-
Notifications
You must be signed in to change notification settings - Fork 1
/
model.py
270 lines (207 loc) · 8.4 KB
/
model.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
from dataclasses import dataclass
from typing import Dict
from typing import Iterable, Optional
import numpy as np
import torch
import torch.nn.functional as F
from torch import Tensor
from torch import nn
@dataclass
class ModelDimensions:
n_mels: int
n_audio_ctx: int
n_audio_state: int
n_audio_head: int
n_audio_layer: int
n_vocab: int
n_text_ctx: int
n_text_state: int
n_text_head: int
n_text_layer: int
class LayerNorm(nn.LayerNorm):
def forward(self, x: Tensor) -> Tensor:
return super().forward(x.float()).type(x.dtype)
class Linear(nn.Linear):
def forward(self, x: Tensor) -> Tensor:
return F.linear(x, self.weight.to(x.dtype), None if self.bias is None else self.bias.to(x.dtype))
class Conv1d(nn.Conv1d):
def _conv_forward(self, x: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor:
return super()._conv_forward(x, weight.to(x.dtype), None if bias is None else bias.to(x.dtype))
def sinusoids(length, channels, max_timescale=10000):
"""Returns sinusoids for positional embedding"""
assert channels % 2 == 0
log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1)
inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2))
scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :]
return torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1)
class MultiHeadAttention(nn.Module):
layer_count = 0
def __init__(self, n_ctx: int, n_state: int, n_head: int):
super().__init__()
self.n_head = n_head
self.query = Linear(n_state, n_state)
self.key = Linear(n_state, n_state, bias=False)
self.value = Linear(n_state, n_state)
self.out = Linear(n_state, n_state)
self.layer_id = MultiHeadAttention.layer_count
MultiHeadAttention.layer_count += 1
self.n_ctx = n_ctx
def forward(
self,
x: Tensor,
xa: Optional[Tensor] = None,
mask: Optional[Tensor] = None,
kv_cache: Optional[dict] = None,
):
q = self.query(x)
k = self.key(x if xa is None else xa)
v = self.value(x if xa is None else xa)
if kv_cache is not None and k.shape[1] <= self.n_ctx:
layer_count = kv_cache.shape[0] // 2
key_id = self.layer_id - layer_count
value_id = key_id + 1
size = k.shape[1]
kv_cache[key_id, :, -size:, :] = k
kv_cache[value_id, :, -size:, :] = v
k = kv_cache[key_id]
v = kv_cache[value_id]
wv, qk = self.qkv_attention(q, k, v, mask)
return self.out(wv), qk
def qkv_attention(self, q: Tensor, k: Tensor, v: Tensor, mask: Optional[Tensor] = None):
n_batch, n_ctx, n_state = q.shape
scale = (n_state // self.n_head)**-0.25
q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) * scale
k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 3, 1) * scale
v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)
qk = q @ k
if mask is not None:
qk = qk + mask[:n_ctx, :n_ctx]
w = F.softmax(qk.float(), dim=-1).to(q.dtype)
return (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2), qk.detach()
class ResidualAttentionBlock(nn.Module):
def __init__(self, n_ctx: int, n_state: int, n_head: int, cross_attention: bool = False):
super().__init__()
self.attn = MultiHeadAttention(n_ctx, n_state, n_head)
self.attn_ln = LayerNorm(n_state)
self.cross_attn = MultiHeadAttention(n_ctx, n_state, n_head) if cross_attention else None
self.cross_attn_ln = LayerNorm(n_state) if cross_attention else None
n_mlp = n_state * 4
self.mlp = nn.Sequential(Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state))
self.mlp_ln = LayerNorm(n_state)
def forward(
self,
x: Tensor,
xa: Optional[Tensor] = None,
mask: Optional[Tensor] = None,
kv_cache: Optional[dict] = None,
):
attentionWeights, attentionQK = self.attn(self.attn_ln(x), mask=mask, kv_cache=kv_cache)
x = x + attentionWeights
if self.cross_attn:
crossAttentionWeights, crossAttentionQK = self.cross_attn(self.cross_attn_ln(x), xa, kv_cache=kv_cache)
x = x + crossAttentionWeights
else:
crossAttentionWeights = None
crossAttentionQK = None
x = x + self.mlp(self.mlp_ln(x))
return x, crossAttentionQK
class AudioEncoder(nn.Module):
def __init__(self, n_mels: int, n_ctx: int, n_state: int, n_head: int, n_layer: int):
super().__init__()
self.conv1 = Conv1d(n_mels, n_state, kernel_size=3, padding=1)
self.conv2 = Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1)
self.register_buffer("positional_embedding", sinusoids(n_ctx, n_state))
self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList([ResidualAttentionBlock(n_ctx, n_state, n_head) for _ in range(n_layer)])
self.ln_post = LayerNorm(n_state)
def forward(self, x: Tensor):
"""
x : torch.Tensor, shape = (batch_size, n_mels, n_ctx)
the mel spectrogram of the audio
"""
x = F.gelu(self.conv1(x))
x = F.gelu(self.conv2(x))
x = x.permute(0, 2, 1)
assert x.shape[1:] == self.positional_embedding.shape, "incorrect audio shape"
x = (x + self.positional_embedding).to(x.dtype)
for block in self.blocks:
x, _, = block(x)
x = self.ln_post(x)
return x
class TextDecoder(nn.Module):
def __init__(self, n_vocab: int, n_ctx: int, n_state: int, n_head: int, n_layer: int):
super().__init__()
self.n_head = n_head
self.token_embedding = nn.Embedding(n_vocab, n_state)
self.positional_embedding = nn.Parameter(torch.empty(n_ctx, n_state))
self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList([ResidualAttentionBlock(n_ctx, n_state, n_head, cross_attention=True) for _ in range(n_layer)])
self.ln = LayerNorm(n_state)
mask = torch.empty(n_ctx, n_ctx).fill_(-np.inf).triu_(1)
self.register_buffer("mask", mask, persistent=False)
def forward(self, x: Tensor, xa: Tensor, kv_cache: Optional[Tensor] = None, offset: Optional[Tensor] = 0):
"""
x : torch.LongTensor, shape = (batch_size, <= n_ctx)
the text tokens
xa : torch.Tensor, shape = (batch_size, n_mels, n_audio_ctx)
the encoded audio features to be attended on
"""
# minus one because we pre allocate kv_cache
x = self.token_embedding(x) + self.positional_embedding[offset:offset + x.shape[-1]]
x = x.to(xa.dtype)
crossAttentionQKs = torch.zeros((len(self.blocks), x.shape[0], self.n_head, x.shape[1], xa.shape[1]))
for blockIndex in range(len(self.blocks)):
block = self.blocks[blockIndex]
x, crossAttentionQK = block(x, xa, mask=self.mask, kv_cache=kv_cache)
#x, crossAttentionQK = block(x, xa, mask=self.mask, kv_cache=None)
crossAttentionQKs[blockIndex] = crossAttentionQK
x = self.ln(x)
logits = (x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1)).float()
return logits, kv_cache, crossAttentionQKs
class Whisper(nn.Module):
def __init__(self, dims: ModelDimensions, model: str):
super().__init__()
self.type = model
self.dims = dims
self.encoder = AudioEncoder(
self.dims.n_mels,
self.dims.n_audio_ctx,
self.dims.n_audio_state,
self.dims.n_audio_head,
self.dims.n_audio_layer,
)
self.decoder = TextDecoder(
self.dims.n_vocab,
self.dims.n_text_ctx,
self.dims.n_text_state,
self.dims.n_text_head,
self.dims.n_text_layer,
)
def embed_audio(self, mel: torch.Tensor):
return self.encoder.forward(mel)
def logits(self, tokens: torch.Tensor, audio_features: torch.Tensor):
kv_cache = self.new_kv_cache(tokens.shape[0], tokens.shape[-1])
output, _, _ = self.decoder.forward(tokens, audio_features, kv_cache=torch.from_numpy(kv_cache), offset=0)
return output
def forward(self, mel: torch.Tensor, tokens: torch.Tensor) -> Dict[str, torch.Tensor]:
kv_cache = self.new_kv_cache(tokens.shape[0], tokens.shape[-1])
output, _, _ = self.decoder(tokens, self.encoder(mel), kv_cache=torch.from_numpy(kv_cache), offset=0)
return output
@property
def device(self):
return torch.device("cpu")
@property
def is_multilingual(self):
return self.dims.n_vocab == 51865
def new_kv_cache(self, n_group: int, length: int):
if self.type == "tiny.en" or self.type == "tiny":
size = [8, n_group, length, 384]
elif self.type == "base.en" or self.type == "base":
size = [12, n_group, length, 512]
elif self.type == "small.en" or self.type == "small":
size = [24, n_group, length, 768]
elif self.type == "medium.en" or self.type == "medium":
size = [48, n_group, length, 1024]
elif self.type == "large" or self.type == "large-v1" or self.type == "large-v2" or self.type == "large-v3":
size = [64, n_group, length, 1280]
else:
raise ValueError(f"Unsupported model type: {self.type}")
return np.zeros(size, dtype=np.float32)