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manual ttgir in bwd pass

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This commit is contained in:
Phil Tillet
2022-12-29 15:53:38 -08:00
parent 71e3143eaf
commit 7388fb1de9
2 changed files with 218 additions and 14 deletions
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#blocked0 = #triton_gpu.blocked<{sizePerThread = [1], threadsPerWarp = [32], warpsPerCTA = [8], order = [0]}>
#blocked1 = #triton_gpu.blocked<{sizePerThread = [1, 8], threadsPerWarp = [4, 8], warpsPerCTA = [8, 1], order = [1, 0]}>
#blocked2 = #triton_gpu.blocked<{sizePerThread = [1, 4], threadsPerWarp = [2, 16], warpsPerCTA = [8, 1], order = [1, 0]}>
#blocked3 = #triton_gpu.blocked<{sizePerThread = [1, 1], threadsPerWarp = [32, 1], warpsPerCTA = [4, 2], order = [0, 1]}>
#mma0 = #triton_gpu.mma<{versionMajor = 2, versionMinor = 0, warpsPerCTA = [4, 2]}>
#mma1 = #triton_gpu.mma<{versionMajor = 2, versionMinor = 0, warpsPerCTA = [8, 1]}>
#shared0 = #triton_gpu.shared<{vec = 1, perPhase = 1, maxPhase = 1, order = [1, 0]}>
#shared1 = #triton_gpu.shared<{vec = 1, perPhase = 1, maxPhase = 1, order = [0, 1]}>
#shared2 = #triton_gpu.shared<{vec = 8, perPhase = 1, maxPhase = 8, order = [1, 0]}>
module attributes {"triton_gpu.num-warps" = 8 : i32} {
func public @_bwd_kernel_0d1d2d34d5d6d7d8d9d10d11d12d13d14d15c16d17d18d19c20d21d22d23c2425d26d27(%arg0: !tt.ptr<f16> {tt.divisibility = 16 : i32}, %arg1: !tt.ptr<f16> {tt.divisibility = 16 : i32}, %arg2: !tt.ptr<f16> {tt.divisibility = 16 : i32}, %arg3: f32, %arg4: !tt.ptr<f16> {tt.divisibility = 16 : i32}, %arg5: !tt.ptr<f16> {tt.divisibility = 16 : i32}, %arg6: !tt.ptr<f32> {tt.divisibility = 16 : i32}, %arg7: !tt.ptr<f16> {tt.divisibility = 16 : i32}, %arg8: !tt.ptr<f16> {tt.divisibility = 16 : i32}, %arg9: !tt.ptr<f32> {tt.divisibility = 16 : i32}, %arg10: !tt.ptr<f32> {tt.divisibility = 16 : i32}, %arg11: !tt.ptr<f32> {tt.divisibility = 16 : i32}, %arg12: i32 {tt.divisibility = 16 : i32}, %arg13: i32 {tt.divisibility = 16 : i32}, %arg14: i32 {tt.divisibility = 16 : i32}, %arg15: i32 {tt.divisibility = 16 : i32}, %arg16: i32 {tt.divisibility = 16 : i32}, %arg17: i32 {tt.divisibility = 16 : i32}, %arg18: i32 {tt.divisibility = 16 : i32}, %arg19: i32 {tt.divisibility = 16 : i32}, %arg20: i32 {tt.divisibility = 16 : i32}, %arg21: i32, %arg22: i32 {tt.divisibility = 16 : i32}, %arg23: i32 {tt.divisibility = 16 : i32}, %arg24: i32) {
%cst = arith.constant dense<0.000000e+00> : tensor<128x128xf32, #mma1>
%cst_0 = arith.constant dense<0.000000e+00> : tensor<128x64xf32, #mma0>
%cst_1 = arith.constant dense<0xFF800000> : tensor<128x128xf32, #blocked3>
%c128 = arith.constant 128 : index
%c128_i32 = arith.constant 128 : i32
%c1 = arith.constant 1 : index
%c0 = arith.constant 0 : index
%c0_i32 = arith.constant 0 : i32
%0 = tt.get_program_id {axis = 0 : i32} : i32
%1 = arith.divsi %0, %arg22 : i32
%2 = arith.remsi %0, %arg22 : i32
%3 = arith.muli %1, %arg12 : i32
%4 = arith.muli %2, %arg13 : i32
%5 = arith.addi %3, %4 : i32
%6 = tt.addptr %arg0, %5 : !tt.ptr<f16>, i32
%7 = tt.addptr %arg1, %5 : !tt.ptr<f16>, i32
%8 = tt.addptr %arg2, %5 : !tt.ptr<f16>, i32
%9 = tt.addptr %arg5, %5 : !tt.ptr<f16>, i32
%10 = tt.addptr %arg6, %5 : !tt.ptr<f32>, i32
%11 = tt.addptr %arg7, %5 : !tt.ptr<f16>, i32
%12 = tt.addptr %arg8, %5 : !tt.ptr<f16>, i32
