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Upload modeling_attn_mask_utils.py with huggingface_hub

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  1. modeling_attn_mask_utils.py +247 -0
modeling_attn_mask_utils.py ADDED
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+ # Copyright 2023 The HuggingFace Team. All rights reserved.
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+ #
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+ # Licensed under the Apache License, Version 2.0 (the "License");
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+ # you may not use this file except in compliance with the License.
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+ # You may obtain a copy of the License at
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+ #
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+ # http://www.apache.org/licenses/LICENSE-2.0
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+ #
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+ # Unless required by applicable law or agreed to in writing, software
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+ # distributed under the License is distributed on an "AS IS" BASIS,
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+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+ # See the License for the specific language governing permissions and
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+ # limitations under the License.
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+ from typing import List, Optional, Tuple, Union
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+
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+ import torch
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+
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+
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+ class AttentionMaskConverter:
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+ """
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+ A utility attention mask class that allows one to:
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+ - Create a causal 4d mask
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+ - Create a causal 4d mask with slided window
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+ - Convert a 2d attention mask (batch_size, query_length) to a 4d attention mask (batch_size, 1, query_length,
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+ key_value_length) that can be multiplied with attention scores
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+
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+ Parameters:
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+ is_causal (`bool`):
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+ Whether the attention mask should be a uni-directional (causal) or bi-directional mask.
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+
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+ sliding_window (`int`, *optional*):
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+ Optionally, the sliding window masks can be created if `sliding_window` is defined to a positive integer.
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+ """
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+
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+ def __init__(self, is_causal: bool, sliding_window: Optional[int] = None):
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+ self.is_causal = is_causal
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+ self.sliding_window = sliding_window
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+
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+ if self.sliding_window is not None and self.sliding_window <= 0:
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+ raise ValueError(
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+ f"Make sure that when passing `sliding_window` that its value is a strictly positive integer, not `{self.sliding_window}`"
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+ )
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+
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+ def to_causal_4d(
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+ self,
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+ batch_size: int,
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+ query_length: int,
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+ key_value_length: int,
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+ dtype: torch.dtype = torch.float32,
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+ device: Union[torch.device, "str"] = "cpu",
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+ ) -> torch.Tensor:
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+ """
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+ Creates a causal 4D mask of (bsz, head_dim=1, query_length, key_value_length) shape and adds large negative
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+ bias to upper right hand triangular matrix (causal mask).
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+ """
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+ if not self.is_causal:
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+ raise ValueError(f"Please use `to_causal_4d` only if {self.__class__} has `is_causal` set to True.")
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+
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+ # If shape is not cached, create a new causal mask and cache it
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+ input_shape = (batch_size, query_length)
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+ past_key_values_length = key_value_length - query_length
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+
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+ # create causal mask
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+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
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+ causal_4d_mask = None
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+ if input_shape[-1] > 1 or self.sliding_window is not None:
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+ causal_4d_mask = self._make_causal_mask(
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+ input_shape,
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+ dtype,
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+ device=device,
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+ past_key_values_length=past_key_values_length,
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+ sliding_window=self.sliding_window,
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+ )
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+
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+ return causal_4d_mask
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+
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+ def to_4d(
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+ self,
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+ attention_mask_2d: torch.Tensor,
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+ query_length: int,
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+ key_value_length: Optional[int] = None,
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+ dtype: torch.dtype = torch.float32,
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+ ) -> torch.Tensor:
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+ """
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+ Converts 2D attention mask to 4D attention mask by expanding mask to (bsz, head_dim=1, query_length,
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+ key_value_length) shape and by adding a large negative bias to not-attended positions. If attention_mask is
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+ causal, a causal mask will be added.
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+ """
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+ input_shape = (attention_mask_2d.shape[0], query_length)
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+
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+ # create causal mask
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+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
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+ causal_4d_mask = None
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+ if (input_shape[-1] > 1 or self.sliding_window is not None) and self.is_causal:
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+ if key_value_length is None:
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+ raise ValueError(
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+ "This attention mask converter is causal. Make sure to pass `key_value_length` to correctly create a causal mask."
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+ )
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+
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+ past_key_values_length = key_value_length - query_length
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+ causal_4d_mask = self._make_causal_mask(
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+ input_shape,
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+ dtype,
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+ device=attention_mask_2d.device,
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+ past_key_values_length=past_key_values_length,
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+ sliding_window=self.sliding_window,
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+ )
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+ elif self.sliding_window is not None:
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+ raise NotImplementedError("Sliding window is currently only implemented for causal masking")
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+
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+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
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+ expanded_attn_mask = self._expand_mask(attention_mask_2d, dtype, tgt_len=input_shape[-1]).to(
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+ attention_mask_2d.device
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+ )
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+ expanded_4d_mask = expanded_attn_mask if causal_4d_mask is None else expanded_attn_mask + causal_4d_mask
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+
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+ return expanded_4d_mask
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+
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+ @staticmethod
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+ def _make_causal_mask(
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+ input_ids_shape: torch.Size,
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+ dtype: torch.dtype,
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+ device: torch.device,
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+ past_key_values_length: int = 0,
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+ sliding_window: Optional[int] = None,
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+ ):
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+ """
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+ Make causal mask used for bi-directional self-attention.
