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finish porting the original logic

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Superjomn
2022-11-04 13:35:49 +08:00
parent da2993e1c7
commit 1f552308c4
2 changed files with 166 additions and 17 deletions
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16
test/Conversion/tritongpu_to_llvm.mlir
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@@ -789,3 +789,19 @@ module attributes {"triton_gpu.num-warps" = 4 : i32} {
return
}
}
// -----
#blocked = #triton_gpu.blocked<{sizePerThread = [1, 4], threadsPerWarp = [2, 16], warpsPerCTA = [1, 4], order = [1, 0]}>
#shared = #triton_gpu.shared<{vec = 1, perPhase = 1, maxPhase = 1, order = [1, 0]}>
#mma = #triton_gpu.mma<{version = 2, warpsPerCTA = [2, 2]}>
#dot_operand_a = #triton_gpu.dot_op<{opIdx=0, parent=#mma}>
#dot_operand_b = #triton_gpu.dot_op<{opIdx=1, parent=#mma}>
module attributes {"triton_gpu.num-warps" = 4 : i32} {
func @matmul884_kernel_dot_operand_layout(%ptr:!tt.ptr<f32> {tt.divisibility = 16 : i32},
%a:tensor<32x16xf32, #shared>, %b:tensor<16x32xf32, #shared>) {
%cst = arith.constant dense<0.000000e+00> : tensor<16x16xf32, #mma>
// CHECK: llvm.intr.fmuladd
%28 = tt.dot %a, %b, %cst {allowTF32 = false, transA = false, transB = false} : tensor<32x16xf32, #shared> * tensor<16x32xf32, #shared> -> tensor<16x16xf32, #mma>
return
}
}