mirror of https://github.com/llvm/torch-mlir
548 lines
22 KiB
C++
548 lines
22 KiB
C++
//===----------------------------------------------------------------------===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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// Also available under a BSD-style license. See LICENSE.
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//
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//===----------------------------------------------------------------------===//
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#include "PassDetail.h"
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#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
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#include "torch-mlir/Dialect/Torch/IR/TorchOps.h"
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#include "torch-mlir/Dialect/Torch/Transforms/Passes.h"
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#include "torch-mlir/Dialect/Torch/Utils/Utils.h"
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using namespace mlir;
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using namespace mlir::torch;
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using namespace mlir::torch::Torch;
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namespace {
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// calculate: (a + b - 1) // b
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// a/b's type should be !torch.int
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Value getIntCeilDiv(PatternRewriter &rewriter, Location loc, Value a, Value b) {
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Value cstOne =
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rewriter.create<ConstantIntOp>(loc, rewriter.getI64IntegerAttr(1));
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Value dividend = rewriter.create<AtenAddIntOp>(loc, a, b);
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dividend = rewriter.create<AtenSubIntOp>(loc, dividend, cstOne);
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Value result = rewriter.create<AtenFloordivIntOp>(loc, dividend, b);
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return result;
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}
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} // namespace
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namespace {
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class RecomposeSliceCopy_ : public OpRewritePattern<AtenCopy_Op> {
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public:
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using OpRewritePattern::OpRewritePattern;
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LogicalResult matchAndRewrite(AtenCopy_Op op,
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PatternRewriter &rewriter) const override {
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// This pattern replaces the in-place mutation of a slice of a tensor with
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// an `index_put` op. Since the slice of the tensor can have a different
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// shape than the full tensor, this pattern requires the `copy_` op to not
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// have users to avoid mismached types. This restriction can be removed by
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// inserting another slice after the `index_put` that creates a tensor of
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// the same shape as the operand to `copy_`.
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if (!op.use_empty())
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return rewriter.notifyMatchFailure(
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op, "`AtenCopy_Op` must not have any users");
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if (!op.getSelf().getDefiningOp() ||
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!isa<AtenSliceTensorOp>(op.getSelf().getDefiningOp()))
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return rewriter.notifyMatchFailure(
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op, "defining op is not `AtenSliceTensorOp`");
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auto sliceOp = cast<AtenSliceTensorOp>(op.getSelf().getDefiningOp());
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// Get indices
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int64_t dim;
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if (!matchPattern(sliceOp.getDim(), m_TorchConstantInt(&dim)))
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return failure();
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int64_t end;
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if (!matchPattern(sliceOp.getEnd(), m_TorchConstantInt(&end)))
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return failure();
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Value newStart = sliceOp.getStart();
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Value newEnd = sliceOp.getEnd();
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Value dimSize = rewriter.create<AtenSizeIntOp>(
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op.getLoc(), sliceOp.getSelf(), sliceOp.getDim());
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if (end < 0) {
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newEnd =
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rewriter.create<AtenAddIntOp>(op.getLoc(), dimSize, sliceOp.getEnd());
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}
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newStart = rewriter.create<PrimMinIntOp>(op.getLoc(), newStart, dimSize);
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newEnd = rewriter.create<PrimMinIntOp>(op.getLoc(), newEnd, dimSize);
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Value noneVal = rewriter.create<ConstantNoneOp>(op.getLoc());
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Value falseVal = rewriter.create<ConstantBoolOp>(op.getLoc(), false);
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// Create IndexPut_Op
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BaseTensorType tensorType = op.getType().cast<BaseTensorType>();
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Type rangeType = tensorType.getWithSizesAndDtype(
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{kUnknownSize}, tensorType.getOptionalDtype());
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Value range = rewriter.create<AtenArangeStartStepOp>(
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op.getLoc(), rangeType, newStart, newEnd, sliceOp.getStep(),
