[aten] Call fbgemm functions for embedding prepack/unpack #44845
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fbgemm functions are vectorized and faster ``` Finished test run: https://our.intern.facebook.com/intern/testinfra/testrun/6473924484856786 Summary (total time 15.08s): PASS: 7 FAIL: 0 SKIP: 0 FATAL: 0 TIMEOUT: 0 OMIT: 0 ``` Performance Before: ``` # ---------------------------------------- # 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) : 68.727 # 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) : 131.500 # 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) : 248.190 # 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) : 172.742 # 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) : 333.008 # 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) : 652.423 # 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) : 167.282 # 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) : 398.901 # 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) : 785.254 # 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) : 122.653 # 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) : 230.617 # 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) : 408.807 # 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) : 176.087 # 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) : 337.514 # 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) : 659.716 # 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) : 342.529 # 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) : 665.197 # 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) : 1307.923 ``` Performance After: ``` # ---------------------------------------- # 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 ``` Differential Revision: [D23675777](https://our.internmc.facebook.com/intern/diff/D23675777/) [ghstack-poisoned]
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fbgemm functions are vectorized and faster ``` Finished test run: https://our.intern.facebook.com/intern/testinfra/testrun/6473924484856786 Summary (total time 15.08s): PASS: 7 FAIL: 0 SKIP: 0 FATAL: 0 TIMEOUT: 0 OMIT: 0 ``` Performance Before: ``` # ---------------------------------------- # 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) : 68.727 # 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) : 131.500 # 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) : 248.190 # 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) : 172.742 # 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) : 333.008 # 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) : 652.423 # 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) : 167.282 # 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) : 398.901 # 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) : 785.254 # 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) : 122.653 # 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) : 230.617 # 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) : 408.807 # 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) : 176.087 # 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) : 337.514 # 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) : 659.716 # 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) : 342.529 # 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) : 665.197 # 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) : 1307.923 ``` Performance After: ``` # ---------------------------------------- # 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 ``` Differential Revision: [D23675777](https://our.internmc.facebook.com/intern/diff/D23675777/) ghstack-source-id: 112188571 Pull Request resolved: #44845
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fbgemm functions are vectorized and faster ``` Finished test run: https://our.intern.facebook.com/intern/testinfra/testrun/6473924484856786 Summary (total time 15.08s): PASS: 7 FAIL: 0 SKIP: 0 FATAL: 0 TIMEOUT: 0 OMIT: 0 ``` Performance Before: ``` # ---------------------------------------- # 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) : 68.727 # 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) : 131.500 # 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) : 248.190 # 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) : 172.742 # 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) : 333.008 # 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) : 652.423 # 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) : 167.282 # 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) : 398.901 # 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) : 785.254 # 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) : 122.653 # 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) : 230.617 # 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) : 408.807 # 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) : 176.087 # 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) : 337.514 # 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) : 659.716 # 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) : 342.529 # 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) : 665.197 # 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) : 1307.923 ``` Performance After: ``` # ---------------------------------------- # 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 ``` Differential Revision: [D23675777](https://our.internmc.facebook.com/intern/diff/D23675777/) [ghstack-poisoned]
dskhudia
added a commit
that referenced
this pull request
Sep 17, 2020
Pull Request resolved: #44845 fbgemm functions are vectorized and faster ``` Finished test run: https://our.intern.facebook.com/intern/testinfra/testrun/6473924484856786 Summary (total time 15.08s): PASS: 7 FAIL: 0 SKIP: 0 FATAL: 0 TIMEOUT: 0 OMIT: 0 ``` Performance Before: ``` # ---------------------------------------- # 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) : 68.727 # 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) : 131.500 # 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) : 248.190 # 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) : 172.742 # 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) : 333.008 # 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) : 652.423 # 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) : 167.282 # 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) : 398.901 # 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) : 785.254 # 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) : 122.653 # 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) : 230.617 # 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) : 408.807 # 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) : 176.087 # 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) : 337.514 # 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) : 659.716 # 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) : 342.529 # 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) : 665.197 # 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) : 1307.923 ``` Performance After: ``` # ---------------------------------------- # 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 ``` ghstack-source-id: 112295758 Differential Revision: [D23675777](https://our.internmc.facebook.com/intern/diff/D23675777/)
