Replace assert with raise in torchaudio.models (#2590)

Summary:
Pull Request resolved: https://github.com/pytorch/audio/pull/2590

Converted assert checks for argument validation to if-else checks so that they are executed in optimized mode as well.

Reviewed By: mthrok

Differential Revision: D38211246

fbshipit-source-id: 922b5bcafe8214980e535527dd94c3345c1ff3e2

torchaudio/models/conformer.py

@@ -37,7 +37,8 @@ class _ConvolutionModule(torch.nn.Module):
         use_group_norm: bool = False,
     ) -> None:
         super().__init__()
-        assert (depthwise_kernel_size - 1) % 2 == 0, "depthwise_kernel_size must be odd to achieve 'SAME' padding."
+        if (depthwise_kernel_size - 1) % 2 != 0:
+            raise ValueError("depthwise_kernel_size must be odd to achieve 'SAME' padding.")
         self.layer_norm = torch.nn.LayerNorm(input_dim)
         self.sequential = torch.nn.Sequential(
             torch.nn.Conv1d(

torchaudio/models/emformer.py

@@ -61,7 +61,8 @@ def _get_weight_init_gains(weight_init_scale_strategy: Optional[str], num_layers
 def _gen_attention_mask_block(
     col_widths: List[int], col_mask: List[bool], num_rows: int, device: torch.device
 ) -> torch.Tensor:
-    assert len(col_widths) == len(col_mask), "Length of col_widths must match that of col_mask"
+    if len(col_widths) != len(col_mask):
+        raise ValueError("Length of col_widths must match that of col_mask")
 
     mask_block = [
         torch.ones(num_rows, col_width, device=device)
@@ -194,11 +195,12 @@ class _EmformerAttention(torch.nn.Module):
 
         # Compute attention.
         attention = torch.bmm(attention_probs, reshaped_value)
-        assert attention.shape == (
+        if attention.shape != (
             B * self.num_heads,
             T,
             self.input_dim // self.num_heads,
-        )
+        ):
+            raise AssertionError("Computed attention has incorrect dimensions")
         attention = attention.transpose(0, 1).contiguous().view(T, B, self.input_dim)
 
         # Apply output projection.
@@ -770,11 +772,12 @@ class _EmformerImpl(torch.nn.Module):
                     output states; list of lists of tensors representing internal state
                     generated in current invocation of ``infer``.
         """
-        assert input.size(1) == self.segment_length + self.right_context_length, (
-            "Per configured segment_length and right_context_length"
-            f", expected size of {self.segment_length + self.right_context_length} for dimension 1 of input"
-            f", but got {input.size(1)}."
-        )
+        if input.size(1) != self.segment_length + self.right_context_length:
+            raise ValueError(
+                "Per configured segment_length and right_context_length"
+                f", expected size of {self.segment_length + self.right_context_length} for dimension 1 of input"
+                f", but got {input.size(1)}."
+            )
         input = input.permute(1, 0, 2)
         right_context_start_idx = input.size(0) - self.right_context_length
         right_context = input[right_context_start_idx:]

torchaudio/models/rnnt_decoder.py

@@ -280,13 +280,13 @@ class RNNTBeamSearch(torch.nn.Module):
         Returns:
             List[Hypothesis]: top-``beam_width`` hypotheses found by beam search.
         """
-        assert input.dim() == 2 or (
-            input.dim() == 3 and input.shape[0] == 1
-        ), "input must be of shape (T, D) or (1, T, D)"
+        if input.dim() != 2 and not (input.dim() == 3 and input.shape[0] == 1):
+            raise ValueError("input must be of shape (T, D) or (1, T, D)")
         if input.dim() == 2:
             input = input.unsqueeze(0)
 
-        assert length.shape == () or length.shape == (1,), "length must be of shape () or (1,)"
+        if length.shape != () and length.shape != (1,):
+            raise ValueError("length must be of shape () or (1,)")
         if input.dim() == 0:
             input = input.unsqueeze(0)
 
@@ -326,13 +326,13 @@ class RNNTBeamSearch(torch.nn.Module):
                     list of lists of tensors representing transcription network
                     internal state generated in current invocation.
         """
-        assert input.dim() == 2 or (
-            input.dim() == 3 and input.shape[0] == 1
-        ), "input must be of shape (T, D) or (1, T, D)"
+        if input.dim() != 2 and not (input.dim() == 3 and input.shape[0] == 1):
+            raise ValueError("input must be of shape (T, D) or (1, T, D)")
         if input.dim() == 2:
             input = input.unsqueeze(0)
 
-        assert length.shape == () or length.shape == (1,), "length must be of shape () or (1,)"
+        if length.shape != () and length.shape != (1,):
+            raise ValueError("length must be of shape () or (1,)")
         if length.dim() == 0:
             length = length.unsqueeze(0)
 

torchaudio/models/tacotron2.py

@@ -83,7 +83,8 @@ def _get_conv1d_layer(
         (torch.nn.Conv1d): The corresponding Conv1D layer.
     """
     if padding is None:
-        assert kernel_size % 2 == 1
+        if kernel_size % 2 != 1:
+            raise ValueError("kernel_size must be odd")
         padding = int(dilation * (kernel_size - 1) / 2)
 
     conv1d = torch.nn.Conv1d(
@@ -1033,7 +1034,6 @@ class Tacotron2(nn.Module):
             lengths = torch.tensor([max_length]).expand(n_batch).to(tokens.device, tokens.dtype)
 
         assert lengths is not None  # For TorchScript compiler
-
         embedded_inputs = self.embedding(tokens).transpose(1, 2)
         encoder_outputs = self.encoder(embedded_inputs, lengths)
         mel_specgram, mel_specgram_lengths, _, alignments = self.decoder.infer(encoder_outputs, lengths)

torchaudio/models/wav2vec2/components.py

@@ -512,7 +512,8 @@ def _get_feature_extractor(
           - Large:
             https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/pretraining/wav2vec2_large_librivox.yaml#L61
     """
-    assert norm_mode in ["group_norm", "layer_norm"]
+    if norm_mode not in ["group_norm", "layer_norm"]:
+        raise ValueError("Invalid norm mode")
     blocks = []
     in_channels = 1
     for i, (out_channels, kernel_size, stride) in enumerate(shapes):

torchaudio/models/wav2vec2/model.py

@@ -155,9 +155,10 @@ class HuBERTPretrainModel(Module):
         self.wav2vec2 = wav2vec2
         self.mask_generator = mask_generator
         self.logit_generator = logit_generator
-        assert (
-            feature_grad_mult is None or 0.0 < feature_grad_mult < 1.0
-        ), f"The value of `feature_grad_mult` must be ``None`` or between (0, 1). Found {feature_grad_mult}"
+        if feature_grad_mult is not None and not 0.0 < feature_grad_mult < 1.0:
+            raise ValueError(
+                f"The value of `feature_grad_mult` must be ``None``or between (0, 1). Found {feature_grad_mult}"
+            )
         self.feature_grad_mult = feature_grad_mult
 
     def forward(
@@ -204,7 +205,8 @@ class HuBERTPretrainModel(Module):
         x, attention_mask = self.wav2vec2.encoder._preprocess(x, lengths)
         x, mask = self.mask_generator(x, padding_mask)
         x = self.wav2vec2.encoder.transformer(x, attention_mask=attention_mask)
-        assert x.shape[1] == labels.shape[1], "The length of label must match that of HuBERT model output"
+        if x.shape[1] != labels.shape[1]:
+            raise ValueError("The length of label must match that of HuBERT model output")
         if padding_mask is not None:
             mask_m = torch.logical_and(~padding_mask, mask)
             mask_u = torch.logical_and(~padding_mask, ~mask_m)

torchaudio/models/wavernn.py

@@ -277,8 +277,10 @@ class WaveRNN(nn.Module):
             Tensor: shape (n_batch, 1, (n_time - kernel_size + 1) * hop_length, n_classes)
         """
 
-        assert waveform.size(1) == 1, "Require the input channel of waveform is 1"
-        assert specgram.size(1) == 1, "Require the input channel of specgram is 1"
+        if waveform.size(1) != 1:
+            raise ValueError("Require the input channel of waveform is 1")
+        if specgram.size(1) != 1:
+            raise ValueError("Require the input channel of specgram is 1")
         # remove channel dimension until the end
         waveform, specgram = waveform.squeeze(1), specgram.squeeze(1)