File size: 19,777 Bytes
e79b770
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
# modified from https://github.com/lifeiteng/vall-e/blob/main/valle/modules/activation.py
from typing import Optional
from typing import Tuple
import torch
from torch import Tensor
from torch.nn import Linear
from torch.nn import Module
from torch.nn.init import constant_
from torch.nn.init import xavier_normal_
from torch.nn.init import xavier_uniform_
from torch.nn.modules.linear import NonDynamicallyQuantizableLinear
from torch.nn.parameter import Parameter

from torch.nn import functional as F
from AR.modules.patched_mha_with_cache import multi_head_attention_forward_patched
F.multi_head_attention_forward=multi_head_attention_forward_patched

class MultiheadAttention(Module):
    r"""Allows the model to jointly attend to information
    from different representation subspaces as described in the paper:
    `Attention Is All You Need <https://arxiv.org/abs/1706.03762>`_.

    Multi-Head Attention is defined as:

    .. math::
        \text{MultiHead}(Q, K, V) = \text{Concat}(head_1,\dots,head_h)W^O

    where :math:`head_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V)`.

    ``forward()`` will use a special optimized implementation if all of the following
    conditions are met:

    - self attention is being computed (i.e., ``query``, ``key``, and ``value`` are the same tensor. This
      restriction will be loosened in the future.)
    - Either autograd is disabled (using ``torch.inference_mode`` or ``torch.no_grad``) or no tensor argument ``requires_grad``
    - training is disabled (using ``.eval()``)
    - dropout is 0
    - ``add_bias_kv`` is ``False``
    - ``add_zero_attn`` is ``False``
    - ``batch_first`` is ``True`` and the input is batched
    - ``kdim`` and ``vdim`` are equal to ``embed_dim``
    - at most one of ``key_padding_mask`` or ``attn_mask`` is passed
    - if a `NestedTensor <https://pytorch.org/docs/stable/nested.html>`_ is passed, neither ``key_padding_mask``
      nor ``attn_mask`` is passed

    If the optimized implementation is in use, a
    `NestedTensor <https://pytorch.org/docs/stable/nested.html>`_ can be passed for
    ``query``/``key``/``value`` to represent padding more efficiently than using a
    padding mask. In this case, a `NestedTensor <https://pytorch.org/docs/stable/nested.html>`_
    will be returned, and an additional speedup proportional to the fraction of the input
    that is padding can be expected.

    Args:
        embed_dim: Total dimension of the model.
        num_heads: Number of parallel attention heads. Note that ``embed_dim`` will be split
            across ``num_heads`` (i.e. each head will have dimension ``embed_dim // num_heads``).
        dropout: Dropout probability on ``attn_output_weights``. Default: ``0.0`` (no dropout).
        bias: If specified, adds bias to input / output projection layers. Default: ``True``.
        add_bias_kv: If specified, adds bias to the key and value sequences at dim=0. Default: ``False``.
        add_zero_attn: If specified, adds a new batch of zeros to the key and value sequences at dim=1.
            Default: ``False``.
        kdim: Total number of features for keys. Default: ``None`` (uses ``kdim=embed_dim``).
        vdim: Total number of features for values. Default: ``None`` (uses ``vdim=embed_dim``).
        batch_first: If ``True``, then the input and output tensors are provided
            as (batch, seq, feature). Default: ``False`` (seq, batch, feature).

    Examples::

        >>> # xdoctest: +SKIP
        >>> multihead_attn = nn.MultiheadAttention(embed_dim, num_heads)
        >>> attn_output, attn_output_weights = multihead_attn(query, key, value)

    """
    __constants__ = ["batch_first"]
    bias_k: Optional[torch.Tensor]
    bias_v: Optional[torch.Tensor]

    def __init__(
            self,
            embed_dim,
            num_heads,
            dropout=0.0,
            bias=True,
            add_bias_kv=False,
            add_zero_attn=False,
            kdim=None,
            vdim=None,
            batch_first=False,
            linear1_cls=Linear,
            linear2_cls=Linear,
            device=None,
            dtype=None, ) -> None:
        factory_kwargs = {"device": device, "dtype": dtype}
        super(MultiheadAttention, self).__init__()
        self.embed_dim = embed_dim
        self.kdim = kdim if kdim is not None else embed_dim
        self.vdim = vdim if vdim is not None else embed_dim
        self._qkv_same_embed_dim = (self.kdim == embed_dim and
                                    self.vdim == embed_dim)

