simplify embedding + first transformer block TP #314
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as titled, we can directly specify the rowwise parallel embedding output layouts be shard on sequence dim, so that we don't need the first layer prepare input. Switching to output_layouts = Shard(1) would also trigger reduce_scatter instead of allreduce for embedding layer, which could give some small perf wins
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…first transformer block" Following changes in pytorch/torchtitan#314, to apply a reduce-scatter instead of the more expensive all-reduce + local chunk. cross PR with pytorch/tutorials#2871 [ghstack-poisoned]
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as titled, we can directly specify the rowwise parallel embedding output layouts be shard on sequence dim, so that we don't need the first layer prepare input. Switching to output_layouts = Shard(1) would also trigger reduce_scatter instead of allreduce for embedding layer, which could give some small perf wins
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as titled, we can directly specify the rowwise parallel embedding output layouts be shard on sequence dim, so that we don't need the first layer prepare input. Switching to output_layouts = Shard(1) would also trigger reduce_scatter instead of allreduce for embedding layer, which could give some small perf wins
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as titled, we can directly specify the rowwise parallel embedding output layouts be shard on sequence dim, so that we don't need the first layer prepare input.
Switching to output_layouts = Shard(1) would also trigger reduce_scatter instead of allreduce for embedding layer, which could give some small perf wins