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[PyTorch] Userbuffers support in operation-based API #1142
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Signed-off-by: Tim Moon <tmoon@nvidia.com>
Signed-off-by: Tim Moon <tmoon@nvidia.com>
Signed-off-by: Tim Moon <tmoon@nvidia.com>
Assumes FP8 RS, which is not a good assumption. Signed-off-by: Tim Moon <tmoon@nvidia.com>
Bias pointers are not properly offset for different data chunks. Also removed logic for FP8 RS. Signed-off-by: Tim Moon <tmoon@nvidia.com>
Test passes with row TP, fails with col TP. Signed-off-by: Tim Moon <tmoon@nvidia.com>
Signed-off-by: Tim Moon <tmoon@nvidia.com>
Signed-off-by: Tim Moon <tmoon@nvidia.com>
Signed-off-by: Tim Moon <tmoon@nvidia.com>
Signed-off-by: Tim Moon <tmoon@nvidia.com>
Signed-off-by: Tim Moon <tmoon@nvidia.com>
for more information, see https://pre-commit.ci
Signed-off-by: Tim Moon <tmoon@nvidia.com>
/te-ci pytorch |
/te-ci pytorch |
denera
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Sep 24, 2024
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LGTM, at least on the Userbuffers code side.
Signed-off-by: Tim Moon <tmoon@nvidia.com>
Signed-off-by: Tim Moon <tmoon@nvidia.com>
Signed-off-by: Tim Moon <tmoon@nvidia.com>
for more information, see https://pre-commit.ci
Signed-off-by: Tim Moon <tmoon@nvidia.com>
/te-ci pytorch |
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Description
This PR adds basic support in the linear operation for using Userbuffers to overlap tensor-parallel communication with GEMMs. This is implemented as fused operations:
I've tried to avoid touching the core UB infrastructure in
transformer_engine/pytorch/module/base.py
, so I've kept the messy API and hackily worked around some bugs. This feature should be considered experimental and unstable.Type of change
Changes
Checklist: