mirror of https://github.com/llvm/torch-mlir
[TOSA] Add div rounding mode, remainder, fmod, and ge.Tensor ops support (#3717)
- Add legalization for aten.div rounding mode: + trunc: rounds division results towards zero + floor: rounds division results down - Add legalization for aten.remainder.Scalar and aten.fmod ops - Add legalization for aten.ge.Tensor op - Update e2e tests in xfail_sets.py - Update basic.mlir with new legalized ops Signed-off-by: Justin Ngo <justin.ngo@arm.com> Change-Id: Icedd23205254fb893ce6f3de08956772b83b4320 Signed-off-by: Justin Ngo <justin.ngo@arm.com>pull/3718/head
Justin Ngo
GitHub
parent
5ce48dfacd
commit
abaff58c6d
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@ -463,6 +463,119 @@ public:
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}
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};
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// Function to perform division with trunc rounding mode (rounding result
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// towards zero) for float type inputs.
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// This function takes in the division result between lhs and rhs rather
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// than takes in the original lhs and rhs tensors as parameters.
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Value truncFloatDivWithDivResult(PatternRewriter &rewriter, Operation *op,
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TensorType outType, Value divResult) {
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// To implement trunc mode for float inputs, multiply the floored abs
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// of the tensor with the elementwise signedness of the tensor.
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// div_result = lhs / rhs
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// trunc_val = floor(abs(div_result)) * sign(div_result)
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auto zero =
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tosa::getConstTensor<float>(rewriter, op, 0, {}, outType.getElementType())
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.value();
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auto one =
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tosa::getConstTensor<float>(rewriter, op, 1, {}, outType.getElementType())
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.value();
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auto minusOne = tosa::getConstTensor<float>(rewriter, op, -1, {},
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outType.getElementType())
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.value();
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auto cond = rewriter.create<tosa::GreaterEqualOp>(
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op->getLoc(),
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RankedTensorType::get(outType.getShape(), rewriter.getIntegerType(1)),
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divResult, zero);
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auto selectOp = rewriter.create<tosa::SelectOp>(op->getLoc(), outType, cond,
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one, minusOne);
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auto absDivResult =
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rewriter.create<tosa::AbsOp>(op->getLoc(), outType, divResult);
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auto flooredAbsDivResult =
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rewriter.create<tosa::FloorOp>(op->getLoc(), outType, absDivResult);
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Value result =
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tosa::createMulOpAndCast(rewriter, op, outType, flooredAbsDivResult,
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selectOp, /*shift=*/0)
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.getResult();
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return result;
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}
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// Function to perform division with trunc rounding mode (rounding result
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// towards zero) for float type inputs
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Value truncFloatDiv(PatternRewriter &rewriter, Operation *op,
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TensorType outType, Value lhs, Value rhs) {
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rhs = tosa::promoteType(rewriter, rhs, outType);
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auto rhsRcp =
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rewriter.create<tosa::ReciprocalOp>(op->getLoc(), rhs.getType(), rhs);
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auto divResult = tosa::createMulOpAndCast(rewriter, op, outType, lhs, rhsRcp,
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/*shift=*/0);
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return truncFloatDivWithDivResult(rewriter, op, outType, divResult);
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}
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// Function to perform division with floor rounding mode (rounding result
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// down) for integer type inputs.
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Value floorIntDiv(PatternRewriter &rewriter, Operation *op, TensorType outType,
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Value lhs, Value rhs) {
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// To implement floor mode int input, utilize tosa::IntDivOp (trunc div
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// result) with the following formula elementwise:
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// floor_val = trunc_val - ((trunc_val * rhs != lhs)
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// && (sign(lhs) != sign(rhs)))
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// TOSA IntDiv requires inputs to be i32
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auto i32Type =
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RankedTensorType::get(outType.getShape(), rewriter.getIntegerType(32));
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lhs = tosa::promoteType(rewriter, lhs, i32Type);
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rhs = tosa::promoteType(rewriter, rhs, i32Type);
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auto intDivOp =
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rewriter.create<tosa::IntDivOp>(op->getLoc(), i32Type, lhs, rhs);
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auto zero = tosa::getConstTensor<int32_t>(rewriter, op, 0, {}).value();
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auto one = tosa::getConstTensor<int32_t>(rewriter, op, 1, {}).value();
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auto boolType =
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RankedTensorType::get(outType.getShape(), rewriter.getIntegerType(1));
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auto lhsMulRhs = rewriter.create<tosa::MulOp>(op->getLoc(), i32Type, lhs, rhs,
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/*shift=*/0);
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auto lhsRhsDifferentSign =
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rewriter.create<tosa::GreaterOp>(op->getLoc(), boolType, zero, lhsMulRhs);
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auto truncMulRhs = rewriter.create<tosa::MulOp>(op->getLoc(), i32Type,
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intDivOp, rhs, /*shift=*/0);
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auto truncMulRhsEqualLhs =
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rewriter.create<tosa::EqualOp>(op->getLoc(), boolType, truncMulRhs, lhs);
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auto truncMulRhsNotEqualLhs = rewriter.create<tosa::LogicalNotOp>(
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op->getLoc(), boolType, truncMulRhsEqualLhs);
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auto truncMinusOne =
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rewriter.create<tosa::SubOp>(op->getLoc(), i32Type, intDivOp, one);
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auto cond = rewriter.create<tosa::LogicalAndOp>(
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op->getLoc(), boolType, lhsRhsDifferentSign, truncMulRhsNotEqualLhs);
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auto selectOp = rewriter.create<tosa::SelectOp>(op->getLoc(), i32Type, cond,
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truncMinusOne, intDivOp);
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Value result = tosa::promoteType(rewriter, selectOp, outType);
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return result;
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}
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template <typename AtenOpT>
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class ConvertAtenDivOp : public OpConversionPattern<AtenOpT> {
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public:
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@ -498,25 +611,64 @@ public:
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OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
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op.getType()));
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// auto result;
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// Get rounding mode for aten.div.Tensor_mode
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std::string roundMode;
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if constexpr (std::is_same<AtenOpT, AtenDivTensorModeOp>() ||
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std::is_same<AtenOpT, AtenDivScalarModeOp>()) {
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if (!matchPattern(op.getRoundingMode(), m_TorchConstantStr(roundMode)))
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return rewriter.notifyMatchFailure(
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op, "Non-const rounding mode parameter unsupported");
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}
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Value result;
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if (isa<mlir::FloatType>(outType.getElementType())) {
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// The input to the reciprocal is an integer sometimes, and we may need to
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// promote it to a floating point. Per TOSA specification, the input types
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// can only be floating point for tosa::ReciprocalOp.
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Value rhsCasted = tosa::promoteType(rewriter, rhsTensor, outType);
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auto rcpOp = rewriter.create<tosa::ReciprocalOp>(
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op->getLoc(), rhsCasted.getType(), rhsCasted);
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// The input to the reciprocal is an integer sometimes, and we may need
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// to promote it to a floating point. Per TOSA specification, the input
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// types can only be floating point for tosa::ReciprocalOp.
