Set to legacy pytorch to onnx exporter for CI.

Author: Talmaj

onnx2pytorch/helpers.py

@@ -23,6 +23,7 @@ def to_onnx(model, inp_size, device=torch.device("cpu"), do_constant_folding=Fal
         do_constant_folding=do_constant_folding,
         input_names=["input"],
         output_names=["output"],
+        dynamo=False,  # Use legacy exporter for compatibility with LSTM
     )
     return onnx.ModelProto.FromString(bitstream.getvalue())
 

tests/onnx2pytorch/convert/test_lstm.py

@@ -40,6 +40,7 @@ def test_single_layer_lstm(
         f=bitstream,
         input_names=["input", "h_0", "c_0"],
         opset_version=11,
+        dynamo=False,  # Use legacy exporter for LSTM compatibility
         dynamic_axes={
             "input": {0: "seq_len", 1: "batch"},
             "h_0": {1: "batch"},

tests/onnx2pytorch/convert/test_maxpool.py

@@ -85,6 +85,7 @@ def test_maxpool_2d_ceil():
         f=bitstream,
         input_names=["x"],
         opset_version=11,
+        dynamo=False,
     )
     onnx_model = onnx.ModelProto.FromString(bitstream.getvalue())
     o2p_model = ConvertModel(onnx_model)
@@ -100,6 +101,7 @@ def test_maxpool_2d_ceil():
         f=bitstream,
         input_names=["x"],
         opset_version=11,
+        dynamo=False,
     )
     onnx_model = onnx.ModelProto.FromString(bitstream.getvalue())
     o2p_model = ConvertModel(onnx_model)

tests/onnx2pytorch/convert/test_train.py

@@ -14,7 +14,9 @@ def test_train_multiple_models():
     original_output = original_model(dummy_input)
 
     bitstream = io.BytesIO()
-    torch.onnx.export(original_model, dummy_input, bitstream, opset_version=11)
+    torch.onnx.export(
+        original_model, dummy_input, bitstream, opset_version=11, dynamo=False
+    )
 
     bitstream.seek(0)
     onnx_model = onnx.load(bitstream)

tests/onnx2pytorch/test_onnx2pytorch.py

@@ -42,6 +42,7 @@ def test_onnx2pytorch2onnx(onnx_model, onnx_model_outputs, onnx_inputs):
         export_params=True,
         opset_version=11,
         do_constant_folding=True,
+        dynamo=False,
         input_names=list(onnx_inputs.keys()),
     )