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This commit is contained in:
Phil Tillet
2023-01-04 00:01:22 -08:00
parent ee86ea9c90
commit e3c3d9fc65
1 changed files with 10 additions and 8 deletions
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python/bwd.ttgir
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@@ -99,8 +99,9 @@ module attributes {"triton_gpu.num-warps" = 8 : i32} {
%87 = tt.splat %86 : (i32) -> tensor<128xi32, #blocked0>
%88 = tt.splat %86 : (i32) -> tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #mma0}>>
%89 = arith.addi %87, %14 : tensor<128xi32, #blocked0>
%90 = tt.load %arg30 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128x64xf16, #blocked1>
%92 = triton_gpu.convert_layout %90 : (tensor<128x64xf16, #blocked1>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma0}>>
%900 = tt.load %arg30 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128x64xf16, #blocked1>
%90 = triton_gpu.convert_layout %900 : (tensor<128x64xf16, #blocked1>) -> tensor<128x64xf16, #shared1>
%92 = triton_gpu.convert_layout %90 : (tensor<128x64xf16, #shared1>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma0}>>
%91 = triton_gpu.convert_layout %67 : (tensor<64x128xf16, #shared1>) -> tensor<64x128xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma0}>>
%93 = tt.dot %92, %91, %cst_0 {allowTF32 = true} : tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma0}>> * tensor<64x128xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma0}>> -> tensor<128x128xf32, #mma0>
%94 = arith.addi %88, %18 : tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #mma0}>>
@@ -116,12 +117,13 @@ module attributes {"triton_gpu.num-warps" = 8 : i32} {
%104 = tt.broadcast %103 : (tensor<128x1xf32, #mma0>) -> tensor<128x128xf32, #mma0>
%105 = arith.subf %101, %104 : tensor<128x128xf32, #mma0>
%106 = math.exp %105 : tensor<128x128xf32, #mma0>
%107 = tt.load %arg31 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128x64xf16, #blocked1>
%1070 = tt.load %arg31 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128x64xf16, #blocked1>
%107 = triton_gpu.convert_layout %1070 : (tensor<128x64xf16, #blocked1>) -> tensor<128x64xf16, #shared1>
%108 = arith.truncf %106 : tensor<128x128xf32, #mma0> to tensor<128x128xf16, #mma0>
%109 = triton_gpu.convert_layout %108 : (tensor<128x128xf16, #mma0>) -> tensor<128x128xf16, #shared1>
%110 = tt.trans %109 : (tensor<128x128xf16, #shared1>) -> tensor<128x128xf16, #shared0>
%111 = triton_gpu.convert_layout %110 : (tensor<128x128xf16, #shared0>) -> tensor<128x128xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma1}>>
%112 = triton_gpu.convert_layout %107 : (tensor<128x64xf16, #blocked1>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma1}>>
%112 = triton_gpu.convert_layout %107 : (tensor<128x64xf16, #shared1>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma1}>>
%113 = tt.dot %111, %112, %arg27 {allowTF32 = true} : tensor<128x128xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma1}>> * tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma1}>> -> tensor<128x64xf32, #mma1>
%114 = tt.addptr %40, %89 : tensor<128x!tt.ptr<f32>, #blocked0>, tensor<128xi32, #blocked0>
%115 = tt.load %114 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128xf32, #blocked0>
@@ -129,7 +131,7 @@ module attributes {"triton_gpu.num-warps" = 8 : i32} {
%117 = tt.expand_dims %116 {axis = 1 : i32} : (tensor<128xf32, #triton_gpu.slice<{dim = 1, parent = #mma0}>>) -> tensor<128x1xf32, #mma0>
%118 = tt.broadcast %117 : (tensor<128x1xf32, #mma0>) -> tensor<128x128xf32, #mma0>
%119 = arith.subf %cst_0, %118 : tensor<128x128xf32, #mma0>
%120 = triton_gpu.convert_layout %107 : (tensor<128x64xf16, #blocked1>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma0}>>
%120 = triton_gpu.convert_layout %107 : (tensor<128x64xf16, #shared1>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma0}>>
%121 = triton_gpu.convert_layout %72 : (tensor<64x128xf16, #shared1>) -> tensor<64x128xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma0}>>
%122 = tt.dot %120, %121, %119 {allowTF32 = true} : tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma0}>> * tensor<64x128xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma0}>> -> tensor<128x128xf32, #mma0>
%123 = arith.mulf %106, %122 : tensor<128x128xf32, #mma0>
@@ -138,12 +140,12 @@ module attributes {"triton_gpu.num-warps" = 8 : i32} {
%126 = triton_gpu.convert_layout %125 : (tensor<128x128xf16, #mma0>) -> tensor<128x128xf16, #shared1>
%127 = tt.trans %126 : (tensor<128x128xf16, #shared1>) -> tensor<128x128xf16, #shared0>
%128 = triton_gpu.convert_layout %127 : (tensor<128x128xf16, #shared0>) -> tensor<128x128xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma1}>>
%129 = triton_gpu.convert_layout %90 : (tensor<128x64xf16, #blocked1>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma1}>>
%129 = triton_gpu.convert_layout %90 : (tensor<128x64xf16, #shared1>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma1}>>
%130 = tt.dot %128, %129, %arg28 {allowTF32 = true} : tensor<128x128xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma1}>> * tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma1}>> -> tensor<128x64xf32, #mma1>
%131 = tt.load %arg29 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128x64xf32, #blocked2>
%132 = triton_gpu.convert_layout %131 : (tensor<128x64xf32, #blocked2>) -> tensor<128x64xf32, #mma1>
%133 = triton_gpu.convert_layout %125 : (tensor<128x128xf16, #mma0>) -> tensor<128x128xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma1}>>
%134 = triton_gpu.convert_layout %59 : (tensor<128x64xf16, #blocked1>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma1}>>
%133 = triton_gpu.convert_layout %126 : (tensor<128x128xf16, #shared1>) -> tensor<128x128xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma1}>>
%134 = triton_gpu.convert_layout %66 : (tensor<128x64xf16, #shared0>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma1}>>
%135 = tt.dot %133, %134, %132 {allowTF32 = true} : tensor<128x128xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma1}>> * tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma1}>> -> tensor<128x64xf32, #mma1>
%136 = triton_gpu.convert_layout %135 : (tensor<128x64xf32, #mma1>) -> tensor<128x64xf32, #blocked2>
tt.store %arg29, %136 : tensor<128x64xf32, #blocked2>