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/*dtype=*/noneVal, /*layout=*/noneVal, /*device=*/noneVal,
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/*pin_memory=*/noneVal);
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SmallVector<Value> indicesVector;
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for (auto i = 0; i < dim; i++)
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indicesVector.push_back(noneVal);
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indicesVector.push_back(range);
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Type indicesType = tensorType.getWithSizesAndDtype(
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/*optionalSizes=*/std::nullopt, /*optionalDtype=*/nullptr);
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Value indices = rewriter.create<PrimListConstructOp>(
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op.getLoc(),
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Torch::ListType::get(op->getContext(),
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Torch::OptionalType::get(indicesType)),
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indicesVector);
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Value sliceOpInput = sliceOp.getSelf();
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rewriter.replaceOpWithNewOp<Aten_IndexPutImpl_Op>(
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op, sliceOpInput.getType(), sliceOpInput, indices, op.getSrc(),
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/*accumulate=*/falseVal, /*unsafe=*/falseVal);
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if (sliceOp->use_empty())
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rewriter.eraseOp(sliceOp);
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return success();
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}
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};
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class RecomposeSelectFill_ : public OpRewritePattern<AtenFill_TensorOp> {
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public:
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using OpRewritePattern::OpRewritePattern;
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LogicalResult matchAndRewrite(AtenFill_TensorOp op,
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PatternRewriter &rewriter) const override {
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if (!op.getSelf().getDefiningOp() ||
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!isa<AtenSelectIntOp>(op.getSelf().getDefiningOp()))
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return failure();
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auto selectOp = cast<AtenSelectIntOp>(op.getSelf().getDefiningOp());
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// Get indices
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int64_t dim;
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if (!matchPattern(selectOp.getDim(), m_TorchConstantInt(&dim)))
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return failure();
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Value noneVal = rewriter.create<ConstantNoneOp>(op.getLoc());
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Value falseVal = rewriter.create<ConstantBoolOp>(op.getLoc(), false);
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// Create IndexPut_Op
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// Convert indexNum to indexTensor for the selectOp
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BaseTensorType selectOutTy =
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selectOp.getType().template cast<BaseTensorType>();
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SmallVector<int64_t> empty;
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auto dtype = getTypeForTorchType(selectOp.getContext(),
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selectOp.getIndex().getType());
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Type emptyTensorType =
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selectOutTy.getWithSizesAndDtype(llvm::ArrayRef(empty), dtype);
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Value indexTensor = rewriter.create<PrimNumToTensorScalarOp>(
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selectOp.getLoc(), emptyTensorType, selectOp.getIndex());
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// Create indicesVector for IndexPut_Op by TorchNone and indexTensor
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BaseTensorType tensorType = op->getResultTypes()[0].cast<BaseTensorType>();
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SmallVector<Value> indicesVector(dim, noneVal);
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indicesVector.push_back(indexTensor);
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Value indices = rewriter.create<PrimListConstructOp>(
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op.getLoc(),
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Torch::ListType::get(op->getContext(),
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Torch::OptionalType::get(tensorType)),
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indicesVector);
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rewriter.replaceOpWithNewOp<Aten_IndexPutImpl_Op>(
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op, op->getResultTypes(), selectOp.getSelf(), indices, op.getValue(),
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/*accumulate=*/falseVal, /*unsafe=*/falseVal);
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return success();
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}
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};
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class RecomposeUnbindListUnpack : public OpRewritePattern<PrimListUnpackOp> {
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public:
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using OpRewritePattern::OpRewritePattern;
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LogicalResult matchAndRewrite(PrimListUnpackOp op,
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PatternRewriter &rewriter) const override {
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// recompose AtenUnbindOp + PrimListUnpackOp to select.int
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auto unbindOp = dyn_cast<AtenUnbindIntOp>(op.getOperand().getDefiningOp());
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if (!unbindOp)
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return rewriter.notifyMatchFailure(op, "Input is not AtenUnbindIntOp");
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if (isListPotentiallyMutated(unbindOp.getResult()))
