Port oss f16_fast_gemv into fbcode #3610
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Summary: X-link: facebookresearch/FBGEMM#688 This diff content includes: 1. Port OSS FastGEMV `fp16` kernel into fbcode and expose to python as a step 1 - `torch.ops.fbgemm.f16_fast_gemv` https://github.com/wangsiping97/FastGEMV/blob/1fdff6f74aade033c02727a419afd6a4b4bfbc3f/fast_gemv.cu#L14 2. Add `fp16_oss_fast_gemv` to quantize ops benchmark script 3. Add two simple tests for custom op`torch.ops.fbgemm.f16_fast_gemv` to test - `torch.compile()` able - correctness **Next step:** Need fp8 mixed precision support for fast gemv kernel which is what we want Differential Revision: D68470488
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Summary: X-link: facebookresearch/FBGEMM#688 This diff content includes: 1. Port OSS FastGEMV `fp16` kernel into fbcode and expose to python as a step 1 - `torch.ops.fbgemm.f16_fast_gemv` https://github.com/wangsiping97/FastGEMV/blob/1fdff6f74aade033c02727a419afd6a4b4bfbc3f/fast_gemv.cu#L14 2. Add `fp16_oss_fast_gemv` to quantize ops benchmark script 3. Add two simple tests for custom op`torch.ops.fbgemm.f16_fast_gemv` to test - `torch.compile()` able - correctness **Next step:** Need fp8 mixed precision support for fast gemv kernel which is what we want Differential Revision: D68470488
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Summary: X-link: facebookresearch/FBGEMM#688 This diff content includes: 1. Port OSS FastGEMV `fp16` kernel into fbcode and expose to python as a step 1 - `torch.ops.fbgemm.f16_fast_gemv` https://github.com/wangsiping97/FastGEMV/blob/1fdff6f74aade033c02727a419afd6a4b4bfbc3f/fast_gemv.cu#L14 2. Add `fp16_oss_fast_gemv` to quantize ops benchmark script 3. Add two simple tests for custom op`torch.ops.fbgemm.f16_fast_gemv` to test - `torch.compile()` able - correctness **Next step:** Need fp8 mixed precision support for fast gemv kernel which is what we want Differential Revision: D68470488
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Summary: X-link: facebookresearch/FBGEMM#688 This diff content includes: 1. Port OSS FastGEMV `fp16` kernel into fbcode and expose to python as a step 1 - `torch.ops.fbgemm.f16_fast_gemv` https://github.com/wangsiping97/FastGEMV/blob/1fdff6f74aade033c02727a419afd6a4b4bfbc3f/fast_gemv.cu#L14 2. Add `fp16_oss_fast_gemv` to quantize ops benchmark script 3. Add two simple tests for custom op`torch.ops.fbgemm.f16_fast_gemv` to test - `torch.compile()` able - correctness Perf numbers: P1720649201 compared with `f16_baseline,fp16_oss_fast_gemv,cuda_lite,marlin_bf16i4,machete_bf16i4` **Next step:** Need fp8 mixed precision support for fast gemv kernel which is what we want Differential Revision: D68470488
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Summary: X-link: facebookresearch/FBGEMM#688 This diff content includes: 1. Port OSS FastGEMV `fp16` kernel into fbcode and expose to python as a step 1 - `torch.ops.fbgemm.f16_fast_gemv` https://github.com/wangsiping97/FastGEMV/blob/1fdff6f74aade033c02727a419afd6a4b4bfbc3f/fast_gemv.cu#L14 2. Add `fp16_oss_fast_gemv` to quantize ops benchmark script 3. Add two simple tests for custom op`torch.ops.fbgemm.f16_fast_gemv` to test - `torch.compile()` able - correctness Perf numbers: P1720649201 compared with `f16_baseline,fp16_oss_fast_gemv,cuda_lite,marlin_bf16i4,machete_bf16i4` **Next step:** Need fp8 mixed precision support for fast gemv kernel which is what we want Differential Revision: D68470488
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Summary: X-link: facebookresearch/FBGEMM#688 This diff content includes: 1. Port OSS FastGEMV `fp16` kernel into fbcode and expose to python as a step 1 - `torch.ops.fbgemm.f16_fast_gemv` https://github.com/wangsiping97/FastGEMV/blob/1fdff6f74aade033c02727a419afd6a4b4bfbc3f/fast_gemv.cu#L14 2. Add `fp16_oss_fast_gemv` to quantize ops benchmark script 3. Add two simple tests for custom op`torch.ops.fbgemm.f16_fast_gemv` to test - `torch.compile()` able - correctness Perf numbers: P1722119058 compared with `f16_baseline,fp16_oss_fast_gemv,cuda_lite,marlin_bf16i4,machete_bf16i4` heuristic sweep results from the 4 problem sizes we care about: P1722043272 **Next step:** Need fp8 mixed precision support for fast gemv kernel which is what we want Differential Revision: D68470488
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Summary: X-link: facebookresearch/FBGEMM#688 This diff content includes: 1. Port OSS FastGEMV `fp16` kernel into fbcode and expose to python as a step 1 - `torch.ops.fbgemm.f16_fast_gemv` https://github.com/wangsiping97/FastGEMV/blob/1fdff6f74aade033c02727a419afd6a4b4bfbc3f/fast_gemv.cu#L14 2. Add `fp16_oss_fast_gemv` to quantize ops benchmark script 3. Add two simple tests for custom op`torch.ops.fbgemm.f16_fast_gemv` to test - `torch.compile()` able - correctness Perf numbers: P1722119058 compared with `f16_baseline,fp16_oss_fast_gemv,cuda_lite,marlin_bf16i4,machete_bf16i4` ====================== ### Benchmark Results | **M** | **N** | **K** | **Method** | **Elapsed Time (ms)** | **TFLOPS** | **GB/s** | | --- | --- | --- | --- | --- | --- | --- | | 1 | 1280 | 8192 | bf16_baseline | 0.024 | 0.860 | 861.042 | | 1 | 1280 | 8192 | fp16_oss_fast_gemv | 0.019 | 1.126 | 1127.391 | | 1 | 1280 | 8192 | cuda_lite | 0.015 | 1.357 | 679.032 | | 1 | 1280 | 8192 | marlin_bf16i4 | 0.027 | 0.768 | 192.612 | | 1 | 1280 | 8192 | machete_bf16i4 | 0.026 | 0.810 | 203.219 | | 1 | 8192 | 1024 | bf16_baseline | 0.018 | 0.952 | 953.176 | | 1 | 8192 | 1024 | fp16_oss_fast_gemv | 0.010 | 1.763 | 1765.033 | | 1 | 8192 | 1024 | cuda_lite | 0.014 | 1.198 | 600.054 | | 1 | 8192 | 1024 | marlin_bf16i4 | 0.015 | 1.144 | 287.150 | | 1 | 8192 | 1024 | machete_bf16i4 | 0.014 | 1.187 | 298.096 | | 1 | 7168 | 8192 | bf16_baseline | 0.073 | 1.609 | 1608.983 | | 1 | 7168 | 8192 | fp16_oss_fast_gemv | 0.069 | 1.697 | 1697.308 | | 1 | 7168 | 8192 | cuda_lite | 0.044 | 2.679 | 1340.093 | | 1 | 7168 | 8192 | marlin_bf16i4 | 0.033 | 3.590 | 898.436 | | 1 | 7168 | 8192 | machete_bf16i4 | 0.039 | 3.017 | 755.147 | | 1 | 8192 | 3584 | bf16_baseline | 0.045 | 1.312 | 1312.239 | | 1 | 8192 | 3584 | fp16_oss_fast_gemv | 0.026 | 2.268 | 1134.843 | | 1 | 8192 | 3584 | cuda_lite | 0.026 | 2.271 | 1136.151 | | 1 | 8192 | 3584 | marlin_bf16i4 | 0.021 | 2.808 | 703.164 | | 1 | 8192 | 3584 | machete_bf16i4 | 0.024 | 2.460 | 615.990 | heuristic sweep results from the 4 problem sizes we care about: P1722043272 **Next step:** Need fp8 mixed precision support for fast gemv kernel which is what we want Differential Revision: D68470488
