Llama-3.1-8B-DALv0.1
/
venv
/lib
/python3.12
/site-packages
/transformers
/models
/falcon
/modeling_falcon.py
# coding=utf-8 | |
# Copyright 2023 the Falcon 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. | |
"""PyTorch Falcon model.""" | |
import math | |
from typing import TYPE_CHECKING, Optional, Tuple, Union | |
import torch | |
import torch.utils.checkpoint | |
from torch import nn | |
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, LayerNorm, MSELoss | |
from torch.nn import functional as F | |
from ...activations import get_activation | |
from ...modeling_attn_mask_utils import ( | |
AttentionMaskConverter, | |
_prepare_4d_causal_attention_mask, | |
_prepare_4d_causal_attention_mask_for_sdpa, | |
) | |
from ...modeling_outputs import ( | |
BaseModelOutputWithPastAndCrossAttentions, | |
CausalLMOutputWithCrossAttentions, | |
QuestionAnsweringModelOutput, | |
SequenceClassifierOutputWithPast, | |
TokenClassifierOutput, | |
) | |
from ...modeling_utils import PreTrainedModel | |
from ...pytorch_utils import is_torch_greater_or_equal_than_2_0 | |
from ...utils import ( | |
add_code_sample_docstrings, | |
add_start_docstrings, | |
add_start_docstrings_to_model_forward, | |
is_flash_attn_2_available, | |
is_flash_attn_greater_or_equal_2_10, | |
logging, | |
) | |
from .configuration_falcon import FalconConfig | |
if TYPE_CHECKING: | |
from ...configuration_utils import PretrainedConfig | |
if is_flash_attn_2_available(): | |
from ...modeling_flash_attention_utils import _flash_attention_forward | |
logger = logging.get_logger(__name__) | |
_CHECKPOINT_FOR_DOC = "Rocketknight1/falcon-rw-1b" | |
_CONFIG_FOR_DOC = "FalconConfig" | |
# NOTE(Hesslow): Unfortunately we did not fuse matmul and bias during training, this means that there's one additional quantization to bfloat16 between the operations. | |
# In order not to degrade the quality of our HF-port, we keep these characteristics in the final model. | |
class FalconLinear(nn.Linear): | |
def forward(self, input: torch.Tensor) -> torch.Tensor: | |
hidden_states = input @ self.weight.T | |
if self.bias is None: | |
return hidden_states | |
return hidden_states + self.bias | |
# Copied from transformers.models.llama.modeling_llama.rotate_half | |
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) | |
# Copied from transformers.models.mixtral.modeling_mixtral.apply_rotary_pos_emb | |
def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1): | |
"""Applies Rotary Position Embedding to the query and key tensors. | |
Args: | |
q (`torch.Tensor`): The query tensor. | |
k (`torch.Tensor`): The key tensor. | |
cos (`torch.Tensor`): The cosine part of the rotary embedding. | |
sin (`torch.Tensor`): The sine part of the rotary embedding. | |
position_ids (`torch.Tensor`): | |
The position indices of the tokens corresponding to the query and key tensors. For example, this can be | |
used to pass offsetted position ids when working with a KV-cache. | |
unsqueeze_dim (`int`, *optional*, defaults to 1): | |
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and | |
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note | |
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and | |
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes | |
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have | |
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. | |
Returns: | |
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. | |
""" | |
cos = cos[position_ids].unsqueeze(unsqueeze_dim) | |
sin = sin[position_ids].unsqueeze(unsqueeze_dim) | |
q_embed = (q * cos) + (rotate_half(q) * sin) | |
k_embed = (k * cos) + (rotate_half(k) * sin) | |
return q_embed, k_embed | |
# Copied from transformers.models.mixtral.modeling_mixtral.MixtralRotaryEmbedding with Mixtral->Falcon | |
class FalconRotaryEmbedding(nn.Module): | |
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): | |
super().__init__() | |
self.dim = dim | |
self.max_position_embeddings = max_position_embeddings | |
self.base = base | |
inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim)) | |
self.register_buffer("inv_freq", inv_freq, persistent=False) | |
# Build here to make `torch.jit.trace` work. | |
self._set_cos_sin_cache( | |
seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype() | |
) | |
def _set_cos_sin_cache(self, seq_len, device, dtype): | |
self.max_seq_len_cached = seq_len | |
t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq) | |
freqs = torch.outer(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().to(dtype), persistent=False) | |
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) | |
def forward(self, x, seq_len=None): | |
# x: [bs, num_attention_heads, seq_len, head_size] | |
if seq_len > self.max_seq_len_cached: | |
self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) | |
return ( | |
self.cos_cached[:seq_len].to(dtype=x.dtype), | |
self.sin_cached[:seq_len].to(dtype=x.dtype), | |
) | |
# copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->Falcon | |
# TODO @joao no longer copied from LLama after static cache, fix me (copied -> Copied) | |
class FalconLinearScalingRotaryEmbedding(FalconRotaryEmbedding): | |
"""FalconRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev""" | |
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): | |
self.scaling_factor = scaling_factor | |
super().__init__(dim, max_position_embeddings, base, device) | |
def _set_cos_sin_cache(self, seq_len, device, dtype): | |
self.max_seq_len_cached = seq_len | |
t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq) | |
t = t / self.scaling_factor | |
freqs = torch.outer(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().to(dtype), persistent=False) | |
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) | |
# copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->Falcon | |
# TODO @joao no longer copied from LLama after static cache, fix me (copied -> Copied) | |
class FalconDynamicNTKScalingRotaryEmbedding(FalconRotaryEmbedding): | |
"""FalconRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla""" | |
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): | |
self.scaling_factor = scaling_factor | |
super().__init__(dim, max_position_embeddings, base, device) | |
def _set_cos_sin_cache(self, seq_len, device, dtype): | |
self.max_seq_len_cached = seq_len | |
if seq_len > self.max_position_embeddings: | |
