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| 1 | +# Copyright 2023 The HuggingFace Team. All rights reserved. |
| 2 | +# |
| 3 | +# Licensed under the Apache License, Version 2.0 (the "License"); |
| 4 | +# you may not use this file except in compliance with the License. |
| 5 | +# You may obtain a copy of the License at |
| 6 | +# |
| 7 | +# http://www.apache.org/licenses/LICENSE-2.0 |
| 8 | +# |
| 9 | +# Unless required by applicable law or agreed to in writing, software |
| 10 | +# distributed under the License is distributed on an "AS IS" BASIS, |
| 11 | +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 12 | +# See the License for the specific language governing permissions and |
| 13 | +# limitations under the License. |
| 14 | +from typing import List, Optional, Tuple, Union |
| 15 | + |
| 16 | +import torch |
| 17 | + |
| 18 | + |
| 19 | +class AttentionMaskConverter: |
| 20 | + """ |
| 21 | + A utility attention mask class that allows one to: |
| 22 | + - Create a causal 4d mask |
| 23 | + - Create a causal 4d mask with slided window |
| 24 | + - Convert a 2d attention mask (batch_size, query_length) to a 4d attention mask (batch_size, 1, query_length, |
| 25 | + key_value_length) that can be multiplied with attention scores |
| 26 | +
|
| 27 | + Parameters: |
| 28 | + is_causal (`bool`): |
| 29 | + Whether the attention mask should be a uni-directional (causal) or bi-directional mask. |
| 30 | +
|
| 31 | + sliding_window (`int`, *optional*): |
| 32 | + Optionally, the sliding window masks can be created if `sliding_window` is defined to a positive integer. |
| 33 | + """ |
| 34 | + |
| 35 | + def __init__(self, is_causal: bool, sliding_window: Optional[int] = None): |
| 36 | + self.is_causal = is_causal |
| 37 | + self.sliding_window = sliding_window |
| 38 | + |
| 39 | + if self.sliding_window is not None and self.sliding_window <= 0: |
| 40 | + raise ValueError( |
| 41 | + f"Make sure that when passing `sliding_window` that its value is a strictly positive integer, not `{self.sliding_window}`" |
| 42 | + ) |
| 43 | + |
| 44 | + def to_causal_4d( |
| 45 | + self, |
| 46 | + batch_size: int, |
| 47 | + query_length: int, |
| 48 | + key_value_length: int, |
| 49 | + dtype: torch.dtype = torch.float32, |
| 50 | + device: Union[torch.device, "str"] = "cpu", |
| 51 | + ) -> torch.Tensor: |
| 52 | + """ |
| 53 | + Creates a causal 4D mask of (bsz, head_dim=1, query_length, key_value_length) shape and adds large negative |
| 54 | + bias to upper right hand triangular matrix (causal mask). |
| 55 | + """ |
| 56 | + if not self.is_causal: |
| 57 | + raise ValueError(f"Please use `to_causal_4d` only if {self.__class__} has `is_causal` set to True.") |
| 58 | + |
| 59 | + # If shape is not cached, create a new causal mask and cache it |
| 60 | + input_shape = (batch_size, query_length) |
| 61 | + past_key_values_length = key_value_length - query_length |
| 62 | + |
| 63 | + # create causal mask |
| 64 | + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] |
| 65 | + causal_4d_mask = None |
| 66 | + if input_shape[-1] > 1 or self.sliding_window is not None: |
| 67 | + causal_4d_mask = self._make_causal_mask( |
| 68 | + input_shape, |
| 69 | + dtype, |
| 70 | + device=device, |
| 71 | + past_key_values_length=past_key_values_length, |
| 72 | + sliding_window=self.sliding_window, |
| 73 | + ) |
| 74 | + |
| 75 | + return causal_4d_mask |
| 76 | + |
| 77 | + def to_4d( |
| 78 | + self, |
| 79 | + attention_mask_2d: torch.Tensor, |
| 80 | + query_length: int, |
| 81 | + key_value_length: Optional[int] = None, |
| 82 | + dtype: torch.dtype = torch.float32, |
| 83 | + ) -> torch.Tensor: |
| 84 | + """ |