%13 = arith.index_cast %arg24 : i32 to index
%14 = tt.make_range {end = 128 : i32, start = 0 : i32} : tensor<128xi32, #blocked0>
%15 = tt.make_range {end = 128 : i32, start = 0 : i32} : tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #blocked1}>>
%16 = tt.make_range {end = 128 : i32, start = 0 : i32} : tensor<128xi32, #triton_gpu.slice<{dim = 0, parent = #blocked3}>>
%17 = tt.make_range {end = 128 : i32, start = 0 : i32} : tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #blocked2}>>
%18 = tt.make_range {end = 128 : i32, start = 0 : i32} : tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #blocked3}>>
%19 = tt.splat %arg14 : (i32) -> tensor<128x1xi32, #blocked1>
%20 = tt.splat %arg14 : (i32) -> tensor<128x1xi32, #blocked2>
%21 = tt.make_range {end = 64 : i32, start = 0 : i32} : tensor<64xi32, #triton_gpu.slice<{dim = 0, parent = #blocked1}>>
%22 = tt.make_range {end = 64 : i32, start = 0 : i32} : tensor<64xi32, #triton_gpu.slice<{dim = 0, parent = #blocked2}>>
%23 = tt.expand_dims %21 {axis = 0 : i32} : (tensor<64xi32, #triton_gpu.slice<{dim = 0, parent = #blocked1}>>) -> tensor<1x64xi32, #blocked1>
%24 = tt.broadcast %23 : (tensor<1x64xi32, #blocked1>) -> tensor<128x64xi32, #blocked1>
%25 = tt.expand_dims %22 {axis = 0 : i32} : (tensor<64xi32, #triton_gpu.slice<{dim = 0, parent = #blocked2}>>) -> tensor<1x64xi32, #blocked2>
%26 = tt.broadcast %25 : (tensor<1x64xi32, #blocked2>) -> tensor<128x64xi32, #blocked2>
%27 = tt.splat %6 : (!tt.ptr<f16>) -> tensor<128x64x!tt.ptr<f16>, #blocked1>
%28 = tt.splat %arg17 : (i32) -> tensor<128x1xi32, #blocked1>
%29 = tt.splat %7 : (!tt.ptr<f16>) -> tensor<128x64x!tt.ptr<f16>, #blocked1>
%30 = tt.splat %8 : (!tt.ptr<f16>) -> tensor<128x64x!tt.ptr<f16>, #blocked1>
%31 = tt.splat %9 : (!tt.ptr<f16>) -> tensor<128x64x!tt.ptr<f16>, #blocked1>
%32 = tt.splat %10 : (!tt.ptr<f32>) -> tensor<128x64x!tt.ptr<f32>, #blocked2>
%33 = arith.muli %0, %arg23 : i32
%34 = tt.addptr %arg11, %33 : !tt.ptr<f32>, i32
%35 = tt.addptr %arg10, %33 : !tt.ptr<f32>, i32
%36 = arith.muli %arg24, %c128_i32 : i32
%37 = arith.index_cast %36 : i32 to index
%38 = tt.splat %35 : (!tt.ptr<f32>) -> tensor<128x!tt.ptr<f32>, #blocked0>
%39 = tt.splat %arg3 : (f32) -> tensor<128x128xf32, #blocked3>
%40 = tt.splat %34 : (!tt.ptr<f32>) -> tensor<128x!tt.ptr<f32>, #blocked0>
%41 = arith.muli %arg14, %c128_i32 : i32
%42 = tt.splat %41 : (i32) -> tensor<128x64xi32, #blocked1>
%43 = tt.splat %41 : (i32) -> tensor<128x64xi32, #blocked2>
%44 = tt.splat %12 : (!tt.ptr<f16>) -> tensor<128x64x!tt.ptr<f16>, #blocked1>
%45 = tt.splat %11 : (!tt.ptr<f16>) -> tensor<128x64x!tt.ptr<f16>, #blocked1>
scf.for %arg25 = %c0 to %13 step %c1 {
%46 = arith.index_cast %arg25 : index to i32
%47 = arith.muli %46, %c128_i32 : i32
%48 = tt.splat %47 : (i32) -> tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #blocked1}>>
%49 = tt.splat %47 : (i32) -> tensor<128xi32, #triton_gpu.slice<{dim = 0, parent = #blocked3}>>
%50 = tt.splat %47 : (i32) -> tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #blocked2}>>
%51 = arith.addi %48, %15 : tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #blocked1}>>
%52 = arith.addi %50, %17 : tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #blocked2}>>
%53 = tt.expand_dims %51 {axis = 1 : i32} : (tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #blocked1}>>) -> tensor<128x1xi32, #blocked1>
