mirror of https://github.com/llvm/torch-mlir
[tosa] support lowering basic torch binary ops with mixed dtypes (#2122)
Lowering torch operations that allow different compatible data types in its operands to tosa end up generating invalid tosa IR with mixed data types. In tosa spec, certain operations (generally element-wise operations) require all operands to have the same data type. Add wrapper functions for those element-wise tosa ops to perform op creation with type conversion if necessary.pull/2134/head
TatWai Chong
GitHub
parent
5698893ae4
commit
ed4ecb072f
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@ -10,8 +10,11 @@
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#ifndef TORCHMLIR_CONVERSION_TORCHTOTOSA_TOSALEGALIZECOMMON_H
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#define TORCHMLIR_CONVERSION_TORCHTOTOSA_TOSALEGALIZECOMMON_H
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#include "mlir/IR/PatternMatch.h" // from @llvm-project
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#include "mlir/Support/LLVM.h" // from @llvm-project
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#include "torch-mlir/Conversion/TorchToTosa/TosaLegalizeUtils.h"
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#include "mlir/Dialect/Tosa/IR/TosaOps.h" // from @llvm-project
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#include "mlir/IR/PatternMatch.h" // from @llvm-project
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#include "mlir/Support/LLVM.h" // from @llvm-project
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namespace mlir {
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namespace tosa {
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@ -21,6 +24,26 @@ createOneDimTfIndices(PatternRewriter &rewriter, Operation *op,
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SmallVector<int64_t> indiceOneDimShape, int32_t dim,
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ArrayRef<int64_t> indexShape);
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mlir::tosa::MulOp createMulOpAndCast(PatternRewriter &rewriter, Operation *op,
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TensorType outType, Value lhs, Value rhs,
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int32_t shift);
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// Create TOSA elementwise binary op with type conversion if necessary.
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template <typename TosaOpT>
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TosaOpT createBinaryOpAndCast(PatternRewriter &rewriter, Operation *op,
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TensorType outType, Value lhs, Value rhs) {
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lhs = promoteType(rewriter, lhs, outType);
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rhs = promoteType(rewriter, rhs, outType);
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return CreateOpAndInfer<TosaOpT>(rewriter, op->getLoc(), outType, lhs, rhs);
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}
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// This specialization is for Div op. Unlike other binary ops, it doesn't support
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// floating type.
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template <>
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tosa::DivOp createBinaryOpAndCast<DivOp>(PatternRewriter &rewriter,
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Operation *op, TensorType outType,
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Value lhs, Value rhs);
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std::optional<Value> convertTorchIndexToTfIndices(PatternRewriter &rewriter,
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Operation *op,
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Value params_value,
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@ -45,6 +45,10 @@ bool isScale32(mlir::quant::UniformQuantizedType output_element_type);
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Value getTosaConstTensorSingleF32(PatternRewriter &rewriter, Operation *op,
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float val);
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// Create a zero constant tensor of the desired type and shape.
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std::optional<Value> getZerosLikeTensor(PatternRewriter &rewriter,
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Operation *op, Type type);
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// Templated function to create a constant op for given type and shape.
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// T: storage C type.
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// Default template creates a constant tensor in T.
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@ -100,17 +100,13 @@ public:
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return rewriter.notifyMatchFailure(op,
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"Only Tensor types supported in TOSA");
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auto lhsElemTy = lhsTy.getElementType();
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auto rhsElemTy = rhsTy.getElementType();
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auto outTy = OpConversionPattern<AtenOpT>::getTypeConverter()
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->convertType(op.getType())
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.template cast<TensorType>();
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if (lhsElemTy != rhsElemTy)
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return rewriter.notifyMatchFailure(op, "Input datatypes mismatched");
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rewriter.replaceOpWithNewOp<TosaOpT>(
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op,
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OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
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op.getType()),
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lhs, rhs);
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auto binaryOp =
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tosa::createBinaryOpAndCast<TosaOpT>(rewriter, op, outTy, lhs, rhs);
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rewriter.replaceOp(op, binaryOp.getResult());
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return success();
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}
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};
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@ -291,52 +287,30 @@ public:
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"alpha in conversion to TOSA operation");
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}
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// make sure input of MulOp is same datetype, otherwise the lowering to
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// arith dialect will bug
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auto multTensor = rewriter.create<tosa::MulOp>(
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op.getLoc(),
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auto mulAlphaOp = tosa::createMulOpAndCast(
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rewriter, op,
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rhsType ? rhsType : RankedTensorType::get({}, rhsAlphaMulElemType),
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rhsTensor, alphaTensor, /*shift=*/0);
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if (outElemTy.isa<mlir::FloatType>() || outElemTy.isInteger(32)) {
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// if outElemTy tensor<f32>, mulTensor must be tensor<f32>,
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// left value could be tensor<f32/i32/i64>, cast left value to
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// tensor<f32> type
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// if outElemTy tensor<i32>, mulTensor must be tensor<i32>,
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// left value could be tensor<f32/i32/i64>, cast left value to
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// tensor<i32> type
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if (lhsType.getElementType() != rhsAlphaMulElemType)
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lhs = rewriter.create<tosa::CastOp>(
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op.getLoc(),
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RankedTensorType::get(lhsType.getShape(), rhsAlphaMulElemType),
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lhs);
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rewriter.replaceOpWithNewOp<TosaOpT>(op, outType, lhs, multTensor);
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return success();
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} else if (outElemTy.isInteger(64)) {
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if (outElemTy.isInteger(64)) {
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// Tosa doesn't support 64-bit elementwise addition and subtraction.
