Add row based sharding support for FeaturedProcessedEBC #3406
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…#3406) Summary: X-link: meta-pytorch#3281 In this diff we introduce row based sharding (TWRW, RW, GRID) type support for feature processors. Previously, feature processors did not support row based sharding since feature processors are data parallel. This means by splitting up the input for row based shards the accessed feature processor weights were in correct. In column/data sharding based approaches, the data is duplicated ensuring the correct weight is accessed across ranks. The indices/buckets are calculated post input split/distribution, to make it compatible with row based sharding we calculate this pre input split/distribution. This couples the train pipeline and feature processors. For each feature, we preprocess the input and place the calculated indices in KJT.weights, this propagates the indices correctly and indexs into the right weight to use for the final step in the feature processing. This applies in both pipelined and non pipelined situations - the input modification is done either at the pipelined forward call or in the input dist of the FPEBC. This is determined by the pipelining flag set through rewrite_model in train pipeline. Differential Revision: D82248545
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…#3406) Summary: X-link: meta-pytorch#3281 In this diff we introduce row based sharding (TWRW, RW, GRID) type support for feature processors. Previously, feature processors did not support row based sharding since feature processors are data parallel. This means by splitting up the input for row based shards the accessed feature processor weights were in correct. In column/data sharding based approaches, the data is duplicated ensuring the correct weight is accessed across ranks. The indices/buckets are calculated post input split/distribution, to make it compatible with row based sharding we calculate this pre input split/distribution. This couples the train pipeline and feature processors. For each feature, we preprocess the input and place the calculated indices in KJT.weights, this propagates the indices correctly and indexs into the right weight to use for the final step in the feature processing. This applies in both pipelined and non pipelined situations - the input modification is done either at the pipelined forward call or in the input dist of the FPEBC. This is determined by the pipelining flag set through rewrite_model in train pipeline. Differential Revision: D82248545
…#3406) Summary: X-link: meta-pytorch#3281 In this diff we introduce row based sharding (TWRW, RW, GRID) type support for feature processors. Previously, feature processors did not support row based sharding since feature processors are data parallel. This means by splitting up the input for row based shards the accessed feature processor weights were in correct. In column/data sharding based approaches, the data is duplicated ensuring the correct weight is accessed across ranks. The indices/buckets are calculated post input split/distribution, to make it compatible with row based sharding we calculate this pre input split/distribution. This couples the train pipeline and feature processors. For each feature, we preprocess the input and place the calculated indices in KJT.weights, this propagates the indices correctly and indexs into the right weight to use for the final step in the feature processing. This applies in both pipelined and non pipelined situations - the input modification is done either at the pipelined forward call or in the input dist of the FPEBC. This is determined by the pipelining flag set through rewrite_model in train pipeline. Differential Revision: D82248545
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Summary:
X-link: #3281
In this diff we introduce row based sharding (TWRW, RW, GRID) type support for feature processors. Previously, feature processors did not support row based sharding since feature processors are data parallel. This means by splitting up the input for row based shards the accessed feature processor weights were in correct. In column/data sharding based approaches, the data is duplicated ensuring the correct weight is accessed across ranks.
The indices/buckets are calculated post input split/distribution, to make it compatible with row based sharding we calculate this pre input split/distribution. This couples the train pipeline and feature processors. For each feature, we preprocess the input and place the calculated indices in KJT.weights, this propagates the indices correctly and indexs into the right weight to use for the final step in the feature processing.
This applies in both pipelined and non pipelined situations - the input modification is done either at the pipelined forward call or in the input dist of the FPEBC. This is determined by the pipelining flag set through rewrite_model in train pipeline.
Differential Revision: D82248545