fbgemm functions are vectorized and faster ``` Finished test run: https://our.intern.facebook.com/intern/testinfra/testrun/6473924484856786 Summary (total time 15.08s): PASS: 7 FAIL: 0 SKIP: 0 FATAL: 0 TIMEOUT: 0 OMIT: 0 ``` Performance Before: ``` # ---------------------------------------- # 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) : 68.727 # 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) : 131.500 # 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) : 248.190 # 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) : 172.742 # 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) : 333.008 # 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) : 652.423 # 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) : 167.282 # 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) : 398.901 # 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) : 785.254 # 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) : 122.653 # 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) : 230.617 # 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) : 408.807 # 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) : 176.087 # 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) : 337.514 # 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) : 659.716 # 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) : 342.529 # 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) : 665.197 # 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) : 1307.923 ``` Performance After: ``` # ---------------------------------------- # 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 ``` Differential Revision: [D23675777](https://our.internmc.facebook.com/intern/diff/D23675777/) [ghstack-poisoned]
dskhudia
added a commit
that referenced
this pull request
Sep 21, 2020
Pull Request resolved: #44845 fbgemm functions are vectorized and faster ``` Finished test run: https://our.intern.facebook.com/intern/testinfra/testrun/6473924484856786 Summary (total time 15.08s): PASS: 7 FAIL: 0 SKIP: 0 FATAL: 0 TIMEOUT: 0 OMIT: 0 ``` Performance Before: ``` # ---------------------------------------- # 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) : 68.727 # 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) : 131.500 # 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) : 248.190 # 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) : 172.742 # 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) : 333.008 # 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) : 652.423 # 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) : 167.282 # 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) : 398.901 # 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) : 785.254 # 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) : 122.653 # 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) : 230.617 # 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) : 408.807 # 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) : 176.087 # 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) : 337.514 # 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) : 659.716 # 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) : 342.529 # 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) : 665.197 # 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) : 1307.923 ``` Performance After: ``` # ---------------------------------------- # 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 ``` ghstack-source-id: 112470869 Differential Revision: [D23675777](https://our.internmc.facebook.com/intern/diff/D23675777/)
fbgemm functions are vectorized and faster ``` Finished test run: https://our.intern.facebook.com/intern/testinfra/testrun/6473924484856786 Summary (total time 15.08s): PASS: 7 FAIL: 0 SKIP: 0 FATAL: 0 TIMEOUT: 0 OMIT: 0 ``` Performance Before: ``` # ---------------------------------------- # 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) : 68.727 # 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) : 131.500 # 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) : 248.190 # 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) : 172.742 # 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) : 333.008 # 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) : 652.423 # 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) : 167.282 # 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) : 398.901 # 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) : 785.254 # 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) : 122.653 # 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) : 230.617 # 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) : 408.807 # 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) : 176.087 # 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) : 337.514 # 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) : 659.716 # 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) : 342.529 # 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) : 665.197 # 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) : 1307.923 ``` Performance After: ``` # ---------------------------------------- # 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 ``` Differential Revision: [D23675777](https://our.internmc.facebook.com/intern/diff/D23675777/) [ghstack-poisoned]
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Pull Request resolved: #44845 fbgemm functions are vectorized and faster ``` Finished test run: https://our.intern.facebook.com/intern/testinfra/testrun/6473924484856786 Summary (total time 15.08s): PASS: 7 FAIL: 0 SKIP: 0 FATAL: 0 TIMEOUT: 0 OMIT: 0 ``` Performance Before: ``` # ---------------------------------------- # 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) : 68.727 # 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) : 131.500 # 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) : 248.190 # 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) : 172.742 # 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) : 333.008 # 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) : 652.423 # 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) : 167.282 # 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) : 398.901 # 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) : 785.254 # 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) : 122.653 # 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) : 230.617 # 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) : 408.807 # 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) : 176.087 # 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) : 337.514 # 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) : 659.716 # 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) : 342.529 # 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) : 665.197 # 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) : 1307.923 ``` Performance After: ``` # ---------------------------------------- # 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 ``` ghstack-source-id: 112496321 Differential Revision: [D23675777](https://our.internmc.facebook.com/intern/diff/D23675777/)
fbgemm functions are vectorized and faster ``` Finished test run: https://our.intern.facebook.com/intern/testinfra/testrun/6473924484856786 Summary (total time 15.08s): PASS: 7 FAIL: 0 SKIP: 0 FATAL: 0 TIMEOUT: 0 OMIT: 0 ``` Performance Before: ``` # ---------------------------------------- # 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) : 68.727 # 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) : 131.500 # 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) : 248.190 # 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) : 172.742 # 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) : 333.008 # 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) : 652.423 # 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) : 167.282 # 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) : 398.901 # 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) : 785.254 # 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) : 122.653 # 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) : 230.617 # 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) : 408.807 # 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) : 176.087 # 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) : 337.514 # 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) : 659.716 # 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) : 342.529 # 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) : 665.197 # 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) : 1307.923 ``` Performance After: ``` # ---------------------------------------- # 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 ``` Differential Revision: [D23675777](https://our.internmc.facebook.com/intern/diff/D23675777/) [ghstack-poisoned]