base = self.base * ( | |
(self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1) | |
) ** (self.dim / (self.dim - 2)) | |
inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim)) | |
self.register_buffer("inv_freq", inv_freq, persistent=False) | |
t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq) | |
freqs = torch.outer(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().to(dtype), persistent=False) | |
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) | |
def build_alibi_tensor(attention_mask: torch.Tensor, num_heads: int, dtype: torch.dtype) -> torch.Tensor: | |
batch_size, seq_length = attention_mask.shape | |
closest_power_of_2 = 2 ** math.floor(math.log2(num_heads)) | |
base = torch.tensor( | |
2 ** (-(2 ** -(math.log2(closest_power_of_2) - 3))), device=attention_mask.device, dtype=torch.float32 | |
) | |
powers = torch.arange(1, 1 + closest_power_of_2, device=attention_mask.device, dtype=torch.int32) | |
slopes = torch.pow(base, powers) | |
if closest_power_of_2 != num_heads: | |
extra_base = torch.tensor( | |
2 ** (-(2 ** -(math.log2(2 * closest_power_of_2) - 3))), device=attention_mask.device, dtype=torch.float32 | |
) | |
num_remaining_heads = min(closest_power_of_2, num_heads - closest_power_of_2) | |
extra_powers = torch.arange(1, 1 + 2 * num_remaining_heads, 2, device=attention_mask.device, dtype=torch.int32) | |
slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0) | |
# Note: alibi will added to the attention bias that will be applied to the query, key product of attention | |
# => therefore alibi will have to be of shape (batch_size, num_heads, query_length, key_length) | |
# => here we set (batch_size=1, num_heads=num_heads, query_length=1, key_length=max_length) | |
# => the query_length dimension will then be broadcasted correctly | |
# This is more or less identical to T5's relative position bias: | |
# https://github.com/huggingface/transformers/blob/f681437203baa7671de3174b0fa583c349d9d5e1/src/transformers/models/t5/modeling_t5.py#L527 | |
arange_tensor = ((attention_mask.cumsum(dim=-1) - 1) * attention_mask)[:, None, :] | |
alibi = slopes[..., None].bfloat16() * arange_tensor | |
return alibi.reshape(batch_size * num_heads, 1, seq_length).to(dtype) | |
# Copied from transformers.models.bloom.modeling_bloom.dropout_add | |
def dropout_add(x: torch.Tensor, residual: torch.Tensor, prob: float, training: bool) -> torch.Tensor: | |
""" | |
Dropout add function | |
Args: | |
x (`torch.tensor`): | |
input tensor | |
residual (`torch.tensor`): | |
residual tensor | |
prob (`float`): | |
dropout probability | |
training (`bool`): | |
training mode | |
""" | |
out = F.dropout(x, p=prob, training=training) | |
out = residual + out | |
return out | |
class FalconAttention(nn.Module): | |
def __init__(self, config: FalconConfig): | |
super().__init__() | |
self.config = config | |
self.hidden_size = config.hidden_size | |
self.num_heads = config.num_attention_heads | |
self.head_dim = self.hidden_size // self.num_heads | |
self.split_size = self.hidden_size | |
self.hidden_dropout = config.hidden_dropout | |
self.max_position_embeddings = config.max_position_embeddings | |
self.rope_theta = config.rope_theta | |
self.is_causal = True | |
self._use_sdpa = config._attn_implementation == "sdpa" | |
if self.head_dim * self.num_heads != self.hidden_size: | |
raise ValueError( | |
f"`hidden_size` must be divisible by num_heads (got `hidden_size`: {self.hidden_size} and `num_heads`:" | |
f" {self.num_heads})." | |
) | |
if config.rotary: | |
self._init_rope() | |
# Layer-wise attention scaling | |
self.inv_norm_factor = 1.0 / math.sqrt(self.head_dim) | |
self.beta = self.inv_norm_factor | |
if config.new_decoder_architecture: | |
qkv_out_dim = (config.num_kv_heads * 2 + config.num_attention_heads) * self.head_dim | |
elif config.multi_query: | |
qkv_out_dim = self.hidden_size + 2 * self.head_dim | |
else: | |
qkv_out_dim = 3 * self.hidden_size | |
self.query_key_value = FalconLinear(self.hidden_size, qkv_out_dim, bias=config.bias) | |
self.new_decoder_architecture = config.new_decoder_architecture | |
self.multi_query = config.multi_query | |
self.dense = FalconLinear(self.hidden_size, self.hidden_size, bias=config.bias) | |
self.attention_dropout = nn.Dropout(config.attention_dropout) | |
self.num_kv_heads = config.num_kv_heads if (self.new_decoder_architecture or not self.multi_query) else 1 | |
def _init_rope(self): | |
if self.config.rope_scaling is None: | |
self.rotary_emb = FalconRotaryEmbedding( | |
self.head_dim, | |
max_position_embeddings=self.max_position_embeddings, | |
base=self.rope_theta, | |
) | |
else: | |
scaling_type = self.config.rope_scaling["type"] | |
scaling_factor = self.config.rope_scaling["factor"] | |
if scaling_type == "linear": | |
self.rotary_emb = FalconLinearScalingRotaryEmbedding( | |
self.head_dim, | |
max_position_embeddings=self.max_position_embeddings, | |
scaling_factor=scaling_factor, | |
base=self.rope_theta, | |
) | |
elif scaling_type == "dynamic": | |
self.rotary_emb = FalconDynamicNTKScalingRotaryEmbedding( | |
self.head_dim, | |
max_position_embeddings=self.max_position_embeddings, | |
scaling_factor=scaling_factor, | |
base=self.rope_theta, | |
) | |
else: | |
raise ValueError(f"Unknown RoPE scaling type {scaling_type}") | |
def _split_heads(self, fused_qkv: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: | |
""" | |
Split the last dimension into (num_heads, head_dim), results share same memory storage as `fused_qkv` | |
Args: | |
fused_qkv (`torch.tensor`): [batch_size, seq_length, num_heads * 3 * head_dim] | |
Returns: | |
query: [batch_size, seq_length, num_heads, head_dim] key: [batch_size, seq_length, num_heads, head_dim] | |
value: [batch_size, seq_length, num_heads, head_dim] | |
""" | |
if self.new_decoder_architecture: | |
batch, seq_len, _ = fused_qkv.shape | |
qkv = fused_qkv.view(batch, seq_len, -1, self.num_heads // self.num_kv_heads + 2, self.head_dim) | |
query = qkv[:, :, :, :-2] | |
key = qkv[:, :, :, [-2]] | |
value = qkv[:, :, :, [-1]] | |
key = torch.broadcast_to(key, query.shape) | |
value = torch.broadcast_to(value, query.shape) | |
query, key, value = [x.flatten(2, 3) for x in (query, key, value)] | |
return query, key, value | |
elif not self.multi_query: | |
batch_size, seq_length, three_times_hidden_size = fused_qkv.shape | |