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// if outElemTy tensor<i64>, mulTensor must be tensor<i32>,
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// left value could be tensor<f32/i32/i64> type, cast left value to
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// tensor<i32> type
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if (lhsType.getElementType() != rhsAlphaMulElemType)
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lhs = rewriter.create<tosa::CastOp>(
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op.getLoc(),
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RankedTensorType::get(lhsType.getShape(), rhsAlphaMulElemType),
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lhs);
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auto tosaOpTOutputTensor = rewriter.create<TosaOpT>(
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op.getLoc(),
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auto addOrSubi64Op = tosa::createBinaryOpAndCast<TosaOpT>(
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rewriter, op,
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RankedTensorType::get(outType.getShape(), rhsAlphaMulElemType), lhs,
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multTensor);
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// cast tensor<i32> back to tensor<i64>
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rewriter.replaceOpWithNewOp<tosa::CastOp>(op, outType,
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tosaOpTOutputTensor);
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mulAlphaOp);
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// cast tensor<i32> back to tensor<i64>
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rewriter.replaceOpWithNewOp<tosa::CastOp>(op, outType, addOrSubi64Op);
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return success();
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} else {
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return rewriter.notifyMatchFailure(
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op, "Only floating-point, i32, i64 datatype legalization supported");
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}
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auto binaryOp = tosa::createBinaryOpAndCast<TosaOpT>(rewriter, op, outType,
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lhs, mulAlphaOp);
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rewriter.replaceOp(op, binaryOp.getResult());
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return success();
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}
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}; // namespace
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@ -457,15 +431,13 @@ public:
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if (outElemTy.isa<mlir::FloatType>() ||
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outElemTy.isa<mlir::IntegerType>()) {
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if (lhsType.getElementType() != outElemTy)
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lhs = rewriter.create<tosa::CastOp>(op.getLoc(), outType, lhs);
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auto outType = OpConversionPattern<AtenOpT>::getTypeConverter()
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->convertType(op.getType())
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.template cast<TensorType>();
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rewriter.replaceOpWithNewOp<tosa::MulOp>(
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op,
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OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
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op.getType()),
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lhs, rhsTensor,
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/*shift=*/0);
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auto mulOp = tosa::createMulOpAndCast(rewriter, op, outType, lhs,
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rhsTensor, /*shift=*/0);
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rewriter.replaceOp(op, mulOp.getResult());
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return success();
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}
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@ -507,23 +479,27 @@ public:
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"conversion in TOSA operation");
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}
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auto rhsTensor = rhsTy ? rhs : rhsAsTensor;
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auto outType = OpConversionPattern<AtenOpT>::getTypeConverter()
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->convertType(op.getType())
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.template cast<TensorType>();
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// auto result;
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Value result;
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if (lhsElemTy.isa<mlir::FloatType>()) {
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auto rcpOp = rewriter.create<tosa::ReciprocalOp>(
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op->getLoc(), rhsTy ? rhsTy : RankedTensorType::get({}, lhsElemTy),
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rhsTensor);
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rewriter.replaceOpWithNewOp<tosa::MulOp>(
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op,
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OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
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op.getType()),
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lhs, rcpOp.getResult(), /*shift=*/0);
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result = tosa::createMulOpAndCast(rewriter, op, outType, lhs,
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rcpOp.getResult(), /*shift=*/0)
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.getResult();
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} else {
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rewriter.replaceOpWithNewOp<tosa::DivOp>(
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op,
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OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
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op.getType()),
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lhs, rhsTensor);
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result = tosa::createBinaryOpAndCast<tosa::DivOp>(rewriter, op, outType,
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lhs, rhsTensor)
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.getResult();
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}
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rewriter.replaceOp(op, {result});
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return success();
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}
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};
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@ -1033,8 +1009,12 @@ LogicalResult ConvertAtenOp<AtenPowTensorScalarOp>::matchAndRewrite(
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op, "Currently only scalar constants are supported for "
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"conversion in TOSA Pow operation");
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rewriter.replaceOpWithNewOp<tosa::PowOp>(
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op, getTypeConverter()->convertType(op.getType()), self, expTensor);
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auto outType =
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getTypeConverter()->convertType(op.getType()).template cast<TensorType>();
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auto powOp = tosa::createBinaryOpAndCast<tosa::PowOp>(rewriter, op, outType,
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self, expTensor);
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rewriter.replaceOp(op, powOp.getResult());
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return success();
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}
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@ -3289,15 +3269,8 @@ LogicalResult ConvertAtenOp<AtenBroadcastToOp>::matchAndRewrite(
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// +0. (sign bit flips). These are probably acceptable in the short term,
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// but we should put a comment acknowledging the danger, as there isn't an
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// op that avoids the denorm flushing.
