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[Attention Mask] Refactor all encoder-decoder attention mask (#27086)
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* [FA2 Bart] Add FA2 to all Bart-like

* better

* Refactor attention mask

* remove all customized atteniton logic

* format

* mass rename

* replace _expand_mask

* replace _expand_mask

* mass rename

* add pt files

* mass replace & rename

* mass replace & rename

* mass replace & rename

* mass replace & rename

* Update src/transformers/models/idefics/modeling_idefics.py

* fix more

* clean more

* fix more

* make style

* fix again

* finish

* finish

* finish

* finish

* finish

* finish

* finish

* finish

* finish

* finish

* Apply suggestions from code review

* Apply suggestions from code review

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* small fix mistral

* finish

* finish

* finish

* finish

---------

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
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@patrickvonplaten @amyeroberts
patrickvonplaten and amyeroberts authored Oct 27, 2023
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247 changes: 247 additions & 0 deletions src/transformers/modeling_attn_mask_utils.py
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# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List, Optional, Tuple, Union

import torch


class AttentionMaskConverter:
"""
A utility attention mask class that allows one to:
- Create a causal 4d mask
- Create a causal 4d mask with slided window
- Convert a 2d attention mask (batch_size, query_length) to a 4d attention mask (batch_size, 1, query_length,
key_value_length) that can be multiplied with attention scores
Parameters:
is_causal (`bool`):
Whether the attention mask should be a uni-directional (causal) or bi-directional mask.
sliding_window (`int`, *optional*):
Optionally, the sliding window masks can be created if `sliding_window` is defined to a positive integer.
"""

def __init__(self, is_causal: bool, sliding_window: Optional[int] = None):
self.is_causal = is_causal
self.sliding_window = sliding_window

if self.sliding_window is not None and self.sliding_window <= 0:
raise ValueError(
f"Make sure that when passing `sliding_window` that its value is a strictly positive integer, not `{self.sliding_window}`"
)

def to_causal_4d(
self,
batch_size: int,
query_length: int,
key_value_length: int,
dtype: torch.dtype = torch.float32,
device: Union[torch.device, "str"] = "cpu",
) -> torch.Tensor:
"""
Creates a causal 4D mask of (bsz, head_dim=1, query_length, key_value_length) shape and adds large negative
bias to upper right hand triangular matrix (causal mask).
"""
if not self.is_causal:
raise ValueError(f"Please use `to_causal_4d` only if {self.__class__} has `is_causal` set to True.")

# If shape is not cached, create a new causal mask and cache it
input_shape = (batch_size, query_length)
past_key_values_length = key_value_length - query_length

# create causal mask
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
causal_4d_mask = None
if input_shape[-1] > 1 or self.sliding_window is not None:
causal_4d_mask = self._make_causal_mask(
input_shape,
dtype,
device=device,
past_key_values_length=past_key_values_length,
sliding_window=self.sliding_window,
)

return causal_4d_mask

def to_4d(
self,
attention_mask_2d: torch.Tensor,
query_length: int,
key_value_length: Optional[int] = None,
dtype: torch.dtype = torch.float32,
) -> torch.Tensor:
"""
Converts 2D attention mask to 4D attention mask by expanding mask to (bsz, head_dim=1, query_length,
key_value_length) shape and by adding a large negative bias to not-attended positions. If attention_mask is
causal, a causal mask will be added.
"""
input_shape = (attention_mask_2d.shape[0], query_length)

# create causal mask
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
causal_4d_mask = None
if (input_shape[-1] > 1 or self.sliding_window is not None) and self.is_causal:
if key_value_length is None:
raise ValueError(
"This attention mask converter is causal. Make sure to pass `key_value_length` to correctly create a causal mask."
)

past_key_values_length = key_value_length - query_length
causal_4d_mask = self._make_causal_mask(
input_shape,
dtype,
device=attention_mask_2d.device,
past_key_values_length=past_key_values_length,
sliding_window=self.sliding_window,
)
elif self.sliding_window is not None:
raise NotImplementedError("Sliding window is currently only implemented for causal masking")

# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
expanded_attn_mask = self._expand_mask(attention_mask_2d, dtype, tgt_len=input_shape[-1]).to(
attention_mask_2d.device
)
expanded_4d_mask = expanded_attn_mask if causal_4d_mask is None else expanded_attn_mask + causal_4d_mask

return expanded_4d_mask

@staticmethod
def _make_causal_mask(
input_ids_shape: torch.Size,
dtype: torch.dtype,
device: torch.device,
past_key_values_length: int = 0,
sliding_window: Optional[int] = None,
):
"""
Make causal mask used for bi-directional self-attention.
"""
bsz, tgt_len = input_ids_shape
mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
mask_cond = torch.arange(mask.size(-1), device=device)
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, device=device), mask], dim=-1)

# add lower triangular sliding window mask if necessary
if sliding_window is not None:
diagonal = past_key_values_length - sliding_window + 1

context_mask = 1 - torch.triu(torch.ones_like(mask, dtype=torch.int), diagonal=diagonal)
mask.masked_fill_(context_mask.bool(), torch.finfo(dtype).min)

return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)