%54 = tt.expand_dims %52 {axis = 1 : i32} : (tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #blocked2}>>) -> tensor<128x1xi32, #blocked2>
%55 = arith.muli %53, %28 : tensor<128x1xi32, #blocked1>
%56 = tt.broadcast %55 : (tensor<128x1xi32, #blocked1>) -> tensor<128x64xi32, #blocked1>
%57 = arith.addi %56, %24 : tensor<128x64xi32, #blocked1>
%58 = tt.addptr %29, %57 : tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64xi32, #blocked1>
%59 = tt.load %58 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128x64xf16, #blocked1>
%60 = arith.muli %53, %19 : tensor<128x1xi32, #blocked1>
%61 = tt.broadcast %60 : (tensor<128x1xi32, #blocked1>) -> tensor<128x64xi32, #blocked1>
%62 = arith.addi %61, %24 : tensor<128x64xi32, #blocked1>
%63 = tt.addptr %30, %62 : tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64xi32, #blocked1>
%64 = tt.load %63 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128x64xf16, #blocked1>
%65 = arith.index_cast %47 : i32 to index
%66 = triton_gpu.convert_layout %59 : (tensor<128x64xf16, #blocked1>) -> tensor<128x64xf16, #shared0>
%67 = tt.trans %66 : (tensor<128x64xf16, #shared0>) -> tensor<64x128xf16, #shared1>
%68 = arith.addi %49, %16 : tensor<128xi32, #triton_gpu.slice<{dim = 0, parent = #blocked3}>>
%69 = tt.expand_dims %68 {axis = 0 : i32} : (tensor<128xi32, #triton_gpu.slice<{dim = 0, parent = #blocked3}>>) -> tensor<1x128xi32, #blocked3>
%70 = tt.broadcast %69 : (tensor<1x128xi32, #blocked3>) -> tensor<128x128xi32, #blocked3>
%71 = triton_gpu.convert_layout %64 : (tensor<128x64xf16, #blocked1>) -> tensor<128x64xf16, #shared0>
%72 = tt.trans %71 : (tensor<128x64xf16, #shared0>) -> tensor<64x128xf16, #shared1>
%73 = arith.muli %54, %20 : tensor<128x1xi32, #blocked2>
%74 = tt.broadcast %73 : (tensor<128x1xi32, #blocked2>) -> tensor<128x64xi32, #blocked2>
%75 = arith.addi %74, %26 : tensor<128x64xi32, #blocked2>
%76 = tt.addptr %32, %75 : tensor<128x64x!tt.ptr<f32>, #blocked2>, tensor<128x64xi32, #blocked2>
%77 = tt.addptr %27, %62 : tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64xi32, #blocked1>
%78 = tt.addptr %31, %62 : tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64xi32, #blocked1>
%79 = triton_gpu.convert_layout %67 : (tensor<64x128xf16, #shared1>) -> tensor<64x128xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma1}>>
%80 = triton_gpu.convert_layout %72 : (tensor<64x128xf16, #shared1>) -> tensor<64x128xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma1}>>
%81 = triton_gpu.alloc_tensor : tensor<1x128x64xf16, #shared2>
%82 = triton_gpu.insert_slice_async %58, %81, %c0_i32 {axis = 0 : i32, cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128x64x!tt.ptr<f16>, #blocked1> -> tensor<1x128x64xf16, #shared2>
triton_gpu.async_wait {num = 0 : i32}
%83 = tensor.extract_slice %82[0, 0, 0] [1, 128, 64] [1, 1, 1] : tensor<1x128x64xf16, #shared2> to tensor<128x64xf16, #shared2>
%84 = triton_gpu.convert_layout %83 : (tensor<128x64xf16, #shared2>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma0}>>
%85:5 = scf.for %arg26 = %65 to %37 step %c128 iter_args(%arg27 = %cst_0, %arg28 = %cst_0, %arg29 = %76, %arg30 = %77, %arg31 = %78) -> (tensor<128x64xf32, #mma0>, tensor<128x64xf32, #mma0>, tensor<128x64x!tt.ptr<f32>, #blocked2>, tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64x!tt.ptr<f16>, #blocked1>) {
%92 = arith.index_cast %arg26 : index to i32