| 85 | + Converts 2D attention mask to 4D attention mask by expanding mask to (bsz, head_dim=1, query_length, |
| 86 | + key_value_length) shape and by adding a large negative bias to not-attended positions. If attention_mask is |
| 87 | + causal, a causal mask will be added. |
| 88 | + """ |
| 89 | + input_shape = (attention_mask_2d.shape[0], query_length) |
| 90 | + |
| 91 | + # create causal mask |
| 92 | + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] |
| 93 | + causal_4d_mask = None |
| 94 | + if (input_shape[-1] > 1 or self.sliding_window is not None) and self.is_causal: |
| 95 | + if key_value_length is None: |
| 96 | + raise ValueError( |
| 97 | + "This attention mask converter is causal. Make sure to pass `key_value_length` to correctly create a causal mask." |
| 98 | + ) |
| 99 | + |
| 100 | + past_key_values_length = key_value_length - query_length |
| 101 | + causal_4d_mask = self._make_causal_mask( |
| 102 | + input_shape, |
| 103 | + dtype, |
| 104 | + device=attention_mask_2d.device, |
| 105 | + past_key_values_length=past_key_values_length, |
| 106 | + sliding_window=self.sliding_window, |
| 107 | + ) |
| 108 | + elif self.sliding_window is not None: |
| 109 | + raise NotImplementedError("Sliding window is currently only implemented for causal masking") |
| 110 | + |
| 111 | + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] |
| 112 | + expanded_attn_mask = self._expand_mask(attention_mask_2d, dtype, tgt_len=input_shape[-1]).to( |
| 113 | + attention_mask_2d.device |
| 114 | + ) |
| 115 | + expanded_4d_mask = expanded_attn_mask if causal_4d_mask is None else expanded_attn_mask + causal_4d_mask |
| 116 | + |
| 117 | + return expanded_4d_mask |
| 118 | + |
| 119 | + @staticmethod |
| 120 | + def _make_causal_mask( |
| 121 | + input_ids_shape: torch.Size, |
| 122 | + dtype: torch.dtype, |
| 123 | + device: torch.device, |
| 124 | + past_key_values_length: int = 0, |
| 125 | + sliding_window: Optional[int] = None, |
| 126 | + ): |
| 127 | + """ |
| 128 | + Make causal mask used for bi-directional self-attention. |
| 129 | + """ |
| 130 | + bsz, tgt_len = input_ids_shape |
| 131 | + mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) |
| 132 | + mask_cond = torch.arange(mask.size(-1), device=device) |
| 133 | + mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) |
| 134 | + |
| 135 | + mask = mask.to(dtype) |
| 136 | + |
| 137 | + if past_key_values_length > 0: |
| 138 | + mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1) |
| 139 | + |
| 140 | + # add lower triangular sliding window mask if necessary |
| 141 | + if sliding_window is not None: |
| 142 | + diagonal = past_key_values_length - sliding_window + 1 |
| 143 | + |
| 144 | + context_mask = 1 - torch.triu(torch.ones_like(mask, dtype=torch.int), diagonal=diagonal) |
| 145 | + mask.masked_fill_(context_mask.bool(), torch.finfo(dtype).min) |
| 146 | + |
| 147 | + return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) |
| 148 | + |
| 149 | + @staticmethod |
| 150 | + def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): |
| 151 | + """ |
| 152 | + Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. |
| 153 | + """ |
| 154 | + bsz, src_len = mask.size() |
| 155 | + tgt_len = tgt_len if tgt_len is not None else src_len |
| 156 | + |
| 157 | + expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) |
| 158 | + |
| 159 | + inverted_mask = 1.0 - expanded_mask |
| 160 | + |
| 161 | + return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) |
| 162 | + |
| 163 | + |
| 164 | +def _prepare_4d_causal_attention_mask( |
| 165 | + attention_mask: Optional[torch.Tensor], |
| 166 | + input_shape: Union[torch.Size, Tuple, List], |
| 167 | + inputs_embeds: torch.Tensor, |
| 168 | + past_key_values_length: int, |
| 169 | + sliding_window: Optional[int] = None, |
| 170 | +): |
| 171 | + """ |
| 172 | + Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape |
| 173 | + `(batch_size, key_value_length)` |
| 174 | +
|
| 175 | + Args: |
| 176 | + attention_mask (`torch.Tensor` or `None`): |
| 177 | + A 2D attention mask of shape `(batch_size, key_value_length)` |
| 178 | + input_shape (`tuple(int)` or `list(int)` or `torch.Size`): |
| 179 | + The input shape should be a tuple that defines `(batch_size, query_length)`. |
| 180 | + inputs_embeds (`torch.Tensor`): |
| 181 | + The embedded inputs as a torch Tensor. |
| 182 | + past_key_values_length (`int`): |
| 183 | + The length of the key value cache. |
| 184 | + sliding_window (`int`, *optional*): |
| 185 | + If the model uses windowed attention, a sliding window should be passed. |
| 186 | + """ |
| 187 | + attn_mask_converter = AttentionMaskConverter(is_causal=True, sliding_window=sliding_window) |
| 188 | + |
| 189 | + key_value_length = input_shape[-1] + past_key_values_length |
| 190 | + |
| 191 | + # 4d mask is passed through the layers |
| 192 | + if attention_mask is not None: |
| 193 | + attention_mask = attn_mask_converter.to_4d( |
| 194 | + attention_mask, input_shape[-1], key_value_length, dtype=inputs_embeds.dtype |
| 195 | + ) |
| 196 | + else: |
| 197 | + attention_mask = attn_mask_converter.to_causal_4d( |
| 198 | + input_shape[0], input_shape[-1], key_value_length, dtype=inputs_embeds.dtype, device=inputs_embeds.device |
| 199 | + ) |
| 200 | + |
| 201 | + return attention_mask |
| 202 | + |
| 203 | + |
| 204 | +def _prepare_4d_attention_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): |
| 205 | + """ |
| 206 | + Creates a non-causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape |
| 207 | + `(batch_size, key_value_length)` |
| 208 | +
|
| 209 | + Args: |
| 210 | + mask (`torch.Tensor` or `None`): |
| 211 | + A 2D attention mask of shape `(batch_size, key_value_length)` |
| 212 | + dtype (`torch.dtype`): |
| 213 | + The torch dtype the created mask shall have. |
| 214 | + tgt_len (`int`): |
| 215 | + The target length or query length the created mask shall have. |
| 216 | + """ |
| 217 | + return AttentionMaskConverter._expand_mask(mask=mask, dtype=dtype, tgt_len=tgt_len) |
| 218 | + |
| 219 | + |
| 220 | +def _create_4d_causal_attention_mask( |
| 221 | + input_shape: Union[torch.Size, Tuple, List], |
| 222 | + dtype: torch.dtype, |
| 223 | + device: torch.device, |
| 224 | + past_key_values_length: int = 0, |
| 225 | + sliding_window: Optional[int] = None, |
| 226 | +): |
| 227 | + """ |
| 228 | + Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` |
| 229 | +
|
| 230 | + Args: |
| 231 | + input_shape (`tuple(int)` or `list(int)` or `torch.Size`): |
| 232 | + The input shape should be a tuple that defines `(batch_size, query_length)`. |
| 233 | + dtype (`torch.dtype`): |
| 234 | + The torch dtype the created mask shall have. |
| 235 | + device (`int`): |
| 236 | + The torch device the created mask shall have. |
| 237 | + sliding_window (`int`, *optional*): |
| 238 | + If the model uses windowed attention, a sliding window should be passed. |
| 239 | + """ |
| 240 | + attn_mask_converter = AttentionMaskConverter(is_causal=True, sliding_window=sliding_window) |
| 241 | + |
| 242 | + key_value_length = past_key_values_length + input_shape[-1] |
| 243 | + attention_mask = attn_mask_converter.to_causal_4d( |
| 244 | + input_shape[0], input_shape[-1], key_value_length, dtype=dtype, device=device |
| 245 | + ) |
| 246 | + |
| 247 | + return attention_mask |
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