Parallelize the quantization conversion operators #45536
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Summary: Pull Request resolved: pytorch#45536 Quantization conversion/reverse conversion operators will be used in critical serving path. The operators can make use of aten::parallel to parallelize the rowwise quantization of tensors. Overall, i see 20-25% improvement with the parallelization optimization added here. The following result is from running benchmark on my `devvm`. Requested a dedicated machine and will post benchmark results again. Easier view to compare results https://our.intern.facebook.com/intern/diffing/?paste_number=143973933 Baseline results are based on D23675777 (pytorch@677a59d) ``` # ---------------------------------------- # PyTorch/Caffe2 Operator Micro-benchmarks # ---------------------------------------- # Tag : short # Benchmarking PyTorch: qembeddingbag_byte_prepack # Mode: Eager # Name: qembeddingbag_byte_prepack_num_embeddings80_embedding_dim128 # Input: num_embeddings: 80, embedding_dim: 128 Forward Execution Time (us) : 10.782 # Benchmarking PyTorch: qembeddingbag_byte_prepack # Mode: Eager # Name: qembeddingbag_byte_prepack_num_embeddings80_embedding_dim256 # Input: num_embeddings: 80, embedding_dim: 256 Forward Execution Time (us) : 17.443 # Benchmarking PyTorch: qembeddingbag_byte_prepack # Mode: Eager # Name: qembeddingbag_byte_prepack_num_embeddings80_embedding_dim512 # Input: num_embeddings: 80, embedding_dim: 512 Forward Execution Time (us) : 25.898 # Benchmarking PyTorch: qembeddingbag_4bit_prepack # Mode: Eager # Name: qembeddingbag_4bit_prepack_num_embeddings80_embedding_dim128 # Input: num_embeddings: 80, embedding_dim: 128 Forward Execution Time (us) : 13.903 # Benchmarking PyTorch: qembeddingbag_4bit_prepack # Mode: Eager # Name: qembeddingbag_4bit_prepack_num_embeddings80_embedding_dim256 # Input: num_embeddings: 80, embedding_dim: 256 Forward Execution Time (us) : 18.575 # Benchmarking PyTorch: qembeddingbag_4bit_prepack # Mode: Eager # Name: qembeddingbag_4bit_prepack_num_embeddings80_embedding_dim512 # Input: num_embeddings: 80, embedding_dim: 512 Forward Execution Time (us) : 30.650 # Benchmarking PyTorch: qembeddingbag_2bit_prepack # Mode: Eager # Name: qembeddingbag_2bit_prepack_num_embeddings80_embedding_dim128 # Input: num_embeddings: 80, embedding_dim: 128 Forward Execution Time (us) : 14.158 # Benchmarking PyTorch: qembeddingbag_2bit_prepack # Mode: Eager # Name: qembeddingbag_2bit_prepack_num_embeddings80_embedding_dim256 # Input: num_embeddings: 80, embedding_dim: 256 Forward Execution Time (us) : 19.818 # Benchmarking PyTorch: qembeddingbag_2bit_prepack # Mode: Eager # Name: qembeddingbag_2bit_prepack_num_embeddings80_embedding_dim512 # Input: num_embeddings: 80, embedding_dim: 512 Forward Execution Time (us) : 30.852 # Benchmarking PyTorch: qembeddingbag_byte_unpack # Mode: Eager # Name: qembeddingbag_byte_unpack_num_embeddings80_embedding_dim128 # Input: num_embeddings: 80, embedding_dim: 128 Forward Execution Time (us) : 47.596 # Benchmarking PyTorch: qembeddingbag_byte_unpack # Mode: Eager # Name: qembeddingbag_byte_unpack_num_embeddings80_embedding_dim256 # Input: num_embeddings: 80, embedding_dim: 256 Forward Execution Time (us) : 91.025 # Benchmarking PyTorch: qembeddingbag_byte_unpack # Mode: Eager # Name: qembeddingbag_byte_unpack_num_embeddings80_embedding_dim512 # Input: num_embeddings: 80, embedding_dim: 512 Forward Execution Time (us) : 131.425 # Benchmarking PyTorch: qembeddingbag_4bit_unpack # Mode: Eager # Name: qembeddingbag_4bit_unpack_num_embeddings80_embedding_dim128 # Input: num_embeddings: 80, embedding_dim: 128 Forward Execution Time (us) : 12.637 # Benchmarking PyTorch: qembeddingbag_4bit_unpack # Mode: Eager # Name: qembeddingbag_4bit_unpack_num_embeddings80_embedding_dim256 # Input: num_embeddings: 80, embedding_dim: 256 Forward Execution Time (us) : 20.856 # Benchmarking PyTorch: qembeddingbag_4bit_unpack # Mode: Eager # Name: qembeddingbag_4bit_unpack_num_embeddings80_embedding_dim512 # Input: num_embeddings: 80, embedding_dim: 512 Forward Execution Time (us) : 33.944 # Benchmarking PyTorch: qembeddingbag_2bit_unpack # Mode: Eager # Name: qembeddingbag_2bit_unpack_num_embeddings80_embedding_dim128 # Input: num_embeddings: 80, embedding_dim: 128 Forward Execution Time (us) : 21.181 # Benchmarking PyTorch: qembeddingbag_2bit_unpack # Mode: Eager # Name: qembeddingbag_2bit_unpack_num_embeddings80_embedding_dim256 # Input: num_embeddings: 80, embedding_dim: 256 Forward Execution Time (us) : 34.213 # Benchmarking PyTorch: qembeddingbag_2bit_unpack # Mode: Eager # Name: qembeddingbag_2bit_unpack_num_embeddings80_embedding_dim512 # Input: num_embeddings: 80, embedding_dim: 512 Forward Execution Time (us) : 59.622 ``` Results with the parallelization ``` # Benchmarking PyTorch: qembeddingbag_byte_prepack # Mode: Eager # Name: qembeddingbag_byte_prepack_num_embeddings80_embedding_dim128 # Input: num_embeddings: 80, embedding_dim: 128 Forward Execution Time (us) : 8.852 # Benchmarking PyTorch: qembeddingbag_byte_prepack # Mode: Eager # Name: qembeddingbag_byte_prepack_num_embeddings80_embedding_dim256 # Input: num_embeddings: 80, embedding_dim: 256 Forward Execution Time (us) : 13.594 # Benchmarking PyTorch: qembeddingbag_byte_prepack # Mode: Eager # Name: qembeddingbag_byte_prepack_num_embeddings80_embedding_dim512 # Input: num_embeddings: 80, embedding_dim: 512 Forward Execution Time (us) : 20.120 # Benchmarking PyTorch: qembeddingbag_4bit_prepack # Mode: Eager # Name: qembeddingbag_4bit_prepack_num_embeddings80_embedding_dim128 # Input: num_embeddings: 80, embedding_dim: 128 Forward Execution Time (us) : 12.049 # Benchmarking PyTorch: qembeddingbag_4bit_prepack # Mode: Eager # Name: qembeddingbag_4bit_prepack_num_embeddings80_embedding_dim256 # Input: num_embeddings: 80, embedding_dim: 256 Forward Execution Time (us) : 20.710 # Benchmarking PyTorch: qembeddingbag_4bit_prepack # Mode: Eager # Name: qembeddingbag_4bit_prepack_num_embeddings80_embedding_dim512 # Input: num_embeddings: 80, embedding_dim: 512 Forward Execution Time (us) : 23.320 # Benchmarking PyTorch: qembeddingbag_2bit_prepack # Mode: Eager # Name: qembeddingbag_2bit_prepack_num_embeddings80_embedding_dim128 # Input: num_embeddings: 80, embedding_dim: 128 Forward Execution Time (us) : 11.998 # Benchmarking PyTorch: qembeddingbag_2bit_prepack # Mode: Eager # Name: qembeddingbag_2bit_prepack_num_embeddings80_embedding_dim256 # Input: num_embeddings: 80, embedding_dim: 256 Forward Execution Time (us) : 15.972 # Benchmarking PyTorch: qembeddingbag_2bit_prepack # Mode: Eager # Name: qembeddingbag_2bit_prepack_num_embeddings80_embedding_dim512 # Input: num_embeddings: 80, embedding_dim: 512 Forward Execution Time (us) : 23.619 # Benchmarking PyTorch: qembeddingbag_byte_unpack # Mode: Eager # Name: qembeddingbag_byte_unpack_num_embeddings80_embedding_dim128 # Input: num_embeddings: 80, embedding_dim: 128 Forward Execution Time (us) : 30.764 # Benchmarking PyTorch: qembeddingbag_byte_unpack # Mode: Eager # Name: qembeddingbag_byte_unpack_num_embeddings80_embedding_dim256 # Input: num_embeddings: 80, embedding_dim: 256 Forward Execution Time (us) : 50.969 # Benchmarking PyTorch: qembeddingbag_byte_unpack # Mode: Eager # Name: qembeddingbag_byte_unpack_num_embeddings80_embedding_dim512 # Input: num_embeddings: 80, embedding_dim: 512 Forward Execution Time (us) : 129.960 # Benchmarking PyTorch: qembeddingbag_4bit_unpack # Mode: Eager # Name: qembeddingbag_4bit_unpack_num_embeddings80_embedding_dim128 # Input: num_embeddings: 80, embedding_dim: 128 Forward Execution Time (us) : 10.797 # Benchmarking PyTorch: qembeddingbag_4bit_unpack # Mode: Eager # Name: qembeddingbag_4bit_unpack_num_embeddings80_embedding_dim256 # Input: num_embeddings: 80, embedding_dim: 256 Forward Execution Time (us) : 15.767 # Benchmarking PyTorch: qembeddingbag_4bit_unpack # Mode: Eager # Name: qembeddingbag_4bit_unpack_num_embeddings80_embedding_dim512 # Input: num_embeddings: 80, embedding_dim: 512 Forward Execution Time (us) : 27.032 # Benchmarking PyTorch: qembeddingbag_2bit_unpack # Mode: Eager # Name: qembeddingbag_2bit_unpack_num_embeddings80_embedding_dim128 # Input: num_embeddings: 80, embedding_dim: 128 Forward Execution Time (us) : 16.521 # Benchmarking PyTorch: qembeddingbag_2bit_unpack # Mode: Eager # Name: qembeddingbag_2bit_unpack_num_embeddings80_embedding_dim256 # Input: num_embeddings: 80, embedding_dim: 256 Forward Execution Time (us) : 26.050 # Benchmarking PyTorch: qembeddingbag_2bit_unpack # Mode: Eager # Name: qembeddingbag_2bit_unpack_num_embeddings80_embedding_dim512 # Input: num_embeddings: 80, embedding_dim: 512 Forward Execution Time (us) : 45.231 ``` Test Plan: 1. buck test //caffe2/test:quantization -- 'test_embedding_bag*' --print-passing-details 2. Ran benchmarks with ```buck build mode/opt caffe2/benchmarks/operator_benchmark/pt:qembedding_pack_test; ./buck-out/gen/caffe2/benchmarks/operator_benchmark/pt/qembedding_pack_test.par``` Reviewed By: qizzzh Differential Revision: D24002456 fbshipit-source-id: 67c652100b9476a519ee86fcc89ac16e57a42d72
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This pull request was exported from Phabricator. Differential Revision: D24002456 |
Codecov Report
@@ Coverage Diff @@
## master #45536 +/- ##
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Coverage 68.27% 68.27%
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Files 410 410
Lines 53572 53572
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Hits 36576 36576
Misses 16996 16996 Continue to review full report at Codecov.
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This pull request has been merged in 31bcd96. |
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Summary:
Quantization conversion/reverse conversion operators will be used in critical serving path.
The operators can make use of aten::parallel to parallelize the rowwise quantization of tensors.
Overall, i see 20-25% improvement with the parallelization optimization added here.
The following result is from running benchmark on my
devvm. Requested a dedicated machine and will post benchmark results again.Easier view to compare results https://our.intern.facebook.com/intern/diffing/?paste_number=143973933
Baseline results are based on D23675777 (677a59d)
Results with the parallelization
Test Plan:
buck test //caffe2/test:quantization -- 'test_embedding_bag*' --print-passing-details
Ran benchmarks with
buck build mode/opt caffe2/benchmarks/operator_benchmark/pt:qembedding_pack_test; ./buck-out/gen/caffe2/benchmarks/operator_benchmark/pt/qembedding_pack_test.parDifferential Revision: D24002456