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rhsTensor = tosa::promoteType(rewriter, rhsTensor, outType);
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auto rhsRcp = rewriter.create<tosa::ReciprocalOp>(
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op->getLoc(), rhsTensor.getType(), rhsTensor);
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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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auto divResult = tosa::createMulOpAndCast(rewriter, op, outType, lhs,
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rhsRcp, /*shift=*/0);
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// Round result based on rounding mode
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if (roundMode.compare("floor") == 0) {
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// "floor": rounds the results of the division down. Equivalent to
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// floor division in Python (the // operator).
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auto floorOp =
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rewriter.create<tosa::FloorOp>(op->getLoc(), outType, divResult);
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result = floorOp.getResult();
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} else if (roundMode.compare("trunc") == 0) {
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// "trunc": rounds the results of the division towards zero. Equivalent
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// to C-style integer division.
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result = truncFloatDivWithDivResult(rewriter, op, outType, divResult);
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} else {
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// The output type can be different than the input types (e.g. dividing an
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// int tensor results in a floating point tensor).
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result = tosa::createBinaryOpAndCast<tosa::IntDivOp>(
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rewriter, op, outType, lhs, rhsTensor)
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.getResult();
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// None: No rounding mode
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result = divResult.getResult();
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}
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} else {
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if (roundMode.compare("floor") == 0) {
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// "floor": rounds the results of the division down. Equivalent to floor
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// division in Python (the // operator).
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result = floorIntDiv(rewriter, op, outType, lhs, rhsTensor);
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} else {
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// "trunc": rounds the results of the division towards zero. Equivalent
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// to C-style integer division.
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// None: no rounding mode.
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// TOSA IntDiv requires inputs to be i32
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auto i32Type = RankedTensorType::get(outType.getShape(),
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rewriter.getIntegerType(32));
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lhs = tosa::promoteType(rewriter, lhs, i32Type);
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rhsTensor = tosa::promoteType(rewriter, rhsTensor, i32Type);
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auto intDivOp = rewriter.create<tosa::IntDivOp>(op->getLoc(), i32Type,
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lhs, rhsTensor);
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result = tosa::promoteType(rewriter, intDivOp, outType);
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}
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}
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rewriter.replaceOp(op, {result});
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@ -4524,20 +4676,24 @@ LogicalResult ConvertAtenOp<AtenToDtypeOp>::matchAndRewrite(
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return success();
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}
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template <>
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LogicalResult ConvertAtenOp<AtenRemainderScalarOp>::matchAndRewrite(
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AtenRemainderScalarOp op, OpAdaptor adaptor,
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ConversionPatternRewriter &rewriter) const {
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template <typename AtenOpT>
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class ConvertAtenRemainderFmodOp : public OpConversionPattern<AtenOpT> {
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public:
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using OpConversionPattern<AtenOpT>::OpConversionPattern;
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using OpAdaptor = typename AtenOpT::Adaptor;
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LogicalResult
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matchAndRewrite(AtenOpT op, OpAdaptor adaptor,
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ConversionPatternRewriter &rewriter) const override {
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Value self = adaptor.getSelf();
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auto selfTy = cast<RankedTensorType>(self.getType());
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if (!selfTy)
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return rewriter.notifyMatchFailure(
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op, "Only ranked tensor types supported in TOSA Remainder");
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op, "Only ranked tensor types supported in TOSA Remainder/Fmod");
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auto outType =
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cast<TensorType>(getTypeConverter()->convertType(op.getType()));
