mirror of https://github.com/llvm/torch-mlir
85 lines
3.0 KiB
C++
85 lines
3.0 KiB
C++
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//===- VerifyInvariantsBeforeBackendLowering.cpp -----------------*- C++-*-===//
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//
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// This file is licensed 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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//
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//===----------------------------------------------------------------------===//
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#include "PassDetail.h"
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#include "mlir/IR/Builders.h"
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#include "mlir/IR/BuiltinOps.h"
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#include "npcomp/Dialect/Torch/IR/TorchOps.h"
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#include "npcomp/Dialect/TorchConversion/IR/TorchConversionOps.h"
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#include "npcomp/Dialect/TorchConversion/Transforms/Passes.h"
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using namespace mlir;
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using namespace mlir::NPCOMP;
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using namespace mlir::NPCOMP::TorchConversion;
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static LogicalResult checkValueInvariants(Operation *errorReportOp, Value v) {
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// TODO: Make this an allowlist instead of a denylist.
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// TODO: Make this stricter.
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auto type = v.getType();
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if (auto valueTensorType = type.dyn_cast<Torch::ValueTensorType>()) {
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if (!valueTensorType.hasDtype() || !valueTensorType.hasSizes())
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return errorReportOp->emitError()
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.append("unsupported by backend lowering: tensor with unknown rank "
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"or dtype")
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.attachNote()
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.append("this is likely due to a missing case in RefineTypes");
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}
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return success();
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}
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namespace {
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class VerifyInvariantsBeforeBackendLoweringPass
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: public VerifyInvariantsBeforeBackendLoweringBase<
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VerifyInvariantsBeforeBackendLoweringPass> {
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void runOnOperation() override {
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// TODO: It seems that the walkers over blocks are not correctly
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// propagating `walkResult.wasInterrupted()` so use a manual `didFail`
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// boolean.
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bool didFail = false;
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getOperation().walk([&](Block *block) {
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// Check invariants on all the Value's in the program.
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// That is, check all BlockArgument's and OpResult's.
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for (BlockArgument arg : block->getArguments()) {
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if (failed(checkValueInvariants(block->getParentOp(), arg))) {
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didFail = true;
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return WalkResult::interrupt();
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}
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}
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for (Operation &op : *block) {
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if (isa<Torch::OperatorOp>(op)) {
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op.emitError()
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.append("unsupported by backend lowering: `torch.operator` op")
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.attachNote()
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.append("this is likely due to a missing op that needs to be "
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"generated by torch_ods_gen.py");
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didFail = true;
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return WalkResult::interrupt();
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}
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for (OpResult result : op.getResults()) {
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if (failed(checkValueInvariants(&op, result))) {
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didFail = true;
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return WalkResult::interrupt();
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}
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}
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}
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return WalkResult::advance();
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});
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if (didFail)
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return signalPassFailure();
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}
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};
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} // namespace
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std::unique_ptr<OperationPass<ModuleOp>> mlir::NPCOMP::TorchConversion::
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createVerifyInvariantsBeforeBackendLoweringPass() {
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return std::make_unique<VerifyInvariantsBeforeBackendLoweringPass>();
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}
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