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Summary: X-link: facebookresearch/FBGEMM#688 This diff content includes: 1. Port OSS FastGEMV `fp16` kernel into fbcode and expose to python as a step 1 - `torch.ops.fbgemm.f16_fast_gemv` https://github.com/wangsiping97/FastGEMV/blob/1fdff6f74aade033c02727a419afd6a4b4bfbc3f/fast_gemv.cu#L14 2. Add `fp16_oss_fast_gemv` to quantize ops benchmark script 3. Add two simple tests for custom op`torch.ops.fbgemm.f16_fast_gemv` to test - `torch.compile()` able - correctness Perf numbers compared with `f16_baseline,fp16_oss_fast_gemv,cuda_lite,marlin_bf16i4,machete_bf16i4` ====================== ### Benchmark Results | **M** | **N** | **K** | **Method** | **Elapsed Time (ms)** | **TFLOPS** | **GB/s** | | --- | --- | --- | --- | --- | --- | --- | | 1 | 1280 | 8192 | bf16_baseline | 0.024 | 0.860 | 861.042 | | 1 | 1280 | 8192 | fp16_oss_fast_gemv | 0.019 | 1.126 | 1127.391 | | 1 | 1280 | 8192 | cuda_lite_fp8 | 0.015 | 1.357 | 679.032 | | 1 | 1280 | 8192 | marlin_bf16i4 | 0.027 | 0.768 | 192.612 | | 1 | 1280 | 8192 | machete_bf16i4 | 0.026 | 0.810 | 203.219 | | 1 | 8192 | 1024 | bf16_baseline | 0.018 | 0.952 | 953.176 | | 1 | 8192 | 1024 | fp16_oss_fast_gemv | 0.010 | 1.763 | 1765.033 | | 1 | 8192 | 1024 | cuda_lite_fp8 | 0.014 | 1.198 | 600.054 | | 1 | 8192 | 1024 | marlin_bf16i4 | 0.015 | 1.144 | 287.150 | | 1 | 8192 | 1024 | machete_bf16i4 | 0.014 | 1.187 | 298.096 | | 1 | 7168 | 8192 | bf16_baseline | 0.073 | 1.609 | 1608.983 | | 1 | 7168 | 8192 | fp16_oss_fast_gemv | 0.069 | 1.697 | 1697.308 | | 1 | 7168 | 8192 | cuda_lite_fp8 | 0.044 | 2.679 | 1340.093 | | 1 | 7168 | 8192 | marlin_bf16i4 | 0.033 | 3.590 | 898.436 | | 1 | 7168 | 8192 | machete_bf16i4 | 0.039 | 3.017 | 755.147 | | 1 | 8192 | 3584 | bf16_baseline | 0.045 | 1.312 | 1312.239 | | 1 | 8192 | 3584 | fp16_oss_fast_gemv | 0.026 | 2.268 | 1134.843 | | 1 | 8192 | 3584 | cuda_lite_fp8 | 0.026 | 2.271 | 1136.151 | | 1 | 8192 | 3584 | marlin_bf16i4 | 0.021 | 2.808 | 703.164 | | 1 | 8192 | 3584 | machete_bf16i4 | 0.024 | 2.460 | 615.990 | Note that currently the precision with fast_gemv kernel and cuda_lite not matched yet. so need fp8 to coming in for a fairer result. heuristic sweep results from the 4 problem sizes we care about: P1722806148 **Next step:** Need fp8 mixed precision support for fast gemv kernel which is what we want Differential Revision: D68470488
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Summary: X-link: facebookresearch/FBGEMM#688 This diff content includes: 1. Port OSS FastGEMV `fp16` kernel into fbcode and expose to python as a step 1 - `torch.ops.fbgemm.f16_fast_gemv` https://github.com/wangsiping97/FastGEMV/blob/1fdff6f74aade033c02727a419afd6a4b4bfbc3f/fast_gemv.cu#L14 2. Add `fp16_oss_fast_gemv` to quantize ops benchmark script 3. Add two simple tests for custom op`torch.ops.fbgemm.f16_fast_gemv` to test - `torch.compile()` able - correctness Perf numbers compared with `f16_baseline,fp16_oss_fast_gemv,cuda_lite,marlin_bf16i4,machete_bf16i4` ====================== ### Benchmark