%93 = tt.splat %92 : (i32) -> tensor<128xi32, #blocked0>
%94 = tt.splat %92 : (i32) -> tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #blocked3}>>
%95 = arith.addi %93, %14 : tensor<128xi32, #blocked0>
%96 = triton_gpu.alloc_tensor : tensor<1x128x64xf16, #shared2>
%97 = triton_gpu.insert_slice_async %arg30, %96, %c0_i32 {axis = 0 : i32, cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128x64x!tt.ptr<f16>, #blocked1> -> tensor<1x128x64xf16, #shared2>
triton_gpu.async_wait {num = 0 : i32}
%98 = tensor.extract_slice %97[0, 0, 0] [1, 128, 64] [1, 1, 1] : tensor<1x128x64xf16, #shared2> to tensor<128x64xf16, #shared2>
%99 = triton_gpu.convert_layout %98 : (tensor<128x64xf16, #shared2>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma1}>>
%100 = tt.dot %99, %79, %cst {allowTF32 = true} : tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma1}>> * tensor<64x128xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma1}>> -> tensor<128x128xf32, #mma1>
%101 = triton_gpu.convert_layout %100 : (tensor<128x128xf32, #mma1>) -> tensor<128x128xf32, #blocked3>
%102 = arith.addi %94, %18 : tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #blocked3}>>
%103 = tt.expand_dims %102 {axis = 1 : i32} : (tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #blocked3}>>) -> tensor<128x1xi32, #blocked3>
%104 = tt.broadcast %103 : (tensor<128x1xi32, #blocked3>) -> tensor<128x128xi32, #blocked3>
%105 = "triton_gpu.cmpi"(%104, %70) {predicate = 5 : i64} : (tensor<128x128xi32, #blocked3>, tensor<128x128xi32, #blocked3>) -> tensor<128x128xi1, #blocked3>
%106 = "triton_gpu.select"(%105, %101, %cst_1) : (tensor<128x128xi1, #blocked3>, tensor<128x128xf32, #blocked3>, tensor<128x128xf32, #blocked3>) -> tensor<128x128xf32, #blocked3>
%107 = tt.addptr %38, %95 : tensor<128x!tt.ptr<f32>, #blocked0>, tensor<128xi32, #blocked0>
%108 = tt.load %107 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128xf32, #blocked0>
%109 = arith.mulf %106, %39 : tensor<128x128xf32, #blocked3>
%110 = triton_gpu.convert_layout %108 : (tensor<128xf32, #blocked0>) -> tensor<128xf32, #triton_gpu.slice<{dim = 1, parent = #blocked3}>>
%111 = tt.expand_dims %110 {axis = 1 : i32} : (tensor<128xf32, #triton_gpu.slice<{dim = 1, parent = #blocked3}>>) -> tensor<128x1xf32, #blocked3>
%112 = tt.broadcast %111 : (tensor<128x1xf32, #blocked3>) -> tensor<128x128xf32, #blocked3>
%113 = arith.subf %109, %112 : tensor<128x128xf32, #blocked3>
%114 = math.exp %113 : tensor<128x128xf32, #blocked3>
%115 = arith.truncf %114 : tensor<128x128xf32, #blocked3> to tensor<128x128xf16, #blocked3>
%116 = triton_gpu.convert_layout %115 : (tensor<128x128xf16, #blocked3>) -> tensor<128x128xf16, #shared1>
%117 = tt.trans %116 : (tensor<128x128xf16, #shared1>) -> tensor<128x128xf16, #shared0>
%118 = triton_gpu.convert_layout %117 : (tensor<128x128xf16, #shared0>) -> tensor<128x128xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma0}>>
%119 = triton_gpu.alloc_tensor : tensor<1x128x64xf16, #shared2>
%120 = triton_gpu.insert_slice_async %arg31, %119, %c0_i32 {axis = 0 : i32, cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128x64x!tt.ptr<f16>, #blocked1> -> tensor<1x128x64xf16, #shared2>
triton_gpu.async_wait {num = 0 : i32}
%121 = tensor.extract_slice %120[0, 0, 0] [1, 128, 64] [1, 1, 1] : tensor<1x128x64xf16, #shared2> to tensor<128x64xf16, #shared2>
