mirror of https://github.com/llvm/torch-mlir
Add Scalarization Patterns for `AtenToDtypeOp`, `AtenNegOp`, `AtenRemainderTensorOp` (#3861)
1. adds a lowering for `aten.neg.int` and `aten.remainder.int` to arith. 2. adds a scalarization pattern for `aten.neg` and `aten.remainder.Tensor` ops. 3. improves folding of `aten.mul.int` 4. adds a scalarization pattern for `aten.to.dtype` which relies on scalar cast ops and basic C++ casting between `double` and `int64_t`. 5. improves rank-0 case handling for `FoldAtenSplatPattern` 6. removes a bug with `aten.unflatten.int` decomposition incorrectly generating a constant size int from a dynamic shape. 7. simplifies the dim list for `aten.unflatten.int` ops generated from the `aten.view` canonicalization in scalarize shapes. All of these changes were necessary to unblock <https://github.com/iree-org/iree/issues/18899>.main
zjgarvey
GitHub
parent
889a836b3d
commit
cd38ecf6c2
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@ -82,6 +82,25 @@ public:
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};
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} // namespace
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namespace {
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class ConvertAtenNegIntOp : public OpConversionPattern<AtenNegIntOp> {
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public:
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using OpConversionPattern<AtenNegIntOp>::OpConversionPattern;
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LogicalResult
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matchAndRewrite(AtenNegIntOp op,
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typename OpConversionPattern<AtenNegIntOp>::OpAdaptor adaptor,
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ConversionPatternRewriter &rewriter) const override {
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Value a = adaptor.getA();
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rewriter.replaceOpWithNewOp<arith::SubIOp>(
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op,
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rewriter.create<arith::ConstantIntOp>(op.getLoc(), /*value=*/0,
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/*bitwidth=*/64),
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a);
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return success();
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}
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};
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} // namespace
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namespace {
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template <typename AtenOp, typename UnaryOp>
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class ConvertAtenUnaryOpToFloatMathOp : public OpConversionPattern<AtenOp> {
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@ -465,11 +484,14 @@ public:
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target.addIllegalOp<AtenAddOp>();
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patterns.add<ConvertAtenAddOp>(typeConverter, context);
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target.addIllegalOp<AtenNegIntOp>();
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patterns.add<ConvertAtenNegIntOp>(typeConverter, context);
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target.addIllegalOp<AtenAddIntOp, AtenAddFloatIntOp, AtenSubIntOp,
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AtenMulIntOp>();
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AtenMulIntOp, AtenRemainderIntOp>();
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patterns.add<ConvertAtenBinaryOp<AtenAddIntOp, arith::AddIOp>>(
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typeConverter, context);
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patterns.add<ConvertAtenBinaryOp<AtenRemainderIntOp, arith::RemSIOp>>(
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typeConverter, context);
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patterns.add<ConvertAtenBinaryOp<AtenAddFloatIntOp, arith::AddFOp>>(
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typeConverter, context);
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patterns.add<ConvertAtenBinaryOp<AtenSubIntOp, arith::SubIOp>>(
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@ -4068,6 +4068,10 @@ OpFoldResult AtenMulIntOp::fold(FoldAdaptor adaptor) {
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int64_t lhs, rhs;
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bool lConstant = matchPattern(getOperand(0), m_TorchConstantInt(&lhs));
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bool rConstant = matchPattern(getOperand(1), m_TorchConstantInt(&rhs));
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if (lConstant && lhs == 1)
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return getOperand(1);
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if (rConstant && rhs == 1)
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return getOperand(0);
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if ((lConstant && lhs == 0) || (rConstant && rhs == 0))
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return getI64IntegerAttr(getContext(), 0);
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if (lConstant && rConstant)
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@ -4587,6 +4587,11 @@ public:
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if (!isValidDim(dimInt, inputRank))
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return rewriter.notifyMatchFailure(op, "dim is not a valid dim");
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if (inputShape[dimInt] == Torch::kUnknownSize &&
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llvm::count(sizesInts, -1) > 0)
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return rewriter.notifyMatchFailure(
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op, "Unimplemented: dynamic unflatten dim with an inferred size.");
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SmallVector<Value> sizesTorchInt;
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if (!getListConstructElements(op.getSizes(), sizesTorchInt))
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return rewriter.notifyMatchFailure(
