mirror of https://github.com/llvm/torch-mlir
401 lines
16 KiB
C++
401 lines
16 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 "SimplifyAbstractInterpCalculationsUtils.h"
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#include "mlir/IR/BlockAndValueMapping.h"
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#include "mlir/Transforms/GreedyPatternRewriteDriver.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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// TODO: Only unroll inside the shape calculation region.
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// Maybe do this by only applying patterns and folding greedily on the ops
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// inside the region + the shape.calculate op itself?
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class FullyUnrollPrimLoopOp : public OpRewritePattern<PrimLoopOp> {
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public:
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using OpRewritePattern::OpRewritePattern;
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LogicalResult matchAndRewrite(PrimLoopOp op,
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PatternRewriter &rewriter) const override {
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Location loc = op->getLoc();
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MLIRContext *context = op->getContext();
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if (!op.isForLike())
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return rewriter.notifyMatchFailure(op, "Loop is not for-like");
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int64_t maxTripCount;
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if (!matchPattern(op.getMaxTripCount(), m_TorchConstantInt(&maxTripCount)))
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return rewriter.notifyMatchFailure(
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op, "Expected `maxTripCount` to be a constant int");
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;
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SmallVector<Value> indices;
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for (int64_t i = 0; i < maxTripCount; i++) {
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// TODO: Add convenience builder.
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indices.push_back(rewriter.create<ConstantIntOp>(
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loc, rewriter.getIntegerAttr(IntegerType::get(context, 64), i)));
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}
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Block *beforeBlock = op->getBlock();
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Block *afterBlock = rewriter.splitBlock(op->getBlock(), op->getIterator());
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SmallVector<Block *> blocksToMerge;
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BlockAndValueMapping bvm;
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// TODO: Helper for region().front()
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auto condition =
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cast<PrimLoopConditionOp>(op.getRegion().front().getTerminator());
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for (int64_t i = 0; i < maxTripCount; i++) {
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SmallVector<Value> iterArgs;
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if (i == 0) {
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llvm::append_range(iterArgs, op.getIterArgsInit());
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} else {
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llvm::append_range(
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iterArgs, llvm::map_range(condition.getIterArgs(),
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[&](Value v) { return bvm.lookup(v); }));
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}
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bvm.clear();
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bvm.map(op.getRegion().front().getArgument(0), indices[i]);
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bvm.map(op.getRegion().front().getArguments().slice(1), iterArgs);
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op.getRegion().cloneInto(afterBlock->getParent(), afterBlock->getIterator(),
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bvm);
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Block *clonedBlock = bvm.lookup(&op.getRegion().front());
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rewriter.eraseOp(clonedBlock->getTerminator());
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blocksToMerge.push_back(clonedBlock);
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}
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blocksToMerge.push_back(afterBlock);
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for (Block *block : blocksToMerge)
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rewriter.mergeBlocks(block, beforeBlock);
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if (maxTripCount == 0) {
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rewriter.replaceOp(op, op.getIterArgsInit());
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} else {
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rewriter.replaceOp(op, llvm::to_vector<6>(llvm::map_range(
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condition.getIterArgs(),
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[&](Value v) { return bvm.lookup(v); })));
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}
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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 AbstractlyInterpretListOpsWithinABlock
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: public OpRewritePattern<PrimListConstructOp> {
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public:
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using OpRewritePattern::OpRewritePattern;
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LogicalResult matchAndRewrite(PrimListConstructOp op,
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PatternRewriter &rewriter) const override {
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Block *block = op->getBlock();
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auto allUsers = llvm::to_vector<6>(op->getUsers());
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// Sort the users into program order.
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auto getParentInBlock = [&](Operation *op) {
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while (op->getBlock() != block)
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op = op->getParentOp();
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return op;
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};
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// Use a stable sort for deterministic results when users are nested in two
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// regions of the same parent op.
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llvm::stable_sort(allUsers, [&](Operation *lhs, Operation *rhs) {
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return getParentInBlock(lhs)->isBeforeInBlock(getParentInBlock(rhs));
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});
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// We cannot interpret all ops. So first do a check to see up until which
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// point we can interpret.