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SmallVector<int64_t> intValues(totalNumElements, 0);
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SmallVector<float> floatValues(totalNumElements, 0.0);
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Value zeroTensor = selfType.getElementType().isa<mlir::FloatType>()
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? tosa::getConstTensor<float>(
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rewriter, op, floatValues, zeroTensorShape)
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.value()
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: tosa::getConstTensor<int64_t>(
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rewriter, op, intValues, zeroTensorShape)
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.value();
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Value zeroTensor =
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tosa::getZerosLikeTensor(rewriter, op, resultType).value();
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// Use add broadcast
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rewriter.replaceOpWithNewOp<tosa::AddOp>(op, resultType, adaptor.getSelf(),
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@ -8,7 +8,6 @@
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//===----------------------------------------------------------------------===//
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#include "torch-mlir/Conversion/TorchToTosa/TosaLegalizeCommon.h"
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#include "torch-mlir/Conversion/TorchToTosa/TosaLegalizeUtils.h"
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#include "torch-mlir/Conversion/Utils/Utils.h"
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#include <climits>
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@ -19,7 +18,6 @@
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#include "mlir/Dialect/Quant/QuantTypes.h" // from @llvm-project
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#include "mlir/Dialect/Tensor/IR/Tensor.h" // from @llvm-project
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#include "mlir/Dialect/Tosa/IR/TosaOps.h" // from @llvm-project
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#include "mlir/IR/BuiltinTypes.h" // from @llvm-project
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#include "mlir/IR/Matchers.h" // from @llvm-project
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#include "mlir/IR/PatternMatch.h" // from @llvm-project
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@ -105,6 +103,32 @@ createOneDimTfIndices(PatternRewriter &rewriter, Operation *op,
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return indicesDim;
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}
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tosa::MulOp createMulOpAndCast(PatternRewriter &rewriter, Operation *op,
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TensorType outType, Value lhs, Value rhs,
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int32_t shift) {
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lhs = promoteType(rewriter, lhs, outType);
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rhs = promoteType(rewriter, rhs, outType);
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return tosa::CreateOpAndInfer<tosa::MulOp>(rewriter, op->getLoc(), outType,
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lhs, rhs, shift);
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}
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template <>
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tosa::DivOp createBinaryOpAndCast<DivOp>(PatternRewriter &rewriter,
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Operation *op, TensorType outType,
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Value lhs, Value rhs) {
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auto lhsElemTy = lhs.getType().cast<TensorType>().getElementType();
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auto rhsElemTy = rhs.getType().cast<TensorType>().getElementType();
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if (lhsElemTy.isa<mlir::FloatType>() || rhsElemTy.isa<mlir::FloatType>()) {
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(void)rewriter.notifyMatchFailure(op,
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"tosa.div only supports integer type");
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}
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lhs = promoteType(rewriter, lhs, outType);
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rhs = promoteType(rewriter, rhs, outType);
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return tosa::CreateOpAndInfer<tosa::DivOp>(rewriter, op->getLoc(), outType,
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lhs, rhs);
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}
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std::optional<Value> convertTorchIndexToTfIndices(PatternRewriter &rewriter,
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Operation *op,
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Value paramsValue,
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@ -149,6 +149,27 @@ Value getTosaConstTensorSingleF32(PatternRewriter &rewriter, Operation *op,
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return const_op.getResult();
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}
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// Create a zero constant tensor of the desired type and shape.
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std::optional<Value> getZerosLikeTensor(PatternRewriter &rewriter,
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Operation *op, Type type) {
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RankedTensorType resultType = type.dyn_cast<RankedTensorType>();
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if (!resultType) {
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(void)rewriter.notifyMatchFailure(op, "not ranked tensor type");
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return std::nullopt;
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}
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auto resultShape = resultType.getShape();
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ShapedType zeroType =
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RankedTensorType::get(resultShape, resultType.getElementType());
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Attribute zeroAttr = rewriter.getZeroAttr(zeroType);
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return CreateOpAndInfer<tosa::ConstOp>(rewriter, op->getLoc(), zeroType,
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zeroAttr.cast<ElementsAttr>())
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.getResult();
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}
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// Templated function to create a constant op for given type and shape.
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// T: storage C type.
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// Default template creates a constant tensor in T.
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@ -0,0 +1,126 @@
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// RUN: torch-mlir-opt -pass-pipeline='builtin.module(torch-backend-to-tosa-backend-pipeline)' -split-input-file -verify-diagnostics %s | FileCheck %s
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// CHECK-LABEL: torch.aten.mul.Scalar$mixed_type
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// CHECK-SAME: %[[VAL_0:.*]]: tensor<5xbf16>
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// CHECK: %[[VAL_1:.*]] = "tosa.const"() <{value = dense<2.000000e+00> : tensor<1xbf16>}> : () -> tensor<1xbf16>
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// CHECK: %[[VAL_2:.*]] = "tosa.mul"(%[[VAL_0]], %[[VAL_1]]) <{shift = 0 : i32}> : (tensor<5xbf16>, tensor<1xbf16>) -> tensor<5xbf16>
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func.func @torch.aten.mul.Scalar$mixed_type(%arg0: !torch.vtensor<[5],bf16>) -> !torch.vtensor<[5],bf16> {
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%float2.000000e00 = torch.constant.float 2.000000e+00
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%0 = torch.aten.mul.Scalar %arg0, %float2.000000e00 : !torch.vtensor<[5],bf16>, !torch.float -> !torch.vtensor<[5],bf16>
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return %0 : !torch.vtensor<[5],bf16>
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}
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// -----
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// CHECK-LABEL: torch.aten.add.Tensor$mixed_type_fp
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// CHECK-SAME: %[[VAL_0:.*]]: tensor<6xbf16>
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// CHECK-SAME: %[[VAL_1:.*]]: tensor<6xf32>
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// CHECK: %[[VAL_3:.*]] = "tosa.cast"(%[[VAL_1]]) : (tensor<6xf32>) -> tensor<6xbf16>
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// CHECK: %[[VAL_4:.*]] = "tosa.add"(%[[VAL_0]], %[[VAL_3]]) : (tensor<6xbf16>, tensor<6xbf16>) -> tensor<6xbf16>
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func.func @torch.aten.add.Tensor$mixed_type_fp(%arg0: !torch.vtensor<[6],bf16>, %arg1: !torch.vtensor<[6],f32>, %arg2: !torch.float) -> !torch.vtensor<[6],bf16> {
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%float1 = torch.constant.float 1.000000e+00
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%0 = torch.aten.add.Tensor %arg0, %arg1, %float1 : !torch.vtensor<[6],bf16>, !torch.vtensor<[6],f32>, !torch.float -> !torch.vtensor<[6],bf16>
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return %0 : !torch.vtensor<[6],bf16>
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}
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// -----
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// CHECK-LABEL: torch.aten.add.Tensor$mixed_type_int
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// CHECK-SAME: %[[VAL_0:.*]]: tensor<5xf32>
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// CHECK-SAME: %[[VAL_1:.*]]: tensor<5xbf16>
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// CHECK: %[[VAL_2:.*]] = "tosa.cast"(%[[VAL_1]]) : (tensor<5xbf16>) -> tensor<5xf32>
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// CHECK: %[[VAL_3:.*]] = "tosa.add"(%[[VAL_0]], %[[VAL_2]]) : (tensor<5xf32>, tensor<5xf32>) -> tensor<5xf32>
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func.func @torch.aten.add.Tensor$mixed_type_int(%arg0: !torch.vtensor<[5],f32>, %arg1: !torch.vtensor<[5],bf16>) -> !torch.vtensor<[5],f32> {
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%int1 = torch.constant.int 1
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%0 = torch.aten.add.Tensor %arg0, %arg1, %int1 : !torch.vtensor<[5],f32>, !torch.vtensor<[5],bf16>, !torch.int -> !torch.vtensor<[5],f32>
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return %0 : !torch.vtensor<[5],f32>
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}
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// -----
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// CHECK-LABEL: torch.aten.Scalar$mixed_type
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// CHECK-SAME: %[[VAL_0:.*]]: tensor<1x1x32x64xi16>
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// CHECK: %[[VAL_1:.*]] = "tosa.const"() <{value = dense<256> : tensor<1x1x1x1xi32>}> : () -> tensor<1x1x1x1xi32>
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// CHECK: %[[VAL_2:.*]] = "tosa.cast"(%[[VAL_0]]) : (tensor<1x1x32x64xi16>) -> tensor<1x1x32x64xi32>
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// CHECK: %[[VAL_3:.*]] = "tosa.add"(%[[VAL_2]], %[[VAL_1]]) : (tensor<1x1x32x64xi32>, tensor<1x1x1x1xi32>) -> tensor<1x1x32x64xi32>
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func.func @torch.aten.Scalar$mixed_type(%arg0: !torch.vtensor<[1,1,32,64],si16>) -> !torch.vtensor<[1,1,32,64],si32> {
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%int1 = torch.constant.int 1
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%int256 = torch.constant.int 256
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%0 = torch.aten.add.Scalar %arg0, %int256, %int1 : !torch.vtensor<[1,1,32,64],si16>, !torch.int, !torch.int -> !torch.vtensor<[1,1,32,64],si32>
|
||||
return %0 : !torch.vtensor<[1,1,32,64],si32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: torch.aten.sub.Scalar$mixed_type
|
||||
// CHECK-SAME: %[[VAL_0:.*]]: tensor<bf16>,
|
||||
// CHECK: %[[VAL_2:.*]] = "tosa.const"() <{value = dense<1.000000e+00> : tensor<bf16>}> : () -> tensor<bf16>
|
||||
// CHECK: %[[VAL_3:.*]] = "tosa.sub"(%[[VAL_0]], %[[VAL_2]]) : (tensor<bf16>, tensor<bf16>) -> tensor<bf16>
|
||||
func.func @torch.aten.sub.Scalar$mixed_type(%arg0: !torch.vtensor<[],bf16>, %arg1: !torch.vtensor<[],bf16>) -> !torch.vtensor<[],bf16> {
|
||||
%int1 = torch.constant.int 1
|
||||
%0 = torch.aten.sub.Scalar %arg0, %int1, %int1 : !torch.vtensor<[],bf16>, !torch.int, !torch.int -> !torch.vtensor<[],bf16>
|
||||
return %0 : !torch.vtensor<[],bf16>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: torch.aten.maximum$mixed_type
|
||||
// CHECK-SAME: %[[VAL_0:.*]]: tensor<1x3x1xi32>,
|
||||
