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Merge pull request #158 from Xilinx/matthias.tosa_matmul
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TorchToTosa: Emit tosa.matmul with legal types
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@mgehre-amd
mgehre-amd authored May 10, 2024
2 parents 6e513b1 + e0bdfee commit f73514a
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Showing 2 changed files with 68 additions and 62 deletions.
41 changes: 15 additions & 26 deletions lib/Conversion/TorchToTosa/TorchToTosa.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -1173,8 +1173,8 @@ class ConvertAtenMatmulBaseOp : public OpConversionPattern<AtenOpT> {
return rewriter.notifyMatchFailure(op,
"Matmul: input datatypes mismatched");

auto outputElemType = getMatMulOutputType(lhsElemTy, rewriter);
if (!outputElemType) {
auto outputElemTy = getMatMulOutputType(lhsElemTy, rewriter);
if (!outputElemTy) {
return rewriter.notifyMatchFailure(
op, "Only i8 and i16 integer and bf16, f16 and "
"f32 float types are valid");
Expand Down Expand Up @@ -1553,12 +1553,6 @@ class ConvertAtenMatmulBaseOp : public OpConversionPattern<AtenOpT> {

SmallVector<int64_t> matmulOutputShape(
{matmulLhsShape[0], matmulLhsShape[1], matmulRhsShape[2]});
Type outputElemTy;
if (lhsElemTy.isa<mlir::FloatType>()) {
outputElemTy = lhsElemTy;
} else { // qint8 emits i32 matmul output
outputElemTy = rewriter.getIntegerType(32);
}

auto mmOutputTy = RankedTensorType::get(
makeShapeLLVMCompatible(matmulOutputShape), outputElemTy);
Expand All @@ -1571,6 +1565,14 @@ class ConvertAtenMatmulBaseOp : public OpConversionPattern<AtenOpT> {
matmulLhs, matmulRhs)
.getResult();

auto castOutputTy = RankedTensorType::get(
makeShapeLLVMCompatible(matmulOutputShape), lhsElemTy);
auto castResult = rewriter.createOrFold<tosa::CastOp>(
op->getLoc(),
OpConversionPattern<AtenOpT>::getTypeConverter()
->convertType(castOutputTy),
mmOpResult);

// Perform the reshape to output shape. This is always required unless max
// input rank=3 and there was no broadcasting, in which case the tosa.matmul
// output itself is correctly shaped.
Expand Down Expand Up @@ -1671,12 +1673,12 @@ class ConvertAtenMatmulBaseOp : public OpConversionPattern<AtenOpT> {

// Perform reshape
auto reshapedOpType = RankedTensorType::get(
makeShapeLLVMCompatible(reshapedOpShape), outputElemTy);
makeShapeLLVMCompatible(reshapedOpShape), lhsElemTy);
auto reshapedOp = rewriter.create<tosa::ReshapeOp>(
op->getLoc(),
OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
reshapedOpType),
mmOpResult, rewriter.getDenseI64ArrayAttr(reshapedOpShape));
castResult, rewriter.getDenseI64ArrayAttr(reshapedOpShape));

if (opNeedsTranspose) {

Expand All @@ -1700,7 +1702,7 @@ class ConvertAtenMatmulBaseOp : public OpConversionPattern<AtenOpT> {
output = reshapedOp.getResult();
}
} else {
output = mmOpResult;
output = castResult;
}

return success();
Expand All @@ -1722,13 +1724,7 @@ class ConvertAtenMatmulBaseOp : public OpConversionPattern<AtenOpT> {
return rewriter.notifyMatchFailure(op,
"Failed to perform matmul operation");

rewriter.replaceOpWithNewOp<tensor::CastOp>(
op,
OpConversionPattern<AtenOpT>::getTypeConverter()
->convertType(op.getType())
.template cast<RankedTensorType>(),
output);

rewriter.replaceOp(op, output);
return success();
}
};
Expand Down Expand Up @@ -1898,14 +1894,7 @@ class ConvertAtenLinearOp : public ConvertAtenMatmulBaseOp<AtenOpT> {
matmulOutput, bias)
.getResult();
}