%122 = triton_gpu.convert_layout %121 : (tensor<128x64xf16, #shared2>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma0}>>
%123 = tt.dot %118, %122, %arg27 {allowTF32 = true} : tensor<128x128xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma0}>> * tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma0}>> -> tensor<128x64xf32, #mma0>
%124 = tt.addptr %40, %95 : tensor<128x!tt.ptr<f32>, #blocked0>, tensor<128xi32, #blocked0>
%125 = tt.load %124 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128xf32, #blocked0>
%126 = triton_gpu.convert_layout %125 : (tensor<128xf32, #blocked0>) -> tensor<128xf32, #triton_gpu.slice<{dim = 1, parent = #mma1}>>
%127 = tt.expand_dims %126 {axis = 1 : i32} : (tensor<128xf32, #triton_gpu.slice<{dim = 1, parent = #mma1}>>) -> tensor<128x1xf32, #mma1>
%128 = tt.broadcast %127 : (tensor<128x1xf32, #mma1>) -> tensor<128x128xf32, #mma1>
%129 = arith.subf %cst, %128 : tensor<128x128xf32, #mma1>
%130 = triton_gpu.alloc_tensor : tensor<1x128x64xf16, #shared2>
%131 = triton_gpu.insert_slice_async %arg31, %130, %c0_i32 {axis = 0 : i32, cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128x64x!tt.ptr<f16>, #blocked1> -> tensor<1x128x64xf16, #shared2>
triton_gpu.async_wait {num = 0 : i32}
%132 = tensor.extract_slice %131[0, 0, 0] [1, 128, 64] [1, 1, 1] : tensor<1x128x64xf16, #shared2> to tensor<128x64xf16, #shared2>
%133 = triton_gpu.convert_layout %132 : (tensor<128x64xf16, #shared2>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma1}>>
%134 = tt.dot %133, %80, %129 {allowTF32 = true} : tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma1}>> * tensor<64x128xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma1}>> -> tensor<128x128xf32, #mma1>
%135 = triton_gpu.convert_layout %134 : (tensor<128x128xf32, #mma1>) -> tensor<128x128xf32, #blocked3>
%136 = arith.mulf %114, %135 : tensor<128x128xf32, #blocked3>
%137 = arith.mulf %136, %39 : tensor<128x128xf32, #blocked3>
%138 = arith.truncf %137 : tensor<128x128xf32, #blocked3> to tensor<128x128xf16, #blocked3>
%139 = triton_gpu.convert_layout %138 : (tensor<128x128xf16, #blocked3>) -> tensor<128x128xf16, #shared1>
%140 = tt.trans %139 : (tensor<128x128xf16, #shared1>) -> tensor<128x128xf16, #shared0>
%141 = triton_gpu.convert_layout %140 : (tensor<128x128xf16, #shared0>) -> tensor<128x128xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma0}>>
%142 = triton_gpu.alloc_tensor : tensor<1x128x64xf16, #shared2>
%143 = triton_gpu.insert_slice_async %arg30, %142, %c0_i32 {axis = 0 : i32, cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128x64x!tt.ptr<f16>, #blocked1> -> tensor<1x128x64xf16, #shared2>
triton_gpu.async_wait {num = 0 : i32}
%144 = tensor.extract_slice %143[0, 0, 0] [1, 128, 64] [1, 1, 1] : tensor<1x128x64xf16, #shared2> to tensor<128x64xf16, #shared2>
%145 = triton_gpu.convert_layout %144 : (tensor<128x64xf16, #shared2>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma0}>>
%146 = tt.dot %141, %145, %arg28 {allowTF32 = true} : tensor<128x128xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma0}>> * tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma0}>> -> tensor<128x64xf32, #mma0>
%147 = tt.load %arg29 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128x64xf32, #blocked2>
%148 = triton_gpu.convert_layout %147 : (tensor<128x64xf32, #blocked2>) -> tensor<128x64xf32, #mma0>
