FM / core

/usr/local/lib64/python3.6/site-packages/torch/include/caffe2/core/

/usr/local/lib64/python3.6/site-packages/torch/include/caffe2/core

Name	Size	Mode	Actions
allocator.h	136	0644	edit dl rm
blob.h	4168	0644	edit dl rm
blob_serialization.h	10791	0644	edit dl rm
blob_serializer_base.h	3905	0644	edit dl rm
blob_stats.h	1127	0644	edit dl rm
common.h	4329	0644	edit dl rm
common_cudnn.h	9893	0644	edit dl rm
common_gpu.h	21414	0644	edit dl rm
common_omp.h	156	0644	edit dl rm
context.h	6174	0644	edit dl rm
context_base.h	4382	0644	edit dl rm
context_gpu.h	11014	0644	edit dl rm
cudnn_wrappers.h	6956	0644	edit dl rm
db.h	9352	0644	edit dl rm
distributions_stubs.h	2161	0644	edit dl rm
event.h	12420	0644	edit dl rm
event_cpu.h	1192	0644	edit dl rm
export_c10_op_to_caffe2.h	9487	0644	edit dl rm
export_caffe2_op_to_c10.h	11101	0644	edit dl rm
flags.h	74	0644	edit dl rm
graph.h	5258	0644	edit dl rm
init.h	6496	0644	edit dl rm
logging.h	75	0644	edit dl rm
macros.h	3426	0644	edit dl rm
memonger.h	817	0644	edit dl rm
module.h	2473	0644	edit dl rm
net.h	4634	0644	edit dl rm
net_async_base.h	7397	0644	edit dl rm
net_async_scheduling.h	993	0644	edit dl rm
net_async_task.h	833	0644	edit dl rm
net_async_task_future.h	1925	0644	edit dl rm
net_async_task_graph.h	2253	0644	edit dl rm
net_async_tracing.h	5093	0644	edit dl rm
net_dag_utils.h	2146	0644	edit dl rm
net_parallel.h	2144	0644	edit dl rm
net_simple.h	2606	0644	edit dl rm
net_simple_refcount.h	2097	0644	edit dl rm
numa.h	72	0644	edit dl rm
observer.h	3809	0644	edit dl rm
operator.h	58872	0644	edit dl rm
operator_gradient.h	10222	0644	edit dl rm
operator_schema.h	18477	0644	edit dl rm
plan_executor.h	219	0644	edit dl rm
prof_dag_counters.h	2751	0644	edit dl rm
qtensor.h	6615	0644	edit dl rm
qtensor_serialization.h	2624	0644	edit dl rm
scope_guard.h	4675	0644	edit dl rm
static_tracepoint.h	398	0644	edit dl rm
static_tracepoint_elfx86.h	5555	0644	edit dl rm
stats.h	10365	0644	edit dl rm
storage.h	733	0644	edit dl rm
tensor.h	18668	0644	edit dl rm
tensor_impl.h	351	0644	edit dl rm
tensor_int8.h	450	0644	edit dl rm
test_utils.h	6285	0644	edit dl rm
timer.h	1218	0644	edit dl rm
transform.h	5741	0644	edit dl rm
types.h	2248	0644	edit dl rm
workspace.h	11305	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/torch/include/caffe2/core/blob_serialization.h (10791B)

#ifndef CAFFE2_CORE_BLOB_SERIALIZATION_H_
#define CAFFE2_CORE_BLOB_SERIALIZATION_H_

#include <limits>
#include <future>

#include <google/protobuf/repeated_field.h>

#include "caffe2/core/blob.h"
#include "caffe2/core/blob_serializer_base.h"
#include "caffe2/core/tensor.h"
#include <c10/util/typeid.h>
#include "caffe2/core/types.h"
#include "caffe2/utils/simple_queue.h"

C10_DECLARE_int(caffe2_tensor_chunk_size);
C10_DECLARE_int(caffe2_max_tensor_serializer_threads);
C10_DECLARE_bool(caffe2_serialize_fp16_as_bytes);

#ifdef _MSC_VER
// It's MSVC, so we just have to guess ... and allow an override
#ifdef FOLLY_ENDIAN_BE
constexpr auto kIsLittleEndian = false;
#else
constexpr auto kIsLittleEndian = true;
#endif
#else
constexpr auto kIsLittleEndian = __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__;
#endif

namespace caffe2 {

constexpr auto kTensorBlobType = "Tensor";
// String used to separate chunk id from the blob name when storing in DB
constexpr auto kChunkIdSeparator = "#%";

/**
 * Serializes the given blob, if possible. Note that this serialization uses
 * the registration mechanism and one has to implement specific serialization
 * approaches for specific classes. Acceptor should take care of writing data
 * to the actual storage.
 */
TORCH_API void SerializeBlob(
    const Blob& blob,
    const string& name,
    BlobSerializerBase::SerializationAcceptor acceptor);