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@ -714,7 +714,7 @@ public:
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ImplicitLocOpBuilder b(op.getLoc(), rewriter);
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// Rank 0 item op prop
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if (selfTy.getSizes().size() == 0) {
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if (selfTy.getSizes().empty()) {
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auto numToTensor = self.getDefiningOp<Torch::PrimNumToTensorScalarOp>();
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auto squeezeDim = self.getDefiningOp<AtenSqueezeDimOp>();
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if (!squeezeDim && !numToTensor)
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@ -746,6 +746,109 @@ public:
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};
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} // namespace
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namespace {
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LogicalResult convertOpFoldResults(ImplicitLocOpBuilder &b,
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SmallVector<OpFoldResult> &converted,
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SmallVector<OpFoldResult> &elements,
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Type inputDtype, Type resultDtype) {
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auto inputIsInt = dyn_cast<mlir::IntegerType>(inputDtype);
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auto resultIsInt = dyn_cast<mlir::IntegerType>(resultDtype);
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if (!inputIsInt && !isa<mlir::FloatType>(inputDtype))
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return failure();
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if (!resultIsInt && !isa<mlir::FloatType>(resultDtype))
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return failure();
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// if dtypes are both int or both float, no conversion needed
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if (static_cast<bool>(inputIsInt) == static_cast<bool>(resultIsInt)) {
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converted = elements;
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return success();
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}
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if (resultIsInt) {
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for (auto &e : elements) {
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auto eValue = dyn_cast<Value>(e);
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if (eValue) {
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converted.push_back(b.createOrFold<AtenIntScalarOp>(eValue));
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continue;
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}
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auto eAttr = dyn_cast<Attribute>(e);
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auto eFloatAttr = dyn_cast_or_null<FloatAttr>(eAttr);
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if (!eFloatAttr)
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return failure();
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converted.push_back(IntegerAttr::get(
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resultDtype, static_cast<int64_t>(eFloatAttr.getValueAsDouble())));
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}
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return success();
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}
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// result is float
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for (auto &e : elements) {
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auto eValue = dyn_cast<Value>(e);
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if (eValue) {
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converted.push_back(b.createOrFold<AtenFloatScalarOp>(eValue));
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continue;
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}
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auto eAttr = dyn_cast<Attribute>(e);
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auto eIntAttr = dyn_cast<IntegerAttr>(eAttr);
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if (!eIntAttr)
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return failure();
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auto eInt = (inputIsInt.isSigned()) ? eIntAttr.getValue().getSExtValue()
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: eIntAttr.getValue().getZExtValue();
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converted.push_back(FloatAttr::get(resultDtype, static_cast<double>(eInt)));
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}
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return success();
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}
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class PropagateAtenToDtypePattern : public OpRewritePattern<AtenToDtypeOp> {
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public:
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using OpRewritePattern<AtenToDtypeOp>::OpRewritePattern;
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LogicalResult matchAndRewrite(AtenToDtypeOp op,
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PatternRewriter &rewriter) const override {
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bool nonBlocking, copyArg;
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// The non_blocking arg must be `False`.
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if (!matchPattern(op.getNonBlocking(), m_TorchConstantBool(&nonBlocking)) ||
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nonBlocking)
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return failure();
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// The copy arg must be `False`.
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if (!matchPattern(op.getCopy(), m_TorchConstantBool(©Arg)) || copyArg)
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return failure();
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// The memory_format arg must be `none`.