rewriter.replaceOpWithNewOp<tensor::CastOp>(
op,
OpConversionPattern<AtenOpT>::getTypeConverter()
->convertType(op.getType())
.template cast<RankedTensorType>(),
matmulPlusBias);

rewriter.replaceOp(op, matmulPlusBias);
return success();
}
};
Expand Down
89 changes: 53 additions & 36 deletions test/Conversion/TorchToTosa/basic.mlir
View file
Original file line number Diff line number Diff line change
Expand Up @@ -30,9 +30,9 @@ func.func @torch.aten.sigmoid$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.
// CHECK-NEXT: %[[VAL_3:.+]] = tosa.reshape %1 {new_shape = array<i64: 1, 8, 16>} : (tensor<8x16xf32>) -> tensor<1x8x16xf32>
// CHECK-NEXT: %[[VAL_4:.+]] = tosa.matmul %[[VAL_2]], %[[VAL_3]] : (tensor<1x4x8xf32>, tensor<1x8x16xf32>) -> tensor<1x4x16xf32>
// CHECK-NEXT: %[[VAL_5:.+]] = tosa.reshape %[[VAL_4]] {new_shape = array<i64: 4, 16>} : (tensor<1x4x16xf32>) -> tensor<4x16xf32>
func.func @torch.aten.mm$basic(%arg0: !torch.vtensor<[4,8],f32>, %arg1: !torch.vtensor<[8,16],f32>) -> !torch.vtensor<[?,?],f32> {
%0 = torch.aten.mm %arg0, %arg1 : !torch.vtensor<[4,8],f32>, !torch.vtensor<[8,16],f32> -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
func.func @torch.aten.mm$basic(%arg0: !torch.vtensor<[4,8],f32>, %arg1: !torch.vtensor<[8,16],f32>) -> !torch.vtensor<[4,16],f32> {
%0 = torch.aten.mm %arg0, %arg1 : !torch.vtensor<[4,8],f32>, !torch.vtensor<[8,16],f32> -> !torch.vtensor<[4,16],f32>
return %0 : !torch.vtensor<[4,16],f32>
}

// -----
Expand All @@ -55,9 +55,9 @@ func.func @torch.aten.matmul_1d(%arg0 : !torch.vtensor<[6],f32>, %arg1 : !torch.
// CHECK-NEXT: %[[VAL_4:.+]] = tosa.reshape %1 {new_shape = array<i64: 1, 6, 1>} : (tensor<6x1xf32>) -> tensor<1x6x1xf32>
// CHECK-NEXT: %[[VAL_5:.+]] = tosa.matmul %[[VAL_3]], %[[VAL_4]] : (tensor<1x1x6xf32>, tensor<1x6x1xf32>) -> tensor<1x1x1xf32>
// CHECK-NEXT: %[[VAL_6:.+]] = tosa.reshape %[[VAL_5]] {new_shape = array<i64: 1>} : (tensor<1x1x1xf32>) -> tensor<1xf32>
func.func @torch.aten.matmul_12d(%arg0 : !torch.vtensor<[6],f32>, %arg1 : !torch.vtensor<[6,1],f32>) -> !torch.vtensor<[?],f32> {
%0 = torch.aten.matmul %arg0, %arg1 : !torch.vtensor<[6],f32>, !torch.vtensor<[6,1],f32> -> !torch.vtensor<[?],f32>
return %0 : !torch.vtensor<[?],f32>
func.func @torch.aten.matmul_12d(%arg0 : !torch.vtensor<[6],f32>, %arg1 : !torch.vtensor<[6,1],f32>) -> !torch.vtensor<[1],f32> {
%0 = torch.aten.matmul %arg0, %arg1 : !torch.vtensor<[6],f32>, !torch.vtensor<[6,1],f32> -> !torch.vtensor<[1],f32>
return %0 : !torch.vtensor<[1],f32>
}

// -----
Expand All @@ -67,9 +67,9 @@ func.func @torch.aten.matmul_12d(%arg0 : !torch.vtensor<[6],f32>, %arg1 : !torch
// CHECK-NEXT: %[[VAL_4:.+]] = tosa.reshape %[[VAL_2]] {new_shape = array<i64: 1, 6, 1>} : (tensor<6x1xf32>) -> tensor<1x6x1xf32>
// CHECK-NEXT: %[[VAL_5:.+]] = tosa.matmul %[[VAL_3]], %[[VAL_4]] : (tensor<1x2x6xf32>, tensor<1x6x1xf32>) -> tensor<1x2x1xf32>
// CHECK-NEXT: %[[VAL_6:.+]] = tosa.reshape %[[VAL_5]] {new_shape = array<i64: 2>} : (tensor<1x2x1xf32>) -> tensor<2xf32>
func.func @torch.aten.matmul_21d(%arg0 : !torch.vtensor<[2,6],f32>, %arg1 : !torch.vtensor<[6],f32>) -> !torch.vtensor<[?],f32> {
%0 = torch.aten.matmul %arg0, %arg1 : !torch.vtensor<[2,6],f32>, !torch.vtensor<[6],f32> -> !torch.vtensor<[?],f32>
return %0 : !torch.vtensor<[?],f32>
func.func @torch.aten.matmul_21d(%arg0 : !torch.vtensor<[2,6],f32>, %arg1 : !torch.vtensor<[6],f32>) -> !torch.vtensor<[2],f32> {
%0 = torch.aten.matmul %arg0, %arg1 : !torch.vtensor<[2,6],f32>, !torch.vtensor<[6],f32> -> !torch.vtensor<[2],f32>
return %0 : !torch.vtensor<[2],f32>
}

// -----
Expand All @@ -78,9 +78,9 @@ func.func @torch.aten.matmul_21d(%arg0 : !torch.vtensor<[2,6],f32>, %arg1 : !tor
// CHECK-NEXT: %[[VAL_3:.+]] = tosa.reshape %1 {new_shape = array<i64: 1, 6, 8>} : (tensor<6x8xf32>) -> tensor<1x6x8xf32>
// CHECK-NEXT: %[[VAL_4:.+]] = tosa.matmul %[[VAL_2]], %[[VAL_3]] : (tensor<1x2x6xf32>, tensor<1x6x8xf32>) -> tensor<1x2x8xf32>
// CHECK-NEXT: %[[VAL_5:.+]] = tosa.reshape %[[VAL_4]] {new_shape = array<i64: 2, 8>} : (tensor<1x2x8xf32>) -> tensor<2x8xf32>
func.func @torch.aten.mm_2d(%arg0 : !torch.vtensor<[2,6],f32>, %arg1 : !torch.vtensor<[6,8],f32>) -> !torch.vtensor<[?,?],f32> {
%0 = torch.aten.mm %arg0, %arg1 : !torch.vtensor<[2,6],f32>, !torch.vtensor<[6,8],f32> -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
func.func @torch.aten.mm_2d(%arg0 : !torch.vtensor<[2,6],f32>, %arg1 : !torch.vtensor<[6,8],f32>) -> !torch.vtensor<[2,8],f32> {
%0 = torch.aten.mm %arg0, %arg1 : !torch.vtensor<[2,6],f32>, !torch.vtensor<[6,8],f32> -> !torch.vtensor<[2,8],f32>
return %0 : !torch.vtensor<[2,8],f32>
}

// -----
Expand All @@ -89,9 +89,27 @@ func.func @torch.aten.mm_2d(%arg0 : !torch.vtensor<[2,6],f32>, %arg1 : !torch.vt
// CHECK-NEXT: %[[VAL_3:.+]] = tosa.reshape %1 {new_shape = array<i64: 100, 2, 6>} : (tensor<10x10x2x6xf32>) -> tensor<100x2x6xf32>
// CHECK-NEXT: %[[VAL_4:.+]] = tosa.matmul %[[VAL_2]], %[[VAL_3]] : (tensor<100x6x2xf32>, tensor<100x2x6xf32>) -> tensor<100x6x6xf32>
// CHECK-NEXT: %[[VAL_5:.+]] = tosa.reshape %[[VAL_4]] {new_shape = array<i64: 10, 10, 6, 6>} : (tensor<100x6x6xf32>) -> tensor<10x10x6x6xf32>
func.func @torch.aten.matmul_4d(%arg0 : !torch.vtensor<[10,10,6,2],f32>, %arg1 : !torch.vtensor<[10,10,2,6],f32>) -> !torch.vtensor<[?,?,?,?],f32> {
%0 = torch.aten.matmul %arg0, %arg1 : !torch.vtensor<[10,10,6,2],f32>, !torch.vtensor<[10,10,2,6],f32> -> !torch.vtensor<[?,?,?,?],f32>
return %0 : !torch.vtensor<[?,?,?,?],f32>
func.func @torch.aten.matmul_4d(%arg0 : !torch.vtensor<[10,10,6,2],f32>, %arg1 : !torch.vtensor<[10,10,2,6],f32>) -> !torch.vtensor<[10,10,6,6],f32> {
%0 = torch.aten.matmul %arg0, %arg1 : !torch.vtensor<[10,10,6,2],f32>, !torch.vtensor<[10,10,2,6],f32> -> !torch.vtensor<[10,10,6,6],f32>
return %0 : !torch.vtensor<[10,10,6,6],f32>
}

// -----

// CHECK: %[[VAL_2:.+]] = tosa.reshape %0 {new_shape = array<i64: 1, 10, 6, 2>} : (tensor<10x6x2xf32>) -> tensor<1x10x6x2xf32>
// CHECK-NEXT: %[[VAL_3:.+]] = "tosa.const"() <{value = dense<[1, 0, 2, 3]> : tensor<4xi32>}> : () -> tensor<4xi32>
// CHECK-NEXT: %[[VAL_4:.+]] = tosa.transpose %[[VAL_2]], %[[VAL_3]] : (tensor<1x10x6x2xf32>, tensor<4xi32>) -> tensor<10x1x6x2xf32>
// CHECK-NEXT: %[[VAL_5:.+]] = tosa.reshape %[[VAL_4]] {new_shape = array<i64: 10, 6, 2>} : (tensor<10x1x6x2xf32>) -> tensor<10x6x2xf32>
// CHECK-NEXT: %[[VAL_6:.+]] = "tosa.const"() <{value = dense<[1, 2, 0, 3]> : tensor<4xi32>}> : () -> tensor<4xi32>
// CHECK-NEXT: %[[VAL_7:.+]] = tosa.transpose %1, %[[VAL_6]] : (tensor<10x10x2x6xf32>, tensor<4xi32>) -> tensor<10x2x10x6xf32>
// CHECK-NEXT: %[[VAL_8:.+]] = tosa.reshape %[[VAL_7]] {new_shape = array<i64: 10, 2, 60>} : (tensor<10x2x10x6xf32>) -> tensor<10x2x60xf32>
// CHECK-NEXT: %[[VAL_9:.+]] = tosa.matmul %[[VAL_5]], %[[VAL_8]] : (tensor<10x6x2xf32>, tensor<10x2x60xf32>) -> tensor<10x6x60xf32>
// CHECK-NEXT: %[[VAL_10:.+]] = tosa.reshape %[[VAL_9]] {new_shape = array<i64: 10, 6, 10, 6>} : (tensor<10x6x60xf32>) -> tensor<10x6x10x6xf32>
// CHECK-NEXT: %[[VAL_11:.+]] = "tosa.const"() <{value = dense<[2, 0, 1, 3]> : tensor<4xi32>}> : () -> tensor<4xi32>
// CHECK-NEXT: %[[VAL_12:.+]] = tosa.transpose %[[VAL_10]], %[[VAL_11]] : (tensor<10x6x10x6xf32>, tensor<4xi32>) -> tensor<10x10x6x6xf32>
func.func @torch.aten.matmul_4d_broadcast(%arg0 : !torch.vtensor<[10,6,2],f32>, %arg1 : !torch.vtensor<[10,10,2,6],f32>) -> !torch.vtensor<[10,10,6,6],f32> {
%0 = torch.aten.matmul %arg0, %arg1 : !torch.vtensor<[10,6,2],f32>, !torch.vtensor<[10,10,2,6],f32> -> !torch.vtensor<[10,10,6,6],f32>
return %0 : !torch.vtensor<[10,10,6,6],f32>
}

// -----
Expand All @@ -104,9 +122,9 @@ func.func @torch.aten.matmul_4d(%arg0 : !torch.vtensor<[10,10,6,2],f32>, %arg1 :
// CHECK-NEXT: %[[VAL_7:.+]] = tosa.reshape %[[VAL_6]] {new_shape = array<i64: 4, 5, 3, 7>} : (tensor<1x20x21xf32>) -> tensor<4x5x3x7xf32>
// CHECK-NEXT: %[[VAL_8:.+]] = "tosa.const"() <{value = dense<[0, 2, 1, 3]> : tensor<4xi32>}> : () -> tensor<4xi32>
// CHECK-NEXT: %[[VAL_9:.+]] = tosa.transpose %[[VAL_7]], %[[VAL_8]] : (tensor<4x5x3x7xf32>, tensor<4xi32>) -> tensor<4x3x5x7xf32>
func.func @torch.aten.matmul_4d_broadcast_2(%arg0 : !torch.vtensor<[4,1,5,6],f32>, %arg1 : !torch.vtensor<[1,3,6,7],f32>) -> !torch.vtensor<[?,?,?,?],f32> {
%0 = torch.aten.matmul %arg0, %arg1 : !torch.vtensor<[4,1,5,6],f32>, !torch.vtensor<[1,3,6,7],f32> -> !torch.vtensor<[?,?,?,?],f32>
return %0 : !torch.vtensor<[?,?,?,?],f32>
func.func @torch.aten.matmul_4d_broadcast_2(%arg0 : !torch.vtensor<[4,1,5,6],f32>, %arg1 : !torch.vtensor<[1,3,6,7],f32>) -> !torch.vtensor<[4,3,5,7],f32> {
%0 = torch.aten.matmul %arg0, %arg1 : !torch.vtensor<[4,1,5,6],f32>, !torch.vtensor<[1,3,6,7],f32> -> !torch.vtensor<[4,3,5,7],f32>
return %0 : !torch.vtensor<[4,3,5,7],f32>
}

// -----
Expand All @@ -118,38 +136,37 @@ func.func @torch.aten.matmul_4d_broadcast_2(%arg0 : !torch.vtensor<[4,1,5,6],f32
// CHECK-NEXT: %[[VAL_6:.+]] = tosa.reshape %[[VAL_5]] {new_shape = array<i64: 1, 8, 16>} : (tensor<8x1x16xf32>) -> tensor<1x8x16xf32>
// CHECK-NEXT: %[[VAL_7:.+]] = tosa.matmul %[[VAL_3]], %[[VAL_6]] : (tensor<1x400x8xf32>, tensor<1x8x16xf32>) -> tensor<1x400x16xf32>
// CHECK-NEXT: %[[VAL_8:.+]] = tosa.reshape %[[VAL_7]] {new_shape = array<i64: 100, 4, 16>} : (tensor<1x400x16xf32>) -> tensor<100x4x16xf32>
func.func @torch.aten.matmul_3d_broadcast(%arg0 : !torch.vtensor<[100,4,8],f32>, %arg1 : !torch.vtensor<[8,16],f32>) -> !torch.vtensor<[?,?,?],f32> {
%0 = torch.aten.matmul %arg0, %arg1 : !torch.vtensor<[100,4,8],f32>, !torch.vtensor<[8,16],f32> -> !torch.vtensor<[?,?,?],f32>
return %0 : !torch.vtensor<[?,?,?],f32>
func.func @torch.aten.matmul_3d_broadcast(%arg0 : !torch.vtensor<[100,4,8],f32>, %arg1 : !torch.vtensor<[8,16],f32>) -> !torch.vtensor<[100,4,16],f32> {
%0 = torch.aten.matmul %arg0, %arg1 : !torch.vtensor<[100,4,8],f32>, !torch.vtensor<[8,16],f32> -> !torch.vtensor<[100,4,16],f32>
return %0 : !torch.vtensor<[100,4,16],f32>
}

