[TOSA] Change PadOp padding to tosa.shape #123133
Merged
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
|
@llvm/pr-subscribers-mlir-tosa @llvm/pr-subscribers-mlir Author: Jerry-Ge (Jerry-Ge) ChangesThis patch changes PadOp's padding input to type !tosa.shape<2 * rank>, (where rank is the rank of the PadOp's input), instead of a <rank x 2> tensor. This patch is also a part of TOSA v1.0 effort: https://discourse.llvm.org/t/rfc-tosa-dialect-increment-to-v1-0/83708 Patch is 42.20 KiB, truncated to 20.00 KiB below, full version: https://github.com/llvm/llvm-project/pull/123133.diff 17 Files Affected:
diff --git a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.h b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.h
index e4f5d09064cd75..d54d4e17de8b54 100644
--- a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.h
+++ b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.h
@@ -39,6 +39,8 @@ ParseResult parseTypeOrAttr(OpAsmParser &parser, TypeAttr &typeAttr,
void printTypeOrAttr(OpAsmPrinter &p, Operation *op, TypeAttr type,
Attribute attr);
+bool collectShapeValue(Operation* op, llvm::SmallVector<int64_t>& newShape);
+
#include "mlir/Dialect/Tosa/IR/TosaInterfaces.h.inc"
} // namespace tosa
diff --git a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
index e1efa7a3001b9f..2953e006bbe8d1 100644
--- a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
+++ b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
@@ -1557,21 +1557,21 @@ def Tosa_PadOp : Tosa_InferShapedTypeOp<"pad"> {
Example:
```mlir
- %0 = arith.constant dense<[1, 2, 3, 4]> : tensor<4xi32>
- tosa.pad %arg0, %0 : (tensor<1x2xf32>, tensor<4xi32>) -> (tensor<4x9xf32>)
+ %0 = tosa.const_shape { value = dense<[1, 2, 3, 4]> : tensor<4xindex> } : () -> !tosa.shape<4>
+ tosa.pad %arg0, %0 : (tensor<1x2xf32>, !tosa.shape<4>) -> (tensor<4x9xf32>)
```
Example 2:
```mlir
- %0 = arith.constant dense<[-1, 2, 3, 4]> : tensor<4xi32>
- tosa.pad %arg0, %0 : (tensor<1x2xf32>, tensor<4xi32>) -> (tensor<?x9xf32>)
+ %0 = tosa.const_shape { value = dense<[-1, 2, 3, 4]> : tensor<4xindex> } : () -> !tosa.shape<4>
+ tosa.pad %arg0, %0 : (tensor<1x2xf32>, !tosa.shape<4>) -> (tensor<?x9xf32>)
```
}];
let arguments = (ins
Tosa_RankedTensor:$input1,
- TosaTensorRankOf<[Tosa_Int32Or64], [1]>:$padding,
+ Tosa_Shape:$padding,
Optional<Tosa_ScalarTensor>:$pad_const,
OptionalAttr<Tosa_PadOpQuantizationAttr>:$quantization_info
);
diff --git a/mlir/include/mlir/Dialect/Tosa/Utils/ConversionUtils.h b/mlir/include/mlir/Dialect/Tosa/Utils/ConversionUtils.h
index 90fea1f68beb58..9b406f1083135c 100644
--- a/mlir/include/mlir/Dialect/Tosa/Utils/ConversionUtils.h
+++ b/mlir/include/mlir/Dialect/Tosa/Utils/ConversionUtils.h
@@ -229,6 +229,11 @@ SmallVector<T> applyTOSAPermutation(ArrayRef<T> input,
return permuted;
}
+// Computes shape value using tosa const_shape op.
