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This commit is contained in:
Phil Tillet
2023-01-06 20:35:57 -08:00
parent 600bcefb12
commit 42421fabc5
1 changed files with 177 additions and 0 deletions
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// TODO: swizzle
// TODO: move opIdx = 0 before opIdx = 1
// TODO: reuse %128 in %137 = triton_gpu.convert_layout %127 : (tensor<128x128xf16, #mma0>) -> tensor<128x128xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma1}>>
// don't convert loaded value to mma for accumulation
#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]}>
#mma0 = #triton_gpu.mma<{versionMajor = 2, versionMinor = 0, warpsPerCTA = [8, 1]}>
#mma1 = #triton_gpu.mma<{versionMajor = 2, versionMinor = 0, warpsPerCTA = [4, 2]}>
#shared0 = #triton_gpu.shared<{vec = 8, perPhase = 1, maxPhase = 8, order = [1, 0]}>
#shared1 = #triton_gpu.shared<{vec = 8, perPhase = 1, maxPhase = 8, 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) {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%c128_i32 = arith.constant 128 : i32
%c128 = arith.constant 128 : index
%cst = arith.constant dense<0.000000e+00> : tensor<128x64xf32, #mma1>
%cst_0 = arith.constant dense<0xFF800000> : tensor<128x128xf32, #mma0>
%cst_1 = arith.constant dense<0.000000e+00> : tensor<128x128xf32, #mma0>
%cst_2 = arith.constant dense<0.000000e+00> : tensor<128x64xf32, #mma1>
%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 = 1, parent = #blocked2}>>
%17 = tt.make_range {end = 128 : i32, start = 0 : i32} : tensor<128xi32, #triton_gpu.slice<{dim = 0, parent = #mma0}>>
%18 = tt.make_range {end = 128 : i32, start = 0 : i32} : tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #mma0}>>
%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, #mma0>
%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 = 1, parent = #blocked2}>>
%50 = tt.splat %47 : (i32) -> tensor<128xi32, #triton_gpu.slice<{dim = 0, parent = #mma0}>>
%51 = arith.addi %48, %15 : tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #blocked1}>>
%52 = arith.addi %49, %16 : 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 = triton_gpu.convert_layout %59 : (tensor<128x64xf16, #blocked1>) -> tensor<128x64xf16, #shared0>
%61 = arith.muli %53, %19 : tensor<128x1xi32, #blocked1>
%62 = tt.broadcast %61 : (tensor<128x1xi32, #blocked1>) -> tensor<128x64xi32, #blocked1>
%63 = arith.addi %62, %24 : tensor<128x64xi32, #blocked1>
%64 = tt.addptr %30, %63 : tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64xi32, #blocked1>
%65 = tt.load %64 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128x64xf16, #blocked1>
%66 = triton_gpu.convert_layout %65 : (tensor<128x64xf16, #blocked1>) -> tensor<128x64xf16, #shared0>
%67 = arith.index_cast %47 : i32 to index
%68 = tt.trans %60 : (tensor<128x64xf16, #shared0>) -> tensor<64x128xf16, #shared1>
%69 = arith.addi %50, %17 : tensor<128xi32, #triton_gpu.slice<{dim = 0, parent = #mma0}>>
%70 = tt.expand_dims %69 {axis = 0 : i32} : (tensor<128xi32, #triton_gpu.slice<{dim = 0, parent = #mma0}>>) -> tensor<1x128xi32, #mma0>
%71 = tt.broadcast %70 : (tensor<1x128xi32, #mma0>) -> tensor<128x128xi32, #mma0>
%72 = tt.trans %66 : (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, %63 : tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64xi32, #blocked1>
%78 = tt.addptr %31, %63 : tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64xi32, #blocked1>
