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This commit is contained in:
Phil Tillet
2023-01-09 15:45:06 -08:00
parent 6c750b6856
commit 8ebb593bbb
5 changed files with 179 additions and 125 deletions
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54
lib/Dialect/TritonGPU/Transforms/Combine.cpp
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@@ -1255,6 +1255,59 @@ public:
}
};
// Convert + trans + convert
// x = convert_layout distributed -> #shared_x
// y = trans x -> #shared_y
// z = convert_layout y -> #dot_operand
class ConvertTransConvert : public mlir::RewritePattern {
public:
ConvertTransConvert(mlir::MLIRContext *context)
: mlir::RewritePattern(triton::gpu::ConvertLayoutOp::getOperationName(),
1, context) {}
LogicalResult matchAndRewrite(mlir::Operation* op,
mlir::PatternRewriter &rewriter) const override {
auto dstOp = cast<triton::gpu::ConvertLayoutOp>(op);
auto tmpOp = dyn_cast_or_null<triton::TransOp>(dstOp.src().getDefiningOp());
if(!tmpOp)
return mlir::failure();
auto srcOp = dyn_cast_or_null<triton::gpu::ConvertLayoutOp>(tmpOp.src().getDefiningOp());
if(!srcOp)
return mlir::failure();
auto arg = srcOp.src();
auto X = tmpOp.src();
auto Y = dstOp.src();
// types
auto argType = arg.getType().cast<RankedTensorType>();
auto XType = X.getType().cast<RankedTensorType>();
auto YType = Y.getType().cast<RankedTensorType>();
auto ZType = dstOp.getResult().getType().cast<RankedTensorType>();
// encodings
auto argEncoding = argType.getEncoding();
auto XEncoding = XType.getEncoding().cast<triton::gpu::SharedEncodingAttr>();
auto YEncoding = YType.getEncoding().cast<triton::gpu::SharedEncodingAttr>();
auto ZEncoding = ZType.getEncoding().dyn_cast<triton::gpu::DotOperandEncodingAttr>();
if(!ZEncoding)
return mlir::failure();
// new X encoding
auto newXOrder = triton::gpu::getOrder(argEncoding);
auto newXEncoding = triton::gpu::SharedEncodingAttr::get(
getContext(), ZEncoding, XType.getShape(), newXOrder,
XType.getElementType());
auto newXType = RankedTensorType::get(XType.getShape(), XType.getElementType(),
newXEncoding);
if(XEncoding == newXEncoding)
return mlir::failure();
auto newX = rewriter.create<triton::gpu::ConvertLayoutOp>(srcOp.getLoc(), newXType, arg);
auto newY = rewriter.create<triton::TransOp>(tmpOp.getLoc(), newX);
rewriter.replaceOpWithNewOp<triton::gpu::ConvertLayoutOp>(dstOp, ZType, newY);
return mlir::success();
}
};
// Correct the versionMinor field in MmaEncodingAttr for Volta.
class UpdateMMAVersionMinorForVolta : public mlir::RewritePattern {
const DenseMap<MmaEncodingAttr, MmaEncodingAttr> &mmaToUpdate;
@@ -1423,6 +1476,7 @@ public:
patterns.add<MoveConvertOutOfLoop>(context);
patterns.add<MoveConvertOutOfIf>(context);
patterns.add<BlockedToMMA>(context, computeCapability);
patterns.add<ConvertTransConvert>(context);
if (applyPatternsAndFoldGreedily(m, std::move(patterns)).failed()) {
signalPassFailure();

9
lib/Dialect/TritonGPU/Transforms/DecomposeConversionsToDotOperand.cpp
View File

@@ -34,16 +34,17 @@ public:
OpBuilder builder(cvtOp);
auto srcType = cvtOp.getOperand().getType().cast<RankedTensorType>();
auto dstType = cvtOp.getType().cast<RankedTensorType>();
auto srcBlocked =
srcType.getEncoding().dyn_cast<triton::gpu::BlockedEncodingAttr>();
auto srcEncoding = srcType.getEncoding();
if(srcEncoding.isa<triton::gpu::SharedEncodingAttr>())
return;
auto dstDotOp =
dstType.getEncoding().dyn_cast<triton::gpu::DotOperandEncodingAttr>();
if (srcBlocked && dstDotOp) {
if (dstDotOp) {
auto tmpType = RankedTensorType::get(
dstType.getShape(), dstType.getElementType(),
triton::gpu::SharedEncodingAttr::get(
mod.getContext(), dstDotOp, srcType.getShape(),
getOrder(srcBlocked), srcType.getElementType()));
triton::gpu::getOrder(srcEncoding), srcType.getElementType()));
auto tmp = builder.create<triton::gpu::ConvertLayoutOp>(
cvtOp.getLoc(), tmpType, cvtOp.getOperand());
auto newConvert = builder.create<triton::gpu::ConvertLayoutOp>(

