Add emformer hubert model architecture (#2836)

Summary: Pull Request resolved: https://github.com/pytorch/audio/pull/2836

Reviewed By: carolineechen

Differential Revision: D41208630

Pulled By: nateanl

fbshipit-source-id: 625e1651f0b8a6e20876409739cf7084cb7c748b

docs/source/prototype.models.rst

@@ -14,6 +14,16 @@ conformer_rnnt_base
 
 .. autofunction:: conformer_rnnt_base
 
+emformer_hubert_model
+~~~~~~~~~~~~~~~~~~~~~
+
+.. autofunction:: emformer_hubert_model
+
+emformer_hubert_base
+~~~~~~~~~~~~~~~~~~~~
+
+.. autofunction:: emformer_hubert_base
+
 ConvEmformer
 ~~~~~~~~~~~~
 

test/torchaudio_unittest/prototype/conformer_wav2vec2_test.py → test/torchaudio_unittest/prototype/ssl_model_test.py

 import torch
 from parameterized import parameterized
-from torchaudio.prototype.models import conformer_wav2vec2_base
+from torchaudio.prototype.models import conformer_wav2vec2_base, emformer_hubert_base
 from torchaudio_unittest.common_utils import skipIfNoCuda, torch_script, TorchaudioTestCase
 
 
-class TestConformerWav2Vec2(TorchaudioTestCase):
-    def _smoke_test(self, model, device, dtype):
+class TestSSLModel(TorchaudioTestCase):
+    def _smoke_test(self, model, feature_dim, device, dtype):
         model = model.to(device=device, dtype=dtype)
         model = model.eval()
 
-        batch_size, num_frames, in_features = 3, 1024, 64
-        features = torch.randn(batch_size, num_frames, in_features, device=device, dtype=dtype)
+        batch_size, num_frames = 3, 1024
+        features = torch.randn(batch_size, num_frames, feature_dim, device=device, dtype=dtype)
         lengths = torch.randint(
             low=0,
             high=num_frames,
@@ -22,25 +22,48 @@ class TestConformerWav2Vec2(TorchaudioTestCase):
 
         model(features, lengths)
 
-    @parameterized.expand([(torch.float32,), (torch.float64,)])
-    def test_cpu_smoke_test(self, dtype):
-        model = conformer_wav2vec2_base()
-        self._smoke_test(model, torch.device("cpu"), dtype)
-
-    @parameterized.expand([(torch.float32,), (torch.float64,)])
+    @parameterized.expand(
+        [
+            (conformer_wav2vec2_base, torch.float32, 64),
+            (conformer_wav2vec2_base, torch.float64, 64),
+            (emformer_hubert_base, torch.float32, 80),
+            (emformer_hubert_base, torch.float64, 80),
+        ]
+    )
+    def test_cpu_smoke_test(self, model, dtype, feature_dim):
+        model = model()
+        self._smoke_test(model, feature_dim, torch.device("cpu"), dtype)
+
+    @parameterized.expand(
+        [
+            (conformer_wav2vec2_base, torch.float32, 64),
+            (conformer_wav2vec2_base, torch.float64, 64),
+            (emformer_hubert_base, torch.float32, 80),
+            (emformer_hubert_base, torch.float64, 80),
+        ]
+    )
     @skipIfNoCuda
-    def test_cuda_smoke_test(self, dtype):
-        model = conformer_wav2vec2_base()
-        self._smoke_test(model, torch.device("cuda"), dtype)
-
-    def test_extract_feature(self):
-        model = conformer_wav2vec2_base()
+    def test_cuda_smoke_test(self, model, dtype, feature_dim):
+        model = model()
+        self._smoke_test(model, feature_dim, torch.device("cuda"), dtype)
+
+    @parameterized.expand(
+        [
+            (conformer_wav2vec2_base, 64),
+            (emformer_hubert_base, 80),
+        ]
+    )
+    def test_extract_feature(self, model, feature_dim):
+        model = model()
         model.eval()
 
-        batch_size, num_frames, in_features = 3, 1024, 64
-        num_layers = len(model.encoder.conformer)
+        batch_size, num_frames = 3, 1024
+        if feature_dim == 64:
+            num_layers = len(model.encoder.conformer)
+        else:
+            num_layers = len(model.encoder.emformer.emformer_layers)
 
-        features = torch.randn(batch_size, num_frames, in_features)
+        features = torch.randn(batch_size, num_frames, feature_dim)
         lengths = torch.randint(
             low=0,
             high=num_frames,
@@ -63,12 +86,18 @@ class TestConformerWav2Vec2(TorchaudioTestCase):
                 self.assertEqual(all_features[i], feats[i])
             assert lengths_.shape == torch.Size([batch_size])
 
-    def test_zero_length(self):
-        model = conformer_wav2vec2_base()
+    @parameterized.expand(
+        [
+            (conformer_wav2vec2_base, 64),
+            (emformer_hubert_base, 80),
+        ]
+    )
+    def test_zero_length(self, model, feature_dim):
+        model = model()
         model.eval()
 
-        batch_size, num_frames, in_features = 3, 1024, 64
-        features = torch.randn(batch_size, num_frames, in_features)
+        batch_size, num_frames = 3, 1024
+        features = torch.randn(batch_size, num_frames, feature_dim)
         input_lengths = torch.zeros(batch_size)
         _, output_lengths = model(features, input_lengths)
         self.assertEqual(torch.zeros_like(output_lengths), output_lengths)
@@ -76,12 +105,18 @@ class TestConformerWav2Vec2(TorchaudioTestCase):
         _, output_lengths = model.extract_features(features, input_lengths)
         self.assertEqual(torch.zeros_like(output_lengths), output_lengths)
 
-    def test_torchscript_consistency(self):
-        model = conformer_wav2vec2_base()
+    @parameterized.expand(
+        [
+            (conformer_wav2vec2_base, 64),
+            (emformer_hubert_base, 80),
+        ]
+    )
+    def test_torchscript_consistency(self, model, feature_dim):
+        model = model()
         model.eval()
 
-        batch_size, num_frames, in_features = 3, 1024, 64
-        features = torch.randn(batch_size, num_frames, in_features)
+        batch_size, num_frames = 3, 1024
+        features = torch.randn(batch_size, num_frames, feature_dim)
         lengths = torch.randint(
             low=0,
             high=num_frames,

torchaudio/prototype/models/__init__.py

 from ._conformer_wav2vec2 import conformer_wav2vec2_base, conformer_wav2vec2_model
+from ._emformer_hubert import emformer_hubert_base, emformer_hubert_model
 from .conv_emformer import ConvEmformer
 from .rnnt import conformer_rnnt_base, conformer_rnnt_model
 
@@ -8,4 +9,6 @@ __all__ = [
     "ConvEmformer",
     "conformer_wav2vec2_model",
     "conformer_wav2vec2_base",
+    "emformer_hubert_base",
+    "emformer_hubert_model",
 ]

torchaudio/prototype/models/_emformer_hubert.py

+from typing import List, Optional, Tuple
+
+import torch
+from torchaudio.models import Wav2Vec2Model
+from torchaudio.models.emformer import Emformer
+from torchaudio.models.rnnt import _TimeReduction
+
+
+class FeatureEncoder(torch.nn.Module):
+    """Extract features from log-mel spectrogram input. Consists of linear layer and time reduction layer.
+
+    Args:
+        input_dim (int): The feature dimension of log-mel spectrogram feature.
+        output_dim (int): The feature dimension after linear layer.
+        use_bias (bool): If ``True``, enable bias parameter in the linear layer.
+        stride (int): Number of frames to merge for the output frame.
+    """
+
+    def __init__(self, input_dim: int, output_dim: int, use_bias: bool, stride: int):
+        super().__init__()
+        self.linear = torch.nn.Linear(input_dim, output_dim, bias=use_bias)
+        self.time_reduction = _TimeReduction(stride)
+
+    def forward(
+        self, input: torch.Tensor, lengths: Optional[torch.Tensor]
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
+        """
+        Args:
+            input (torch.Tensor): The log-mel spectrogram input.
+                Tensor with dimensions `(batch, time, input_dim)`.
+            lengths (torch.Tensor or None): Valid length of each input sample.
+                Tensor with dimension `(batch, )`.
+
+        Returns:
+            (torch.Tensor, torch.Tensor or None):
+                torch.Tensor