        self.num_heads = num_heads
        self.dropout = dropout
        self.batch_first = batch_first
        self.head_dim = embed_dim // num_heads
        assert (self.head_dim * num_heads == self.embed_dim
                ), "embed_dim must be divisible by num_heads"

        if add_bias_kv:
            self.bias_k = Parameter(
                torch.empty((1, 1, embed_dim), **factory_kwargs))
            self.bias_v = Parameter(
                torch.empty((1, 1, embed_dim), **factory_kwargs))
        else:
            self.bias_k = self.bias_v = None

        if linear1_cls == Linear:
            if not self._qkv_same_embed_dim:
                self.q_proj_weight = Parameter(
                    torch.empty((embed_dim, embed_dim), **factory_kwargs))
                self.k_proj_weight = Parameter(
                    torch.empty((embed_dim, self.kdim), **factory_kwargs))
                self.v_proj_weight = Parameter(
                    torch.empty((embed_dim, self.vdim), **factory_kwargs))
                self.register_parameter("in_proj_weight", None)
            else:
                self.in_proj_weight = Parameter(
                    torch.empty((3 * embed_dim, embed_dim), **factory_kwargs))
                self.register_parameter("q_proj_weight", None)
                self.register_parameter("k_proj_weight", None)
                self.register_parameter("v_proj_weight", None)

            if bias:
                self.in_proj_bias = Parameter(
                    torch.empty(3 * embed_dim, **factory_kwargs))
            else:
                self.register_parameter("in_proj_bias", None)
            self.out_proj = NonDynamicallyQuantizableLinear(
                embed_dim, embed_dim, bias=bias, **factory_kwargs)

            self._reset_parameters()
        else:
            if not self._qkv_same_embed_dim:
                raise NotImplementedError
            else:
                self.in_proj_linear = linear1_cls(
                    embed_dim, 3 * embed_dim, bias=bias, **factory_kwargs)
                self.in_proj_weight = self.in_proj_linear.weight

                self.register_parameter("q_proj_weight", None)
                self.register_parameter("k_proj_weight", None)
                self.register_parameter("v_proj_weight", None)

                if bias:
                    self.in_proj_bias = self.in_proj_linear.bias
                else:
                    self.register_parameter("in_proj_bias", None)

            self.out_proj = linear2_cls(
                embed_dim, embed_dim, bias=bias, **factory_kwargs)

            if self.bias_k is not None:
                xavier_normal_(self.bias_k)
            if self.bias_v is not None:
                xavier_normal_(self.bias_v)

        self.add_zero_attn = add_zero_attn

    def _reset_parameters(self):
        if self._qkv_same_embed_dim:
            xavier_uniform_(self.in_proj_weight)
        else:
            xavier_uniform_(self.q_proj_weight)
            xavier_uniform_(self.k_proj_weight)
            xavier_uniform_(self.v_proj_weight)

        if self.in_proj_bias is not None:
            constant_(self.in_proj_bias, 0.0)
            constant_(self.out_proj.bias, 0.0)

        if self.bias_k is not None:
            xavier_normal_(self.bias_k)
        if self.bias_v is not None:
            xavier_normal_(self.bias_v)

    def __setstate__(self, state):
        # Support loading old MultiheadAttention checkpoints generated by v1.1.0
        if "_qkv_same_embed_dim" not in state:
            state["_qkv_same_embed_dim"] = True

        super(MultiheadAttention, self).__setstate__(state)