182
python/being-optimized.ttgir
View File

@@ -9,17 +9,16 @@
#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 = 1, perPhase = 1, maxPhase = 1, order = [1, 0]}>
#shared2 = #triton_gpu.shared<{vec = 1, perPhase = 1, maxPhase = 1, order = [0, 1]}>
#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<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 = arith.constant dense<0.000000e+00> : tensor<128x128xf32, #mma0>
%cst_0 = arith.constant dense<0xFF800000> : tensor<128x128xf32, #mma0>
%cst_1 = arith.constant dense<0.000000e+00> : tensor<128x128xf32, #mma0>
%cst_1 = arith.constant dense<0.000000e+00> : tensor<128x64xf32, #mma1>
%c128 = arith.constant 128 : index
%c128_i32 = arith.constant 128 : i32
%c1 = arith.constant 1 : index
%c0 = arith.constant 0 : index
%0 = tt.get_program_id {axis = 0 : i32} : i32
%1 = arith.divsi %0, %arg22 : i32
%2 = arith.remsi %0, %arg22 : i32
@@ -82,93 +81,92 @@ module attributes {"triton_gpu.num-warps" = 8 : i32} {
%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 = triton_gpu.convert_layout %59 : (tensor<128x64xf16, #blocked1>) -> tensor<128x64xf16, #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<f16>, #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, #shared1>
%68 = arith.index_cast %47 : i32 to index
%69 = tt.trans %61 : (tensor<128x64xf16, #shared1>) -> tensor<64x128xf16, #shared2>
%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 = tt.trans %67 : (tensor<128x64xf16, #shared1>) -> tensor<64x128xf16, #shared2>
%74 = arith.muli %54, %20 : tensor<128x1xi32, #blocked2>
%75 = tt.broadcast %74 : (tensor<128x1xi32, #blocked2>) -> tensor<128x64xi32, #blocked2>
%76 = arith.addi %75, %26 : tensor<128x64xi32, #blocked2>
%77 = tt.addptr %32, %76 : tensor<128x64x!tt.ptr<f32>, #blocked2>, tensor<128x64xi32, #blocked2>
%78 = tt.addptr %27, %64 : tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64xi32, #blocked1>
%79 = tt.addptr %31, %64 : tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64xi32, #blocked1>
%80:5 = scf.for %arg26 = %68 to %37 step %c128 iter_args(%arg27 = %cst, %arg28 = %cst, %arg29 = %77, %arg30 = %78, %arg31 = %79) -> (tensor<128x64xf32, #mma1>, tensor<128x64xf32, #mma1>, tensor<128x64x!tt.ptr<f32>, #blocked2>, tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64x!tt.ptr<f16>, #blocked1>) {
%87 = arith.index_cast %arg26 : index to i32
%88 = tt.splat %87 : (i32) -> tensor<128xi32, #blocked0>
%89 = tt.splat %87 : (i32) -> tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #mma0}>>
%90 = arith.addi %88, %14 : tensor<128xi32, #blocked0>
%91 = tt.load %arg30 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128x64xf16, #blocked1>
%92 = triton_gpu.convert_layout %91 : (tensor<128x64xf16, #blocked1>) -> tensor<128x64xf16, #shared0>
%93 = triton_gpu.convert_layout %69 : (tensor<64x128xf16, #shared2>) -> tensor<64x128xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma0}>>
%94 = triton_gpu.convert_layout %92 : (tensor<128x64xf16, #shared0>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma0}>>
%95 = tt.dot %94, %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>
%96 = arith.addi %89, %18 : tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #mma0}>>
%97 = tt.expand_dims %96 {axis = 1 : i32} : (tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #mma0}>>) -> tensor<128x1xi32, #mma0>
%98 = tt.broadcast %97 : (tensor<128x1xi32, #mma0>) -> tensor<128x128xi32, #mma0>
%99 = "triton_gpu.cmpi"(%98, %72) {predicate = 5 : i64} : (tensor<128x128xi32, #mma0>, tensor<128x128xi32, #mma0>) -> tensor<128x128xi1, #mma0>
%100 = "triton_gpu.select"(%99, %95, %cst_0) : (tensor<128x128xi1, #mma0>, tensor<128x128xf32, #mma0>, tensor<128x128xf32, #mma0>) -> tensor<128x128xf32, #mma0>
%101 = tt.addptr %38, %90 : tensor<128x!tt.ptr<f32>, #blocked0>, tensor<128xi32, #blocked0>
%102 = tt.load %101 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128xf32, #blocked0>
%103 = triton_gpu.convert_layout %102 : (tensor<128xf32, #blocked0>) -> tensor<128xf32, #triton_gpu.slice<{dim = 1, parent = #mma0}>>
%104 = arith.mulf %100, %39 : tensor<128x128xf32, #mma0>
%105 = tt.expand_dims %103 {axis = 1 : i32} : (tensor<128xf32, #triton_gpu.slice<{dim = 1, parent = #mma0}>>) -> tensor<128x1xf32, #mma0>