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return rewriter.notifyMatchFailure(
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op, "AtenUnbindIntOp result is potentially mutated");
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Location loc = op.getLoc();
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Value dim = unbindOp.getDim();
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Value input = unbindOp.getSelf();
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// add runtime.assert to check unbind's dim size == numResults
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Value totalSize = rewriter.create<AtenSizeIntOp>(loc, input, dim);
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Value cstNumResults = rewriter.create<ConstantIntOp>(
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loc, rewriter.getI64IntegerAttr(op.getNumResults()));
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Value eqOrNot = rewriter.create<AtenEqIntOp>(loc, totalSize, cstNumResults);
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rewriter.create<RuntimeAssertOp>(
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loc, eqOrNot,
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rewriter.getStringAttr("unbind's dim size should equal to "
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"prim.list_unpack's num results"));
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SmallVector<Value> slices;
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for (size_t i = 0; i < op.getNumResults(); i++) {
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// rewrite to select.int op
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auto resultTy = op.getResult(i).getType();
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auto index = rewriter.create<Torch::ConstantIntOp>(
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op->getLoc(), rewriter.getI64IntegerAttr(i));
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auto newSelect = rewriter.create<AtenSelectIntOp>(op->getLoc(), resultTy,
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input, dim, index);
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slices.push_back(newSelect);
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}
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rewriter.replaceOp(op, slices);
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if (unbindOp.getResult().use_empty())
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rewriter.eraseOp(unbindOp);
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return success();
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}
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};
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class RecomposeUnbindGetItem : public OpRewritePattern<Aten__Getitem__TOp> {
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public:
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using OpRewritePattern::OpRewritePattern;
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LogicalResult matchAndRewrite(Aten__Getitem__TOp op,
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PatternRewriter &rewriter) const override {
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// recompose AtenUnbindIntOp + __getitem__t to select.int
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auto unbind = dyn_cast<AtenUnbindIntOp>(op.getList().getDefiningOp());
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if (!unbind)
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return rewriter.notifyMatchFailure(op, "Input is not AtenUnbindIntOp");
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if (isListPotentiallyMutated(unbind.getResult()))
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return rewriter.notifyMatchFailure(
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op, "AtenUnbindIntOp result is potentially mutated");
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int64_t index;
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if (!matchPattern(op.getIdx(), m_TorchConstantInt(&index)))
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return rewriter.notifyMatchFailure(
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op, "Expected `idx` of `Aten__Getitem__TOp` to be a constant int");
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if (index < 0)
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return rewriter.notifyMatchFailure(
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op, "Expected `idx` of `Aten__Getitem__TOp` to be a positive int");
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Location loc = op.getLoc();
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Value dim = unbind.getDim();
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Value input = unbind.getSelf();
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// add runtime.assert to check: index
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Value totalSize = rewriter.create<AtenSizeIntOp>(loc, input, dim);
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Value ltOrNot = rewriter.create<AtenLtIntOp>(loc, op.getIdx(), totalSize);
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rewriter.create<RuntimeAssertOp>(
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loc, ltOrNot,
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rewriter.getStringAttr("index should less than unbind's dim size"));
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// rewrite to slice op
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auto resultTy = op.getResult().getType();
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Value newSelect = rewriter.create<AtenSelectIntOp>(loc, resultTy, input,
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dim, op.getIdx());
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rewriter.replaceOp(op, newSelect);
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if (unbind.getResult().use_empty())
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rewriter.eraseOp(unbind);
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return success();
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}
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};
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class RecomposeSplitTensorGetItemOp
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: public OpRewritePattern<Aten__Getitem__TOp> {
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public:
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using OpRewritePattern::OpRewritePattern;
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LogicalResult matchAndRewrite(Aten__Getitem__TOp op,
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PatternRewriter &rewriter) const override {
