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torch-mlir/lib/Dialect/Torch/Transforms/MaximizeValueSemantics.cpp

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//===- MaximizeValueSemantics.cpp --------------------------------*- C++-*-===//
//
// This file is licensed under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// Also available under a BSD-style license. See LICENSE.
//
//===----------------------------------------------------------------------===//
#include "PassDetail.h"
#include "mlir/IR/BuiltinOps.h"
#include "mlir/IR/PatternMatch.h"
#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
#include "torch-mlir/Dialect/Torch/IR/TorchOps.h"
#include "torch-mlir/Dialect/Torch/Transforms/Passes.h"
#include "torch-mlir/Dialect/Torch/Utils/Utils.h"
using namespace mlir;
using namespace mlir::torch;
using namespace mlir::torch::Torch;
static Value assertNonValueTensor(Value tensor) {
assert(isa<NonValueTensorType>(tensor.getType()) &&
"tensor is expected to be a non-value tensor");
return tensor;
}
// A cast-like op is an op that does not modify the contents, shape, and dtype
// of the input tensor. In other words, it is an op that only serves to encode
// compile time information, but at runtime the op behaves like a no-op.
static bool isCastLikeOp(Operation *op) {
return isa<TensorStaticInfoCastOp>(op);
}
// Given a `value`, this function goes up the use-def chain and finds the
// largest sequence of consecutive cast-like ops. The returned set contains all
// the aliases that are identical to `value`, and have only been transformed by
// cast-like ops.
static DenseSet<Value> getCastLikeAliasesOf(Value value) {
Operation *currentOp = value.getDefiningOp();
DenseSet<Value> result;
while (isCastLikeOp(currentOp)) {
Value operand = assertNonValueTensor(currentOp->getOperand(0));
result.insert(operand);
currentOp = operand.getDefiningOp();
}
return result;
}
namespace {
class AbstractlyInterpretCopyToNonValueTensorOpUsersWithinABlock
: public OpRewritePattern<CopyToNonValueTensorOp> {
public:
using OpRewritePattern::OpRewritePattern;
// Used to represent all of the interpreted ops that have at least
// one non-value tensor as input or output.
struct InterpretedOps {
SmallVector<Operation *> copyLikeOps;
SmallVector<Operation *> viewLikeOps;
SmallVector<OverwriteTensorContentsOp> overwriteTensorContentsOps;
std::optional<mlir::func::ReturnOp> returnOp;
};
// Check that graph rewriting is possible by doing an abstract
// interpretation within a single basic block. If rewriting is
// possible, the interpreted ops are returned split into their
// respective categories.
static FailureOr<InterpretedOps> abstractlyInterpretSlice(
CopyToNonValueTensorOp copyToNonValueTensor,
const DenseMap<Operation *, SmallVector<Value>> &nonValueTensorsUsedByOp,
PatternRewriter &rewriter) {
// Sort by order in the block, so we can abstractly interpret the ops.
SmallVector<Operation *> nonValueTensorUsers(
llvm::make_first_range(nonValueTensorsUsedByOp));
llvm::sort(nonValueTensorUsers, [](Operation *lhs, Operation *rhs) {
return lhs->isBeforeInBlock(rhs);
});
// We track the available aliases at each point as well as split the
// users into view-like, copy-to-value, and overwrite ops as we walk
// forward.
InterpretedOps result;
result.copyLikeOps.push_back(copyToNonValueTensor);
DenseSet<Value> availableAliases{
assertNonValueTensor(copyToNonValueTensor.getResult())};
for (Operation *user : nonValueTensorUsers) {
for (Value operand : nonValueTensorsUsedByOp.lookup(user)) {
if (!availableAliases.contains(operand)) {
return rewriter.notifyMatchFailure(
copyToNonValueTensor,
"operand of op is not a valid tensor alias");
}
}
if (isViewLikeOp(user)) {
Value userResult = user->getResult(0);
// View-like ops produce a new alias available to later ops.
// However, if the view-like op has been partially converted
// to use value semantics (which happens for example with ops
// that take two aliases as input), then it is possible that the
// op no longer generates an alias.
if (isa<NonValueTensorType>(userResult.getType()))
availableAliases.insert(userResult);
result.viewLikeOps.push_back(user);
} else if (auto copyToValueTensor = dyn_cast<CopyToValueTensorOp>(user)) {
result.copyLikeOps.push_back(copyToValueTensor);
} else if (auto overwrite = dyn_cast<OverwriteTensorContentsOp>(user)) {
// To simplify the analysis, we only support the case where the
// only aliases used after an overwrite are the aliases generated
// after plus the alias being overwritten and any aliases that are
// simply a cast of the overwritten alias.
availableAliases.clear();
Value overwritten = overwrite.getOverwritten();
availableAliases.insert(assertNonValueTensor(overwritten));
DenseSet<Value> castLikeAliases = getCastLikeAliasesOf(overwritten);
availableAliases.insert(castLikeAliases.begin(), castLikeAliases.end());
result.overwriteTensorContentsOps.push_back(overwrite);
} else if (auto returnOp = dyn_cast<mlir::func::ReturnOp>(user)) {
result.returnOp = returnOp;
} else {
return rewriter.notifyMatchFailure(
copyToNonValueTensor, "unsupported op `" +
user->getName().getStringRef() +
"` encountered during abstract analysis");
}
}
return result;
}
// Rewrite slice composed of the interpreted ops so that the slice uses
// value semantics everywhere.
static void rewriteSlice(const InterpretedOps &ops,
PatternRewriter &rewriter) {
DenseMap<int, Type> originalReturnTypes;
if (ops.returnOp.has_value()) {
auto returnOp = ops.returnOp.value();
for (auto operand : llvm::enumerate(returnOp->getOperands())) {
auto type = operand.value().getType();
if (!isa<NonValueTensorType>(type))
continue;
originalReturnTypes[operand.index()] = type;
}
}
// The rewriting for the overwrite op involves replacing all uses of its
// non-value tensor operand with its value tensor operand. Since the
// rewriting of other ops can potentially change the non-value tensor
// operand to a value tensor, this rewriting MUST happen first to avoid
// wrongly replacing operands that were previously not a view of the
// overwritten tensor.
for (OverwriteTensorContentsOp overwrite :
llvm::reverse(ops.overwriteTensorContentsOps)) {
Value overwritten = assertNonValueTensor(overwrite.getOverwritten());
// Cast-like aliases represent the exact same tensor at runtime as the
// overwritten alias, since casts only encode compile time information.
// Therefore, here we replace the overwritten value and any cast-like
// aliases of it with the overwrite value.
DenseSet<Value> overwrittenAliases = getCastLikeAliasesOf(overwritten);
overwrittenAliases.insert(overwritten);
for (Value alias : overwrittenAliases) {
alias.replaceUsesWithIf(
overwrite.getValue(), [&](const OpOperand &operand) {
return !operand.getOwner()->isBeforeInBlock(overwrite);
});
}
rewriter.eraseOp(overwrite);
}
for (Operation *copyLikeOp : ops.copyLikeOps)
rewriter.replaceOp(copyLikeOp, copyLikeOp->getOperand(0));
// Replace return type of view-like ops with value-semantics type variant.
for (Operation *viewLikeOp : ops.viewLikeOps) {
rewriter.modifyOpInPlace(viewLikeOp, [&] {
Value result = viewLikeOp->getResult(0);
auto resultType = dyn_cast<NonValueTensorType>(result.getType());
if (resultType)
result.setType(resultType.getWithValueSemantics());
});
}
if (ops.returnOp.has_value()) {
auto returnOp = ops.returnOp.value();
for (int i = 0, e = returnOp->getNumOperands(); i < e; i++) {
OpOperand &operand = returnOp->getOpOperand(i);
auto it = originalReturnTypes.find(i);
if (it == originalReturnTypes.end())
continue;
auto originalType = it->second.cast<NonValueTensorType>();
rewriter.setInsertionPoint(returnOp);
Value newReturnValue = copyTensorToType(rewriter, returnOp->getLoc(),
originalType, operand.get());
operand.set(newReturnValue);
}
}
}
LogicalResult matchAndRewrite(CopyToNonValueTensorOp copy,
PatternRewriter &rewriter) const override {
// Find a subgraph starting with this CopyToNonValueTensorOp, and
// terminating at CopyToValueTensorOp's, possibly with intervening view-like
// ops and overwrites. This also catches the special case of a
// CopyToNonValueTensorOp that trivially feeds into CopyToValueTensorOp's.
