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CUTLASS 2.8 (#363)

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CUTLASS 2.8
This commit is contained in:
Manish Gupta
2021-11-19 13:26:35 -08:00
committed by GitHub
parent 6fc5008803
commit 808c25337a
127 changed files with 18555 additions and 1338 deletions
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media/docs/implicit_gemm_convolution.md
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@@ -152,13 +152,13 @@ a hierarchical GEMM kernel targeting Tensor Cores.
# CUTLASS Convolution Implementation
The CUTLASS Implicit GEMM implementation makes several assumptions.
To get the best performance, the following parameters are recommended.
- All tensors are 128-bit aligned NHWC tensors
- Channel count (C) is a multiple of 32 elements
- Filter count (K) is a multiple of 32 elements
This enables 128-bit vector memory acceses which lead to efficient CUDA kernels.
This enables 128-bit vector memory acceses which lead to efficient CUDA kernels. Smaller alignment is supported even on tensor cores by setting AlignmentA and AlignmentB in conv::kernel::DefaultConv2dFprop, but the performance is lower than 128-bit aligned tesnors.
# CUTLASS Device-level Convolution Operator
@@ -187,7 +187,7 @@ using Conv2dFpropKernel = typename cutlass::conv::kernel::DefaultConv2dFprop<
SwizzleThreadBlock, // optional function to reorder threadblocks for locality
NumStages, // number of pipeline stages in threadblock-scoped GEMM
cutlass::arch::OpMultiplyAddSaturate, // math operation on data of element a and b
cutlass::conv::IteratorAlgorithm::kAnalytic // globabl memory iterator algorithm
cutlass::conv::IteratorAlgorithm::kOptimized // globabl memory iterator algorithm
>::Kernel
```
@@ -219,7 +219,7 @@ using Conv2dFpropKernel = typename cutlass::conv::kernel::DefaultConv2dFprop<
SwizzleThreadBlock, // optional function to reorder threadblocks for locality
2, // number of pipeline stages in threadblock-scoped GEMM
cutlass::arch::OpMultiplyAddSaturate, // math operation on data of element a and b
cutlass::conv::IteratorAlgorithm::kAnalytic // globabl memory iterator algorithm
cutlass::conv::IteratorAlgorithm::kOptimized // globabl memory iterator algorithm
>::Kernel
```