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int numUsersToInterpret = 0;
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for (int i = 0, e = allUsers.size(); i != e; i++, numUsersToInterpret++) {
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Operation *user = allUsers[i];
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// If a user potentially mutates the list, then we require it to be in the
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// same block for our simple abstract interpretation to work (we can't,
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// for example, handle an "append" operation in a loop or other region).
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// However, if the op is read-only, then from the purpose of our abstract
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// interpretation, we can handle it effectively as though it was at the
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// same position as the corresponding parent op in the block under
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// consideration.
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if (potentiallyMutatesListOperands(user)) {
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if (user->getBlock() != block)
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break;
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}
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}
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// Truncate the list of users to the number of users we're going to
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// interpret.
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allUsers.resize(numUsersToInterpret);
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auto usersToInterpret =
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makeArrayRef(allUsers).take_front(numUsersToInterpret);
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// For each mutating op (which must be in the same block), we save the
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// current state of the list as a vector of Value's. These will then
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// be converted to PrimListConstructOp's at the correct program points.
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SmallVector<SmallVector<Value>> listLiterals;
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SmallVector<Value> runningList;
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llvm::append_range(runningList, op->getOperands());
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bool generatedNewLiteral = false;
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for (Operation *user : usersToInterpret) {
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if (auto append = dyn_cast<AtenAppendTOp>(user)) {
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if (!append.use_empty())
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return rewriter.notifyMatchFailure(
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op, "Expected `AtenAppendTOp` to not have users");
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if (append.getSelf() == op) {
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runningList.push_back(append.getEl());
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generatedNewLiteral = true;
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}
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listLiterals.push_back(runningList);
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continue;
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}
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if (auto insert = dyn_cast<AtenInsertTOp>(user)) {
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if (!insert.use_empty())
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return rewriter.notifyMatchFailure(
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op, "Expected `AtenInsertTOp` to not have users");
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int64_t index;
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if (!matchPattern(insert.getIdx(), m_TorchConstantInt(&index)))
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return rewriter.notifyMatchFailure(
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op, "Expected `idx` of `AtenInsertTOp` to be a constant int");
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// The index might be statically out of bounds.
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if (index < 0 || index > static_cast<int64_t>(runningList.size()))
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return rewriter.notifyMatchFailure(
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op, "Index in `AtenInsertTOp` is out of bounds");
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if (insert.getSelf() == op) {
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runningList.insert(runningList.begin() + index, insert.getEl());
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generatedNewLiteral = true;
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}
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listLiterals.push_back(runningList);
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continue;
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}
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if (auto setItem = dyn_cast<Aten_SetItemTOp>(user)) {
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if (!setItem.use_empty())
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return rewriter.notifyMatchFailure(
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op, "Expected `Aten_SetItemTOp` to not have users");
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llvm::Optional<int64_t> indexOpt =
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matchLegalConstantIndexIntoListOfSize(setItem.getIdx(),
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runningList.size());
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// The index might be statically out of bounds.
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if (!indexOpt)
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return rewriter.notifyMatchFailure(
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op, "Index in `Aten_SetItemTOp` is out of bounds");
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if (setItem.getL() == op) {
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runningList[*indexOpt] = setItem.getEl();
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generatedNewLiteral = true;
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}
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listLiterals.push_back(runningList);
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continue;
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}
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// If this user potentially mutates the list and isn't handled above, then
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// we can't abstractly interpret any further.
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if (potentiallyMutatesListOperands(user))
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break;
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}
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if (!generatedNewLiteral)
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return rewriter.notifyMatchFailure(op, "No new literal created");
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// Rewrite all users to use the appropriate list literals.