DenseMap<Operation *, SmallVector<Value>> nonValueTensorsUsedByOp;
// Some view-like ops take more than one non-value tensor as input (such as
// `aten.view_as`). For these ops, we assume that the tensor view that gets
// returned by the op is a view of the first operand of the op.
// View-like ops that return a non-value tensor and have a view of the
// operand of `copy.to_tensor` as the first operand.
DenseSet<Operation *> validViewLikeOps;
// View-like ops that return a non-value tensor and have a view of the
// operand of `copy.to_tensor` as an operand other than the first operand.
DenseSet<Operation *> viewLikeOpsToCheck;
using OpOperandRefs = SmallVector<std::reference_wrapper<OpOperand>>;
OpOperandRefs workList(copy.getResult().getUses());
while (!workList.empty()) {
OpOperand &operand = workList.pop_back_val();
Operation *op = operand.getOwner();
if (op->getBlock() != copy->getBlock()) {
return rewriter.notifyMatchFailure(
copy, "can only analyze within a single basic block");
}
if (isViewLikeOp(op)) {
// We currently only support view-like ops with one tensor output.
if (op->getNumResults() != 1 ||
!isa<BaseTensorType>(op->getResult(0).getType())) {
return rewriter.notifyMatchFailure(
copy, "unsupported: view-like ops must have one tensor output, "
"and the tensor output must be the first result");
}
Value opResult = op->getResult(0);
// There are cases where a view-like op will be partially converted to
// value semantics, resulting in at least one of the inputs being a
// non-value tensor and the output being a value tensor. If this is the
// case then there is no need to look at the users of the result of the
// op.
if (isa<NonValueTensorType>(opResult.getType())) {
if (operand.getOperandNumber() == 0) {
validViewLikeOps.insert(op);
llvm::append_range(workList, opResult.getUses());
} else {
viewLikeOpsToCheck.insert(op);
}
}
}
nonValueTensorsUsedByOp[op].push_back(
assertNonValueTensor(operand.get()));
}
// Nothing to do if there is just a ReturnOp -- we know that we won't be
// rewriting anything, since we must preserve the ReturnOp's original type.
if (llvm::hasSingleElement(nonValueTensorsUsedByOp) &&
isa<mlir::func::ReturnOp>(nonValueTensorsUsedByOp.begin()->first)) {
return failure();
}
if (llvm::any_of(viewLikeOpsToCheck, [&](Operation *op) {
return !validViewLikeOps.contains(op);
})) {
return rewriter.notifyMatchFailure(
copy, "if a view-like op returns a non-value tensor, the first "
"operand must be a view of the operand of the `copy.to_tensor` "
"op");
}
FailureOr<InterpretedOps> interpretedOps =
abstractlyInterpretSlice(copy, nonValueTensorsUsedByOp, rewriter);
if (failed(LogicalResult(interpretedOps)))
return failure();
rewriteSlice(*interpretedOps, rewriter);
return success();
}
};
} // namespace
namespace {
// Calculate a forward slice starting from a CopyToNonValueTensorOp
// and ending at CopyToValueTensorOp's. If all intervening ops
// are just view-like operations (i.e. no mutation), then we can trivially
// convert them all to value semantics.