Results | **M** | **N** | **K** | **Method** | **Elapsed Time (ms)** | **TFLOPS** | **GB/s** | | --- | --- | --- | --- | --- | --- | --- | | 1 | 1280 | 8192 | bf16_baseline | 0.024 | 0.860 | 861.042 | | 1 | 1280 | 8192 | fp16_oss_fast_gemv | 0.019 | 1.126 | 1127.391 | | 1 | 1280 | 8192 | cuda_lite_fp8 | 0.015 | 1.357 | 679.032 | | 1 | 1280 | 8192 | marlin_bf16i4 | 0.027 | 0.768 | 192.612 | | 1 | 1280 | 8192 | machete_bf16i4 | 0.026 | 0.810 | 203.219 | | 1 | 8192 | 1024 | bf16_baseline | 0.018 | 0.952 | 953.176 | | 1 | 8192 | 1024 | fp16_oss_fast_gemv | 0.010 | 1.763 | 1765.033 | | 1 | 8192 | 1024 | cuda_lite_fp8 | 0.014 | 1.198 | 600.054 | | 1 | 8192 | 1024 | marlin_bf16i4 | 0.015 | 1.144 | 287.150 | | 1 | 8192 | 1024 | machete_bf16i4 | 0.014 | 1.187 | 298.096 | | 1 | 7168 | 8192 | bf16_baseline | 0.073 | 1.609 | 1608.983 | | 1 | 7168 | 8192 | fp16_oss_fast_gemv | 0.069 | 1.697 | 1697.308 | | 1 | 7168 | 8192 | cuda_lite_fp8 | 0.044 | 2.679 | 1340.093 | | 1 | 7168 | 8192 | marlin_bf16i4 | 0.033 | 3.590 | 898.436 | | 1 | 7168 | 8192 | machete_bf16i4 | 0.039 | 3.017 | 755.147 | | 1 | 8192 | 3584 | bf16_baseline | 0.045 | 1.312 | 1312.239 | | 1 | 8192 | 3584 | fp16_oss_fast_gemv | 0.026 | 2.268 | 1134.843 | | 1 | 8192 | 3584 | cuda_lite_fp8 | 0.026 | 2.271 | 1136.151 | | 1 | 8192 | 3584 | marlin_bf16i4 | 0.021 | 2.808 | 703.164 | | 1 | 8192 | 3584 | machete_bf16i4 | 0.024 | 2.460 | 615.990 | Note that currently the precision with fast_gemv kernel and cuda_lite not matched yet. so need fp8 to coming in for a fairer result. heuristic sweep results from the 4 problem sizes we care about: P1722806148 **Next step:** Need fp8 mixed precision support for fast gemv kernel which is what we want Differential Revision: D68470488
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Summary: X-link: facebookresearch/FBGEMM#688 This diff content includes: 1. Port OSS FastGEMV `fp16` kernel into fbcode and expose to python as a step 1 - `torch.ops.fbgemm.f16_fast_gemv` https://github.com/wangsiping97/FastGEMV/blob/1fdff6f74aade033c02727a419afd6a4b4bfbc3f/fast_gemv.cu#L14 2. Add `fp16_oss_fast_gemv` to quantize ops benchmark script 3. Add two simple tests for custom op`torch.ops.fbgemm.f16_fast_gemv` to test - `torch.compile()` able - correctness Perf numbers compared with `f16_baseline,fp16_oss_fast_gemv,cuda_lite,marlin_bf16i4,machete_bf16i4` ====================== ### Benchmark Results | **M** | **N** | **K** | **Method** | **Elapsed Time (ms)** | **TFLOPS** | **GB/s** | | --- | --- | --- | --- | --- | --- | --- | | 1 | 1280 | 8192 | bf16_baseline | 0.024 | 0.860 | 861.042 | | 1 | 1280 | 8192 | fp16_oss_fast_gemv | 0.019 | 1.126 | 1127.391 | | 1 | 1280 | 8192 | cuda_lite_fp8 | 0.015 | 1.357 | 679.032 | | 1 | 1280 | 8192 | marlin_bf16i4 | 0.027 | 0.768 | 192.612 | | 1 | 1280 | 8192 | machete_bf16i4 | 0.026 | 0.810 | 203.219 | | 1 | 8192 | 1024 | bf16_baseline | 0.018 | 0.952 | 953.176 | | 1 | 8192 | 1024 | fp16_oss_fast_gemv | 0.010 | 1.763 | 1765.033 | | 1 | 8192 | 1024 | cuda_lite_fp8 | 0.014 | 1.198 | 600.054 | | 1 | 8192 | 1024 | marlin_bf16i4 | 0.015 | 1.144 | 287.150 | | 1 | 8192 | 1024 | machete_bf16i4 | 0.014 | 1.187 | 298.096 | | 1 | 7168 | 8192 | bf16_baseline | 0.073 | 1.609 | 1608.983 | | 1 | 7168 | 8192 | fp16_oss_fast_gemv | 0.069 | 1.697 | 1697.308 | | 1 | 7168 | 8192 | cuda_lite_fp8 | 0.044 | 2.679 | 1340.093 | | 1 | 7168 | 8192 | marlin_bf16i4 | 0.033 | 3.590 | 898.436 | | 1 | 7168 | 8192 | machete_bf16i4 | 0.039 | 3.017 | 755.147 | | 1 | 8192 | 3584 | bf16_baseline | 0.045 | 1.312 | 1312.239 | | 1 | 8192 | 3584 | fp16_oss_fast_gemv | 0.026 | 2.268 | 1134.843 | | 1 | 8192 | 3584 | cuda_lite_fp8 | 0.026 | 2.271 | 1136.151 | | 1 | 8192 | 3584 | marlin_bf16i4 | 0.021 | 2.808 | 703.164 | | 1 | 8192 | 3584 | machete_bf16i4 | 0.024 | 2.460 | 615.990 | Note that currently the precision with fast_gemv kernel and cuda_lite not matched yet. so need fp8 to coming in for a fairer result. heuristic sweep results from the 4 problem sizes we care about: P1722806148 **Next step:** Need fp8 mixed precision support for fast gemv kernel which is what we want Differential Revision: D68470488
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Feb 1, 2025
Summary: X-link: facebookresearch/FBGEMM#688 This diff content includes: 1. Port OSS FastGEMV `bf16` kernel into fbcode and expose to python as a step 1 - `torch.ops.fbgemm.bf16_fast_gemv` https://github.com/wangsiping97/FastGEMV/blob/1fdff6f74aade033c02727a419afd6a4b4bfbc3f/fast_gemv.cu#L14 2. Add `bf16_oss_fast_gemv` to quantize ops benchmark script 3. Add two simple tests for custom op`torch.ops.fbgemm.f16_fast_gemv` to test - `torch.compile()` able - correctness Perf numbers compared with `bf16_baseline,bf16_oss_fast_gemv,cuda_lite,marlin_bf16i4,machete_bf16i4` ====================== ### Benchmark Results on H100 | **M** | **N** | **K** | **Method** | **Elapsed Time (ms)** | **TFLOPS** | **GB/s** | | --- | --- | --- | --- | --- | --- | --- | | 1 | 1280 | 8192 | bf16_baseline | 0.024 | 0.860 | 861.042 | | 1 | 1280 | 8192 | fp16_oss_fast_gemv | 0.019 | 1.126 | 1127.391 | | 1 | 1280 | 8192 | cuda_lite_fp8 | 0.015 | 1.357 | 679.032 | | 1 | 1280 | 8192 | marlin_bf16i4 | 0.027 | 0.768 | 192.612 | | 1 | 1280 | 8192 | machete_bf16i4 | 0.026 | 0.810 | 203.219 | | 1 | 8192 | 1024 | bf16_baseline | 0.018 | 0.952 | 953.176 | | 1 | 8192 | 1024 | fp16_oss_fast_gemv | 0.015 | 1.100 | 1100.900 | | 1 | 8192 | 1024 | cuda_lite_fp8 | 0.014 | 1.198 | 600.054 | | 1 | 8192 | 1024 | marlin_bf16i4 | 0.015 | 1.144 | 287.150 | | 1 | 8192 | 1024 | machete_bf16i4 | 0.014 | 1.187 | 298.096 | | 1 | 7168 | 8192 | bf16_baseline | 0.073 | 1.609 | 1608.983 | | 1 | 7168 | 8192 | fp16_oss_fast_gemv | 0.069 | 1.697 | 1697.308 | | 1 | 7168 | 8192 | cuda_lite_fp8 | 0.044 | 2.679 | 1340.093 | | 1 | 7168 | 8192 | marlin_bf16i4 | 0.033 | 3.590 | 898.436 | | 1 | 7168 | 8192 | machete_bf16i4 | 0.039 | 3.017 | 755.147 | | 1 | 8192 | 3584 | bf16_baseline | 0.045 | 1.312 | 1312.239 | | 1 | 8192 | 3584 | fp16_oss_fast_gemv | 0.041 | 1.427 | 1427.166 | | 1 | 8192 | 3584 | cuda_lite_fp8 | 0.026 | 2.271 | 1136.151 | | 1 | 8192 | 3584 | marlin_bf16i4 | 0.021 | 2.808 | 703.164 | | 1 | 8192 | 3584 | machete_bf16i4 | 0.024 | 2.460 | 615.990 | Note that currently the precision with `fast_gemv` kernel and `cuda_lite` does not match yet. so need fp8 to coming in for a fairer result. Also no_cuda_graph flag enabled when running the quantize_bench heuristic sweep results from the 4 problem sizes we care about: P1722806148 **Next step:** Need fp8 mixed precision support for fast gemv kernel which is what we want Differential Revision: D68470488
