-
Notifications
You must be signed in to change notification settings - Fork 7
/
mock.py
326 lines (293 loc) · 15.2 KB
/
mock.py
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
from torch.nn.modules.activation import torch, Optional, Tuple, Tensor, Parameter, NonDynamicallyQuantizableLinear, MultiheadAttention
from torch.nn.functional import (
_mha_shape_check, _scaled_dot_product_attention, has_torch_function, handle_torch_function, _in_projection_packed, _in_projection,
math, linear, warnings, pad)
#############################################################################################################
############################## Mocking torch.nn.functional in Pytorch 1.11 ##################################
#############################################################################################################
def multi_head_attention_forward(
query: Tensor,
key: Tensor,
value: Tensor,
embed_dim_to_check: int,
num_heads: int,
in_proj_weight: Tensor,
in_proj_bias: Optional[Tensor],
bias_k: Optional[Tensor],
bias_v: Optional[Tensor],
add_zero_attn: bool,
dropout_p: float,
out_proj_weight: Tensor,
out_proj_bias: Optional[Tensor],
training: bool = True,
key_padding_mask: Optional[Tensor] = None,
need_weights: bool = True,
attn_mask: Optional[Tensor] = None,
use_separate_proj_weight: bool = False,
q_proj_weight: Optional[Tensor] = None,
k_proj_weight: Optional[Tensor] = None,
v_proj_weight: Optional[Tensor] = None,
static_k: Optional[Tensor] = None,
static_v: Optional[Tensor] = None,
average_attn_weights: bool = True,
alpha: Optional[Tensor] = None,
) -> Tuple[Tensor, Optional[Tensor]]:
tens_ops = (query, key, value, in_proj_weight, in_proj_bias, bias_k, bias_v, out_proj_weight, out_proj_bias)
if has_torch_function(tens_ops):
return handle_torch_function(
multi_head_attention_forward,
tens_ops,
query,
key,
value,
embed_dim_to_check,
num_heads,
in_proj_weight,
in_proj_bias,
bias_k,
bias_v,
add_zero_attn,
dropout_p,
out_proj_weight,
out_proj_bias,
training=training,
key_padding_mask=key_padding_mask,
need_weights=need_weights,
attn_mask=attn_mask,
use_separate_proj_weight=use_separate_proj_weight,
q_proj_weight=q_proj_weight,
k_proj_weight=k_proj_weight,
v_proj_weight=v_proj_weight,
static_k=static_k,
static_v=static_v,
)
is_batched = _mha_shape_check(query, key, value, key_padding_mask, attn_mask, num_heads)
# For unbatched input, we unsqueeze at the expected batch-dim to pretend that the input
# is batched, run the computation and before returning squeeze the
# batch dimension so that the output doesn't carry this temporary batch dimension.
if not is_batched:
# unsqueeze if the input is unbatched
query = query.unsqueeze(1)
key = key.unsqueeze(1)
value = value.unsqueeze(1)
if key_padding_mask is not None:
key_padding_mask = key_padding_mask.unsqueeze(0)
# set up shape vars
tgt_len, bsz, embed_dim = query.shape
src_len, _, _ = key.shape
assert embed_dim == embed_dim_to_check, \
f"was expecting embedding dimension of {embed_dim_to_check}, but got {embed_dim}"
if isinstance(embed_dim, torch.Tensor):
# embed_dim can be a tensor when JIT tracing
head_dim = embed_dim.div(num_heads, rounding_mode='trunc')
else:
head_dim = embed_dim // num_heads
assert head_dim * num_heads == embed_dim, f"embed_dim {embed_dim} not divisible by num_heads {num_heads}"
if use_separate_proj_weight:
# allow MHA to have different embedding dimensions when separate projection weights are used
assert key.shape[:2] == value.shape[:2], \
f"key's sequence and batch dims {key.shape[:2]} do not match value's {value.shape[:2]}"
else:
assert key.shape == value.shape, f"key shape {key.shape} does not match value shape {value.shape}"
#
# compute in-projection
#
if not use_separate_proj_weight:
q, k, v = _in_projection_packed(query, key, value, in_proj_weight, in_proj_bias)
else:
assert q_proj_weight is not None, "use_separate_proj_weight is True but q_proj_weight is None"
assert k_proj_weight is not None, "use_separate_proj_weight is True but k_proj_weight is None"
assert v_proj_weight is not None, "use_separate_proj_weight is True but v_proj_weight is None"
if in_proj_bias is None:
b_q = b_k = b_v = None
else:
b_q, b_k, b_v = in_proj_bias.chunk(3)
q, k, v = _in_projection(query, key, value, q_proj_weight, k_proj_weight, v_proj_weight, b_q, b_k, b_v)
# prep attention mask
if attn_mask is not None:
if attn_mask.dtype == torch.uint8:
warnings.warn("Byte tensor for attn_mask in nn.MultiheadAttention is deprecated. Use bool tensor instead.")