+Value getTosaConstShape(PatternRewriter& rewriter, Location loc,
+ llvm::ArrayRef<int64_t> shape);
+SmallVector<int64_t> convertFromMlirShape(ArrayRef<int64_t> shape);
+
} // namespace tosa
} // namespace mlir
diff --git a/mlir/lib/Conversion/TosaToTensor/TosaToTensor.cpp b/mlir/lib/Conversion/TosaToTensor/TosaToTensor.cpp
index b5a0da15e780e0..5aa0269a675cbe 100644
--- a/mlir/lib/Conversion/TosaToTensor/TosaToTensor.cpp
+++ b/mlir/lib/Conversion/TosaToTensor/TosaToTensor.cpp
@@ -306,7 +306,16 @@ class PadConverter : public OpConversionPattern<tosa::PadOp> {
ConversionPatternRewriter &rewriter) const final {
auto loc = padOp.getLoc();
auto input = padOp.getInput1();
- auto padding = padOp.getPadding();
+
+ ElementsAttr paddingElems;
+ if (!matchPattern(padOp.getPadding(), m_Constant(&paddingElems))) {
+ return rewriter.notifyMatchFailure(
+ padOp, "padding must be a static shape value");
+ }
+ llvm::SmallVector<int64_t> paddingVals;
+ for (auto idx : paddingElems.getValues<IntegerAttr>()) {
+ paddingVals.push_back(static_cast<int64_t>(idx.getInt()));
+ }
ShapedType inputTy = cast<ShapedType>(input.getType());
Type elementTy = inputTy.getElementType();
@@ -345,18 +354,10 @@ class PadConverter : public OpConversionPattern<tosa::PadOp> {
highValues.reserve(rank);
for (int i = 0; i < rank; i++) {
- Value lowIndex = rewriter.create<arith::ConstantIndexOp>(loc, 2 * i);
- Value highIndex = rewriter.create<arith::ConstantIndexOp>(loc, 2 * i + 1);
- Value lowVal = rewriter.createOrFold<tensor::ExtractOp>(
- loc, padding, ValueRange({lowIndex}));
- Value highVal = rewriter.createOrFold<tensor::ExtractOp>(
- loc, padding, ValueRange({highIndex}));
-
- lowVal = rewriter.createOrFold<arith::IndexCastOp>(
- loc, rewriter.getIndexType(), lowVal);
- highVal = rewriter.createOrFold<arith::IndexCastOp>(
- loc, rewriter.getIndexType(), highVal);
-
+ Value lowVal = rewriter.create<arith::ConstantOp>(
+ loc, rewriter.getIndexAttr(paddingVals[2 * i]));
+ Value highVal = rewriter.create<arith::ConstantOp>(
+ loc, rewriter.getIndexAttr(paddingVals[2 * i + 1]));
lowValues.push_back(lowVal);
highValues.push_back(highVal);
}
diff --git a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
index 83cf4a9415fe68..ee0f31652ba36a 100644
--- a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
+++ b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
@@ -210,6 +210,26 @@ void mlir::tosa::printTypeOrAttr(OpAsmPrinter &p, Operation *op, TypeAttr type,
}
}
+//===----------------------------------------------------------------------===//
+// TOSA shape inference helper
+//===----------------------------------------------------------------------===//
+bool mlir::tosa::collectShapeValue(Operation* op, llvm::SmallVector<int64_t>& newShape) {
+ if (!op) {
+ return false;
+ }
+ if (auto constOp = mlir::dyn_cast<tosa::ConstShapeOp>(op)) {
+ Attribute constOpAttr = constOp->getAttr("value");
+ DenseElementsAttr elementsAttr = cast<DenseElementsAttr>(constOpAttr);
+ for (int i = 0; i < elementsAttr.size(); i++) {
+ int64_t val = elementsAttr.getValues<int64_t>()[i];
+ newShape.push_back(val);
+ }
+ return true;
+ }
+ // for undefined op, return false.
+ return false;
+}
+
//===----------------------------------------------------------------------===//
// TOSA Operator Verifiers.
//===----------------------------------------------------------------------===//
@@ -823,51 +843,42 @@ LogicalResult tosa::PadOp::inferReturnTypeComponents(
PadOp::Adaptor adaptor,
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
ShapeAdaptor inputShape(adaptor.getInput1().getType());
- ShapeAdaptor paddingShape(adaptor.getPadding().getType());
+ auto paddingRank =
+ cast<tosa::shapeType>(adaptor.getPadding().getType()).getRank();
SmallVector<int64_t> outputShape;
- // If both inputs have unknown shape, we cannot determine the shape of the
- // output.
- if (!inputShape.hasRank() && !paddingShape.hasRank()) {
- inferredReturnShapes.push_back(ShapedTypeComponents());
- return success();
- }
-
- // If the input rank is unknown we can info the output rank using the
- // padding shape's first dim.
+ // If the input rank is unknown, we can infer the output rank using the
+ // padding shape's rank divided by 2.
if (!inputShape.hasRank()) {
- if (paddingShape.isDynamicDim(0)) {
- inferredReturnShapes.push_back(ShapedTypeComponents());
- return success();
- }
-
- outputShape.resize(paddingShape.getDimSize(0) / 2, ShapedType::kDynamic);
+ outputShape.resize(paddingRank / 2, ShapedType::kDynamic);
inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
return success();
}
- DenseIntElementsAttr paddings;
+ SmallVector<int64_t> paddingValues;
// If the paddings value is not a constant, all dimensions must be dynamic.
- if (!matchPattern(adaptor.getPadding(), m_Constant(&paddings))) {
+ if (!tosa::collectShapeValue(adaptor.getPadding().getDefiningOp(),
+ paddingValues)) {
outputShape.resize(inputShape.getRank(), ShapedType::kDynamic);
inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
return success();
}
- SmallVector<int64_t> paddingValues;
- for (auto val : paddings) {
- paddingValues.push_back(val.getSExtValue());
- }
-
outputShape.reserve(inputShape.getRank());
for (int i = 0, s = inputShape.getRank(); i < s; i++) {
if (inputShape.isDynamicDim(i)) {
outputShape.push_back(ShapedType::kDynamic);
continue;
}
+ auto padFront = paddingValues[i * 2];
+ auto padBack = paddingValues[i * 2 + 1];
+ if (padFront < 0 || padBack < 0) {
+ // if either padding for dim i is -1, output dim is unknown
+ outputShape.push_back(ShapedType::kDynamic);
+ continue;
+ }
- outputShape.push_back(inputShape.getDimSize(i) + paddingValues[i * 2] +
- paddingValues[i * 2 + 1]);
+ outputShape.push_back(inputShape.getDimSize(i) + padFront + padBack);
}
inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
@@ -877,17 +888,16 @@ LogicalResult tosa::PadOp::inferReturnTypeComponents(
LogicalResult tosa::PadOp::verify() {
RankedTensorType inputType = getInput1().getType();
RankedTensorType outputType = getOutput().getType();
- RankedTensorType paddingType = getPadding().getType();
+ auto paddingRank = cast<tosa::shapeType>(getPadding().getType()).getRank();
if (inputType.getRank() != outputType.getRank())
return emitOpError() << "expect same input and output tensor rank.";
-
- if (!paddingType.isDynamicDim(0) &&
- paddingType.getDimSize(0) != inputType.getRank() * 2)
+
+ if (paddingRank != inputType.getRank() * 2)
return emitOpError() << "expected padding tensor dim 0 to have size "
<< inputType.getRank() * 2
<< " (2*rank(shape1)) but got size "
- << paddingType.getDimSize(0);
+ << paddingRank;
return success();
}
diff --git a/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeConv2D.cpp b/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeConv2D.cpp
index 04a709c5967795..cb08360f902286 100644
--- a/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeConv2D.cpp
+++ b/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeConv2D.cpp
@@ -81,11 +81,7 @@ struct Conv2DIsFullyConnected : public OpRewritePattern<tosa::Conv2DOp> {
}
}
- auto padSizeTy = RankedTensorType::get({8}, rewriter.getI64Type());
- auto padSize =
- DenseIntElementsAttr::get(padSizeTy, ArrayRef<int64_t>(pad));
- Value padSizeVal =
- rewriter.create<tosa::ConstOp>(op->getLoc(), padSizeTy, padSize);
+ Value padSizeVal = getTosaConstShape(rewriter, op->getLoc(), pad);
auto padTy = RankedTensorType::get({}, inputETy);
auto padAttr = DenseElementsAttr::get(padTy, zeroAttr);
diff --git a/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeDepthwise.cpp b/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeDepthwise.cpp
index 14f392ab8c45c1..45f4419875b485 100644
--- a/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeDepthwise.cpp
+++ b/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeDepthwise.cpp
@@ -108,11 +108,7 @@ struct DepthwiseConv2DIsMul : public OpRewritePattern<tosa::DepthwiseConv2DOp> {
}
}
- auto padSizeTy = RankedTensorType::get({10}, rewriter.getI64Type());
- auto padSize =
- DenseIntElementsAttr::get(padSizeTy, ArrayRef<int64_t>(pad));
- Value padSizeVal =
- rewriter.create<tosa::ConstOp>(op->getLoc(), padSizeTy, padSize);
+ Value padSizeVal = getTosaConstShape(rewriter, op->getLoc(), pad);
auto padTy = RankedTensorType::get({}, inputETy);
auto padAttr = DenseElementsAttr::get(padTy, zeroAttr);
diff --git a/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeTransposeConv.cpp b/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeTransposeConv.cpp
index db1e219b601b30..1b97f0b245d9ba 100644
--- a/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeTransposeConv.cpp
+++ b/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeTransposeConv.cpp
@@ -135,15 +135,14 @@ class TransposeConvStridedConverter
int64_t inputChannels = weightTy.getDimSize(3);
// Pad the weight so that it is modulo of the striding.
- llvm::SmallVector<int32_t, 8> weightPadding = {0, 0, 0, 0, 0, 0, 0, 0};
+ llvm::SmallVector<int64_t, 8> weightPadding = {0, 0, 0, 0, 0, 0, 0, 0};
weightPadding[3] =
(weightHeight % stride[0]) ? (stride[0] - weightHeight % stride[0]) : 0;
weightPadding[5] =
- (weightWidth % stride[1]) ? (stride[1] - weightWidth % stride[1]) : 0;
- DenseElementsAttr weightPaddingAttr = DenseIntElementsAttr::get(
- RankedTensorType::get({8}, rewriter.getI32Type()), weightPadding);
- Value weightPaddingVal = CreateOpAndInferShape<tosa::ConstOp>(
- rewriter, loc, weightPaddingAttr.getType(), weightPaddingAttr);
+ weightWidth % stride[1] ? stride[1] - weightWidth % stride[1] : 0;
+
+ Value weightPaddingVal =
+ getTosaConstShape(rewriter, op->getLoc(), weightPadding);
if (op.getQuantizationInfo().has_value()) {
auto quantInfo = op.getQuantizationInfo().value();
@@ -197,17 +196,14 @@ class TransposeConvStridedConverter
/* axis = */ rewriter.getI32IntegerAttr(2));
// We need to pad the input far enough that we can pull all values.
- llvm::SmallVector<int32_t, 8> inputPadding = {0, 0, 0, 0, 0, 0, 0, 0};
+ llvm::SmallVector<int64_t, 8> inputPadding = {0, 0, 0, 0, 0, 0, 0, 0};
inputPadding[2] += restridedWeightTy.getDimSize(1) - 1;
inputPadding[3] += restridedWeightTy.getDimSize(1) - 1;
inputPadding[4] += restridedWeightTy.getDimSize(2) - 1;
inputPadding[5] += restridedWeightTy.getDimSize(2) - 1;
- DenseElementsAttr inputPaddingAttr = DenseIntElementsAttr::get(
- RankedTensorType::get({8}, rewriter.getI32Type()), inputPadding);
-
- Value inputPaddingVal = CreateOpAndInferShape<tosa::ConstOp>(
- rewriter, loc, inputPaddingAttr.getType(), inputPaddingAttr);
+ Value inputPaddingVal =
+ getTosaConstShape(rewriter, op->getLoc(), inputPadding);
if (op.getQuantizationInfo().has_value()) {
auto quantInfo = op.getQuantizationInfo().value();
@@ -310,17 +306,14 @@ class TransposeConvStridedConverter
rewriter.getDenseI64ArrayAttr(sliceSize))
.getResult();
- llvm::SmallVector<int32_t, 8> resultPadding = {0, 0, 0, 0, 0, 0, 0, 0};
+ llvm::SmallVector<int64_t, 8> resultPadding = {0, 0, 0, 0, 0, 0, 0, 0};
resultPadding[2] = resultPadTop;
resultPadding[3] = resultTy.getDimSize(1) - resultPadTop - sliceSize[1];
resultPadding[4] = resultPadLeft;
resultPadding[5] = resultTy.getDimSize(2) - resultPadLeft - sliceSize[2];
- DenseElementsAttr resultPaddingAttr = DenseIntElementsAttr::get(
- RankedTensorType::get({8}, rewriter.getI32Type()), resultPadding);
-
- Value resultPaddingVal = CreateOpAndInferShape<tosa::ConstOp>(
- rewriter, loc, resultPaddingAttr.getType(), resultPaddingAttr);
+ Value resultPaddingVal =
+ getTosaConstShape(rewriter, op->getLoc(), resultPadding);
Value resultPad = CreateOpAndInferShape<tosa::PadOp>(
rewriter, loc, UnrankedTensorType::get(resultETy), slice,
diff --git a/mlir/lib/Dialect/Tosa/Utils/ConversionUtils.cpp b/mlir/lib/Dialect/Tosa/Utils/ConversionUtils.cpp
index 1f6e3b2ab83919..3a56ec2a10bcf6 100644
--- a/mlir/lib/Dialect/Tosa/Utils/ConversionUtils.cpp
+++ b/mlir/lib/Dialect/Tosa/Utils/ConversionUtils.cpp
@@ -160,3 +160,17 @@ LogicalResult mlir::tosa::EqualizeRanks(ImplicitLocOpBuilder &builder,
return success();
}
+
+Value mlir::tosa::getTosaConstShape(PatternRewriter& rewriter, Location loc,
+ llvm::ArrayRef<int64_t> shape) {
+ auto attr = rewriter.getIndexTensorAttr(shape);
+ auto type = mlir::tosa::shapeType::get(rewriter.getContext(), shape.size());
+ mlir::Operation *mlir_op = rewriter.create<tosa::ConstShapeOp>(loc, type, attr);
+ return mlir_op->getResult(0);
+}
+
+SmallVector<int64_t> mlir::tosa::convertFromMlirShape(ArrayRef<int64_t> shape) {
+ return to_vector(llvm::map_range(shape, [](int64_t dim) {
+ return ShapedType::isDynamic(dim) ? -1 : dim;
+ }));
+}
diff --git a/mlir/test/Conversion/TosaToTensor/tosa-to-tensor.mlir b/mlir/test/Conversion/TosaToTensor/tosa-to-tensor.mlir
index 0b9a64494bc0f1..2f11b31aad2307 100644
--- a/mlir/test/Conversion/TosaToTensor/tosa-to-tensor.mlir
+++ b/mlir/test/Conversion/TosaToTensor/tosa-to-tensor.mlir
@@ -459,65 +459,84 @@ func.func @slice_dyn(%arg0: tensor<?xf32>) -> (tensor<?xf32>) {
// CHECK-LABEL: @pad_float
// CHECK-SAME: (%[[ARG0:[0-9a-zA-Z_]*]]:
func.func @pad_float(%arg0 : tensor<1x2xf32>) -> (tensor<4x9xf32>) {
- %0 = arith.constant dense<[1, 2, 3, 4]> : tensor<4xi32>
+ %0 = tosa.const_shape {value = dense<[1, 2, 3, 4]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ // CHECK-DAG: [[INDEX1:%.+]] = arith.constant 1 : index
+ // CHECK-DAG: [[INDEX2:%.+]] = arith.constant 2 : index
+ // CHECK-DAG: [[INDEX3:%.+]] = arith.constant 3 : index
+ // CHECK-DAG: [[INDEX4:%.+]] = arith.constant 4 : index
// CHECK-DAG: [[CST:%.+]] = arith.constant 0.000000e+00 : f32
- // CHECK: tensor.pad %[[ARG0]] low{{\[}}%{{.*}}, %{{.*}}] high{{\[}}%{{.*}}, %{{.*}}] {
+ // CHECK: tensor.pad %[[ARG0]] low{{\[}}[[INDEX1]], [[INDEX3]]] high{{\[}}[[INDEX2]], [[INDEX4]]] {
// CHECK: tensor.yield [[CST]]
// CHECK: } : tensor<1x2xf32> to tensor<4x9xf32>
- %1 = "tosa.pad"(%arg0, %0) : (tensor<1x2xf32>, tensor<4xi32>) -> (tensor<4x9xf32>)
+ %1 = "tosa.pad"(%arg0, %0) : (tensor<1x2xf32>, !tosa.shape<4>) -> (tensor<4x9xf32>)
return %1 : tensor<4x9xf32>
}
+// -----
func.func @pad_int(%arg0 : tensor<1x2xi32>) -> (tensor<4x9xi32>) {
- %0 = arith.constant dense<[1, 2, 3, 4]> : tensor<4xi32>
+ %0 = tosa.const_shape {value = dense<[1, 2, 3, 4]> : tensor<4xindex>} : () -> !tosa.shape<4>
// CHECK: [[CST:%.+]] = arith.constant 0 : i32
// CHECK: tensor.pad
// CHECK: tensor.yield [[CST]]
- %1 = "tosa.pad"(%arg0, %0) : (tensor<1x2xi32>, tensor<4xi32>) -> (tensor<4x9xi32>)
+ %1 = "tosa.pad"(%arg0, %0) : (tensor<1x2xi32>, !tosa.shape<4>) -> (tensor<4x9xi32>)
return %1 : tensor<4x9xi32>
}
+// -----
func.func @pad_quant(%arg0 : tensor<1x2xi32>) -> (tensor<4x9xi32>) {
- %0 = arith.constant dense<[1, 2, 3, 4]> : tensor<4xi32>
+ %0 = tosa.const_shape {value = dense<[1, 2, 3, 4]> : tensor<4xindex>} : () -> !tosa.shape<4>
// CHECK: [[CST:%.+]] = arith.constant 42 : i32
// CHECK: tensor.pad
// CHECK: tensor.yield [[CST]]
- %1 = "tosa.pad"(%arg0, %0) {quantization_info = #tosa.pad_quant<input_zp = 42>} : (tensor<1x2xi32>, tensor<4xi32>) -> (tensor<4x9xi32>)
+ %1 = "tosa.pad"(%arg0, %0) {quantization_info = #tosa.pad_quant<input_zp = 42>} : (tensor<1x2xi32>, !tosa.shape<4>) -> (tensor<4x9xi32>)
return %1 : tensor<4x9xi32>
}
// -----
func.func @pad_float_explicit(%arg0 : tensor<1x2xf32>) -> (tensor<4x9xf32>) {
- %0 = arith.constant dense<[1, 2, 3, 4]> : tensor<4xi32>
+ %0 = tosa.const_shape {value = dense<[1, 2, 3, 4]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ // CHECK-DAG: [[INDEX1:%.+]] = arith.constant 1 : index
+ // CHECK-DAG: [[INDEX2:%.+]] = arith.constant 2 : index
+ // CHECK-DAG: [[INDEX3:%.+]] = arith.constant 3 : index
+ // CHECK-DAG: [[INDEX4:%.+]] = arith.constant 4 : index
// CHECK-DAG: [[CST:%.+]] = arith.constant 4.200000e+01 : f32
- // CHECK: tensor.pad %[[ARG0]] low{{\[}}%{{.*}}, %{{.*}}] high{{\[}}%{{.*}}, %{{.*}}] {
+ // CHECK: tensor.pad %[[ARG0]] low{{\[}}[[INDEX1]], [[INDEX3]]] high{{\[}}[[INDEX2]], [[INDEX4]]] {
// CHECK: tensor.yield [[CST]]
