Add algorithm search to FP8 sparse linear benchmark (#4397)#4397
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Add algorithm search to FP8 sparse linear benchmark (#4397)#4397bbeckca wants to merge 1 commit into
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/ao/4397
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Summary: What: Adds algorithm search support (--search-alg) to the FP8 sparse linear benchmark. Threads a new alg_id parameter through the quantization config (Float8DynamicActivationFloat8WeightConfig), the sparse tensor class, and down into the _cslt_sparse_mm kernel call. When --search-alg is passed, the benchmark calls _cslt_sparse_mm_search to find the best algorithm for each shape and benchmarks with that algorithm to report the speedup. Why: hipSPARSELt supports multiple algorithms for sparse matmul, and the default (alg_id=0) isn't always the fastest. This lets us find the best algorithm for a given shape and measure the performance benefit. Differential Revision: D102683062
bbeckca
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Summary: What: Adds algorithm search support (--search-alg) to the FP8 sparse linear benchmark. Threads a new alg_id parameter through the quantization config (Float8DynamicActivationFloat8WeightConfig), the sparse tensor class, and down into the _cslt_sparse_mm kernel call. When --search-alg is passed, the benchmark calls _cslt_sparse_mm_search to find the best algorithm for each shape and benchmarks with that algorithm to report the speedup. Why: hipSPARSELt supports multiple algorithms for sparse matmul, and the default (alg_id=0) isn't always the fastest. This lets us find the best algorithm for a given shape and measure the performance benefit. Differential Revision: D102683062
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Summary: What: Adds algorithm search support (--search-alg) to the FP8 sparse linear benchmark. Threads a new alg_id parameter through the quantization config (Float8DynamicActivationFloat8WeightConfig), the sparse tensor class, and down into the _cslt_sparse_mm kernel call. When --search-alg is passed, the benchmark calls _cslt_sparse_mm_search to find the best algorithm for each shape and benchmarks with that algorithm to report the speedup. Why: hipSPARSELt supports multiple algorithms for sparse matmul, and the default (alg_id=0) isn't always the fastest. This lets us find the best algorithm for a given shape and measure the performance benefit. Differential Revision: D102683062
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Summary:
What: Adds algorithm search support (--search-alg) to the FP8 sparse linear benchmark. Threads a new alg_id parameter through the quantization config (Float8DynamicActivationFloat8WeightConfig), the sparse tensor class, and down into the _cslt_sparse_mm kernel call. When --search-alg is passed, the benchmark calls _cslt_sparse_mm_search to find the best algorithm for each shape and benchmarks with that algorithm to report the speedup.
Why: hipSPARSELt supports multiple algorithms for sparse matmul, and the default (alg_id=0) isn't always the fastest. This lets us find the best algorithm for a given shape and measure the performance benefit.
Differential Revision: D102683062