Add microbenchmark for A@B^t #2408
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Egor-Krivov
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alexbaden
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Oct 4, 2024
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The benchmark implementation looks fine. One note - this example is almost identical to our tutorial 10 experimental block pointer code, with some small changes to handle the transpose case and a user provided bias vector. We might be able to unify this microbenchmark with tutorial 10 in the future.
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Egor-Krivov
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Based on this feedback #2408 (review) Changed GEMM benchmark to include transposed matrices case. Closes #2424 Relates to #1795 A@B^t case is important because weight matrix is often stored in [M, K] format. For example, in https://pytorch.org/docs/stable/generated/torch.nn.Linear.html Right now we are about 1.5 times slower on XPU against raw torch for that case. A^t@B case is important because it's part of matmul backprop. Right now we are about 4 times slower on XPU against raw torch for that case.
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PR adds microbenchmark for gemm with A@B^t, which closes #2414