Minor speedup for model lowering: Skip redundant run_decompositions when no ops match decomp table (#18496)#18496
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Minor speedup for model lowering: Skip redundant run_decompositions when no ops match decomp table (#18496)#18496apullin wants to merge 1 commit intopytorch:mainfrom
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/18496
Note: Links to docs will display an error until the docs builds have been completed. ❌ 1 New Failure, 4 Unrelated FailuresAs of commit e10f77c with merge base 401ea8e ( NEW FAILURE - The following job has failed:
FLAKY - The following jobs failed but were likely due to flakiness present on trunk:
BROKEN TRUNK - The following jobs failed but were present on the merge base:👉 Rebase onto the `viable/strict` branch to avoid these failures
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…orch#18496) Summary: Adds an early-exit check to _gen_edge_manager_for_partitioners: before calling program.run_decompositions(table), scan the graph for ops that appear in the decomposition table. If none are found, skip the call entirely. Each run_decompositions call performs a full re-export of the program via make_fx(), re-tracing every node through FakeTensor dispatch. On the EDGE_DO_NOT_DECOMP path this function is called up to 3 times; the early-exit eliminates at least one redundant call where the previous pass already decomposed all matching ops. The check recursively walks control flow submodules (cond/map/scan) to avoid incorrectly skipping when decomposable ops are nested. ## Benchmark Model: small CNN feature extractor (~50K params, 9 conv layers with LayerNorm, targeting Ethos-U55 via the ARM/TOSA lowering pipeline). Graph: ~1200 nodes. lower() before: 82 s lower() after: 71 s Delta: -11 s (-13 %) Differential Revision: D96489903
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…orch#18496) Summary: Adds an early-exit check to _gen_edge_manager_for_partitioners: before calling program.run_decompositions(table), scan the graph for ops that appear in the decomposition table. If none are found, skip the call entirely. Each run_decompositions call performs a full re-export of the program via make_fx(), re-tracing every node through FakeTensor dispatch. On the EDGE_DO_NOT_DECOMP path this function is called up to 3 times; the early-exit eliminates at least one redundant call where the previous pass already decomposed all matching ops. The check recursively walks control flow submodules (cond/map/scan) to avoid incorrectly skipping when decomposable ops are nested. ## Benchmark Model: small CNN feature extractor (~50K params, 9 conv layers with LayerNorm, targeting Ethos-U55 via the ARM/TOSA lowering pipeline). Graph: ~1200 nodes. lower() before: 82 s lower() after: 71 s Delta: -11 s (-13 %) Differential Revision: D96489903
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…orch#18496) Summary: Adds an early-exit check to _gen_edge_manager_for_partitioners: before calling program.run_decompositions(table), scan the graph for ops that appear in the decomposition table. If none are found, skip the call entirely. Each run_decompositions call performs a full re-export of the program via make_fx(), re-tracing every node through FakeTensor dispatch. On the EDGE_DO_NOT_DECOMP path this function is called up to 3 times; the early-exit eliminates at least one redundant call where the previous pass already decomposed all matching ops. The check recursively walks control flow submodules (cond/map/scan) to avoid incorrectly skipping when decomposable ops are nested. ## Benchmark Model: small CNN feature extractor (~50K params, 9 conv layers with LayerNorm, targeting Ethos-U55 via the ARM/TOSA lowering pipeline). Graph: ~1200 nodes. lower() before: 82 s lower() after: 71 s Delta: -11 s (-13 %) Differential Revision: D96489903
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…orch#18496) Summary: Pull Request resolved: pytorch#18496 Adds an early-exit check to _gen_edge_manager_for_partitioners: before calling program.run_decompositions(table), scan the graph for ops that appear in the decomposition table. If none are found, skip the call entirely. Each run_decompositions call performs a full re-export of the program via make_fx(), re-tracing every node through FakeTensor dispatch. On the EDGE_DO_NOT_DECOMP path this function is called up to 3 times; the early-exit eliminates at least one redundant call where the previous pass already decomposed all matching ops. The check recursively walks control flow submodules (cond/map/scan) to avoid incorrectly skipping when decomposable ops are nested. ## Benchmark Model: small CNN feature extractor (~50K params, 9 conv layers with LayerNorm, targeting Ethos-U55 via the ARM/TOSA lowering pipeline). Graph: ~1200 nodes. lower() before: 82 s lower() after: 71 s Delta: -11 s (-13 %) Differential Revision: D96489903
