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[Triton] Use UnitAttr in tt.reshape definition #2497

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[Triton] Use UnitAttr in tt.reshape definition #2497

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99abe0f
[Triton] Use `UnitAttr` in `tt.reshape` definition
victor-eds Oct 16, 2024
9e60885
Revert semantic changes to tests and add new ones
victor-eds Oct 16, 2024
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9 changes: 2 additions & 7 deletions include/triton/Dialect/Triton/IR/TritonOps.td
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Expand Up @@ -460,17 +460,12 @@ def TT_ReshapeOp : TT_Op<"reshape", [Pure,
If efficient_layout is set, this is a hint that the destination layout should be kept for performance reason.
The compiler is still free to change it for better performance.
}];
let arguments = (ins TT_Tensor:$src, BoolAttr:$allow_reorder, OptionalAttr<UnitAttr>:$efficient_layout);
let arguments = (ins TT_Tensor:$src, UnitAttr:$allow_reorder, UnitAttr:$efficient_layout);
let results = (outs TT_Tensor:$result);
let assemblyFormat = "$src attr-dict `:` type($src) `->` type($result)";
let assemblyFormat = "$src (`allow_reorder` $allow_reorder^)? (`efficient_layout` $efficient_layout^)? attr-dict `:` type($src) `->` type($result)";
let hasCanonicalizeMethod = 1;
let hasFolder = 1;
let hasVerifier = 1;
let builders = [
OpBuilder<(ins "Type":$type, "Value":$src, "bool":$allow_reorder),
[{
build($_builder, $_state, type, src, allow_reorder, /*efficient_layout=*/UnitAttr());
}]>];
}

def TT_BroadcastOp : TT_Op<"broadcast", [Pure,
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2 changes: 1 addition & 1 deletion lib/Dialect/Triton/IR/Ops.cpp
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Expand Up @@ -678,7 +678,7 @@ LogicalResult canonicalizeViewOrBroadcast(OpType op,
}

LogicalResult ReshapeOp::canonicalize(ReshapeOp op, PatternRewriter &rewriter) {
if (!op.getAllowReorder() || op.getEfficientLayout().has_value())
if (!op.getAllowReorder() || op.getEfficientLayout())
return failure();
return canonicalizeViewOrBroadcast(op, rewriter);
}
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5 changes: 2 additions & 3 deletions lib/Dialect/TritonGPU/IR/Dialect.cpp
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Expand Up @@ -2764,7 +2764,7 @@ struct CanonicalizeConvertFromReshape
return failure();
if (isExpensiveView(convert.getSrc().getType(), op.getType()))
return failure();
if (!op.getAllowReorder() || op.getEfficientLayout().has_value())
if (!op.getAllowReorder() || op.getEfficientLayout())
return failure();

rewriter.replaceOpWithNewOp<triton::ReshapeOp>(
Expand Down Expand Up @@ -2885,8 +2885,7 @@ struct CanonicalizeConvertFromConvert

// cvt(reshape) -> reshape
if (auto reshape = dyn_cast<ReshapeOp>(arg)) {
if (!reshape.getAllowReorder() ||
reshape.getEfficientLayout().has_value() ||
if (!reshape.getAllowReorder() || reshape.getEfficientLayout() ||
isExpensiveView(reshape.getSrc().getType(), op.getType()))
return failure();

