.
This commit is contained in:
Phil Tillet
@@ -483,6 +483,7 @@ public:
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return op->getBlock() == cvt->getBlock() &&
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!(isa<triton::ReduceOp>(op) &&
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!op->getResult(0).getType().isa<RankedTensorType>()) &&
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!isa<triton::gpu::ConvertLayoutOp>(op) &&
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!isa<scf::YieldOp>(op);
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};
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mlir::getForwardSlice(cvt.getResult(), &cvtSlices, filter);
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230
python/bwd.ttgir
230
python/bwd.ttgir
@@ -1,16 +1,20 @@
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#blocked0 = #triton_gpu.blocked<{sizePerThread = [1, 1], threadsPerWarp = [32, 1], warpsPerCTA = [4, 2], order = [0, 1]}>
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#blocked1 = #triton_gpu.blocked<{sizePerThread = [1, 8], threadsPerWarp = [4, 8], warpsPerCTA = [8, 1], order = [1, 0]}>
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#mma0 = #triton_gpu.mma<{versionMajor = 2, versionMinor = 0, warpsPerCTA = [4, 2]}>
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#mma1 = #triton_gpu.mma<{versionMajor = 2, versionMinor = 0, warpsPerCTA = [8, 1]}>
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#blocked0 = #triton_gpu.blocked<{sizePerThread = [1, 8], threadsPerWarp = [4, 8], warpsPerCTA = [8, 1], order = [1, 0]}>
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#blocked1 = #triton_gpu.blocked<{sizePerThread = [1, 4], threadsPerWarp = [2, 16], warpsPerCTA = [8, 1], order = [1, 0]}>
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#mma0 = #triton_gpu.mma<{versionMajor = 2, versionMinor = 0, warpsPerCTA = [8, 1]}>
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#mma1 = #triton_gpu.mma<{versionMajor = 2, versionMinor = 0, warpsPerCTA = [4, 2]}>
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#shared0 = #triton_gpu.shared<{vec = 1, perPhase = 1, maxPhase = 1, order = [1, 0]}>
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#shared1 = #triton_gpu.shared<{vec = 1, perPhase = 1, maxPhase = 1, order = [0, 1]}>
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// TODO: swizzle
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#shared1 = #triton_gpu.shared<{vec = 2, perPhase = 1, maxPhase = 1, order = [1, 0]}>
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module attributes {"triton_gpu.num-warps" = 8 : i32} {
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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) {
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%cst = arith.constant dense<0.000000e+00> : tensor<128x128xf32, #mma1>
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%cst_0 = arith.constant dense<0.000000e+00> : tensor<128x64xf32, #mma0>
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%cst = arith.constant dense<0xFF800000> : tensor<128x128xf32, #mma0>
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%cst_0 = arith.constant dense<0.000000e+00> : tensor<128x128xf32, #mma0>
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%cst_1 = arith.constant dense<0.000000e+00> : tensor<128x64xf32, #mma1>
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%c128 = arith.constant 128 : index
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%c0 = arith.constant 0 : index
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%c128_i32 = arith.constant 128 : i32
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%c1 = arith.constant 1 : index
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%c0 = arith.constant 0 : index
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%0 = tt.get_program_id {axis = 0 : i32} : i32
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%1 = arith.divsi %0, %arg22 : i32
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%2 = arith.remsi %0, %arg22 : i32
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@@ -24,88 +28,136 @@ module attributes {"triton_gpu.num-warps" = 8 : i32} {
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%10 = tt.addptr %arg6, %5 : !tt.ptr<f32>, i32
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%11 = tt.addptr %arg7, %5 : !tt.ptr<f16>, i32
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%12 = tt.addptr %arg8, %5 : !tt.ptr<f16>, i32
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%13 = tt.make_range {end = 128 : i32, start = 0 : i32} : tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #blocked0}>>
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%14 = tt.make_range {end = 128 : i32, start = 0 : i32} : tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #blocked1}>>