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cast<TensorType>(this->getTypeConverter()->convertType(op.getType()));
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Type outElemTy = outType.getElementType();
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if (!outElemTy.isIntOrFloat())
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@ -4545,35 +4701,69 @@ LogicalResult ConvertAtenOp<AtenRemainderScalarOp>::matchAndRewrite(
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op, "Only floating-point or integer datatype legalization supported");
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Value otherTensor;
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if constexpr (std::is_same<AtenOpT, AtenRemainderScalarOp>()) {
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Value other = op.getOther();
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if (failed(torchScalarToTosaTensor(rewriter, op, other, otherTensor,
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outElemTy, {})))
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return rewriter.notifyMatchFailure(
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op, "Currently only scalar constants are supported for "
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"conversion in TOSA Remainder operation");
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"conversion in TOSA Remainder/Fmod operation");
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} else {
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otherTensor = adaptor.getOther();
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auto otherTy = cast<RankedTensorType>(otherTensor.getType());
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if (!otherTy)
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return rewriter.notifyMatchFailure(
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op, "Only ranked tensor types supported in TOSA Remainder/Fmod");
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}
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constexpr bool isRemainderOp =
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std::is_same<AtenOpT, AtenRemainderScalarOp>() ||
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std::is_same<AtenOpT, AtenRemainderTensorOp>() ||
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std::is_same<AtenOpT, AtenRemainderIntOp>();
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if (selfTy.getElementType() != outElemTy)
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self = rewriter.create<tosa::CastOp>(op.getLoc(), outType, self);
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auto divTensor = self;
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Value divTensor;
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if (isRemainderOp) {
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// torch.remainder(a, b) == a - a.div(b, rounding_mode="floor") * b
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if (isa<mlir::FloatType>(outElemTy)) {
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auto otherTensorReciprocal = rewriter.create<tosa::ReciprocalOp>(
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op.getLoc(), otherTensor.getType(), otherTensor);
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divTensor = rewriter.create<tosa::MulOp>(
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op.getLoc(), outType, self, otherTensorReciprocal, /*shift=*/0);
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divTensor = rewriter.create<tosa::FloorOp>(op.getLoc(), outType, divTensor);
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divTensor =
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rewriter.create<tosa::FloorOp>(op.getLoc(), outType, divTensor);
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} else {
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divTensor = rewriter.create<tosa::IntDivOp>(op.getLoc(), outType, self,
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otherTensor);
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divTensor = floorIntDiv(rewriter, op, outType, self, otherTensor);
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}
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} else {
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// torch.fmod(a, b) == a - a.div(b, rounding_mode="trunc") * b
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if (isa<mlir::FloatType>(outElemTy)) {
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divTensor = truncFloatDiv(rewriter, op, outType, self, otherTensor);
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} else {
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// TOSA IntDiv requires inputs to be i32
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auto i32Type = RankedTensorType::get(outType.getShape(),
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rewriter.getIntegerType(32));
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self = tosa::promoteType(rewriter, self, i32Type);
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otherTensor = tosa::promoteType(rewriter, otherTensor, i32Type);
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auto intDivTensor = rewriter.create<tosa::IntDivOp>(
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op->getLoc(), i32Type, self, otherTensor);
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divTensor = tosa::promoteType(rewriter, intDivTensor, outType);
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}
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}
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auto mulTensor =
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rewriter.create<tosa::MulOp>(op.getLoc(), outType, otherTensor, divTensor,
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auto mulTensor = rewriter.create<tosa::MulOp>(op.getLoc(), outType,
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otherTensor, divTensor,
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/*shift=*/0);
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rewriter.replaceOpWithNewOp<tosa::SubOp>(op, outType, self, mulTensor);
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return success();
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}
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};
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template <typename AtenOpT, typename TosaOpT>
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class ConvertAtenPoolingBaseOp : public OpConversionPattern<AtenOpT> {