Expand Down
3 changes: 1 addition & 2 deletions lib/Dialect/TritonGPU/Transforms/Utility.cpp
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Expand Up @@ -556,8 +556,7 @@ bool canFoldIntoConversion(Operation *op, Attribute targetEncoding) {
RankedTensorType newDstType =
RankedTensorType::get(reshapeDstType.getShape(),
reshapeDstType.getElementType(), targetEncoding);
return reshape.getAllowReorder() &&
!reshape.getEfficientLayout().has_value() &&
return reshape.getAllowReorder() && !reshape.getEfficientLayout() &&
!triton::gpu::isExpensiveView(reshape.getSrc().getType(),
newDstType);
}
Expand Down
2 changes: 1 addition & 1 deletion test/Conversion/intel/tritongpu_to_gen.mlir
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Expand Up @@ -506,7 +506,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 4 :
// CHECK-NEXT: [[STRUCT2:%.*]] = llvm.insertvalue [[ARG0_1]], [[STRUCT1]][1]
// CHECK-NEXT: [[T0:%.*]] = llvm.extractvalue [[STRUCT2]][0]
// CHECK-NEXT: [[T1:%.*]] = llvm.extractvalue [[STRUCT2]][1]
%0 = tt.reshape %arg {allow_reorder = true} : tensor<256xf32, #blocked0> -> tensor<256x1xf32,#blocked2>
%0 = tt.reshape %arg allow_reorder : tensor<256xf32, #blocked0> -> tensor<256x1xf32,#blocked2>
// CHECK: [[RES:%.*]] = llvm.mlir.undef : !llvm.struct<(f32, f32, f32, f32, f32, f32, f32, f32)>
// CHECK-NEXT: [[RES1:%.*]] = llvm.insertvalue [[T0]], [[RES]][0]
// CHECK-NEXT: [[RES2:%.*]] = llvm.insertvalue [[T1]], [[RES1]][1]
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2 changes: 1 addition & 1 deletion test/Conversion/tritongpu_to_llvm.mlir
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Expand Up @@ -357,7 +357,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 4 :
// CHECK: llvm.mlir.undef
// CHECK: %[[T0:.*]] = llvm.extractvalue
// CHECK: %[[T1:.*]] = llvm.extractvalue
%0 = tt.reshape %arg {allow_reorder = true} : tensor<256xf32, #blocked0> -> tensor<256x1xf32,#blocked2>
%0 = tt.reshape %arg allow_reorder : tensor<256xf32, #blocked0> -> tensor<256x1xf32,#blocked2>
// CHECK: llvm.mlir.undef
// CHECK: llvm.insertvalue %[[T0]]
// CHECK: llvm.insertvalue %[[T1]]
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12 changes: 6 additions & 6 deletions test/Triton/combine.mlir
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Expand Up @@ -292,15 +292,15 @@ tt.func @test_canonicalize_expand_dims(%arg0: tensor<f32>, %arg1: tensor<1xf32>)

// CHECK-LABEL: @test_canonicalize_view
tt.func @test_canonicalize_view(%arg0: tensor<8xf32>, %arg1: tensor<f32>) -> (tensor<4x2xf32>, tensor<2x2x2xf32>, tensor<8xf32>) {
%view0 = tt.reshape %arg0 {allow_reorder = true} : tensor<8xf32> -> tensor<2x4xf32>
// CHECK: %{{.*}} = tt.reshape %arg0 {allow_reorder = true} : tensor<8xf32> -> tensor<4x2xf32>
%view1 = tt.reshape %view0 {allow_reorder = true} : tensor<2x4xf32> -> tensor<4x2xf32>
%view0 = tt.reshape %arg0 allow_reorder : tensor<8xf32> -> tensor<2x4xf32>
// CHECK: %{{.*}} = tt.reshape %arg0 allow_reorder : tensor<8xf32> -> tensor<4x2xf32>
%view1 = tt.reshape %view0 allow_reorder : tensor<2x4xf32> -> tensor<4x2xf32>

%splat = tt.splat %arg1 : tensor<f32> -> tensor<8xf32>
// CHECK: %{{.*}} = tt.splat %arg1 : tensor<f32> -> tensor<2x2x2xf32>
%view2 = tt.reshape %splat {allow_reorder = true} : tensor<8xf32> -> tensor<2x2x2xf32>
%view2 = tt.reshape %splat allow_reorder : tensor<8xf32> -> tensor<2x2x2xf32>

