| //===- TFUtils.h - utilities for tensorflow C API ---------------*- C++ -*-===// |
| // |
| // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. |
| // See https://llvm.org/LICENSE.txt for license information. |
| // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception |
| // |
| //===----------------------------------------------------------------------===// |
| // |
| #ifndef LLVM_ANALYSIS_UTILS_TFUTILS_H |
| #define LLVM_ANALYSIS_UTILS_TFUTILS_H |
| |
| #include "llvm/Config/llvm-config.h" |
| |
| #ifdef LLVM_HAVE_TF_API |
| #include "llvm/ADT/StringMap.h" |
| #include "llvm/Analysis/TensorSpec.h" |
| #include "llvm/IR/LLVMContext.h" |
| #include "llvm/Support/JSON.h" |
| |
| #include <memory> |
| #include <vector> |
| |
| namespace llvm { |
| |
| /// Load a SavedModel, find the given inputs and outputs, and setup storage |
| /// for input tensors. The user is responsible for correctly dimensioning the |
| /// input tensors and setting their values before calling evaluate(). |
| /// To initialize: |
| /// - construct the object |
| /// - initialize the input tensors using initInput. Indices must correspond to |
| /// indices in the InputNames used at construction. |
| /// To use: |
| /// - set input values by using getInput to get each input tensor, and then |
| /// setting internal scalars, for all dimensions (tensors are row-major: |
| /// https://github.com/tensorflow/tensorflow/blob/r1.5/tensorflow/c/c_api.h#L205) |
| /// - call evaluate. The input tensors' values are not consumed after this, and |
| /// may still be read. |
| /// - use the outputs in the output vector |
| class TFModelEvaluatorImpl; |
| class EvaluationResultImpl; |
| |
| /// Logging utility - given an ordered specification of features, and assuming |
| /// a scalar reward, allow logging feature values and rewards, and then print |
| /// as tf.train.SequenceExample text protobuf. |
| /// The assumption is that, for an event to be logged (i.e. a set of feature |
| /// values and a reward), the user calls the log* API for each feature exactly |
| /// once, providing the index matching the position in the feature spec list |
| /// provided at construction. The example assumes the first feature's element |
| /// type is float, the second is int64, and the reward is float: |
| /// |
| /// event 0: |
| /// logFloatValue(0, ...) |
| /// logInt64Value(1, ...) |
| /// ... |
| /// logFloatReward(...) |
| /// event 1: |
| /// logFloatValue(0, ...) |
| /// logInt64Value(1, ...) |
| /// ... |
| /// logFloatReward(...) |
| /// |
| /// At the end, call print to generate the protobuf. |
| /// Alternatively, don't call logReward at the end of each event, just |
| /// log{Float|Int32|Int64}FinalReward at the end. |
| class LoggerDataImpl; |
| class Logger final { |
| public: |
| /// Construct a Logger. If IncludeReward is false, then logReward or |
| /// logFinalReward shouldn't be called, and the reward feature won't be |
| /// printed out. |
| /// NOTE: the FeatureSpecs are expected to be in the same order (i.e. have |
| /// corresponding indices) with any MLModelRunner implementations |
| /// corresponding to the model being trained/logged. |
| Logger(const std::vector<LoggedFeatureSpec> &FeatureSpecs, |
| const TensorSpec &RewardSpec, bool IncludeReward); |
| |
| ~Logger(); |
| |
| void logFloatReward(float Value); |
| void logInt32Reward(int32_t Value); |
| void logInt64Reward(int64_t Value); |
| |
| void logFloatFinalReward(float Value); |
| void logInt32FinalReward(int32_t Value); |
| void logInt64FinalReward(int64_t Value); |
| |
| void logFloatValue(size_t FeatureID, const float *Value); |
| void logInt32Value(size_t FeatureID, const int32_t *Value); |
| void logInt64Value(size_t FeatureID, const int64_t *Value); |
| |
| void logSpecifiedTensorValue(size_t FeatureID, const char *RawData); |
| |
| // Warning! For int32_t, the return is set up for int64_t, so the caller needs |
| // to piecemeal cast their int32_t values. |
| // FIXME: let's drop int32_t support. While it's supported by evaluator, it's |
| // not supported by the tensorflow::SequenceExample proto. For small values, |
| // we can consider using bytes. |
| char *addEntryAndGetFloatOrInt64Buffer(size_t FeatureID); |
| |
| // Flush the content of the log to the stream, clearing the stored data in the |
| // process. |
| void flush(std::string *Str); |
| void flush(raw_ostream &OS); |
| |
| // Flush a set of logs that are produced from the same module, e.g. |
| // per-function regalloc traces, as a google::protobuf::Struct message. |
| static void flushLogs(raw_ostream &OS, |
| const StringMap<std::unique_ptr<Logger>> &Loggers); |
| |
| private: |
| std::vector<LoggedFeatureSpec> FeatureSpecs; |
| TensorSpec RewardSpec; |
| const bool IncludeReward; |
| std::unique_ptr<LoggerDataImpl> LoggerData; |
| }; |
| |
| class TFModelEvaluator final { |
| public: |
| /// The result of a model evaluation. Handles the lifetime of the output |
| /// tensors, which means that their values need to be used before |
| /// the EvaluationResult's dtor is called. |
| class EvaluationResult { |
| public: |
| EvaluationResult(const EvaluationResult &) = delete; |
| EvaluationResult &operator=(const EvaluationResult &Other) = delete; |
| |
| EvaluationResult(EvaluationResult &&Other); |
| EvaluationResult &operator=(EvaluationResult &&Other); |
| |
| ~EvaluationResult(); |
| |
| /// Get a (const) pointer to the first element of the tensor at Index. |
| template <typename T> T *getTensorValue(size_t Index) { |
| return static_cast<T *>(getUntypedTensorValue(Index)); |
| } |
| |
| template <typename T> const T *getTensorValue(size_t Index) const { |
| return static_cast<T *>(getUntypedTensorValue(Index)); |
| } |
| |
| /// Get a (const) pointer to the untyped data of the tensor. |
| void *getUntypedTensorValue(size_t Index); |
| const void *getUntypedTensorValue(size_t Index) const; |
| |
| private: |
| friend class TFModelEvaluator; |
| EvaluationResult(std::unique_ptr<EvaluationResultImpl> Impl); |
| std::unique_ptr<EvaluationResultImpl> Impl; |
| }; |
| |
| TFModelEvaluator(StringRef SavedModelPath, |
| const std::vector<TensorSpec> &InputSpecs, |
| const std::vector<TensorSpec> &OutputSpecs, |
| const char *Tags = "serve"); |
| TFModelEvaluator(StringRef SavedModelPath, |
| const std::vector<TensorSpec> &InputSpecs, |
| function_ref<TensorSpec(size_t)> GetOutputSpecs, |
| size_t OutputSpecsSize, const char *Tags = "serve"); |
| |
| ~TFModelEvaluator(); |
| TFModelEvaluator(const TFModelEvaluator &) = delete; |
| TFModelEvaluator(TFModelEvaluator &&) = delete; |
| |
| /// Evaluate the model, assuming it is valid. Returns None if the evaluation |
| /// fails or the model is invalid, or an EvaluationResult otherwise. The |
| /// inputs are assumed to have been already provided via getInput(). When |
| /// returning None, it also invalidates this object. |
| Optional<EvaluationResult> evaluate(); |
| |
| /// Provides access to the input vector. |
| template <typename T> T *getInput(size_t Index) { |
| return static_cast<T *>(getUntypedInput(Index)); |
| } |
| |
| /// Returns true if the tensorflow model was loaded successfully, false |
| /// otherwise. |
| bool isValid() const { return !!Impl; } |
| |
| /// Untyped access to input. |
| void *getUntypedInput(size_t Index); |
| |
| private: |
| std::unique_ptr<TFModelEvaluatorImpl> Impl; |
| }; |
| |
| } // namespace llvm |
| |
| #endif // LLVM_HAVE_TF_API |
| #endif // LLVM_ANALYSIS_UTILS_TFUTILS_H |