    def forward(
            self,
            query: Tensor,
            key: Tensor,
            value: Tensor,
            key_padding_mask: Optional[Tensor]=None,
            need_weights: bool=True,
            attn_mask: Optional[Tensor]=None,
            average_attn_weights: bool=True,cache=None
    ) -> Tuple[Tensor, Optional[Tensor]]:
        r"""
        Args:
            query: Query embeddings of shape :math:`(L, E_q)` for unbatched input, :math:`(L, N, E_q)` when ``batch_first=False``
                or :math:`(N, L, E_q)` when ``batch_first=True``, where :math:`L` is the target sequence length,
                :math:`N` is the batch size, and :math:`E_q` is the query embedding dimension ``embed_dim``.
                Queries are compared against key-value pairs to produce the output.
                See "Attention Is All You Need" for more details.
            key: Key embeddings of shape :math:`(S, E_k)` for unbatched input, :math:`(S, N, E_k)` when ``batch_first=False``
                or :math:`(N, S, E_k)` when ``batch_first=True``, where :math:`S` is the source sequence length,
                :math:`N` is the batch size, and :math:`E_k` is the key embedding dimension ``kdim``.
                See "Attention Is All You Need" for more details.
            value: Value embeddings of shape :math:`(S, E_v)` for unbatched input, :math:`(S, N, E_v)` when
                ``batch_first=False`` or :math:`(N, S, E_v)` when ``batch_first=True``, where :math:`S` is the source
                sequence length, :math:`N` is the batch size, and :math:`E_v` is the value embedding dimension ``vdim``.
                See "Attention Is All You Need" for more details.
            key_padding_mask: If specified, a mask of shape :math:`(N, S)` indicating which elements within ``key``
                to ignore for the purpose of attention (i.e. treat as "padding"). For unbatched `query`, shape should be :math:`(S)`.
                Binary and byte masks are supported.
                For a binary mask, a ``True`` value indicates that the corresponding ``key`` value will be ignored for
                the purpose of attention. For a float mask, it will be directly added to the corresponding ``key`` value.
            need_weights: If specified, returns ``attn_output_weights`` in addition to ``attn_outputs``.
                Default: ``True``.
            attn_mask: If specified, a 2D or 3D mask preventing attention to certain positions. Must be of shape
                :math:`(L, S)` or :math:`(N\cdot\text{num\_heads}, L, S)`, where :math:`N` is the batch size,
                :math:`L` is the target sequence length, and :math:`S` is the source sequence length. A 2D mask will be
                broadcasted across the batch while a 3D mask allows for a different mask for each entry in the batch.
                Binary, byte, and float masks are supported. For a binary mask, a ``True`` value indicates that the
                corresponding position is not allowed to attend. For a byte mask, a non-zero value indicates that the
                corresponding position is not allowed to attend. For a float mask, the mask values will be added to
                the attention weight.
            average_attn_weights: If true, indicates that the returned ``attn_weights`` should be averaged across
                heads. Otherwise, ``attn_weights`` are provided separately per head. Note that this flag only has an
                effect when ``need_weights=True``. Default: ``True`` (i.e. average weights across heads)

        Outputs:
            - **attn_output** - Attention outputs of shape :math:`(L, E)` when input is unbatched,
              :math:`(L, N, E)` when ``batch_first=False`` or :math:`(N, L, E)` when ``batch_first=True``,
              where :math:`L` is the target sequence length, :math:`N` is the batch size, and :math:`E` is the
              embedding dimension ``embed_dim``.
            - **attn_output_weights** - Only returned when ``need_weights=True``. If ``average_attn_weights=True``,
              returns attention weights averaged across heads of shape :math:`(L, S)` when input is unbatched or
              :math:`(N, L, S)`, where :math:`N` is the batch size, :math:`L` is the target sequence length, and
              :math:`S` is the source sequence length. If ``average_attn_weights=False``, returns attention weights per
              head of shape :math:`(\text{num\_heads}, L, S)` when input is unbatched or :math:`(N, \text{num\_heads}, L, S)`.

            .. note::
                `batch_first` argument is ignored for unbatched inputs.
        """
        is_batched = query.dim() == 3
        if key_padding_mask is not None:
            _kpm_dtype = key_padding_mask.dtype
            if _kpm_dtype != torch.bool and not torch.is_floating_point(
                    key_padding_mask):
                raise AssertionError(
                    "only bool and floating types of key_padding_mask are supported"
                )
        why_not_fast_path = ""
        if not is_batched:
            why_not_fast_path = f"input not batched; expected query.dim() of 3 but got {query.dim()}"
        elif query is not key or key is not value:
            # When lifting this restriction, don't forget to either
            # enforce that the dtypes all match or test cases where
            # they don't!
            why_not_fast_path = "non-self attention was used (query, key, and value are not the same Tensor)"
        elif (self.in_proj_bias is not None and
              query.dtype != self.in_proj_bias.dtype):
            why_not_fast_path = f"dtypes of query ({query.dtype}) and self.in_proj_bias ({self.in_proj_bias.dtype}) don't match"
        elif (self.in_proj_weight is not None and
              query.dtype != self.in_proj_weight.dtype):
            # this case will fail anyway, but at least they'll get a useful error message.
            why_not_fast_path = f"dtypes of query ({query.dtype}) and self.in_proj_weight ({self.in_proj_weight.dtype}) don't match"
        elif self.training:
            why_not_fast_path = "training is enabled"
        elif not self.batch_first:
            why_not_fast_path = "batch_first was not True"
        elif self.bias_k is not None:
            why_not_fast_path = "self.bias_k was not None"
        elif self.bias_v is not None:
            why_not_fast_path = "self.bias_v was not None"
        elif self.dropout:
            why_not_fast_path = f"dropout was {self.dropout}, required zero"
        elif self.add_zero_attn:
            why_not_fast_path = "add_zero_attn was enabled"
        elif not self._qkv_same_embed_dim:
            why_not_fast_path = "_qkv_same_embed_dim was not True"
        elif attn_mask is not None:
            why_not_fast_path = "attn_mask was not None"
        elif query.is_nested and key_padding_mask is not None:
            why_not_fast_path = (
                "key_padding_mask is not supported with NestedTensor input")
        elif self.num_heads % 2 == 1:
            why_not_fast_path = "num_heads is odd"
        elif torch.is_autocast_enabled():
            why_not_fast_path = "autocast is enabled"

