Fixed the issue in comments

examples/dynamo/mutable_torchtrt_module_example.py

@@ -14,6 +14,7 @@
 1. Sample workflow of Mutable Torch TensorRT Module with ResNet 18
 2. Save a Mutable Torch TensorRT Module
 3. Integration with Huggingface pipeline in LoRA use case
+4. Usage of dynamic shape with Mutable Torch TensorRT Module
 """
 
 import numpy as np
@@ -63,16 +64,14 @@
 # Saving Mutable Torch TensorRT Module
 # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-# Currently, saving is only enabled for C++ runtime, not python runtime.
+# Currently, saving is only when "use_python" = False in settings
 torch_trt.MutableTorchTensorRTModule.save(mutable_module, "mutable_module.pkl")
 reload = torch_trt.MutableTorchTensorRTModule.load("mutable_module.pkl")
 
 # %%
 # Stable Diffusion with Huggingface
 # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-# The LoRA checkpoint is from https://civitai.com/models/12597/moxin
-
 from diffusers import DiffusionPipeline
 
 with torch.no_grad():
@@ -111,3 +110,45 @@
     # Refit triggered
     image = pipe(prompt, negative_prompt=negative, num_inference_steps=30).images[0]
     image.save("./with_LoRA_mutable.jpg")
+
+
+# %%
+# Use Mutable Torch TensorRT module with dynamic shape
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+class Model(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, a, b, c={}):
+        x = torch.matmul(a, b)
+        x = torch.matmul(c["a"], c["b"].T)
+        print(c["b"][0])
+        x = 2 * c["b"]
+        return x
+
+
+device = "cuda:0"
+model = Model().eval().to(device)
+inputs = (torch.rand(10, 3).to(device), torch.rand(3, 30).to(device))
+kwargs = {
+    "c": {"a": torch.rand(10, 30).to(device), "b": torch.rand(10, 30).to(device)},
+}
+dim_0 = torch.export.Dim("dim", min=1, max=50)
+dim_1 = torch.export.Dim("dim", min=1, max=50)
+dim_2 = torch.export.Dim("dim2", min=1, max=50)
+args_dynamic_shapes = ({1: dim_1}, {0: dim_0})
+kwarg_dynamic_shapes = {
+    "c": {"a": {}, "b": {0: dim_2}},
+}
+# Export the model first with custom dynamic shape constraints
+model = torch_trt.MutableTorchTensorRTModule(model, debug=True, min_block_size=1)
+model.set_expected_dynamic_shape_range(args_dynamic_shapes, kwarg_dynamic_shapes)
+# Compile
+model(*inputs, **kwargs)
+# Change input shape
+inputs_2 = (torch.rand(10, 5).to(device), torch.rand(10, 30).to(device))
+kwargs_2 = {
+    "c": {"a": torch.rand(10, 30).to(device), "b": torch.rand(5, 30).to(device)},
+}
+# Run without recompiling
+model(*inputs_2, **kwargs_2)

py/torch_tensorrt/dynamo/_refit.py

@@ -395,9 +395,12 @@ def refit_module_weights(
                     try:
                         weight_name_map = compiled_submodule.weight_name_map
                     except AttributeError:
-                        logger.warning(
-                            "The module was compiled with an old version of Torch-TensorRT. Rebuilding the weight map."
-                        )
+                        if not isinstance(
+                            compiled_submodule, torch.fx.graph_module.GraphModule
+                        ):
+                            logger.warning(
+                                "The module was compiled with an old version of Torch-TensorRT. Rebuilding the weight map."
+                            )
                     if not weight_name_map:
                         use_weight_map_cache = False
                         logger.warning(

py/torch_tensorrt/dynamo/conversion/_TRTInterpreter.py

@@ -375,7 +375,10 @@ def _construct_trt_network_def(self) -> None:
 
     @staticmethod
     def find_weight(
-        weight_name: str, np_map: dict[str, Any], state_dict: dict[str, Any]
+        weight_name: str,
+        np_map: dict[str, Any],
+        state_dict: dict[str, Any],
+        device: torch.device,
     ) -> str:
         """
         We need to build map from engine weight name to state_dict weight name.
@@ -385,19 +388,21 @@ def find_weight(
         np_map: the map from weight name to np values in INetworkDefinition
         state_dict: state of the graph module
         """
-        network_weight = torch.from_numpy(np_map[weight_name]).cuda()
+        network_weight = torch.from_numpy(np_map[weight_name]).to(device)
         for sd_w_name, sd_weight in state_dict.items():
-            if TRTInterpreter.check_weight_equal(sd_weight, network_weight):
+            if TRTInterpreter.check_weight_equal(sd_weight, network_weight, device):
                 del state_dict[sd_w_name]
                 return sd_w_name
         return ""
 
     @staticmethod
     def check_weight_equal(
-        sd_weight: torch.tensor, network_weight: Union[torch.Tensor, np.ndarray]
+        sd_weight: torch.tensor,
+        network_weight: Union[torch.Tensor, np.ndarray],
+        device: torch.device,
     ) -> Any:
         if not isinstance(network_weight, torch.Tensor):
-            network_weight = torch.from_numpy(network_weight).cuda()
+            network_weight = torch.from_numpy(network_weight).to(device)
         try:
             return sd_weight.shape == network_weight.shape and torch.all(
                 torch.abs(sd_weight - network_weight) < 0.01
@@ -530,10 +535,10 @@ def _save_weight_mapping(self) -> None:
                 # There is no direct connection in batch_norm layer. So skip it
                 pass
             elif sd_weight_name not in sd or not TRTInterpreter.check_weight_equal(
-                sd[sd_weight_name], np_map[engine_weight_name]
+                sd[sd_weight_name], np_map[engine_weight_name], torch_device
             ):
                 weight_name_map[engine_weight_name] = TRTInterpreter.find_weight(
-                    engine_weight_name, np_map, sd
+                    engine_weight_name, np_map, sd, torch_device
                 )
                 if (
                     weight_name_map[engine_weight_name] != ""