chore: update test case

Author: keehyuna

py/torch_tensorrt/_compile.py

@@ -12,6 +12,9 @@
 from torch_tensorrt._features import ENABLED_FEATURES
 from torch_tensorrt._Input import Input
 from torch_tensorrt.dynamo import _defaults
+from torch_tensorrt.dynamo.runtime._WrapperTorchTensorRTModule import (
+    WrapperTorchTensorRTModule,
+)
 from torch_tensorrt.fx import InputTensorSpec
 from torch_tensorrt.fx.lower import compile as fx_compile
 from torch_tensorrt.fx.utils import LowerPrecision
@@ -586,14 +589,16 @@ def save(
     Save the model to disk in the specified output format.
 
     Arguments:
-        module (Optional(torch.jit.ScriptModule | torch.export.ExportedProgram | torch.fx.GraphModule)): Compiled Torch-TensorRT module
+        module (Optional(torch.jit.ScriptModule | torch.export.ExportedProgram | torch.fx.GraphModule | WrapperTorchTensorRTModule)): Compiled Torch-TensorRT module
         inputs (torch.Tensor): Torch input tensors
         arg_inputs (Tuple[Any, ...]): Same as inputs. Alias for better understanding with kwarg_inputs.
         kwarg_inputs (dict[Any, ...]): Optional, kwarg inputs to the module forward function.
         output_format (str): Format to save the model. Options include exported_program | torchscript.
         retrace (bool): When the module type is a fx.GraphModule, this option re-exports the graph using torch.export.export(strict=False) to save it.
                 This flag is experimental for now.
     """
+    if isinstance(module, WrapperTorchTensorRTModule):
+        module = module.original_module
     module_type = _parse_module_type(module)
     accepted_formats = {"exported_program", "torchscript"}
     if arg_inputs is not None and not all(

py/torch_tensorrt/dynamo/_compiler.py

@@ -835,7 +835,7 @@ def contains_metadata(gm: torch.fx.GraphModule) -> bool:
 
     dryrun_stats_display(dryrun_tracker, settings.dryrun)
 
-    if len(trt_modules) > 1:
+    if len(dryrun_tracker.to_run_in_torch) > 0:
         # Capture/replay a series of CUDA operations in subgraphs in a wrapped runtime module.
         partitioned_module = WrapperTorchTensorRTModule(
             partitioned_module,

py/torch_tensorrt/dynamo/runtime/_PythonTorchTensorRTModule.py

@@ -78,6 +78,7 @@ def __init__(
         self.cudagraph: Optional[torch.cuda.CUDAGraph] = None
         self._caller_stream: Optional[torch.cuda.Stream] = None
         self._engine_stream: Optional[torch.cuda.Stream] = None
+
         # TODO: Make the below a Dictionary {shape: cudagraph}
         self.shape_key: Optional[str] = None
 

py/torch_tensorrt/dynamo/runtime/_WrapperTorchTensorRTModule.py

@@ -46,7 +46,7 @@ def __init__(
             if "_run_on_acc" in name:
                 rt_mod.set_cudagraphs_enabled_parent_module(True)
 
-        # TODO: check if only torch needs warm up.
+        # Warm up is necessary to ensure that memory allocations and initializations are not recorded in cuda graphs
         with unset_fake_temporarily():
             inputs_tensor = [spec.torch_tensor.cuda() for spec in self.inputs]
             s = torch.cuda.Stream()
@@ -256,7 +256,6 @@ def forward(self, *inputs: torch.Tensor) -> torch.Tensor | Tuple[torch.Tensor, .
                 self._caller_stream.wait_stream(self._engine_stream)
 
             if cudagraphs_enabled:
-                # TODO: submodule to return list only
                 if isinstance(self._output_buffers, (list, tuple)):
                     output_buffers = self._output_buffers
                 else:

py/torch_tensorrt/runtime/_weight_streaming.py

@@ -3,6 +3,9 @@
 
 import torch
 from torch_tensorrt.dynamo.runtime import PythonTorchTensorRTModule, TorchTensorRTModule
+from torch_tensorrt.dynamo.runtime._WrapperTorchTensorRTModule import (
+    WrapperTorchTensorRTModule,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -12,9 +15,14 @@ class _WeightStreamingContextManager(object):
     Helper class used to setup weight streaming budget
     """
 