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@ -5649,6 +5839,7 @@ public:
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patterns.add<ConvertAtenCompareOp<AtenOp, TosaOp>>(typeConverter, context);
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INSERT_BINARY_COMPARE_PATTERN(AtenGtTensorOp, tosa::GreaterOp)
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INSERT_BINARY_COMPARE_PATTERN(AtenGeScalarOp, tosa::GreaterEqualOp)
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INSERT_BINARY_COMPARE_PATTERN(AtenGeTensorOp, tosa::GreaterEqualOp)
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INSERT_BINARY_COMPARE_PATTERN(AtenGtScalarOp, tosa::GreaterOp)
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INSERT_BINARY_COMPARE_PATTERN(AtenLtTensorOp, tosa::GreaterOp)
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INSERT_BINARY_COMPARE_PATTERN(AtenLtScalarOp, tosa::GreaterOp)
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@ -5673,8 +5864,19 @@ public:
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patterns.add<ConvertAtenDivOp<AtenOp>>(typeConverter, context);
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INSERT_BINARY_DIV_PATTERN(AtenDivTensorOp);
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INSERT_BINARY_DIV_PATTERN(AtenDivScalarOp);
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INSERT_BINARY_DIV_PATTERN(AtenDivTensorModeOp);
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INSERT_BINARY_DIV_PATTERN(AtenDivScalarModeOp);
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#undef INSERT_BINARY_DIV_PATTERN
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#define INSERT_REMAINDER_FMOD_OP_PATTERN(AtenOp) \
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target.addIllegalOp<AtenOp>(); \
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patterns.add<ConvertAtenRemainderFmodOp<AtenOp>>(typeConverter, context);
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INSERT_REMAINDER_FMOD_OP_PATTERN(AtenRemainderScalarOp);
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INSERT_REMAINDER_FMOD_OP_PATTERN(AtenRemainderTensorOp);
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INSERT_REMAINDER_FMOD_OP_PATTERN(AtenFmodScalarOp);
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INSERT_REMAINDER_FMOD_OP_PATTERN(AtenFmodTensorOp);
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#undef INSERT_REMAINDER_FMOD_OP_PATTERN
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#define INSERT_NDIMS_REDUCTION_OP_PATTERN(AtenOp, ConversionFunc) \
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target.addIllegalOp<AtenOp>(); \
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patterns.add<ConvertAtenMultipleDimsReductionOp<AtenOp, ConversionFunc>>( \
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@ -5828,7 +6030,6 @@ public:
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INSERT_ATENOP_PATTERN(AtenCopyOp);
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INSERT_ATENOP_PATTERN(AtenToDtypeOp);
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INSERT_ATENOP_PATTERN(AtenConstantPadNdOp);
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INSERT_ATENOP_PATTERN(AtenRemainderScalarOp);
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INSERT_ATENOP_PATTERN(AtenCatOp);
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INSERT_ATENOP_PATTERN(AtenSqrtOp);
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INSERT_ATENOP_PATTERN(AtenIscloseOp);
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@ -1668,6 +1668,40 @@ FX_IMPORTER_TOSA_CRASHING_SET = {
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# Write the TOSA set as a "passing" set as it is very early in development
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# and very few tests work yet.
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TOSA_PASS_SET = {
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"ElementwiseAtenFloorDivideBroadcastModule_basic",
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"ElementwiseAtenFloorDivideScalarModule_basic",
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"ElementwiseAtenFloorDivideScalarNegativeModule_basic",
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"ElementwiseAtenFloorDivideTensorNegativeModule_basic",
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"ElementwiseAtenFloorDivideTensorPositiveModule_basic",
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"ElementwiseDivScalarRoundingModeFloorIntStaticModule_basic",
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"ElementwiseDivScalarRoundingModeFloorModule_basic",
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"ElementwiseDivScalarRoundingModeFloorStaticModule_basic",
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"ElementwiseDivScalarRoundingModeTruncIntStaticModule_basic",
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"ElementwiseDivScalarRoundingModeTruncModule_basic",
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"ElementwiseDivScalarRoundingModeTruncStaticModule_basic",
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"ElementwiseDivTensorRoundingModeFloorIntStaticModule_basic",
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"ElementwiseDivTensorRoundingModeFloorModule_basic",
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"ElementwiseDivTensorRoundingModeFloorStaticModule_basic",
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"ElementwiseDivTensorRoundingModeTruncIntStaticModule_basic",
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"ElementwiseDivTensorRoundingModeTruncModule_basic",
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"ElementwiseDivTensorRoundingModeTruncStaticModule_basic",
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"ElementwiseGeFloatTensorModule_basic",
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"ElementwiseGeIntTensorModule_basic",
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"ElementwiseRemainderScalarModule_Bool_NegativeDivisor_basic",
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"ElementwiseRemainderScalarModule_Bool_basic",
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"ElementwiseRemainderScalarModule_Int_NegativeDividend_basic",
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"ElementwiseRemainderScalarModule_Int_NegativeDivisor_basic",
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"ElementwiseRemainderTensorModule_Float_NegativeDividend_basic",