%149 = triton_gpu.convert_layout %138 : (tensor<128x128xf16, #blocked3>) -> tensor<128x128xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma0}>>
%150 = tt.dot %149, %84, %148 {allowTF32 = true} : tensor<128x128xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma0}>> * tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma0}>> -> tensor<128x64xf32, #mma0>
%151 = triton_gpu.convert_layout %150 : (tensor<128x64xf32, #mma0>) -> tensor<128x64xf32, #blocked2>
tt.store %arg29, %151 : tensor<128x64xf32, #blocked2>
%152 = tt.addptr %arg29, %43 : tensor<128x64x!tt.ptr<f32>, #blocked2>, tensor<128x64xi32, #blocked2>
%153 = tt.addptr %arg30, %42 : tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64xi32, #blocked1>
%154 = tt.addptr %arg31, %42 : tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64xi32, #blocked1>
scf.yield %123, %146, %152, %153, %154 : tensor<128x64xf32, #mma0>, tensor<128x64xf32, #mma0>, tensor<128x64x!tt.ptr<f32>, #blocked2>, tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64x!tt.ptr<f16>, #blocked1>
}
%86 = triton_gpu.convert_layout %85#1 : (tensor<128x64xf32, #mma0>) -> tensor<128x64xf32, #blocked1>
%87 = triton_gpu.convert_layout %85#0 : (tensor<128x64xf32, #mma0>) -> tensor<128x64xf32, #blocked1>
%88 = tt.addptr %44, %62 : tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64xi32, #blocked1>
%89 = arith.truncf %87 : tensor<128x64xf32, #blocked1> to tensor<128x64xf16, #blocked1>
tt.store %88, %89 : tensor<128x64xf16, #blocked1>
%90 = tt.addptr %45, %57 : tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64xi32, #blocked1>
%91 = arith.truncf %86 : tensor<128x64xf32, #blocked1> to tensor<128x64xf16, #blocked1>
tt.store %90, %91 : tensor<128x64xf16, #blocked1>
}
return
}
}

41
python/tutorials/06-fused-attention.py
View File

@@ -191,6 +191,7 @@ def _bwd_kernel(
tl.store(dv_ptrs, dv)
tl.store(dk_ptrs, dk)
_bwd_kernel = triton.compile("./bwd.ttgir", num_warps=8)
# _fwd_kernel = triton.compile("./fails.ptx", num_warps=4, shared=18432)
empty = torch.empty(128, device="cuda")
@@ -255,21 +256,35 @@ class _attention(torch.autograd.Function):
BLOCK_M=ctx.BLOCK, D_HEAD=ctx.BLOCK_DMODEL,
)
pgm = _bwd_kernel[(ctx.grid[1],)](
q, k, v, ctx.sm_scale,
o, do_scaled,
dq, dk, dv,
l, m,
delta,
q.stride(0), q.stride(1), q.stride(2), q.stride(3),
k.stride(0), k.stride(1), k.stride(2), k.stride(3),
v.stride(0), v.stride(1), v.stride(2), v.stride(3),
_bwd_kernel[(ctx.grid[1],1,1)](
q.data_ptr(), k.data_ptr(), v.data_ptr(), ctx.sm_scale,
o.data_ptr(), do_scaled.data_ptr(),
dq.data_ptr(), dk.data_ptr(), dv.data_ptr(),
l.data_ptr(), m.data_ptr(),
delta.data_ptr(),
q.stride(0), q.stride(1), q.stride(2),
k.stride(0), k.stride(1), k.stride(2),
v.stride(0), v.stride(1), v.stride(2),
q.shape[0], q.shape[1], q.shape[2],
ctx.grid[0],
BLOCK_M=ctx.BLOCK, BLOCK_N=ctx.BLOCK,
BLOCK_DMODEL=ctx.BLOCK_DMODEL, num_warps=8,
num_stages=1,
ctx.grid[0]
)
# pgm = _bwd_kernel[(ctx.grid[1],)](
# q, k, v, ctx.sm_scale,
# o, do_scaled,
# dq, dk, dv,
# l, m,
# delta,
# q.stride(0), q.stride(1), q.stride(2), q.stride(3),
# k.stride(0), k.stride(1), k.stride(2), k.stride(3),
# v.stride(0), v.stride(1), v.stride(2), v.stride(3),
# q.shape[0], q.shape[1], q.shape[2],
# ctx.grid[0],
# BLOCK_M=ctx.BLOCK, BLOCK_N=ctx.BLOCK,
# BLOCK_DMODEL=ctx.BLOCK_DMODEL, num_warps=8,
# num_stages=1,
# )
# print(pgm.asm["ttgir"])
return dq, dk, dv, None