        if not why_not_fast_path:
            tensor_args = (query, key, value, self.in_proj_weight,
                           self.in_proj_bias, self.out_proj.weight,
                           self.out_proj.bias, )
            # We have to use list comprehensions below because TorchScript does not support
            # generator expressions.
            if torch.overrides.has_torch_function(tensor_args):
                why_not_fast_path = "some Tensor argument has_torch_function"
            elif not all([(x is None or x.is_cuda or "cpu" in str(x.device))
                          for x in tensor_args]):
                why_not_fast_path = (
                    "some Tensor argument is neither CUDA nor CPU")
            elif torch.is_grad_enabled() and any(
                [x is not None and x.requires_grad for x in tensor_args]):
                why_not_fast_path = (
                    "grad is enabled and at least one of query or the "
                    "input/output projection weights or biases requires_grad")
            if not why_not_fast_path:
                return torch._native_multi_head_attention(
                    query,
                    key,
                    value,
                    self.embed_dim,
                    self.num_heads,
                    self.in_proj_weight,
                    self.in_proj_bias,
                    self.out_proj.weight,
                    self.out_proj.bias,
                    key_padding_mask
                    if key_padding_mask is not None else attn_mask,
                    need_weights,
                    average_attn_weights,
                    1 if key_padding_mask is not None else 0
                    if attn_mask is not None else None, )

        any_nested = query.is_nested or key.is_nested or value.is_nested
        assert not any_nested, (
            "MultiheadAttention does not support NestedTensor outside of its fast path. "
            + f"The fast path was not hit because {why_not_fast_path}")

        if self.batch_first and is_batched:
            # make sure that the transpose op does not affect the "is" property
            if key is value:
                if query is key:
                    query = key = value = query.transpose(1, 0)
                else:
                    query, key = [x.transpose(1, 0) for x in (query, key)]
                    value = key
            else:
                query, key, value = [
                    x.transpose(1, 0) for x in (query, key, value)
                ]

        if not self._qkv_same_embed_dim:
            attn_output, attn_output_weights = F.multi_head_attention_forward(
                query,
                key,
                value,
                self.embed_dim,
                self.num_heads,
                self.in_proj_weight,
                self.in_proj_bias,
                self.bias_k,
                self.bias_v,
                self.add_zero_attn,
                self.dropout,
                self.out_proj.weight,
                self.out_proj.bias,
                training=self.training,
                key_padding_mask=key_padding_mask,
                need_weights=need_weights,
                attn_mask=attn_mask,
                use_separate_proj_weight=True,
                q_proj_weight=self.q_proj_weight,
                k_proj_weight=self.k_proj_weight,
                v_proj_weight=self.v_proj_weight,
                average_attn_weights=average_attn_weights,cache=cache )
        else:
            attn_output, attn_output_weights = F.multi_head_attention_forward(
                query,
                key,
                value,
                self.embed_dim,
                self.num_heads,
                self.in_proj_weight,
                self.in_proj_bias,
                self.bias_k,
                self.bias_v,
                self.add_zero_attn,
                self.dropout,
                self.out_proj.weight,
                self.out_proj.bias,
                training=self.training,
                key_padding_mask=key_padding_mask,
                need_weights=need_weights,
                attn_mask=attn_mask,
                average_attn_weights=average_attn_weights,cache=cache )
        if self.batch_first and is_batched:
            return attn_output.transpose(1, 0), attn_output_weights
        else:
            return attn_output, attn_output_weights