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+ """
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+ bsz, tgt_len = input_ids_shape
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+ mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
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+ mask_cond = torch.arange(mask.size(-1), device=device)
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+ mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
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+
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+ mask = mask.to(dtype)
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+
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+ if past_key_values_length > 0:
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+ mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
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+
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+ # add lower triangular sliding window mask if necessary
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+ if sliding_window is not None:
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+ diagonal = past_key_values_length - sliding_window + 1
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+
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+ context_mask = 1 - torch.triu(torch.ones_like(mask, dtype=torch.int), diagonal=diagonal)
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+ mask.masked_fill_(context_mask.bool(), torch.finfo(dtype).min)
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+
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+ return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
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+
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+ @staticmethod
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+ def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
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+ """
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+ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
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+ """
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+ bsz, src_len = mask.size()
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+ tgt_len = tgt_len if tgt_len is not None else src_len
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+
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+ expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
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+
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+ inverted_mask = 1.0 - expanded_mask
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+
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+ return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
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+
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+
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+ def _prepare_4d_causal_attention_mask(
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+ attention_mask: Optional[torch.Tensor],
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+ input_shape: Union[torch.Size, Tuple, List],
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+ inputs_embeds: torch.Tensor,
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+ past_key_values_length: int,
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+ sliding_window: Optional[int] = None,
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+ ):
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+ """
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+ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
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+ `(batch_size, key_value_length)`
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+
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+ Args:
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+ attention_mask (`torch.Tensor` or `None`):
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+ A 2D attention mask of shape `(batch_size, key_value_length)`
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+ input_shape (`tuple(int)` or `list(int)` or `torch.Size`):
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+ The input shape should be a tuple that defines `(batch_size, query_length)`.
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+ inputs_embeds (`torch.Tensor`):
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+ The embedded inputs as a torch Tensor.
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+ past_key_values_length (`int`):
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+ The length of the key value cache.
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+ sliding_window (`int`, *optional*):
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+ If the model uses windowed attention, a sliding window should be passed.
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+ """
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+ attn_mask_converter = AttentionMaskConverter(is_causal=True, sliding_window=sliding_window)
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+
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+ key_value_length = input_shape[-1] + past_key_values_length
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+
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+ # 4d mask is passed through the layers
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+ if attention_mask is not None:
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+ attention_mask = attn_mask_converter.to_4d(
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+ attention_mask, input_shape[-1], key_value_length, dtype=inputs_embeds.dtype
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+ )
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+ else:
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+ attention_mask = attn_mask_converter.to_causal_4d(
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+ input_shape[0], input_shape[-1], key_value_length, dtype=inputs_embeds.dtype, device=inputs_embeds.device
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+ )
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+
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+ return attention_mask
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+
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+
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+ def _prepare_4d_attention_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
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+ """
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+ Creates a non-causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
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+ `(batch_size, key_value_length)`
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+
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+ Args:
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+ mask (`torch.Tensor` or `None`):
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+ A 2D attention mask of shape `(batch_size, key_value_length)`
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+ dtype (`torch.dtype`):
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+ The torch dtype the created mask shall have.
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+ tgt_len (`int`):
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+ The target length or query length the created mask shall have.
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+ """
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+ return AttentionMaskConverter._expand_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)
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+
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+
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+ def _create_4d_causal_attention_mask(
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+ input_shape: Union[torch.Size, Tuple, List],
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+ dtype: torch.dtype,
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+ device: torch.device,
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+ past_key_values_length: int = 0,
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+ sliding_window: Optional[int] = None,
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+ ):
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+ """
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+ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)`
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+
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+ Args:
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+ input_shape (`tuple(int)` or `list(int)` or `torch.Size`):
232
+ The input shape should be a tuple that defines `(batch_size, query_length)`.
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+ dtype (`torch.dtype`):
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+ The torch dtype the created mask shall have.
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+ device (`int`):
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+ The torch device the created mask shall have.
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+ sliding_window (`int`, *optional*):
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+ If the model uses windowed attention, a sliding window should be passed.
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+ """
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+ attn_mask_converter = AttentionMaskConverter(is_causal=True, sliding_window=sliding_window)
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+
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+ key_value_length = past_key_values_length + input_shape[-1]
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+ attention_mask = attn_mask_converter.to_causal_4d(
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+ input_shape[0], input_shape[-1], key_value_length, dtype=dtype, device=device
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+ )
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+
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+ return attention_mask