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// recompose AtenSplitTensorOp + __getitem__t to AtenSliceTensorOp
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auto splitTensorOp =
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dyn_cast<AtenSplitTensorOp>(op.getList().getDefiningOp());
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if (!splitTensorOp)
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return rewriter.notifyMatchFailure(op, "Input is not AtenSplitTensorOp");
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if (isListPotentiallyMutated(splitTensorOp.getResult()))
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return rewriter.notifyMatchFailure(
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op, "SplitTensorOp result is potentially mutated");
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int64_t index;
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if (!matchPattern(op.getIdx(), m_TorchConstantInt(&index)))
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return rewriter.notifyMatchFailure(
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op, "Expected `idx` of `Aten__Getitem__TOp` to be a constant int");
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if (index < 0)
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return rewriter.notifyMatchFailure(
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op, "Expected `idx` of `Aten__Getitem__TOp` to be a positive int");
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int64_t splitSize;
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if (!matchPattern(splitTensorOp.getSplitSize(),
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m_TorchConstantInt(&splitSize)))
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return rewriter.notifyMatchFailure(
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op,
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"Expected `SplitSize` of `AtenSplitTensorOp` to be a constant int");
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Location loc = op.getLoc();
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Value input = splitTensorOp.getSelf();
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Value dim = splitTensorOp.getDim();
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// add runtime.assert to check rank constraint: index < split_result_size
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Value totalSize = rewriter.create<AtenSizeIntOp>(loc, input, dim);
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Value splitResultSize =
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getIntCeilDiv(rewriter, loc, totalSize, splitTensorOp.getSplitSize());
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Value ltOrNot =
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rewriter.create<AtenLtIntOp>(loc, op.getIdx(), splitResultSize);
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rewriter.create<RuntimeAssertOp>(
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loc, ltOrNot,
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rewriter.getStringAttr("index should less than split_result_size"));
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Value step =
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rewriter.create<ConstantIntOp>(loc, rewriter.getI64IntegerAttr(1));
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Value start = rewriter.create<ConstantIntOp>(
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loc, rewriter.getI64IntegerAttr(index * splitSize));
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Value end = rewriter.create<ConstantIntOp>(
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loc, rewriter.getI64IntegerAttr(index * splitSize + splitSize));
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Value sliceTensorOp = rewriter.create<AtenSliceTensorOp>(
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loc, op.getResult().getType(), input, dim, start, end, step);
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rewriter.replaceOp(op, sliceTensorOp);
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if (splitTensorOp.getResult().use_empty())
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rewriter.eraseOp(splitTensorOp);
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return success();
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}
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};
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class RecomposeSplitTensorListUnpack
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: public OpRewritePattern<PrimListUnpackOp> {
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public:
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using OpRewritePattern::OpRewritePattern;
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LogicalResult matchAndRewrite(PrimListUnpackOp op,
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PatternRewriter &rewriter) const override {
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// recompose AtenSplitTensorOp + PrimListUnpackOp to AtenSliceTensorOps
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auto splitTensorOp =
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dyn_cast<AtenSplitTensorOp>(op.getOperand().getDefiningOp());
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if (!splitTensorOp)
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return rewriter.notifyMatchFailure(op, "Input is not AtenSplitTensorOp");
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if (isListPotentiallyMutated(splitTensorOp.getResult()))
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return rewriter.notifyMatchFailure(
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op, "SplitTensorOp result is potentially mutated");
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int64_t splitSize;
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if (!matchPattern(splitTensorOp.getSplitSize(),
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m_TorchConstantInt(&splitSize)))
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return rewriter.notifyMatchFailure(
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op,
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"Expected `SplitSize` of `AtenSplitTensorOp` to be a constant int");
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Location loc = op.getLoc();
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Value input = splitTensorOp.getSelf();
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Value dim = splitTensorOp.getDim();
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// add runtime.assert to check rank constraint