fused_qkv = fused_qkv.view(batch_size, seq_length, self.num_heads, 3, self.head_dim) | |
return fused_qkv[..., 0, :], fused_qkv[..., 1, :], fused_qkv[..., 2, :] | |
else: | |
batch_size, seq_length, three_times_hidden_size = fused_qkv.shape | |
fused_qkv = fused_qkv.view(batch_size, seq_length, self.num_heads + 2, self.head_dim) | |
return fused_qkv[..., :-2, :], fused_qkv[..., [-2], :], fused_qkv[..., [-1], :] | |
# Copied from transformers.models.bloom.modeling_bloom.BloomAttention._merge_heads | |
def _merge_heads(self, x: torch.Tensor) -> torch.Tensor: | |
""" | |
Merge heads together over the last dimension | |
Args: | |
x (`torch.tensor`): [batch_size * num_heads, seq_length, head_dim] | |
Returns: | |
torch.tensor: [batch_size, seq_length, num_heads * head_dim] | |
""" | |
# What we want to achieve is: | |
# batch_size * num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads * head_dim | |
batch_size_and_num_heads, seq_length, _ = x.shape | |
batch_size = batch_size_and_num_heads // self.num_heads | |
# First view to decompose the batch size | |
# batch_size * num_heads, seq_length, head_dim -> batch_size, num_heads, seq_length, head_dim | |
x = x.view(batch_size, self.num_heads, seq_length, self.head_dim) | |
# batch_size, num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads, head_dim | |
x = x.permute(0, 2, 1, 3) | |
# batch_size, seq_length, num_heads, head_dim -> batch_size, seq_length, num_heads * head_dim | |
return x.reshape(batch_size, seq_length, self.num_heads * self.head_dim) | |
def forward( | |
self, | |
hidden_states: torch.Tensor, | |
alibi: Optional[torch.Tensor], | |
attention_mask: torch.Tensor, | |
position_ids: Optional[torch.LongTensor] = None, | |
layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, | |
head_mask: Optional[torch.Tensor] = None, | |
use_cache: bool = False, | |
output_attentions: bool = False, | |
): | |
fused_qkv = self.query_key_value(hidden_states) # [batch_size, seq_length, 3 x hidden_size] | |
num_kv_heads = self.num_heads if self.new_decoder_architecture else self.num_kv_heads | |
# 3 x [batch_size, seq_length, num_heads, head_dim] | |
(query_layer, key_layer, value_layer) = self._split_heads(fused_qkv) | |
batch_size, query_length, _, _ = query_layer.shape | |
query_layer = query_layer.transpose(1, 2).reshape(batch_size, self.num_heads, query_length, self.head_dim) | |
key_layer = key_layer.transpose(1, 2).reshape(batch_size, num_kv_heads, query_length, self.head_dim) | |
value_layer = value_layer.transpose(1, 2).reshape(batch_size, num_kv_heads, query_length, self.head_dim) | |
kv_seq_len = key_layer.shape[-2] | |
if layer_past is not None: | |
kv_seq_len += layer_past[0].shape[-2] | |
if alibi is None: | |
cos, sin = self.rotary_emb(value_layer, seq_len=kv_seq_len) | |
query_layer, key_layer = apply_rotary_pos_emb(query_layer, key_layer, cos, sin, position_ids) | |
if layer_past is not None: | |
past_key, past_value = layer_past | |
# concatenate along seq_length dimension: | |
# - key: [batch_size, self.num_heads, kv_length, head_dim] | |
# - value: [batch_size, self.num_heads, kv_length, head_dim] | |
key_layer = torch.cat((past_key, key_layer), dim=-2) | |
value_layer = torch.cat((past_value, value_layer), dim=-2) | |
kv_length = key_layer.shape[-2] | |
if use_cache: | |
present = (key_layer, value_layer) | |
else: | |
present = None | |
if self._use_sdpa and query_layer.device.type == "cuda" and attention_mask is not None: | |
# For torch<=2.1.2, SDPA with memory-efficient backend is bugged with non-contiguous inputs with custom attn_mask, | |
# Reference: https://github.com/pytorch/pytorch/issues/112577. | |
query_layer = query_layer.contiguous() | |
key_layer = key_layer.contiguous() | |
value_layer = value_layer.contiguous() | |
if alibi is None: | |
if self._use_sdpa and not output_attentions: | |
# We dispatch to SDPA's Flash Attention or Efficient kernels via this if statement instead of an | |
# inline conditional assignment to support both torch.compile's `dynamic=True` and `fullgraph=True` | |
# The query_length > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not | |
# create a causal mask in case query_length == 1. | |
is_causal = True if self.is_causal and attention_mask is None and query_length > 1 else False | |
attn_output = torch.nn.functional.scaled_dot_product_attention( | |
query_layer, | |
key_layer, | |
value_layer, | |
attn_mask=attention_mask, | |
dropout_p=0.0, | |
is_causal=is_causal, | |
) | |
attention_scores = None | |
else: | |
attention_scores = query_layer @ key_layer.transpose(-1, -2) | |
attention_scores /= math.sqrt(self.head_dim) | |
attention_scores = F.softmax(attention_scores + attention_mask, dim=-1, dtype=hidden_states.dtype) | |
# It is unclear why neither dropout nor head_mask is applied here (while it is with alibi). | |
attn_output = attention_scores @ value_layer | |
attn_output = attn_output.view(batch_size, self.num_heads, query_length, self.head_dim) | |
attn_output = attn_output.permute(0, 2, 1, 3) | |
attn_output = attn_output.reshape(batch_size, query_length, self.num_heads * self.head_dim) | |
attn_output = self.dense(attn_output) | |
if output_attentions: | |
return attn_output, present, attention_scores | |
else: | |
return attn_output, present | |
else: | |
if self._use_sdpa and not output_attentions and head_mask is None: | |
# We dispatch to SDPA's Flash Attention or Efficient kernels via this if statement instead of an | |
# inline conditional assignment to support both torch.compile's `dynamic=True` and `fullgraph=True` | |
is_causal = True if self.is_causal and attention_mask is None and query_length > 1 else False | |
attn_output = torch.nn.functional.scaled_dot_product_attention( | |
query_layer, | |
key_layer, | |
value_layer, | |
attn_mask=attention_mask, | |
dropout_p=self.attention_dropout.p if self.training else 0.0, | |
is_causal=is_causal, | |
) | |
attn_output = attn_output.transpose(1, 2) | |
attn_output = attn_output.reshape(batch_size, query_length, self.num_heads * self.head_dim) | |
attn_output = self.dense(attn_output) | |
else: | |
matmul_result = query_layer @ key_layer.transpose(-1, -2) | |
# change view to [batch_size, num_heads, q_length, kv_length] | |
attention_scores = matmul_result.view(batch_size, self.num_heads, query_length, kv_length) | |
# cast attention scores to fp32, compute scaled softmax and cast back to initial dtype - [batch_size, num_heads, q_length, kv_length] | |
input_dtype = attention_scores.dtype | |
# `float16` has a minimum value of -65504.0, whereas `bfloat16` and `float32` have a minimum value of `-3.4e+38` | |
if input_dtype == torch.float16 or input_dtype == torch.bfloat16: | |
attention_scores = attention_scores.to(torch.float32) | |
attention_logits = attention_scores + alibi.view(batch_size, self.num_heads, 1, -1) | |
attention_logits *= self.inv_norm_factor | |
attention_probs = F.softmax(attention_logits + attention_mask, dim=-1, dtype=hidden_states.dtype) | |
# [batch_size, num_heads, q_length, kv_length] | |
attention_probs = self.attention_dropout(attention_probs) | |
if head_mask is not None: | |
attention_probs = attention_probs * head_mask | |
# change view [batch_size, num_heads, q_length, kv_length] | |
attention_probs_reshaped = attention_probs.view(batch_size, self.num_heads, query_length, kv_length) | |
# matmul: [batch_size * num_heads, q_length, head_dim] | |
attn_output = (attention_probs_reshaped @ value_layer).flatten(0, 1) | |
# change view [batch_size, q_length, num_heads * head_dim] | |
attn_output = self._merge_heads(attn_output) | |
attn_output = self.dense(attn_output) | |
if output_attentions: | |
return attn_output, present, attention_probs | |
else: | |
return attn_output, present | |
class FalconFlashAttention2(FalconAttention): | |
""" | |
Falcon flash attention module. This module inherits from `FalconAttention` as the weights of the module stays | |
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of | |
flash attention and deal with padding tokens in case the input contains any of them. | |
""" | |
# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__ | |
def __init__(self, *args, **kwargs): | |
super().__init__(*args, **kwargs) | |
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. | |
# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0. | |
# Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left). | |
self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() | |
def forward( | |
self, | |
hidden_states: torch.Tensor, | |
alibi: Optional[torch.Tensor], | |
attention_mask: torch.Tensor, | |
position_ids: Optional[torch.LongTensor] = None, | |
layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, | |
head_mask: Optional[torch.Tensor] = None, | |
use_cache: bool = False, | |
output_attentions: bool = False, | |
): | |
fused_qkv = self.query_key_value(hidden_states) # [batch_size, seq_length, 3 x hidden_size] | |
num_kv_heads = self.num_heads if self.new_decoder_architecture else self.num_kv_heads | |
# 3 x [batch_size, seq_length, num_heads, head_dim] | |
(query_layer, key_layer, value_layer) = self._split_heads(fused_qkv) | |
batch_size, query_length, _, _ = query_layer.shape | |
query_layer = query_layer.transpose(1, 2).reshape(batch_size, self.num_heads, query_length, self.head_dim) | |
key_layer = key_layer.transpose(1, 2).reshape(batch_size, num_kv_heads, query_length, self.head_dim) | |
value_layer = value_layer.transpose(1, 2).reshape(batch_size, num_kv_heads, query_length, self.head_dim) | |
kv_seq_len = key_layer.shape[-2] | |
if layer_past is not None: | |
kv_seq_len += layer_past[0].shape[-2] | |
if alibi is None: | |
cos, sin = self.rotary_emb(value_layer, seq_len=kv_seq_len) | |
query_layer, key_layer = apply_rotary_pos_emb(query_layer, key_layer, cos, sin, position_ids) | |
if layer_past is not None and use_cache: | |
past_key, past_value = layer_past | |
# concatenate along seq_length dimension: | |
# - key: [batch_size, self.num_heads, kv_length, head_dim] | |
# - value: [batch_size, self.num_heads, kv_length, head_dim] | |
key_layer = torch.cat((past_key, key_layer), dim=-2) | |
value_layer = torch.cat((past_value, value_layer), dim=-2) | |
past_key_value = (key_layer, value_layer) if use_cache else None | |
# TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache | |
# to be able to avoid many of these transpose/reshape/view. | |
query_layer = query_layer.transpose(1, 2) | |
key_layer = key_layer.transpose(1, 2) | |
value_layer = value_layer.transpose(1, 2) | |
if alibi is not None: | |
raise ValueError("`alibi` is not supported when `use_flash_attn` is True") | |
attn_dropout = self.config.attention_dropout if self.training else 0.0 | |
# In PEFT, usually we cast the layer norms in float32 for training stability reasons | |
# therefore the input hidden states gets silently casted in float32. Hence, we need | |
# cast them back in float16 just to be sure everything works as expected. | |
input_dtype = query_layer.dtype | |
if input_dtype == torch.float32: | |
if torch.is_autocast_enabled(): | |
target_dtype = torch.get_autocast_gpu_dtype() | |
# Handle the case where the model is quantized | |
elif hasattr(self.config, "_pre_quantization_dtype"): | |
target_dtype = self.config._pre_quantization_dtype | |
else: | |
target_dtype = self.query_key_value.weight.dtype | |
logger.warning_once( | |
f"The input hidden states seems to be silently casted in float32, this might be related to" | |
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" | |
f" {target_dtype}." | |
) | |
query_layer = query_layer.to(target_dtype) | |
key_layer = key_layer.to(target_dtype) | |
value_layer = value_layer.to(target_dtype) | |
attn_output = _flash_attention_forward( | |
query_layer, | |
key_layer, | |
value_layer, | |
attention_mask, | |
query_length, | |
position_ids=position_ids, | |
dropout=attn_dropout, | |
is_causal=self.is_causal, | |
use_top_left_mask=self._flash_attn_uses_top_left_mask, | |
) | |
attn_weights = attn_output.reshape(batch_size, query_length, self.num_heads * self.head_dim) | |
attn_output = self.dense(attn_weights) | |
if not output_attentions: | |
attn_weights = None | |
return attn_output, past_key_value, attn_weights | |
class FalconMLP(nn.Module): | |
def __init__(self, config: FalconConfig): | |
super().__init__() | |
hidden_size = config.hidden_size | |
self.dense_h_to_4h = FalconLinear(hidden_size, config.ffn_hidden_size, bias=config.bias) | |
self.act = get_activation(config.activation) | |
self.dense_4h_to_h = FalconLinear(config.ffn_hidden_size, hidden_size, bias=config.bias) | |
self.hidden_dropout = config.hidden_dropout | |
def forward(self, x: torch.Tensor) -> torch.Tensor: | |
x = self.act(self.dense_h_to_4h(x)) | |
x = self.dense_4h_to_h(x) | |
return x | |
FALCON_ATTENTION_CLASSES = { | |
"eager": FalconAttention, | |
"sdpa": FalconAttention, # FalconAttention originally implemented both a forward with & without SDPA | |
"flash_attention_2": FalconFlashAttention2, | |
} | |
class FalconDecoderLayer(nn.Module): | |
def __init__(self, config: FalconConfig): | |
super().__init__() | |
hidden_size = config.hidden_size | |
self.num_heads = config.num_attention_heads | |
self.self_attention = FALCON_ATTENTION_CLASSES[config._attn_implementation](config) | |
self.mlp = FalconMLP(config) | |
self.hidden_dropout = config.hidden_dropout | |
self.config = config | |
if config.num_ln_in_parallel_attn is None and config.new_decoder_architecture: | |
config.num_ln_in_parallel_attn = 2 | |
if not config.parallel_attn: | |
self.post_attention_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon) | |
self.input_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon) | |
else: | |
if config.num_ln_in_parallel_attn == 2: | |
# The layer norm before self-attention | |
self.ln_attn = LayerNorm(hidden_size, eps=config.layer_norm_epsilon) | |
# The layer norm before the MLP | |
self.ln_mlp = LayerNorm(hidden_size, eps=config.layer_norm_epsilon) | |
else: | |
self.input_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon) | |
def forward( | |
self, | |
hidden_states: torch.Tensor, | |
alibi: Optional[torch.Tensor], | |
attention_mask: torch.Tensor, | |
position_ids: Optional[torch.LongTensor] = None, | |
layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, | |
head_mask: Optional[torch.Tensor] = None, | |
use_cache: bool = False, | |
output_attentions: bool = False, | |
**kwargs, | |
): | |
residual = hidden_states | |
if self.config.new_decoder_architecture and self.config.num_ln_in_parallel_attn == 2: | |
attention_layernorm_out = self.ln_attn(hidden_states) | |
mlp_layernorm_out = self.ln_mlp(hidden_states) | |
else: | |
attention_layernorm_out = self.input_layernorm(hidden_states) | |
# Self attention. | |
attn_outputs = self.self_attention( | |
attention_layernorm_out, | |
layer_past=layer_past, | |
attention_mask=attention_mask, | |
position_ids=position_ids, | |
alibi=alibi, | |
head_mask=head_mask, | |
use_cache=use_cache, | |
output_attentions=output_attentions, | |
) | |
attention_output = attn_outputs[0] | |
if not self.config.new_decoder_architecture: | |
if self.config.parallel_attn: | |
mlp_layernorm_out = attention_layernorm_out | |
else: | |
residual = dropout_add( | |
attention_output, residual, self.config.attention_dropout, training=self.training | |
) | |
mlp_layernorm_out = self.post_attention_layernorm(residual) | |
if ( | |
self.config.new_decoder_architecture | |
and self.config.parallel_attn | |
and self.config.num_ln_in_parallel_attn == 1 | |
): | |
mlp_layernorm_out = attention_layernorm_out | |
outputs = attn_outputs[1:] | |
# MLP. | |
mlp_output = self.mlp(mlp_layernorm_out) | |
if self.config.new_decoder_architecture or self.config.parallel_attn: | |
mlp_output += attention_output | |
output = dropout_add(mlp_output, residual, self.config.hidden_dropout, training=self.training) | |
if use_cache: | |
outputs = (output,) + outputs | |
else: | |
outputs = (output,) + outputs[1:] | |
return outputs # hidden_states, present, attentions | |
FALCON_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 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 ([`FalconConfig`]): 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. | |
""" | |
FALCON_INPUTS_DOCSTRING = r""" | |
Args: | |
input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`): | |
`input_ids_length` = `sequence_length` if `past_key_values` is `None` else `past_key_values[0][0].shape[2]` | |
(`sequence_length` of input past key value states). Indices of input sequence tokens in the vocabulary. | |
If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as | |
`input_ids`. | |
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and | |
[`PreTrainedTokenizer.__call__`] for details. | |
[What are input IDs?](../glossary#input-ids) | |
past_key_values (`Tuple[Tuple[torch.Tensor]]` of length `config.num_hidden_layers`): | |
Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see | |
`past_key_values` output below). Can be used to speed up sequential decoding. The `input_ids` which have | |
their past given to this model should not be passed as `input_ids` as they have already been computed. | |
Each element of `past_key_values` is a tuple (past_key, past_value): | |
- past_key: [batch_size * num_heads, head_dim, kv_length] | |
- past_value: [batch_size * num_heads, kv_length, head_dim] | |
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) | |
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) | |
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. | |
If `past_key_values` is used, optionally only the last `inputs_embeds` have to be input (see | |
`past_key_values`). | |
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 [`~file_utils.ModelOutput`] instead of a plain tuple. | |
""" | |
class FalconPreTrainedModel(PreTrainedModel): | |
""" | |
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained | |
models. | |
""" | |
config_class = FalconConfig | |
base_model_prefix = "transformer" | |
supports_gradient_checkpointing = True | |
_no_split_modules = ["FalconDecoderLayer"] | |
_supports_flash_attn_2 = True | |
_supports_sdpa = True | |
def __init__(self, *inputs, **kwargs): | |
super().__init__(*inputs, **kwargs) | |
def _init_weights(self, module: nn.Module): | |
"""Initialize the weights.""" | |
if isinstance(module, nn.Linear) or isinstance(module, FalconLinear): | |
# Slightly different from the TF version which uses truncated_normal for initialization | |
# cf https://github.com/pytorch/pytorch/pull/5617 | |
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) | |
if module.bias is not None: | |
module.bias.data.zero_() | |
elif isinstance(module, nn.Embedding): | |
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) | |
if module.padding_idx is not None: | |
module.weight.data[module.padding_idx].zero_() | |
elif isinstance(module, LayerNorm): | |
module.bias.data.zero_() | |
module.weight.data.fill_(1.0) | |
# Adapted from transformers.modeling_utils.PreTrainedModel._check_and_enable_sdpa | |
def _check_and_enable_sdpa(cls, config, hard_check_only: bool = False) -> "PretrainedConfig": | |
# NOTE: Falcon supported SDPA from PyTorch 2.0. We keep it like that for backward compatibility (automatically use SDPA for torch>=2.0). | |
if hard_check_only: | |
if not is_torch_greater_or_equal_than_2_0: | |
raise ImportError("PyTorch SDPA requirements in Transformers are not met. Please install torch>=2.0.") | |
if not is_torch_greater_or_equal_than_2_0: | |
return config | |
_is_bettertransformer = getattr(cls, "use_bettertransformer", False) | |
if _is_bettertransformer: | |
return config | |
if not hard_check_only: | |
config._attn_implementation = "sdpa" | |
return config | |
class FalconModel(FalconPreTrainedModel): | |
def __init__(self, config: FalconConfig): | |
super().__init__(config) | |
self.embed_dim = config.hidden_size | |
self.num_heads = config.num_attention_heads | |
self.use_alibi = config.alibi | |
# Embedding + LN Embedding | |
self.word_embeddings = nn.Embedding(config.vocab_size, self.embed_dim) | |
# Transformer blocks | |
self.h = nn.ModuleList([FalconDecoderLayer(config) for _ in range(config.num_hidden_layers)]) | |
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2" | |
self._use_sdpa = config._attn_implementation == "sdpa" | |
# Final Layer Norm | |
self.ln_f = LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon) | |
self.gradient_checkpointing = False | |
# Initialize weights and apply final processing | |
self.post_init() | |
def get_input_embeddings(self): | |
return self.word_embeddings | |
def set_input_embeddings(self, new_embeddings: torch.Tensor): | |
self.word_embeddings = new_embeddings | |
def forward( | |
self, | |
input_ids: Optional[torch.LongTensor] = None, | |
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, | |
attention_mask: Optional[torch.Tensor] = None, | |
position_ids: Optional[torch.LongTensor] = None, | |
head_mask: Optional[torch.LongTensor] = None, | |
inputs_embeds: 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[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]: | |
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 | |
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: | |
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 input_ids or inputs_embeds") | |
if past_key_values is None: | |
past_key_values = tuple([None] * len(self.h)) | |
if inputs_embeds is None: | |
inputs_embeds = self.word_embeddings(input_ids) | |
hidden_states = inputs_embeds | |
if self.gradient_checkpointing and self.training: | |
if use_cache: | |
logger.warning( | |
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." | |
) | |
use_cache = False | |
presents = () if use_cache else None | |
all_self_attentions = () if output_attentions else None | |
all_hidden_states = () if output_hidden_states else None | |
# Compute alibi tensor: check build_alibi_tensor documentation | |
past_key_values_length = 0 | |
if past_key_values[0] is not None: | |
past_key_values_length = past_key_values[0][0].shape[-2] | |
if self.use_alibi: | |
mask = ( | |
torch.ones( | |
(batch_size, seq_length + past_key_values_length), device=inputs_embeds.device, dtype=torch.long | |
) | |
if attention_mask is None | |
else attention_mask | |
) | |
alibi = build_alibi_tensor(mask, self.num_heads, dtype=hidden_states.dtype) | |
else: | |
alibi = None | |
if 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) | |
if self._use_flash_attention_2: | |
# 2d mask is passed through the layers | |
attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None | |
elif self._use_sdpa and not output_attentions: | |
# output_attentions=True can not be supported when using SDPA, and we fall back on | |
# the manual implementation that requires a 4D causal mask in all cases. | |
if alibi is None: | |
attention_mask = _prepare_4d_causal_attention_mask_for_sdpa( | |
attention_mask, | |
(batch_size, seq_length), | |
inputs_embeds, | |
past_key_values_length, | |
) | |
elif head_mask is None: | |
alibi = alibi.reshape(batch_size, -1, *alibi.shape[1:]) | |
# We don't call _prepare_4d_causal_attention_mask_for_sdpa as we need to mask alibi using the 4D attention_mask untouched. | |
attention_mask = _prepare_4d_causal_attention_mask( | |
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length | |
) | |
# We take care to integrate alibi bias in the attention_mask here. | |
min_dtype = torch.finfo(alibi.dtype).min | |
attention_mask = torch.masked_fill( | |
alibi / math.sqrt(self.config.hidden_size // self.num_heads), | |
attention_mask < -1, | |
min_dtype, | |
) | |
# From PyTorch 2.1 onwards, F.scaled_dot_product_attention with the memory-efficient attention backend | |
# produces nans if sequences are completely unattended in the attention mask. Details: https://github.com/pytorch/pytorch/issues/110213 | |
if seq_length > 1 and attention_mask.device.type == "cuda": | |
attention_mask = AttentionMaskConverter._unmask_unattended(attention_mask, min_dtype=min_dtype) | |
else: | |
# PyTorch SDPA does not support head_mask, we fall back on the eager implementation in this case. | |
attention_mask = _prepare_4d_causal_attention_mask( | |
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length | |
) | |
else: | |
# 4d mask is passed through the layers | |
attention_mask = _prepare_4d_causal_attention_mask( | |
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length | |
) | |
# Prepare head mask if needed | |
# 1.0 in head_mask indicate we keep the head | |
# attention_probs has shape batch_size x num_heads x N x N | |
# head_mask has shape n_layer x batch x num_heads x N x N | |
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) | |
for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)): | |
if output_hidden_states: | |
all_hidden_states = all_hidden_states + (hidden_states,) | |
if self.gradient_checkpointing and self.training: | |
outputs = self._gradient_checkpointing_func( | |
block.__call__, | |
hidden_states, | |
alibi, | |
attention_mask, | |
position_ids, | |
head_mask[i], | |
layer_past, | |
use_cache, | |
output_attentions, | |
) | |
else: | |
outputs = block( | |
hidden_states, | |
layer_past=layer_past, | |
attention_mask=attention_mask, | |
position_ids=position_ids, | |
head_mask=head_mask[i], | |
use_cache=use_cache, | |
output_attentions=output_attentions, | |
alibi=alibi, | |
) | |
hidden_states = outputs[0] | |
if use_cache is True: | |
presents = presents + (outputs[1],) | |
if output_attentions: | |
all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],) | |
# Add last hidden state | |
hidden_states = self.ln_f(hidden_states) | |
if output_hidden_states: | |
all_hidden_states = all_hidden_states + (hidden_states,) | |
if not return_dict: | |
return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None) | |
return BaseModelOutputWithPastAndCrossAttentions( | |
last_hidden_state=hidden_states, | |
past_key_values=presents, | |
hidden_states=all_hidden_states, | |
attentions=all_self_attentions, | |
) | |
class FalconForCausalLM(FalconPreTrainedModel): | |
_tied_weights_keys = ["lm_head.weight"] | |
def __init__(self, config: FalconConfig): | |
super().__init__(config) | |
self.transformer = FalconModel(config) | |
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) | |
# Initialize weights and apply final processing | |
self.post_init() | |
def get_output_embeddings(self): | |
return self.lm_head | |
def set_output_embeddings(self, new_embeddings: torch.Tensor): | |
self.lm_head = new_embeddings | |
def prepare_inputs_for_generation( | |
self, | |
input_ids: torch.LongTensor, | |
past_key_values: Optional[torch.Tensor] = None, | |
attention_mask: Optional[torch.Tensor] = None, | |
position_ids: Optional[torch.Tensor] = None, | |
inputs_embeds: Optional[torch.Tensor] = None, | |
**kwargs, | |
) -> dict: | |
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:] | |
# Note: versions of Falcon with alibi do not use position_ids. It is used with RoPE. | |
if not self.transformer.use_alibi and 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) | |
if past_key_values: | |
position_ids = position_ids[:, -input_ids.shape[1] :] | |
if inputs_embeds is not None and past_key_values is None: | |
model_inputs = {"inputs_embeds": inputs_embeds} | |
else: | |
model_inputs = {"input_ids": input_ids} | |
model_inputs.update( | |
{ | |
"position_ids": position_ids, | |
"past_key_values": past_key_values, | |
"use_cache": kwargs.get("use_cache"), | |
"attention_mask": attention_mask, | |
} | |
) | |
return model_inputs | |
def forward( | |
self, | |
input_ids: Optional[torch.LongTensor] = None, | |
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, | |
attention_mask: Optional[torch.Tensor] = None, | |
position_ids: Optional[torch.LongTensor] = None, | |
head_mask: Optional[torch.Tensor] = None, | |
inputs_embeds: Optional[torch.Tensor] = None, | |
labels: 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[torch.Tensor], CausalLMOutputWithCrossAttentions]: | |
r""" | |
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): | |
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set | |
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100` | |
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` | |
""" | |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | |
transformer_outputs = self.transformer( | |
input_ids, | |
past_key_values=past_key_values, | |
attention_mask=attention_mask, | |
position_ids=position_ids, | |
head_mask=head_mask, | |
inputs_embeds=inputs_embeds, | |
use_cache=use_cache, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
hidden_states = transformer_outputs[0] | |
lm_logits = self.lm_head(hidden_states) | |
loss = None | |
if labels is not None: | |
# Shift so that tokens < n predict n | |
shift_logits = lm_logits[..., :-1, :].contiguous() | |
shift_labels = labels[..., 1:].contiguous() | |
batch_size, seq_length, vocab_size = shift_logits.shape | |
# Flatten the tokens | |
loss_fct = CrossEntropyLoss() | |
loss = loss_fct( | |
shift_logits.view(batch_size * seq_length, vocab_size), shift_labels.view(batch_size * seq_length) | |
) | |
if not return_dict: | |
output = (lm_logits,) + transformer_outputs[1:] | |
return ((loss,) + output) if loss is not None else output | |
return CausalLMOutputWithCrossAttentions( | |
loss=loss, | |
logits=lm_logits, | |
past_key_values=transformer_outputs.past_key_values, | |
hidden_states=transformer_outputs.hidden_states, | |
attentions=transformer_outputs.attentions, | |
) | |
def _reorder_cache( | |
self, past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor | |
) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]: | |
""" | |
This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or | |
[`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct | |
beam_idx at every generation step. | |
Output shares the same memory storage as `past`. | |
""" | |
# Get a copy of `beam_idx` on all the devices where we need those indices. | |
device_to_beam_idx = { | |
past_state.device: beam_idx.to(past_state.device) for layer_past in past for past_state in layer_past | |
} | |
reordered_past = tuple( | |
( | |
layer_past[0].index_select(0, device_to_beam_idx[layer_past[0].device]), | |
layer_past[1].index_select(0, device_to_beam_idx[layer_past[0].device]), | |
) | |
for layer_past in past | |
) | |
return reordered_past | |
class FalconForSequenceClassification(FalconPreTrainedModel): | |
def __init__(self, config: FalconConfig): | |
super().__init__(config) | |
self.num_labels = config.num_labels | |
self.transformer = FalconModel(config) | |
self.score = nn.Linear(config.hidden_size, config.num_labels, bias=False) | |
# Initialize weights and apply final processing | |
self.post_init() | |
def forward( | |
self, | |
input_ids: Optional[torch.LongTensor] = None, | |
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, | |
attention_mask: Optional[torch.Tensor] = None, | |
head_mask: Optional[torch.Tensor] = None, | |
inputs_embeds: Optional[torch.Tensor] = None, | |
labels: 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[torch.Tensor], SequenceClassifierOutputWithPast]: | |
r""" | |
labels (`torch.LongTensor` 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). | |
""" | |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | |
transformer_outputs = self.transformer( | |
input_ids, | |
past_key_values=past_key_values, | |
attention_mask=attention_mask, | |
head_mask=head_mask, | |
inputs_embeds=inputs_embeds, | |
use_cache=use_cache, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
hidden_states = transformer_outputs[0] | |
logits = self.score(hidden_states) | |
if input_ids is not None: | |
batch_size = input_ids.shape[0] | |
else: | |
batch_size = inputs_embeds.shape[0] | |
if self.config.pad_token_id is None and batch_size != 1: | |
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") | |
if self.config.pad_token_id is None: | |