%106 = tt.broadcast %105 : (tensor<128x1xf32, #mma0>) -> tensor<128x128xf32, #mma0>
%107 = arith.subf %104, %106 : tensor<128x128xf32, #mma0>
%108 = math.exp %107 : tensor<128x128xf32, #mma0>
%109 = tt.load %arg31 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128x64xf16, #blocked1>
%110 = triton_gpu.convert_layout %109 : (tensor<128x64xf16, #blocked1>) -> tensor<128x64xf16, #shared0>
%111 = arith.truncf %108 : tensor<128x128xf32, #mma0> to tensor<128x128xf16, #mma0>
%112 = triton_gpu.convert_layout %111 : (tensor<128x128xf16, #mma0>) -> tensor<128x128xf16, #shared1>
%113 = tt.trans %112 : (tensor<128x128xf16, #shared1>) -> tensor<128x128xf16, #shared2>
%114 = triton_gpu.convert_layout %113 : (tensor<128x128xf16, #shared2>) -> tensor<128x128xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma1}>>
%115 = triton_gpu.convert_layout %110 : (tensor<128x64xf16, #shared0>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma1}>>
%116 = tt.dot %114, %115, %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>
%117 = tt.addptr %40, %90 : tensor<128x!tt.ptr<f32>, #blocked0>, tensor<128xi32, #blocked0>
%118 = tt.load %117 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128xf32, #blocked0>
%119 = triton_gpu.convert_layout %118 : (tensor<128xf32, #blocked0>) -> tensor<128xf32, #triton_gpu.slice<{dim = 1, parent = #mma0}>>
%120 = tt.expand_dims %119 {axis = 1 : i32} : (tensor<128xf32, #triton_gpu.slice<{dim = 1, parent = #mma0}>>) -> tensor<128x1xf32, #mma0>
%121 = tt.broadcast %120 : (tensor<128x1xf32, #mma0>) -> tensor<128x128xf32, #mma0>
%122 = arith.subf %cst_1, %121 : tensor<128x128xf32, #mma0>
%123 = triton_gpu.convert_layout %110 : (tensor<128x64xf16, #shared0>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma0}>>
%124 = triton_gpu.convert_layout %73 : (tensor<64x128xf16, #shared2>) -> tensor<64x128xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma0}>>
%125 = tt.dot %123, %124, %122 {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>
%126 = arith.mulf %108, %125 : tensor<128x128xf32, #mma0>
%127 = arith.mulf %126, %39 : tensor<128x128xf32, #mma0>
%128 = arith.truncf %127 : tensor<128x128xf32, #mma0> to tensor<128x128xf16, #mma0>
%129 = triton_gpu.convert_layout %128 : (tensor<128x128xf16, #mma0>) -> tensor<128x128xf16, #shared1>
%130 = tt.trans %129 : (tensor<128x128xf16, #shared1>) -> tensor<128x128xf16, #shared2>
%131 = triton_gpu.convert_layout %92 : (tensor<128x64xf16, #shared0>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma1}>>
%132 = triton_gpu.convert_layout %130 : (tensor<128x128xf16, #shared2>) -> tensor<128x128xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma1}>>
%133 = tt.dot %132, %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>
%134 = tt.load %arg29 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128x64xf32, #blocked2>
%135 = triton_gpu.convert_layout %134 : (tensor<128x64xf32, #blocked2>) -> tensor<128x64xf32, #mma1>
%136 = triton_gpu.convert_layout %128 : (tensor<128x128xf16, #mma0>) -> tensor<128x128xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma1}>>
%137 = triton_gpu.convert_layout %60 : (tensor<128x64xf16, #shared0>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma1}>>
%138 = tt.dot %136, %137, %135 {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>
tt.store %arg29, %139 : 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 %116, %133, %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>
%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 = 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<f32>, #blocked2>, tensor<128x64xi32, #blocked2>
%75 = tt.addptr %27, %63 : tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64xi32, #blocked1>
%76 = tt.addptr %31, %63 : tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64xi32, #blocked1>
%77:5 = scf.for %arg26 = %67 to %37 step %c128 iter_args(%arg27 = %cst_1, %arg28 = %cst_1, %arg29 = %74, %arg30 = %75, %arg31 = %76) -> (tensor<128x64xf32, #mma1>, tensor<128x64xf32, #mma1>, tensor<128x64x!tt.ptr<f32>, #blocked2>, tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64x!tt.ptr<f16>, #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 = tt.trans %60 : (tensor<128x64xf16, #shared0>) -> tensor<64x128xf16, #shared1>