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Value totalSize = rewriter.create<AtenSizeIntOp>(loc, input, dim);
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Value cstNumResults = rewriter.create<ConstantIntOp>(
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loc, rewriter.getI64IntegerAttr(op.getNumResults()));
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Value cstOne =
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rewriter.create<ConstantIntOp>(loc, rewriter.getI64IntegerAttr(1));
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// assert: numResults == floordiv(totalSize + splitSize - 1, splitSize)
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Value splitResultSize =
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getIntCeilDiv(rewriter, loc, totalSize, splitTensorOp.getSplitSize());
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Value eqOrNot =
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rewriter.create<AtenEqIntOp>(loc, splitResultSize, cstNumResults);
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rewriter.create<RuntimeAssertOp>(
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loc, eqOrNot,
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rewriter.getStringAttr("numResults should equal to floordiv(totalSize "
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"+ splitSize - 1, splitSize)"));
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SmallVector<Value> slices;
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for (size_t i = 0; i < op.getNumResults(); i++) {
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auto resultTy = op.getResult(i).getType();
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auto start = rewriter.create<Torch::ConstantIntOp>(
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loc, rewriter.getI64IntegerAttr(i * splitSize));
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auto end = rewriter.create<Torch::ConstantIntOp>(
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loc, rewriter.getI64IntegerAttr((i + 1) * splitSize));
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Value sliceTensorOp = rewriter.create<AtenSliceTensorOp>(
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loc, resultTy, input, dim, start, end, /*step=*/cstOne);
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slices.push_back(sliceTensorOp);
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}
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rewriter.replaceOp(op, slices);
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// erase splitTensorOp if no user left
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if (splitTensorOp.getResult().use_empty())
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rewriter.eraseOp(splitTensorOp);
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return success();
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}
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};
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class RecomposeSplitWithSizesListUnpack
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: public OpRewritePattern<PrimListUnpackOp> {
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public:
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using OpRewritePattern::OpRewritePattern;
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LogicalResult matchAndRewrite(PrimListUnpackOp op,
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PatternRewriter &rewriter) const override {
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// recompose AtenSplitWithSizesOp + PrimListUnpackOp to AtenSliceTensorOps
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auto splitOp =
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dyn_cast<AtenSplitWithSizesOp>(op.getOperand().getDefiningOp());
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if (!splitOp) {
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return rewriter.notifyMatchFailure(op,
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"Input is not AtenSplitWithSizesOp");
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}
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if (isListPotentiallyMutated(splitOp.getResult())) {
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return rewriter.notifyMatchFailure(
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op, "splitWithSizesOp result is potentially mutated");
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}
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if (isListPotentiallyMutated(splitOp.getSplitSizes())) {
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return rewriter.notifyMatchFailure(
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op, "splitWithSizesOp's split_sizes is potentially mutated");
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}
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auto splitSizesConstruct =
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splitOp.getSplitSizes().getDefiningOp<Torch::PrimListConstructOp>();
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if (!splitSizesConstruct) {
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return rewriter.notifyMatchFailure(
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op, "split_sizes is not from PrimListConstructOp");
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}
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int64_t sumSplitSize = 0;
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SmallVector<int64_t> splitSizes;
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for (auto operand : splitSizesConstruct.getOperands()) {
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int64_t value = -1;
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// TODO: support when split_sizes are not constant int
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if (!matchPattern(operand, m_TorchConstantInt(&value))) {
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return rewriter.notifyMatchFailure(
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op, "one of split_sizes is not constant int");
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}
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if (value < 0) {
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return rewriter.notifyMatchFailure(op, "all of split_sizes must > 0");
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}
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sumSplitSize += value;
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splitSizes.push_back(value);
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}
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if (splitSizes.size() != op.getNumResults()) {