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"ElementwiseRemainderTensorModule_Float_NegativeDivisor_basic",
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"ElementwiseRemainderTensorModule_Float_basic",
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"ElementwiseRemainderTensorModule_Int_Float_NegativeDividend_basic",
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"ElementwiseRemainderTensorModule_Int_Float_NegativeDivisor_basic",
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"ElementwiseRemainderTensorModule_Int_Float_basic",
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"ElementwiseRemainderTensorModule_Int_NegativeDividend_basic",
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"ElementwiseRemainderTensorModule_Int_NegativeDivisor_basic",
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"ElementwiseRemainderTensorModule_Int_basic",
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"TriuBroadcastModule_basic",
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"TriuModule_basic",
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"ArgminIntModule_basic",
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||||
"ArgminIntModule_multiple_mins",
|
||||
"ArgminModule_basic",
|
||||
|
|
@ -2210,6 +2244,7 @@ MAKE_FX_TOSA_PASS_SET = (
|
|||
TOSA_PASS_SET
|
||||
| {
|
||||
### Tests additionally passing in make_fx_tosa
|
||||
"AdaptiveAvgPool1dStaticLargerOutput_basic",
|
||||
"ArgminIntModule_basic",
|
||||
"ArgminIntModule_multiple_mins",
|
||||
"ArgminModule_basic",
|
||||
|
|
@ -2318,7 +2353,6 @@ MAKE_FX_TOSA_PASS_SET = (
|
|||
"ViewNoChange1dModule_basic",
|
||||
"ViewNoChange2dModule_basic",
|
||||
"ViewNoChange3dModule_basic",
|
||||
"AdaptiveAvgPool2dFixedKernelStrideSizeStaticModule_basic",
|
||||
}
|
||||
|
||||
if torch_version_for_comparison() < version.parse("2.5.0.dev"):
|
||||
|
|
@ -3137,7 +3171,6 @@ FX_IMPORTER_TOSA_XFAIL_SET = {
|
|||
"Rot90MultipleRotationsModule_basic",
|
||||
"Rot90NegativeEvenRotationsModule_basic",
|
||||
"Rot90NegativeOddRotationsModule_basic",
|
||||
"AdaptiveAvgPool2dFixedKernelStrideSizeStaticModule_basic",
|
||||
"AtenIntMM_basic",
|
||||
"AtenKthvalueDynamicDimsModule_basic",
|
||||
"AtenKthvalueFloat64DynamicDimsModule_basic",
|
||||
|
|
@ -3153,15 +3186,6 @@ FX_IMPORTER_TOSA_XFAIL_SET = {
|
|||
"EinsumStaticDiagonalDimensionModule_basic",
|
||||
"ElementwiseFloatTensorGtIntTensorModule_basic",
|
||||
"ElementwiseIntTensorLtFloatTensorModule_basic",
|
||||
"ElementwiseRemainderScalarModule_Bool_NegativeDivisor_basic",
|
||||
"ElementwiseRemainderScalarModule_Int_NegativeDividend_basic",
|
||||
"ElementwiseRemainderScalarModule_Int_NegativeDivisor_basic",
|
||||
"ElementwiseRemainderTensorModule_Float_NegativeDividend_basic",
|
||||
"ElementwiseRemainderTensorModule_Float_NegativeDivisor_basic",
|
||||
"ElementwiseRemainderTensorModule_Int_Float_NegativeDividend_basic",
|
||||
"ElementwiseRemainderTensorModule_Int_Float_NegativeDivisor_basic",
|
||||
"ElementwiseRemainderTensorModule_Int_NegativeDividend_basic",
|
||||
"ElementwiseRemainderTensorModule_Int_NegativeDivisor_basic",
|
||||
"ElementwiseRreluEvalModule_basic",
|
||||
"ElementwiseRreluEvalStaticModule_basic",
|
||||
"ElementwiseRreluTrainModule_basic",
|
||||
|
|
@ -3194,11 +3218,6 @@ FX_IMPORTER_TOSA_XFAIL_SET = {
|
|||
"TriuIndicesNegativeOffsetModule_basic",
|
||||
"TypeConversionUint8ToF32Module_basic",
|
||||
"WeightNormInterfaceModule_basic",
|
||||
"AdaptiveAvgPool1dGeneralDynamicNoBatches_basic",
|
||||
"AdaptiveAvgPool1dGeneralDynamic_basic",
|
||||
"AdaptiveAvgPool1dStaticLargerOutput_basic",
|
||||
"AdaptiveAvgPool2dDynamicNoBatch_basic",
|
||||
"AdaptiveAvgPool2dDynamic_basic",
|
||||
"AdaptiveAvgPool3dDynamicNoBatch_basic",
|
||||
"AdaptiveAvgPool3dDynamic_basic",
|
||||
"AdaptiveMaxPool1dDynamicNoBatch_basic",
|
||||
|
|
@ -3370,11 +3389,6 @@ FX_IMPORTER_TOSA_XFAIL_SET = {
|
|||
"ElementwiseAtanTensorIntModule_basic",
|
||||
"ElementwiseAtanhIntModule_basic",
|
||||
"ElementwiseAtanhModule_basic",
|
||||
"ElementwiseAtenFloorDivideBroadcastModule_basic",
|
||||
"ElementwiseAtenFloorDivideScalarModule_basic",
|
||||
"ElementwiseAtenFloorDivideScalarNegativeModule_basic",
|
||||
"ElementwiseAtenFloorDivideTensorNegativeModule_basic",
|
||||
"ElementwiseAtenFloorDivideTensorPositiveModule_basic",
|
||||
"ElementwiseAtenLogicalAndOpModule_basic",
|
||||
"ElementwiseAtenLogicalAndOpPromoteBroadcastModule_basic",
|
||||
"ElementwiseAtenLogicalAndOpPromoteBroadcastStaticShapeModule_basic",
|
||||
|
|
@ -3402,25 +3416,11 @@ FX_IMPORTER_TOSA_XFAIL_SET = {
|
|||
"ElementwiseCoshModule_basic",
|
||||
"ElementwiseDequantizePerChannelModule_basic",
|
||||
"ElementwiseDequantizePerTensorModule_basic",
|
||||
"ElementwiseDivScalarRoundingModeFloorIntStaticModule_basic",
|
||||
"ElementwiseDivScalarRoundingModeFloorModule_basic",
|
||||
"ElementwiseDivScalarRoundingModeFloorStaticModule_basic",
|
||||
"ElementwiseDivScalarRoundingModeTruncIntStaticModule_basic",
|
||||
"ElementwiseDivScalarRoundingModeTruncModule_basic",
|
||||
"ElementwiseDivScalarRoundingModeTruncStaticModule_basic",
|
||||
"ElementwiseDivTensorRoundingModeFloorIntStaticModule_basic",
|
||||
"ElementwiseDivTensorRoundingModeFloorModule_basic",
|
||||
"ElementwiseDivTensorRoundingModeFloorStaticModule_basic",
|
||||
"ElementwiseDivTensorRoundingModeTruncIntStaticModule_basic",
|
||||
"ElementwiseDivTensorRoundingModeTruncModule_basic",
|
||||
"ElementwiseDivTensorRoundingModeTruncStaticModule_basic",
|
||||
"ElementwiseErfIntModule_basic",
|
||||
"ElementwiseErfModule_basic",
|
||||
"ElementwiseExpIntModule_basic",
|
||||
"ElementwiseExpm1IntModule_basic",
|
||||
"ElementwiseExpm1Module_basic",
|
||||
"ElementwiseGeFloatTensorModule_basic",
|
||||
"ElementwiseGeIntTensorModule_basic",
|
||||
"ElementwiseGeluApproximateTanhModule_basic",
|
||||
"ElementwiseHardshrinkModule_basic",
|
||||
"ElementwiseHardshrinkStaticModule_basic",
|
||||
|
|
@ -3448,10 +3448,6 @@ FX_IMPORTER_TOSA_XFAIL_SET = {
|
|||
"ElementwiseQuantizePerTensorModule_basic",
|
||||
"ElementwiseQuantizePerTensorUIntModule_basic",
|
||||
"ElementwiseReciprocalIntModule_basic",
|
||||
"ElementwiseRemainderScalarModule_Bool_basic",
|
||||
"ElementwiseRemainderTensorModule_Float_basic",
|
||||
"ElementwiseRemainderTensorModule_Int_Float_basic",
|
||||
"ElementwiseRemainderTensorModule_Int_basic",
|
||||
"ElementwiseRsqrtIntModule_basic",
|
||||
"ElementwiseSigmoidIntModule_basic",
|
||||
"ElementwiseSinIntModule_basic",
|
||||
|
|
@ -3850,6 +3846,7 @@ ONNX_TOSA_CRASHING_SET = {
|
|||
}
|
||||
|
||||
ONNX_TOSA_XFAIL_SET = {