%79:5 = scf.for %arg26 = %67 to %37 step %c128 iter_args(%arg27 = %cst, %arg28 = %cst, %arg29 = %76, %arg30 = %77, %arg31 = %78) -> (tensor<128x64xf32, #mma1>, tensor<128x64xf32, #mma1>, tensor<128x64x!tt.ptr<f32>, #blocked2>, tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64x!tt.ptr<f16>, #blocked1>) {
%86 = arith.index_cast %arg26 : index to 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>
%91 = triton_gpu.convert_layout %90 : (tensor<128x64xf16, #blocked1>) -> tensor<128x64xf16, #shared2>
%92 = triton_gpu.convert_layout %91 : (tensor<128x64xf16, #shared2>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma0}>>
%93 = triton_gpu.convert_layout %68 : (tensor<64x128xf16, #shared1>) -> tensor<64x128xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma0}>>
%94 = tt.dot %92, %93, %cst_1 {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>
%95 = arith.addi %88, %18 : tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #mma0}>>
%96 = tt.expand_dims %95 {axis = 1 : i32} : (tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #mma0}>>) -> tensor<128x1xi32, #mma0>
%97 = tt.broadcast %96 : (tensor<128x1xi32, #mma0>) -> tensor<128x128xi32, #mma0>
%98 = "triton_gpu.cmpi"(%97, %71) {predicate = 5 : i64} : (tensor<128x128xi32, #mma0>, tensor<128x128xi32, #mma0>) -> tensor<128x128xi1, #mma0>
%99 = "triton_gpu.select"(%98, %94, %cst_0) : (tensor<128x128xi1, #mma0>, tensor<128x128xf32, #mma0>, tensor<128x128xf32, #mma0>) -> tensor<128x128xf32, #mma0>
%100 = tt.addptr %38, %89 : tensor<128x!tt.ptr<f32>, #blocked0>, tensor<128xi32, #blocked0>
%101 = tt.load %100 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128xf32, #blocked0>
%102 = triton_gpu.convert_layout %101 : (tensor<128xf32, #blocked0>) -> tensor<128xf32, #triton_gpu.slice<{dim = 1, parent = #mma0}>>
%103 = arith.mulf %99, %39 : tensor<128x128xf32, #mma0>
%104 = tt.expand_dims %102 {axis = 1 : i32} : (tensor<128xf32, #triton_gpu.slice<{dim = 1, parent = #mma0}>>) -> tensor<128x1xf32, #mma0>
%105 = tt.broadcast %104 : (tensor<128x1xf32, #mma0>) -> tensor<128x128xf32, #mma0>
%106 = arith.subf %103, %105 : tensor<128x128xf32, #mma0>
%107 = math.exp %106 : tensor<128x128xf32, #mma0>
%108 = tt.load %arg31 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128x64xf16, #blocked1>
%109 = arith.truncf %107 : tensor<128x128xf32, #mma0> to tensor<128x128xf16, #mma0>
%110 = triton_gpu.convert_layout %109 : (tensor<128x128xf16, #mma0>) -> tensor<128x128xf16, #shared0>
%111 = tt.trans %110 : (tensor<128x128xf16, #shared0>) -> tensor<128x128xf16, #shared1>
%112 = triton_gpu.convert_layout %108 : (tensor<128x64xf16, #blocked1>) -> tensor<128x64xf16, #shared2>
%113 = triton_gpu.convert_layout %112 : (tensor<128x64xf16, #shared2>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma1}>>
%114 = triton_gpu.convert_layout %111 : (tensor<128x128xf16, #shared1>) -> tensor<128x128xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma1}>>
%115 = tt.dot %114, %113, %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>
%116 = tt.addptr %40, %89 : tensor<128x!tt.ptr<f32>, #blocked0>, tensor<128xi32, #blocked0>
%117 = tt.load %116 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128xf32, #blocked0>
%118 = triton_gpu.convert_layout %117 : (tensor<128xf32, #blocked0>) -> tensor<128xf32, #triton_gpu.slice<{dim = 1, parent = #mma0}>>