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Sep 23, 2020
Pull Request resolved: #44845 fbgemm functions are vectorized and faster ``` Finished test run: https://our.intern.facebook.com/intern/testinfra/testrun/6473924484856786 Summary (total time 15.08s): PASS: 7 FAIL: 0 SKIP: 0 FATAL: 0 TIMEOUT: 0 OMIT: 0 ``` Performance Before: ``` # ---------------------------------------- # 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) : 68.727 # 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) : 131.500 # 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) : 248.190 # 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) : 172.742 # 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) : 333.008 # 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) : 652.423 # 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) : 167.282 # 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) : 398.901 # 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) : 785.254 # 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) : 122.653 # 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) : 230.617 # 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) : 408.807 # 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) : 176.087 # 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) : 337.514 # 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) : 659.716 # 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) : 342.529 # 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) : 665.197 # 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) : 1307.923 ``` Performance After: ``` # ---------------------------------------- # 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 ``` ghstack-source-id: 112664490 Differential Revision: [D23675777](https://our.internmc.facebook.com/intern/diff/D23675777/)
fbgemm functions are vectorized and faster ``` Finished test run: https://our.intern.facebook.com/intern/testinfra/testrun/6473924484856786 Summary (total time 15.08s): PASS: 7 FAIL: 0 SKIP: 0 FATAL: 0 TIMEOUT: 0 OMIT: 0 ``` Performance Before: ``` # ---------------------------------------- # 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) : 68.727 # 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) : 131.500 # 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) : 248.190 # 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) : 172.742 # 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) : 333.008 # 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) : 652.423 # 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) : 167.282 # 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) : 398.901 # 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) : 785.254 # 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) : 122.653 # 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) : 230.617 # 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) : 408.807 # 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) : 176.087 # 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) : 337.514 # 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) : 659.716 # 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) : 342.529 # 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) : 665.197 # 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) : 1307.923 ``` Performance After: ``` # ---------------------------------------- # 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 ``` Differential Revision: [D23675777](https://our.internmc.facebook.com/intern/diff/D23675777/) [ghstack-poisoned]
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Pull Request resolved: #44845 fbgemm functions are vectorized and faster ``` Finished test run: https://our.intern.facebook.com/intern/testinfra/testrun/6473924484856786 Summary (total time 15.08s): PASS: 7 FAIL: 0 SKIP: 0 FATAL: 0 TIMEOUT: 0 OMIT: 0 ``` Performance Before: ``` # ---------------------------------------- # 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) : 68.727 # 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) : 131.500 # 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) : 248.190 # 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) : 172.742 # 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) : 333.008 # 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) : 652.423 # 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) : 167.282 # 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) : 398.901 # 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) : 785.254 # 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) : 122.653 # 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) : 230.617 # 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) : 408.807 # 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) : 176.087 # 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) : 337.514 # 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) : 659.716 # 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) : 342.529 # 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) : 665.197 # 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) : 1307.923 ``` Performance After: ``` # ---------------------------------------- # 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 ``` ghstack-source-id: 112699964 Differential Revision: [D23675777](https://our.internmc.facebook.com/intern/diff/D23675777/)
fbgemm functions are vectorized and faster ``` Finished test run: https://our.intern.facebook.com/intern/testinfra/testrun/6473924484856786 Summary (total time 15.08s): PASS: 7 FAIL: 0 SKIP: 0 FATAL: 0 TIMEOUT: 0 OMIT: 0 ``` Performance Before: ``` # ---------------------------------------- # 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) : 68.727 # 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) : 131.500 # 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) : 248.190 # 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) : 172.742 # 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) : 333.008 # 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) : 652.423 # 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) : 167.282 # 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) : 398.901 # 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) : 785.254 # 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) : 122.653 # 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) : 230.617 # 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) : 408.807 # 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) : 176.087 # 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) : 337.514 # 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) : 659.716 # 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) : 342.529 # 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) : 665.197 # 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) : 1307.923 ``` Performance After: ``` # ---------------------------------------- # 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 ``` Differential Revision: [D23675777](https://our.internmc.facebook.com/intern/diff/D23675777/) [ghstack-poisoned]