attn_mask = attn_mask.to(torch.bool)
else:
assert attn_mask.is_floating_point() or attn_mask.dtype == torch.bool, \
f"Only float, byte, and bool types are supported for attn_mask, not {attn_mask.dtype}"
# ensure attn_mask's dim is 3
if attn_mask.dim() == 2:
correct_2d_size = (tgt_len, src_len)
if attn_mask.shape != correct_2d_size:
raise RuntimeError(f"The shape of the 2D attn_mask is {attn_mask.shape}, but should be {correct_2d_size}.")
attn_mask = attn_mask.unsqueeze(0)
elif attn_mask.dim() == 3:
correct_3d_size = (bsz * num_heads, tgt_len, src_len)
if attn_mask.shape != correct_3d_size:
raise RuntimeError(f"The shape of the 3D attn_mask is {attn_mask.shape}, but should be {correct_3d_size}.")
else:
raise RuntimeError(f"attn_mask's dimension {attn_mask.dim()} is not supported")
# prep key padding mask
if key_padding_mask is not None and key_padding_mask.dtype == torch.uint8:
warnings.warn("Byte tensor for key_padding_mask in nn.MultiheadAttention is deprecated. Use bool tensor instead.")
key_padding_mask = key_padding_mask.to(torch.bool)
# add bias along batch dimension (currently second)
if bias_k is not None and bias_v is not None:
assert static_k is None, "bias cannot be added to static key."
assert static_v is None, "bias cannot be added to static value."
k = torch.cat([k, bias_k.repeat(1, bsz, 1)])
v = torch.cat([v, bias_v.repeat(1, bsz, 1)])
if attn_mask is not None:
attn_mask = pad(attn_mask, (0, 1))
if key_padding_mask is not None:
key_padding_mask = pad(key_padding_mask, (0, 1))
else:
assert bias_k is None
assert bias_v is None
#
# reshape q, k, v for multihead attention and make em batch first
#
head_dim = q.shape[-1] // num_heads ###
q = q.contiguous().view(tgt_len, bsz * num_heads, head_dim).transpose(0, 1)
if static_k is None:
k = k.contiguous().view(k.shape[0], bsz * num_heads, head_dim).transpose(0, 1)
else:
# TODO finish disentangling control flow so we don't do in-projections when statics are passed
assert static_k.size(0) == bsz * num_heads, \
f"expecting static_k.size(0) of {bsz * num_heads}, but got {static_k.size(0)}"
assert static_k.size(2) == head_dim, \
f"expecting static_k.size(2) of {head_dim}, but got {static_k.size(2)}"
k = static_k
if static_v is None:
v = v.contiguous().view(v.shape[0], bsz * num_heads, head_dim).transpose(0, 1)
else:
# TODO finish disentangling control flow so we don't do in-projections when statics are passed
assert static_v.size(0) == bsz * num_heads, \
f"expecting static_v.size(0) of {bsz * num_heads}, but got {static_v.size(0)}"
assert static_v.size(2) == head_dim, \
f"expecting static_v.size(2) of {head_dim}, but got {static_v.size(2)}"
v = static_v
if alpha is not None:
q, k, v = q * alpha, k * alpha, v * alpha ###
# add zero attention along batch dimension (now first)
if add_zero_attn:
zero_attn_shape = (bsz * num_heads, 1, head_dim)
k = torch.cat([k, torch.zeros(zero_attn_shape, dtype=k.dtype, device=k.device)], dim=1)
v = torch.cat([v, torch.zeros(zero_attn_shape, dtype=v.dtype, device=v.device)], dim=1)
if attn_mask is not None:
attn_mask = pad(attn_mask, (0, 1))
if key_padding_mask is not None:
key_padding_mask = pad(key_padding_mask, (0, 1))
# update source sequence length after adjustments
src_len = k.size(1)
# merge key padding and attention masks
if key_padding_mask is not None:
assert key_padding_mask.shape == (bsz, src_len), \
f"expecting key_padding_mask shape of {(bsz, src_len)}, but got {key_padding_mask.shape}"
key_padding_mask = key_padding_mask.view(bsz, 1, 1, src_len). \
expand(-1, num_heads, -1, -1).reshape(bsz * num_heads, 1, src_len)
if attn_mask is None:
attn_mask = key_padding_mask
elif attn_mask.dtype == torch.bool:
attn_mask = attn_mask.logical_or(key_padding_mask)
else:
attn_mask = attn_mask.masked_fill(key_padding_mask, float("-inf"))
# convert mask to float
if attn_mask is not None and attn_mask.dtype == torch.bool:
new_attn_mask = torch.zeros_like(attn_mask, dtype=q.dtype)
new_attn_mask.masked_fill_(attn_mask, float("-inf"))
attn_mask = new_attn_mask
# adjust dropout probability
if not training:
dropout_p = 0.0
#
# (deep breath) calculate attention and out projection
#
attn_output, attn_output_weights = _scaled_dot_product_attention(q, k, v, attn_mask, dropout_p)
embed_dim = head_dim * num_heads ###
attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
attn_output = linear(attn_output, out_proj_weight, out_proj_bias)
if need_weights:
# optionally average attention weights over heads
attn_output_weights = attn_output_weights.view(bsz, num_heads, tgt_len, src_len)
if average_attn_weights:
attn_output_weights = attn_output_weights.sum(dim=1) / num_heads
if not is_batched:
# squeeze the output if input was unbatched
attn_output = attn_output.squeeze(1)
attn_output_weights = attn_output_weights.squeeze(0)
return attn_output, attn_output_weights
else:
if not is_batched:
# squeeze the output if input was unbatched
attn_output = attn_output.squeeze(1)
return attn_output, None
#############################################################################################################
########################## Mocking torch.nn.modules.activation in Pytorch 1.11 ##############################
#############################################################################################################
def __init__(self, embed_dim, num_heads, dropout=0., bias=True, add_bias_kv=False, add_zero_attn=False,
kdim=None, vdim=None, batch_first=False, device=None, dtype=None, search=False) -> 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 self._qkv_same_embed_dim is False:
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)
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
self.add_zero_attn = add_zero_attn
self._reset_parameters()
if search:
self.alpha = Parameter(torch.ones(1, 1, self.head_dim))
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) -> Tuple[Tensor, Optional[Tensor]]:
is_batched = query.dim() == 3
if self.batch_first and is_batched:
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 = 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)
else:
attn_output, attn_output_weights = 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,
alpha=None if not hasattr(self, 'alpha') else self.alpha)
if self.batch_first and is_batched:
return attn_output.transpose(1, 0), attn_output_weights
else:
return attn_output, attn_output_weights
MultiheadAttention.__init__ = __init__
MultiheadAttention.forward = forward