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Value latestLiteral = rewriter.create<PrimListConstructOp>(
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op->getLoc(), op.getType(), op->getOperands());
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int nextLiteral = 0;
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for (Operation *user : usersToInterpret) {
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if (auto append = dyn_cast<AtenAppendTOp>(user)) {
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rewriter.setInsertionPoint(append);
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latestLiteral = rewriter.create<PrimListConstructOp>(
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append->getLoc(), op.getType(), listLiterals[nextLiteral++]);
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if (append.getSelf() == op)
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rewriter.eraseOp(append);
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continue;
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}
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if (auto insert = dyn_cast<AtenInsertTOp>(user)) {
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rewriter.setInsertionPoint(insert);
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latestLiteral = rewriter.create<PrimListConstructOp>(
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insert->getLoc(), op.getType(), listLiterals[nextLiteral++]);
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if (insert.getSelf() == op)
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rewriter.eraseOp(insert);
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continue;
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}
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if (auto setItem = dyn_cast<Aten_SetItemTOp>(user)) {
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rewriter.setInsertionPoint(setItem);
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latestLiteral = rewriter.create<PrimListConstructOp>(
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setItem->getLoc(), op.getType(), listLiterals[nextLiteral++]);
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if (setItem.getL() == op)
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rewriter.eraseOp(setItem);
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continue;
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}
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for (OpOperand &opOperand : user->getOpOperands()) {
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if (opOperand.get() == op.getResult()) {
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opOperand.set(latestLiteral);
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}
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}
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}
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// Any remaining uses should use the updated value of the latest literal.
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rewriter.replaceOp(op, latestLiteral);
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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 DecomposeAtenSizeOp : public OpRewritePattern<AtenSizeOp> {
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public:
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using OpRewritePattern::OpRewritePattern;
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LogicalResult matchAndRewrite(AtenSizeOp op,
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PatternRewriter &rewriter) const override {
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Location loc = op.getLoc();
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Value self = op.getSelf();
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MLIRContext *context = op.getContext();
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auto tensorType = self.getType().cast<BaseTensorType>();
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if (!tensorType.hasSizes())
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return rewriter.notifyMatchFailure(op, "unranked tensor");
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int64_t rank = tensorType.getSizes().size();
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SmallVector<Value> sizes;
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for (int i = 0; i < rank; i++) {
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Value dim = rewriter.create<Torch::ConstantIntOp>(
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loc, rewriter.getI64IntegerAttr(i));
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sizes.push_back(rewriter.create<AtenSizeIntOp>(loc, self, dim));
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}
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Value sizeList = rewriter.create<PrimListConstructOp>(
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loc, Torch::ListType::get(Torch::IntType::get(context)), sizes);
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rewriter.replaceOp(op, sizeList);
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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 FoldPrimUncheckedCastOp : public OpRewritePattern<PrimUncheckedCastOp> {
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public:
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using OpRewritePattern::OpRewritePattern;
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LogicalResult matchAndRewrite(PrimUncheckedCastOp op,
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PatternRewriter &rewriter) const override {
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if (!isValidSubtype(op.getX().getType(), op.getResult().getType())) {
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return rewriter.notifyMatchFailure(
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op, "input tensor type is not a valid subtype of result type");
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}
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rewriter.replaceOp(op, op.getX());
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return success();
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}
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};
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} // namespace
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static LogicalResult refineShapeCalculateResult(ShapeCalculateOp op,
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int resultNum,
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PatternRewriter &rewriter) {
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auto yieldShapes = op.getCalculation().front().getTerminator();
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auto shape = yieldShapes->getOperand(resultNum);
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auto result = op->getResult(resultNum);
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// If the yielded shape is not a list literal, we can't analyze it.
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// AbstractlyInterpretListOpsWithinABlock should already have converted as
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// much as possible to literals.
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auto listConstruct = shape.getDefiningOp<PrimListConstructOp>();
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if (!listConstruct)
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return rewriter.notifyMatchFailure(
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op, "Expected result from ShapeCalculateOp calculation to be a "
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"`PrimListConstructOp`");
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llvm::BitVector clobberedElements(listConstruct->getNumOperands());
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// Analyze the users to determine if we can refine the shape.
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for (Operation *user : listConstruct->getUsers()) {
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// If an op doesn't mutate the list, then we can handle it.
