Upload seamless_communication/models/monotonic_decoder/p_choose.py with huggingface_hub
Browse files
seamless_communication/models/monotonic_decoder/p_choose.py
ADDED
@@ -0,0 +1,148 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
2 |
+
# All rights reserved.
|
3 |
+
#
|
4 |
+
# This source code is licensed under the license found in the
|
5 |
+
# MIT_LICENSE file in the root directory of this source tree.
|
6 |
+
|
7 |
+
from typing import Optional, final
|
8 |
+
|
9 |
+
import torch
|
10 |
+
from fairseq2.nn.projection import Linear
|
11 |
+
from fairseq2.typing import DataType, Device, finaloverride
|
12 |
+
from torch import Tensor
|
13 |
+
from torch.nn import AvgPool1d, Module, ModuleList, ReLU
|
14 |
+
from torch.nn.parameter import Parameter
|
15 |
+
|
16 |
+
|
17 |
+
class EnergyProjection(Module):
|
18 |
+
def __init__(
|
19 |
+
self,
|
20 |
+
model_dim: int,
|
21 |
+
num_layers: int,
|
22 |
+
bias: bool = True,
|
23 |
+
device: Optional[Device] = None,
|
24 |
+
dtype: Optional[DataType] = None,
|
25 |
+
) -> None:
|
26 |
+
super().__init__()
|
27 |
+
|
28 |
+
if num_layers < 1:
|
29 |
+
raise ValueError(
|
30 |
+
f"Invalid `num_layers`: {num_layers} for EnergyProjectionLayer."
|
31 |
+
)
|
32 |
+
|
33 |
+
self.layers = ModuleList()
|
34 |
+
|
35 |
+
for _ in range(num_layers):
|
36 |
+
self.layers.append(
|
37 |
+
Linear(model_dim, model_dim, bias, device=device, dtype=dtype)
|
38 |
+
)
|
39 |
+
self.layers.append(ReLU())
|
40 |
+
|
41 |
+
def forward(self, seqs: Tensor) -> Tensor:
|
42 |
+
for layer in self.layers:
|
43 |
+
seqs = layer(seqs)
|
44 |
+
return seqs
|
45 |
+
|
46 |
+
|
47 |
+
@final
|
48 |
+
class PChooseLayer(Module):
|
49 |
+
"""Represents a PChoose layer."""
|
50 |
+
|
51 |
+
model_dim: int
|
52 |
+
num_heads: int
|
53 |
+
energy_bias: Parameter
|
54 |
+
monotonic_temperature: float
|
55 |
+
q_energy_proj: EnergyProjection
|
56 |
+
k_energy_proj: EnergyProjection
|
57 |
+
keys_pooling: AvgPool1d
|
58 |
+
|
59 |
+
def __init__(
|
60 |
+
self,
|
61 |
+
model_dim: int,
|
62 |
+
num_heads: int,
|
63 |
+
energy_bias_value: float,
|
64 |
+
monotonic_temperature: float,
|
65 |
+
num_monotonic_energy_layers: int,
|
66 |
+
pre_decision_ratio: int,
|
67 |
+
*,
|
68 |
+
bias: bool = True,
|
69 |
+
device: Optional[Device] = None,
|
70 |
+
dtype: Optional[DataType] = None,
|
71 |
+
) -> None:
|
72 |
+
"""
|
73 |
+
:param model_dim:
|
74 |
+
The dimensionality of the model.
|
75 |
+
:param num_heads:
|
76 |
+
The number of attention heads.
|
77 |
+
:param bias:
|
78 |
+
If ``True``, query, key energy projection layers learn an
|
79 |
+
additive bias.
|
80 |
+
"""
|
81 |
+
super().__init__()
|
82 |
+
|
83 |
+
self.model_dim = model_dim
|
84 |
+
self.num_heads = num_heads
|
85 |
+
|
86 |
+
if energy_bias_value != 0.0:
|
87 |
+
self.energy_bias = Parameter(
|
88 |
+
torch.full([1], energy_bias_value, device=device, dtype=dtype)
|
89 |
+
)
|
90 |
+
else:
|
91 |
+
self.register_module("energy_bias", None)
|
92 |
+
|
93 |
+
self.monotonic_temperature = monotonic_temperature
|
94 |
+
|
95 |
+
if num_monotonic_energy_layers <= 0:
|
96 |
+
raise ValueError("Number of monotonic energy layers must be > 0.")
|
97 |
+
|
98 |
+
self.q_energy_proj = EnergyProjection(
|
99 |
+
self.model_dim,
|
100 |
+
num_monotonic_energy_layers,
|
101 |
+
bias,
|
102 |
+
device=device,
|
103 |
+
dtype=dtype,
|
104 |
+
)
|
105 |
+
self.k_energy_proj = EnergyProjection(
|
106 |
+
self.model_dim,
|
107 |
+
num_monotonic_energy_layers,
|
108 |
+
bias,
|
109 |
+
device=device,
|
110 |
+
dtype=dtype,
|
111 |
+
)
|
112 |
+
|
113 |
+
self.keys_pooling = AvgPool1d(
|
114 |
+
kernel_size=pre_decision_ratio,
|
115 |
+
stride=pre_decision_ratio,
|
116 |
+
ceil_mode=True,
|
117 |
+
)
|
118 |
+
|
119 |
+
@finaloverride
|
120 |
+
def forward(self, seqs: Tensor, keys: Tensor) -> Tensor:
|
121 |
+
q = self.q_energy_proj(seqs)
|
122 |
+
|
123 |
+
# (N, S, M) -> (N, H, S, K)
|
124 |
+
q = q.unflatten(-1, (self.num_heads, -1)).transpose(1, 2)
|
125 |
+
|
126 |
+
# (N, S_kv, M) -> (N, M, S_kv) -> (N, M, S_p)
|
127 |
+
pooled_keys = self.keys_pooling(keys.transpose(1, 2))
|
128 |
+
|
129 |
+
# (N, M, S_p) -> (N, S_p, M)
|
130 |
+
pooled_keys = pooled_keys.transpose(1, 2)
|
131 |
+
|
132 |
+
k = self.k_energy_proj(pooled_keys)
|
133 |
+
|
134 |
+
# (N, S_p, M) -> (N, H, S_p, K)
|
135 |
+
k = k.unflatten(-1, (self.num_heads, -1)).transpose(1, 2)
|
136 |
+
|
137 |
+
# (N, H, S, K) @ (N, H, K, S_p) = (N, H, S, S_p)
|
138 |
+
monotonic_energy = torch.matmul(q, k.transpose(-1, -2))
|
139 |
+
|
140 |
+
monotonic_energy = monotonic_energy * (q.size(-1) ** -0.5)
|
141 |
+
|
142 |
+
if self.energy_bias is not None:
|
143 |
+
monotonic_energy += self.energy_bias
|
144 |
+
|
145 |
+
# p_choose: (N, H, S, S_p)
|
146 |
+
p_choose = torch.sigmoid(monotonic_energy / self.monotonic_temperature)
|
147 |
+
|
148 |
+
return p_choose
|