Use upstream PyTorch cost models instead of local copy#305
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pianpwk wants to merge 1 commit intometa-pytorch:mainfrom
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Use upstream PyTorch cost models instead of local copy#305pianpwk wants to merge 1 commit intometa-pytorch:mainfrom
pianpwk wants to merge 1 commit intometa-pytorch:mainfrom
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DEPENDENCY: Requires pytorch/pytorch branch `cost-model-consolidation` which adds `all_to_all_cost` and `include_compute_cost` to `torch.distributed.tensor._collective_utils`. Changes: - Remove local `all_to_all_cost` function (now imported from PyTorch) - Remove copy-pasted `redistribute_cost` function - Use PyTorch's `redistribute_cost` with `include_compute_cost=True` - Deprecate `order` parameter (PyTorch now uses _gen_transform_infos for optimal ordering) - Add comprehensive tests for cost model functions This consolidation: - Reduces code duplication between PyTorch and Autoparallel - Ensures cost models stay in sync across projects - Removes the 5x hack in all_to_all_cost (calibration moved to PyTorch) Authored with Claude.
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DEPENDENCY: Requires pytorch/pytorch branch
cost-model-consolidationwhich addsall_to_all_costandinclude_compute_costtotorch.distributed.tensor._collective_utils.Changes:
all_to_all_costfunction (now imported from PyTorch)redistribute_costfunctionredistribute_costwithinclude_compute_cost=Trueorderparameter (PyTorch now uses _gen_transform_infos for optimal ordering)This consolidation:
Authored with Claude.