Add lazy Inductor compilation to graph_pp_runner#360
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LGTM, can merge after CI passes
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The module-level `dispatcher.sharding_propagator = CustomShardingPropagator()` was leaking into other test files (e.g. test_api.py) when run in the same pytest process, causing `aten.copy_` failures because the custom propagator doesn't have rules for ops that the default DTensor propagator handles. test_dtensor.py's two test classes (ImplicitRegistrationTest, DimShardingTest) inherit from DTensorTestBase which uses MultiProcessTestCase -- each test spawns subprocesses that re-import the module. Those subprocesses don't run pytest fixtures, so they need the custom propagator installed at module level. We gate the module-level install on `multiprocessing.current_process().name` to only run in spawned workers, and use a module-scoped autouse pytest fixture to install/restore the propagator in the main process. Authored with Claude. stack-info: PR: #367, branch: xmfan/stack/32
Add _execute_graph() that lazily compiles graph modules with compile_fx_inner on first invocation. Controlled by an inductor kwarg threaded through all _run_* functions. GraphPPRunner accepts inductor=True and propagates it to all GraphPipelineStage instances, which the stage_* action functions read when calling _run_*. Authored with Claude. stack-info: PR: #360, branch: xmfan/stack/30
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* Scope CustomShardingPropagator to test_dtensor tests via pytest fixture The module-level `dispatcher.sharding_propagator = CustomShardingPropagator()` was leaking into other test files (e.g. test_api.py) when run in the same pytest process, causing `aten.copy_` failures because the custom propagator doesn't have rules for ops that the default DTensor propagator handles. test_dtensor.py's two test classes (ImplicitRegistrationTest, DimShardingTest) inherit from DTensorTestBase which uses MultiProcessTestCase -- each test spawns subprocesses that re-import the module. Those subprocesses don't run pytest fixtures, so they need the custom propagator installed at module level. We gate the module-level install on `multiprocessing.current_process().name` to only run in spawned workers, and use a module-scoped autouse pytest fixture to install/restore the propagator in the main process. Authored with Claude. stack-info: PR: #367, branch: xmfan/stack/32 * Add lazy Inductor compilation to graph_pp_runner Add _execute_graph() that lazily compiles graph modules with compile_fx_inner on first invocation. Controlled by an inductor kwarg threaded through all _run_* functions. GraphPPRunner accepts inductor=True and propagates it to all GraphPipelineStage instances, which the stage_* action functions read when calling _run_*. Authored with Claude. stack-info: PR: #360, branch: xmfan/stack/30 * Add --inductor flag to example_ds3_pp with FORCE_BALANCED_ROUTING The DSv3 MoE implementation uses .tolist() and data-dependent grouped_mm offsets that Inductor cannot compile. Add FORCE_BALANCED_ROUTING to the model that makes token-per-expert counts uniform and uses balanced all-to-all splits, eliminating all data-dependent ops. The --inductor CLI flag enables both Inductor compilation and forced balanced routing together. Authored with Claude. stack-info: PR: #361, branch: xmfan/stack/31
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Stacked PRs:
Add lazy Inductor compilation to graph_pp_runner
Add _execute_graph() that lazily compiles graph modules with
compile_fx_inner on first invocation. Controlled by an inductor kwarg
threaded through all run* functions.
GraphPPRunner accepts inductor=True and propagates it to all
GraphPipelineStage instances, which the stage_* action functions
read when calling run*.
Authored with Claude.