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v4.1 release update v2. (#2481)

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Junkai-Wu
2025-07-22 10:03:55 +08:00
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parent 9baa06dd57
commit fd6cfe1ed0
179 changed files with 7878 additions and 1286 deletions
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examples/07_volta_tensorop_gemm/volta_tensorop_gemm.cu
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@@ -64,7 +64,7 @@ ElementAccumulator (float), ElementComputeEpilogue (float), ElementInputA (cutla
ElementInputB (cutlass::half_t), ElementOutput (float). Communicating just the data type is not
enough. As the data is laid out linearly in memory, we have to convey the layout of matrices. We do
that by initializing template variable LayoutInputA to column major cutlass variable, LayoutInputB
to row major and LayoutOutput to row major. Next, we setup rules to comptue alpha * X + beta * C
to row major and LayoutOutput to row major. Next, we setup rules to compute alpha * X + beta * C
which is called epilogue of the kernel. We initialize template variable EpilogueOp, which takes the
data type of output ElementOutput (int32_t), the number of elements per vector memory access (16),
data type of accumulator (int32_t) and data type of computation of linear combination (alpha * X +