aboutsummaryrefslogtreecommitdiffstats
path: root/contrib/libs/llvm12/lib/Analysis/TFUtils.cpp
blob: 3f26bdfdc05b8584aebe200d8635ee809678ebaf (plain) (blame)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
//===- TFUtils.cpp - tensorflow evaluation utilities ----------------------===//
//
//                     The LLVM Compiler Infrastructure
//
// This file is distributed under the University of Illinois Open Source
// License. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
//
// This file implements utilities for interfacing with tensorflow C APIs.
//
//===----------------------------------------------------------------------===//
#include "llvm/Config/config.h"
#if defined(LLVM_HAVE_TF_API)

#include "llvm/ADT/Twine.h"
#include "llvm/Analysis/Utils/TFUtils.h"
#include "llvm/Support/Debug.h"
#include "llvm/Support/JSON.h"
#include "llvm/Support/ManagedStatic.h"
#include "llvm/Support/MemoryBuffer.h"
#include "llvm/Support/Path.h"
#include "llvm/Support/raw_ostream.h"

#error #include "tensorflow/c/c_api.h"
#error #include "tensorflow/c/c_api_experimental.h"

#include <cassert>
#include <numeric>

using namespace llvm;

namespace {

using TFGraphPtr = std::unique_ptr<TF_Graph, decltype(&TF_DeleteGraph)>;
using TFSessionOptionsPtr =
    std::unique_ptr<TF_SessionOptions, decltype(&TF_DeleteSessionOptions)>;
using TFStatusPtr = std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)>;

struct TFInitializer {
  TFInitializer() {
    assert(!IsInitialized && "TFInitialized should be called only once");
    int Argc = 1;
    const char *Name = "";
    const char **NamePtr = &Name;
    TF_InitMain(Name, &Argc, const_cast<char ***>(&NamePtr));
    IsInitialized = true;
  }
  bool IsInitialized = false;
};

llvm::ManagedStatic<TFInitializer> TFLibInitializer;

bool ensureInitTF() { return TFLibInitializer->IsInitialized; }

TFGraphPtr createTFGraph() {
  return TFGraphPtr(TF_NewGraph(), &TF_DeleteGraph);
}

TFStatusPtr createTFStatus() {
  return TFStatusPtr(TF_NewStatus(), &TF_DeleteStatus);
}

TFSessionOptionsPtr createTFSessionOptions() {
  return TFSessionOptionsPtr(TF_NewSessionOptions(), &TF_DeleteSessionOptions);
}

/// Write the values of one tensor as a list.
template <typename T>
void writeTensorValues(raw_ostream &OutFile, const char *TensorData,
                       size_t ElemCount) {
  OutFile << "[";
  const T *TypedData = reinterpret_cast<const T *>(TensorData);
  for (size_t I = 0; I < ElemCount; ++I) {
    if (I > 0)
      OutFile << ", ";
    OutFile << TypedData[I];
  }
  OutFile << "]";
}

/// Write a list of tensors as a sequence of TensorFlow FeatureList protobufs.
/// The tensors are assumed to be stored contiguously, in row-major format,
/// in the TensorData buffer. Each tensor has the shape given by Spec. The
/// feature name in the output is either the provided LoggingName, if
/// specified, otherwise it's the name of the tensor (as given by Spec).
void writeRawTensorsAsFeatureLists(raw_ostream &OutFile,
                                   const LoggedFeatureSpec &LoggedSpec,
                                   const char *TensorData, size_t TensorCount,
                                   bool FinalReward = false) {
  const char *FieldName = "<invalid>";
  std::function<void(const char *)> ValueWriter;
  const auto &Spec = LoggedSpec.Spec;
  // The 'Feature' protobuf only has 3 possible fields: float_list,
  // int64_list, or bytes_list, so we capture int32 values as int64. We don't
  // support any other types.
  if (Spec.isElementType<int64_t>()) {
    FieldName = "int64_list";
    ValueWriter = [&](const char *Data) {
      writeTensorValues<int64_t>(OutFile, Data, Spec.getElementCount());
    };
  } else if (Spec.isElementType<int32_t>()) {
    FieldName = "int64_list";
    ValueWriter = [&](const char *Data) {
      writeTensorValues<int32_t>(OutFile, Data, Spec.getElementCount());
    };

  } else if (Spec.isElementType<float>()) {
    FieldName = "float_list";
    ValueWriter = [&](const char *Data) {
      writeTensorValues<float>(OutFile, Data, Spec.getElementCount());
    };

  } else {
    llvm_unreachable("Unsupported tensor type.");
  }

  OutFile << "  feature_list: {\n";
  OutFile << "    key: "
          << "\""
          << (LoggedSpec.LoggingName ? *LoggedSpec.LoggingName : Spec.name())
          << "\" ";
  OutFile << "value: {\n";
  size_t TensorByteSize = Spec.getElementCount() * Spec.getElementByteSize();

  auto WriteFeatureProto = [&](const char *P) {
    OutFile << "      feature: { " << FieldName << ": { value: ";
    ValueWriter(P);
    OutFile << " } }\n";
  };

  const char *CurrentTensor = TensorData;
  static int64_t Zero = 0;
  // Write all but the last value. If this is the final reward, don't increment
  // the CurrentTensor, and just write 0.
  for (size_t I = 0; I < TensorCount - 1; ++I) {
    if (FinalReward)
      WriteFeatureProto(reinterpret_cast<const char *>(&Zero));
    else {
      WriteFeatureProto(CurrentTensor);
      CurrentTensor += TensorByteSize;
    }
  }

  WriteFeatureProto(CurrentTensor);

  OutFile << "    }\n";
  OutFile << "  }\n";
}
} // namespace

namespace llvm {
class EvaluationResultImpl {
public:
  EvaluationResultImpl(size_t OutputSize)
      : OutputSize(OutputSize), Output(OutputSize){};

  ~EvaluationResultImpl() {
    for (auto *P : Output)
      if (P)
        TF_DeleteTensor(P);
  }

  EvaluationResultImpl(const EvaluationResultImpl &) = delete;
  EvaluationResultImpl(EvaluationResultImpl &&Other) = delete;
  std::vector<TF_Tensor *> &getOutput() { return Output; }

private:
  const size_t OutputSize;
  std::vector<TF_Tensor *> Output;
};

size_t TensorSpec::getElementByteSize() const {
  return TF_DataTypeSize(static_cast<TF_DataType>(TypeIndex));
}

TensorSpec::TensorSpec(const std::string &Name, int Port, int TypeIndex,
                       const std::vector<int64_t> &Shape)
    : Name(Name), Port(Port), TypeIndex(TypeIndex), Shape(Shape),
      ElementCount(std::accumulate(Shape.begin(), Shape.end(), 1,
                                   std::multiplies<int64_t>())) {}

Optional<TensorSpec> getTensorSpecFromJSON(LLVMContext &Ctx,
                                           const json::Value &Value) {
  auto EmitError = [&](const llvm::Twine &Message) -> Optional<TensorSpec> {
    std::string S;
    llvm::raw_string_ostream OS(S);
    OS << Value;
    Ctx.emitError("Unable to parse JSON Value as spec (" + Message + "): " + S);
    return None;
  };
  // FIXME: accept a Path as a parameter, and use it for error reporting.
  json::Path::Root Root("tensor_spec");
  json::ObjectMapper Mapper(Value, Root);
  if (!Mapper)
    return EmitError("Value is not a dict");

  std::string TensorName;
  int TensorPort = -1;
  std::string TensorType;
  std::vector<int64_t> TensorShape;

  if (!Mapper.map<std::string>("name", TensorName))
    return EmitError("'name' property not present or not a string");
  if (!Mapper.map<std::string>("type", TensorType))
    return EmitError("'type' property not present or not a string");
  if (!Mapper.map<int>("port", TensorPort))
    return EmitError("'port' property not present or not an int");
  if (!Mapper.map<std::vector<int64_t>>("shape", TensorShape))
    return EmitError("'shape' property not present or not an int array");

#define PARSE_TYPE(T, E)                                                       \
  if (TensorType == #T)                                                        \
    return TensorSpec::createSpec<T>(TensorName, TensorShape, TensorPort);
  TFUTILS_SUPPORTED_TYPES(PARSE_TYPE)
#undef PARSE_TYPE
  return None;
}

Optional<std::vector<LoggedFeatureSpec>>
loadOutputSpecs(LLVMContext &Ctx, StringRef ExpectedDecisionName,
                StringRef ModelPath, StringRef SpecFileOverride) {
  SmallVector<char, 128> OutputSpecsPath;
  StringRef FileName = SpecFileOverride;
  if (FileName.empty()) {
    llvm::sys::path::append(OutputSpecsPath, ModelPath, "output_spec.json");
    FileName = {OutputSpecsPath.data(), OutputSpecsPath.size()};
  }

  auto BufferOrError = MemoryBuffer::getFileOrSTDIN(FileName);
  if (!BufferOrError) {
    Ctx.emitError("Error opening output specs file: " + FileName + " : " +
                  BufferOrError.getError().message());
    return None;
  }
  auto ParsedJSONValues = json::parse(BufferOrError.get()->getBuffer());
  if (!ParsedJSONValues) {
    Ctx.emitError("Could not parse specs file: " + FileName);
    return None;
  }
  auto ValuesArray = ParsedJSONValues->getAsArray();
  if (!ValuesArray) {
    Ctx.emitError("Expected an array of {tensor_spec:<TensorSpec>, "
                  "logging_name:<name>} dictionaries");
    return None;
  }
  std::vector<LoggedFeatureSpec> Ret;
  for (const auto &Value : *ValuesArray)
    if (const auto *Obj = Value.getAsObject())
      if (const auto *SpecPart = Obj->get("tensor_spec"))
        if (auto TensorSpec = getTensorSpecFromJSON(Ctx, *SpecPart))
          if (auto LoggingName = Obj->getString("logging_name")) {
            if (!TensorSpec->isElementType<int64_t>() &&
                !TensorSpec->isElementType<int32_t>() &&
                !TensorSpec->isElementType<float>()) {
              Ctx.emitError(
                  "Only int64, int32, and float tensors are supported. "
                  "Found unsupported type for tensor named " +
                  TensorSpec->name());
              return None;
            }
            Ret.push_back({*TensorSpec, LoggingName->str()});
          }

  if (ValuesArray->size() != Ret.size()) {
    Ctx.emitError(
        "Unable to parse output spec. It should be a json file containing an "
        "array of dictionaries. Each dictionary must have a 'tensor_spec' key, "
        "with a json object describing a TensorSpec; and a 'logging_name' key, "
        "which is a string to use as name when logging this tensor in the "
        "training log.");
    return None;
  }
  if (Ret.empty() || *Ret[0].LoggingName != ExpectedDecisionName) {
    Ctx.emitError("The first output spec must describe the decision tensor, "
                  "and must have the logging_name " +
                  StringRef(ExpectedDecisionName));
    return None;
  }
  return Ret;
}

class TFModelEvaluatorImpl {
public:
  TFModelEvaluatorImpl(StringRef SavedModelPath,
                       const std::vector<TensorSpec> &InputSpecs,
                       function_ref<TensorSpec(size_t)> GetOutputSpecs,
                       size_t OutputSpecsSize, const char *Tags);

  bool isValid() const { return IsValid; }
  size_t OutputSize() const { return OutputFeed.size(); }

  void evaluate(TF_Tensor **Output, TF_Status *Status) {
    TF_SessionRun(Session, nullptr, InputFeed.data(), Input.data(),
                  Input.size(), OutputFeed.data(), Output, OutputFeed.size(),
                  nullptr, 0, nullptr, Status);
  }

  void initInput(size_t Index, TF_DataType Type,
                 const std::vector<int64_t> &Dimensions);
  const std::vector<TF_Tensor *> &getInput() const { return Input; }

  ~TFModelEvaluatorImpl();

private:
  /// The objects necessary for carrying out an evaluation of the SavedModel.
  /// They are expensive to set up, and we maintain them accross all the
  /// evaluations of the model.
  TF_Session *Session = nullptr;
  TFGraphPtr Graph;
  TFSessionOptionsPtr Options;

  /// The specification of the input nodes.
  std::vector<TF_Output> InputFeed;

  /// The input tensors. They must match by index of the corresponding InputFeed
  /// value. We set up the tensors once and just mutate theirs scalars before
  /// each evaluation. The input tensors keep their value after an evaluation.
  std::vector<TF_Tensor *> Input;

  /// The specification of the output nodes. When evaluating, the tensors in the
  /// output tensor vector must match by index the corresponding element in the
  /// OutputFeed.
  std::vector<TF_Output> OutputFeed;

  void invalidate() { IsValid = false; }

  bool IsValid = true;

  /// Reusable utility for ensuring we can bind the requested Name to a node in
  /// the SavedModel Graph.
  bool checkReportAndInvalidate(const TF_Output &Output,
                                const TensorSpec &OutputSpec);
};
} // namespace llvm

TFModelEvaluatorImpl::TFModelEvaluatorImpl(
    StringRef SavedModelPath, const std::vector<TensorSpec> &InputSpecs,
    function_ref<TensorSpec(size_t)> GetOutputSpecs, size_t OutputSpecsSize,
    const char *Tags = "serve")
    : Graph(createTFGraph()), Options(createTFSessionOptions()),
      InputFeed(InputSpecs.size()), Input(InputSpecs.size()),
      OutputFeed(OutputSpecsSize) {
  if (!ensureInitTF()) {
    errs() << "Tensorflow should have been initialized";
    return;
  }
  auto Status = createTFStatus();

  Session = TF_LoadSessionFromSavedModel(Options.get(), nullptr,
                                         SavedModelPath.str().c_str(), &Tags, 1,
                                         Graph.get(), nullptr, Status.get());
  if (TF_GetCode(Status.get()) != TF_Code::TF_OK) {
    errs() << TF_Message(Status.get());
    invalidate();
  }
  for (size_t I = 0; I < InputSpecs.size(); ++I) {
    auto &InputSpec = InputSpecs[I];
    InputFeed[I] = {
        TF_GraphOperationByName(Graph.get(), (InputSpec.name()).c_str()),
        InputSpec.port()};
    if (!checkReportAndInvalidate(InputFeed[I], InputSpec))
      return;
    initInput(I, static_cast<TF_DataType>(InputSpec.typeIndex()),
              InputSpec.shape());
  }
  for (size_t I = 0; I < OutputSpecsSize; ++I) {
    auto OutputSpec = GetOutputSpecs(I);
    OutputFeed[I] = {
        TF_GraphOperationByName(Graph.get(), (OutputSpec.name()).c_str()),
        OutputSpec.port()};
    if (!checkReportAndInvalidate(OutputFeed[I], OutputSpec))
      return;
  }
}

TFModelEvaluator::TFModelEvaluator(
    StringRef SavedModelPath, const std::vector<TensorSpec> &InputSpecs,
    function_ref<TensorSpec(size_t)> GetOutputSpecs, size_t OutputSpecsSize,
    const char *Tags)
    : Impl(new TFModelEvaluatorImpl(SavedModelPath, InputSpecs, GetOutputSpecs,
                                    OutputSpecsSize, Tags)) {
  if (!Impl->isValid())
    Impl.reset();
}

TFModelEvaluator::TFModelEvaluator(StringRef SavedModelPath,
                                   const std::vector<TensorSpec> &InputSpecs,
                                   const std::vector<TensorSpec> &OutputSpecs,
                                   const char *Tags)
    : TFModelEvaluator(
          SavedModelPath, InputSpecs, [&](size_t I) { return OutputSpecs[I]; },
          OutputSpecs.size(), Tags) {}

TFModelEvaluatorImpl::~TFModelEvaluatorImpl() {
  for (auto *T : Input) {
    TF_DeleteTensor(T);
  }
  if (Session == nullptr)
    return;
  auto Status = createTFStatus();
  TF_DeleteSession(Session, Status.get());
  Session = nullptr;
  if (TF_GetCode(Status.get()) != TF_Code::TF_OK)
    errs() << "Could not delete TF session";
}

bool TFModelEvaluatorImpl::checkReportAndInvalidate(
    const TF_Output &Output, const TensorSpec &OutputSpec) {
  if (Output.oper)
    return true;
  errs() << "Could not find TF_Output named: " + OutputSpec.name();
  IsValid = false;
  return IsValid;
}

Optional<TFModelEvaluator::EvaluationResult> TFModelEvaluator::evaluate() {
  if (!isValid())
    return None;
  std::unique_ptr<EvaluationResultImpl> Ret =
      std::make_unique<EvaluationResultImpl>(Impl->OutputSize());
  auto Status = createTFStatus();
  Impl->evaluate(Ret->getOutput().data(), Status.get());
  if (TF_GetCode(Status.get()) != TF_Code::TF_OK) {
    errs() << TF_Message(Status.get());
    Impl.reset();
    return None;
  }
  return EvaluationResult(std::move(Ret));
}

void TFModelEvaluatorImpl::initInput(size_t Index, TF_DataType Type,
                                     const std::vector<int64_t> &Dimensions) {
  int64_t TotalSize = TF_DataTypeSize(Type);
  for (auto &D : Dimensions)
    TotalSize *= D;

  Input[Index] =
      TF_AllocateTensor(Type, Dimensions.data(), Dimensions.size(), TotalSize);
  std::memset(TF_TensorData(Input[Index]), 0, TotalSize);
}

void *TFModelEvaluator::getUntypedInput(size_t Index) {
  return TF_TensorData(Impl->getInput()[Index]);
}

TFModelEvaluator::EvaluationResult::EvaluationResult(
    std::unique_ptr<EvaluationResultImpl> Impl)
    : Impl(std::move(Impl)) {}

TFModelEvaluator::EvaluationResult::EvaluationResult(EvaluationResult &&Other)
    : Impl(std::move(Other.Impl)) {}

TFModelEvaluator::EvaluationResult &
TFModelEvaluator::EvaluationResult::operator=(EvaluationResult &&Other) {
  Impl = std::move(Other.Impl);
  return *this;
}

void *TFModelEvaluator::EvaluationResult::getUntypedTensorValue(size_t Index) {
  return TF_TensorData(Impl->getOutput()[Index]);
}

const void *
TFModelEvaluator::EvaluationResult::getUntypedTensorValue(size_t Index) const {
  return TF_TensorData(Impl->getOutput()[Index]);
}

#define TFUTILS_GETDATATYPE_IMPL(T, E)                                         \
  template <> int TensorSpec::getDataType<T>() { return E; }

TFUTILS_SUPPORTED_TYPES(TFUTILS_GETDATATYPE_IMPL)

#undef TFUTILS_GETDATATYPE_IMPL

TFModelEvaluator::EvaluationResult::~EvaluationResult() {}
TFModelEvaluator::~TFModelEvaluator() {}

void Logger::print(raw_ostream &OS) {
  if (RawLogData.empty())
    return;
  if (RawLogData[0].empty())
    return;
  size_t Tensor0Size = FeatureSpecs[0].Spec.getElementCount() *
                       FeatureSpecs[0].Spec.getElementByteSize();
  size_t NumberOfRecords = RawLogData[0].size() / Tensor0Size;
  if (NumberOfRecords == 0)
    return;
  size_t RewardSize =
      RewardSpec.getElementCount() * RewardSpec.getElementByteSize();
  size_t NumberOfRewards = RawLogData.back().size() / RewardSize;

  OS << "feature_lists: {\n";
  for (size_t I = 0; I < FeatureSpecs.size(); ++I)
    writeRawTensorsAsFeatureLists(OS, FeatureSpecs[I], RawLogData[I].data(),
                                  NumberOfRecords);

  if (IncludeReward)
    writeRawTensorsAsFeatureLists(OS, {RewardSpec, None},
                                  RawLogData.back().data(), NumberOfRecords,
                                  NumberOfRewards == 1);

  OS << "}\n";
}
#endif // defined(LLVM_HAVE_TF_API)