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 //===- TrainingLogger.h - mlgo feature/reward logging  ----------*- 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
 //
 //===----------------------------------------------------------------------===//
 //
 // The design goals of the logger are:
 // - no dependencies that llvm doesn't already have.
 // - support streaming, so that we don't need to buffer data during compilation
 // - 0-decoding tensor values. Tensor values are potentially very large buffers
 // of scalars. Because of their potentially large size, avoiding
 // serialization/deserialization overhead is preferred.
 //
 // The simple logger produces an output of the form (each line item on its line)
 // - header: a json object describing the data that will follow.
 // - context: e.g. function name, for regalloc, or "default" for module-wide
 // optimizations like the inliner. This is the context to which the subsequent
 // data corresponds.
 // - observation number.
 // - tensor values - raw bytes of the tensors, in the order given in the header.
 // The values are in succession, i.e. no separator is found between successive
 // tensor values. At the end, there is a new line character.
 // - [score] - this is optional, and is present if it was present in the header.
 // Currently, for final rewards, we output "0" scores after each observation,
 // except for the last one.
 // <repeat>
 // The file should be read as binary, but the reason we use newlines is mostly
 // ease of debugging: the log can be opened in a text editor and, while tensor
 // values are inscrutable, at least the sequence of data can be easily observed.
 // Of course, the buffer of tensor values could contain '\n' bytes. A reader
 // should use the header information to know how much data to read for the
 // tensor values, and not use line information for that.
 //
 // An example reader, used for test, is available at
 // Analysis/models/log_reader.py
 //
 // Example:
 // {"features":[list of TensorSpecs], "score":<a tensor spec>}
 // {"context": "aFunction"}
 // {"observation": 0}
 // <bytes>
 // {"outcome": 0}
 // <bytes for the tensor corresponding to the "score" spec in the header>
 // {"observation": 1}
 // ...
 // {"context": "anotherFunction"}
 // {"observation": 0}
 // ...
 //
 
 #ifndef LLVM_ANALYSIS_UTILS_TRAININGLOGGER_H
 #define LLVM_ANALYSIS_UTILS_TRAININGLOGGER_H
 
 #include "llvm/Config/llvm-config.h"
 
 #include "llvm/ADT/StringMap.h"
 #include "llvm/Analysis/TensorSpec.h"
 #include "llvm/IR/LLVMContext.h"
 #include "llvm/Support/JSON.h"
 
 #include <memory>
 #include <optional>
 #include <vector>
 
 namespace llvm {
 
 /// Logging utility - given an ordered specification of features, and assuming
 /// a scalar reward, allow logging feature values and rewards.
 /// 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 log.
 /// Alternatively, don't call logReward at the end of each event, just
 /// log{Float|Int32|Int64}FinalReward at the end.
 class Logger final {
   std::unique_ptr<raw_ostream> OS;
   const std::vector<TensorSpec> FeatureSpecs;
   const TensorSpec RewardSpec;
   const bool IncludeReward;
   StringMap<size_t> ObservationIDs;
   std::string CurrentContext;
 
   void writeHeader(std::optional<TensorSpec> AdviceSpec);
   void writeTensor(const TensorSpec &Spec, const char *RawData) {
     OS->write(RawData, Spec.getTotalTensorBufferSize());
   }
   void logRewardImpl(const char *RawData);
 
 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(std::unique_ptr<raw_ostream> OS,
          const std::vector<TensorSpec> &FeatureSpecs,
          const TensorSpec &RewardSpec, bool IncludeReward,
          std::optional<TensorSpec> AdviceSpec = std::nullopt);
 
   void switchContext(StringRef Name);
   void startObservation();
   void endObservation();
   void flush() { OS->flush(); }
 
   const std::string &currentContext() const { return CurrentContext; }
 
   /// Check if there is at least an observation for `currentContext()`.
   bool hasObservationInProgress() const {
     return hasAnyObservationForContext(CurrentContext);
   }
 
   /// Check if there is at least an observation for the context `Ctx`.
   bool hasAnyObservationForContext(StringRef Ctx) const {
     return ObservationIDs.contains(Ctx);
   }
 
   template <typename T> void logReward(T Value) {
     logRewardImpl(reinterpret_cast<const char *>(&Value));
   }
 
   void logTensorValue(size_t FeatureID, const char *RawData) {
     writeTensor(FeatureSpecs[FeatureID], RawData);
   }
 };
 
 } // namespace llvm
 #endif // LLVM_ANALYSIS_UTILS_TRAININGLOGGER_H

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