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return rewriter.notifyMatchFailure(
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op, "split_sizes must be same as splitOp result size");
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}
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Location loc = op.getLoc();
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Value input = splitOp.getSelf();
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Value dim = splitOp.getDim();
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// add runtime.assert to check rank constraint
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Value totalSize = rewriter.create<AtenSizeIntOp>(loc, input, dim);
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Value cstSumSplitSize = rewriter.create<ConstantIntOp>(
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loc, rewriter.getI64IntegerAttr(sumSplitSize));
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Value eqOrNot =
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rewriter.create<AtenEqIntOp>(loc, totalSize, cstSumSplitSize);
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rewriter.create<RuntimeAssertOp>(
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loc, eqOrNot,
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rewriter.getStringAttr("split dim must be sum of split_sizes"));
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// calculate slice op's lower bound and up bound
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SmallVector<int64_t> boundaryOfSliceOp(splitSizes.size() + 1, 0);
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for (size_t i = 1; i < boundaryOfSliceOp.size(); i++) {
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boundaryOfSliceOp[i] = boundaryOfSliceOp[i - 1] + splitSizes[i - 1];
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}
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SmallVector<Value> slices;
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Value cstOne =
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rewriter.create<ConstantIntOp>(loc, rewriter.getI64IntegerAttr(1));
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for (size_t i = 0; i < op.getNumResults(); i++) {
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auto resultTy = op.getResult(i).getType();
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auto start = rewriter.create<Torch::ConstantIntOp>(
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loc, rewriter.getI64IntegerAttr(boundaryOfSliceOp[i]));
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auto end = rewriter.create<Torch::ConstantIntOp>(
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loc, rewriter.getI64IntegerAttr((boundaryOfSliceOp[i + 1])));
|
|
Value sliceTensorOp = rewriter.create<AtenSliceTensorOp>(
|
|
loc, resultTy, input, dim, start, end, /*step=*/cstOne);
|
|
slices.push_back(sliceTensorOp);
|
|
}
|
|
rewriter.replaceOp(op, slices);
|
|
// erase splitOp if no user left
|
|
if (splitOp.getResult().use_empty())
|
|
rewriter.eraseOp(splitOp);
|
|
return success();
|
|
}
|
|
};
|
|
|
|
class RecomposeChunkListUnpack : public OpRewritePattern<PrimListUnpackOp> {
|
|
public:
|
|
using OpRewritePattern::OpRewritePattern;
|
|
LogicalResult matchAndRewrite(PrimListUnpackOp op,
|
|
PatternRewriter &rewriter) const override {
|
|
// recompose AtenChunkOp + PrimListUnpackOp to AtenSliceTensorOps
|
|
auto chunkOp = dyn_cast<AtenChunkOp>(op.getOperand().getDefiningOp());
|
|
if (!chunkOp)
|
|
return rewriter.notifyMatchFailure(op, "Input is not AtenChunkOp");
|
|
if (isListPotentiallyMutated(chunkOp.getResult()))
|
|
return rewriter.notifyMatchFailure(
|
|
op, "AtenChunkOp result is potentially mutated");
|
|
Value dim = chunkOp.getDim();
|
|
Value input = chunkOp.getSelf();
|
|
Value chunks = chunkOp.getChunks();
|
|
Location loc = chunkOp.getLoc();
|
|
Value totalSize = rewriter.create<Torch::AtenSizeIntOp>(loc, input, dim);
|
|
// chunkSize = floordiv(totalSize + chunks - 1, chunks)
|
|
Value chunkSize = getIntCeilDiv(rewriter, loc, totalSize, chunks);
|
|
|
|
// add runtime.assert to check chunks == NumResults
|
|
Value cstNumResults = rewriter.create<ConstantIntOp>(
|
|
loc, rewriter.getI64IntegerAttr(op.getNumResults()));
|
|
Value eqOrNot = rewriter.create<AtenEqIntOp>(loc, chunks, cstNumResults);
|
|
rewriter.create<RuntimeAssertOp>(
|
|
loc, eqOrNot,
|
|
rewriter.getStringAttr(
|
|
"chunks should equal to prim.list_unpack's num results"));
|
|
|
|
Value cstOne =
|
|
rewriter.create<ConstantIntOp>(loc, rewriter.getI64IntegerAttr(1));
|
|
SmallVector<Value> slices;
|
|
for (size_t i = 0; i < op.getNumResults(); i++) {
|
|
// rewrite to slice op with
|
|
// start = chunkSize * i,
|
|
// end = lastIndex ? totalSize : chunkSize * (i+1)
|
|
auto resultTy = op.getResult(i).getType();
|
|
auto index = rewriter.create<Torch::ConstantIntOp>(
|
|
op->getLoc(), rewriter.getI64IntegerAttr(i));
|
|
auto start = rewriter.create<AtenMulIntOp>(loc, index, chunkSize);
|
|
Value end;
|
|
if (i == op.getNumResults() - 1) {
|
|
end = totalSize;
|
|
} else {
|
|
auto nextIdx = rewriter.create<AtenAddIntOp>(loc, index, cstOne);
|
|
end = rewriter.create<AtenMulIntOp>(loc, nextIdx, chunkSize);
|
|
}
|
|
Value sliceTensorOp = rewriter.create<AtenSliceTensorOp>(
|
|
loc, resultTy, input, dim, start, end, /*step=*/cstOne);
|
|
slices.push_back(sliceTensorOp);
|
|
}
|
|
rewriter.replaceOp(op, slices);
|
|
// erase chunkOp if no user left
|
|
if (chunkOp.getResult().use_empty())
|
|
rewriter.eraseOp(chunkOp);
|
|
return success();
|
|
}
|
|
};
|
|
} // namespace
|
|
|
|
namespace {
|
|
class RecomposeComplexOpsPass
|
|
: public RecomposeComplexOpsBase<RecomposeComplexOpsPass> {
|
|
public:
|
|
void runOnOperation() override {
|
|
MLIRContext *context = &getContext();
|
|
RewritePatternSet patterns(context);
|
|
|
|
// pattern.add calls go here
|
|
patterns.add<RecomposeSliceCopy_>(context);
|
|
patterns.add<RecomposeSelectFill_>(context);
|
|
patterns.add<RecomposeSplitTensorGetItemOp>(context);
|
|
patterns.add<RecomposeSplitTensorListUnpack>(context);
|
|
patterns.add<RecomposeSplitWithSizesListUnpack>(context);
|
|
patterns.add<RecomposeUnbindListUnpack>(context);
|
|
patterns.add<RecomposeUnbindGetItem>(context);
|
|
patterns.add<RecomposeChunkListUnpack>(context);
|
|
|
|
GreedyRewriteConfig config;
|
|
config.useTopDownTraversal = true;
|
|
config.maxIterations = GreedyRewriteConfig::kNoLimit;
|
|
|
|
if (failed(applyPatternsAndFoldGreedily(getOperation(), std::move(patterns),
|
|
config))) {
|
|
return signalPassFailure();
|
|
}
|
|
}
|
|
};
|
|
} // namespace
|
|
|
|
std::unique_ptr<OperationPass<func::FuncOp>>
|
|
mlir::torch::Torch::createRecomposeComplexOpsPass() {
|
|
return std::make_unique<RecomposeComplexOpsPass>();
|
|
}
|