[https://nvbugs/5606166][fix] AutoDeploy: use tuples for cudagraph shape lookup #8658
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📝 WalkthroughWalkthroughThis PR normalizes shape type handling in CUDA graph computations by converting input shapes to tuples for hashability consistency, and adds a test verification mechanism that temporarily replaces the forward method to ensure graph capture usage. Changes
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⚠️ Outside diff range comments (1)
tensorrt_llm/_torch/auto_deploy/compile/backends/torch_cudagraph.py (1)
1-1: Add missing NVIDIA Apache-2.0 copyright header.Per coding guidelines, all Python source files must include the NVIDIA Apache-2.0 copyright header at the top of the file with the current year (2025).
As per coding guidelines
Add the copyright header before the docstring:
+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. """Compile backend with cudagraph."""
🧹 Nitpick comments (1)
tensorrt_llm/_torch/auto_deploy/compile/backends/torch_cudagraph.py (1)
160-166: LGTM! Consistent tuple conversion for shape lookup.The
tuple()conversion ensures thecombined_shapekey used for CUDA graph lookup at line 166 matches the key format created during capture (line 142).Optional: Consider iterable unpacking for a more Pythonic approach:
rounded_shapes = [ - (self.round_to_cuda_batch_size(input.shape[0]),) + tuple(input.shape[1:]) + (self.round_to_cuda_batch_size(input.shape[0]), *input.shape[1:]) for input in args_batched ]
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📒 Files selected for processing (2)
tensorrt_llm/_torch/auto_deploy/compile/backends/torch_cudagraph.py(2 hunks)tests/unittest/_torch/auto_deploy/unit/singlegpu/compile/test_cuda_graph_batch_sizes.py(2 hunks)
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**/*.{h,hpp,hh,hxx,cpp,cxx,cc,cu,cuh,py}
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tests/unittest/_torch/auto_deploy/unit/singlegpu/compile/test_cuda_graph_batch_sizes.pytensorrt_llm/_torch/auto_deploy/compile/backends/torch_cudagraph.py
**/*.py
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Files:
tests/unittest/_torch/auto_deploy/unit/singlegpu/compile/test_cuda_graph_batch_sizes.pytensorrt_llm/_torch/auto_deploy/compile/backends/torch_cudagraph.py
**/*.{cpp,cxx,cc,h,hpp,hh,hxx,cu,cuh,py}
📄 CodeRabbit inference engine (CODING_GUIDELINES.md)
Prepend the NVIDIA Apache-2.0 copyright header with current year to the top of all source files (e.g., .cpp, .h, .cu, .py).
Files:
tests/unittest/_torch/auto_deploy/unit/singlegpu/compile/test_cuda_graph_batch_sizes.pytensorrt_llm/_torch/auto_deploy/compile/backends/torch_cudagraph.py
🧬 Code graph analysis (1)
tests/unittest/_torch/auto_deploy/unit/singlegpu/compile/test_cuda_graph_batch_sizes.py (2)
tensorrt_llm/_torch/auto_deploy/compile/backends/torch_cudagraph.py (1)
forward(145-179)tests/unittest/_torch/auto_deploy/unit/singlegpu/compile/test_captured_graph.py (1)
forward(21-27)
🪛 Ruff (0.14.1)
tests/unittest/_torch/auto_deploy/unit/singlegpu/compile/test_cuda_graph_batch_sizes.py
39-39: Unused static method argument: args
(ARG004)
39-39: Unused static method argument: kwargs
(ARG004)
40-40: Avoid specifying long messages outside the exception class
(TRY003)
tensorrt_llm/_torch/auto_deploy/compile/backends/torch_cudagraph.py
160-160: Consider iterable unpacking instead of concatenation
Replace with iterable unpacking
(RUF005)
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🔇 Additional comments (3)
tensorrt_llm/_torch/auto_deploy/compile/backends/torch_cudagraph.py (1)
142-143: LGTM! Ensures consistent tuple keys for CUDA graph lookup.Converting
input.shapetotuple()ensures the dictionary key is consistently a tuple rather than atorch.Sizeobject, which resolves the shape lookup issue mentioned in the PR.tests/unittest/_torch/auto_deploy/unit/singlegpu/compile/test_cuda_graph_batch_sizes.py (2)
38-40: LGTM! Clever test helper to verify CUDA graph usage.This helper function is used in the test to temporarily replace
model.forward, ensuring that if the model's forward method is called (instead of the CUDA graph path), the test will fail. This validates that the CUDA graph is actually being used for valid batch sizes.Note: The static analysis hints about unused arguments are false positives—the signature must match the method being replaced. The message style hint is a minor style preference and can be safely ignored in test code.
199-205: LGTM! Proper verification of CUDA graph execution path.The test correctly verifies that
captured_graph.forward()uses the CUDA graph replay path rather than falling back tomodel.forward(). The try/finally pattern ensures the original forward method is restored even if the test fails.
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…ape lookup Signed-off-by: Lucas Liebenwein <[email protected]>
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Summary by CodeRabbit
Bug Fixes
Tests
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