+                    Returned feature Tensor after linear layer and time reduction layer.
+                    Tensor with dimensions `(batch, time // stride, output_dim)`.
+                torch.Tensor or None
+                    The reduced lengths Tensor.
+        """
+        output = self.linear(input)
+        if lengths is None:
+            B, T, _ = input.shape
+            dummy_lengths = torch.full((B,), T)
+            output, _ = self.time_reduction(output, dummy_lengths)
+        else:
+            output, lengths = self.time_reduction(output, lengths)
+        return output, lengths
+
+
+class EmformerEncoder(torch.nn.Module):
+    """Emformer Encoder class for HuBERT pre-training. Consists of emformer module,
+        linear layer and layer normalization layer.
+
+    Args:
+        emformer (torch.nn.Module):
+            :py:class:`torchaudio.models.Emformer` module that consists of a list of emformer layers.
+        output_linear (torch.nn.Module):
+            Linear layer after emformer module.
+        layer_norm (torch.nn.Module or None, optional):
+            If ``None``, don't apply layer normalization to the output.
+    """
+
+    def __init__(
+        self,
+        emformer: torch.nn.Module,
+        output_linear: torch.nn.Module,
+        layer_norm: Optional[torch.nn.Module] = None,
+    ):
+        super().__init__()
+        self.emformer = emformer
+        self.output_linear = output_linear
+        self.layer_norm = layer_norm
+
+    def forward(
+        self,
+        input: torch.Tensor,
+        lengths: Optional[torch.Tensor],
+    ) -> torch.Tensor:
+        """
+        Args:
+            input (torch.Tensor): The input feature for emformer encoder.
+                Tensor with dimensions `(batch, time, feature_dim)`.
+            lengths (torch.Tensor or None): Valid length of each input sample.
+                Tensor with dimension `(batch, )`.
+
+        Returns:
+            torch.Tensor: The feature Tensor after emformer encoder.
+        """
+        if lengths is None:
+            B, T, _ = input.shape
+            dummy_lengths = torch.full((B,), T)
+            output, _ = self.emformer(input, dummy_lengths)
+        else:
+            output, lengths = self.emformer(input, lengths)
+        output = self.output_linear(output)
+        if self.layer_norm is not None:
+            output = self.layer_norm(output)
+        return output
+
+    def extract_features(
+        self,
+        input: torch.Tensor,
+        lengths: Optional[torch.Tensor],
+        num_layers: Optional[int] = None,
+    ) -> List[torch.Tensor]:
+        """Extract output Tensors of the emformer layers.
+
+        Args:
+            input (torch.Tensor): The input feature for emformer encoder.
+                Tensor with dimensions `(batch, time, feature_dim)`.
+            lengths (torch.Tensor or None): Valid length of each input sample.
+                Tensor with dimension `(batch, )`.
+            num_layers (int or None, optional): If not ``None``, returns the first
+                `num_layers` layers of Tensors as the output, otherwise returns the
+                Tensors from all emformer layers.
+
+        Returns:
+            List[torch.Tensor]:
+                Output Tensors of selected emformer layers.
+        """
+        if num_layers is not None:
+            if not 0 < num_layers <= len(self.emformer.emformer_layers):
+                raise ValueError(f"`num_layers` must be between [1, {len(self.emformer.emformer_layers)}]")
+
+        ret: List[torch.Tensor] = []
+
+        input = input.permute(1, 0, 2)
+        right_context = self.emformer._gen_right_context(input)
+        utterance = input[: input.size(0) - self.emformer.right_context_length]
+        attention_mask = self.emformer._gen_attention_mask(utterance)
+        mems = (
+            self.emformer.memory_op(utterance.permute(1, 2, 0)).permute(2, 0, 1)[:-1]
+            if self.emformer.use_mem