-    def __init__(self, module: torch.fx.GraphModule) -> None:
+    def __init__(
+        self, module: torch.fx.GraphModule | WrapperTorchTensorRTModule
+    ) -> None:
         rt_mods = []
         self.current_device_budget = 0
+
+        if isinstance(module, WrapperTorchTensorRTModule):
+            module = module.original_module
         for name, rt_mod in module.named_children():
             if "_run_on_acc" in name and isinstance(
                 rt_mod, (PythonTorchTensorRTModule, TorchTensorRTModule)

tests/py/dynamo/runtime/test_002_cudagraphs_py.py

@@ -158,76 +158,6 @@ def forward(self, x):
                 msg=f"CUDA Graph Python TRT outputs don't match with the original model. (trial: {i})",
             )
 
-    def test_cudagraphs_dynamic_py(self):
-        class SampleModel(torch.nn.Module):
-            def forward(self, x):
-                return torch.relu((x + 2) * 0.5)
-
-        # TODO: more dynamic dim
-        # TODO: multiple output
-        # TODO: module that graph cannot be used
-        inputs = torch_tensorrt.Input(
-            min_shape=(1, 3, 224, 224),
-            opt_shape=(8, 3, 224, 224),
-            max_shape=(16, 3, 224, 224),
-            dtype=torch.float,
-            name="x",
-        )
-        fx_graph = torch.fx.symbolic_trace(SampleModel())
-
-        # Validate that the results between Torch and Torch-TRT are similar
-        optimized_model = torch_tensorrt.compile(
-            fx_graph,
-            "dynamo",
-            inputs,
-            min_block_size=1,
-            pass_through_build_failures=True,
-            torch_executed_ops={"torch.ops.aten.mul.Tensor"},
-            use_python_runtime=True,
-        )
-
-        result_samples = []
-        torch_results_samples = []
-
-        inputs = []
-        for i in [1, 3, 8, 11, 16]:
-            inputs.append(torch.randn((i, 3, 224, 224)).cuda())
-
-        for n in range(len(inputs) * TRIALS):
-            i = n // TRIALS
-            # disable cuda graph at all index for all trials
-            if n % TRIALS == n // TRIALS:
-                torch_tensorrt.runtime.set_cudagraphs_mode(False)
-            else:
-                torch_tensorrt.runtime.set_cudagraphs_mode(True)
-
-            result_samples.append(optimized_model(inputs[i]).detach().cpu())
-            torch_results_samples.append(fx_graph(inputs[i]).detach().cpu())
-
-        for n in range(len(inputs) * TRIALS):
-            i = n // TRIALS
-            # enable cuda graph at all index for all trials
-            if n % TRIALS == n // TRIALS:
-                torch_tensorrt.runtime.set_cudagraphs_mode(True)
-            else:
-                torch_tensorrt.runtime.set_cudagraphs_mode(False)
-
-            result_samples.append(optimized_model(inputs[i]).detach().cpu())
-            torch_results_samples.append(fx_graph(inputs[i]).detach().cpu())
-
-        for i, (optimized_model_results, torch_model_results) in enumerate(
-            zip(result_samples, torch_results_samples)
-        ):
-            max_diff = float(
-                torch.max(torch.abs(optimized_model_results - torch_model_results))
-            )
-            self.assertAlmostEqual(
-                max_diff,
-                0,
-                DECIMALS_OF_AGREEMENT,
-                msg=f"CUDA Graph Python TRT outputs don't match with the original model. (trial: {i})",
-            )
-
 
 if __name__ == "__main__":
     run_tests()