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Fuse shared expert MLP with grouped GEMM - #5604
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/ok to test b687d5b |
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@sraman-rgb, there was an error processing your request: See the following link for more information: https://docs.gha-runners.nvidia.com/cpr/e/2/ |
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/ok to test a05f54d |
Phlip79
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done |
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@jiemingz @fanshiqing can you please take a look at this MR? |
Signed-off-by: Siddhartha Raman Sundara Raman <sraman@nvidia.com>
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/ok to test 30edd6c |
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| glu_interleave_size = self.config.moe_shared_expert_glu_interleave_size | ||
| fc1_weight = self.linear_fc1.weight | ||
| op = te.pytorch.ops.GroupedLinear( |
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LGTM
The te op fuser code is starting to proliferate around the code base, we might want to consider collecting it in one location, probably under the extensions/transformer_engine directory
Signed-off-by: Siddhartha Raman Sundara Raman <sraman@nvidia.com>
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/ok to test 5fe4e6b |
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🔄 Merge queue validation started! You can track the progress here: https://github.com/NVIDIA/Megatron-LM/actions/runs/28886850036 |
What does this PR do?
This PR is for enabling usage of groupedGEMM based shared expert MLP consisting of dense GEMMs. This is needed for fusion of shared expert GEMM+swiglu+quant fusion
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