|
||||
"ScaledDotProductAttentionDifferentCausalModule_basic",
|
||||
"HstackBasicComplexModule_basic",
|
||||
"HstackBasicFloatModule_basic",
|
||||
"HstackBasicIntFloatModule_basic",
|
||||
|
|
@ -3890,8 +3887,6 @@ ONNX_TOSA_XFAIL_SET = {
|
|||
"ElementwiseRemainderScalarModule_Bool_NegativeDivisor_basic",
|
||||
"ElementwiseRemainderScalarModule_Int_Float_NegativeDividend_basic",
|
||||
"ElementwiseRemainderScalarModule_Int_Float_NegativeDivisor_basic",
|
||||
"ElementwiseRemainderScalarModule_Int_NegativeDividend_basic",
|
||||
"ElementwiseRemainderScalarModule_Int_NegativeDivisor_basic",
|
||||
"ElementwiseRemainderTensorModule_Int_Float_NegativeDividend_basic",
|
||||
"ElementwiseRemainderTensorModule_Int_Float_NegativeDivisor_basic",
|
||||
"ElementwiseRemainderTensorModule_Int_NegativeDividend_basic",
|
||||
|
|
@ -4223,11 +4218,6 @@ ONNX_TOSA_XFAIL_SET = {
|
|||
"ElementwiseFloatTensorGtIntTensorModule_basic",
|
||||
"ElementwiseFmodTensor_Int_Float_basic",
|
||||
"ElementwiseFmodTensor_Int_basic",
|
||||
"ElementwiseGeFloatIntScalarModule_basic",
|
||||
"ElementwiseGeFloatScalarModule_basic",
|
||||
"ElementwiseGeFloatTensorModule_basic",
|
||||
"ElementwiseGeIntScalarModule_basic",
|
||||
"ElementwiseGeIntTensorModule_basic",
|
||||
"ElementwiseGeMixedIntScalarModule_basic",
|
||||
"ElementwiseGtMixed2ScalarModule_basic",
|
||||
"ElementwiseIntTensorLtFloatScalarModule_basic",
|
||||
|
|
@ -4259,7 +4249,6 @@ ONNX_TOSA_XFAIL_SET = {
|
|||
"ElementwiseRelu6Module_basic",
|
||||
"ElementwiseRemainderScalarModule_Bool_basic",
|
||||
"ElementwiseRemainderScalarModule_Int_Float_basic",
|
||||
"ElementwiseRemainderScalarModule_Int_basic",
|
||||
"ElementwiseRemainderTensorModule_Int_Float_basic",
|
||||
"ElementwiseRemainderTensorModule_Int_basic",
|
||||
"ElementwiseRsqrtIntModule_basic",
|
||||
|
|
@ -4682,7 +4671,6 @@ ONNX_TOSA_XFAIL_SET = {
|
|||
"ScalarImplicitIntModule_basic",
|
||||
# REMOVE WHEN ENABLE_GQA IS ADDED
|
||||
"ScaledDotProductAttentionBoolMaskModule_basic",
|
||||
"ScaledDotProductAttentionDifferentDynamicCausalModule_basic",
|
||||
"ScaledDotProductAttentionSameCausalModule_basic",
|
||||
"ScaledDotProductAttentionSameDynamicModule_basic",
|
||||
"ScatterReduceFloatMaxModule",
|
||||
|
|
@ -4819,8 +4807,6 @@ ONNX_TOSA_XFAIL_SET = {
|
|||
"TraceSignedIntModule_basic",
|
||||
"TraceUnsignedIntModule_basic",
|
||||
"TraceUnsignedIntModule_empty",
|
||||
"TriuBroadcastModule_basic",
|
||||
"TriuModule_basic",
|
||||
"TupleModule_basic",
|
||||
"TypeAsDifferentModule_basic",
|
||||
"TypeConversionF32ToF64Module_basic",
|
||||
|
|
|
|||
|
|
@ -213,10 +213,10 @@ func.func @torch.aten.mul$basic(%arg0: !torch.vtensor<[?, ?],f32>, %arg1: !torch
|
|||
// CHECK-LABEL: func.func @torch.aten.div$basic(
|
||||
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>,
|
||||
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
|
||||
// CHECK-DAG: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
|
||||
// CHECK-DAG: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
|
||||
// CHECK: %[[VAL_4:.*]] = tosa.reciprocal %[[VAL_3]] : (tensor<?x?xf32>) -> tensor<?x?xf32>
|
||||
// CHECK: %[[VAL_5:.*]] = tosa.mul %[[VAL_2]], %[[VAL_4]] {shift = 0 : i8} : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
|
||||
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
|
||||
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
|
||||
// CHECK: %[[VAL_4:.*]] = tosa.reciprocal %[[VAL_2]] : (tensor<?x?xf32>) -> tensor<?x?xf32>
|
||||
// CHECK: %[[VAL_5:.*]] = tosa.mul %[[VAL_3]], %[[VAL_4]] {shift = 0 : i8} : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
|
||||
// CHECK: %[[VAL_6:.*]] = torch_c.from_builtin_tensor %[[VAL_5]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
|
||||
// CHECK: return %[[VAL_6]] : !torch.vtensor<[?,?],f32>
|
||||
// CHECK: }
|
||||
|
|
@ -1470,3 +1470,205 @@ func.func @torch.aten.all.dim$basic(%arg0: !torch.vtensor<[3,2,3],i1>) -> !torch
|
|||
%0 = torch.aten.all.dim %arg0, %dim, %keepdims: !torch.vtensor<[3,2,3],i1> , !torch.int, !torch.bool -> !torch.vtensor<[3,2,1],i1>
|
||||
return %0 : !torch.vtensor<[3,2,1],i1>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func.func @torch.aten.div.Tensor_mode$float_trunc(
|
||||
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>,
|
||||
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
|
||||
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
|
||||
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
|
||||
// CHECK: %[[VAL_4:.*]] = torch.constant.str "trunc"
|
||||
// CHECK: %[[VAL_5:.*]] = tosa.reciprocal %[[VAL_2]] : (tensor<?x?xf32>) -> tensor<?x?xf32>
|
||||
// CHECK: %[[VAL_6:.*]] = tosa.mul %[[VAL_3]], %[[VAL_5]] {shift = 0 : i8} : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
|
||||
// CHECK: %[[VAL_7:.*]] = "tosa.const"() <{value = dense<0.000000e+00> : tensor<f32>}> : () -> tensor<f32>
|
||||
// CHECK: %[[VAL_8:.*]] = "tosa.const"() <{value = dense<1.000000e+00> : tensor<f32>}> : () -> tensor<f32>
|
||||
// CHECK: %[[VAL_9:.*]] = "tosa.const"() <{value = dense<-1.000000e+00> : tensor<f32>}> : () -> tensor<f32>
|
||||
// CHECK: %[[VAL_10:.*]] = tosa.greater_equal %[[VAL_6]], %[[VAL_7]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xi1>
|
||||
// CHECK: %[[VAL_11:.*]] = tosa.select %[[VAL_10]], %[[VAL_8]], %[[VAL_9]] : (tensor<?x?xi1>, tensor<f32>, tensor<f32>) -> tensor<?x?xf32>
|
||||
// CHECK: %[[VAL_12:.*]] = tosa.abs %[[VAL_6]] : (tensor<?x?xf32>) -> tensor<?x?xf32>
|
||||
// CHECK: %[[VAL_13:.*]] = tosa.floor %[[VAL_12]] : (tensor<?x?xf32>) -> tensor<?x?xf32>
|
||||
// CHECK: %[[VAL_14:.*]] = tosa.mul %[[VAL_13]], %[[VAL_11]] {shift = 0 : i8} : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
|
||||
// CHECK: %[[VAL_15:.*]] = torch_c.from_builtin_tensor %[[VAL_14]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
|
||||
// CHECK: return %[[VAL_15]] : !torch.vtensor<[?,?],f32>
|
||||
// CHECK: }
|
||||
func.func @torch.aten.div.Tensor_mode$float_trunc(%arg0: !torch.vtensor<[?, ?],f32>, %arg1: !torch.vtensor<[?, ?],f32>) -> !torch.vtensor<[?, ?],f32> {
|
||||
%str = torch.constant.str "trunc"
|
||||
%0 = torch.aten.div.Tensor_mode %arg0, %arg1, %str : !torch.vtensor<[?, ?],f32>, !torch.vtensor<[?, ?],f32>, !torch.str -> !torch.vtensor<[?, ?],f32>
|
||||
return %0 : !torch.vtensor<[?, ?],f32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func.func @torch.aten.div.Tensor_mode$int_trunc(
|
||||
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],si64>,
|
||||
