Fix exporting streaming zipformer models. (#1755)

egs/librispeech/ASR/zipformer/export-onnx-streaming.py

@@ -74,7 +74,6 @@ import onnx
 import torch
 import torch.nn as nn
 from decoder import Decoder
-from onnxconverter_common import float16
 from onnxruntime.quantization import QuantType, quantize_dynamic
 from scaling_converter import convert_scaled_to_non_scaled
 from train import add_model_arguments, get_model, get_params
@@ -756,6 +755,7 @@ def main():
     logging.info(f"Exported joiner to {joiner_filename}")
 
     if(params.fp16) :
+        from onnxconverter_common import float16
         logging.info("Generate fp16 models")
 
         encoder = onnx.load(encoder_filename)

egs/librispeech/ASR/zipformer/zipformer.py

@@ -191,6 +191,7 @@ class Zipformer2(EncoderInterface):
                     dim=encoder_dim[i],
                     downsample=downsampling_factor[i],
                     dropout=dropout,
+                    causal=causal,
                 )
 
             encoders.append(encoder)
@@ -198,7 +199,10 @@ class Zipformer2(EncoderInterface):
         self.encoders = nn.ModuleList(encoders)
 
         self.downsample_output = SimpleDownsample(
-            max(encoder_dim), downsample=output_downsampling_factor, dropout=dropout
+            max(encoder_dim),
+            downsample=output_downsampling_factor,
+            dropout=dropout,
+            causal=causal,
         )
 
     def get_feature_masks(self, x: Tensor) -> Union[List[float], List[Tensor]]:
@@ -1217,11 +1221,16 @@ class DownsampledZipformer2Encoder(nn.Module):
     """
 
     def __init__(
-        self, encoder: nn.Module, dim: int, downsample: int, dropout: FloatLike
+        self,
+        encoder: nn.Module,
+        dim: int,
+        downsample: int,
+        dropout: FloatLike,
+        causal: bool,
     ):
         super(DownsampledZipformer2Encoder, self).__init__()
         self.downsample_factor = downsample
-        self.downsample = SimpleDownsample(dim, downsample, dropout)
+        self.downsample = SimpleDownsample(dim, downsample, dropout, causal)
         self.num_layers = encoder.num_layers
         self.encoder = encoder
         self.upsample = SimpleUpsample(dim, downsample)
@@ -1310,9 +1319,12 @@ class SimpleDownsample(torch.nn.Module):
     Does downsampling with attention, by weighted sum, and a projection..
     """
 
-    def __init__(self, channels: int, downsample: int, dropout: FloatLike):
+    def __init__(
+        self, channels: int, downsample: int, dropout: FloatLike, causal: bool
+    ):
         super(SimpleDownsample, self).__init__()
 
+        self.causal = causal
         self.bias = nn.Parameter(torch.zeros(downsample))
 
         self.name = None  # will be set from training code
@@ -1333,9 +1345,18 @@ class SimpleDownsample(torch.nn.Module):
         # Pad to an exact multiple of self.downsample
         # right-pad src, repeating the last element.
         pad = d_seq_len * ds - seq_len
+
+        if self.causal and torch.jit.is_tracing():
+            assert (
+                pad == 0
+            ), f"pad should be zero for exporting streaming models. Given {pad}"
+
+        # If we are exporting a streaming model, then we skip the if statement
+        if not self.causal or not torch.jit.is_tracing():
             src_extra = src[src.shape[0] - 1 :].expand(pad, src.shape[1], src.shape[2])
             src = torch.cat((src, src_extra), dim=0)
-        assert src.shape[0] == d_seq_len * ds
+
+        assert src.shape[0] == d_seq_len * ds, (src.shape, d_seq_len, ds)
 
         src = src.reshape(d_seq_len, ds, batch_size, in_channels)
 
@@ -1609,7 +1630,11 @@ class RelPositionMultiheadAttentionWeights(nn.Module):
         k = x[..., query_dim : 2 * query_dim]
         # p is the position-encoding query
         p = x[..., 2 * query_dim :]
-        assert p.shape[-1] == num_heads * pos_head_dim, (p.shape[-1], num_heads, pos_head_dim)
+        assert p.shape[-1] == num_heads * pos_head_dim, (
+            p.shape[-1],
+            num_heads,
+            pos_head_dim,
+        )
 
         q = self.copy_query(q)  # for diagnostics only, does nothing.
         k = self.whiten_keys(self.balance_keys(k))  # does nothing in the forward pass.