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if (!isa<Torch::NoneType>(op.getMemoryFormat().getType()))
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return failure();
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auto inputType = dyn_cast<ValueTensorType>(op.getSelf().getType());
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auto resultType = dyn_cast<ValueTensorType>(op.getType());
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if (!inputType || !resultType || !inputType.hasDtype() ||
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!resultType.hasDtype())
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return failure();
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auto inputDtype = inputType.getDtype();
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auto resultDtype = resultType.getDtype();
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SmallVector<OpFoldResult> elements;
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if (failed(getListFromTensor(op.getSelf(), elements)))
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return failure();
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ImplicitLocOpBuilder b(op.getLoc(), rewriter);
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SmallVector<OpFoldResult> converted;
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if (failed(convertOpFoldResults(b, converted, elements, inputDtype,
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resultDtype)))
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return rewriter.notifyMatchFailure(
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op, "Unhandled attribute type encountered.");
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SmallVector<Value> vals;
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if (failed(materializeFolds(b, converted, vals)))
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return failure();
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Value result = constructAtenTensorOpFromList(b, op.getType(), vals);
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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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} // namespace
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namespace {
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template <typename AtenViewLikeOp>
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class PropagateAtenViewLikePattern : public OpRewritePattern<AtenViewLikeOp> {
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@ -828,7 +931,7 @@ public:
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if (failed(materializeFolds(b, resultFolds, resultVals)))
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return failure();
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if (resultTy.getSizes().size() == 0) {
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if (resultTy.getSizes().empty()) {
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rewriter.replaceOpWithNewOp<Torch::PrimNumToTensorScalarOp>(
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op, resultTy, resultVals.front());
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return success();
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@ -841,6 +944,48 @@ public:
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};
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} // namespace
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namespace {
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template <typename OpTy, typename ScalarOpTy>
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class PropagateAtenUnaryPattern : public OpRewritePattern<OpTy> {
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public:
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using OpRewritePattern<OpTy>::OpRewritePattern;
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LogicalResult matchAndRewrite(OpTy op,
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PatternRewriter &rewriter) const override {
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// Check type
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auto resultTy = cast<ValueTensorType>(op.getType());
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if (resultTy.getSizes().size() > 1)
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return rewriter.notifyMatchFailure(op, "unsupported: rank > 1");
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if (!resultTy.hasDtype() || !isa<mlir::IntegerType>(resultTy.getDtype()))
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return rewriter.notifyMatchFailure(op, "not an int type");
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ImplicitLocOpBuilder b(op.getLoc(), rewriter);
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SmallVector<OpFoldResult> selfFold;
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if (failed(getListFromTensor(op.getSelf(), selfFold)))
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return failure();
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SmallVector<Value> selfVals;
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if (failed(materializeFolds(b, selfFold, selfVals)))
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return failure();
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SmallVector<OpFoldResult> resultFolds;
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for (uint64_t i = 0; i < selfVals.size(); i++) {