// CHECK-SAME: %[[VAL_1:.*]]: tensor<1x3x1xf32>
|
||||
// CHECK: %[[VAL_2:.*]] = "tosa.cast"(%[[VAL_0]]) : (tensor<1x3x1xi32>) -> tensor<1x3x1xf32>
|
||||
// CHECK: %[[VAL_3:.*]] = "tosa.maximum"(%[[VAL_2]], %[[VAL_1]]) : (tensor<1x3x1xf32>, tensor<1x3x1xf32>) -> tensor<1x3x1xf32>
|
||||
func.func @torch.aten.maximum$mixed_type(%arg0: !torch.vtensor<[1,3,1],si32>, %arg1: !torch.vtensor<[1,3,1],f32>) -> !torch.vtensor<[1,3,1],f32> {
|
||||
%0 = torch.aten.maximum %arg0, %arg1 : !torch.vtensor<[1,3,1],si32>, !torch.vtensor<[1,3,1],f32> -> !torch.vtensor<[1,3,1],f32>
|
||||
return %0 : !torch.vtensor<[1,3,1],f32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: torch.aten.bitwise_and.Tensor$mixed_type
|
||||
// CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?xi16>,
|
||||
// CHECK-SAME: %[[VAL_1:.*]]: tensor<?x?xi32>
|
||||
// CHECK: %[[VAL_2:.*]] = "tosa.cast"(%[[VAL_0]]) : (tensor<?x?xi16>) -> tensor<?x?xi32>
|
||||
// CHECK: %[[VAL_3:.*]] = "tosa.bitwise_and"(%[[VAL_2]], %[[VAL_1]]) : (tensor<?x?xi32>, tensor<?x?xi32>) -> tensor<?x?xi32>
|
||||
func.func @torch.aten.bitwise_and.Tensor$mixed_type(%arg0: !torch.vtensor<[?,?],si16>, %arg1: !torch.vtensor<[?,?],si32>) -> !torch.vtensor<[?,?],si32> {
|
||||
%0 = torch.aten.bitwise_and.Tensor %arg0, %arg1 : !torch.vtensor<[?,?],si16>, !torch.vtensor<[?,?],si32> -> !torch.vtensor<[?,?],si32>
|
||||
return %0 : !torch.vtensor<[?,?],si32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: torch.aten.div.Tensor$mixed_type_fp
|
||||
// CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?xf32>,
|
||||
// CHECK-SAME: %[[VAL_1:.*]]: tensor<?x?xi32>
|
||||
// CHECK: %[[VAL_2:.*]] = "tosa.reciprocal"(%[[VAL_1]]) : (tensor<?x?xi32>) -> tensor<?x?xi32>
|
||||
// CHECK: %[[VAL_3:.*]] = "tosa.cast"(%[[VAL_2]]) : (tensor<?x?xi32>) -> tensor<?x?xf32>
|
||||
// CHECK: %[[VAL_4:.*]] = "tosa.mul"(%[[VAL_0]], %[[VAL_3]]) <{shift = 0 : i32}> : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
|
||||
func.func @torch.aten.div.Tensor$mixed_type_fp(%arg0: !torch.vtensor<[?, ?],f32>, %arg1: !torch.vtensor<[?, ?],si32>) -> !torch.vtensor<[?, ?],f32> {
|
||||
%0 = torch.aten.div.Tensor %arg0, %arg1 : !torch.vtensor<[?, ?],f32>, !torch.vtensor<[?, ?],si32> -> !torch.vtensor<[?, ?],f32>
|
||||
return %0 : !torch.vtensor<[?, ?],f32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: torch.aten.div.Tensor$mixed_type_int
|
||||
// CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?xi16>,
|
||||
// CHECK-SAME: %[[VAL_1:.*]]: tensor<?x?xi32>
|
||||
// CHECK: %[[VAL_2:.*]] = "tosa.cast"(%[[VAL_0]]) : (tensor<?x?xi16>) -> tensor<?x?xi32>
|
||||
// CHECK: %[[VAL_3:.*]] = "tosa.div"(%[[VAL_2]], %[[VAL_1]]) : (tensor<?x?xi32>, tensor<?x?xi32>) -> tensor<?x?xi32>
|
||||
func.func @torch.aten.div.Tensor$mixed_type_int(%arg0: !torch.vtensor<[?, ?],si16>, %arg1: !torch.vtensor<[?, ?],si32>) -> !torch.vtensor<[?, ?],si32> {
|
||||
%0 = torch.aten.div.Tensor %arg0, %arg1 : !torch.vtensor<[?, ?],si16>, !torch.vtensor<[?, ?],si32> -> !torch.vtensor<[?, ?],si32>
|
||||
return %0 : !torch.vtensor<[?, ?],si32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: torch.aten.pow.Tensor$mixed_type
|
||||
// CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?xf16>
|
||||
// CHECK: %[[VAL_1:.*]] = "tosa.const"() <{value = dense<3.123400e+00> : tensor<1x1xf32>}> : () -> tensor<1x1xf32>
|
||||
// CHECK: %[[VAL_2:.*]] = "tosa.cast"(%[[VAL_0]]) : (tensor<?x?xf16>) -> tensor<?x?xf32>
|
||||
// CHECK: %[[VAL_3:.*]] = "tosa.pow"(%[[VAL_2]], %[[VAL_1]]) : (tensor<?x?xf32>, tensor<1x1xf32>) -> tensor<?x?xf32>
|
||||
func.func @torch.aten.pow.Tensor$mixed_type(%arg0: !torch.vtensor<[?,?],f16>) -> !torch.vtensor<[?,?],f32> {
|
||||
%fp0 = torch.constant.float 3.123400e+00
|
||||
%0 = torch.aten.pow.Tensor_Scalar %arg0, %fp0 : !torch.vtensor<[?,?],f16>, !torch.float -> !torch.vtensor<[?,?],f32>
|
||||
return %0 : !torch.vtensor<[?,?],f32>
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue