feat: cherry-pick of torch.compile dynamic shapes (#2750)

Author: peri044

.github/workflows/build-test.yml

@@ -21,6 +21,7 @@ jobs:
       os: linux
       test-infra-repository: pytorch/test-infra
       test-infra-ref: main
+      channel: test
       with-rocm: false
       with-cpu: false
 
@@ -208,6 +209,7 @@ jobs:
         ${CONDA_RUN} python -m pip install --pre pytest-xdist timm transformers parameterized expecttest==0.1.6 --use-deprecated=legacy-resolver
         ${CONDA_RUN} python -m pytest -n 10 --junitxml=${RUNNER_TEST_RESULTS_DIR}/torch_compile_be_test_results.xml backend/
         ${CONDA_RUN} python -m pytest -n 4 --junitxml=${RUNNER_TEST_RESULTS_DIR}/torch_comple_be_e2e_test_results.xml --ir torch_compile models/test_models.py
+        ${CONDA_RUN} python -m pytest --junitxml=${RUNNER_TEST_RESULTS_DIR}/torch_compile_dyn_models_export.xml --ir torch_compile models/test_dyn_models.py
         popd
 
   tests-py-dynamo-core:

py/torch_tensorrt/dynamo/_compiler.py

@@ -273,14 +273,12 @@ def contains_metadata(gm: torch.fx.GraphModule) -> bool:
                 return False
         return True
 
-    # Check if the module has metadata (shape, dtype). If not, run symbolic shape propagation.
+    # Check if the module has metadata (shape, dtype).
     if not contains_metadata(gm):
-        from torch._inductor.compile_fx import fake_tensor_prop
-
-        torch_inputs = get_torch_inputs(sample_inputs, settings.device)
-        with torch.no_grad():
-            # This fails if the module has data-dependent shape operators.
-            fake_tensor_prop(gm, torch_inputs)
+        # TODO: For future, explore when nodes don't have metadata and if fake_tensor_prop can resolve this.
+        logger.warning(
+            "Some nodes do not have metadata (shape and dtype information). This could lead to problems sometimes if the graph has PyTorch and TensorRT segments."
+        )
 
     # Partition module into components that can be TRT-accelerated
     fast_partitioner_failed = False

py/torch_tensorrt/dynamo/backend/backends.py

@@ -13,6 +13,7 @@
 from torch_tensorrt.dynamo.lowering import (
     apply_lowering_passes,
     get_decompositions,
+    remove_sym_nodes,
     repair_input_aliasing,
 )
 from torch_tensorrt.dynamo.utils import (
@@ -27,7 +28,7 @@
 @td.register_backend(name="tensorrt")  # type: ignore[misc]
 @td.register_backend(name="torch_tensorrt")  # type: ignore[misc]
 def torch_tensorrt_backend(
-    gm: torch.fx.GraphModule, sample_inputs: Sequence[torch.Tensor], **kwargs: Any
+    gm: torch.fx.GraphModule, sample_inputs: Sequence[Any], **kwargs: Any
 ) -> torch.nn.Module:
     # Set log level at the top of compilation (torch_tensorrt.dynamo)
     if (
@@ -44,15 +45,15 @@ def torch_tensorrt_backend(
 
 @td.register_backend(name="aot_torch_tensorrt_aten")  # type: ignore[misc]
 def aot_torch_tensorrt_aten_backend(
-    gm: torch.fx.GraphModule, sample_inputs: Sequence[torch.Tensor], **kwargs: Any
+    gm: torch.fx.GraphModule, sample_inputs: Sequence[Any], **kwargs: Any
 ) -> torch.nn.Module:
     settings = parse_dynamo_kwargs(kwargs)
     return _pretraced_backend(gm, sample_inputs, settings)
 
 
 def _pretraced_backend(
     gm: torch.fx.GraphModule,
-    sample_inputs: Sequence[torch.Tensor],
+    sample_inputs: Sequence[Any],
     settings: CompilationSettings = CompilationSettings(),
 ) -> torch.fx.GraphModule | Callable[..., Any]:
     """Helper function to manage translation of traced FX module to TRT engines
@@ -74,10 +75,17 @@ def _pretraced_backend(
             fake_mode, "allow_non_fake_inputs", True
         ), fake_mode:
             repair_input_aliasing(gm)
+
+            # Remove sym_int placeholders and inputs
+            remove_sym_nodes(gm)
+            torch_inputs = [
+                input for input in sample_inputs if isinstance(input, torch.Tensor)
+            ]
+
             # Invoke AOTAutograd to translate operators to aten
             gm = aot_export_joint_simple(
                 gm,
-                sample_inputs,
+                torch_inputs,
                 trace_joint=False,
                 decompositions=get_decompositions(
                     settings.enable_experimental_decompositions
@@ -86,10 +94,10 @@ def _pretraced_backend(
 
             logger.debug("Post-AOT Autograd graph:\n" + str(gm.graph))
 
-            gm = apply_lowering_passes(gm, sample_inputs)
+            gm = apply_lowering_passes(gm, torch_inputs)
 
             torchtrt_inputs = prepare_inputs(
-                sample_inputs, disable_memory_format_check=True
+                torch_inputs, disable_memory_format_check=True
             )
             trt_compiled = compile_module(
                 gm,

py/torch_tensorrt/dynamo/conversion/impl/shape.py

@@ -104,6 +104,7 @@ def get_shape_with_dynamic_shape(
     scale_res = scale_layer.get_output(0)
 
     length = input_shape.shape[0]
+
     zero_layer = ctx.net.add_constant(
         input_shape.shape, np.zeros((length), dtype=np.int32)
     )

py/torch_tensorrt/dynamo/lowering/__init__.py

@@ -3,6 +3,6 @@
     torch_enabled_decompositions,
 )
 from ._decompositions import get_decompositions  # noqa: F401
-from ._fusers import *  # noqa: F401
+from ._remove_sym_nodes import remove_sym_nodes
 from ._repair_input_aliasing import repair_input_aliasing
 from .passes import apply_lowering_passes

py/torch_tensorrt/dynamo/lowering/_fusers.py

@@ -0,0 +1,30 @@
+import logging
+
+import torch
+
+logger = logging.getLogger(__name__)
+
+
+def remove_sym_nodes(gm: torch.fx.GraphModule) -> torch.fx.GraphModule:
+    """Remove sym_int placeholders which get inserted due to torch.compile's
+    dynamic=True behavior
+    """
+    # Extract SymInt placeholder Tensors
+    placeholders = [
+        node
+        for node in gm.graph.nodes
+        if (
+            node.op == "placeholder"
+            and isinstance(node.type, type)
+            and issubclass(node.type, torch.SymInt)
+        )
+    ]
+
+    for node in placeholders:
+        gm.graph.erase_node(node)
+
+    gm.graph.lint()
+    gm.recompile()
+    logger.debug(f"Removed SymInt placeholders:\n{gm.graph}")
+
+    return gm