// This pattern handles the case where views span multiple basic blocks,
// which is currently not supported by
// `AbstractlyInterpretCopyToNonValueTensorOpUsersWithinABlock`.
class RewriteViewLikeSubgraph
: public OpRewritePattern<CopyToNonValueTensorOp> {
public:
using OpRewritePattern::OpRewritePattern;
LogicalResult matchAndRewrite(CopyToNonValueTensorOp copy,
PatternRewriter &rewriter) const override {
// Find a subgraph starting with this CopyToNonValueTensorOp, and
// terminating at CopyToValueTensorOp's or ReturnOp's, possibly with
// intervening view-like ops.
// This also catches the special case of a CopyToNonValueTensorOp that
// trivially feeds into CopyToValueTensorOp's.
SmallVector<Operation *> viewLikeOps;
SmallVector<CopyToValueTensorOp> copyToValueTensorOps;
SmallVector<mlir::func::ReturnOp> returnOps;
auto workList = llvm::to_vector<6>(copy.getResult().getUsers());
// We currently only support view-like ops with one tensor input and one
// tensor output, meaning that the tensor use-def chains form a tree.
// This will not be the case for an op like `torch.aten.view_as`, so
// we will need to add a set to prune duplicate visitation.
while (!workList.empty()) {
Operation *op = workList.pop_back_val();
if (auto copyToValueTensor = dyn_cast<CopyToValueTensorOp>(op)) {
copyToValueTensorOps.push_back(copyToValueTensor);
} else if (auto returnOp = dyn_cast<mlir::func::ReturnOp>(op)) {
returnOps.push_back(returnOp);
} else if (isViewLikeOp(op)) {
viewLikeOps.push_back(op);
llvm::append_range(workList, op->getResult(0).getUsers());
} else {
return rewriter.notifyMatchFailure(
copy, "can only handle these transitive user ops");
}
}
if (copyToValueTensorOps.empty() && viewLikeOps.empty())
return rewriter.notifyMatchFailure(copy, "no types to change");
// All CopyToValueTensorOp operands will be changed to the correct type
// by the logic below.
for (CopyToValueTensorOp op : copyToValueTensorOps)
rewriter.replaceOp(op, op.getOperand());
// All uses of `copy` will be updated by the logic below.
copy.replaceAllUsesWith(copy.getOperand());
// Keep track of the original types of any view-like ops, so that we can
// correctly copy them back to their mlir::func::ReturnOp's expected types.
DenseMap<Value, Type> originalTypes;
for (Operation *op : viewLikeOps) {
rewriter.modifyOpInPlace(op, [&]() {
if (auto nonValueTensorType =
dyn_cast<NonValueTensorType>(op->getResult(0).getType())) {
originalTypes[op->getResult(0)] = nonValueTensorType;
op->getResult(0).setType(nonValueTensorType.getWithValueSemantics());
}
});
}
// For ReturnOp's, we need to update the operands to their original types.
for (mlir::func::ReturnOp op : returnOps) {
for (int i = 0, e = op->getNumOperands(); i < e; i++) {
OpOperand &operand = op->getOpOperand(i);
auto it = originalTypes.find(operand.get());
if (it == originalTypes.end())
continue;
auto originalType = it->second.cast<BaseTensorType>();
rewriter.setInsertionPoint(op);
Value newReturnValue = copyTensorToType(rewriter, op->getLoc(),
originalType, operand.get());
operand.set(newReturnValue);
}
}
return success();
}
};
} // namespace
namespace {
class MaximizeValueSemanticsPass
: public MaximizeValueSemanticsBase<MaximizeValueSemanticsPass> {
void runOnOperation() override {
MLIRContext *context = &getContext();
auto func = getOperation();
RewritePatternSet patterns(context);
patterns.insert<AbstractlyInterpretCopyToNonValueTensorOpUsersWithinABlock,
RewriteViewLikeSubgraph>(context);
(void)applyPatternsAndFoldGreedily(func, std::move(patterns));
}
};
} // namespace
std::unique_ptr<OperationPass<func::FuncOp>>
mlir::torch::Torch::createMaximizeValueSemanticsPass() {
return std::make_unique<MaximizeValueSemanticsPass>();
}
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