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// The utility to read checkpoints for google brain tensor ops and v3
// checkpoints for dist_belief.
//
#ifndef TENSORFLOW_UTIL_TENSOR_SLICE_READER_H_
#define TENSORFLOW_UTIL_TENSOR_SLICE_READER_H_
#include <unordered_map>
#include "tensorflow/core/framework/tensor_slice.h"
#include "tensorflow/core/framework/types.pb.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/platform/port.h"
#include "tensorflow/core/platform/protobuf.h"
#include "tensorflow/core/public/status.h"
#include "tensorflow/core/public/tensor_shape.h"
#include "tensorflow/core/util/saved_tensor_slice.pb.h"
#include "tensorflow/core/util/saved_tensor_slice_util.h"
#include "tensorflow/core/util/tensor_slice_set.h"
#include "tensorflow/core/util/tensor_slice_util.h"
namespace tensorflow {
namespace checkpoint {
// The reader reads in all the meta data about all the tensor slices. Then it
// will try to read the relevant data on-demand to produce the data for the
// slices needed.
// NOTE(yangke): another way to do this is to first load a list of the tensor
// slices needed and then just selectively read some of the meta data. That
// might optimize the loading but makes the logic a bit more complicated. We
// might want to revisit that.
// TODO(yangke): consider moving to TensorProto.
class TensorSliceReader {
public:
// Abstract interface for reading data out of a tensor slice checkpoint file
class Table {
public:
virtual ~Table();
virtual bool Get(const string& key, string* value) = 0;
};
typedef std::function<Status(const string&, Table**)> OpenTableFunction;
static const int kLoadAllShards = -1;
TensorSliceReader(const string& filepattern, OpenTableFunction open_function);
TensorSliceReader(const string& filepattern, OpenTableFunction open_function,
int preferred_shard);
virtual ~TensorSliceReader();
// Get the filename this reader is attached to.
const string& filepattern() const { return filepattern_; }
// Get the number of files matched.
int num_files() const { return sss_.size(); }
// Get the status of the reader.
const Status status() const { return status_; }
// Checks if the reader contains any slice of a tensor. In case the reader
// does contain the tensor, if "shape" is not nullptr, fill "shape" with the
// shape of the tensor; if "type" is not nullptr, fill "type" with the type
// of the tensor.
bool HasTensor(const string& name, TensorShape* shape, DataType* type) const;
// Checks if the reader contains all the data about a tensor slice, and if
// yes, copies the data of the slice to "data". The caller needs to make sure
// that "data" points to a buffer that holds enough data.
// This is a slow function since it needs to read sstables.
template <typename T>
bool CopySliceData(const string& name, const TensorSlice& slice,
T* data) const;
// Get the tensors.
const std::unordered_map<string, TensorSliceSet*>& Tensors() const {
return tensors_;
}
private:
friend class TensorSliceWriteTestHelper;
void LoadShard(int shard) const;
void LoadAllShards() const;
void RegisterTensorSlice(const string& name, const TensorShape& shape,
DataType type, const string& tag,
const TensorSlice& slice) const;
const TensorSliceSet* FindTensorSlice(
const string& name, const TensorSlice& slice,
std::vector<std::pair<TensorSlice, string>>* details) const;
const string filepattern_;
const OpenTableFunction open_function_;
std::vector<string> fnames_;
std::unordered_map<string, int> fname_to_index_;
// Guards the attributes below.
mutable mutex mu_;
mutable bool all_shards_loaded_ = false;
mutable std::vector<std::unique_ptr<Table>> sss_;
mutable std::unordered_map<string, TensorSliceSet*> tensors_;
mutable Status status_;
TF_DISALLOW_COPY_AND_ASSIGN(TensorSliceReader);
};
Status OpenTableTensorSliceReader(const string& fname,
TensorSliceReader::Table** table);
template <typename T>
bool TensorSliceReader::CopySliceData(const string& name,
const TensorSlice& slice, T* data) const {
std::vector<std::pair<TensorSlice, string>> details;
const TensorSliceSet* tss;
{
mutex_lock l(mu_);
tss = FindTensorSlice(name, slice, &details);
if (!tss && !all_shards_loaded_) {
VLOG(1) << "Did not find slice in preferred shard, loading all shards."
<< name << ": " << slice.DebugString();
LoadAllShards();
tss = FindTensorSlice(name, slice, &details);
}
if (!tss) {
// No such tensor
return false;
}
}
// We have the data -- copy it over.
string value;
for (const auto& x : details) {
const TensorSlice& slice_s = x.first;
const string& fname = x.second;
int idx = gtl::FindWithDefault(fname_to_index_, fname, -1);
CHECK_GE(idx, 0) << "Failed to find the index for filename " << fname;
// We read a record in the corresponding sstable
const string key = EncodeTensorNameSlice(name, slice_s);
CHECK(sss_[idx]->Get(key, &value))
<< "Failed to seek to the record for tensor " << name << ", slice "
<< slice_s.DebugString() << ": computed key = " << key;
SavedTensorSlices sts;
CHECK(ParseProtoUnlimited(&sts, value))
<< "Failed to parse the record for tensor " << name << ", slice "
<< slice_s.DebugString() << ": computed key = " << key;
CopyDataFromTensorSliceToTensorSlice(
tss->shape(), slice_s, slice,
checkpoint::TensorProtoData<T>(sts.data().data()), data);
}
return true;
}
} // namespace checkpoint
} // namespace tensorflow
#endif // TENSORFLOW_UTIL_TENSOR_SLICE_READER_H_
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