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resultFolds.push_back(
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b.createOrFold<ScalarOpTy>(selfVals[i].getType(), selfVals[i]));
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}
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SmallVector<Value> resultVals;
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if (failed(materializeFolds(b, resultFolds, resultVals)))
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return failure();
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if (resultTy.getSizes().size() == 0) {
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rewriter.replaceOpWithNewOp<Torch::PrimNumToTensorScalarOp>(
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op, resultTy, resultVals.front());
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return success();
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}
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Value result = constructAtenTensorOpFromList(b, resultTy, resultVals);
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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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} // namespace
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/// ------ Fold Patterns ------ ///
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// These are shape-specific folding patterns
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@ -915,6 +1060,11 @@ public:
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auto resultTy = cast<BaseTensorType>(op.getType());
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if (!resultTy.hasSizes() || !resultTy.areAllSizesKnown())
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return rewriter.notifyMatchFailure(op, "dynamic output shape");
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if (resultTy.getSizes().size() == 0) {
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rewriter.replaceOpWithNewOp<Torch::PrimNumToTensorScalarOp>(
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op, op.getType(), elements.front());
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return success();
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}
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auto loc = op.getLoc();
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SmallVector<Value> sizes;
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@ -922,12 +1072,10 @@ public:
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sizes.push_back(rewriter.create<Torch::ConstantIntOp>(
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loc, rewriter.getI64IntegerAttr(size)));
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Value one = rewriter.create<Torch::ConstantIntOp>(
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loc, rewriter.getType<Torch::IntType>(), 1);
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Value sizeList = rewriter.create<Torch::PrimListConstructOp>(
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loc,
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rewriter.getType<Torch::ListType>(rewriter.getType<Torch::IntType>()),
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one);
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sizes);
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Value none = rewriter.create<Torch::ConstantNoneOp>(loc);
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Value cstFalse = rewriter.create<Torch::ConstantBoolOp>(loc, false);
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@ -1031,6 +1179,24 @@ public:
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};
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} // namespace
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namespace {
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// fold ridiculous patterns like size.int -> float.scalar -> int.scalar
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class FoldAtenIntScalarPattern : public OpRewritePattern<AtenIntScalarOp> {
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public:
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using OpRewritePattern<AtenIntScalarOp>::OpRewritePattern;
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LogicalResult matchAndRewrite(AtenIntScalarOp op,
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PatternRewriter &rewriter) const override {
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auto floatScalarOp = op.getA().getDefiningOp<AtenFloatScalarOp>();
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if (!floatScalarOp)
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return failure();
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auto sizeOp = floatScalarOp.getA().getDefiningOp<AtenSizeIntOp>();
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if (!sizeOp)
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return failure();
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rewriter.replaceOp(op, floatScalarOp.getA());
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return success();
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}
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};
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} // namespace
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namespace {
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class FoldAtenUnsqueezePattern : public OpRewritePattern<AtenUnsqueezeOp> {
