From c98c889d7f91e39091eb440fefa31b2c55e04e45 Mon Sep 17 00:00:00 2001 From: Phil Tillet Date: Mon, 9 Jan 2023 19:08:51 -0800 Subject: [PATCH] . --- .../Transforms/SinkConversionsFromShared.cpp | 20 ++- python/slow.ttgir | 155 +++++++++--------- python/triton/compiler.py | 2 +- 3 files changed, 90 insertions(+), 87 deletions(-) diff --git a/lib/Dialect/TritonGPU/Transforms/SinkConversionsFromShared.cpp b/lib/Dialect/TritonGPU/Transforms/SinkConversionsFromShared.cpp index 458086316..7e3fde937 100644 --- a/lib/Dialect/TritonGPU/Transforms/SinkConversionsFromShared.cpp +++ b/lib/Dialect/TritonGPU/Transforms/SinkConversionsFromShared.cpp @@ -41,13 +41,6 @@ public: void runOnOperation() override { MLIRContext *context = &getContext(); ModuleOp m = getOperation(); - // Move convert(load) immediately after dependent load - m.walk([&](triton::gpu::ConvertLayoutOp op){ - auto load = dyn_cast(op.getOperand().getDefiningOp()); - if(!load) - return; - op->moveAfter(load); - }); // Sink conversions into loops when they will increase // register pressure DenseMap opToMove; @@ -62,7 +55,18 @@ public: }); for(auto &kv: opToMove) kv.first->moveBefore(kv.second); - + // Move convert(load) immediately after dependent load + m.walk([&](triton::gpu::ConvertLayoutOp op){ + auto dstType = op.getResult().getType().cast(); + auto dstEncoding = dstType.getEncoding(); + if(!dstEncoding.isa()) + return; + Operation* argOp = op.getOperand().getDefiningOp(); + if(!argOp) + return; + llvm::outs() << "moving " << *op << "\n"; + op->moveAfter(argOp); + }); // Move transpositions just after their definition opToMove.clear(); m.walk([&](triton::TransOp op){ diff --git a/python/slow.ttgir b/python/slow.ttgir index b36ec08de..8e8ca9de9 100644 --- a/python/slow.ttgir +++ b/python/slow.ttgir @@ -7,14 +7,13 @@ #shared1 = #triton_gpu.shared<{vec = 8, perPhase = 1, maxPhase = 8, order = [0, 1]}> module attributes {"triton_gpu.num-warps" = 8 : i32} { func public @_bwd_kernel_0d1d2d34d5d6d7d8d9d10d11d12d13d14d15c16d17d18d19c20d21d22d23c2425d26d27(%arg0: !tt.ptr {tt.divisibility = 16 : i32}, %arg1: !tt.ptr {tt.divisibility = 16 : i32}, %arg2: !tt.ptr {tt.divisibility = 16 : i32}, %arg3: f32, %arg4: !tt.ptr {tt.divisibility = 16 : i32}, %arg5: !tt.ptr {tt.divisibility = 16 : i32}, %arg6: !tt.ptr {tt.divisibility = 16 : i32}, %arg7: !tt.ptr {tt.divisibility = 16 : i32}, %arg8: !tt.ptr {tt.divisibility = 16 : i32}, %arg9: !tt.ptr {tt.divisibility = 16 : i32}, %arg10: !tt.ptr {tt.divisibility = 16 : i32}, %arg11: !tt.ptr {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<128x64xf32, #mma1> - %cst_0 = arith.constant dense<0.000000e+00> : tensor<128x128xf32, #mma0> - %cst_1 = arith.constant dense<0xFF800000> : tensor<128x128xf32, #mma0> - %cst_2 = arith.constant dense<0.000000e+00> : tensor<128x64xf32, #mma1> %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> %0 = tt.get_program_id {axis = 0 : i32} : i32 %1 = arith.divsi %0, %arg22 : i32 %2 = arith.remsi %0, %arg22 : i32 @@ -77,92 +76,92 @@ module attributes {"triton_gpu.num-warps" = 8 : i32} { %58 = tt.addptr %29, %57 : tensor<128x64x!tt.ptr, #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, #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 = arith.addi %50, %17 : tensor<128xi32, #triton_gpu.slice<{dim = 0, parent = #mma0}>> - %69 = tt.expand_dims %68 {axis = 0 : i32} : (tensor<128xi32, #triton_gpu.slice<{dim = 0, parent = #mma0}>>) -> tensor<1x128xi32, #mma0> - %70 = tt.broadcast %69 : (tensor<1x128xi32, #mma0>) -> tensor<128x128xi32, #mma0> - %71 = arith.muli %54, %20 : tensor<128x1xi32, #blocked2> - %72 = tt.broadcast %71 : (tensor<128x1xi32, #blocked2>) -> tensor<128x64xi32, #blocked2> - %73 = arith.addi %72, %26 : tensor<128x64xi32, #blocked2> - %74 = tt.addptr %32, %73 : tensor<128x64x!tt.ptr, #blocked2>, tensor<128x64xi32, #blocked2> - %75 = tt.addptr %27, %63 : tensor<128x64x!tt.ptr, #blocked1>, tensor<128x64xi32, #blocked1> - %76 = tt.addptr %31, %63 : tensor<128x64x!tt.ptr, #blocked1>, tensor<128x64xi32, #blocked1> - %77:5 = scf.for %arg26 = %67 to %37 step %c128 iter_args(%arg27 = %cst, %arg28 = %cst, %arg29 = %74, %arg30 = %75, %arg31 = %76) -> (tensor<128x64xf32, #mma1>, tensor<128x64xf32, #mma1>, tensor<128x64x!tt.ptr, #blocked2>, tensor<128x64x!tt.ptr, #blocked1>, tensor<128x64x!tt.ptr, #blocked1>) { - %84 = arith.index_cast %arg26 : index to i32 - %85 = tt.splat %84 : (i32) -> tensor<128xi32, #blocked0> - %86 = tt.splat %84 : (i32) -> tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #mma0}>> - %87 = arith.addi %85, %14 : tensor<128xi32, #blocked0> - %88 = tt.load %arg30 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128x64xf16, #blocked1> - %89 = triton_gpu.convert_layout %88 : (tensor<128x64xf16, #blocked1>) -> tensor<128x64xf16, #shared0> - %90 = triton_gpu.convert_layout %89 : (tensor<128x64xf16, #shared0>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma0}>> - %91 = tt.trans %60 : (tensor<128x64xf16, #shared0>) -> tensor<64x128xf16, #shared1> - %92 = triton_gpu.convert_layout %91 : (tensor<64x128xf16, #shared1>) -> tensor<64x128xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma0}>> - %93 = tt.dot %90, %92, %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 %86, %18 : tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #mma0}>> - %95 = tt.expand_dims %94 {axis = 1 : i32} : (tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #mma0}>>) -> tensor<128x1xi32, #mma0> - %96 = tt.broadcast %95 : (tensor<128x1xi32, #mma0>) -> tensor<128x128xi32, #mma0> - %97 = "triton_gpu.cmpi"(%96, %70) {predicate = 5 : i64} : (tensor<128x128xi32, #mma0>, tensor<128x128xi32, #mma0>) -> tensor<128x128xi1, #mma0> - %98 = "triton_gpu.select"(%97, %93, %cst_1) : (tensor<128x128xi1, #mma0>, tensor<128x128xf32, #mma0>, tensor<128x128xf32, #mma0>) -> tensor<128x128xf32, #mma0> - %99 = tt.addptr %38, %87 : tensor<128x!tt.ptr, #blocked0>, tensor<128xi32, #blocked0> - %100 = tt.load %99 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128xf32, #blocked0> - %101 = triton_gpu.convert_layout %100 : (tensor<128xf32, #blocked0>) -> tensor<128xf32, #triton_gpu.slice<{dim = 1, parent = #mma0}>> - %102 = arith.mulf %98, %39 : tensor<128x128xf32, #mma0> - %103 = tt.expand_dims %101 {axis = 1 : i32} : (tensor<128xf32, #triton_gpu.slice<{dim = 1, parent = #mma0}>>) -> tensor<128x1xf32, #mma0> - %104 = tt.broadcast %103 : (tensor<128x1xf32, #mma0>) -> tensor<128x128xf32, #mma0> - %105 = arith.subf %102, %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> - %108 = triton_gpu.convert_layout %107 : (tensor<128x64xf16, #blocked1>) -> tensor<128x64xf16, #shared0> - %109 = arith.truncf %106 : 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 %111 : (tensor<128x128xf16, #shared1>) -> tensor<128x128xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma1}>> - %113 = triton_gpu.convert_layout %108 : (tensor<128x64xf16, #shared0>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma1}>> - %114 = tt.dot %112, %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> - %115 = tt.addptr %40, %87 : tensor<128x!tt.ptr, #blocked0>, tensor<128xi32, #blocked0> - %116 = tt.load %115 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128xf32, #blocked0> - %117 = triton_gpu.convert_layout %116 : (tensor<128xf32, #blocked0>) -> tensor<128xf32, #triton_gpu.slice<{dim = 1, parent = #mma0}>> - %118 = tt.expand_dims %117 {axis = 1 : i32} : (tensor<128xf32, #triton_gpu.slice<{dim = 1, parent = #mma0}>>) -> tensor<128x1xf32, #mma0> - %119 = tt.broadcast %118 : (tensor<128x1xf32, #mma0>) -> tensor<128x128xf32, #mma0> - %120 = arith.subf %cst_0, %119 : tensor<128x128xf32, #mma0> - %121 = tt.trans %66 : (tensor<128x64xf16, #shared0>) -> tensor<64x128xf16, #shared1> - %122 = triton_gpu.convert_layout %108 : (tensor<128x64xf16, #shared0>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma0}>> - %123 = triton_gpu.convert_layout %121 : (tensor<64x128xf16, #shared1>) -> tensor<64x128xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma0}>> - %124 = tt.dot %122, %123, %120 {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 %106, %124 : tensor<128x128xf32, #mma0> + %61 = tt.trans %60 : (tensor<128x64xf16, #shared0>) -> tensor<64x128xf16, #shared1> + %62 = arith.muli %53, %19 : tensor<128x1xi32, #blocked1> + %63 = tt.broadcast %62 : (tensor<128x1xi32, #blocked1>) -> tensor<128x64xi32, #blocked1> + %64 = arith.addi %63, %24 : tensor<128x64xi32, #blocked1> + %65 = tt.addptr %30, %64 : tensor<128x64x!tt.ptr, #blocked1>, tensor<128x64xi32, #blocked1> + %66 = tt.load %65 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128x64xf16, #blocked1> + %67 = triton_gpu.convert_layout %66 : (tensor<128x64xf16, #blocked1>) -> tensor<128x64xf16, #shared0> + %68 = tt.trans %67 : (tensor<128x64xf16, #shared0>) -> tensor<64x128xf16, #shared1> + %69 = arith.index_cast %47 : i32 to index + %70 = arith.addi %50, %17 : 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 = 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, #blocked2>, tensor<128x64xi32, #blocked2> + %77 = tt.addptr %27, %64 : tensor<128x64x!tt.ptr, #blocked1>, tensor<128x64xi32, #blocked1> + %78 = tt.addptr %31, %64 : tensor<128x64x!tt.ptr, #blocked1>, tensor<128x64xi32, #blocked1> + %79:5 = scf.for %arg26 = %69 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, #blocked2>, tensor<128x64x!tt.ptr, #blocked1>, tensor<128x64x!tt.ptr, #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, #shared0> + %92 = triton_gpu.convert_layout %61 : (tensor<64x128xf16, #shared1>) -> tensor<64x128xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma0}>> + %93 = triton_gpu.convert_layout %91 : (tensor<128x64xf16, #shared0>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma0}>> + %94 = tt.dot %93, %92, %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, %72) {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, #blocked0>, tensor<128xi32, #blocked0> + %101 = tt.load %100 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128xf32, #blocked0> + %102 = arith.mulf %99, %39 : tensor<128x128xf32, #mma0> + %103 = triton_gpu.convert_layout %101 : (tensor<128xf32, #blocked0>) -> tensor<128xf32, #triton_gpu.slice<{dim = 1, parent = #mma0}>> + %104 = tt.expand_dims %103 {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 %102, %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 = triton_gpu.convert_layout %108 : (tensor<128x64xf16, #blocked1>) -> tensor<128x64xf16, #shared0> + %110 = arith.truncf %107 : tensor<128x128xf32, #mma0> to tensor<128x128xf16, #mma0> + %111 = triton_gpu.convert_layout %110 : (tensor<128x128xf16, #mma0>) -> tensor<128x128xf16, #shared0> + %112 = tt.trans %111 : (tensor<128x128xf16, #shared0>) -> tensor<128x128xf16, #shared1> + %113 = triton_gpu.convert_layout %112 : (tensor<128x128xf16, #shared1>) -> tensor<128x128xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma1}>> + %114 = triton_gpu.convert_layout %109 : (tensor<128x64xf16, #shared0>) -> 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 = tt.addptr %40, %89 : tensor<128x!tt.ptr, #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 %68 : (tensor<64x128xf16, #shared1>) -> tensor<64x128xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma0}>> + %123 = triton_gpu.convert_layout %109 : (tensor<128x64xf16, #shared0>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma0}>> + %124 = tt.dot %123, %122, %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 %129 : (tensor<128x128xf16, #shared1>) -> tensor<128x128xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma1}>> - %131 = triton_gpu.convert_layout %89 : (tensor<128x64xf16, #shared0>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma1}>> - %132 = tt.dot %130, %131, %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> + %130 = triton_gpu.convert_layout %91 : (tensor<128x64xf16, #shared0>) -> 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> %134 = triton_gpu.convert_layout %133 : (tensor<128x64xf32, #blocked2>) -> tensor<128x64xf32, #mma1> - %135 = triton_gpu.convert_layout %128 : (tensor<128x128xf16, #shared0>) -> tensor<128x128xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma1}>> - %136 = triton_gpu.convert_layout %60 : (tensor<128x64xf16, #shared0>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma1}>> - %137 = tt.dot %135, %136, %134 {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> + %135 = triton_gpu.convert_layout %60 : (tensor<128x64xf16, #shared0>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma1}>> + %136 = triton_gpu.convert_layout %128 : (tensor<128x128xf16, #shared0>) -> tensor<128x128xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma1}>> + %137 = tt.dot %136, %135, %134 {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> %138 = triton_gpu.convert_layout %137 : (tensor<128x64xf32, #mma1>) -> tensor<128x64xf32, #blocked2> tt.store %arg29, %138 : tensor<128x64xf32, #blocked2> %139 = tt.addptr %arg29, %43 : tensor<128x64x!tt.ptr, #blocked2>, tensor<128x64xi32, #blocked2> %140 = tt.addptr %arg30, %42 : tensor<128x64x!tt.ptr, #blocked1>, tensor<128x64xi32, #blocked1> %141 = tt.addptr %arg31, %42 : tensor<128x64x!tt.ptr, #blocked1>, tensor<128x64xi32, #blocked1> - scf.yield %114, %132, %139, %140, %141 : tensor<128x64xf32, #mma1>, tensor<128x64xf32, #mma1>, tensor<128x64x!tt.ptr, #blocked2>, tensor<128x64x!tt.ptr, #blocked1>, tensor<128x64x!tt.ptr, #blocked1> + scf.yield %115, %132, %139, %140, %141 : tensor<128x64xf32, #mma1>, tensor<128x64xf32, #mma1>, tensor<128x64x!tt.ptr, #blocked2>, tensor<128x64x!tt.ptr, #blocked1>, tensor<128x64x!tt.ptr, #blocked1> } - %78 = arith.truncf %77#0 : tensor<128x64xf32, #mma1> to tensor<128x64xf16, #mma1> - %79 = tt.addptr %44, %63 : tensor<128x64x!tt.ptr, #blocked1>, tensor<128x64xi32, #blocked1> - %80 = triton_gpu.convert_layout %78 : (tensor<128x64xf16, #mma1>) -> tensor<128x64xf16, #blocked1> - tt.store %79, %80 : tensor<128x64xf16, #blocked1> - %81 = arith.truncf %77#1 : tensor<128x64xf32, #mma1> to tensor<128x64xf16, #mma1> - %82 = tt.addptr %45, %57 : tensor<128x64x!tt.ptr, #blocked1>, tensor<128x64xi32, #blocked1> - %83 = triton_gpu.convert_layout %81 : (tensor<128x64xf16, #mma1>) -> tensor<128x64xf16, #blocked1> - tt.store %82, %83 : tensor<128x64xf16, #blocked1> + %80 = arith.truncf %79#0 : tensor<128x64xf32, #mma1> to tensor<128x64xf16, #mma1> + %81 = tt.addptr %44, %64 : tensor<128x64x!tt.ptr, #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, #blocked1>, tensor<128x64xi32, #blocked1> + %85 = triton_gpu.convert_layout %83 : (tensor<128x64xf16, #mma1>) -> tensor<128x64xf16, #blocked1> + tt.store %84, %85 : tensor<128x64xf16, #blocked1> } return } diff --git a/python/triton/compiler.py b/python/triton/compiler.py index 14f87d7a0..b40fe65fb 100644 --- a/python/triton/compiler.py +++ b/python/triton/compiler.py @@ -906,10 +906,10 @@ def ttir_to_ttgir(mod, num_warps, num_stages, compute_capability): pm.add_tritongpu_combine_pass(compute_capability) pm.add_cse_pass() # pm.add_tritongpu_optimize_load_convert_pass() - pm.add_tritongpu_sink_conversions_from_shared_pass() pm.add_tritongpu_decompose_conversions_to_dot_operand_pass() pm.add_cse_pass() pm.add_symbol_dce_pass() + pm.add_tritongpu_sink_conversions_from_shared_pass() pm.run(mod) return mod