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[?,?],si64>) -> !torch.vtensor<[?,?],si64> {
|
||||
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],si64> -> tensor<?x?xi64>
|
||||
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],si64> -> tensor<?x?xi64>
|
||||
// CHECK: %[[VAL_4:.*]] = torch.constant.str "trunc"
|
||||
// CHECK: %[[VAL_5:.*]] = tosa.cast %[[VAL_3]] : (tensor<?x?xi64>) -> tensor<?x?xi32>
|
||||
// CHECK: %[[VAL_6:.*]] = tosa.cast %[[VAL_2]] : (tensor<?x?xi64>) -> tensor<?x?xi32>
|
||||
// CHECK: %[[VAL_7:.*]] = tosa.int_div %[[VAL_5]], %[[VAL_6]] : (tensor<?x?xi32>, tensor<?x?xi32>) -> tensor<?x?xi32>
|
||||
// CHECK: %[[VAL_8:.*]] = tosa.cast %[[VAL_7]] : (tensor<?x?xi32>) -> tensor<?x?xi64>
|
||||
// CHECK: %[[VAL_9:.*]] = torch_c.from_builtin_tensor %[[VAL_8]] : tensor<?x?xi64> -> !torch.vtensor<[?,?],si64>
|
||||
// CHECK: return %[[VAL_9]] : !torch.vtensor<[?,?],si64>
|
||||
// CHECK: }
|
||||
func.func @torch.aten.div.Tensor_mode$int_trunc(%arg0: !torch.vtensor<[?, ?],si64>, %arg1: !torch.vtensor<[?, ?],si64>) -> !torch.vtensor<[?, ?],si64> {
|
||||
%str = torch.constant.str "trunc"
|
||||
%0 = torch.aten.div.Tensor_mode %arg0, %arg1, %str : !torch.vtensor<[?, ?],si64>, !torch.vtensor<[?, ?],si64>, !torch.str -> !torch.vtensor<[?, ?],si64>
|
||||
return %0 : !torch.vtensor<[?, ?],si64>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func.func @torch.aten.div.Tensor_mode$float_floor(
|
||||
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>,
|
||||
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
|
||||
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
|
||||
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
|
||||
// CHECK: %[[VAL_4:.*]] = torch.constant.str "floor"
|
||||
// CHECK: %[[VAL_5:.*]] = tosa.reciprocal %[[VAL_2]] : (tensor<?x?xf32>) -> tensor<?x?xf32>
|
||||
// CHECK: %[[VAL_6:.*]] = tosa.mul %[[VAL_3]], %[[VAL_5]] {shift = 0 : i8} : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
|
||||
// CHECK: %[[VAL_7:.*]] = tosa.floor %[[VAL_6]] : (tensor<?x?xf32>) -> tensor<?x?xf32>
|
||||
// CHECK: %[[VAL_8:.*]] = torch_c.from_builtin_tensor %[[VAL_7]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
|
||||
// CHECK: return %[[VAL_8]] : !torch.vtensor<[?,?],f32>
|
||||
// CHECK: }
|
||||
func.func @torch.aten.div.Tensor_mode$float_floor(%arg0: !torch.vtensor<[?, ?],f32>, %arg1: !torch.vtensor<[?, ?],f32>) -> !torch.vtensor<[?, ?],f32> {
|
||||
%str = torch.constant.str "floor"
|
||||
%0 = torch.aten.div.Tensor_mode %arg0, %arg1, %str : !torch.vtensor<[?, ?],f32>, !torch.vtensor<[?, ?],f32>, !torch.str -> !torch.vtensor<[?, ?],f32>
|
||||
return %0 : !torch.vtensor<[?, ?],f32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func.func @torch.aten.div.Tensor_mode$int_floor(
|
||||
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],si64>,
|
||||
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[?,?],si64>) -> !torch.vtensor<[?,?],si64> {
|
||||
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],si64> -> tensor<?x?xi64>
|
||||
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],si64> -> tensor<?x?xi64>
|
||||
// CHECK: %[[VAL_4:.*]] = torch.constant.str "floor"
|
||||
// CHECK: %[[VAL_5:.*]] = tosa.cast %[[VAL_3]] : (tensor<?x?xi64>) -> tensor<?x?xi32>
|
||||
// CHECK: %[[VAL_6:.*]] = tosa.cast %[[VAL_2]] : (tensor<?x?xi64>) -> tensor<?x?xi32>
|
||||
// CHECK: %[[VAL_7:.*]] = tosa.int_div %[[VAL_5]], %[[VAL_6]] : (tensor<?x?xi32>, tensor<?x?xi32>) -> tensor<?x?xi32>
|
||||
// CHECK: %[[VAL_8:.*]] = "tosa.const"() <{value = dense<0> : tensor<i32>}> : () -> tensor<i32>
|
||||
// CHECK: %[[VAL_9:.*]] = "tosa.const"() <{value = dense<1> : tensor<i32>}> : () -> tensor<i32>
|
||||
// CHECK: %[[VAL_10:.*]] = tosa.mul %[[VAL_5]], %[[VAL_6]] {shift = 0 : i8} : (tensor<?x?xi32>, tensor<?x?xi32>) -> tensor<?x?xi32>
|
||||
// CHECK: %[[VAL_11:.*]] = tosa.greater %[[VAL_8]], %[[VAL_10]] : (tensor<i32>, tensor<?x?xi32>) -> tensor<?x?xi1>
|
||||
// CHECK: %[[VAL_12:.*]] = tosa.mul %[[VAL_7]], %[[VAL_6]] {shift = 0 : i8} : (tensor<?x?xi32>, tensor<?x?xi32>) -> tensor<?x?xi32>
|
||||
// CHECK: %[[VAL_13:.*]] = tosa.equal %[[VAL_12]], %[[VAL_5]] : (tensor<?x?xi32>, tensor<?x?xi32>) -> tensor<?x?xi1>
|
||||
// CHECK: %[[VAL_14:.*]] = tosa.logical_not %[[VAL_13]] : (tensor<?x?xi1>) -> tensor<?x?xi1>
|
||||
// CHECK: %[[VAL_15:.*]] = tosa.sub %[[VAL_7]], %[[VAL_9]] : (tensor<?x?xi32>, tensor<i32>) -> tensor<?x?xi32>
|
||||
// CHECK: %[[VAL_16:.*]] = tosa.logical_and %[[VAL_11]], %[[VAL_14]] : (tensor<?x?xi1>, tensor<?x?xi1>) -> tensor<?x?xi1>
|
||||
// CHECK: %[[VAL_17:.*]] = tosa.select %[[VAL_16]], %[[VAL_15]], %[[VAL_7]] : (tensor<?x?xi1>, tensor<?x?xi32>, tensor<?x?xi32>) -> tensor<?x?xi32>
|
||||
// CHECK: %[[VAL_18:.*]] = tosa.cast %[[VAL_17]] : (tensor<?x?xi32>) -> tensor<?x?xi64>
|
||||
// CHECK: %[[VAL_19:.*]] = torch_c.from_builtin_tensor %[[VAL_18]] : tensor<?x?xi64> -> !torch.vtensor<[?,?],si64>
|
||||
// CHECK: return %[[VAL_19]] : !torch.vtensor<[?,?],si64>
|
||||
// CHECK: }
|
||||
func.func @torch.aten.div.Tensor_mode$int_floor(%arg0: !torch.vtensor<[?, ?],si64>, %arg1: !torch.vtensor<[?, ?],si64>) -> !torch.vtensor<[?, ?],si64> {
|
||||
%str = torch.constant.str "floor"
|
||||
%0 = torch.aten.div.Tensor_mode %arg0, %arg1, %str : !torch.vtensor<[?, ?],si64>, !torch.vtensor<[?, ?],si64>, !torch.str -> !torch.vtensor<[?, ?],si64>
|
||||
return %0 : !torch.vtensor<[?, ?],si64>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func.func @torch.aten.div.Tensor_mode$float_basic(
|
||||
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>,
|
||||
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
|
||||
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
|
||||
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
|
||||
// CHECK: %[[VAL_4:.*]] = torch.constant.str ""
|
||||
// CHECK: %[[VAL_5:.*]] = tosa.reciprocal %[[VAL_2]] : (tensor<?x?xf32>) -> tensor<?x?xf32>
|
||||
// CHECK: %[[VAL_6:.*]] = tosa.mul %[[VAL_3]], %[[VAL_5]] {shift = 0 : i8} : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
|
||||
// CHECK: %[[VAL_7:.*]] = torch_c.from_builtin_tensor %[[VAL_6]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
|
||||
// CHECK: return %[[VAL_7]] : !torch.vtensor<[?,?],f32>
|
||||
// CHECK: }
|
||||
func.func @torch.aten.div.Tensor_mode$float_basic(%arg0: !torch.vtensor<[?, ?],f32>, %arg1: !torch.vtensor<[?, ?],f32>) -> !torch.vtensor<[?, ?],f32> {
|
||||
%str = torch.constant.str ""
|
||||
%0 = torch.aten.div.Tensor_mode %arg0, %arg1, %str : !torch.vtensor<[?, ?],f32>, !torch.vtensor<[?, ?],f32>, !torch.str -> !torch.vtensor<[?, ?],f32>