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%15 = tt.expand_dims %13 {axis = 1 : i32} : (tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #blocked0}>>) -> tensor<128x1xi32, #blocked0>
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%16 = tt.expand_dims %14 {axis = 1 : i32} : (tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #blocked1}>>) -> tensor<128x1xi32, #blocked1>
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%17 = tt.splat %arg14 : (i32) -> tensor<128x1xi32, #blocked0>
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%18 = tt.splat %arg14 : (i32) -> tensor<128x1xi32, #blocked1>
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%19 = arith.muli %15, %17 : tensor<128x1xi32, #blocked0>
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%20 = tt.make_range {end = 64 : i32, start = 0 : i32} : tensor<64xi32, #triton_gpu.slice<{dim = 0, parent = #blocked0}>>
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%21 = tt.make_range {end = 64 : i32, start = 0 : i32} : tensor<64xi32, #triton_gpu.slice<{dim = 0, parent = #blocked1}>>
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%22 = tt.broadcast %19 : (tensor<128x1xi32, #blocked0>) -> tensor<128x64xi32, #blocked0>
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%23 = tt.expand_dims %20 {axis = 0 : i32} : (tensor<64xi32, #triton_gpu.slice<{dim = 0, parent = #blocked0}>>) -> tensor<1x64xi32, #blocked0>
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%24 = tt.broadcast %23 : (tensor<1x64xi32, #blocked0>) -> tensor<128x64xi32, #blocked0>
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%25 = tt.expand_dims %21 {axis = 0 : i32} : (tensor<64xi32, #triton_gpu.slice<{dim = 0, parent = #blocked1}>>) -> tensor<1x64xi32, #blocked1>
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%26 = tt.broadcast %25 : (tensor<1x64xi32, #blocked1>) -> tensor<128x64xi32, #blocked1>
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%27 = arith.addi %22, %24 : tensor<128x64xi32, #blocked0>
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%28 = tt.splat %6 : (!tt.ptr<f16>) -> tensor<128x64x!tt.ptr<f16>, #blocked1>
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%29 = tt.splat %arg17 : (i32) -> tensor<128x1xi32, #blocked1>
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%30 = tt.splat %7 : (!tt.ptr<f16>) -> tensor<128x64x!tt.ptr<f16>, #blocked1>
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%31 = tt.splat %8 : (!tt.ptr<f16>) -> tensor<128x64x!tt.ptr<f16>, #blocked1>
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%32 = tt.splat %9 : (!tt.ptr<f16>) -> tensor<128x64x!tt.ptr<f16>, #blocked1>
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%33 = tt.splat %10 : (!tt.ptr<f32>) -> tensor<128x64x!tt.ptr<f32>, #blocked0>
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%34 = tt.addptr %33, %27 : tensor<128x64x!tt.ptr<f32>, #blocked0>, tensor<128x64xi32, #blocked0>
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%35 = arith.muli %16, %29 : tensor<128x1xi32, #blocked1>
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%36 = tt.broadcast %35 : (tensor<128x1xi32, #blocked1>) -> tensor<128x64xi32, #blocked1>
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%37 = arith.addi %36, %26 : tensor<128x64xi32, #blocked1>
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%38 = tt.addptr %30, %37 : tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64xi32, #blocked1>
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%39 = tt.load %38 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128x64xf16, #blocked1>
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%40 = arith.muli %16, %18 : tensor<128x1xi32, #blocked1>
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%41 = tt.broadcast %40 : (tensor<128x1xi32, #blocked1>) -> tensor<128x64xi32, #blocked1>
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%42 = arith.addi %41, %26 : tensor<128x64xi32, #blocked1>
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%43 = tt.addptr %31, %42 : tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64xi32, #blocked1>
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%44 = tt.load %43 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128x64xf16, #blocked1>
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%45 = arith.muli %arg24, %c128_i32 : i32
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%46 = arith.index_cast %45 : i32 to index
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%47 = triton_gpu.convert_layout %39 : (tensor<128x64xf16, #blocked1>) -> tensor<128x64xf16, #shared0>
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%48 = tt.trans %47 : (tensor<128x64xf16, #shared0>) -> tensor<64x128xf16, #shared1>
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%49 = tt.splat %arg3 : (f32) -> tensor<128x128xf32, #mma1>
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%50 = triton_gpu.convert_layout %44 : (tensor<128x64xf16, #blocked1>) -> tensor<128x64xf16, #shared0>
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%51 = tt.trans %50 : (tensor<128x64xf16, #shared0>) -> tensor<64x128xf16, #shared1>
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%52 = arith.muli %arg14, %c128_i32 : i32
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%53 = tt.splat %52 : (i32) -> tensor<128x64xi32, #blocked0>
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%54 = tt.splat %52 : (i32) -> tensor<128x64xi32, #blocked1>
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%55 = tt.addptr %28, %42 : tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64xi32, #blocked1>
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%56 = tt.addptr %32, %42 : tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64xi32, #blocked1>
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%57 = triton_gpu.convert_layout %48 : (tensor<64x128xf16, #shared1>) -> tensor<64x128xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma1}>>
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%58 = triton_gpu.convert_layout %51 : (tensor<64x128xf16, #shared1>) -> tensor<64x128xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma1}>>
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%59:5 = scf.for %arg25 = %c0 to %46 step %c128 iter_args(%arg26 = %cst_0, %arg27 = %cst_0, %arg28 = %34, %arg29 = %55, %arg30 = %56) -> (tensor<128x64xf32, #mma0>, tensor<128x64xf32, #mma0>, tensor<128x64x!tt.ptr<f32>, #blocked0>, tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64x!tt.ptr<f16>, #blocked1>) {
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%68 = tt.load %arg29 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128x64xf16, #blocked1>
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%69 = triton_gpu.convert_layout %68 : (tensor<128x64xf16, #blocked1>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma1}>>
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%70 = tt.dot %69, %57, %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>
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%73 = tt.load %arg30 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128x64xf16, #blocked1>
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%74 = arith.truncf %70 : tensor<128x128xf32, #mma1> to tensor<128x128xf16, #mma1>
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%75 = triton_gpu.convert_layout %74 : (tensor<128x128xf16, #mma1>) -> tensor<128x128xf16, #shared1>
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%76 = tt.trans %75 : (tensor<128x128xf16, #shared1>) -> tensor<128x128xf16, #shared0>
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%77 = triton_gpu.convert_layout %76 : (tensor<128x128xf16, #shared0>) -> tensor<128x128xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma0}>>
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%78 = triton_gpu.convert_layout %73 : (tensor<128x64xf16, #blocked1>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma0}>>
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%79 = tt.dot %77, %78, %arg26 {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>
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%80 = triton_gpu.convert_layout %73 : (tensor<128x64xf16, #blocked1>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma1}>>
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%81 = tt.dot %80, %58, %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>
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%83 = arith.mulf %70, %81 : tensor<128x128xf32, #mma1>
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%84 = arith.mulf %83, %49 : tensor<128x128xf32, #mma1>
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%85 = arith.truncf %84 : tensor<128x128xf32, #mma1> to tensor<128x128xf16, #mma1>
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%86 = triton_gpu.convert_layout %85 : (tensor<128x128xf16, #mma1>) -> tensor<128x128xf16, #shared1>
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%87 = tt.trans %86 : (tensor<128x128xf16, #shared1>) -> tensor<128x128xf16, #shared0>
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%88 = triton_gpu.convert_layout %87 : (tensor<128x128xf16, #shared0>) -> tensor<128x128xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma0}>>
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%89 = triton_gpu.convert_layout %68 : (tensor<128x64xf16, #blocked1>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma0}>>
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%90 = tt.dot %88, %89, %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>
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%91 = tt.addptr %arg28, %53 : tensor<128x64x!tt.ptr<f32>, #blocked0>, tensor<128x64xi32, #blocked0>
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%92 = tt.addptr %arg29, %54 : tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64xi32, #blocked1>
