Use upstream DTensor sharding strategies#505
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AutoParallel now relies on PyTorch DTensor propagation for the upstream-backed strategy removals instead of carrying local duplicates. This covers reductions, tensor movement/scatter variants, normalization, convolution, reshape, and expand while keeping the AutoParallel-specific rules in place. Authored with Claude. stack-info: PR: #505, branch: aditvenk/stack/17
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This was referenced Jun 15, 2026
AutoParallel now relies on PyTorch DTensor propagation for the upstream-backed strategy removals instead of carrying local duplicates. This covers reductions, tensor movement/scatter variants, normalization, convolution, reshape, and expand while keeping the AutoParallel-specific rules in place. Authored with Claude. stack-info: PR: #505, branch: aditvenk/stack/17
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AutoParallel now relies on PyTorch DTensor propagation for the upstream-backed strategy removals instead of carrying local duplicates. This covers reductions, tensor movement/scatter variants, normalization, convolution, reshape, and expand while keeping the AutoParallel-specific rules in place.
Authored with Claude.