[DTensor] Document redistribute_costs#158495
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wconstab wants to merge 6 commits intogh/wconstab/430/basefrom
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[DTensor] Document redistribute_costs#158495wconstab wants to merge 6 commits intogh/wconstab/430/basefrom
wconstab wants to merge 6 commits intogh/wconstab/430/basefrom
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/158495
Note: Links to docs will display an error until the docs builds have been completed. ✅ You can merge normally! (1 Unrelated Failure)As of commit 87b7510 with merge base 900fba4 ( UNSTABLE - The following job is marked as unstable, possibly due to flakiness on trunk:
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cc H-Huang awgu wanchaol fegin fduwjj wz337 d4l3k [ghstack-poisoned]
cc H-Huang awgu wanchaol fegin fduwjj wz337 d4l3k [ghstack-poisoned]
cc H-Huang awgu wanchaol fegin fduwjj wz337 d4l3k [ghstack-poisoned]
zpcore
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Jul 17, 2025
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| 0.0, # cost of redistributing tensor_a from 'Replicate()' | ||
| K, # cost of redistributing tensor_a from 'Shard(0)' |
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What about:
0.0, # cost of redistributing tensor_a from Replicate() -> Replicate()
K, # cost of redistributing tensor_a from 'Shard(0)' -> Replicate()
cc H-Huang awgu wanchaol fegin fduwjj wz337 d4l3k [ghstack-poisoned]
[ghstack-poisoned]
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@pytorchbot merge -i |
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Merge startedYour change will be merged while ignoring the following 1 checks: pull / cuda12.8-py3.10-gcc9-sm75 / test (pr_time_benchmarks, 1, 1, linux.g4dn.metal.nvidia.gpu, unstable) Learn more about merging in the wiki. Questions? Feedback? Please reach out to the PyTorch DevX Team |
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Starting merge as part of PR stack under #158490 |
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Jul 18, 2025
Fixes several bugs in the original. - foremost, fixes a serious bug where we returned incorrect strategies by mixing input_specs that were frozen from select_strategy.strategies[0] with output_specs that varied across select_strategy.strategies[0..N] (e.g. we could create a nonsense strategy like input:Shard(0) output(Replicate) for an op like clone - fixes the redistribute costs: they should not actually be 0, they should be the cost of redistributing our single input from another strategy to the current strategy, in our list of output strategies - adds a note, wondering if we should have just literally returned the input strategy instead of creating this new object - Currently, using default_strategy is incorrect becuase it maps 'self' tensor's strategies directly onto 'src' tensor without accounting for the fact that copy_ supports broadcasting a smaller rank tensor into a larger one. Separates out copy_ op from default strategy, adds missing test case, but does not fix the underlying issue with copy_, leaves that for future PR Renames to `propagate_single_input_strategy` since that's more descriptive Pull Request resolved: #158490 Approved by: https://github.com/wanchaol, https://github.com/XilunWu ghstack dependencies: #158495
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Jul 18, 2025
The previous strategy directly used 'self' input strategy for 'src' input. The fixed strategy correctly maps the self dim to src dim so that it works even if the src input is broadcast. E.g. for this program, broadcasting will occur on dims 0,1,3 of self. ``` self = torch.ones((2,3,4,5)) src = torch.ones((4,1)) self.copy_(src) ``` These are the correct sharding combinations: | self | src | |-------|------| | Shard(0) | Replicate() | | Shard(1) | Replicate() | | Shard(2) | Shard(0) | | Shard(3) | Shard(1) | Pull Request resolved: #158538 Approved by: https://github.com/zpcore, https://github.com/XilunWu, https://github.com/wanchaol ghstack dependencies: #158495, #158490
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