Fixed the CI problem (#3680)

Author: cehongwang

py/torch_tensorrt/dynamo/_refit.py

@@ -101,7 +101,7 @@ def construct_refit_mapping_from_weight_name_map(
                 params[w.split(".")[-1]] = state_dict[w].cuda()
             # Batch norm constant folding
 
-            scale, shift = batch_norm_constant_folding(**params, eps=1e-7)
+            scale, shift = batch_norm_constant_folding(**params, eps=1e-5)
             # Set scale to scale or shift to shift
             engine_weight_map[engine_weight_name] = eval(
                 engine_weight_name.split(" ")[-1].lower()

tests/py/dynamo/models/test_model_refit.py

@@ -89,6 +89,70 @@ def test_mapping():
     torch._dynamo.reset()
 
 
+@unittest.skipIf(
+    not torch_trt.ENABLED_FEATURES.torch_tensorrt_runtime,
+    "TorchScript Frontend is not available",
+)
+@unittest.skipIf(
+    not torch_trt.ENABLED_FEATURES.refit,
+    "Refit feature is not supported in Python 3.13 or higher",
+)
+@unittest.skipIf(
+    not importlib.util.find_spec("torchvision"),
+    "torchvision is not installed",
+)
+@pytest.mark.unit
+def test_conv_refit_with_weightmap():
+    class net(nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.conv = nn.Conv2d(3, 3, 1)
+
+        def forward(self, x):
+            return self.conv(x)
+
+    model = net().eval().to("cuda")
+    model2 = net().eval().to("cuda")
+    inputs = [torch.randn((1, 3, 224, 224)).to("cuda")]
+    enabled_precisions = {torch.float}
+    min_block_size = 1
+    use_python_runtime = True
+
+    exp_program = torch.export.export(model, tuple(inputs))
+    exp_program2 = torch.export.export(model2, tuple(inputs))
+
+    trt_gm = torchtrt.dynamo.compile(
+        exp_program,
+        tuple(inputs),
+        use_python_runtime=use_python_runtime,
+        enabled_precisions=enabled_precisions,
+        min_block_size=min_block_size,
+        immutable_weights=False,
+    )
+
+    new_trt_gm = refit_module_weights(
+        compiled_module=trt_gm,
+        new_weight_module=exp_program2,
+        arg_inputs=inputs,
+        use_weight_map_cache=True,
+        verify_output=True,
+    )
+
+    # Check the output
+    model2.to("cuda")
+    expected_outputs, refitted_outputs = exp_program2.module()(*inputs), new_trt_gm(
+        *inputs
+    )
+    for expected_output, refitted_output in zip(expected_outputs, refitted_outputs):
+        assertions.assertTrue(
+            torch.allclose(expected_output, refitted_output, 1e-2, 1e-2),
+            "Refit Result is not correct. Refit failed",
+        )
+        # Clean up model env
+
+    torch._dynamo.reset()
+
+
 @unittest.skipIf(
     not torch_trt.ENABLED_FEATURES.torch_tensorrt_runtime,
     "TorchScript Frontend is not available",

tests/py/dynamo/runtime/test_mutable_torchtrt_module.py

@@ -317,9 +317,7 @@ def test_resnet18_modify_attribute():
     mutable_module = torch_trt.MutableTorchTensorRTModule(model, **compile_spec)
     mutable_module(*inputs)
 
-    mutable_module.conv1.weight = nn.Parameter(
-        torch.rand_like(mutable_module.conv1.weight)
-    )
+    mutable_module.fc.weight = nn.Parameter(torch.rand_like(mutable_module.fc.weight))
     assertions.assertEqual(
         mutable_module.refit_state.get_state(),
         RefitFlag.UNKNOWN,