+            else torch.empty(0).to(dtype=input.dtype, device=input.device)
+        )
+        output = utterance
+        if lengths is None:
+            B, T, _ = input.shape
+            lengths = torch.full((B,), T)
+        for layer in self.emformer.emformer_layers:
+            output, right_context, mems = layer(output, lengths, right_context, mems, attention_mask)
+            ret.append(output.permute(1, 0, 2))
+            if num_layers is not None and len(ret) >= num_layers:
+                return ret
+        return ret
+
+
+def _get_emformer_feature_extractor(input_dim: int, output_dim: int, use_bias: bool, stride: int) -> FeatureEncoder:
+    """Construct FeatureEncoder for emformer model.
+
+    Args:
+        input_dim (int): The feature dimension of log-mel spectrogram feature.
+        output_dim (int): The feature dimension after linear layer.
+        use_bias (bool): If ``True``, enable bias parameter in the linear layer.
+        stride (int): Number of frames to merge for the output frame.
+
+    Returns:
+        FeatureEncoder: The resulting FeatureEncoder module.
+    """
+    return FeatureEncoder(input_dim, output_dim, use_bias, stride)
+
+
+def _get_emformer_encoder(
+    input_dim: int,
+    output_dim: int,
+    num_heads: int,
+    ffn_dim: int,
+    num_layers: int,
+    segment_length: int,
+    left_context_length: int,
+    right_context_length: int,
+    dropout: float,
+    activation: str,
+    max_memory_size: int,
+    weight_init_scale_strategy: Optional[str],
+    tanh_on_mem: bool,
+) -> EmformerEncoder:
+    """Construct EmformerEncoder for emformer model.
+
+    Args:
+        input_dim (int): The feature dimension of input Tensor.
+        output_dim (int): The feature dimension after EmformerEncoder.
+        num_heads (int): Number of attention heads in each Emformer layer.
+        ffn_dim: (int): Hidden layer dimension of feedforward network.
+        num_layers (int): Number of Emformer layers to instantiate.
+        segment_length (int): Length of each input segment.
+        left_context_length (int): Length of left context.
+        right_context_length (int): Length of right context.
+        dropout (float): Dropout probability.
+        activation (str): Activation function to use in each Emformer layer's
+            feedforward network. Must be one of ("relu", "gelu", "silu").
+        max_memory_size (int): Maximum number of memory elements to use.
+        weight_init_scale_strategy (str or None): Per-layer weight initialization scaling
+            strategy. Must be one of ("depthwise", "constant", ``None``).
+        tanh_on_mem (bool): If ``True``, applies tanh to memory elements.
+
+    Returns:
+        EmformerEncoder: The resulting EmformerEncoder module.
+    """
+    emformer = Emformer(
+        input_dim=input_dim,
+        num_heads=num_heads,
+        ffn_dim=ffn_dim,
+        num_layers=num_layers,
+        segment_length=segment_length,
+        left_context_length=left_context_length,
+        right_context_length=right_context_length,
+        dropout=dropout,
+        activation=activation,
+        max_memory_size=max_memory_size,
+        weight_init_scale_strategy=weight_init_scale_strategy,
+        tanh_on_mem=tanh_on_mem,
+    )
+    output_linear = torch.nn.Linear(input_dim, output_dim)
+    return EmformerEncoder(emformer, output_linear)
+
+
+def emformer_hubert_model(
+    extractor_input_dim: int,
+    extractor_output_dim: int,
+    extractor_use_bias: bool,
+    extractor_stride: int,
+    encoder_input_dim: int,
+    encoder_output_dim: int,
+    encoder_num_heads: int,
+    encoder_ffn_dim: int,
+    encoder_num_layers: int,
+    encoder_segment_length: int,
+    encoder_left_context_length: int,
+    encoder_right_context_length: int,
+    encoder_dropout: float,
+    encoder_activation: str,
+    encoder_max_memory_size: int,
+    encoder_weight_init_scale_strategy: Optional[str],
+    encoder_tanh_on_mem: bool,
+) -> Wav2Vec2Model:
+    """Build a custom Emformer HuBERT model.
+
+    Args:
+        extractor_input_dim (int): The input dimension for feature extractor.
+        extractor_output_dim (int): The output dimension after feature extractor.
+        extractor_use_bias (bool): If ``True``, enable bias parameter in the linear layer of feature extractor.
+        extractor_stride (int): Number of frames to merge for the output frame in feature extractor.
+        encoder_input_dim (int): The input dimension for Emformer layer.
+        encoder_output_dim (int): The output dimension after EmformerEncoder.
+        encoder_num_heads (int): Number of attention heads in each Emformer layer.
+        encoder_ffn_dim (int): Hidden layer dimension of feedforward network in Emformer.
+        encoder_num_layers (int): Number of Emformer layers to instantiate.
+        encoder_segment_length (int): Length of each input segment.
+        encoder_left_context_length (int): Length of left context.
+        encoder_right_context_length (int): Length of right context.
+        encoder_dropout (float): Dropout probability.
+        encoder_activation (str): Activation function to use in each Emformer layer's
+            feedforward network. Must be one of ("relu", "gelu", "silu").
+        encoder_max_memory_size (int): Maximum number of memory elements to use.
+        encoder_weight_init_scale_strategy (str or None): Per-layer weight initialization scaling
+            strategy. Must be one of ("depthwise", "constant", ``None``).
+        encoder_tanh_on_mem (bool): If ``True``, applies tanh to memory elements.
+
+    Returns:
+        Wav2Vec2Model:
+            The resulting :py:class:`torchaudio.models.Wav2Vec2Model` model
+            with a :py:class:`torchaudio.models.Emformer` encoder.
+    """
+    feature_extractor = _get_emformer_feature_extractor(
+        extractor_input_dim, extractor_output_dim, extractor_use_bias, extractor_stride
+    )
+    emformer = _get_emformer_encoder(
+        encoder_input_dim,
+        encoder_output_dim,
+        encoder_num_heads,
+        encoder_ffn_dim,
+        encoder_num_layers,
+        encoder_segment_length,
+        encoder_left_context_length,
+        encoder_right_context_length,
+        encoder_dropout,
+        encoder_activation,
+        encoder_max_memory_size,
+        encoder_weight_init_scale_strategy,
+        encoder_tanh_on_mem,
+    )
+    return Wav2Vec2Model(feature_extractor, emformer)
+
+
+def emformer_hubert_base(
+    extractor_input_dim: int = 80,
+    extractor_output_dim: int = 128,
+    encoder_dropout: float = 0.1,
+) -> Wav2Vec2Model:
+    """Build Emformer HuBERT Model with 20 Emformer layers.
+
+    Args:
+        extractor_input_dim (int, optional): The input dimension for feature extractor. (Default: 80)
+        extractor_output_dim (int, optional): The output dimension after feature extractor. (Default: 128)
+        encoder_dropout (float, optional): Dropout probability in Emformer. (Default: 0.1)
+
+    Returns:
+        Wav2Vec2Model:
+            The resulting :py:class:`torchaudio.models.Wav2Vec2Model` model
+            with a :py:class:`torchaudio.models.Emformer` encoder.
+    """
+    return emformer_hubert_model(
+        extractor_input_dim=extractor_input_dim,
+        extractor_output_dim=extractor_output_dim,
+        extractor_use_bias=False,
+        extractor_stride=4,
+        encoder_input_dim=512,
+        encoder_output_dim=1024,
+        encoder_num_heads=8,
+        encoder_ffn_dim=2048,
+        encoder_num_layers=20,
+        encoder_segment_length=4,
+        encoder_left_context_length=30,
+        encoder_right_context_length=1,
+        encoder_dropout=encoder_dropout,
+        encoder_activation="gelu",
+        encoder_max_memory_size=0,
+        encoder_weight_init_scale_strategy="depthwise",
+        encoder_tanh_on_mem=True,
+    )