%119 = tt.expand_dims %118 {axis = 1 : i32} : (tensor<128xf32, #triton_gpu.slice<{dim = 1, parent = #mma0}>>) -> tensor<128x1xf32, #mma0>
%120 = tt.broadcast %119 : (tensor<128x1xf32, #mma0>) -> tensor<128x128xf32, #mma0>
%121 = arith.subf %cst_1, %120 : tensor<128x128xf32, #mma0>
%122 = triton_gpu.convert_layout %112 : (tensor<128x64xf16, #shared2>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma0}>>
%123 = triton_gpu.convert_layout %72 : (tensor<64x128xf16, #shared1>) -> tensor<64x128xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma0}>>
%124 = tt.dot %122, %123, %121 {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>
%125 = arith.mulf %107, %124 : tensor<128x128xf32, #mma0>
%126 = arith.mulf %125, %39 : tensor<128x128xf32, #mma0>
%127 = arith.truncf %126 : tensor<128x128xf32, #mma0> to tensor<128x128xf16, #mma0>
%128 = triton_gpu.convert_layout %127 : (tensor<128x128xf16, #mma0>) -> tensor<128x128xf16, #shared0>
%129 = tt.trans %128 : (tensor<128x128xf16, #shared0>) -> tensor<128x128xf16, #shared1>
%130 = triton_gpu.convert_layout %91 : (tensor<128x64xf16, #shared2>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma1}>>
%131 = triton_gpu.convert_layout %129 : (tensor<128x128xf16, #shared1>) -> tensor<128x128xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma1}>>
%132 = tt.dot %131, %130, %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>
%133 = tt.load %arg29 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128x64xf32, #blocked2>
%135 = triton_gpu.convert_layout %59 : (tensor<128x64xf16, #blocked1>) -> tensor<128x64xf16, #shared2>
%136 = triton_gpu.convert_layout %135 : (tensor<128x64xf16, #shared2>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma1}>>
%137 = triton_gpu.convert_layout %128 : (tensor<128x128xf16, #shared0>) -> tensor<128x128xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma1}>>
%138 = tt.dot %137, %136, %cst_2 {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>
%139 = triton_gpu.convert_layout %138 : (tensor<128x64xf32, #mma1>) -> tensor<128x64xf32, #blocked2>
%1000 = arith.addf %133, %139: tensor<128x64xf32, #blocked2>
tt.store %arg29, %133 : tensor<128x64xf32, #blocked2>
%140 = tt.addptr %arg29, %43 : tensor<128x64x!tt.ptr<f32>, #blocked2>, tensor<128x64xi32, #blocked2>
%141 = tt.addptr %arg30, %42 : tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64xi32, #blocked1>
%142 = tt.addptr %arg31, %42 : tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64xi32, #blocked1>
scf.yield %115, %132, %140, %141, %142 : tensor<128x64xf32, #mma1>, tensor<128x64xf32, #mma1>, tensor<128x64x!tt.ptr<f32>, #blocked2>, tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64x!tt.ptr<f16>, #blocked1>
}
%80 = arith.truncf %79#0 : tensor<128x64xf32, #mma1> to tensor<128x64xf16, #mma1>
%81 = tt.addptr %44, %63 : tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64xi32, #blocked1>
%82 = triton_gpu.convert_layout %80 : (tensor<128x64xf16, #mma1>) -> tensor<128x64xf16, #blocked1>
tt.store %81, %82 : tensor<128x64xf16, #blocked1>
%83 = arith.truncf %79#1 : tensor<128x64xf32, #mma1> to tensor<128x64xf16, #mma1>
%84 = tt.addptr %45, %57 : tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64xi32, #blocked1>
%85 = triton_gpu.convert_layout %83 : (tensor<128x64xf16, #mma1>) -> tensor<128x64xf16, #blocked1>
tt.store %84, %85 : tensor<128x64xf16, #blocked1>
}
return
}
}