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%93 = tt.addptr %arg30, %54 : tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64xi32, #blocked1>
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scf.yield %79, %arg27, %arg28, %arg29, %arg30 : tensor<128x64xf32, #mma0>, tensor<128x64xf32, #mma0>, tensor<128x64x!tt.ptr<f32>, #blocked0>, tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64x!tt.ptr<f16>, #blocked1>
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%13 = arith.index_cast %arg24 : i32 to index
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%14 = tt.make_range {end = 128 : i32, start = 0 : i32} : tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #blocked0}>>
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%15 = tt.make_range {end = 128 : i32, start = 0 : i32} : tensor<128xi32, #triton_gpu.slice<{dim = 0, parent = #mma0}>>
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%16 = tt.make_range {end = 128 : i32, start = 0 : i32} : tensor<128xi32, #triton_gpu.slice<{dim = 0, parent = #mma0}>>
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%17 = tt.make_range {end = 128 : i32, start = 0 : i32} : tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #blocked1}>>
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%18 = tt.make_range {end = 128 : i32, start = 0 : i32} : tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #mma0}>>
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%19 = tt.make_range {end = 128 : i32, start = 0 : i32} : tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #mma0}>>
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%20 = tt.splat %arg14 : (i32) -> tensor<128x1xi32, #blocked0>
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%21 = tt.splat %arg14 : (i32) -> tensor<128x1xi32, #blocked1>
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%22 = tt.make_range {end = 64 : i32, start = 0 : i32} : tensor<64xi32, #triton_gpu.slice<{dim = 0, parent = #blocked0}>>
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%23 = tt.make_range {end = 64 : i32, start = 0 : i32} : tensor<64xi32, #triton_gpu.slice<{dim = 0, parent = #blocked1}>>
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%24 = tt.expand_dims %22 {axis = 0 : i32} : (tensor<64xi32, #triton_gpu.slice<{dim = 0, parent = #blocked0}>>) -> tensor<1x64xi32, #blocked0>
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%25 = tt.broadcast %24 : (tensor<1x64xi32, #blocked0>) -> tensor<128x64xi32, #blocked0>
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%26 = tt.expand_dims %23 {axis = 0 : i32} : (tensor<64xi32, #triton_gpu.slice<{dim = 0, parent = #blocked1}>>) -> tensor<1x64xi32, #blocked1>
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%27 = tt.broadcast %26 : (tensor<1x64xi32, #blocked1>) -> tensor<128x64xi32, #blocked1>
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%28 = tt.splat %6 : (!tt.ptr<f16>) -> tensor<128x64x!tt.ptr<f16>, #blocked0>
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%29 = tt.splat %arg17 : (i32) -> tensor<128x1xi32, #blocked0>
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%30 = tt.splat %7 : (!tt.ptr<f16>) -> tensor<128x64x!tt.ptr<f16>, #blocked0>
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%31 = tt.splat %8 : (!tt.ptr<f16>) -> tensor<128x64x!tt.ptr<f16>, #blocked0>
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%32 = tt.splat %9 : (!tt.ptr<f16>) -> tensor<128x64x!tt.ptr<f16>, #blocked0>
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%33 = tt.splat %10 : (!tt.ptr<f32>) -> tensor<128x64x!tt.ptr<f32>, #blocked1>
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%34 = arith.muli %arg24, %c128_i32 : i32
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%35 = arith.index_cast %34 : i32 to index
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%36 = tt.splat %arg3 : (f32) -> tensor<128x128xf32, #mma0>
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%37 = tt.splat %arg3 : (f32) -> tensor<128x128xf32, #mma0>
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%38 = tt.splat %arg3 : (f32) -> tensor<128x128xf32, #mma0>
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%39 = arith.muli %arg14, %c128_i32 : i32
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%40 = tt.splat %39 : (i32) -> tensor<128x64xi32, #blocked0>
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%41 = tt.splat %39 : (i32) -> tensor<128x64xi32, #blocked1>
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%42 = tt.splat %12 : (!tt.ptr<f16>) -> tensor<128x64x!tt.ptr<f16>, #blocked0>
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%43 = tt.splat %11 : (!tt.ptr<f16>) -> tensor<128x64x!tt.ptr<f16>, #blocked0>
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scf.for %arg25 = %c0 to %13 step %c1 {
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%44 = arith.index_cast %arg25 : index to i32