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public:
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@ -1182,8 +1348,29 @@ public:
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if (inputUnmatched == 1 && outputUnmatched > 1) {
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Value dimVal =
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rewriter.create<Torch::ConstantIntOp>(op.getLoc(), leftMatchEnd);
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ArrayRef<Value> unflattenSizes(viewSizes.begin() + leftMatchEnd,
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viewSizes.end() - rightMatchEnd);
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SmallVector<Value> unflattenSizes(viewSizes.begin() + leftMatchEnd,
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viewSizes.end() - rightMatchEnd);
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// try to convert a single dynamic size input to -1
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int64_t dynCount = 0;
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int64_t dynIdx = 0;
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for (auto [i, v] : llvm::enumerate(unflattenSizes)) {
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int64_t szeInt;
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if (!matchPattern(v, m_TorchConstantInt(&szeInt))) {
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dynCount++;
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dynIdx = i;
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continue;
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}
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// if we have a -1 already, make dynCount invalid and break
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if (szeInt == -1) {
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dynCount = -1;
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break;
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}
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}
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// if only one size is dynamic, make it -1
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if (dynCount == 1)
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unflattenSizes[dynIdx] =
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rewriter.create<Torch::ConstantIntOp>(op.getLoc(), -1);
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Value unflattenList = rewriter.create<Torch::PrimListConstructOp>(
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op.getLoc(), op.getSize().getType(), unflattenSizes);
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rewriter.replaceOpWithNewOp<AtenUnflattenIntOp>(
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@ -1227,6 +1414,18 @@ public:
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namespace {
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bool isItemForSliceOp(Operation *op) {
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auto itemOp = dyn_cast_or_null<AtenItemOp>(op);
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if (!itemOp)
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return false;
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for (OpOperand &use : op->getUses()) {
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Operation *userOp = use.getOwner();
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if (isa<AtenSliceTensorOp>(userOp))
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return true;
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}
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return false;
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}
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bool isSourceOpForShapeScalarization(Operation *op) {
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return llvm::isa<AtenSizeIntOp, Torch::ConstantIntOp, Torch::ConstantBoolOp,
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Aten_ShapeAsTensorOp, Torch::ValueTensorLiteralOp>(op);
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@ -1244,7 +1443,7 @@ bool isPrimListOfInts(Operation *op) {
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bool isAnchorOp(Operation *op) {
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return isa<Torch::RuntimeAssertOp>(op) || isa<AtenArangeStartStepOp>(op) ||
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isPrimListOfInts(op);
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isPrimListOfInts(op) || isItemForSliceOp(op);
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}
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// The argument to this function, op, is the use of some source op, srcOp. If
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@ -1278,9 +1477,9 @@ bool isInvalidValidViewConsumer(Operation *op,
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void populateScalarizationFoldPatterns(RewritePatternSet &patterns) {
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patterns.insert<FoldAtenSqueezePattern<AtenSqueezeOp>,
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FoldAtenSqueezePattern<AtenSqueezeDimOp>,
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FoldAtenUnsqueezePattern, FoldAtenWhereSelf,
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FoldAtenTensorSplatPattern, FoldAtenEqIntPattern>(
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patterns.getContext());
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FoldAtenIntScalarPattern, FoldAtenUnsqueezePattern,
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FoldAtenWhereSelf, FoldAtenTensorSplatPattern,
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FoldAtenEqIntPattern>(patterns.getContext());
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}
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void populateScalarizationCanonicalizePatterns(RewritePatternSet &patterns) {
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@ -1303,10 +1502,12 @@ void populateScalarizationPropagationPatterns(RewritePatternSet &patterns) {
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PropagateAtenItemPattern, PropagateAtenShapeToTensorPattern,
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PropagateAtenSliceTensorPattern, PropagateAtenEqTensorPattern,
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PropagateAtenWhereSelfPattern, PropagateAtenBroadcastToPattern,
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PropagateAtenTransposeIntPattern,
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PropagateAtenTransposeIntPattern, PropagateAtenToDtypePattern,
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PropagateAtenUnaryPattern<AtenNegOp, AtenNegIntOp>,
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PropagateAtenArithmeticPattern<AtenAddTensorOp, AtenAddIntOp>,
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PropagateAtenArithmeticPattern<AtenSubTensorOp, AtenSubIntOp>,
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PropagateAtenArithmeticPattern<AtenMulTensorOp, AtenMulIntOp>,
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PropagateAtenArithmeticPattern<AtenRemainderTensorOp, AtenRemainderIntOp>,
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PropagateAtenArithmeticPattern<AtenDivTensorOp, AtenFloordivIntOp>>(
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patterns.getContext());
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}
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@ -1314,6 +1515,7 @@ void populateScalarizationPropagationPatterns(RewritePatternSet &patterns) {
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void populateScalarizationRemovePatterns(RewritePatternSet &patterns) {
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patterns.insert<RemoveUnusedPattern<Torch::AtenIntBoolOp>,
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RemoveUnusedPattern<Torch::AtenEqIntOp>,
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RemoveUnusedPattern<Torch::AtenToDtypeOp>,
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RemoveUnusedPattern<Torch::PrimNumToTensorScalarOp>,
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RemoveUnusedPattern<Torch::AtenFullOp>,
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RemoveUnusedPattern<Torch::AtenUnsqueezeOp>,
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@ -1321,6 +1523,8 @@ void populateScalarizationRemovePatterns(RewritePatternSet &patterns) {
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RemoveUnusedPattern<Torch::AtenSizeIntOp>,
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RemoveUnusedPattern<Torch::AtenSliceTensorOp>,
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RemoveUnusedPattern<Torch::AtenTensorOp>,
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RemoveUnusedPattern<Torch::AtenFloatScalarOp>,
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RemoveUnusedPattern<Torch::AtenIntScalarOp>,
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RemoveUnusedPattern<Torch::PrimListConstructOp>>(
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patterns.getContext());
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}
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@ -105,9 +105,9 @@ func.func @torch.aten.fake_quantize_per_channel_affine_cachemask(%arg0: !torch.v
|
|||
|
||||
// CHECK-LABEL: test_einsum_inner_prod
|
||||
func.func @test_einsum_inner_prod(%arg0: !torch.vtensor<[5],f64>, %arg1: !torch.vtensor<[5],f64>) -> !torch.vtensor<[],f64> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 17 : si64} {
|
||||
// CHECK: %[[INT5:.+]] = torch.constant.int 5
|
||||
// CHECK: %[[INT1:.+]] = torch.constant.int 1
|
||||
// CHECK: %[[INT0:.+]] = torch.constant.int 0
|
||||
// CHECK-DAG: %[[INT5:.+]] = torch.constant.int 5
|
||||
// CHECK-DAG: %[[INT1:.+]] = torch.constant.int 1
|
||||
// CHECK-DAG: %[[INT0:.+]] = torch.constant.int 0
|
||||
// CHECK: %[[LHS_LIST:.+]] = torch.prim.ListConstruct %[[INT0]]
|
||||
// CHECK: %[[LHS_PERM:.+]] = torch.aten.permute %arg0, %[[LHS_LIST]]
|
||||
// CHECK: %[[RHS_LIST:.+]] = torch.prim.ListConstruct %[[INT0]]
|
||||
|
|
|
|||
|
|
@ -27,12 +27,8 @@ func.func @shape_as_tensor(%arg0 : !torch.vtensor<[5,?,?],f32>) -> !torch.vtenso
|
|||
// CHECK-LABEL: @shape_as_tensor_dim
|
||||
func.func @shape_as_tensor_dim(%arg0 : !torch.vtensor<[5,?,?],f32>) -> !torch.vtensor<[],si32> {
|
||||
// CHECK: %[[INT1:.+]] = torch.constant.int 1
|
||||
// CHECK: %[[SZ:.+]] = torch.aten.size.int %arg0, %[[INT1]]
|
||||
// CHECK: %[[INT1_0:.+]] = torch.constant.int 1
|
||||
// CHECK-DAG: %[[FALSE:.+]] = torch.constant.bool false
|
||||
// CHECK-DAG: %[[NONE:.+]] = torch.constant.none
|
||||
// CHECK-DAG: %[[LIST:.+]] = torch.prim.ListConstruct %[[INT1_0]]
|
||||
// CHECK: %[[TENSOR:.+]] = torch.aten.full %[[LIST]], %[[SZ]], %[[NONE]], %[[NONE]], %[[NONE]], %[[FALSE]]
|
||||
// CHECK-DAG: %[[SZ:.+]] = torch.aten.size.int %arg0, %[[INT1]]
|
||||
