blob: 07c8a8b1f1d99a160f3ef9aff5f4c365fc72407f [file] [log] [blame]
/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#include <string>
#include "llvm/ADT/StringRef.h"
#include "mlir/Dialect/Quant/QuantTypes.h" // from @llvm-project
#include "mlir/IR/Block.h" // from @llvm-project
#include "mlir/IR/Builders.h" // from @llvm-project
#include "mlir/IR/BuiltinTypes.h" // from @llvm-project
#include "mlir/IR/Operation.h" // from @llvm-project
#include "mlir/IR/Value.h" // from @llvm-project
#include "mlir/Pass/Pass.h" // from @llvm-project
#include "mlir/Pass/PassRegistry.h" // from @llvm-project
#include "tensorflow/compiler/mlir/tfr/ir/tfr_ops.h"
#include "tensorflow/compiler/mlir/tfr/passes/passes.h"
namespace mlir {
namespace TFR {
class RewriteQuantizedIOPass
: public PassWrapper<RewriteQuantizedIOPass, OperationPass<ModuleOp>> {
public:
StringRef getArgument() const final { return "tfr-rewrite-quantized-io"; }
StringRef getDescription() const final {
return "Replaces operands and results that has quantized type with their "
"storage types.";
}
void runOnOperation() override;
};
void RewriteQuantizedIOPass::runOnOperation() {
ModuleOp module = getOperation();
OpBuilder builder(module);
module.walk([&](func::FuncOp func) {
Block& block = func.front();
Operation* terminator = block.getTerminator();
// Replace input_arg(tensor<quant_type>) -> tfr.cast
// with input_arg(tensor<storage_type>) -> tfr.cast
for (BlockArgument arg : block.getArguments()) {
Type arg_type = arg.getType();
if (auto quant_type = arg_type.cast<TensorType>()
.getElementType()
.dyn_cast<quant::QuantizedType>()) {
if (arg.hasOneUse() && llvm::isa<TFR::CastOp>(*arg.user_begin())) {
arg.setType(
arg_type.cast<TensorType>().clone(quant_type.getStorageType()));
} else {
std::string error_message;
llvm::raw_string_ostream os{error_message};
os << "The argument with type ";
arg.getType().print(os);
os << " should have one user, which should be tfr.cast.";
func->emitError(error_message);
return;
}
}
}
builder.setInsertionPoint(terminator);
// Replace tfr.cast(tensor<quant_type>) -> output
// with tfr.cast(tensor<storage_type>) -> output
for (OpOperand& returned_value : terminator->getOpOperands()) {
auto returned_type =
returned_value.get().getType().dyn_cast<TensorType>();
if (!returned_type ||
!returned_type.getElementType().isa<quant::QuantizedType>()) {
continue;
}
if (auto returned_op =
returned_value.get().getDefiningOp<TFR::CastOp>()) {
auto new_type = returned_type.clone(returned_type.getElementType()
.cast<quant::QuantizedType>()
.getStorageType());
auto new_op = builder.create<TFR::CastOp>(returned_op->getLoc(),
new_type, returned_op.arg());
returned_value.set(new_op.getResult());
if (returned_op.use_empty()) {
returned_op.erase();
}
} else {
returned_value.get().getDefiningOp()->emitError(
"The producer of quantized type result should be a tfr.cast op.");
return;
}
}
auto new_func_type = builder.getFunctionType(block.getArgumentTypes(),
terminator->getOperandTypes());
func.setType(new_func_type);
});
}
// Creates an instance of the pass to decompose the TF ops.
std::unique_ptr<OperationPass<ModuleOp>> CreateRewriteQuantizedIOPass() {
return std::make_unique<RewriteQuantizedIOPass>();
}
static PassRegistration<RewriteQuantizedIOPass> pass([] {
return CreateRewriteQuantizedIOPass();
});
} // namespace TFR
} // namespace mlir