%view3 = tt.reshape %arg0 {allow_reorder = true} : tensor<8xf32> -> tensor<8xf32>
%view3 = tt.reshape %arg0 allow_reorder : tensor<8xf32> -> tensor<8xf32>
// CHECK: %{{.*}} = arith.addf %arg0, %arg0 : tensor<8xf32>
%add = arith.addf %view3, %arg0 : tensor<8xf32>

Expand Down Expand Up @@ -329,7 +329,7 @@ tt.func @test_fold_views() -> (tensor<16x8xf32>, tensor<16x128xf32>, tensor<1x1x
%a = arith.constant dense<1.0> : tensor<1x128xf32>

// CHECK-DAG: %{{.*}} = arith.constant dense<1.{{.*}}> : tensor<16x8xf32>
%b = tt.reshape %a {allow_reorder = true} : tensor<1x128xf32> -> tensor<16x8xf32>
%b = tt.reshape %a allow_reorder : tensor<1x128xf32> -> tensor<16x8xf32>

// CHECK-DAG: %{{.*}} = arith.constant dense<1.{{.*}}> : tensor<16x128xf32>
%c = tt.broadcast %a : tensor<1x128xf32> -> tensor<16x128xf32>
Expand Down
2 changes: 1 addition & 1 deletion test/Triton/invalid.mlir
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Expand Up @@ -38,7 +38,7 @@ tt.func public @fn(%arg0: tensor<128xf32>, %arg1: tensor<64xf32>) {

tt.func public @reshape_different_num_elements(%arg0: tensor<32x128xf16>) {
// expected-error @+1 {{number of src and dst elements of reshape must be the same}}
%a = tt.reshape %arg0 {allow_reorder = false} : tensor<32x128xf16> -> tensor<64x32xf16>
%a = tt.reshape %arg0 : tensor<32x128xf16> -> tensor<64x32xf16>
tt.return
}

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25 changes: 23 additions & 2 deletions test/Triton/ops.mlir
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Expand Up @@ -225,8 +225,29 @@ tt.func @inline_asm_scalar(%0: i32) {

// CHECK-LABEL: reshape
tt.func @reshape(%0: tensor<512xi32>) {
// CHECK: tt.reshape %{{.+}} {allow_reorder = false} : tensor<512xi32> -> tensor<16x32xi32>
%1 = tt.reshape %0 {allow_reorder = false} : tensor<512xi32> -> tensor<16x32xi32>
// CHECK: tt.reshape %{{.+}} : tensor<512xi32> -> tensor<16x32xi32>
%1 = tt.reshape %0 : tensor<512xi32> -> tensor<16x32xi32>
tt.return
}

// CHECK-LABEL: reshape_allow_reorder
tt.func @reshape_allow_reorder(%0: tensor<512xi32>) {
// CHECK: tt.reshape %{{.+}} allow_reorder : tensor<512xi32> -> tensor<16x32xi32>
%1 = tt.reshape %0 allow_reorder : tensor<512xi32> -> tensor<16x32xi32>
tt.return
}

// CHECK-LABEL: reshape_efficient_layout
tt.func @reshape_efficient_layout(%0: tensor<512xi32>) {
// CHECK: tt.reshape %{{.+}} efficient_layout : tensor<512xi32> -> tensor<16x32xi32>
%1 = tt.reshape %0 efficient_layout : tensor<512xi32> -> tensor<16x32xi32>
tt.return
}

// CHECK-LABEL: reshape_allow_reorder_efficient_layout
tt.func @reshape_allow_reorder_efficient_layout(%0: tensor<512xi32>) {
// CHECK: tt.reshape %{{.+}} allow_reorder efficient_layout : tensor<512xi32> -> tensor<16x32xi32>
%1 = tt.reshape %0 allow_reorder efficient_layout : tensor<512xi32> -> tensor<16x32xi32>
tt.return
}