Summary: X-link: facebookresearch/FBGEMM#688 This diff content includes: 1. Port OSS FastGEMV `bf16` kernel into fbcode and expose to python as a step 1 - `torch.ops.fbgemm.bf16_fast_gemv` https://github.com/wangsiping97/FastGEMV/blob/1fdff6f74aade033c02727a419afd6a4b4bfbc3f/fast_gemv.cu#L14 2. Add `bf16_oss_fast_gemv` to quantize ops benchmark script 3. Add two simple tests for custom op`torch.ops.fbgemm.f16_fast_gemv` to test - `torch.compile()` able - correctness Perf numbers compared with `bf16_baseline,bf16_oss_fast_gemv,cuda_lite,marlin_bf16i4,machete_bf16i4` ====================== ### Benchmark Results on H100 | **M** | **N** | **K** | **Method** | **Elapsed Time (ms)** | **TFLOPS** | **GB/s** | | --- | --- | --- | --- | --- | --- | --- | | 1 | 1280 | 8192 | bf16_baseline | 0.024 | 0.860 | 861.042 | | 1 | 1280 | 8192 | bf16_oss_fast_gemv | 0.019 | 1.126 | 1127.391 | | 1 | 1280 | 8192 | cuda_lite_fp8 | 0.015 | 1.357 | 679.032 | | 1 | 1280 | 8192 | marlin_bf16i4 | 0.027 | 0.768 | 192.612 | | 1 | 1280 | 8192 | machete_bf16i4 | 0.026 | 0.810 | 203.219 | | 1 | 8192 | 1024 | bf16_baseline | 0.018 | 0.952 | 953.176 | | 1 | 8192 | 1024 | bf16_oss_fast_gemv | 0.015 | 1.100 | 1100.900 | | 1 | 8192 | 1024 | cuda_lite_fp8 | 0.014 | 1.198 | 600.054 | | 1 | 8192 | 1024 | marlin_bf16i4 | 0.015 | 1.144 | 287.150 | | 1 | 8192 | 1024 | machete_bf16i4 | 0.014 | 1.187 | 298.096 | | 1 | 7168 | 8192 | bf16_baseline | 0.073 | 1.609 | 1608.983 | | 1 | 7168 | 8192 | bf16_oss_fast_gemv | 0.069 | 1.697 | 1697.308 | | 1 | 7168 | 8192 | cuda_lite_fp8 | 0.044 | 2.679 | 1340.093 | | 1 | 7168 | 8192 | marlin_bf16i4 | 0.033 | 3.590 | 898.436 | | 1 | 7168 | 8192 | machete_bf16i4 | 0.039 | 3.017 | 755.147 | | 1 | 8192 | 3584 | bf16_baseline | 0.045 | 1.312 | 1312.239 | | 1 | 8192 | 3584 | bf16_oss_fast_gemv | 0.041 | 1.427 | 1427.166 | | 1 | 8192 | 3584 | cuda_lite_fp8 | 0.026 | 2.271 | 1136.151 | | 1 | 8192 | 3584 | marlin_bf16i4 | 0.021 | 2.808 | 703.164 | | 1 | 8192 | 3584 | machete_bf16i4 | 0.024 | 2.460 | 615.990 | Note that currently the precision with `fast_gemv` kernel and `cuda_lite` does not match yet. so need fp8 to coming in for a fairer result. Also no_cuda_graph flag enabled when running the quantize_bench heuristic sweep results from the 4 problem sizes we care about: P1722806148 **Next step:** Need fp8 mixed precision support for fast gemv kernel which is what we want Differential Revision: D68470488
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Summary:
This diff content includes:
fp16kernel into fbcode and expose to python as a step 1 -torch.ops.fbgemm.f16_fast_gemvhttps://github.com/wangsiping97/FastGEMV/blob/1fdff6f74aade033c02727a419afd6a4b4bfbc3f/fast_gemv.cu#L14
fp16_oss_fast_gemvto quantize ops benchmark scripttorch.ops.fbgemm.f16_fast_gemvto testtorch.compile()ableNext step:
Need fp8 mixed precision support for fast gemv kernel which is what we want
Differential Revision: D68470488