sequence_lengths = -1 | |
else: | |
if input_ids is not None: | |
# if no pad token found, use modulo instead of reverse indexing for ONNX compatibility | |
sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1 | |
sequence_lengths = sequence_lengths % input_ids.shape[-1] | |
sequence_lengths = sequence_lengths.to(logits.device) | |
else: | |
sequence_lengths = -1 | |
logger.warning_once( | |
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be " | |
"unexpected if using padding tokens in conjunction with `inputs_embeds.`" | |
) | |
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] | |
loss = None | |
if labels is not None: | |
if self.config.problem_type is None: | |
if self.num_labels == 1: | |
self.config.problem_type = "regression" | |
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): | |
self.config.problem_type = "single_label_classification" | |
else: | |
self.config.problem_type = "multi_label_classification" | |
if self.config.problem_type == "regression": | |
loss_fct = MSELoss() | |
if self.num_labels == 1: | |
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) | |
else: | |
loss = loss_fct(pooled_logits, labels) | |
elif self.config.problem_type == "single_label_classification": | |
loss_fct = CrossEntropyLoss() | |
loss = loss_fct(pooled_logits, labels) | |
elif self.config.problem_type == "multi_label_classification": | |
loss_fct = BCEWithLogitsLoss() | |
loss = loss_fct(pooled_logits, labels) | |
if not return_dict: | |
output = (pooled_logits,) + transformer_outputs[1:] | |
return ((loss,) + output) if loss is not None else output | |
return SequenceClassifierOutputWithPast( | |
loss=loss, | |
logits=pooled_logits, | |
past_key_values=transformer_outputs.past_key_values, | |
hidden_states=transformer_outputs.hidden_states, | |
attentions=transformer_outputs.attentions, | |
) | |
class FalconForTokenClassification(FalconPreTrainedModel): | |
def __init__(self, config: FalconConfig): | |
super().__init__(config) | |
self.num_labels = config.num_labels | |
self.transformer = FalconModel(config) | |
if getattr(config, "classifier_dropout", None) is not None: | |
classifier_dropout = config.classifier_dropout | |
elif getattr(config, "hidden_dropout", None) is not None: | |
classifier_dropout = config.hidden_dropout | |
else: | |
classifier_dropout = 0.1 | |
self.dropout = nn.Dropout(classifier_dropout) | |
self.classifier = nn.Linear(config.hidden_size, config.num_labels) | |
# Initialize weights and apply final processing | |
self.post_init() | |
def forward( | |
self, | |
input_ids: Optional[torch.LongTensor] = None, | |
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, | |
attention_mask: Optional[torch.Tensor] = None, | |
head_mask: Optional[torch.Tensor] = None, | |
inputs_embeds: Optional[torch.Tensor] = None, | |
labels: 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[torch.Tensor], TokenClassifierOutput]: | |
r""" | |
labels (`torch.LongTensor` 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). | |
""" | |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | |
transformer_outputs = self.transformer( | |
input_ids, | |
past_key_values=past_key_values, | |
attention_mask=attention_mask, | |
head_mask=head_mask, | |
inputs_embeds=inputs_embeds, | |
use_cache=use_cache, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
hidden_states = transformer_outputs[0] | |
hidden_states = self.dropout(hidden_states) | |
logits = self.classifier(hidden_states) | |
loss = None | |
if labels is not None: | |
batch_size, seq_length = labels.shape | |
loss_fct = CrossEntropyLoss() | |
loss = loss_fct( | |
logits.view(batch_size * seq_length, self.num_labels), labels.view(batch_size * seq_length) | |
) | |
if not return_dict: | |
output = (logits,) + transformer_outputs[2:] | |
return ((loss,) + output) if loss is not None else output | |
return TokenClassifierOutput( | |
loss=loss, | |
logits=logits, | |
hidden_states=transformer_outputs.hidden_states, | |
attentions=transformer_outputs.attentions, | |
) | |
class FalconForQuestionAnswering(FalconPreTrainedModel): | |
def __init__(self, config): | |
super().__init__(config) | |
self.transformer = FalconModel(config) | |
self.qa_outputs = nn.Linear(config.hidden_size, 2) | |
# Initialize weights and apply final processing | |
self.post_init() | |
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, | |
start_positions: Optional[torch.LongTensor] = None, | |
end_positions: Optional[torch.LongTensor] = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
) -> Union[Tuple, QuestionAnsweringModelOutput]: | |
r""" | |
start_positions (`torch.LongTensor` 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 (`torch.LongTensor` 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. | |
""" | |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | |
outputs = self.transformer( | |
input_ids, | |
attention_mask=attention_mask, | |
head_mask=head_mask, | |
inputs_embeds=inputs_embeds, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
sequence_output = outputs[0] | |
logits = self.qa_outputs(sequence_output) | |
start_logits, end_logits = logits.split(1, dim=-1) | |
start_logits = start_logits.squeeze(-1).contiguous() | |
end_logits = end_logits.squeeze(-1).contiguous() | |
total_loss = None | |
if start_positions is not None and end_positions is not None: | |
# If we are on multi-GPU, split add a dimension | |
if len(start_positions.size()) > 1: | |
start_positions = start_positions.squeeze(-1) | |
if len(end_positions.size()) > 1: | |
end_positions = end_positions.squeeze(-1) | |
# sometimes the start/end positions are outside our model inputs, we ignore these terms | |
ignored_index = start_logits.size(1) | |
start_positions = start_positions.clamp(0, ignored_index) | |
end_positions = end_positions.clamp(0, ignored_index) | |
loss_fct = CrossEntropyLoss(ignore_index=ignored_index) | |
start_loss = loss_fct(start_logits, start_positions) | |
end_loss = loss_fct(end_logits, end_positions) | |
total_loss = (start_loss + end_loss) / 2 | |
if not return_dict: | |
output = (start_logits, end_logits) + outputs[2:] | |
return ((total_loss,) + output) if total_loss is not None else output | |
return QuestionAnsweringModelOutput( | |
loss=total_loss, | |
start_logits=start_logits, | |
end_logits=end_logits, | |
hidden_states=outputs.hidden_states, | |
attentions=outputs.attentions, | |
) | |