Introduces new operator to get the lemma logits for factored vocabulary models for GPU inference#776
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rhenry-nv wants to merge 18 commits into
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Introduces new operator to get the lemma logits for factored vocabulary models for GPU inference#776rhenry-nv wants to merge 18 commits into
rhenry-nv wants to merge 18 commits into
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…erence when processing the lemmas
…since the allocator has a memory pool that it manages for it won't get released by a cuda free. Additionally, two kernels may get the same pointer but they cannot execute concurrently since a single thread does not launch concurrent kernels. Since there is an allocator per thread, this means that no two kernels can ever race on the same pointer (I think). I have not seen any issues after removing this sync
…expose more parallelism when adding into the lemmas
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FYI: I am currently requesting internally to remove the notices in each file and for NVIDIA to be added to the license file. I will take care of the licensing once I get confirmation. |
rhenry-nv
commented
Dec 15, 2020
| auto factorMaxima = max(logits_[g]->loss(), -1); | ||
| auto factorMasks = constant(getFactorMasks(g, shortlist ? shortlist->indices() : std::vector<WordIndex>())); | ||
| sel = sel + factorMaxima * factorMasks; // those lemmas that don't have a factor get multiplied with 0 | ||
| if(numGroups > 1 && graph()->isInference() && graph()->getBackend()->getDeviceId().type == DeviceType::gpu) { |
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This fork is something I wasn't sure how to remove. It would be better if it was under the expression operator but moving it down causes the operator interface to be a bit ugly and introduces some code duplication. Feedback on this in particular would be greatly appreciated.
rhenry-nv
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Description
This PR adds a new inference operator to GPU for getting the lemma logits for a factored vocabulary. It demonstrates significant speedup on GPU inference over PR #772
Here are some perf numbers relative to PR #772
Times from a proxy model with 1 stream as measured on a Titan V.
Times from a proxy model with two streams as measured on a Titan V
List of changes:
Added dependencies: cub
How to test
I ran the regression tests and they all passed. I also manually tested on a proxy model and the outputs from master exactly match the outputs after this change was made.
CMake command: cmake .. -DCOMPILE_CPU=on -DCOMPILE_CUDA=on -DUSE_SENTENCEPIECE=on -DUSE_STATIC_LIBS=off -DCOMPILE_SERVER=off -DUSE_FBGEMM=on -DCOMPILE_CUDA_SM35=off -DCOMPILE_CUDA_SM50=off -DCOMPILE_CUDA_SM60=off -DCOMPILE_CUDA_SM70=on -DCOMPILE_CUDA_SM75=off -DCOMPILE_TESTS=on
Ubuntu - 18.04.3 LTS
nvcc - 10.1.243
gcc - 7.5.0
Checklist