mirror of https://github.com/llvm/torch-mlir
111 lines
4.6 KiB
C++
111 lines
4.6 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/Dialect/Affine/IR/AffineOps.h"
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#include "mlir/Dialect/Arith/IR/Arith.h"
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#include "mlir/Dialect/Complex/IR/Complex.h"
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#include "mlir/Dialect/ControlFlow/IR/ControlFlowOps.h"
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#include "mlir/Dialect/Func/IR/FuncOps.h"
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#include "mlir/Dialect/Linalg/IR/Linalg.h"
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#include "mlir/Dialect/MLProgram/IR/MLProgram.h"
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#include "mlir/Dialect/Math/IR/Math.h"
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#include "mlir/Dialect/SCF/IR/SCF.h"
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#include "mlir/Dialect/Tensor/IR/Tensor.h"
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#include "mlir/IR/OpDefinition.h"
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#include "mlir/Transforms/DialectConversion.h"
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#include "torch-mlir-dialects/Dialect/TMTensor/IR/TMTensorDialect.h"
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#include "torch-mlir-dialects/Dialect/TMTensor/IR/TMTensorOps.h"
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#include "torch-mlir/Dialect/TorchConversion/IR/TorchConversionOps.h"
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#include "torch-mlir/Dialect/TorchConversion/Transforms/Passes.h"
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#include "mlir/IR/BuiltinOps.h"
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using namespace mlir;
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using namespace mlir::torch;
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using namespace mlir::torch::TorchConversion;
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using namespace TMTensor;
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namespace {
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class VerifyLinalgOnTensorsBackendContractPass
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: public VerifyLinalgOnTensorsBackendContractBase<
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VerifyLinalgOnTensorsBackendContractPass> {
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void runOnOperation() override {
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MLIRContext *context = &getContext();
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auto module = getOperation();
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TypeConverter converter;
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converter.addConversion([](RankedTensorType type) -> Type {
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if (BaseMemRefType::isValidElementType(type.getElementType()))
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return type;
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return nullptr;
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});
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TypeConverter scalarConverter;
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for (TypeConverter *c : {&converter, &scalarConverter}) {
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c->addConversion([](FloatType type) { return type; });
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c->addConversion([](IntegerType type) { return type; });
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c->addConversion([](IndexType type) { return type; });
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c->addConversion([](ComplexType type) { return type; });
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}
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auto opHasLegalTypes = [&](Operation *op) { return converter.isLegal(op); };
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auto isLegalScalarOp = [&](Operation *op) {
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// We recognize basic scalar ops by them having the trait "Elementwise",
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// even though we don't expect them to operate on tensors.
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return scalarConverter.isLegal(op) &&
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op->hasTrait<OpTrait::Elementwise>();
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};
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ConversionTarget target(*context);
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// Structural operations.
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target.addDynamicallyLegalOp<ModuleOp, func::FuncOp, func::ReturnOp>(
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opHasLegalTypes);
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target.addDynamicallyLegalOp<GetNextSeedOp>(opHasLegalTypes);
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// Basic scalar operations.
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target.addDynamicallyLegalDialect<func::FuncDialect>(isLegalScalarOp);
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target.addDynamicallyLegalDialect<math::MathDialect>(isLegalScalarOp);
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target.addDynamicallyLegalDialect<arith::ArithDialect>(isLegalScalarOp);
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target.addDynamicallyLegalDialect<complex::ComplexDialect>(isLegalScalarOp);
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// Tensor operations should go through linalg and the tensor dialect.
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target.addDynamicallyLegalDialect<linalg::LinalgDialect>(opHasLegalTypes);
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target.addDynamicallyLegalDialect<tensor::TensorDialect>(opHasLegalTypes);
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target.addDynamicallyLegalDialect<affine::AffineDialect>(opHasLegalTypes);
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target.addDynamicallyLegalDialect<cf::ControlFlowDialect>(opHasLegalTypes);
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target.addDynamicallyLegalDialect<TMTensorDialect>(opHasLegalTypes);
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target.addDynamicallyLegalDialect<scf::SCFDialect>(opHasLegalTypes);
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target.addDynamicallyLegalDialect<ml_program::MLProgramDialect>(
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opHasLegalTypes);
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// ConstantOp is used for tensors and for scalars.
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target.addDynamicallyLegalOp<arith::ConstantOp>(opHasLegalTypes);
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target.addDynamicallyLegalOp<complex::CreateOp>(opHasLegalTypes);
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RewritePatternSet patterns(context);
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if (failed(applyFullConversion(module, target, std::move(patterns)))) {
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// We avoid `module.emitError()` so that mlir-print-op-on-diagnostics
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// doesn't unnecessarily spew out the entire module.
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emitError(module.getLoc())
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<< "Module does not conform to the linalg-on-tensors backend contract. "
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"See dialect conversion legality information above.";
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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<ModuleOp>>
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mlir::torch::TorchConversion::createVerifyLinalgOnTensorsBackendContractPass() {
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return std::make_unique<VerifyLinalgOnTensorsBackendContractPass>();
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}
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