[DTensor] single-dim expander raises clear inplace error#173572
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[DTensor] single-dim expander raises clear inplace error#173572wconstab wants to merge 2 commits intogh/wconstab/508/basefrom
wconstab wants to merge 2 commits intogh/wconstab/508/basefrom
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Inplace ops for dtensor have a restriction: you're not allowed to redistribute the 'inplace' tensor. This means in some cases, sharding propagation has to fail becuase the inplace input is not compatible with any of the possible sharding strategies. This PR makes sure this case raises the expected informative error rather than a confusing error about selecting min cost over an empty sharding strategies list. [ghstack-poisoned]
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/173572
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This was referenced Jan 27, 2026
pianpwk
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Jan 27, 2026
| f"{op_schema.op}: in-place operations that require placement changes " | ||
| f"are not supported. The input has placement {blocking_inplace_input_placements}, " | ||
| f"but no valid strategy preserves this placement. " | ||
| f"Please use the out-of-place version of this operation instead." |
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nit: or redistribute to {one of the possible placements}?
pianpwk
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Jan 27, 2026
Inplace ops for dtensor have a restriction: you're not allowed to redistribute the 'inplace' tensor. This means in some cases, sharding propagation has to fail becuase the inplace input is not compatible with any of the possible sharding strategies. This PR makes sure this case raises the expected informative error rather than a confusing error about selecting min cost over an empty sharding strategies list. [ghstack-poisoned]
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Pull Request resolved: #173567 Approved by: https://github.com/tianyu-l, https://github.com/fegin ghstack dependencies: #172479, #173572
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) Pull Request resolved: pytorch#173567 Approved by: https://github.com/tianyu-l, https://github.com/fegin ghstack dependencies: pytorch#172479, pytorch#173572
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Inplace ops for dtensor have a restriction: you're not allowed to redistribute the 'inplace' tensor. This means in some cases, sharding propagation has to fail becuase the inplace input is not compatible with any of the possible sharding strategies. This PR makes sure this case raises the expected informative error rather than a confusing error about selecting min cost over an empty sharding strategies list. ghstack-source-id: 76bf5f7 Pull Request resolved: pytorch/pytorch#173572
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Stack from ghstack (oldest at bottom):
Inplace ops for dtensor have a restriction: you're not allowed to
redistribute the 'inplace' tensor. This means in some cases, sharding
propagation has to fail becuase the inplace input is not compatible with
any of the possible sharding strategies.
This PR makes sure this case raises the expected informative error
rather than a confusing error about selecting min cost over an
empty sharding strategies list.