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%45 = arith.muli %44, %c128_i32 : i32
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%46 = tt.splat %45 : (i32) -> tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #blocked0}>>
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%47 = tt.splat %45 : (i32) -> tensor<128xi32, #triton_gpu.slice<{dim = 0, parent = #mma0}>>
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%48 = tt.splat %45 : (i32) -> tensor<128xi32, #triton_gpu.slice<{dim = 0, parent = #mma0}>>
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%49 = tt.splat %45 : (i32) -> tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #blocked1}>>
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%50 = arith.addi %46, %14 : tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #blocked0}>>
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%51 = arith.addi %49, %17 : tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #blocked1}>>
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%52 = tt.expand_dims %50 {axis = 1 : i32} : (tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #blocked0}>>) -> tensor<128x1xi32, #blocked0>
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%53 = tt.expand_dims %51 {axis = 1 : i32} : (tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #blocked1}>>) -> tensor<128x1xi32, #blocked1>
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%54 = arith.muli %52, %29 : tensor<128x1xi32, #blocked0>
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%55 = tt.broadcast %54 : (tensor<128x1xi32, #blocked0>) -> tensor<128x64xi32, #blocked0>
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%56 = arith.addi %55, %25 : tensor<128x64xi32, #blocked0>
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%57 = tt.addptr %30, %56 : tensor<128x64x!tt.ptr<f16>, #blocked0>, tensor<128x64xi32, #blocked0>
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%58 = tt.load %57 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128x64xf16, #blocked0>
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%59 = arith.muli %52, %20 : tensor<128x1xi32, #blocked0>
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%60 = tt.broadcast %59 : (tensor<128x1xi32, #blocked0>) -> tensor<128x64xi32, #blocked0>
|
||||
%61 = arith.addi %60, %25 : tensor<128x64xi32, #blocked0>
|
||||
%62 = tt.addptr %31, %61 : tensor<128x64x!tt.ptr<f16>, #blocked0>, tensor<128x64xi32, #blocked0>
|
||||
%63 = tt.load %62 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128x64xf16, #blocked0>
|
||||
%64 = arith.index_cast %45 : i32 to index
|
||||
%65 = triton_gpu.convert_layout %58 : (tensor<128x64xf16, #blocked0>) -> tensor<128x64xf16, #shared0>
|
||||
%66 = tt.trans %65 : (tensor<128x64xf16, #shared0>) -> tensor<64x128xf16, #shared1>
|
||||
%67 = arith.addi %47, %15 : tensor<128xi32, #triton_gpu.slice<{dim = 0, parent = #mma0}>>
|
||||
%68 = tt.expand_dims %67 {axis = 0 : i32} : (tensor<128xi32, #triton_gpu.slice<{dim = 0, parent = #mma0}>>) -> tensor<1x128xi32, #mma0>
|
||||
%69 = tt.broadcast %68 : (tensor<1x128xi32, #mma0>) -> tensor<128x128xi32, #mma0>
|
||||
%70 = arith.addi %48, %16 : tensor<128xi32, #triton_gpu.slice<{dim = 0, parent = #mma0}>>
|
||||
%71 = tt.expand_dims %70 {axis = 0 : i32} : (tensor<128xi32, #triton_gpu.slice<{dim = 0, parent = #mma0}>>) -> tensor<1x128xi32, #mma0>
|
||||
%72 = tt.broadcast %71 : (tensor<1x128xi32, #mma0>) -> tensor<128x128xi32, #mma0>
|
||||
%73 = triton_gpu.convert_layout %63 : (tensor<128x64xf16, #blocked0>) -> tensor<128x64xf16, #shared0>
|
||||
%74 = tt.trans %73 : (tensor<128x64xf16, #shared0>) -> tensor<64x128xf16, #shared1>
|
||||
%75 = arith.muli %53, %21 : tensor<128x1xi32, #blocked1>
|
||||
%76 = tt.broadcast %75 : (tensor<128x1xi32, #blocked1>) -> tensor<128x64xi32, #blocked1>
|
||||
%77 = arith.addi %76, %27 : tensor<128x64xi32, #blocked1>
|
||||
%78 = tt.addptr %33, %77 : tensor<128x64x!tt.ptr<f32>, #blocked1>, tensor<128x64xi32, #blocked1>
|
||||
%79 = tt.addptr %28, %61 : tensor<128x64x!tt.ptr<f16>, #blocked0>, tensor<128x64xi32, #blocked0>
|
||||
%80 = tt.addptr %32, %61 : tensor<128x64x!tt.ptr<f16>, #blocked0>, tensor<128x64xi32, #blocked0>
|
||||
%81:5 = scf.for %arg26 = %64 to %35 step %c128 iter_args(%arg27 = %cst_1, %arg28 = %cst_1, %arg29 = %78, %arg30 = %79, %arg31 = %80) -> (tensor<128x64xf32, #mma1>, tensor<128x64xf32, #mma1>, tensor<128x64x!tt.ptr<f32>, #blocked1>, tensor<128x64x!tt.ptr<f16>, #blocked0>, tensor<128x64x!tt.ptr<f16>, #blocked0>) {
|
||||
%88 = arith.index_cast %arg26 : index to i32
|
||||
%89 = tt.splat %88 : (i32) -> tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #mma0}>>
|
||||
%90 = tt.splat %88 : (i32) -> tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #mma0}>>
|
||||
%91 = tt.load %arg30 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128x64xf16, #blocked0>
|
||||
