| /* Copyright 2019 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 "absl/memory/memory.h" |
| #include "absl/strings/str_split.h" |
| #include "llvm/ADT/APFloat.h" |
| #include "llvm/ADT/DenseMap.h" |
| #include "llvm/ADT/STLExtras.h" |
| #include "llvm/ADT/SmallVector.h" |
| #include "llvm/ADT/StringMap.h" |
| #include "llvm/ADT/StringSwitch.h" |
| #include "llvm/Support/Regex.h" |
| #include "llvm/Support/raw_ostream.h" |
| #include "mlir/Dialect/Func/IR/FuncOps.h" // from @llvm-project |
| #include "mlir/Dialect/Quant/FakeQuantSupport.h" // from @llvm-project |
| #include "mlir/Dialect/Quant/QuantOps.h" // from @llvm-project |
| #include "mlir/IR/AffineExpr.h" // from @llvm-project |
| #include "mlir/IR/AffineMap.h" // from @llvm-project |
| #include "mlir/IR/Attributes.h" // from @llvm-project |
| #include "mlir/IR/BuiltinTypes.h" // from @llvm-project |
| #include "mlir/IR/Location.h" // from @llvm-project |
| #include "mlir/IR/PatternMatch.h" // from @llvm-project |
| #include "mlir/Pass/Pass.h" // from @llvm-project |
| #include "mlir/Support/LLVM.h" // from @llvm-project |
| #include "tensorflow/compiler/mlir/lite/quantization/quantization_info.pb.h" |
| #include "tensorflow/compiler/mlir/lite/quantization/quantization_passes.h" |
| #include "tensorflow/compiler/mlir/tensorflow/utils/import_utils.h" |
| #include "tensorflow/compiler/mlir/tensorflow/utils/location_utils.h" |
| |
| // NOLINTNEXTLINE |
| static llvm::cl::opt<std::string> quantize_stats( |
| "quant-test-stats", llvm::cl::value_desc("string"), |
| llvm::cl::desc("serialized quant info string. Only used in tests"), |
| llvm::cl::init("")); |
| |
| //===----------------------------------------------------------------------===// |
| // The Pass to import quantization stats to the ops in a function. This requires |
| // a custom method to retrieve the unique name of the operation. |
| |
| namespace mlir { |
| namespace quant { |
| |
| using QuantParamsEntry = QuantizationInfo::QuantParams; |
| |
| namespace { |
| class ImportQuantStatsPass |
| : public PassWrapper<ImportQuantStatsPass, OperationPass<func::FuncOp>> { |
| public: |
| MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(ImportQuantStatsPass) |
| |
| explicit ImportQuantStatsPass(OperationToName op_to_name) |
| : op_to_name_(op_to_name) {} |
| |
| StringRef getArgument() const final { |
| // This is the argument used to refer to the pass in |
| // the textual format (on the commandline for example). |
| return "quant-import-stats"; |
| } |
| StringRef getDescription() const final { |
| // This is a brief description of the pass. |
| return "Import quantization stats to the model"; |
| } |
| |
| void runOnOperation() override; |
| |
| void getDependentDialects(DialectRegistry ®istry) const override { |
| registry.insert<quant::QuantizationDialect>(); |
| } |
| |
| // Parses the serialized quant stats protobuf and initialize the internal |
| // data structure. This method must be called after the pass is created. |
| bool ParseQuantStats(const std::string &stats_str); |
| |
| private: |
| void ImportAsStatsOps(OpBuilder b, Operation *op, int index, |
| const QuantParamsEntry &info); |
| |
| void InsertStatsOpAtResult(OpBuilder b, Value res, ElementsAttr layer_stats, |
| ElementsAttr axis_stats, IntegerAttr axis); |
| |
| // If the index is out of range, this method returns false. Otherwise it |
| // returns true if the value is a float tensor. |
| bool IsQuantizableResult(Operation *op, int index) { |
| if (index < 0 || index >= static_cast<int>(op->getNumResults())) |
| return false; |
| Value res = op->getResult(index); |
| return res.getType().isa<ShapedType>() && |
| res.getType().cast<ShapedType>().getElementType().isa<FloatType>(); |
| } |
| |
| // A method to retrieve the name for the given op. |
| OperationToName op_to_name_; |
| |
| // We split the normal names and regex names, since the former can use hash |
| // map to lookup and the latter needs to iterate all the regex to find the |
| // match. |
| // The `int` in the following two containers are to specify the result index |
| // of the given op. -1 indicates all the floating-point results. |
| llvm::StringMap<std::pair<int, const QuantParamsEntry>> name_to_info_; |
| llvm::StringMap<std::pair<int, const QuantParamsEntry>> regex_to_info_; |
| }; |
| } // namespace |
| |
| bool ImportQuantStatsPass::ParseQuantStats(const std::string &stats_str) { |
| QuantizationInfo quant_stats; |
| if (!tensorflow::LoadProtoFromBuffer(stats_str, &quant_stats).ok()) { |
| return true; |
| } |
| |
| for (const auto &entry : quant_stats.entries()) { |
| if (!entry.name().empty()) { |
| std::vector<std::string> name_and_port = |
| absl::StrSplit(entry.name(), ':'); |
| int port = name_and_port.size() == 2 ? std::stoi(name_and_port[1]) : -1; |
| name_to_info_.insert({name_and_port[0], {port, entry}}); |
| } else if (!entry.name_regex().empty()) { |
| std::vector<std::string> name_and_port = |
| absl::StrSplit(entry.name_regex(), ':'); |
| int port = name_and_port.size() == 2 ? std::stoi(name_and_port[1]) : -1; |
| regex_to_info_.insert({name_and_port[0], {port, entry}}); |
| } |
| } |
| return false; |
| } |
| |
| void ImportQuantStatsPass::InsertStatsOpAtResult(OpBuilder b, Value res, |
| ElementsAttr layer_stats, |
| ElementsAttr axis_stats, |
| IntegerAttr axis) { |
| auto stats_op = b.create<quant::StatisticsOp>(b.getUnknownLoc(), res, |
| layer_stats, axis_stats, axis); |
| res.replaceAllUsesWith(stats_op); |
| stats_op.getOperation()->replaceUsesOfWith(stats_op, res); |
| } |
| |
| void ImportQuantStatsPass::ImportAsStatsOps(OpBuilder b, Operation *op, |
| int index, |
| const QuantParamsEntry &info) { |
| if (info.params_size() == 0) return; |
| |
| SmallVector<APFloat, 4> min_maxs; |
| min_maxs.reserve(info.params_size() * 2); |
| for (const auto ¶m : info.params()) { |
| llvm::APFloat min(param.min_max().min()); |
| llvm::APFloat max(param.min_max().max()); |
| min_maxs.push_back(min); |
| min_maxs.push_back(max); |
| } |
| // The layer stats contain only the first min/max pairs. |
| ElementsAttr layer_stats = DenseFPElementsAttr::get( |
| RankedTensorType::get({2}, b.getF32Type()), {min_maxs[0], min_maxs[1]}); |
| ElementsAttr axis_stats; |
| IntegerAttr axis; |
| |
| if (info.params_size() > 1) { |
| SmallVector<int64_t, 4> axis_stats_shape{info.params_size(), 2}; |
| axis_stats = DenseFPElementsAttr::get( |
| RankedTensorType::get(axis_stats_shape, b.getF32Type()), min_maxs); |
| axis = b.getI64IntegerAttr(info.meta().quantize_axis()); |
| } |
| |
| b.setInsertionPointAfter(op); |
| if (IsQuantizableResult(op, index)) { |
| InsertStatsOpAtResult(b, op->getResult(index), layer_stats, axis_stats, |
| axis); |
| } else { |
| for (int i = 0, e = op->getNumResults(); i < e; ++i) { |
| if (IsQuantizableResult(op, i)) { |
| InsertStatsOpAtResult(b, op->getResult(i), layer_stats, axis_stats, |
| axis); |
| } |
| } |
| } |
| } |
| |
| void ImportQuantStatsPass::runOnOperation() { |
| func::FuncOp func = getOperation(); |
| OpBuilder builder(func); |
| |
| func.walk([&](Operation *op) { |
| if (op->hasTrait<OpTrait::IsTerminator>()) return; |
| auto op_name = op_to_name_(op); |
| |
| // Check the named info collection first. |
| auto it = name_to_info_.find(op_name); |
| if (it != name_to_info_.end()) { |
| ImportAsStatsOps(builder, op, it->second.first, it->second.second); |
| return; |
| } |
| |
| // Iterate all the regex names and matches the first one. |
| for (auto ®ex : regex_to_info_) { |
| if (llvm::Regex(regex.first()).match(op_name)) { |
| ImportAsStatsOps(builder, op, regex.second.first, regex.second.second); |
| break; |
| } |
| } |
| }); |
| } |
| |
| // Creates an instance of the default quant parameters pass. |
| std::unique_ptr<OperationPass<func::FuncOp>> CreateImportQuantStatsPass( |
| OperationToName op_to_name, const std::string &stats_str) { |
| auto pass = absl::make_unique<ImportQuantStatsPass>(op_to_name); |
| if (pass->ParseQuantStats(stats_str)) return nullptr; |
| return pass; |
| } |
| |
| // Creates an instance pass to import quantization stats to the operations in |
| // the function. A custom method to get the name from the op is used because |
| // different dialect ops might have different ways to assign the name. |
| std::unique_ptr<OperationPass<func::FuncOp>> |
| CreateImportQuantStatsPassForTFControlDialect(const std::string &stats_str) { |
| auto get_name_func = [](Operation *op) { |
| Location loc = tensorflow::GetLocationWithoutOpType(op->getLoc()); |
| if (auto name = loc.dyn_cast<NameLoc>()) { |
| return name.getName().strref(); |
| } else if (auto fused_name = loc.dyn_cast<FusedLoc>()) { |
| for (auto sub_loc : fused_name.getLocations()) { |
| if (auto named_sub_loc = sub_loc.dyn_cast<NameLoc>()) { |
| return named_sub_loc.getName().strref(); |
| } |
| } |
| } |
| return llvm::StringRef(""); |
| }; |
| |
| return CreateImportQuantStatsPass(get_name_func, stats_str); |
| } |
| |
| // Registers this pass with default values, only for test |
| static PassRegistration<ImportQuantStatsPass> pass([] { |
| return CreateImportQuantStatsPassForTFControlDialect(quantize_stats); |
| }); |
| |
| } // namespace quant |
| } // namespace mlir |