@staticmethod
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)


def _prepare_4d_causal_attention_mask(
attention_mask: Optional[torch.Tensor],
input_shape: Union[torch.Size, Tuple, List],
inputs_embeds: torch.Tensor,
past_key_values_length: int,
sliding_window: Optional[int] = None,
):
"""
Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
`(batch_size, key_value_length)`
Args:
attention_mask (`torch.Tensor` or `None`):
A 2D attention mask of shape `(batch_size, key_value_length)`
input_shape (`tuple(int)` or `list(int)` or `torch.Size`):
The input shape should be a tuple that defines `(batch_size, query_length)`.
inputs_embeds (`torch.Tensor`):
The embedded inputs as a torch Tensor.
past_key_values_length (`int`):
The length of the key value cache.
sliding_window (`int`, *optional*):
If the model uses windowed attention, a sliding window should be passed.
"""
attn_mask_converter = AttentionMaskConverter(is_causal=True, sliding_window=sliding_window)

key_value_length = input_shape[-1] + past_key_values_length

# 4d mask is passed through the layers
if attention_mask is not None:
attention_mask = attn_mask_converter.to_4d(
attention_mask, input_shape[-1], key_value_length, dtype=inputs_embeds.dtype
)
else:
attention_mask = attn_mask_converter.to_causal_4d(
input_shape[0], input_shape[-1], key_value_length, dtype=inputs_embeds.dtype, device=inputs_embeds.device
)

return attention_mask


def _prepare_4d_attention_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
"""
Creates a non-causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
`(batch_size, key_value_length)`
Args:
mask (`torch.Tensor` or `None`):
A 2D attention mask of shape `(batch_size, key_value_length)`
dtype (`torch.dtype`):
The torch dtype the created mask shall have.
tgt_len (`int`):
The target length or query length the created mask shall have.
"""
return AttentionMaskConverter._expand_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)


def _create_4d_causal_attention_mask(
input_shape: Union[torch.Size, Tuple, List],
dtype: torch.dtype,
device: torch.device,
past_key_values_length: int = 0,
sliding_window: Optional[int] = None,
):
"""
Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)`
Args:
input_shape (`tuple(int)` or `list(int)` or `torch.Size`):
The input shape should be a tuple that defines `(batch_size, query_length)`.
dtype (`torch.dtype`):
The torch dtype the created mask shall have.
device (`int`):
The torch device the created mask shall have.
sliding_window (`int`, *optional*):
If the model uses windowed attention, a sliding window should be passed.
"""
attn_mask_converter = AttentionMaskConverter(is_causal=True, sliding_window=sliding_window)

key_value_length = past_key_values_length + input_shape[-1]
attention_mask = attn_mask_converter.to_causal_4d(
input_shape[0], input_shape[-1], key_value_length, dtype=dtype, device=device
)

return attention_mask
63 changes: 5 additions & 58 deletions src/transformers/models/autoformer/modeling_autoformer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,6 +26,7 @@
from torch import nn

from ...activations import ACT2FN
from ...modeling_attn_mask_utils import _prepare_4d_attention_mask
from ...modeling_outputs import (
BaseModelOutput,
ModelOutput,
Expand Down Expand Up @@ -357,39 +358,6 @@ def nll(input: torch.distributions.Distribution, target: torch.Tensor) -> torch.
return -input.log_prob(target)


# Copied from transformers.models.bart.modeling_bart._make_causal_mask
def _make_causal_mask(
input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, 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.finfo(dtype).min, device=device)
mask_cond = torch.arange(mask.size(-1), device=device)
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, device=device), mask], dim=-1)
return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)


# Copied from transformers.models.bart.modeling_bart._expand_mask
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)


# Copied from transformers.models.marian.modeling_marian.MarianSinusoidalPositionalEmbedding with Marian->Autoformer
class AutoformerSinusoidalPositionalEmbedding(nn.Embedding):
"""This module produces sinusoidal positional embeddings of any length."""
Expand Down Expand Up @@ -1176,7 +1144,7 @@ def forward(
# expand attention_mask
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
attention_mask = _expand_mask(attention_mask, inputs_embeds.dtype)
attention_mask = _prepare_4d_attention_mask(attention_mask, inputs_embeds.dtype)

encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
Expand Down Expand Up @@ -1262,29 +1230,6 @@ def __init__(self, config: AutoformerConfig):
# Initialize weights and apply final processing
self.post_init()

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,
device=inputs_embeds.device,
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

def forward(
self,
trend: Optional[torch.Tensor] = None,
Expand Down Expand Up @@ -1374,7 +1319,9 @@ def forward(
# expand encoder attention mask
if encoder_hidden_states is not None and encoder_attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
encoder_attention_mask = _expand_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])
encoder_attention_mask = _prepare_4d_attention_mask(
encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
)

hidden_states = self.value_embedding(inputs_embeds)
embed_pos = self.embed_positions(
Expand Down
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