%91 = triton_gpu.convert_layout %89 : (tensor<128x64xf16, #shared0>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma0}>>
%92 = triton_gpu.convert_layout %90 : (tensor<64x128xf16, #shared1>) -> tensor<64x128xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma0}>>
%93 = tt.dot %91, %92, %cst {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_0) : (tensor<128x128xi1, #mma0>, tensor<128x128xf32, #mma0>, tensor<128x128xf32, #mma0>) -> tensor<128x128xf32, #mma0>
%99 = tt.addptr %38, %87 : tensor<128x!tt.ptr<f32>, #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 %108 : (tensor<128x64xf16, #shared0>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma1}>>
%113 = triton_gpu.convert_layout %111 : (tensor<128x128xf16, #shared1>) -> tensor<128x128xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma1}>>
%114 = tt.dot %113, %112, %arg27 {allowTF32 = true} : tensor<128x128xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma1}>> * tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma1}>> -> tensor<128x64xf32, #mma1>
%115 = tt.addptr %40, %87 : tensor<128x!tt.ptr<f32>, #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, %119 : tensor<128x128xf32, #mma0>
%121 = tt.trans %66 : (tensor<128x64xf16, #shared0>) -> tensor<64x128xf16, #shared1>
%122 = triton_gpu.convert_layout %121 : (tensor<64x128xf16, #shared1>) -> tensor<64x128xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma0}>>
%123 = triton_gpu.convert_layout %108 : (tensor<128x64xf16, #shared0>) -> tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma0}>>
%124 = tt.dot %123, %122, %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>
%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 %89 : (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>
%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<f32>, #blocked2>, tensor<128x64xi32, #blocked2>
%140 = tt.addptr %arg30, %42 : tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64xi32, #blocked1>
%141 = tt.addptr %arg31, %42 : tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64xi32, #blocked1>
scf.yield %114, %132, %139, %140, %141 : tensor<128x64xf32, #mma1>, tensor<128x64xf32, #mma1>, tensor<128x64x!tt.ptr<f32>, #blocked2>, tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64x!tt.ptr<f16>, #blocked1>
}
%81 = arith.truncf %80#0 : tensor<128x64xf32, #mma1> to tensor<128x64xf16, #mma1>
%82 = tt.addptr %44, %64 : tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64xi32, #blocked1>
%78 = arith.truncf %77#0 : tensor<128x64xf32, #mma1> to tensor<128x64xf16, #mma1>
%79 = tt.addptr %44, %63 : tensor<128x64x!tt.ptr<f16>, #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<f16>, #blocked1>, tensor<128x64xi32, #blocked1>
%83 = triton_gpu.convert_layout %81 : (tensor<128x64xf16, #mma1>) -> tensor<128x64xf16, #blocked1>
tt.store %82, %83 : tensor<128x64xf16, #blocked1>
%84 = arith.truncf %80#1 : tensor<128x64xf32, #mma1> to tensor<128x64xf16, #mma1>
%85 = tt.addptr %45, %57 : tensor<128x64x!tt.ptr<f16>, #blocked1>, tensor<128x64xi32, #blocked1>
%86 = triton_gpu.convert_layout %84 : (tensor<128x64xf16, #mma1>) -> tensor<128x64xf16, #blocked1>
tt.store %85, %86 : tensor<128x64xf16, #blocked1>
}
return
}

1
python/triton/compiler.py
View File

@@ -909,6 +909,7 @@ def ttir_to_ttgir(mod, num_warps, num_stages, compute_capability):
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.run(mod)
return mod

58
python/tutorials/06-fused-attention.py
View File

@@ -191,7 +191,7 @@ def _bwd_kernel(
tl.store(dv_ptrs, dv)
tl.store(dk_ptrs, dk)
# _bwd_kernel = triton.compile("./being-optimized.ttgir", num_warps=8)
_bwd_kernel = triton.compile("./being-optimized.ttgir", num_warps=8)
# _bwd_kernel = triton.compile("./unoptimized.ttgir", num_warps=8)
# _bwd_kernel = triton.compile("./bwd.ttgir", num_warps=8)
# _fwd_kernel = triton.compile("./fails.ptx", num_warps=4, shared=18432)
@@ -260,36 +260,36 @@ class _attention(torch.autograd.Function):
BLOCK_M=ctx.BLOCK, D_HEAD=ctx.BLOCK_DMODEL,
)
# _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]
# )
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]
)
print(pgm.asm["ttgir"])
exit()
# 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"])
# exit()
return dq, dk, dv, None