0, "width has to be divisible by 8");
width
} else {
1024
};
StableDiffusionConfig {
width,
height,
clip: clip::Config::sdxl(),
clip2: Some(clip::Config::sdxl2()),
autoencoder,
scheduler,
unet,
}
}
fn sdxl_turbo_(
sliced_attention_size: Option<usize>,
height: Option<usize>,
width: Option<usize>,
prediction_type: schedulers::PredictionType,
) -> Self {
let bc = |out_channels, use_cross_attn, attention_head_dim| unet_2d::BlockConfig {
out_channels,
use_cross_attn,
attention_head_dim,
};
// https://huggingface.co/stabilityai/sdxl-turbo/blob/main/unet/config.json
let unet = unet_2d::UNet2DConditionModelConfig {
blocks: vec![
bc(320, None, 5),
bc(640, Some(2), 10),
bc(1280, Some(10), 20),
],
center_input_sample: false,
cross_attention_dim: 2048,
downsample_padding: 1,
flip_sin_to_cos: true,
freq_shift: 0.,
layers_per_block: 2,
mid_block_scale_factor: 1.,
norm_eps: 1e-5,
norm_num_groups: 32,
sliced_attention_size,
use_linear_projection: true,
};
// https://huggingface.co/stabilityai/sdxl-turbo/blob/main/vae/config.json
let autoencoder = vae::AutoEncoderKLConfig {
block_out_channels: vec![128, 256, 512, 512],
layers_per_block: 2,
latent_channels: 4,
norm_num_groups: 32,
};
let scheduler = Arc::new(
euler_ancestral_discrete::EulerAncestralDiscreteSchedulerConfig {
prediction_type,
timestep_spacing: schedulers::TimestepSpacing::Trailing,
..Default::default()
},
);
let height = if let Some(height) = height {
assert_eq!(height % 8, 0, "height has to be divisible by 8");
height
} else {
512
};
let width = if let Some(width) = width {
assert_eq!(width % 8, 0, "width has to be divisible by 8");
width
} else {
512
};
Self {
width,
height,
clip: clip::Config::sdxl(),
clip2: Some(clip::Config::sdxl2()),
autoencoder,
scheduler,
unet,
}
}
pub fn sdxl(
sliced_attention_size: Option<usize>,
height: Option<usize>,
width: Option<usize>,
) -> Self {
Self::sdxl_(
sliced_attention_size,
height,
width,
// https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/scheduler/scheduler_config.json
schedulers::PredictionType::Epsilon,
)
}
pub fn sdxl_turbo(
sliced_attention_size: Option<usize>,
height: Option<usize>,
width: Option<usize>,
) -> Self {
Self::sdxl_turbo_(
sliced_attention_size,
height,
width,
// https://huggingface.co/stabilityai/sdxl-turbo/blob/main/scheduler/scheduler_config.json
schedulers::PredictionType::Epsilon,
)
}
pub fn ssd1b(
sliced_attention_size: Option<usize>,
height: Option<usize>,
width: Option<usize>,
) -> Self {
let bc = |out_channels, use_cross_attn, attention_head_dim| unet_2d::BlockConfig {
out_channels,
use_cross_attn,
attention_head_dim,
};
// https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/unet/config.json
let unet = unet_2d::UNet2DConditionModelConfig {
blocks: vec![
bc(320, None, 5),
bc(640, Some(2), 10),
bc(1280, Some(10), 20),
],
center_input_sample: false,
cross_attention_dim: 2048,
downsample_padding: 1,
flip_sin_to_cos: true,
freq_shift: 0.,
layers_per_block: 2,
mid_block_scale_factor: 1.,
norm_eps: 1e-5,
norm_num_groups: 32,
sliced_attention_size,
use_linear_projection: true,
};
// https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/vae/config.json
let autoencoder = vae::AutoEncoderKLConfig {
block_out_channels: vec![128, 256, 512, 512],
layers_per_block: 2,
latent_channels: 4,
norm_num_groups: 32,
};
let scheduler = Arc::new(ddim::DDIMSchedulerConfig {
..Default::default()
});
let height = if let Some(height) = height {
assert_eq!(height % 8, 0, "height has to be divisible by 8");
height
} else {
1024
};
let width = if let Some(width) = width {
assert_eq!(width % 8, 0, "width has to be divisible by 8");
width
} else {
1024
};
Self {
width,
height,
clip: clip::Config::ssd1b(),
clip2: Some(clip::Config::ssd1b2()),
autoencoder,
scheduler,
unet,
}
}
pub fn build_vae<P: AsRef<std::path::Path>>(
&self,
vae_weights: P,
device: &Device,
dtype: DType,
) -> Result<vae::AutoEncoderKL> {
let vs_ae =
unsafe { nn::VarBuilder::from_mmaped_safetensors(&[vae_weights], dtype, device)? };
// https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/vae/config.json
let autoencoder = vae::AutoEncoderKL::new(vs_ae, 3, 3, self.autoencoder.clone())?;
Ok(autoencoder)
}
pub fn build_unet<P: AsRef<std::path::Path>>(
&self,
unet_weights: P,
device: &Device,
in_channels: usize,
use_flash_attn: bool,
dtype: DType,
) -> Result<unet_2d::UNet2DConditionModel> {
let vs_unet =
unsafe { nn::VarBuilder::from_mmaped_safetensors(&[unet_weights], dtype, device)? };
let unet = unet_2d::UNet2DConditionModel::new(
vs_unet,
in_channels,
4,
use_flash_attn,
self.unet.clone(),
)?;
Ok(unet)
}
pub fn build_scheduler(&self, n_steps: usize) -> Result<Box<dyn Scheduler>> {
self.scheduler.build(n_steps)
}
}
pub fn build_clip_transformer<P: AsRef<std::path::Path>>(
clip: &clip::Config,
clip_weights: P,
device: &Device,
dtype: DType,
) -> Result<clip::ClipTextTransformer> {
let vs = unsafe { nn::VarBuilder::from_mmaped_safetensors(&[clip_weights], dtype, device)? };
let text_model = clip::ClipTextTransformer::new(vs, clip)?;
Ok(text_model)
}
| candle/candle-transformers/src/models/stable_diffusion/mod.rs/0 | {
"file_path": "candle/candle-transformers/src/models/stable_diffusion/mod.rs",
"repo_id": "candle",
"token_count": 7668
} | 36 |
use candle::{Device, Result, Tensor};
use candle_transformers::generation::LogitsProcessor;
#[test]
fn sample_with_zero_temperature() -> Result<()> {
let mut logits_process = LogitsProcessor::new(1337, None, None);
let logits = Tensor::new(&[0.1, 0.2, 0.3, 0.4], &Device::Cpu)?;
let token = logits_process.sample(&logits)?;
assert_eq!(token, 3);
Ok(())
}
#[test]
fn sample_with_temperature() -> Result<()> {
let mut logits_process = LogitsProcessor::new(42, Some(0.9), None);
let logits = Tensor::new(&[0.1, 0.2, 0.3, 0.4], &Device::Cpu)?;
let token = logits_process.sample(&logits)?;
assert_eq!(token, 0);
Ok(())
}
#[test]
fn sample_with_top_p() -> Result<()> {
let mut logits_process = LogitsProcessor::new(42, Some(1.0), Some(0.5));
let logits = Tensor::new(&[0.1, 0.2, 0.3, 0.4], &Device::Cpu)?;
let token = logits_process.sample(&logits)?;
assert_eq!(token, 2);
Ok(())
}
| candle/candle-transformers/tests/generation_tests.rs/0 | {
"file_path": "candle/candle-transformers/tests/generation_tests.rs",
"repo_id": "candle",
"token_count": 408
} | 37 |
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