Support Linear operation with fp16 weights in ATen #22023
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module: operators
module: nn
Summary: Pull Request resolved: #22023 This diff implements Linear operation with fp16 weights based on FBGEMM. At a hight level, we want to perform the following operation: Y = X * W + B with dtypes: (fp32, fp32, fp16, fp32) To do that, three steps are needed: 1. Quantize weights from fp32 to fp16, this is done using `PackedGemmMatrixFP16` in the `fbgemm_pack_gemm_matrix_fp16` 2. Conduct matrix multiplication with quantized weights using `cblas_gemm_compute` in `fbgemm_linear_fp16_weight` 3. Add bias to the result from step2 and return the final Y Reviewed By: jianyuh Differential Revision: D15921768 fbshipit-source-id: f48ed23c4a446e6454b1334ede492b7efec45260
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This pull request has been merged in 573d9e6. |
zdevito
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Jul 13, 2019
Summary: Pull Request resolved: pytorch/pytorch#22023 This diff implements Linear operation with fp16 weights based on FBGEMM. At a hight level, we want to perform the following operation: Y = X * W + B with dtypes: (fp32, fp32, fp16, fp32) To do that, three steps are needed: 1. Quantize weights from fp32 to fp16, this is done using `PackedGemmMatrixFP16` in the `fbgemm_pack_gemm_matrix_fp16` 2. Conduct matrix multiplication with quantized weights using `cblas_gemm_compute` in `fbgemm_linear_fp16_weight` 3. Add bias to the result from step2 and return the final Y Reviewed By: jianyuh Differential Revision: D15921768 fbshipit-source-id: dc4e5b366f846ce9d58975876940a9b3372b8b8d
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Summary:
This diff implements Linear operation with fp16 weights based on FBGEMM. At a hight level, we want to perform the following operation:
Y = X * W + B with dtypes:
(fp32, fp32, fp16, fp32)
To do that, three steps are needed:
PackedGemmMatrixFP16in thefbgemm_pack_gemm_matrix_fp16cblas_gemm_computeinfbgemm_linear_fp16_weightDifferential Revision: D15921768