// CHECK: %[[TENSOR:.+]] = torch.prim.NumToTensor.Scalar %[[SZ]] : !torch.int -> !torch.vtensor<[],si32>
|
||||
// CHECK: return %[[TENSOR]] : !torch.vtensor<[],si32>
|
||||
%shape = torch.aten._shape_as_tensor %arg0 : !torch.vtensor<[5,?,?],f32> -> !torch.vtensor<[3],si32>
|
||||
%dim = torch.constant.int 0
|
||||
|
|
@ -43,6 +39,49 @@ func.func @shape_as_tensor_dim(%arg0 : !torch.vtensor<[5,?,?],f32>) -> !torch.vt
|
|||
return %select : !torch.vtensor<[],si32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: @cast_int_int
|
||||
func.func @cast_int_int(%arg0 : !torch.vtensor<[5,?,?],f32>) -> !torch.vtensor<[],si64> {
|
||||
// CHECK: %[[I1:.*]] = torch.constant.int 1
|
||||
// CHECK: %[[SZE:.*]] = torch.aten.size.int %arg0, %[[I1]] : !torch.vtensor<[5,?,?],f32>, !torch.int -> !torch.int
|
||||
// CHECK: %[[TENSOR:.*]] = torch.prim.NumToTensor.Scalar %[[SZE]] : !torch.int -> !torch.vtensor<[],si64>
|
||||
// CHECK: return %[[TENSOR]] : !torch.vtensor<[],si64>
|
||||
%int4 = torch.constant.int 4
|
||||
%false = torch.constant.bool false
|
||||
%none = torch.constant.none
|
||||
%shape = torch.aten._shape_as_tensor %arg0 : !torch.vtensor<[5,?,?],f32> -> !torch.vtensor<[3],si32>
|
||||
%cast_shape = torch.aten.to.dtype %shape, %int4, %false, %false, %none : !torch.vtensor<[3],si32>, !torch.int, !torch.bool, !torch.bool, !torch.none -> !torch.vtensor<[3],si64>
|
||||
%dim = torch.constant.int 0
|
||||
%idx = torch.vtensor.literal(dense<1> : tensor<si32>) : !torch.vtensor<[],si32>
|
||||
%select = torch.aten.index_select %cast_shape, %dim, %idx : !torch.vtensor<[3],si64>, !torch.int, !torch.vtensor<[],si32> -> !torch.vtensor<[],si64>
|
||||
%item = torch.aten.item %select : !torch.vtensor<[],si64> -> !torch.int
|
||||
%list = torch.prim.ListConstruct %item : (!torch.int) -> !torch.list<int>
|
||||
return %select : !torch.vtensor<[],si64>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: @cast_int_float
|
||||
func.func @cast_int_float(%arg0 : !torch.vtensor<[5,?,?],f32>) -> !torch.vtensor<[],f32> {
|
||||
// CHECK: %[[I1:.*]] = torch.constant.int 1
|
||||
// CHECK: %[[SZE:.*]] = torch.aten.size.int %arg0, %[[I1]] : !torch.vtensor<[5,?,?],f32>, !torch.int -> !torch.int
|
||||
// CHECK: %[[FLOAT:.*]] = torch.aten.Float.Scalar %[[SZE]] : !torch.int -> !torch.float
|
||||
// CHECK: %[[TENSOR:.*]] = torch.prim.NumToTensor.Scalar %[[FLOAT]] : !torch.float -> !torch.vtensor<[],f32>
|
||||
// CHECK: return %[[TENSOR]] : !torch.vtensor<[],f32>
|
||||
%int6 = torch.constant.int 6
|
||||
%false = torch.constant.bool false
|
||||
%none = torch.constant.none
|
||||
%shape = torch.aten._shape_as_tensor %arg0 : !torch.vtensor<[5,?,?],f32> -> !torch.vtensor<[3],si32>
|
||||
%cast_shape = torch.aten.to.dtype %shape, %int6, %false, %false, %none : !torch.vtensor<[3],si32>, !torch.int, !torch.bool, !torch.bool, !torch.none -> !torch.vtensor<[3],f32>
|
||||
%dim = torch.constant.int 0
|
||||
%idx = torch.vtensor.literal(dense<1> : tensor<si32>) : !torch.vtensor<[],si32>
|
||||
%select = torch.aten.index_select %cast_shape, %dim, %idx : !torch.vtensor<[3],f32>, !torch.int, !torch.vtensor<[],si32> -> !torch.vtensor<[],f32>
|
||||
%item = torch.aten.item %select : !torch.vtensor<[],f32> -> !torch.float
|
||||
%item_int = torch.aten.Int.Scalar %item : !torch.float -> !torch.int
|
||||
%list = torch.prim.ListConstruct %item_int : (!torch.int) -> !torch.list<int>
|
||||
return %select : !torch.vtensor<[],f32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
|
|
@ -89,14 +128,12 @@ func.func @arith_prop(%arg0 : !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?]
|
|||
// CHECK: %[[x2:.*]] = torch.aten.floordiv.int %[[x0]], %[[int12]] : !torch.int, !torch.int -> !torch.int
|
||||
// CHECK: %[[x3:.*]] = torch.aten.floordiv.int %[[x1]], %[[int1_0]] : !torch.int, !torch.int -> !torch.int
|
||||
// CHECK: %[[int12_1:.*]] = torch.constant.int 12
|
||||
// CHECK: %[[int1_2:.*]] = torch.constant.int 1
|
||||
// CHECK: %[[x4:.*]] = torch.aten.mul.int %[[x2]], %[[int12_1]] : !torch.int, !torch.int -> !torch.int
|
||||
// CHECK: %[[x5:.*]] = torch.aten.mul.int %[[x3]], %[[int1_2]] : !torch.int, !torch.int -> !torch.int
|
||||
// CHECK: %[[x6:.*]] = torch.aten.sub.int %[[x0]], %[[x4]] : !torch.int, !torch.int -> !torch.int
|
||||
// CHECK: %[[x7:.*]] = torch.aten.sub.int %[[x1]], %[[x5]] : !torch.int, !torch.int -> !torch.int
|
||||
// CHECK: %[[x8:.*]] = torch.prim.ListConstruct %[[x7]], %[[x6]] : (!torch.int, !torch.int) -> !torch.list<int>
|
||||
// CHECK: %[[x9:.*]] = torch.aten.constant_pad_nd %arg0, %[[x8]], %[[float0]] : !torch.vtensor<[?,?],f32>, !torch.list<int>, !torch.float -> !torch.vtensor<[?,?],f32>
|
||||
// CHECK: return %[[x9]] : !torch.vtensor<[?,?],f32>
|
||||
// CHECK: %[[x5:.*]] = torch.aten.sub.int %[[x0]], %[[x4]] : !torch.int, !torch.int -> !torch.int
|
||||
// CHECK: %[[x6:.*]] = torch.aten.sub.int %[[x1]], %[[x3]] : !torch.int, !torch.int -> !torch.int
|
||||
// CHECK: %[[x7:.*]] = torch.prim.ListConstruct %[[x6]], %[[x5]] : (!torch.int, !torch.int) -> !torch.list<int>
|
||||
// CHECK: %[[x8:.*]] = torch.aten.constant_pad_nd %arg0, %[[x7]], %[[float0]] : !torch.vtensor<[?,?],f32>, !torch.list<int>, !torch.float -> !torch.vtensor<[?,?],f32>
|
||||
// CHECK: return %[[x8]] : !torch.vtensor<[?,?],f32>
|
||||
%0 = torch.vtensor.literal(dense<1> : tensor<si64>) : !torch.vtensor<[],si64>
|
||||
%1 = torch.vtensor.literal(dense<0> : tensor<si64>) : !torch.vtensor<[],si64>
|
||||
%float0.000000e00 = torch.constant.float 0.000000e+00
|
||||
|
|
|
|||
Loading…
Reference in New Issue