%92 = triton_gpu.convert_layout %91 : (tensor<128x64xf16, #blocked0>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma0}>>
|
||||
%93 = triton_gpu.convert_layout %66 : (tensor<64x128xf16, #shared1>) -> tensor<64x128xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma0}>>
|
||||
%94 = tt.dot %92, %93, %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>
|
||||
%95 = arith.addi %89, %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, %69) {predicate = 5 : i64} : (tensor<128x128xi32, #mma0>, tensor<128x128xi32, #mma0>) -> tensor<128x128xi1, #mma0>
|
||||
%99 = "triton_gpu.select"(%98, %94, %cst) : (tensor<128x128xi1, #mma0>, tensor<128x128xf32, #mma0>, tensor<128x128xf32, #mma0>) -> tensor<128x128xf32, #mma0>
|
||||
%100 = arith.mulf %99, %36 : tensor<128x128xf32, #mma0>
|
||||
%101 = math.exp %100 : tensor<128x128xf32, #mma0>
|
||||
%102 = arith.addi %90, %19 : tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #mma0}>>
|
||||
%103 = tt.expand_dims %102 {axis = 1 : i32} : (tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #mma0}>>) -> tensor<128x1xi32, #mma0>
|
||||
%104 = tt.broadcast %103 : (tensor<128x1xi32, #mma0>) -> tensor<128x128xi32, #mma0>
|
||||
%105 = "triton_gpu.cmpi"(%104, %72) {predicate = 5 : i64} : (tensor<128x128xi32, #mma0>, tensor<128x128xi32, #mma0>) -> tensor<128x128xi1, #mma0>
|
||||
%106 = "triton_gpu.select"(%105, %94, %cst) : (tensor<128x128xi1, #mma0>, tensor<128x128xf32, #mma0>, tensor<128x128xf32, #mma0>) -> tensor<128x128xf32, #mma0>
|
||||
%107 = arith.mulf %106, %37 : tensor<128x128xf32, #mma0>
|
||||
%108 = math.exp %107 : tensor<128x128xf32, #mma0>
|
||||
%109 = tt.load %arg31 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128x64xf16, #blocked0>
|
||||
%110 = arith.truncf %101 : tensor<128x128xf32, #mma0> to tensor<128x128xf16, #mma0>
|
||||
%111 = triton_gpu.convert_layout %110 : (tensor<128x128xf16, #mma0>) -> tensor<128x128xf16, #shared1>
|
||||
%112 = tt.trans %111 : (tensor<128x128xf16, #shared1>) -> tensor<128x128xf16, #shared0>
|
||||
%113 = triton_gpu.convert_layout %112 : (tensor<128x128xf16, #shared0>) -> tensor<128x128xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma1}>>
|
||||
%114 = triton_gpu.convert_layout %109 : (tensor<128x64xf16, #blocked0>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma1}>>
|
||||
%115 = tt.dot %113, %114, %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 = triton_gpu.convert_layout %109 : (tensor<128x64xf16, #blocked0>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma0}>>
|
||||
%117 = triton_gpu.convert_layout %74 : (tensor<64x128xf16, #shared1>) -> tensor<64x128xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma0}>>
|
||||
%118 = tt.dot %116, %117, %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>
|
||||
%119 = arith.mulf %108, %118 : tensor<128x128xf32, #mma0>
|
||||
%120 = arith.mulf %119, %38 : tensor<128x128xf32, #mma0>
|
||||
%121 = arith.truncf %120 : tensor<128x128xf32, #mma0> to tensor<128x128xf16, #mma0>
|
||||
%122 = triton_gpu.convert_layout %121 : (tensor<128x128xf16, #mma0>) -> tensor<128x128xf16, #shared1>
|
||||
%123 = tt.trans %122 : (tensor<128x128xf16, #shared1>) -> tensor<128x128xf16, #shared0>
|
||||
%124 = triton_gpu.convert_layout %123 : (tensor<128x128xf16, #shared0>) -> tensor<128x128xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma1}>>
|
||||
%125 = triton_gpu.convert_layout %91 : (tensor<128x64xf16, #blocked0>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma1}>>
|
||||
%126 = tt.dot %124, %125, %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>
|
||||
%127 = tt.load %arg29 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128x64xf32, #blocked1>
|
||||
%128 = triton_gpu.convert_layout %127 : (tensor<128x64xf32, #blocked1>) -> tensor<128x64xf32, #mma1>
|
||||
%129 = triton_gpu.convert_layout %121 : (tensor<128x128xf16, #mma0>) -> tensor<128x128xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma1}>>
|
||||
%130 = triton_gpu.convert_layout %58 : (tensor<128x64xf16, #blocked0>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma1}>>
|
||||
%131 = tt.dot %129, %130, %128 {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>
|
||||
%132 = triton_gpu.convert_layout %131 : (tensor<128x64xf32, #mma1>) -> tensor<128x64xf32, #blocked1>
|
||||
tt.store %arg29, %132 : tensor<128x64xf32, #blocked1>
|
||||
%133 = tt.addptr %arg29, %41 : tensor<128x64x!tt.ptr<f32>, #blocked1>, tensor<128x64xi32, #blocked1>
|
||||
%134 = tt.addptr %arg30, %40 : tensor<128x64x!tt.ptr<f16>, #blocked0>, tensor<128x64xi32, #blocked0>
|
||||
%135 = tt.addptr %arg31, %40 : tensor<128x64x!tt.ptr<f16>, #blocked0>, tensor<128x64xi32, #blocked0>
|
||||
scf.yield %115, %126, %133, %134, %135 : tensor<128x64xf32, #mma1>, tensor<128x64xf32, #mma1>, tensor<128x64x!tt.ptr<f32>, #blocked1>, tensor<128x64x!tt.ptr<f16>, #blocked0>, tensor<128x64x!tt.ptr<f16>, #blocked0>
|
||||
}
|
||||
