6024e5c395
clang 20.0.0 (based on r547379) from build 12806354. Bug: http://b/379133546 Test: N/A Change-Id: I2eb8938af55d809de674be63cb30cf27e801862b Upstream-Commit: ad834e67b1105d15ef907f6255d4c96e8e733f57
79 lines
2.8 KiB
C++
79 lines
2.8 KiB
C++
//===- ModelUnderTrainingRunner.h -- 'development' mode runner --*- 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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//
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//===----------------------------------------------------------------------===//
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//
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#ifndef LLVM_ANALYSIS_MODELUNDERTRAININGRUNNER_H
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#define LLVM_ANALYSIS_MODELUNDERTRAININGRUNNER_H
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#include "llvm/ADT/STLExtras.h"
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#include "llvm/ADT/iterator_range.h"
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#include "llvm/Analysis/TensorSpec.h"
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#include "llvm/Config/llvm-config.h"
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#ifdef LLVM_HAVE_TFLITE
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#include "llvm/Analysis/MLModelRunner.h"
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#include "llvm/Analysis/Utils/TFUtils.h"
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#include "llvm/IR/LLVMContext.h"
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#include "llvm/IR/PassManager.h"
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namespace llvm {
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/// ModelUnderTrainingRunner - training mode implementation. It uses TFLite
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/// to dynamically load and evaluate a TF SavedModel
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/// (https://www.tensorflow.org/guide/saved_model) converted to TFLite. see
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/// lib/Analysis/models/saved-model-to-tflite.py. Runtime performance is
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/// sacrificed for ease of use while training.
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class ModelUnderTrainingRunner final : public MLModelRunner {
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public:
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// Disallows copy and assign.
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ModelUnderTrainingRunner(const ModelUnderTrainingRunner &) = delete;
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ModelUnderTrainingRunner &
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operator=(const ModelUnderTrainingRunner &) = delete;
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const std::vector<TensorSpec> &extraOutputsForLoggingSpecs() const {
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return ExtraOutputsForLogging;
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}
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const void *getUntypedExtraOutputValue(size_t ExtraOutputIndex) const {
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return lastEvaluationResult()->getUntypedTensorValue(ExtraOutputIndex + 1);
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}
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const std::optional<TFModelEvaluator::EvaluationResult> &
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lastEvaluationResult() const {
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return LastEvaluationResult;
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}
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static bool classof(const MLModelRunner *R) {
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return R->getKind() == MLModelRunner::Kind::Development;
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}
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static std::unique_ptr<ModelUnderTrainingRunner>
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createAndEnsureValid(LLVMContext &Ctx, const std::string &ModelPath,
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StringRef DecisionName,
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const std::vector<TensorSpec> &InputSpecs,
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StringRef OutputSpecsPathOverride = "");
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ModelUnderTrainingRunner(
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LLVMContext &Ctx, const std::string &ModelPath,
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const std::vector<TensorSpec> &InputSpecs,
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const std::vector<TensorSpec> &OutputSpecs,
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const std::vector<TensorSpec> &ExtraOutputsForLogging = {});
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bool isValid() const { return !!Evaluator; }
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private:
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std::unique_ptr<TFModelEvaluator> Evaluator;
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const std::vector<TensorSpec> OutputSpecs;
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const std::vector<TensorSpec> ExtraOutputsForLogging;
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std::optional<TFModelEvaluator::EvaluationResult> LastEvaluationResult;
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void *evaluateUntyped() override;
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};
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} // namespace llvm
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#endif // define(LLVM_HAVE_TFLITE)
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#endif // LLVM_ANALYSIS_MODELUNDERTRAININGRUNNER_H
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