Merge pull request #2 from csukuangfj/export_zipformer2_onnx

Use torch.jit.script() for position encoding

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

@@ -296,6 +296,8 @@ def export_encoder_model_onnx(
     x = torch.zeros(1, 100, 80, dtype=torch.float32)
     x_lens = torch.tensor([100], dtype=torch.int64)
 
+    encoder_model = torch.jit.trace(encoder_model, (x, x_lens))
+
     torch.onnx.export(
         encoder_model,
         (x, x_lens),
@@ -523,7 +525,7 @@ def main():
     model.to("cpu")
     model.eval()
 
-    convert_scaled_to_non_scaled(model, inplace=True)
+    convert_scaled_to_non_scaled(model, inplace=True, is_onnx=True)
 
     encoder = OnnxEncoder(
         encoder=model.encoder,

egs/librispeech/ASR/zipformer/scaling_converter.py

@@ -27,6 +27,7 @@ from typing import List, Tuple
 import torch
 import torch.nn as nn
 from scaling import Balancer, Dropout3, ScaleGrad, Whiten
+from zipformer import CompactRelPositionalEncoding
 
 
 # Copied from https://pytorch.org/docs/1.9.0/_modules/torch/nn/modules/module.html#Module.get_submodule  # noqa
@@ -51,6 +52,7 @@ def convert_scaled_to_non_scaled(
     model: nn.Module,
     inplace: bool = False,
     is_pnnx: bool = False,
+    is_onnx: bool = False,
 ):
     """
     Args:
@@ -61,6 +63,8 @@ def convert_scaled_to_non_scaled(
         If False, the input model is copied and we modify the copied version.
       is_pnnx:
         True if we are going to export the model for PNNX.
+      is_onnx:
+        True if we are going to export the model for ONNX.
     Return:
       Return a model without scaled layers.
     """
@@ -71,6 +75,11 @@ def convert_scaled_to_non_scaled(
     for name, m in model.named_modules():
         if isinstance(m, (Balancer, Dropout3, ScaleGrad, Whiten)):
             d[name] = nn.Identity()
+        elif is_onnx and isinstance(m, CompactRelPositionalEncoding):
+            # We want to recreate the positional encoding vector when
+            # the input changes, so we have to use torch.jit.script()
+            # to replace torch.jit.trace()
+            d[name] = torch.jit.script(m)
 
     for k, v in d.items():
         if "." in k:

egs/librispeech/ASR/zipformer/zipformer.py

@@ -1305,12 +1305,6 @@ class CompactRelPositionalEncoding(torch.nn.Module):
     ) -> None:
         """Construct a CompactRelPositionalEncoding object."""
         super(CompactRelPositionalEncoding, self).__init__()
-        if torch.jit.is_tracing():
-            # It assumes that the maximum input, after downsampling, won't have more than 
-            # 10k frames.
-            # The first downsampling factor is 2, so the maximum input 
-            # should contain less than 20k frames, e.g., less than 200 seconds, i.e., 3.33 minutes
-            max_len = 10000
         self.embed_dim = embed_dim
         assert embed_dim % 2 == 0
         self.dropout = Dropout2(dropout_rate)
@@ -1327,11 +1321,7 @@ class CompactRelPositionalEncoding(torch.nn.Module):
             # self.pe contains both positive and negative parts
             # the length of self.pe is 2 * input_len - 1
             if self.pe.size(0) >= T * 2 - 1:
-                # Note: TorchScript doesn't implement operator== for torch.Device
-                if self.pe.dtype != x.dtype or str(self.pe.device) != str(
-                    x.device
-                ):
-                    self.pe = self.pe.to(dtype=x.dtype, device=x.device)
+                self.pe = self.pe.to(dtype=x.dtype, device=x.device)
                 return
 
         # if T == 4, x would contain [ -3, -2, 1, 0, 1, 2, 3 ]