1 //===- TFUtils.h - utilities for tensorflow C API ---------------*- C++ -*-===// 2 // 3 // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. 4 // See https://llvm.org/LICENSE.txt for license information. 5 // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception 6 // 7 //===----------------------------------------------------------------------===// 8 // 9 #ifndef LLVM_ANALYSIS_UTILS_TFUTILS_H 10 #define LLVM_ANALYSIS_UTILS_TFUTILS_H 11 12 #include "llvm/Config/llvm-config.h" 13 14 #ifdef LLVM_HAVE_TF_API 15 #include "llvm/ADT/StringMap.h" 16 #include "llvm/IR/LLVMContext.h" 17 #include "llvm/Support/JSON.h" 18 19 #include <memory> 20 #include <vector> 21 22 namespace llvm { 23 24 /// Load a SavedModel, find the given inputs and outputs, and setup storage 25 /// for input tensors. The user is responsible for correctly dimensioning the 26 /// input tensors and setting their values before calling evaluate(). 27 /// To initialize: 28 /// - construct the object 29 /// - initialize the input tensors using initInput. Indices must correspond to 30 /// indices in the InputNames used at construction. 31 /// To use: 32 /// - set input values by using getInput to get each input tensor, and then 33 /// setting internal scalars, for all dimensions (tensors are row-major: 34 /// https://github.com/tensorflow/tensorflow/blob/r1.5/tensorflow/c/c_api.h#L205) 35 /// - call evaluate. The input tensors' values are not consumed after this, and 36 /// may still be read. 37 /// - use the outputs in the output vector 38 class TFModelEvaluatorImpl; 39 class EvaluationResultImpl; 40 41 /// TensorSpec encapsulates the specification of a tensor: its dimensions, or 42 /// "shape" (row-major), its type (see TensorSpec::getDataType specializations 43 /// for supported types), its name and port (see "TensorFlow: Large-Scale 44 /// Machine Learning on Heterogeneous Distributed Systems", section 4.2, para 2: 45 /// https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45166.pdf) 46 /// 47 /// TensorSpec is used to set up a TFModelEvaluator by describing the expected 48 /// inputs and outputs. 49 class TensorSpec final { 50 public: 51 template <typename T> 52 static TensorSpec createSpec(const std::string &Name, 53 const std::vector<int64_t> &Shape, 54 int Port = 0) { 55 return TensorSpec(Name, Port, getDataType<T>(), Shape); 56 } 57 58 const std::string &name() const { return Name; } 59 int port() const { return Port; } 60 int typeIndex() const { return TypeIndex; } 61 const std::vector<int64_t> &shape() const { return Shape; } 62 63 bool operator==(const TensorSpec &Other) const { 64 return Name == Other.Name && Port == Other.Port && 65 TypeIndex == Other.TypeIndex && Shape == Other.Shape; 66 } 67 68 bool operator!=(const TensorSpec &Other) const { return !(*this == Other); } 69 70 /// Get the number of elements in a tensor with this shape. 71 size_t getElementCount() const { return ElementCount; } 72 /// Get the size, in bytes, of one element. 73 size_t getElementByteSize() const; 74 75 template <typename T> bool isElementType() const { 76 return getDataType<T>() == TypeIndex; 77 } 78 79 private: 80 TensorSpec(const std::string &Name, int Port, int TypeIndex, 81 const std::vector<int64_t> &Shape); 82 83 template <typename T> static int getDataType() { 84 llvm_unreachable("Undefined tensor type"); 85 } 86 87 std::string Name; 88 int Port = 0; 89 int TypeIndex = 0; 90 std::vector<int64_t> Shape; 91 size_t ElementCount = 0; 92 }; 93 94 /// Construct a TensorSpec from a JSON dictionary of the form: 95 /// { "name": <string>, 96 /// "port": <int>, 97 /// "type": <string. Use LLVM's types, e.g. float, double, int64_t>, 98 /// "shape": <array of ints> } 99 /// For the "type" field, see the C++ primitive types used in 100 /// TFUTILS_SUPPORTED_TYPES. 101 Optional<TensorSpec> getTensorSpecFromJSON(LLVMContext &Ctx, 102 const json::Value &Value); 103 104 struct LoggedFeatureSpec { 105 TensorSpec Spec; 106 Optional<std::string> LoggingName; 107 }; 108 109 /// Load the output specs. If SpecFileOverride is not empty, that path is used. 110 /// Otherwise, the file is assumed to be called 'output_spec.json' and be found 111 /// under ModelPath (the model directory). 112 /// The first output tensor name must match ExpectedDecisionName. 113 /// In case of error, the return is None and the error is logged. 114 Optional<std::vector<LoggedFeatureSpec>> 115 loadOutputSpecs(LLVMContext &Ctx, StringRef ExpectedDecisionName, 116 StringRef ModelPath, StringRef SpecFileOverride = StringRef()); 117 118 /// Logging utility - given an ordered specification of features, and assuming 119 /// a scalar reward, allow logging feature values and rewards, and then print 120 /// as tf.train.SequenceExample text protobuf. 121 /// The assumption is that, for an event to be logged (i.e. a set of feature 122 /// values and a reward), the user calls the log* API for each feature exactly 123 /// once, providing the index matching the position in the feature spec list 124 /// provided at construction. The example assumes the first feature's element 125 /// type is float, the second is int64, and the reward is float: 126 /// 127 /// event 0: 128 /// logFloatValue(0, ...) 129 /// logInt64Value(1, ...) 130 /// ... 131 /// logFloatReward(...) 132 /// event 1: 133 /// logFloatValue(0, ...) 134 /// logInt64Value(1, ...) 135 /// ... 136 /// logFloatReward(...) 137 /// 138 /// At the end, call print to generate the protobuf. 139 /// Alternatively, don't call logReward at the end of each event, just 140 /// log{Float|Int32|Int64}FinalReward at the end. 141 class LoggerDataImpl; 142 class Logger final { 143 public: 144 /// Construct a Logger. If IncludeReward is false, then logReward or 145 /// logFinalReward shouldn't be called, and the reward feature won't be 146 /// printed out. 147 Logger(const std::vector<LoggedFeatureSpec> &FeatureSpecs, 148 const TensorSpec &RewardSpec, bool IncludeReward); 149 150 ~Logger(); 151 152 void logFloatReward(float Value); 153 void logInt32Reward(int32_t Value); 154 void logInt64Reward(int64_t Value); 155 156 void logFloatFinalReward(float Value); 157 void logInt32FinalReward(int32_t Value); 158 void logInt64FinalReward(int64_t Value); 159 160 void logFloatValue(size_t FeatureID, const float *Value); 161 void logInt32Value(size_t FeatureID, const int32_t *Value); 162 void logInt64Value(size_t FeatureID, const int64_t *Value); 163 164 void logSpecifiedTensorValue(size_t FeatureID, const char *RawData); 165 166 // Warning! For int32_t, the return is set up for int64_t, so the caller needs 167 // to piecemeal cast their int32_t values. 168 // FIXME: let's drop int32_t support. While it's supported by evaluator, it's 169 // not supported by the tensorflow::SequenceExample proto. For small values, 170 // we can consider using bytes. 171 char *addEntryAndGetFloatOrInt64Buffer(size_t FeatureID); 172 173 void print(raw_ostream &OS); 174 175 private: 176 std::vector<LoggedFeatureSpec> FeatureSpecs; 177 TensorSpec RewardSpec; 178 const bool IncludeReward; 179 std::unique_ptr<LoggerDataImpl> LoggerData; 180 }; 181 182 class TFModelEvaluator final { 183 public: 184 /// The result of a model evaluation. Handles the lifetime of the output 185 /// tensors, which means that their values need to be used before 186 /// the EvaluationResult's dtor is called. 187 class EvaluationResult { 188 public: 189 EvaluationResult(const EvaluationResult &) = delete; 190 EvaluationResult &operator=(const EvaluationResult &Other) = delete; 191 192 EvaluationResult(EvaluationResult &&Other); 193 EvaluationResult &operator=(EvaluationResult &&Other); 194 195 ~EvaluationResult(); 196 197 /// Get a (const) pointer to the first element of the tensor at Index. 198 template <typename T> T *getTensorValue(size_t Index) { 199 return static_cast<T *>(getUntypedTensorValue(Index)); 200 } 201 202 template <typename T> const T *getTensorValue(size_t Index) const { 203 return static_cast<T *>(getUntypedTensorValue(Index)); 204 } 205 206 /// Get a (const) pointer to the untyped data of the tensor. 207 void *getUntypedTensorValue(size_t Index); 208 const void *getUntypedTensorValue(size_t Index) const; 209 210 private: 211 friend class TFModelEvaluator; 212 EvaluationResult(std::unique_ptr<EvaluationResultImpl> Impl); 213 std::unique_ptr<EvaluationResultImpl> Impl; 214 }; 215 216 TFModelEvaluator(StringRef SavedModelPath, 217 const std::vector<TensorSpec> &InputSpecs, 218 const std::vector<TensorSpec> &OutputSpecs, 219 const char *Tags = "serve"); 220 TFModelEvaluator(StringRef SavedModelPath, 221 const std::vector<TensorSpec> &InputSpecs, 222 function_ref<TensorSpec(size_t)> GetOutputSpecs, 223 size_t OutputSpecsSize, const char *Tags = "serve"); 224 225 ~TFModelEvaluator(); 226 TFModelEvaluator(const TFModelEvaluator &) = delete; 227 TFModelEvaluator(TFModelEvaluator &&) = delete; 228 229 /// Evaluate the model, assuming it is valid. Returns None if the evaluation 230 /// fails or the model is invalid, or an EvaluationResult otherwise. The 231 /// inputs are assumed to have been already provided via getInput(). When 232 /// returning None, it also invalidates this object. 233 Optional<EvaluationResult> evaluate(); 234 235 /// Provides access to the input vector. 236 template <typename T> T *getInput(size_t Index) { 237 return static_cast<T *>(getUntypedInput(Index)); 238 } 239 240 /// Returns true if the tensorflow model was loaded successfully, false 241 /// otherwise. 242 bool isValid() const { return !!Impl; } 243 244 private: 245 void *getUntypedInput(size_t Index); 246 std::unique_ptr<TFModelEvaluatorImpl> Impl; 247 }; 248 249 /// List of supported types, as a pair: 250 /// - C++ type 251 /// - enum name (implementation-specific) 252 #define TFUTILS_SUPPORTED_TYPES(M) \ 253 M(float, TF_FLOAT) \ 254 M(double, TF_DOUBLE) \ 255 M(int8_t, TF_INT8) \ 256 M(uint8_t, TF_UINT8) \ 257 M(int16_t, TF_INT16) \ 258 M(uint16_t, TF_UINT16) \ 259 M(int32_t, TF_INT32) \ 260 M(uint32_t, TF_UINT32) \ 261 M(int64_t, TF_INT64) \ 262 M(uint64_t, TF_UINT64) 263 264 #define TFUTILS_GETDATATYPE_DEF(T, E) \ 265 template <> int TensorSpec::getDataType<T>(); 266 267 TFUTILS_SUPPORTED_TYPES(TFUTILS_GETDATATYPE_DEF) 268 269 #undef TFUTILS_GETDATATYPE_DEF 270 } // namespace llvm 271 272 #endif // LLVM_HAVE_TF_API 273 #endif // LLVM_ANALYSIS_UTILS_TFUTILS_H 274