// -----

// CHECK-LABEL: torch.aten.bmm_3d_fp16
// CHECK: %[[VAL_2:.+]] = tosa.matmul %0, %1 : (tensor<100x4x8xf16>, tensor<100x8x16xf16>) -> tensor<100x4x16xf16>
func.func @torch.aten.bmm_3d_fp16(%arg0 : !torch.vtensor<[100,4,8],f16>, %arg1 : !torch.vtensor<[100,8,16],f16>) -> !torch.vtensor<[?,?,?],f16> {
%0 = torch.aten.bmm %arg0, %arg1 : !torch.vtensor<[100,4,8],f16>, !torch.vtensor<[100,8,16],f16> -> !torch.vtensor<[?,?,?],f16>
return %0 : !torch.vtensor<[?,?,?],f16>
// CHECK: %[[VAL_2:.+]] = tosa.matmul %0, %1 : (tensor<100x4x8xf16>, tensor<100x8x16xf16>) -> tensor<100x4x16xf32>
// CHECK-NEXT: %[[VAL_3:.+]] = tosa.cast %[[VAL_2]] : (tensor<100x4x16xf32>) -> tensor<100x4x16xf16>
func.func @torch.aten.bmm_3d_fp16(%arg0 : !torch.vtensor<[100,4,8],f16>, %arg1 : !torch.vtensor<[100,8,16],f16>) -> !torch.vtensor<[100,4,16],f16> {
%0 = torch.aten.bmm %arg0, %arg1 : !torch.vtensor<[100,4,8],f16>, !torch.vtensor<[100,8,16],f16> -> !torch.vtensor<[100,4,16],f16>
return %0 : !torch.vtensor<[100,4,16],f16>
}

// -----
// CHECK-LABEL: torch.aten.bmm_3d_bf16
// CHECK: %[[VAL_2:.+]] = tosa.matmul %0, %1 : (tensor<100x4x8xbf16>, tensor<100x8x16xbf16>) -> tensor<100x4x16xbf16>
func.func @torch.aten.bmm_3d_bf16(%arg0 : !torch.vtensor<[100,4,8],bf16>, %arg1 : !torch.vtensor<[100,8,16],bf16>) -> !torch.vtensor<[?,?,?],bf16> {
%0 = torch.aten.bmm %arg0, %arg1 : !torch.vtensor<[100,4,8],bf16>, !torch.vtensor<[100,8,16],bf16> -> !torch.vtensor<[?,?,?],bf16>
return %0 : !torch.vtensor<[?,?,?],bf16>

// CHECK: %[[VAL_2:.+]] = tosa.matmul %0, %1 : (tensor<100x4x8xbf16>, tensor<100x8x16xbf16>) -> tensor<100x4x16xf32>
// CHECK-NEXT: %[[VAL_3:.+]] = tosa.cast %[[VAL_2]] : (tensor<100x4x16xf32>) -> tensor<100x4x16xbf16>
func.func @torch.aten.bmm_3d_bf16(%arg0 : !torch.vtensor<[100,4,8],bf16>, %arg1 : !torch.vtensor<[100,8,16],bf16>) -> !torch.vtensor<[100,4,16],bf16> {
%0 = torch.aten.bmm %arg0, %arg1 : !torch.vtensor<[100,4,8],bf16>, !torch.vtensor<[100,8,16],bf16> -> !torch.vtensor<[100,4,16],bf16>
return %0 : !torch.vtensor<[100,4,16],bf16>
}

// -----

// CHECK: %[[VAL_2:.+]] = tosa.matmul %0, %1 : (tensor<100x4x8xf32>, tensor<100x8x16xf32>) -> tensor<100x4x16xf32>
func.func @torch.aten.bmm_3d_fp32(%arg0 : !torch.vtensor<[100,4,8],f32>, %arg1 : !torch.vtensor<[100,8,16],f32>) -> !torch.vtensor<[?,?,?],f32> {
%0 = torch.aten.bmm %arg0, %arg1 : !torch.vtensor<[100,4,8],f32>, !torch.vtensor<[100,8,16],f32> -> !torch.vtensor<[?,?,?],f32>
return %0 : !torch.vtensor<[?,?,?],f32>
func.func @torch.aten.bmm_3d_fp32(%arg0 : !torch.vtensor<[100,4,8],f32>, %arg1 : !torch.vtensor<[100,8,16],f32>) -> !torch.vtensor<[100,4,16],f32> {
%0 = torch.aten.bmm %arg0, %arg1 : !torch.vtensor<[100,4,8],f32>, !torch.vtensor<[100,8,16],f32> -> !torch.vtensor<[100,4,16],f32>
return %0 : !torch.vtensor<[100,4,16],f32>
}



// -----

// CHECK-LABEL: func.func @torch.aten.relu$basic(
Expand Down

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