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// The utility to write checkpoints for google brain tensor ops and v3
// checkpoints for dist_belief.
//
#ifndef TENSORFLOW_UTIL_TENSOR_SLICE_WRITER_H_
#define TENSORFLOW_UTIL_TENSOR_SLICE_WRITER_H_
#include <unordered_map>
#include "tensorflow/core/framework/tensor_slice.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/lib/core/errors.h"
#include "tensorflow/core/platform/port.h"
#include "tensorflow/core/lib/core/stringpiece.h"
#include "tensorflow/core/lib/gtl/map_util.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/public/tensor_shape.h"
#include "tensorflow/core/public/status.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();
private:
// Allocate "num_elements" elements in "ss" and save the data in "data"
// there.
template <typename T>
static void SaveData(const T* data, int num_elements, SavedSlice* ss);
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));
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, "");
sts.AppendToString(&key_value.second);
data_.insert(key_value);
}
++slices_;
return Status::OK();
}
template <typename T>
void TensorSliceWriter::SaveData(const T* data, int num_elements,
SavedSlice* ss) {
Fill(data, num_elements, ss->mutable_data());
}
// 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_UTIL_TENSOR_SLICE_WRITER_H_
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