|
||||
return %0 : !torch.vtensor<[?, ?],f32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func.func @torch.aten.div.Tensor_mode$int_basic(
|
||||
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],si64>,
|
||||
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[?,?],si64>) -> !torch.vtensor<[?,?],si64> {
|
||||
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],si64> -> tensor<?x?xi64>
|
||||
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],si64> -> tensor<?x?xi64>
|
||||
// CHECK: %[[VAL_4:.*]] = torch.constant.str ""
|
||||
// CHECK: %[[VAL_5:.*]] = tosa.cast %[[VAL_3]] : (tensor<?x?xi64>) -> tensor<?x?xi32>
|
||||
// CHECK: %[[VAL_6:.*]] = tosa.cast %[[VAL_2]] : (tensor<?x?xi64>) -> tensor<?x?xi32>
|
||||
// CHECK: %[[VAL_7:.*]] = tosa.int_div %[[VAL_5]], %[[VAL_6]] : (tensor<?x?xi32>, tensor<?x?xi32>) -> tensor<?x?xi32>
|
||||
// CHECK: %[[VAL_8:.*]] = tosa.cast %[[VAL_7]] : (tensor<?x?xi32>) -> tensor<?x?xi64>
|
||||
// CHECK: %[[VAL_9:.*]] = torch_c.from_builtin_tensor %[[VAL_8]] : tensor<?x?xi64> -> !torch.vtensor<[?,?],si64>
|
||||
// CHECK: return %[[VAL_9]] : !torch.vtensor<[?,?],si64>
|
||||
// CHECK: }
|
||||
func.func @torch.aten.div.Tensor_mode$int_basic(%arg0: !torch.vtensor<[?, ?],si64>, %arg1: !torch.vtensor<[?, ?],si64>) -> !torch.vtensor<[?, ?],si64> {
|
||||
%str = torch.constant.str ""
|
||||
%0 = torch.aten.div.Tensor_mode %arg0, %arg1, %str : !torch.vtensor<[?, ?],si64>, !torch.vtensor<[?, ?],si64>, !torch.str -> !torch.vtensor<[?, ?],si64>
|
||||
return %0 : !torch.vtensor<[?, ?],si64>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func.func @torch.aten.ge.Tensor$basic(
|
||||
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>,
|
||||
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],i1> {
|
||||
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
|
||||
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
|
||||
// CHECK: %[[VAL_4:.*]] = tosa.greater_equal %[[VAL_3]], %[[VAL_2]] : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xi1>
|
||||
// CHECK: %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<?x?xi1> -> !torch.vtensor<[?,?],i1>
|
||||
// CHECK: return %[[VAL_5]] : !torch.vtensor<[?,?],i1>
|
||||
// CHECK: }
|
||||
func.func @torch.aten.ge.Tensor$basic(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],i1> {
|
||||
%0 = torch.aten.ge.Tensor %arg0, %arg1 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],i1>
|
||||
return %0 : !torch.vtensor<[?,?],i1>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func.func @torch.aten.remainder.Tensor(
|
||||
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[2,4],f32>,
|
||||
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[2,4],f32>) -> !torch.vtensor<[2,4],f32> {
|
||||
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[2,4],f32> -> tensor<2x4xf32>
|
||||
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[2,4],f32> -> tensor<2x4xf32>
|
||||
// CHECK: %[[VAL_4:.*]] = tosa.reciprocal %[[VAL_2]] : (tensor<2x4xf32>) -> tensor<2x4xf32>
|
||||
// CHECK: %[[VAL_5:.*]] = tosa.mul %[[VAL_3]], %[[VAL_4]] {shift = 0 : i8} : (tensor<2x4xf32>, tensor<2x4xf32>) -> tensor<2x4xf32>
|
||||
// CHECK: %[[VAL_6:.*]] = tosa.floor %[[VAL_5]] : (tensor<2x4xf32>) -> tensor<2x4xf32>
|
||||
// CHECK: %[[VAL_7:.*]] = tosa.mul %[[VAL_2]], %[[VAL_6]] {shift = 0 : i8} : (tensor<2x4xf32>, tensor<2x4xf32>) -> tensor<2x4xf32>
|
||||
// CHECK: %[[VAL_8:.*]] = tosa.sub %[[VAL_3]], %[[VAL_7]] : (tensor<2x4xf32>, tensor<2x4xf32>) -> tensor<2x4xf32>
|
||||
// CHECK: %[[VAL_9:.*]] = torch_c.from_builtin_tensor %[[VAL_8]] : tensor<2x4xf32> -> !torch.vtensor<[2,4],f32>
|
||||
// CHECK: return %[[VAL_9]] : !torch.vtensor<[2,4],f32>
|
||||
// CHECK: }
|
||||
func.func @torch.aten.remainder.Tensor(%arg0: !torch.vtensor<[2, 4],f32>, %arg1: !torch.vtensor<[2, 4],f32>) -> !torch.vtensor<[2, 4],f32> {
|
||||
%0 = torch.aten.remainder.Tensor %arg0, %arg1 : !torch.vtensor<[2, 4],f32>, !torch.vtensor<[2, 4],f32> -> !torch.vtensor<[2, 4],f32>
|
||||
return %0 : !torch.vtensor<[2, 4],f32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func.func @torch.aten.fmod.Tensor(
|
||||
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[2,4],f32>,
|
||||
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[2,4],f32>) -> !torch.vtensor<[2,4],f32> {
|
||||
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[2,4],f32> -> tensor<2x4xf32>
|
||||
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[2,4],f32> -> tensor<2x4xf32>
|
||||
// CHECK: %[[VAL_4:.*]] = tosa.reciprocal %[[VAL_2]] : (tensor<2x4xf32>) -> tensor<2x4xf32>
|
||||
// CHECK: %[[VAL_5:.*]] = tosa.mul %[[VAL_3]], %[[VAL_4]] {shift = 0 : i8} : (tensor<2x4xf32>, tensor<2x4xf32>) -> tensor<2x4xf32>
|
||||
// CHECK: %[[VAL_6:.*]] = "tosa.const"() <{value = dense<0.000000e+00> : tensor<f32>}> : () -> tensor<f32>
|
||||
// CHECK: %[[VAL_7:.*]] = "tosa.const"() <{value = dense<1.000000e+00> : tensor<f32>}> : () -> tensor<f32>
|
||||
// CHECK: %[[VAL_8:.*]] = "tosa.const"() <{value = dense<-1.000000e+00> : tensor<f32>}> : () -> tensor<f32>
|
||||
// CHECK: %[[VAL_9:.*]] = tosa.greater_equal %[[VAL_5]], %[[VAL_6]] : (tensor<2x4xf32>, tensor<f32>) -> tensor<2x4xi1>
|
||||
// CHECK: %[[VAL_10:.*]] = tosa.select %[[VAL_9]], %[[VAL_7]], %[[VAL_8]] : (tensor<2x4xi1>, tensor<f32>, tensor<f32>) -> tensor<2x4xf32>
|
||||
// CHECK: %[[VAL_11:.*]] = tosa.abs %[[VAL_5]] : (tensor<2x4xf32>) -> tensor<2x4xf32>
|
||||
// CHECK: %[[VAL_12:.*]] = tosa.floor %[[VAL_11]] : (tensor<2x4xf32>) -> tensor<2x4xf32>
|
||||
// CHECK: %[[VAL_13:.*]] = tosa.mul %[[VAL_12]], %[[VAL_10]] {shift = 0 : i8} : (tensor<2x4xf32>, tensor<2x4xf32>) -> tensor<2x4xf32>
|
||||
// CHECK: %[[VAL_14:.*]] = tosa.mul %[[VAL_2]], %[[VAL_13]] {shift = 0 : i8} : (tensor<2x4xf32>, tensor<2x4xf32>) -> tensor<2x4xf32>
|
||||
// CHECK: %[[VAL_15:.*]] = tosa.sub %[[VAL_3]], %[[VAL_14]] : (tensor<2x4xf32>, tensor<2x4xf32>) -> tensor<2x4xf32>
|
||||
// CHECK: %[[VAL_16:.*]] = torch_c.from_builtin_tensor %[[VAL_15]] : tensor<2x4xf32> -> !torch.vtensor<[2,4],f32>
|
||||
// CHECK: return %[[VAL_16]] : !torch.vtensor<[2,4],f32>
|
||||
// CHECK: }
|
||||
func.func @torch.aten.fmod.Tensor(%arg0: !torch.vtensor<[2, 4],f32>, %arg1: !torch.vtensor<[2, 4],f32>) -> !torch.vtensor<[2, 4],f32> {
|
||||
%0 = torch.aten.fmod.Tensor %arg0, %arg1 : !torch.vtensor<[2, 4],f32>, !torch.vtensor<[2, 4],f32> -> !torch.vtensor<[2, 4],f32>
|
||||
return %0 : !torch.vtensor<[2, 4],f32>
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue