# coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# 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 LLaMA model."""
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from transformers.activations import ACT2FN
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from transformers.modeling_utils import PretrainedConfig
from transformers.utils import (
ContextManagers,
add_start_docstrings,
add_start_docstrings_to_model_forward,
replace_return_docstrings,
)
from m4.models import DecoupledEmbedding, DecoupledLinear
from m4.models.common import (
expand_inputs_for_generation,
prepare_inputs_for_generation,
update_model_kwargs_for_generation,
)
from m4.models.custom_modules import VLOOMPreTrainedModelBase
from m4.models.perceiver.perceiver import PerceiverResampler
from m4.models.vllama.configuration_vllama import VLlamaConfig
from m4.training.utils import (
compute_perceiver_tflops_per_batch_per_gpu,
compute_tflops_per_batch_per_gpu,
deepspeed_gathered_parameters_context_manager,
freeze_model,
)
from m4.utils import logging
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "VLlamaConfig"
def _make_causal_mask(input_ids_shape: torch.Size, dtype: torch.dtype, past_key_values_length: int = 0):
"""
Make causal mask used for bi-directional self-attention.
"""
bsz, tgt_len = input_ids_shape
mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min))
mask_cond = torch.arange(mask.size(-1))
mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
mask = mask.to(dtype)
if past_key_values_length > 0:
mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype), mask], dim=-1)
return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
bsz, src_len = mask.size()
tgt_len = tgt_len if tgt_len is not None else src_len
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
inverted_mask = 1.0 - expanded_mask
return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
class LlamaRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
LlamaRMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
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
class LlamaRotaryEmbedding(torch.nn.Module):
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
super().__init__()
inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
self.register_buffer("inv_freq", inv_freq)
# Build here to make `torch.jit.trace` work.
self.max_seq_len_cached = max_position_embeddings
t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
emb = torch.cat((freqs, freqs), dim=-1)
self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
def forward(self, x, seq_len=None):
# x: [bs, num_attention_heads, seq_len, head_size]
# This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
if seq_len > self.max_seq_len_cached:
self.max_seq_len_cached = seq_len
t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype)
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
return (
self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
)
def rotate_half(x):
"""Rotates half the hidden dims of the input."""
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2 :]
return torch.cat((-x2, x1), dim=-1)
def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
gather_indices = position_ids[:, None, :, None] # [bs, 1, seq_len, 1]
gather_indices = gather_indices.repeat(1, cos.shape[1], 1, cos.shape[3])
cos = torch.gather(cos.repeat(gather_indices.shape[0], 1, 1, 1), 2, gather_indices)
sin = torch.gather(sin.repeat(gather_indices.shape[0], 1, 1, 1), 2, gather_indices)
q_embed = (q * cos) + (rotate_half(q) * sin)
k_embed = (k * cos) + (rotate_half(k) * sin)
return q_embed, k_embed
class LlamaMLP(nn.Module):
def __init__(
self,
hidden_size: int,
intermediate_size: int,
hidden_act: str,
):
super().__init__()
self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
self.act_fn = ACT2FN[hidden_act]
def forward(self, x):
return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
class LlamaAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
hidden_size: int,
num_heads: int,
dropout: float = 0.0,
is_cross_attention: bool = False,
config: PretrainedConfig = None,
qk_layer_norms: bool = False,
):
super().__init__()
self.hidden_size = hidden_size
self.num_heads = num_heads
self.head_dim = hidden_size // num_heads
self.dropout = dropout
if (self.head_dim * num_heads) != self.hidden_size:
raise ValueError(
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
f" and `num_heads`: {num_heads})."
)
self.is_cross_attention = is_cross_attention
if self.is_cross_attention:
kv_input_dim = self.hidden_size if not hasattr(config, "vision_embed_dim") else config.vision_embed_dim
self.q_proj = nn.Linear(
self.hidden_size,
num_heads * self.head_dim,
bias=False,
)
self.k_proj = nn.Linear(kv_input_dim, num_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(
kv_input_dim,
num_heads * self.head_dim,
bias=False,
)
else:
self.q_proj = nn.Linear(
self.hidden_size,
num_heads * self.head_dim,
bias=False,
)
self.k_proj = nn.Linear(
self.hidden_size,
num_heads * self.head_dim,
bias=False,
)
self.v_proj = nn.Linear(
self.hidden_size,
num_heads * self.head_dim,
bias=False,
)
self.o_proj = nn.Linear(
num_heads * self.head_dim,
hidden_size,
bias=False,
)
self.rotary_emb = LlamaRotaryEmbedding(self.head_dim)
self.qk_layer_norms = qk_layer_norms
if self.qk_layer_norms:
self.q_layer_norm = LlamaRMSNorm(self.head_dim, eps=config.rms_norm_eps)
self.k_layer_norm = LlamaRMSNorm(self.head_dim, eps=config.rms_norm_eps)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: bool = False,
use_cache: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
# if key_value_states are provided this layer is used as a cross-attention layer
is_cross_attention = self.is_cross_attention or key_value_states is not None
bsz, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
if not is_cross_attention:
key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
else:
_, kv_len, _ = key_value_states.size() # Note that, in this case, `kv_len` == `kv_seq_len`
key_states = self.k_proj(key_value_states).view(bsz, kv_len, self.num_heads, self.head_dim).transpose(1, 2)
value_states = (
self.v_proj(key_value_states).view(bsz, kv_len, self.num_heads, self.head_dim).transpose(1, 2)
)
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
kv_seq_len += past_key_value[0].shape[-2]
if not is_cross_attention:
cos, sin = self.rotary_emb(value_states, seq_len=max(kv_seq_len, q_len))
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
# [bsz, nh, t, hd]
if past_key_value is not None:
# reuse k, v, self_attention
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
past_key_value = (key_states, value_states) if use_cache else None
if self.qk_layer_norms:
query_states = self.q_layer_norm(query_states)
key_states = self.k_layer_norm(key_states)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
)
attn_output = nn.functional.scaled_dot_product_attention(
query_states,
key_states,
value_states,
attn_mask=attention_mask,
dropout_p=self.dropout,
)
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.transpose(1, 2)
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
attn_output = self.o_proj(attn_output)
attn_weights = None
logger.warning_once(
"attn_weights are not extracted in scaled_dot_product_attention. The model returns None instead"
)
return attn_output, attn_weights, past_key_value
class LlamaDecoderLayer(nn.Module):
def __init__(self, config: VLlamaConfig):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = LlamaAttention(
hidden_size=self.hidden_size,
num_heads=config.num_attention_heads,
dropout=config.dropout,
config=config,
)
self.mlp = LlamaMLP(
hidden_size=self.hidden_size,
intermediate_size=config.intermediate_size,
hidden_act=config.hidden_act,
)
self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.dropout = config.dropout
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative 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.
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`).
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
"""
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
# Fully Connected
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
return outputs
class VLlamaGatedCrossAttentionLayer(nn.Module):
def __init__(self, config: VLlamaConfig):
super().__init__()
self.hidden_size = config.hidden_size
self.cross_attn = LlamaAttention(
hidden_size=self.hidden_size,
num_heads=config.num_attention_heads,
is_cross_attention=True,
dropout=config.dropout,
config=config,
qk_layer_norms=config.qk_layer_norms,
)
self.mlp = LlamaMLP(
hidden_size=self.hidden_size,
intermediate_size=config.intermediate_size,
hidden_act=config.hidden_act,
)
self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.config = config.dropout
self.act_cross_attn = nn.Tanh()
self.act_dense = nn.Tanh()
if config.alpha_initializer == "zeros":
if config.alpha_type == "vector":
self.alpha_cross_attn = nn.Parameter(torch.zeros(1, 1, self.hidden_size))
self.alpha_dense = nn.Parameter(torch.zeros(1, 1, self.hidden_size))
elif config.alpha_type == "float":
self.alpha_cross_attn = nn.Parameter(torch.zeros(1))
self.alpha_dense = nn.Parameter(torch.zeros(1))
else:
raise ValueError(f"Unknown value for `alpha_type` ({config.alpha_type})")
elif config.alpha_initializer == "ones":
if config.alpha_type == "vector":
self.alpha_cross_attn = nn.Parameter(torch.ones(1, 1, self.hidden_size))
self.alpha_dense = nn.Parameter(torch.ones(1, 1, self.hidden_size))
elif config.alpha_type == "float":
self.alpha_cross_attn = nn.Parameter(torch.ones(1))
self.alpha_dense = nn.Parameter(torch.ones(1))
else:
raise ValueError(f"Unknown value for `alpha_type` ({config.alpha_type})")
elif config.alpha_initializer in {"normal", "gaussian", "random"}:
if config.alpha_type == "vector":
self.alpha_cross_attn = nn.Parameter(
torch.normal(mean=0.0, std=config.alphas_initializer_range, size=(1, 1, self.hidden_size))
)
self.alpha_dense = nn.Parameter(
torch.normal(mean=0.0, std=config.alphas_initializer_range, size=(1, 1, self.hidden_size))
)
elif config.alpha_type == "float":
self.alpha_cross_attn = nn.Parameter(
torch.normal(mean=0.0, std=config.alphas_initializer_range, size=(1))
)
self.alpha_dense = nn.Parameter(torch.normal(mean=0.0, std=config.alphas_initializer_range, size=(1)))
else:
raise ValueError(f"Unknown value for `alpha_type` ({config.alpha_type})")
else:
raise NotImplementedError(f"Alpha initialization scheme {config.alpha_initializer} not yet implemented!")
if not (hasattr(self, "alpha_cross_attn") and hasattr(self, "alpha_dense")):
raise ValueError("Alpha parameters not initialized correctly!")
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
image_hidden_states: Optional[torch.Tensor] = None,
image_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative 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.
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`).
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
"""
if image_hidden_states is None:
raise ValueError(
"`image_hidden_states` is required for VLlama cross attention module which are visual features to be"
" conditioned on."
)
if past_key_value is not None:
raise NotImplementedError("Past key value states are not implemented for VLlama cross attention module.")
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.cross_attn(
hidden_states=hidden_states,
key_value_states=image_hidden_states,
attention_mask=image_attention_mask,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.config, training=self.training)
hidden_states = residual + self.act_cross_attn(self.alpha_cross_attn) * hidden_states
# Fully Connected
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.config, training=self.training)
hidden_states = residual + self.act_dense(self.alpha_dense) * hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
return outputs
LLAMA_START_DOCSTRING = r"""
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 ([`VLlamaConfig`]):
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.
"""
@add_start_docstrings(
"The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
LLAMA_START_DOCSTRING,
)
class VLlamaPreTrainedModel(VLOOMPreTrainedModelBase):
config_class = VLlamaConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["LlamaDecoderLayer", "VLlamaGatedCrossAttentionLayer"]
_keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
def _init_weights(self, module):
def init_a_linear(module, mean=0.0, std=self.config.initializer_range):
with ContextManagers(deepspeed_gathered_parameters_context_manager(module.weight, modify=True)):
module.weight.data.normal_(mean=mean, std=std)
if module.bias is not None:
with ContextManagers(deepspeed_gathered_parameters_context_manager(module.bias, modify=True)):
module.bias.data.zero_()
if isinstance(module, VLlamaGatedCrossAttentionLayer):
for sub_module_name, sub_module in module.named_modules():
if isinstance(sub_module, nn.Linear):
if "down_proj" in sub_module_name:
factor = 2 * self.config.num_hidden_layers
else:
factor = 1.0
init_a_linear(sub_module, std=(0.4 / (sub_module.in_features * factor)) ** 0.5)
elif isinstance(module, PerceiverResampler):
with ContextManagers(deepspeed_gathered_parameters_context_manager(module.latents, modify=True)):
module.latents.data.normal_(mean=0.0, std=(1.0 / self.config.vision_embed_dim) ** 0.5)
for sub_module_name, sub_module in module.named_modules():
if isinstance(sub_module, nn.Linear):
if "c_proj" in sub_module_name:
factor = 2 * self.config.num_hidden_layers
else:
factor = 1.0
init_a_linear(sub_module, std=(0.4 / (self.config.vision_embed_dim * factor)) ** 0.5)
elif isinstance(module, nn.Embedding):
with ContextManagers(deepspeed_gathered_parameters_context_manager(module.weight, modify=True)):
module.weight.data.normal_(mean=0.0, std=(1.0 / self.config.hidden_size) ** 0.5)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, DecoupledLinear):
if hasattr(module, "additional_fc"):
init_a_linear(module.additional_fc, std=(1.0 / (module.additional_fc.in_features)) ** 0.5)
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, VLlamaModel):
module.gradient_checkpointing = value
@classmethod
def override_vision_model_wrapper(cls, model, config, vision_model_name, vision_model_params, torch_dtype):
# this can be called via from_pretrained from a class w/ head or w/o head so we extract the beheaded model version
beheaded_model = model.model if hasattr(model, "model") else model
cls.override_vision_model(beheaded_model, vision_model_name, vision_model_params, torch_dtype)
beheaded_model.freeze_relevant_params(config)
LLAMA_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` 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)
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
`past_key_values`).
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
information on the default strategy.
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.n_positions - 1]`.
[What are position IDs?](../glossary#position-ids)
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential 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.
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.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
LLAMA_START_DOCSTRING,
)
class VLlamaModel(VLlamaPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]
Args:
config: VLlamaConfig
"""
def __init__(self, config: VLlamaConfig, vision_model=None):
super().__init__(config)
self.config = config
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = DecoupledEmbedding(
num_embeddings=config.vocab_size,
num_additional_embeddings=config.additional_vocab_size,
embedding_dim=config.hidden_size,
partially_freeze=config.freeze_text_layers,
padding_idx=self.padding_idx,
)
# Load an uninitialized model and later in from_pretrained will load the pre-trained model -
# this solves the losing of weights in `from_pretrained` on the main model
self.vision_model = vision_model
# Perceiver Resampler
if config.use_resampler:
self.perceiver_resampler = PerceiverResampler(
self.config,
self.config.vision_embed_dim,
config.resampler_depth,
config.resampler_n_heads,
config.resampler_head_dim,
config.resampler_n_latents,
)
self.layers = nn.ModuleList([LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)])
self.cross_layer_interval = config.cross_layer_interval
num_cross_layers = config.num_hidden_layers // self.cross_layer_interval
self.gated_cross_attn_layers = nn.ModuleList(
[VLlamaGatedCrossAttentionLayer(config) for _ in range(num_cross_layers)]
)
self.gradient_checkpointing = False
self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
self.freeze_relevant_params(config)
def freeze_relevant_params(self, config=None):
if config is None:
config = self.config
if config.freeze_text_layers:
self.freeze_text_layers(config.freeze_text_module_exceptions)
if config.freeze_vision_layers:
freeze_model(self.vision_model, module_exceptions=config.freeze_vision_module_exceptions)
def freeze_text_layers(self, module_exceptions):
for module in [self.layers, self.norm]:
freeze_model(module, module_exceptions=module_exceptions)
def get_input_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, value):
self.embed_tokens = value
# Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
# create causal mask
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
combined_attention_mask = None
if input_shape[-1] > 1:
combined_attention_mask = _make_causal_mask(
input_shape, inputs_embeds.dtype, past_key_values_length=past_key_values_length
).to(inputs_embeds.device)
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
inputs_embeds.device
)
combined_attention_mask = (
expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
)
return combined_attention_mask
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
image_embeddings: Optional[torch.FloatTensor] = None,
image_attention_mask: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPast]:
device = input_ids.device if input_ids is not None else inputs_embeds.device
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
)
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
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
elif input_ids is not None:
batch_size, seq_length = input_ids.shape
elif inputs_embeds is not None:
batch_size, seq_length, _ = inputs_embeds.shape
else:
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
seq_length_with_past = seq_length
past_key_values_length = 0
if past_key_values is not None:
past_key_values_length = past_key_values[0][0].shape[2]
seq_length_with_past = seq_length_with_past + past_key_values_length
if attention_mask is not None and position_ids is None:
# create position_ids on the fly for batch generation
position_ids = attention_mask.long().cumsum(-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
elif position_ids is None:
device = input_ids.device if input_ids is not None else inputs_embeds.device
position_ids = torch.arange(
past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
)
position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
else:
position_ids = position_ids.view(-1, seq_length).long()
if pixel_values is not None and image_embeddings is not None:
raise ValueError("You cannot specify both pixel_values and image_embeddings at the same time")
elif pixel_values is not None:
pixel_values = pixel_values.to(dtype=self.dtype, device=input_ids.device) # fp16 compatibility
batch_size, num_images = pixel_values.size(0), pixel_values.size(1)
pixel_values = pixel_values.contiguous().view(batch_size * num_images, *pixel_values.shape[2:])
# Get sequence from the vision encoder
image_hidden_states = self.vision_model(pixel_values=pixel_values).last_hidden_state
elif image_embeddings is not None:
batch_size, num_images, image_seq_len, image_hidden_size = image_embeddings.size()
image_hidden_states = image_embeddings.to(dtype=self.dtype, device=input_ids.device)
image_hidden_states = image_hidden_states.view(batch_size * num_images, image_seq_len, image_hidden_size)
if self.config.use_resampler:
image_hidden_states = self.perceiver_resampler(image_hidden_states)
image_seq_len, image_hidden_size = image_hidden_states.size(1), image_hidden_states.size(2)
image_hidden_states = image_hidden_states.view(batch_size, num_images * image_seq_len, image_hidden_size)
# Make image_attention_mask compatible with hidden states
text_seq_len = image_attention_mask.size(1)
image_attention_mask = image_attention_mask.unsqueeze(-1)
image_attention_mask = image_attention_mask.repeat(1, 1, 1, image_seq_len)
image_attention_mask = image_attention_mask.view(batch_size, text_seq_len, num_images * image_seq_len)
if image_hidden_states is not None:
image_batch_size, image_sequence_length, _ = image_hidden_states.size()
image_hidden_shape = (image_batch_size, image_sequence_length)
if image_attention_mask is None:
image_attention_mask = torch.ones(image_hidden_shape, device=device)
image_attention_mask = self.invert_attention_mask(image_attention_mask)
else:
image_attention_mask = None
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
# embed positions
if attention_mask is None:
attention_mask = torch.ones(
(batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
)
attention_mask = self._prepare_decoder_attention_mask(
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
)
hidden_states = inputs_embeds
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
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
next_decoder_cache = () if use_cache else None
for idx, decoder_layer in enumerate(self.layers):
if output_hidden_states:
all_hidden_states += (hidden_states,)
past_key_value = past_key_values[idx] if past_key_values is not None else None
def vblock(
main_block,
hidden_states,
attention_mask,
position_ids,
past_key_value,
image_hidden_states,
image_attention_mask,
output_attentions,
use_cache,
layer_idx,
cross_layer_interval,
gated_cross_attn_layers,
):
# TODO(ls): Add cross attention values to respective lists
if layer_idx % cross_layer_interval == 0:
xblock = gated_cross_attn_layers[layer_idx // cross_layer_interval]
outputs = xblock(
hidden_states,
attention_mask=attention_mask,
image_hidden_states=image_hidden_states,
image_attention_mask=image_attention_mask,
output_attentions=output_attentions,
use_cache=use_cache,
past_key_value=None, # not implemented
)
hidden_states = outputs[0]
layer_outputs = main_block(
hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
return layer_outputs
if self.gradient_checkpointing and self.training:
past_key_value = None
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
layer_outputs = torch.utils.checkpoint.checkpoint(
vblock,
decoder_layer,
hidden_states,
attention_mask,
position_ids,
past_key_value,
image_hidden_states,
image_attention_mask,
output_attentions,
use_cache,
idx,
self.cross_layer_interval,
self.gated_cross_attn_layers,
)
else:
layer_outputs = vblock(
decoder_layer,
hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
image_hidden_states=image_hidden_states,
image_attention_mask=image_attention_mask,
output_attentions=output_attentions,
use_cache=use_cache,
layer_idx=idx,
cross_layer_interval=self.cross_layer_interval,
gated_cross_attn_layers=self.gated_cross_attn_layers,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
if not return_dict:
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
)
class VLlamaForCausalLM(VLlamaPreTrainedModel):
_keys_to_ignore_on_load_missing = [r"lm_head.weight"]
def __init__(self, config, vision_model=None):
super().__init__(config)
self.model = VLlamaModel(config, vision_model=vision_model)
self.lm_head = DecoupledLinear(
in_features=config.hidden_size,
out_features=config.vocab_size,
out_additional_features=config.additional_vocab_size,
bias=False,
partially_freeze=config.freeze_lm_head,
)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.model.embed_tokens
def set_input_embeddings(self, value):
self.model.embed_tokens = value
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def set_decoder(self, decoder):
self.model = decoder
def get_decoder(self):
return self.model
def tie_weights(self):
"""
Overwrite `transformers.modeling_utils.PreTrainedModel.tie_weights` to handle the case of DecoupledLinear and DecoupledEmbedding.
"""
output_embeddings = self.get_output_embeddings()
input_embeddings = self.get_input_embeddings()
if getattr(self.config, "tie_word_embeddings", True):
output_embeddings.weight = input_embeddings.weight
if input_embeddings.num_additional_embeddings > 0:
assert output_embeddings.out_additional_features == input_embeddings.num_additional_embeddings
output_embeddings.additional_fc.weight = input_embeddings.additional_embedding.weight
if hasattr(output_embeddings, "out_features") and hasattr(input_embeddings, "num_embeddings"):
output_embeddings.out_features = input_embeddings.num_embeddings
if hasattr(output_embeddings, "out_additional_features") and hasattr(
input_embeddings, "num_additional_embeddings"
):
output_embeddings.out_additional_features = input_embeddings.num_additional_embeddings
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
image_embeddings: Optional[torch.FloatTensor] = None,
image_attention_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithPast]:
r"""
Args:
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, LlamaForCausalLM
>>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
>>> prompt = "Hey, are you consciours? Can you talk to me?"
>>> inputs = tokenizer(prompt, return_tensors="pt")
>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
```"""
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
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
pixel_values=pixel_values,
image_embeddings=image_embeddings,
image_attention_mask=image_attention_mask,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
logits = self.lm_head(hidden_states)
loss = None
if labels is not None:
# Shift so that tokens < n predict n
if attention_mask is not None:
shift_attention_mask = attention_mask[..., 1:]
shift_logits = logits[..., :-1, :][shift_attention_mask != 0].contiguous()
shift_labels = labels[..., 1:][shift_attention_mask != 0].contiguous()
else:
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs):
inputs = prepare_inputs_for_generation(input_ids, past=past, **kwargs)
unwanted_kwargs = ["token_type_ids"]
for kwarg in unwanted_kwargs:
inputs.pop(kwarg, None)
return inputs
@staticmethod
def _expand_inputs_for_generation(
*args,
**model_kwargs,
):
return expand_inputs_for_generation(*args, **model_kwargs)
@staticmethod
def _update_model_kwargs_for_generation(outputs, model_kwargs, is_encoder_decoder=False):
return update_model_kwargs_for_generation(outputs, model_kwargs, is_encoder_decoder=is_encoder_decoder)
@staticmethod
def _reorder_cache(past, beam_idx):
reordered_past = ()
for layer_past in past:
reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
return reordered_past
def get_model_tflops_per_batch_per_gpu(self, hparams, data_param, tokenizer, max_num_images):
config_vl_model = self.config
language_embed_size = config_vl_model.hidden_size
num_language_layers = config_vl_model.num_hidden_layers
ffn_inner_size = config_vl_model.intermediate_size
vision_config = self.model.vision_model.config
if hasattr(vision_config, "vision_config"):
vision_config = vision_config.vision_config
# Get vision model blocks infos
vision_patch_size = vision_config.patch_size
vision_hidden_size = vision_config.hidden_size
num_vision_layers = vision_config.num_hidden_layers
# The +1 is for the CLS token
single_image_seq_len = (vision_config.image_size // vision_patch_size) ** 2 + 1
vision_exp_factor = vision_config.intermediate_size // vision_hidden_size
# Get language and cross-att blocks infos
num_cross_attn_layers = num_language_layers // config_vl_model.cross_layer_interval
language_seq_len = data_param.max_seq_len
language_exp_factor = (ffn_inner_size // language_embed_size) if ffn_inner_size is not None else 4
cross_att_exp_factor = (ffn_inner_size // language_embed_size) if ffn_inner_size is not None else 4
k_v_cross_attn_seq_len = (
(self.config.resampler_n_latents * max_num_images)
if self.config.use_resampler
else (single_image_seq_len * max_num_images)
)
language_tflops_per_batch_per_gpu = compute_tflops_per_batch_per_gpu(
num_layers=num_language_layers,
batch_size=hparams.batch_size_per_gpu,
q_seq_len=language_seq_len,
k_seq_len=language_seq_len,
hidden_size=language_embed_size,
kv_in_dim=language_embed_size,
ff_exp_factor=language_exp_factor,
grad_acc_size=hparams.grad_acc_size,
swiglu=True,
vocab_size=tokenizer.vocab_size,
count_backward=True, # Always True regardless of freezing, because gradients are computed for cross-attentions
use_grad_checkpointing=hparams.gradient_checkpointing,
)
cross_attention_tflops_per_batch_per_gpu = compute_tflops_per_batch_per_gpu(
num_layers=num_cross_attn_layers,
batch_size=hparams.batch_size_per_gpu,
q_seq_len=language_seq_len,
k_seq_len=k_v_cross_attn_seq_len,
hidden_size=language_embed_size,
kv_in_dim=vision_hidden_size,
ff_exp_factor=cross_att_exp_factor,
grad_acc_size=hparams.grad_acc_size,
swiglu=True,
vocab_size=None,
count_backward=True,
use_grad_checkpointing=hparams.gradient_checkpointing,
)
vision_tflops_per_batch_per_gpu = compute_tflops_per_batch_per_gpu(
num_layers=num_vision_layers,
batch_size=hparams.batch_size_per_gpu * max_num_images,
q_seq_len=single_image_seq_len,
k_seq_len=single_image_seq_len,
hidden_size=vision_hidden_size,
kv_in_dim=vision_hidden_size,
ff_exp_factor=vision_exp_factor,
grad_acc_size=hparams.grad_acc_size,
swiglu=False,
vocab_size=None,
count_backward=not hparams.model_params["freeze_vision_layers"],
use_grad_checkpointing=hparams.gradient_checkpointing,
)
if self.config.use_resampler:
perceiver_tflops_per_batch_per_gpu = compute_perceiver_tflops_per_batch_per_gpu(
num_layers=self.config.resampler_depth,
batch_size=hparams.batch_size_per_gpu * max_num_images,
q_seq_len=self.config.resampler_n_latents,
vision_embed_seq_len=single_image_seq_len,
q_k_v_input_dim=vision_hidden_size,
attention_hidden_size=self.config.resampler_n_heads * self.config.resampler_head_dim,
ff_exp_factor=cross_att_exp_factor,
count_backward=True,
use_grad_checkpointing=hparams.gradient_checkpointing,
)
flop_count = (
language_tflops_per_batch_per_gpu
+ cross_attention_tflops_per_batch_per_gpu
+ vision_tflops_per_batch_per_gpu
+ perceiver_tflops_per_batch_per_gpu
)
else:
flop_count = (
language_tflops_per_batch_per_gpu
+ cross_attention_tflops_per_batch_per_gpu
+ vision_tflops_per_batch_per_gpu
)
return flop_count