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ai-content-maker/.venv/Lib/site-packages/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 dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import torch
@dataclass
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
Examples:
```python
>>> import torch
>>> from transformers.modeling_attn_mask_utils import AttentionMaskConverter
>>> converter = AttentionMaskConverter(True)
>>> converter.to_4d(torch.tensor([[0, 0, 0, 1, 1]]), 5, key_value_length=5, dtype=torch.float32)
tensor([[[[-3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38],
[-3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38],
[-3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38],
[-3.4028e+38, -3.4028e+38, -3.4028e+38, 0.0000e+00, -3.4028e+38],
[-3.4028e+38, -3.4028e+38, -3.4028e+38, 0.0000e+00, 0.0000e+00]]]])
```
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.
"""
is_causal: bool
sliding_window: int
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,
device: Union[torch.device, "str"] = "cpu",
) -> Optional[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,
dtype: torch.dtype,
key_value_length: Optional[int] = None,
) -> 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
)
if causal_4d_mask is not None:
expanded_attn_mask = causal_4d_mask.masked_fill(expanded_attn_mask.bool(), torch.finfo(dtype).min)
# expanded_attn_mask + causal_4d_mask can cause some overflow
expanded_4d_mask = expanded_attn_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 = torch.tril(torch.ones_like(mask, dtype=torch.bool), diagonal=diagonal)
mask.masked_fill_(context_mask, 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)
@staticmethod
def _unmask_unattended(
expanded_mask: torch.FloatTensor,
min_dtype: float,
):
# fmt: off
"""
Attend to all tokens in masked rows from the expanded attention mask, for example the relevant first rows when
using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
Details: https://github.com/pytorch/pytorch/issues/110213
`expanded_mask` is [bsz, num_masks, tgt_seq_len, src_seq_len] or [bsz, tgt_seq_len, src_seq_len].
`attention_mask` is [bsz, src_seq_len].
The dimension num_masks of `expanded_mask` is most often 1, but it can also be the number of heads in the case of alibi attention bias.
For example, if `expanded_mask` is (e.g. here left-padding case)
```
[[[[0, 0, 0],
[0, 0, 0],
[0, 0, 1]]],
[[[1, 0, 0],
[1, 1, 0],
[1, 1, 1]]],
[[[0, 0, 0],
[0, 1, 0],
[0, 1, 1]]]]
```
then the modified `expanded_mask` will be
```
[[[[1, 1, 1], <-- modified
[1, 1, 1], <-- modified
[0, 0, 1]]],
[[[1, 0, 0],
[1, 1, 0],
[1, 1, 1]]],
[[[1, 1, 1], <-- modified
[0, 1, 0],
[0, 1, 1]]]]
```
"""
# fmt: on
if expanded_mask.dtype == torch.bool:
raise ValueError(
"AttentionMaskConverter._unmask_unattended expects a float `expanded_mask`, got a BoolTensor."
)
return expanded_mask.mul(~torch.all(expanded_mask == min_dtype, dim=-1, keepdim=True))
@staticmethod
def _ignore_causal_mask_sdpa(
attention_mask: Optional[torch.Tensor],
inputs_embeds: torch.Tensor,
past_key_values_length: int,
sliding_window: Optional[int] = None,
) -> bool:
"""
Detects whether the optional user-specified attention_mask & the automatically created causal mask can be ignored in case PyTorch's SDPA is used, rather relying on SDPA's `is_causal` argument.
In case no token is masked in the `attention_mask` argument, if `query_length == 1` or
`key_value_length == query_length`, we rather rely on SDPA `is_causal` argument to use causal/non-causal masks,
allowing to dispatch to the flash attention kernel (that can otherwise not be used if a custom `attn_mask` is passed).
"""
batch_size, query_length = inputs_embeds.shape[0], inputs_embeds.shape[1]
key_value_length = query_length + past_key_values_length
is_tracing = (
torch.jit.is_tracing()
or isinstance(inputs_embeds, torch.fx.Proxy)
or (hasattr(torch, "_dynamo") and torch._dynamo.is_compiling())
)
ignore_causal_mask = False
if attention_mask is None:
# TODO: When tracing with TorchDynamo with fullgraph=True, the model is recompiled depending on the input shape, thus SDPA's `is_causal` argument is rightfully updated (see https://gist.github.com/fxmarty/1313f39037fc1c112508989628c57363). However, when using `torch.export` or
# or `torch.onnx.dynamo_export`, we must pass an example input, and `is_causal` behavior is hard-coded. If a user exports a model with q_len > 1, the exported model will hard-code `is_causal=True` which is in general wrong (see https://github.com/pytorch/pytorch/issues/108108).
# Thus, we currently can NOT set `ignore_causal_mask = True` here. We would need a `torch._dynamo.is_exporting()` flag.
#
# Besides, jit.trace can not handle the `q_len > 1` condition for `is_causal` (`TypeError: scaled_dot_product_attention(): argument 'is_causal' must be bool, not Tensor`).
if (
not is_tracing
and (query_length == 1 or key_value_length == query_length)
and (sliding_window is None or key_value_length < sliding_window)
):
ignore_causal_mask = True
elif sliding_window is None or key_value_length < sliding_window:
if len(attention_mask.shape) == 4:
expected_shape = (batch_size, 1, query_length, key_value_length)
if tuple(attention_mask.shape) != expected_shape:
raise ValueError(
f"Incorrect 4D attention_mask shape: {tuple(attention_mask.shape)}; expected: {expected_shape}."
)
elif not is_tracing and torch.all(attention_mask == 1):
if query_length == 1 or key_value_length == query_length:
# For query_length == 1, causal attention and bi-directional attention are the same.
ignore_causal_mask = True
# Unfortunately, for query_length > 1 and key_value_length != query_length, we cannot generally ignore the attention mask, as SDPA causal mask generation
# may be wrong. We will set `is_causal=False` in SDPA and rely on Transformers attention_mask instead, hence not setting it to None here.
# Reference: https://github.com/pytorch/pytorch/issues/108108
# TODO: maybe revisit this with https://github.com/pytorch/pytorch/pull/114823 in PyTorch 2.3.
return ignore_causal_mask
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 and len(attention_mask.shape) == 2:
attention_mask = attn_mask_converter.to_4d(
attention_mask, input_shape[-1], key_value_length=key_value_length, dtype=inputs_embeds.dtype
)
elif attention_mask is not None and len(attention_mask.shape) == 4:
expected_shape = (input_shape[0], 1, input_shape[1], key_value_length)
if tuple(attention_mask.shape) != expected_shape:
raise ValueError(
f"Incorrect 4D attention_mask shape: {tuple(attention_mask.shape)}; expected: {expected_shape}."
)
else:
# if the 4D mask has correct shape - invert it and fill with negative infinity
inverted_mask = 1.0 - attention_mask
attention_mask = inverted_mask.masked_fill(
inverted_mask.to(torch.bool), torch.finfo(inputs_embeds.dtype).min
)
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
# Adapted from _prepare_4d_causal_attention_mask
def _prepare_4d_causal_attention_mask_for_sdpa(
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,
):
"""
Prepares the correct `attn_mask` argument to be used by `torch.nn.functional.scaled_dot_product_attention`.
In case no token is masked in the `attention_mask` argument, we simply set it to `None` for the cases `query_length == 1` and
`key_value_length == query_length`, and rely instead on SDPA `is_causal` argument to use causal/non-causal masks,
allowing to dispatch to the flash attention kernel (that can otherwise not be used if a custom `attn_mask` is passed).
"""
attn_mask_converter = AttentionMaskConverter(is_causal=True, sliding_window=sliding_window)
key_value_length = input_shape[-1] + past_key_values_length
# torch.jit.trace, symbolic_trace and torchdynamo with fullgraph=True are unable to capture the controlflow `is_causal=attention_mask is None and q_len > 1`
# used as an SDPA argument. We keep compatibility with these tracing tools by always using SDPA's `attn_mask` argument in case we are tracing.
# TODO: For dynamo, rather use a check on fullgraph=True once this is possible (https://github.com/pytorch/pytorch/pull/120400).
is_tracing = (
torch.jit.is_tracing()
or isinstance(inputs_embeds, torch.fx.Proxy)
or (hasattr(torch, "_dynamo") and torch._dynamo.is_compiling())
)
ignore_causal_mask = AttentionMaskConverter._ignore_causal_mask_sdpa(
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
past_key_values_length=past_key_values_length,
sliding_window=sliding_window,
)
if ignore_causal_mask:
expanded_4d_mask = None
elif attention_mask is None:
expanded_4d_mask = attn_mask_converter.to_causal_4d(
input_shape[0], input_shape[-1], key_value_length, dtype=inputs_embeds.dtype, device=inputs_embeds.device
)
else:
expanded_4d_mask = attn_mask_converter.to_4d(
attention_mask,
input_shape[-1],
dtype=inputs_embeds.dtype,
key_value_length=key_value_length,
)
# Attend to all tokens in masked rows from the causal_mask, for example the relevant first rows when
# using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
# Details: https://github.com/pytorch/pytorch/issues/110213
if not is_tracing and expanded_4d_mask.device.type == "cuda":
expanded_4d_mask = AttentionMaskConverter._unmask_unattended(
expanded_4d_mask, min_dtype=torch.finfo(inputs_embeds.dtype).min
)
return expanded_4d_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 _prepare_4d_attention_mask_for_sdpa(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.
"""
batch_size, key_value_length = mask.shape
tgt_len = tgt_len if tgt_len is not None else key_value_length
# torch.jit.trace, symbolic_trace and torchdynamo with fullgraph=True are unable to capture the controlflow `is_causal=attention_mask is None and q_len > 1`
# used as an SDPA argument. We keep compatibility with these tracing tools by always using SDPA's `attn_mask` argument in case we are tracing.
# TODO: For dynamo, rather use a check on fullgraph=True once this is possible (https://github.com/pytorch/pytorch/pull/120400).
is_tracing = (
torch.jit.is_tracing()
or isinstance(mask, torch.fx.Proxy)
or (hasattr(torch, "_dynamo") and torch._dynamo.is_compiling())
)
if torch.all(mask == 1):
if is_tracing:
pass
elif tgt_len == 1:
# For query_length == 1, causal attention and bi-directional attention are the same.
return None
elif key_value_length == tgt_len:
return None
else:
# Unfortunately, for query_length > 1 and key_value_length != query_length, we can not generally ignore the attention mask, as SDPA causal mask generation
# may be wrong. We will set is_causal=False in SDPA and rely on Transformers attention_mask instead, hence not setting it to None here.
# Reference: https://github.com/pytorch/pytorch/issues/108108
return AttentionMaskConverter._expand_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)
else:
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,
) -> Optional[torch.Tensor]:
"""
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
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