// CHECK: } : tensor<1x2xf32> to tensor<4x9xf32>
%1 = arith.constant dense<42.0> : tensor<f32>
- %2 = "tosa.pad"(%arg0, %0, %1) : (tensor<1x2xf32>, tensor<4xi32>, tensor<f32>) -> (tensor<4x9xf32>)
+ %2 = "tosa.pad"(%arg0, %0, %1) : (tensor<1x2xf32>, !tosa.shape<4>, tensor<f32>) -> (tensor<4x9xf32>)
return %2 : tensor<4x9xf32>
}
// -----
func.func @pad_dyn_input(%arg0 : tensor<?x2xf32>) -> (tensor<?x9xf32>) {
- %0 = arith.constant dense<[1, 2, 3, 4]> : tensor<4xi32>
+ %0 = tosa.const_shape {value = dense<[1, 2, 3, 4]> : tensor<4xindex>} : () -> !tosa.shape<4>
+ // CHECK-DAG: [[INDEX1:%.+]] = arith.constant 1 : index
+ // CHECK-DAG: [[INDEX2:%.+]] = arith.constant 2 : index
+ // CHECK-DAG: [[INDEX3:%.+]] = arith.constant 3 : index
+ // CHECK-DAG: [[INDEX4:%.+]] = arith.constant 4 : index
// CHECK-DAG: [[CST:%.+]] = arith.constant 0.000000e+00 : f32
- // CHECK: tensor.pad %[[ARG0]] low{{\[}}%{{.*}}, %{{.*}}] high{{\[}}%{{.*}}, %{{.*}}] {
+ // CHECK: tens...
[truncated]
|
|
✅ With the latest revision this PR passed the C/C++ code formatter. |
GeorgeARM
reviewed
Jan 16, 2025
lhutton1
reviewed
Jan 21, 2025
| auto padding = padOp.getPadding(); | ||
|
|
||
| ElementsAttr paddingElems; | ||
| if (!matchPattern(padOp.getPadding(), m_Constant(&paddingElems))) { |
There was a problem hiding this comment.
A bit surprised this works, maybe I'm missing something. Should we extract padding in a similar way as below?
if (!tosa::ExtractConstShapeValue(adaptor.getPadding().getDefiningOp(),
paddingValues))
There was a problem hiding this comment.
shape constant value is intended to work properly with matchPattern
There was a problem hiding this comment.
Thanks, in that case it’s a non-blocking comment, just a suggestion for consistency
GeorgeARM
reviewed
Jan 21, 2025
| llvm::ArrayRef<int64_t> shape); | ||
| SmallVector<int64_t> convertFromMlirShape(ArrayRef<int64_t> shape); | ||
|
|
||
| bool ExtractConstShapeValue(Operation *op, |
There was a problem hiding this comment.
Sorry to ask for a change here again but let's do getConstShapeValue instead of ExtractConstShapeValue to align with the rest of accessors' interfaces.
There was a problem hiding this comment.
thanks for the review. updated ExtractConstShapeValue to getConstShapeValue
This patch changes PadOp's padding input to type !tosa.shape<2 * rank>, (where rank is the rank of the PadOp's input), instead of a <rank x 2> tensor. Signed-off-by: Tai Ly <[email protected]> Signed-off-by: Jerry Ge <[email protected]> Change-Id: I08526a699d6b8ebbaf9ee092cd37580e5d78f919
GeorgeARM
approved these changes
Jan 22, 2025
sjarus
approved these changes
Jan 22, 2025
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
This patch changes PadOp's padding input to type !tosa.shape<2 * rank>, (where rank is the rank of the PadOp's input), instead of a <rank x 2> tensor.
This patch is also a part of TOSA v1.0 effort: https://discourse.llvm.org/t/rfc-tosa-dialect-increment-to-v1-0/83708
This patch updates the PadOp to match all against the TOSA v1.0 form.
Original Authors include:
@Tai78641
@wonjeon