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if (!potentiallyMutatesListOperands(user))
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continue;
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// We can handle Aten_SetItemTOp specially, since we know that it doesn't
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// change the size of the list. It might clobber some elements, which then
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// become dimensions with unknown size.
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if (auto setItem = dyn_cast<Aten_SetItemTOp>(user)) {
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// If the index is statically known, we can clobber only a single index.
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// Otherwise, we conservatively clobber all of them.
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llvm::Optional<int64_t> indexOpt = matchLegalConstantIndexIntoListOfSize(
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setItem.getIdx(), listConstruct->getNumOperands());
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if (indexOpt)
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clobberedElements.set(*indexOpt);
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else
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clobberedElements.set();
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continue;
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}
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// An unhandled op! We can't make any assumptions about the shape.
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return rewriter.notifyMatchFailure(op, "Unhandled op that mutates lists");
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}
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// Construct the list of sizes implied by the yielded shape.
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SmallVector<int64_t> sizes;
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for (auto operand : llvm::enumerate(listConstruct->getOperands())) {
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int64_t size;
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if (matchPattern(operand.value(), m_TorchConstantInt(&size)) &&
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!clobberedElements[operand.index()])
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sizes.push_back(size);
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else
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sizes.push_back(kUnknownSize);
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}
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auto originalResultType = result.getType().cast<BaseTensorType>();
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auto impliedTypesFromShape =
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originalResultType.cast<BaseTensorType>()
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.getWithSizesAndDtype(makeArrayRef(sizes),
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originalResultType.getOptionalDtype())
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.cast<BaseTensorType>();
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return updateCalculateOpResultTypes(op, resultNum, impliedTypesFromShape,
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rewriter);
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}
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namespace {
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// This pattern propagates information out of the shape calculation region and
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// into the ShapeCalculateOp result types.
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class RefineShapeCalculateOp : public OpRewritePattern<ShapeCalculateOp> {
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public:
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using OpRewritePattern::OpRewritePattern;
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LogicalResult matchAndRewrite(ShapeCalculateOp op,
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PatternRewriter &rewriter) const override {
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LogicalResult result = failure();
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for (int i = 0, e = op->getNumResults(); i != e; i++)
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if (succeeded(refineShapeCalculateResult(op, i, rewriter)))
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result = success();
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return result;
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}
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};
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} // namespace
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namespace {
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class SimplifyShapeCalculationsPass
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: public SimplifyShapeCalculationsBase<SimplifyShapeCalculationsPass> {
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void runOnOperation() override {
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MLIRContext *context = &getContext();
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RewritePatternSet patterns(context);
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patterns.insert<FullyUnrollPrimLoopOp>(context);
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patterns.insert<AbstractlyInterpretListOpsWithinABlock>(context);
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patterns.insert<DecomposeAtenSizeOp>(context);
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patterns.insert<RefineShapeCalculateOp>(context);
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patterns.insert<FoldPrimUncheckedCastOp>(context);
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PrimIfOp::getCanonicalizationPatterns(patterns, context);
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Aten__Getitem__TOp::getCanonicalizationPatterns(patterns, context);
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AtenSizeOp::getCanonicalizationPatterns(patterns, context);
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AtenLenTOp::getCanonicalizationPatterns(patterns, context);
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AtenAddTOp::getCanonicalizationPatterns(patterns, context);
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// TODO: Debug visitation order to make this more efficient.
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// A single linear scan should suffice.
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GreedyRewriteConfig config;
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config.useTopDownTraversal = true;
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config.maxIterations = GreedyRewriteConfig::kNoIterationLimit;
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if (failed(applyPatternsAndFoldGreedily(getOperation(), std::move(patterns),
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config))) {
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return signalPassFailure();
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}
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}
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};
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} // namespace
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std::unique_ptr<OperationPass<func::FuncOp>>
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mlir::torch::Torch::createSimplifyShapeCalculationsPass() {
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return std::make_unique<SimplifyShapeCalculationsPass>();
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}
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