EVOLUTION-MANAGER

Edit File: tensor_slice_writer.h

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
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http://www.apache.org/licenses/LICENSE-2.0

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distributed under the License is distributed on an "AS IS" BASIS,
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==============================================================================*/

// The utility to write checkpoints for google brain tensor ops and v3
// checkpoints for dist_belief.

#ifndef TENSORFLOW_CORE_UTIL_TENSOR_SLICE_WRITER_H_
#define TENSORFLOW_CORE_UTIL_TENSOR_SLICE_WRITER_H_

#include <unordered_map>

#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/tensor_slice.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/lib/core/errors.h"
#include "tensorflow/core/lib/core/status.h"
#include "tensorflow/core/lib/core/stringpiece.h"
#include "tensorflow/core/lib/gtl/map_util.h"
#include "tensorflow/core/lib/strings/stringprintf.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/macros.h"
#include "tensorflow/core/platform/types.h"
#include "tensorflow/core/util/saved_tensor_slice.pb.h"
#include "tensorflow/core/util/saved_tensor_slice_util.h"

namespace tensorflow {

namespace checkpoint {

class TensorSliceWriter {
 public:
  // Abstract interface that TensorSliceWriter uses for building
  class Builder {
   public:
    virtual ~Builder() {}
    virtual void Add(StringPiece key, StringPiece value) = 0;
    virtual Status Finish(int64* file_size) = 0;
  };
  typedef std::function<Status(const string&, Builder**)> CreateBuilderFunction;

TensorSliceWriter(const string& filename,
                    CreateBuilderFunction create_builder);
  virtual ~TensorSliceWriter() {}
  // Adds a slice. We support float and int32 for now.
  // TODO(yangke): add more supports
  template <typename T>
  Status Add(const string& name, const TensorShape& shape,
             const TensorSlice& slice, const T* data);
  Status Finish();

// Allocate "num_elements" elements in "ss" and save the data in "data"
  // there.
  template <typename T>
  static Status SaveData(const T* data, int64 num_elements, SavedSlice* ss);

static size_t MaxBytesPerElement(DataType dt);

private:
  static constexpr size_t kMaxMessageBytes = 1LL << 31;
  // Filling in the TensorProto in a SavedSlice will add the following
  // header bytes, in addition to the data:
  // - 1 byte: TensorProto tag and wire format
  // - <= 5 bytes: TensorProto length
  // - 1 byte: Repeated *_val tag and wire format
  // - <= 5 bytes: *_val length
  // However, we add 1KB of slack, to be conservative and guard
  // against other additions to the TensorProto.
  static constexpr size_t kTensorProtoHeaderBytes = 1 << 10;

const string filename_;
  const CreateBuilderFunction create_builder_;
  const string tmpname_;

// A mapping from the tensor names to their index in meta_.saved_slice_meta()
  std::unordered_map<string, int> name_to_index_;
  // The metadata that holds all the saved tensor slices.
  SavedTensorSlices sts_;
  // The data to be written to the builder
  std::map<string, string> data_;
  // Total number of slices written
  int slices_;
  TF_DISALLOW_COPY_AND_ASSIGN(TensorSliceWriter);
};

template <typename T>
Status TensorSliceWriter::Add(const string& name, const TensorShape& shape,
                              const TensorSlice& slice, const T* data) {
  // The tensor and the slice have to be compatible
  if (shape.dims() != slice.dims()) {
    return errors::Internal("Incompatible tensor shape and slice: ", "shape = ",
                            shape.DebugString(),
                            ", slice = ", slice.DebugString());
  }
  DataType dt = DataTypeToEnum<T>::value;
  // We need to add an entry for "name" if there isn't an entry already.
  int index = gtl::FindWithDefault(name_to_index_, name, -1);
  if (index >= 0) {
    // The same tensor has been registered -- we verify that the shapes and the
    // type agree.
    const SavedSliceMeta& ssm = sts_.meta().tensor(index);
    CHECK_EQ(name, ssm.name()) << ssm.ShortDebugString();
    TensorShape ssm_shape(ssm.shape());
    if (!shape.IsSameSize(ssm_shape)) {
      return errors::Internal(
          "Mismatching shapes: existing tensor = ", ssm_shape.DebugString(),
          ", trying to add name ", name, ", shape = ", shape.DebugString());
    }
    if (dt != ssm.type()) {
      return errors::Internal(
          "Mismatching types: existing type = ", DataTypeString(ssm.type()),
          ", trying to add name ", name, ", type = ", DataTypeString(dt));
    }
  } else {
    // Insert the new tensor name with the shape information
    index = sts_.meta().tensor_size();
    name_to_index_.insert(std::make_pair(name, index));
    SavedSliceMeta* ssm = sts_.mutable_meta()->add_tensor();
    ssm->set_name(name);
    shape.AsProto(ssm->mutable_shape());
    ssm->set_type(dt);
  }
  // Now we need to add the slice info the list of slices.
  SavedSliceMeta* ssm = sts_.mutable_meta()->mutable_tensor(index);
  slice.AsProto(ssm->add_slice());

// Now we need to add the real data.
  {
    SavedTensorSlices sts;
    SavedSlice* ss = sts.mutable_data();
    ss->set_name(name);
    slice.AsProto(ss->mutable_slice());
    TensorShape saved_shape(ssm->shape());
    TensorShape sliced_shape;
    TF_RETURN_IF_ERROR(slice.SliceTensorShape(saved_shape, &sliced_shape));
    TF_RETURN_IF_ERROR(SaveData(data, sliced_shape.num_elements(), ss));
    string key = EncodeTensorNameSlice(name, slice);
    // TODO(yangke): consider doing a two-pass thing where the first pass just
    // list the tensor slices we want to save and then another pass to actually
    // set the data. Need to figure out if the interface works well.
    std::pair<string, string> key_value(key, "");
    if (!sts.AppendToString(&key_value.second)) {
      return errors::Internal("Error writing Tensor. Possible size overflow.");
    }
    data_.insert(key_value);
  }
  ++slices_;
  return Status::OK();
}

template <typename T>
Status TensorSliceWriter::SaveData(const T* data, int64 num_elements,
                                   SavedSlice* ss) {
  size_t size_bound =
      ss->ByteSize() + kTensorProtoHeaderBytes +
      (MaxBytesPerElement(DataTypeToEnum<T>::value) * num_elements);
  if (size_bound > kMaxMessageBytes) {
    return errors::InvalidArgument(
        "Tensor slice is too large to serialize (conservative estimate: ",
        size_bound, " bytes)");
  }
  Fill(data, num_elements, ss->mutable_data());
  DCHECK_GE(ss->ByteSize(), 0);
  DCHECK_LE(ss->ByteSize(), size_bound);
  return Status::OK();
}

template <>
Status TensorSliceWriter::SaveData(const tstring* data, int64 num_elements,
                                   SavedSlice* ss);

// Create a table builder that will write to "filename" in
// tensorflow::io::Table format.  If successful, return OK
// and set "*builder" to the allocated builder.  Otherwise, return a
// non-OK status.
Status CreateTableTensorSliceBuilder(const string& filename,
                                     TensorSliceWriter::Builder** builder);

}  // namespace checkpoint

}  // namespace tensorflow

#endif  // TENSORFLOW_CORE_UTIL_TENSOR_SLICE_WRITER_H_