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Pull Request resolved: #44845 fbgemm functions are vectorized and faster ``` Finished test run: https://our.intern.facebook.com/intern/testinfra/testrun/6473924484856786 Summary (total time 15.08s): PASS: 7 FAIL: 0 SKIP: 0 FATAL: 0 TIMEOUT: 0 OMIT: 0 ``` Performance Before: ``` # ---------------------------------------- # 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) : 68.727 # 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) : 131.500 # 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) : 248.190 # 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) : 172.742 # 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) : 333.008 # 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) : 652.423 # 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) : 167.282 # 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) : 398.901 # 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) : 785.254 # 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) : 122.653 # 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) : 230.617 # 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) : 408.807 # 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) : 176.087 # 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) : 337.514 # 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) : 659.716 # 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) : 342.529 # 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) : 665.197 # 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) : 1307.923 ``` Performance After: ``` # ---------------------------------------- # 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 ``` ghstack-source-id: 112812505 Differential Revision: [D23675777](https://our.internmc.facebook.com/intern/diff/D23675777/)
fbgemm functions are vectorized and faster ``` Finished test run: https://our.intern.facebook.com/intern/testinfra/testrun/6473924484856786 Summary (total time 15.08s): PASS: 7 FAIL: 0 SKIP: 0 FATAL: 0 TIMEOUT: 0 OMIT: 0 ``` Performance Before: ``` # ---------------------------------------- # 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) : 68.727 # 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) : 131.500 # 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) : 248.190 # 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) : 172.742 # 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) : 333.008 # 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) : 652.423 # 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) : 167.282 # 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) : 398.901 # 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) : 785.254 # 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) : 122.653 # 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) : 230.617 # 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) : 408.807 # 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) : 176.087 # 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) : 337.514 # 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) : 659.716 # 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) : 342.529 # 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) : 665.197 # 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) : 1307.923 ``` Performance After: ``` # ---------------------------------------- # 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 ``` Differential Revision: [D23675777](https://our.internmc.facebook.com/intern/diff/D23675777/) [ghstack-poisoned]
fbgemm functions are vectorized and faster ``` Finished test run: https://our.intern.facebook.com/intern/testinfra/testrun/6473924484856786 Summary (total time 15.08s): PASS: 7 FAIL: 0 SKIP: 0 FATAL: 0 TIMEOUT: 0 OMIT: 0 ``` Performance Before: ``` # ---------------------------------------- # 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) : 68.727 # 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) : 131.500 # 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) : 248.190 # 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) : 172.742 # 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) : 333.008 # 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) : 652.423 # 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) : 167.282 # 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) : 398.901 # 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) : 785.254 # 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) : 122.653 # 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) : 230.617 # 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) : 408.807 # 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) : 176.087 # 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) : 337.514 # 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) : 659.716 # 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) : 342.529 # 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) : 665.197 # 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) : 1307.923 ``` Performance After: ``` # ---------------------------------------- # 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 ``` Differential Revision: [D23675777](https://our.internmc.facebook.com/intern/diff/D23675777/) [ghstack-poisoned]
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Sep 24, 2020
Pull Request resolved: #44845 fbgemm functions are vectorized and faster ``` Finished test run: https://our.intern.facebook.com/intern/testinfra/testrun/6473924484856786 Summary (total time 15.08s): PASS: 7 FAIL: 0 SKIP: 0 FATAL: 0 TIMEOUT: 0 OMIT: 0 ``` Performance Before: ``` # ---------------------------------------- # 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) : 68.727 # 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) : 131.500 # 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) : 248.190 # 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) : 172.742 # 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) : 333.008 # 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) : 652.423 # 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) : 167.282 # 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) : 398.901 # 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) : 785.254 # 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) : 122.653 # 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) : 230.617 # 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) : 408.807 # 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) : 176.087 # 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) : 337.514 # 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) : 659.716 # 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) : 342.529 # 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) : 665.197 # 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) : 1307.923 ``` Performance After: ``` # ---------------------------------------- # 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 ``` ghstack-source-id: 112836216 Differential Revision: [D23675777](https://our.internmc.facebook.com/intern/diff/D23675777/)
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Stack from ghstack:
fbgemm functions are vectorized and faster
Performance Before:
Performance After:
Differential Revision: D23675777