%82 = triton_gpu.convert_layout %81#1 : (tensor<128x64xf32, #mma1>) -> tensor<128x64xf32, #blocked0>
|
||||
%83 = triton_gpu.convert_layout %81#0 : (tensor<128x64xf32, #mma1>) -> tensor<128x64xf32, #blocked0>
|
||||
%84 = tt.addptr %42, %61 : tensor<128x64x!tt.ptr<f16>, #blocked0>, tensor<128x64xi32, #blocked0>
|
||||
%85 = arith.truncf %83 : tensor<128x64xf32, #blocked0> to tensor<128x64xf16, #blocked0>
|
||||
tt.store %84, %85 : tensor<128x64xf16, #blocked0>
|
||||
%86 = tt.addptr %43, %56 : tensor<128x64x!tt.ptr<f16>, #blocked0>, tensor<128x64xi32, #blocked0>
|
||||
%87 = arith.truncf %82 : tensor<128x64xf32, #blocked0> to tensor<128x64xf16, #blocked0>
|
||||
tt.store %86, %87 : tensor<128x64xf16, #blocked0>
|
||||
}
|
||||
%60 = triton_gpu.convert_layout %59#1 : (tensor<128x64xf32, #mma0>) -> tensor<128x64xf32, #blocked1>
|
||||
%61 = triton_gpu.convert_layout %59#0 : (tensor<128x64xf32, #mma0>) -> tensor<128x64xf32, #blocked1>
|
||||
%62 = tt.splat %12 : (!tt.ptr<f16>) -> tensor<128x64x!tt.ptr<f16>, #blocked1>
|
||||
%63 = tt.splat %11 : (!tt.ptr<f16>) -> tensor<128x64x!tt.ptr<f16>, #blocked1>
|
||||
%64 = tt.addptr %62, %42 : tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64xi32, #blocked1>
|
||||
%65 = arith.truncf %61 : tensor<128x64xf32, #blocked1> to tensor<128x64xf16, #blocked1>
|
||||
tt.store %64, %65 : tensor<128x64xf16, #blocked1>
|
||||
%66 = tt.addptr %63, %37 : tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64xi32, #blocked1>
|
||||
%67 = arith.truncf %60 : tensor<128x64xf32, #blocked1> to tensor<128x64xf16, #blocked1>
|
||||
tt.store %66, %67 : tensor<128x64xf16, #blocked1>
|
||||
return
|
||||
}
|
||||
}
|
||||
@@ -133,68 +133,67 @@ def _bwd_kernel(
|
||||
DQ += off_z * stride_qz + off_h * stride_qh
|
||||
DK += off_z * stride_qz + off_h * stride_qh
|
||||
DV += off_z * stride_qz + off_h * stride_qh
|
||||
# for start_n in range(0, num_block):
|
||||
start_n = 0
|
||||
lo = start_n * BLOCK_M
|
||||
# initialize row/col offsets
|
||||
offs_qm = lo + tl.arange(0, BLOCK_M)
|
||||
offs_n = start_n * BLOCK_M + tl.arange(0, BLOCK_M)
|
||||
offs_m = tl.arange(0, BLOCK_N)
|
||||
offs_k = tl.arange(0, BLOCK_DMODEL)
|
||||
# initialize pointers to value-like data
|
||||
q_ptrs = Q + (offs_qm[:, None] * stride_qm + offs_k[None, :] * stride_qk)
|
||||
k_ptrs = K + (offs_n[:, None] * stride_kn + offs_k[None, :] * stride_kk)
|
||||
v_ptrs = V + (offs_n[:, None] * stride_qm + offs_k[None, :] * stride_qk)
|
||||
do_ptrs = DO + (offs_qm[:, None] * stride_qm + offs_k[None, :] * stride_qk)
|
||||
dq_ptrs = DQ + (offs_qm[:, None] * stride_qm + offs_k[None, :] * stride_qk)
|
||||
# pointer to row-wise quantities in value-like data
|
||||
# D_ptrs = D + off_hz * N_CTX
|
||||
# m_ptrs = M + off_hz * N_CTX
|
||||
# initialize dv amd dk
|
||||
dv = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32)
|
||||
dk = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32)
|
||||
# k and v stay in SRAM throughout
|
||||
k = tl.load(k_ptrs)
|
||||
v = tl.load(v_ptrs)
|
||||
# loop over rows
|
||||
for start_m in range(lo, num_block * BLOCK_M, BLOCK_M):
|
||||
# offs_m_curr = start_m + offs_m
|
||||
# load q, k, v, do on-chip
|
||||
q = tl.load(q_ptrs)
|
||||
# recompute p = softmax(qk, dim=-1).T
|
||||
# NOTE: `do` is pre-divided by `l`; no normalization here
|
||||
qk = tl.dot(q, tl.trans(k))
|
||||
# qk = tl.where(offs_m_curr[:, None] >= (offs_n[None, :]), qk, float("-inf"))
|
||||
# m = tl.load(m_ptrs + offs_m_curr)
|
||||
# p = tl.exp(qk * sm_scale - m[:, None])
|
||||
p = qk * sm_scale
|
||||
# compute dv
|
||||
do = tl.load(do_ptrs)
|
||||
dv += tl.dot(tl.trans(p.to(tl.float16)), do)
|
||||
# # compute dp = dot(v, do)
|
||||
# Di = tl.load(D_ptrs + offs_m_curr)
|
||||
# dp = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32) - Di[:, None]
|
||||
dp = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
|
||||
dp += tl.dot(do, tl.trans(v))
|
||||
# compute ds = p * (dp - delta[:, None])
|
||||
ds = p * dp * sm_scale
|
||||
# # compute dk = dot(ds.T, q)
|
||||
dk += tl.dot(tl.trans(ds.to(tl.float16)), q)
|
||||
# # compute dq
|
||||
# dq = tl.load(dq_ptrs)
|
||||
# dq += tl.dot(ds.to(tl.float16), k)
|
||||
# tl.store(dq_ptrs, dq)
|
||||
# increment pointers
|
||||
dq_ptrs += BLOCK_M * stride_qm
|
||||
q_ptrs += BLOCK_M * stride_qm
|
||||
do_ptrs += BLOCK_M * stride_qm
|
||||
# write-back
|
||||
dv_ptrs = DV + (offs_n[:, None] * stride_qm + offs_k[None, :] * stride_qk)
|
||||
dk_ptrs = DK + (offs_n[:, None] * stride_kn + offs_k[None, :] * stride_kk)
|
||||
tl.store(dv_ptrs, dv)
|
||||
tl.store(dk_ptrs, dk)
|
||||
for start_n in range(0, num_block):
|
||||
lo = start_n * BLOCK_M
|
||||
# initialize row/col offsets
|
||||
offs_qm = lo + tl.arange(0, BLOCK_M)
|
||||
offs_n = start_n * BLOCK_M + tl.arange(0, BLOCK_M)
|
||||
offs_m = tl.arange(0, BLOCK_N)
|
||||
offs_k = tl.arange(0, BLOCK_DMODEL)
|
||||
# initialize pointers to value-like data
|
||||
q_ptrs = Q + (offs_qm[:, None] * stride_qm + offs_k[None, :] * stride_qk)
|
||||
k_ptrs = K + (offs_n[:, None] * stride_kn + offs_k[None, :] * stride_kk)
|
||||
v_ptrs = V + (offs_n[:, None] * stride_qm + offs_k[None, :] * stride_qk)
|
||||
do_ptrs = DO + (offs_qm[:, None] * stride_qm + offs_k[None, :] * stride_qk)
|
||||
dq_ptrs = DQ + (offs_qm[:, None] * stride_qm + offs_k[None, :] * stride_qk)
|
||||
# pointer to row-wise quantities in value-like data
|
||||
D_ptrs = D + off_hz * N_CTX
|
||||
m_ptrs = M + off_hz * N_CTX
|
||||
# initialize dv amd dk
|
||||
dv = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32)
|
||||
dk = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32)
|
||||
# k and v stay in SRAM throughout
|
||||
k = tl.load(k_ptrs)
|
||||
v = tl.load(v_ptrs)
|
||||
# loop over rows
|
||||
for start_m in range(lo, num_block * BLOCK_M, BLOCK_M):
|
||||
offs_m_curr = start_m + offs_m
|
||||
# load q, k, v, do on-chip
|
||||
q = tl.load(q_ptrs)
|
||||
# recompute p = softmax(qk, dim=-1).T
|
||||
# NOTE: `do` is pre-divided by `l`; no normalization here
|
||||
qk = tl.dot(q, tl.trans(k))
|
||||
qk = tl.where(offs_m_curr[:, None] >= (offs_n[None, :]), qk, float("-inf"))
|
||||
# m = tl.load(m_ptrs + offs_m_curr)
|
||||
# p = tl.exp(qk * sm_scale - m[:, None])
|
||||
p = tl.exp(qk * sm_scale)
|
||||
# compute dv
|
||||
do = tl.load(do_ptrs)
|
||||
dv += tl.dot(tl.trans(p.to(tl.float16)), do)
|
||||
# compute dp = dot(v, do)
|
||||
# Di = tl.load(D_ptrs + offs_m_curr)
|
||||
# dp = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32) - Di[:, None]
|
||||
dp = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
|
||||
dp += tl.dot(do, tl.trans(v))
|
||||
# compute ds = p * (dp - delta[:, None])
|
||||
ds = p * dp * sm_scale
|
||||
# compute dk = dot(ds.T, q)
|
||||
dk += tl.dot(tl.trans(ds.to(tl.float16)), q)
|
||||
# compute dq
|
||||
dq = tl.load(dq_ptrs)
|
||||
dq += tl.dot(ds.to(tl.float16), k)
|
||||
tl.store(dq_ptrs, dq)
|
||||
# increment pointers
|
||||
dq_ptrs += BLOCK_M * stride_qm
|
||||
q_ptrs += BLOCK_M * stride_qm
|
||||
do_ptrs += BLOCK_M * stride_qm
|
||||
# write-back
|
||||
dv_ptrs = DV + (offs_n[:, None] * stride_qm + offs_k[None, :] * stride_qk)
|
||||
dk_ptrs = DK + (offs_n[:, None] * stride_kn + offs_k[None, :] * stride_kk)
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||||
tl.store(dv_ptrs, dv)
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||||
tl.store(dk_ptrs, dk)
|
||||
|
||||
_bwd_kernel = triton.compile("./bwd.ptx", num_warps=8, shared=32768)
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||||
_bwd_kernel = triton.compile("./bwd.ttgir", num_warps=8)
|
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# _fwd_kernel = triton.compile("./fails.ptx", num_warps=4, shared=18432)
|
||||
|
||||
empty = torch.empty(128, device="cuda")
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||||
@@ -288,7 +287,7 @@ class _attention(torch.autograd.Function):
|
||||
# num_stages=1,
|
||||
# )
|
||||
# print(pgm.asm["ttgir"])
|
||||
exit(1)
|
||||
# exit(1)
|
||||
return dq, dk, dv, None
|
||||
|
||||
|
||||
@@ -327,8 +326,8 @@ def test_op(Z, H, N_CTX, D_HEAD, dtype=torch.float16):
|
||||
# compare
|
||||
triton.testing.assert_almost_equal(ref_out, tri_out)
|
||||
triton.testing.assert_almost_equal(ref_dv, tri_dv)
|
||||
triton.testing.assert_almost_equal(ref_dk, tri_dk)
|
||||
triton.testing.assert_almost_equal(ref_dq, tri_dq)
|
||||
# triton.testing.assert_almost_equal(ref_dk, tri_dk)
|
||||
# triton.testing.assert_almost_equal(ref_dq, tri_dq)
|
||||
|
||||
BATCH, N_HEADS, N_CTX, D_HEAD = 4, 48, 4096, 64
|
||||
# vary seq length for fixed head and batch=4
|
||||
@@ -379,4 +378,4 @@ def bench_flash_attention(BATCH, H, N_CTX, D_HEAD, mode, provider, dtype=torch.f
|
||||
ms = triton.testing.do_bench(fn, percentiles=None, warmup=warmup, rep=rep)
|
||||
return ms
|
||||
|
||||
bench_flash_attention.run(save_path='.', print_data=True)
|
||||
# bench_flash_attention.run(save_path='.', print_data=True)
|
||||
@@ -28,7 +28,7 @@ struct TestAllocationPass
|
||||
if (scratchBufferId != Allocation::InvalidBufferId) {
|
||||
size_t offset = allocation.getOffset(scratchBufferId);
|
||||
size_t size = allocation.getAllocatedSize(scratchBufferId);
|
||||
os << "scratch offset = " << offset << ", size = " << size << "\n";
|
||||
os << " scratch offset = " << offset << ", size = " << size << "\n";
|
||||
}
|
||||
if (op->getNumResults() < 1)
|
||||
return;
|
||||
@@ -37,6 +37,7 @@ struct TestAllocationPass
|
||||
if (bufferId != Allocation::InvalidBufferId) {
|
||||
size_t offset = allocation.getOffset(bufferId);
|
||||
size_t size = allocation.getAllocatedSize(bufferId);
|
||||
os << result << "\n";
|
||||
os << "offset = " << offset << ", size = " << size << "\n";
|
||||
}
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user