Implement ConformerFeedForwardV1 Part

i6_models/parts/conformer/feedforward.py

@@ -0,0 +1,48 @@
+from __future__ import annotations
+from dataclasses import dataclass
+from typing import Callable
+
+import torch
+from torch import nn
+
+from i6_models.config import ModelConfiguration
+
+
+@dataclass
+class ConformerPositionwiseFeedForwardV1Config(ModelConfiguration):
+    input_dim: int
+    """input dimension"""
+    hidden_dim: int
+    """hidden dimension (normally set to 4*input_dim as suggested by the paper)"""
+    dropout: float
+    """dropout probability"""
+    activation: Callable[[torch.Tensor], torch.Tensor] = nn.functional.silu
+    """activation function"""
+
+
+class ConformerPositionwiseFeedForwardV1(nn.Module):
+    """
+    Conformer feedforward module
+    """
+
+    def __init__(self, cfg: ConformerPositionwiseFeedForwardV1Config):
+        super().__init__()
+
+        self.layer_norm = nn.LayerNorm(cfg.input_dim)
+        self.linear_ff = nn.Linear(in_features=cfg.input_dim, out_features=cfg.hidden_dim, bias=True)
+        self.activation = cfg.activation
+        self.linear_out = nn.Linear(in_features=cfg.hidden_dim, out_features=cfg.input_dim, bias=True)
+        self.dropout = cfg.dropout
+
+    def forward(self, tensor: torch.Tensor) -> torch.Tensor:
+        """
+        :param tensor: shape [B,T,F], F=input_dim
+        :return: shape [B,T,F], F=input_dim
+        """
+        tensor = self.layer_norm(tensor)
+        tensor = self.linear_ff(tensor)  # [B,T,F]
+        tensor = self.activation(tensor)  # [B,T,F]
+        tensor = nn.functional.dropout(tensor, p=self.dropout, training=self.training)  # [B,T,F]
+        tensor = self.linear_out(tensor)  # [B,T,F]
+        tensor = nn.functional.dropout(tensor, p=self.dropout, training=self.training)  # [B,T,F]
+        return tensor

requirements.txt

@@ -1 +1,2 @@
-typeguard
+typeguard
+torch

tests/test_conformer.py

@@ -0,0 +1,24 @@
+from itertools import product
+
+import torch
+from torch import nn
+
+from i6_models.parts.conformer.feedforward import (
+    ConformerPositionwiseFeedForwardV1,
+    ConformerPositionwiseFeedForwardV1Config,
+)
+
+
+def test_ConformerPositionwiseFeedForwardV1():
+    def get_output_shape(input_shape, input_dim, hidden_dim, dropout, activation):
+        x = torch.randn(input_shape)
+        cfg = ConformerPositionwiseFeedForwardV1Config(input_dim, hidden_dim, dropout, activation)
+        conf_ffn_part = ConformerPositionwiseFeedForwardV1(cfg)
+        y = conf_ffn_part(x)
+        return y.shape
+
+    for input_dim, hidden_dim, dropout, activation in product(
+        [10, 20], [100, 200], [0.1, 0.3], [nn.functional.silu, nn.functional.relu]
+    ):
+        input_shape = (10, 100, input_dim)
+        assert get_output_shape(input_shape, input_dim, hidden_dim, dropout, activation) == input_shape