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…hen no ops match decomp table (pytorch#18496) Summary: Pull Request resolved: pytorch#18496 Adds an early-exit check to _gen_edge_manager_for_partitioners: before calling program.run_decompositions(table), scan the graph for ops that appear in the decomposition table. If none are found, skip the call entirely. Each run_decompositions call performs a full re-export of the program via make_fx(), re-tracing every node through FakeTensor dispatch. On the EDGE_DO_NOT_DECOMP path this function is called up to 3 times; the early-exit eliminates at least one redundant call where the previous pass already decomposed all matching ops. The check recursively walks control flow submodules (cond/map/scan) to avoid incorrectly skipping when decomposable ops are nested. ## Benchmark Model: small CNN feature extractor (~50K params, 9 conv layers with LayerNorm, targeting Ethos-U55 via the ARM/TOSA lowering pipeline). Graph: ~1200 nodes. lower() before: 82 s lower() after: 71 s Delta: -11 s (-13 %) Differential Revision: D96489903
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…hen no ops match decomp table (pytorch#18496) Summary: Adds an early-exit check to _gen_edge_manager_for_partitioners: before calling program.run_decompositions(table), scan the graph for ops that appear in the decomposition table. If none are found, skip the call entirely. Each run_decompositions call performs a full re-export of the program via make_fx(), re-tracing every node through FakeTensor dispatch. On the EDGE_DO_NOT_DECOMP path this function is called up to 3 times; the early-exit eliminates at least one redundant call where the previous pass already decomposed all matching ops. The check recursively walks control flow submodules (cond/map/scan) to avoid incorrectly skipping when decomposable ops are nested. ## Benchmark Model: small CNN feature extractor (~50K params, 9 conv layers with LayerNorm, targeting Ethos-U55 via the ARM/TOSA lowering pipeline). Graph: ~1200 nodes. lower() before: 82 s lower() after: 71 s Delta: -11 s (-13 %) Differential Revision: D96489903
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…hen no ops match decomp table (pytorch#18496) Summary: Adds an early-exit check to _gen_edge_manager_for_partitioners: before calling program.run_decompositions(table), scan the graph for ops that appear in the decomposition table. If none are found, skip the call entirely. Each run_decompositions call performs a full re-export of the program via make_fx(), re-tracing every node through FakeTensor dispatch. On the EDGE_DO_NOT_DECOMP path this function is called up to 3 times; the early-exit eliminates at least one redundant call where the previous pass already decomposed all matching ops. The check recursively walks control flow submodules (cond/map/scan) to avoid incorrectly skipping when decomposable ops are nested. ## Benchmark Model: small CNN feature extractor (~50K params, 9 conv layers with LayerNorm, targeting Ethos-U55 via the ARM/TOSA lowering pipeline). Graph: ~1200 nodes. lower() before: 82 s lower() after: 71 s Delta: -11 s (-13 %) Differential Revision: D96489903
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Summary:
Adds an early-exit check to _gen_edge_manager_for_partitioners: before
calling program.run_decompositions(table), scan the graph for ops that
appear in the decomposition table. If none are found, skip the call
entirely.
Each run_decompositions call performs a full re-export of the program
via make_fx(), re-tracing every node through FakeTensor dispatch.
On the EDGE_DO_NOT_DECOMP path this function is called up to 3 times;
the early-exit eliminates at least one redundant call where the previous
pass already decomposed all matching ops.
The check recursively walks control flow submodules (cond/map/scan) to
avoid incorrectly skipping when decomposable ops are nested.
Benchmark
Model: small CNN feature extractor (~50K params, 9 conv layers with
LayerNorm, targeting Ethos-U55 via the ARM/TOSA lowering pipeline).
Graph: ~1200 nodes.
lower() before: 82 s
lower() after: 71 s
Delta: -11 s (-13 %)
Differential Revision: D96489903