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8 changes: 4 additions & 4 deletions test/TritonGPU/canonicalize.mlir
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Expand Up @@ -4,7 +4,7 @@
// CHECK-LABEL: @test_canonicalize_convert_view
// CHECK-SAME: (%[[ARG:.+]]: tensor<64x64xf32
// CHECK-NOT: triton_gpu.convert_layout
// CHECK: %[[V:.+]] = tt.reshape %[[ARG]] {allow_reorder = true}
// CHECK: %[[V:.+]] = tt.reshape %[[ARG]] allow_reorder
// CHECK: tt.return %[[V]]
#blocked0 = #triton_gpu.blocked<{sizePerThread = [1, 8], threadsPerWarp = [4, 8], warpsPerCTA = [8, 1], order = [1, 0], CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [0, 1]}>
#blocked1 = #triton_gpu.blocked<{sizePerThread = [1], threadsPerWarp = [32], warpsPerCTA = [8], order = [0], CTAsPerCGA = [1], CTASplitNum = [1], CTAOrder = [0]}>
Expand All @@ -13,7 +13,7 @@
module attributes {"triton_gpu.num-warps" = 8 : i32, "triton_gpu.num-ctas" = 1 : i32, "triton_gpu.target" = "cuda:80"} {
tt.func @test_canonicalize_convert_view(%arg0: tensor<64x64xf32, #blocked0>) -> tensor<4096xf32, #blocked1> {
%c = triton_gpu.convert_layout %arg0 : tensor<64x64xf32, #blocked0> -> tensor<64x64xf32, #blocked2>
%r = tt.reshape %c {allow_reorder = true} : tensor<64x64xf32, #blocked2> -> tensor<4096xf32, #blocked1>
%r = tt.reshape %c allow_reorder : tensor<64x64xf32, #blocked2> -> tensor<4096xf32, #blocked1>
tt.return %r : tensor<4096xf32, #blocked1>
}
} // end module
Expand All @@ -25,15 +25,15 @@ tt.func @test_canonicalize_convert_view(%arg0: tensor<64x64xf32, #blocked0>) ->
// CHECK-LABEL: @test_canonicalize_convert_expensive_view
// CHECK-SAME: (%[[ARG:.+]]: tensor<256x16xf32
// CHECK: %[[C:.+]] = triton_gpu.convert_layout %[[ARG]]
// CHECK: %[[V:.+]] = tt.reshape %[[C]] {allow_reorder = true}
// CHECK: %[[V:.+]] = tt.reshape %[[C]] allow_reorder
// CHECK: tt.return %[[V]]
#blocked0 = #triton_gpu.blocked<{sizePerThread = [1, 8], threadsPerWarp = [4, 8], warpsPerCTA = [8, 1], order = [1, 0], CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [0, 1]}>
#blocked1 = #triton_gpu.blocked<{sizePerThread = [1], threadsPerWarp = [32], warpsPerCTA = [8], order = [0], CTAsPerCGA = [1], CTASplitNum = [1], CTAOrder = [0]}>
#blocked2 = #triton_gpu.blocked<{sizePerThread = [1, 1], threadsPerWarp = [32, 1], warpsPerCTA = [8, 1], order = [0, 1], CTAsPerCGA = [1, 1], CTASplitNum = [1, 1], CTAOrder = [0, 1]}>
module attributes {"triton_gpu.num-warps" = 8 : i32, "triton_gpu.num-ctas" = 1 : i32, "triton_gpu.target" = "cuda:80"} {
tt.func @test_canonicalize_convert_expensive_view(%arg0: tensor<256x16xf32, #blocked0>) -> tensor<4096xf32, #blocked1> {
%c = triton_gpu.convert_layout %arg0 : tensor<256x16xf32, #blocked0> -> tensor<256x16xf32, #blocked2>
%r = tt.reshape %c {allow_reorder = true} : tensor<256x16xf32, #blocked2> -> tensor<4096xf32, #blocked1>
%r = tt.reshape %c allow_reorder : tensor<256x16xf32, #blocked2> -> tensor<4096xf32, #blocked1>
tt.return %r : tensor<4096xf32, #blocked1>
}
} // end module
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10 changes: 5 additions & 5 deletions test/TritonGPU/combine.mlir
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Expand Up @@ -2097,7 +2097,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 :
tt.func public @reshape_propagate(%arg0: tensor<16x2xf32, #blocked>) -> tensor<32xf32, #blocked3> {
// CHECK-NOT: triton_gpu.convert_layout
%a = triton_gpu.convert_layout %arg0 : tensor<16x2xf32, #blocked> -> tensor<16x2xf32, #blocked1>
%b = tt.reshape %a {allow_reorder = false} : tensor<16x2xf32, #blocked1> -> tensor<32xf32, #blocked2>
%b = tt.reshape %a : tensor<16x2xf32, #blocked1> -> tensor<32xf32, #blocked2>
%c = triton_gpu.convert_layout %b : tensor<32xf32, #blocked2> -> tensor<32xf32, #blocked3>
tt.return %c : tensor<32xf32, #blocked3>
}
Expand All @@ -2116,7 +2116,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 :
// CHECK: tt.reshape
// CHECK: triton_gpu.convert_layout
%a = triton_gpu.convert_layout %arg0 : tensor<16x2xf32, #blocked> -> tensor<16x2xf32, #blocked1>
%b = tt.reshape %a {allow_reorder = false} : tensor<16x2xf32, #blocked1> -> tensor<32xf32, #blocked2>
%b = tt.reshape %a : tensor<16x2xf32, #blocked1> -> tensor<32xf32, #blocked2>
tt.return %b : tensor<32xf32, #blocked2>
}
}
Expand All @@ -2133,7 +2133,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 :
// CHECK-NOT: triton_gpu.convert_layout
// CHECK: arith.truncf
// CHECK: triton_gpu.convert_layout
%a = tt.reshape %arg0 {allow_reorder = true, efficient_layout} : tensor<16x2xf32, #blocked> -> tensor<32xf32, #blocked1>
%a = tt.reshape %arg0 allow_reorder efficient_layout : tensor<16x2xf32, #blocked> -> tensor<32xf32, #blocked1>
%b = triton_gpu.convert_layout %a : tensor<32xf32, #blocked1> -> tensor<32xf32, #blocked2>
%c = arith.truncf %b : tensor<32xf32, #blocked2> to tensor<32xf16, #blocked2>
tt.return %c : tensor<32xf16, #blocked2>
Expand Down Expand Up @@ -2536,9 +2536,9 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 2 :
%1 = tt.expand_dims %0 {axis = 0 : i32} : tensor<2xi32, #triton_gpu.slice<{dim = 0, parent = #triton_gpu.slice<{dim = 2, parent = #blocked2}>}>> -> tensor<1x2xi32, #triton_gpu.slice<{dim = 2, parent = #blocked2}>>
%2 = tt.expand_dims %1 {axis = 2 : i32} : tensor<1x2xi32, #triton_gpu.slice<{dim = 2, parent = #blocked2}>> -> tensor<1x2x1xi32, #blocked2>
%3 = tt.broadcast %2 : tensor<1x2x1xi32, #blocked2> -> tensor<1x2x128xi32, #blocked2>
%4 = tt.reshape %3 {allow_reorder = false} : tensor<1x2x128xi32, #blocked2> -> tensor<1x256xi32, #blocked1>
%4 = tt.reshape %3 : tensor<1x2x128xi32, #blocked2> -> tensor<1x256xi32, #blocked1>
%5 = tt.broadcast %2 : tensor<1x2x1xi32, #blocked2> -> tensor<2x2x64xi32, #blocked2>
%6 = tt.reshape %5 {allow_reorder = false} : tensor<2x2x64xi32, #blocked2> -> tensor<1x256xi32, #blocked1>
%6 = tt.reshape %5 : tensor<2x2x64xi32, #blocked2> -> tensor<1x256xi32, #blocked1>
%7 = arith.cmpi ne, %4, %cst : tensor<1x256xi32, #blocked1>
%8 = arith.select %7, %6, %cst : tensor<1x256xi1, #blocked1>, tensor<1x256xi32, #blocked1>
%9 = triton_gpu.convert_layout %8 : tensor<1x256xi32, #blocked1> -> tensor<1x256xi32, #blocked>
Expand Down
2 changes: 1 addition & 1 deletion test/TritonGPU/loop-pipeline-hip.mlir
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Expand Up @@ -221,7 +221,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 4 :
%33:3 = scf.for %arg7 = %c0_i32 to %c5_i32 step %c1_i32 iter_args(%arg8 = %cst, %arg9 = %arg0, %arg10 = %arg1) -> (tensor<64x1x32xf32, #blocked>, tensor<1x512x!tt.ptr<f32>, #blocked2>, tensor<64x8x32x!tt.ptr<f32>, #blocked1>) : i32 {
%39 = tt.load %arg9 : tensor<1x512x!tt.ptr<f32>, #blocked2>
%40 = tt.load %arg10 : tensor<64x8x32x!tt.ptr<f32>, #blocked1>
%41 = tt.reshape %39 {allow_reorder = true} : tensor<1x512xf32, #blocked2> -> tensor<64x1x8xf32, #blocked5>
%41 = tt.reshape %39 allow_reorder : tensor<1x512xf32, #blocked2> -> tensor<64x1x8xf32, #blocked5>
%43 = triton_gpu.convert_layout %41 : tensor<64x1x8xf32, #blocked5> -> tensor<64x1x8xf32, #triton_gpu.dot_op<{opIdx = 0, parent = #blocked}>>
%44 = triton_gpu.convert_layout %40 : tensor<64x8x32xf32, #blocked1> -> tensor<64x8x32xf32, #triton_gpu.dot_op<{opIdx = 1, parent = #blocked}>>
%45 = tt.dot %43, %44, %arg8 : tensor<64x1x8xf32, #triton_gpu.dot_op<{opIdx = 0, parent = #blocked}>> * tensor<64x8x32xf32, #triton_gpu.dot_op<{opIdx = 1, parent = #blocked}>> -> tensor<64x1x32xf32, #blocked>
Expand Down
2 changes: 1 addition & 1 deletion test/TritonGPU/loop-pipeline.mlir
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Expand Up @@ -1341,7 +1341,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 8 :
%84 = arith.sitofp %82 : tensor<64x256xi8, #blocked> to tensor<64x256xf16, #blocked>
%85 = tt.join %83, %84 : tensor<64x256xf16, #blocked> -> tensor<64x256x2xf16, #blocked3>
%86 = tt.trans %85 {order = array<i32: 0, 2, 1>} : tensor<64x256x2xf16, #blocked3> -> tensor<64x2x256xf16, #blocked4>
%87 = tt.reshape %86 {allow_reorder = false} : tensor<64x2x256xf16, #blocked4> -> tensor<128x256xf16, #blocked5>
%87 = tt.reshape %86 : tensor<64x2x256xf16, #blocked4> -> tensor<128x256xf16, #blocked5>
%88 = triton_gpu.convert_layout %78 : tensor<16x128xf16, #blocked1> -> tensor<16x128xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma, kWidth = 2}>>
%89 = triton_gpu.convert_layout %87 : tensor<128x256xf16, #blocked5> -> tensor<128x256xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma, kWidth = 2}>>
%90 = tt.dot %88, %89, %arg10 : tensor<16x128xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma, kWidth = 2}>> * tensor<128x256xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma, kWidth = 2}>> -> tensor<16x256xf32, #mma>
Expand Down
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