TORCH_API void SerializeBlob(
    const Blob& blob,
    const string& name,
    BlobSerializerBase::SerializationAcceptor acceptor,
    const BlobSerializationOptions& options);

TORCH_API size_t EstimateSerializedBlobSize(
    const Blob& blob,
    c10::string_view name,
    const BlobSerializationOptions& options);

/**
 * @brief Convenience function to serialize a blob to a string.
 *
 * This is a convenience function to serialize small Blobs that produce
 * manageable serialized strings. To serialize big blobs such as
 * large sparse tensors, use the fully-functional interface in
 * blob_serializer_base.h.
 *
 * NOTE: this function doesn't do chunking and might break with big tensors.
 */
TORCH_API string SerializeBlob(const Blob& blob, const string& name);

/**
 * Deserializes from a string containing either BlobProto or TensorProto. If
 * the deserialization fails, the content in the blob should no longer be
 * trusted.
 */
TORCH_API void DeserializeBlob(const string& content, Blob* result);
TORCH_API void DeserializeBlob(const BlobProto& proto, Blob* result);

/*
 * Get an empty Tensor from the TensorProto given the meta data in proto (data
 * type and size of the Tensor) without actually filling in the data.
 *
 * We need this function because we want to construct a fully initialized Tensor
 * in the beginning instead of keeping partially initialized Tensor around the
 * process. Consider the case when we have a Tensor that is split into multiple
 * protos during serialization, in deserialization, we have to fill the Tensor
 * in multiple calls to Deserialize, therefore we need to create a new Tensor
 * with the correct size and data type before the call to Deserialize, because
 * otherwise we will have to check whether the function call is the first call
 * to initialize the underlying Tensor, which makes the function stateful and
 * complicated.
 *
 * The legacy code get away with this problem by passing in a partially
 * initialized Tensor and use Resize and mutable_data to set the correct size,
 * data type and allocate memory for the Tensor, so the state is encoded in
 * these function calls. e.g. mutable_data will allocate memory on the first
 * call and it will return a pointer to the allocated memory on later calls.
 */
TORCH_API Tensor EmptyTensorFromProto(const TensorProto& proto);

/**
 * @brief TensorSerializer is the serializer for Tensors.
 *
 * TensorSerializer takes in a blob that contains a Tensor, and serializes it
 * into a TensorProto protocol buffer.
 */
class TORCH_API TensorSerializer : public BlobSerializerBase {
 public:
  TensorSerializer() {}
  ~TensorSerializer() override {}
  /**
   * Serializes a Blob. Note that this blob has to contain Tensor,
   * otherwise this function produces a fatal error.
   */
  void Serialize(
      const void* pointer,
      TypeMeta typeMeta,
      const string& name,
      SerializationAcceptor acceptor) override;
  void SerializeWithOptions(
      const void* pointer,
      TypeMeta typeMeta,
      const string& name,
      SerializationAcceptor acceptor,
      const BlobSerializationOptions& options) override;

void Serialize(
      const Tensor& tensor,
      const string& name,
      TensorProto* proto,
      const BlobSerializationOptions& options,
      size_t chunkBegin,
      int32_t chunkSize);

void Serialize(
      const Tensor& tensor,
      const string& name,
      TensorProto* proto,
      size_t chunkBegin,
      int32_t chunkSize) {
    BlobSerializationOptions options;
    Serialize(tensor, name, proto, options, chunkBegin, chunkSize);
  }

size_t EstimateSerializedBlobSize(
      const void* pointer,
      TypeMeta typeMeta,
      c10::string_view name,
      const BlobSerializationOptions& options) override;

private:
  // A utility function to store the device context detauls.
  void StoreDeviceDetail(const Tensor& input, TensorProto* proto);
  unique_ptr<BaseContext> context_;
};

/**
 * @brief TensorDeserializer is the deserializer for Tensors.
 *
 * The device that the deserialized Tensor will live under is determined by the
 * device_detail field. If you want to specify the device of the deserialized
 * tensor, change the TensorProto's corresponding fields before calling
 * Deserialize.
 */
class TORCH_API TensorDeserializer : public BlobDeserializerBase {
 public:
  void Deserialize(const BlobProto& proto, Blob* blob) override;

/* There are cases when a Tensor is split into multiple protos and
   * we have to call Deserialize multiple times to get the complete deserialized
   * Tensor, each call will fill part of the Tensor given the segment begin and
   * end information in proto, therefore we have to pass in the Tensor pointer
   * rather than create a new Tensor every time.
   *
   * Precondition: Tensor must be initialized
   */
  void DeserializeToTensor(const TensorProto& proto, Tensor* tensor);

/* Deserialize the proto and return a new Tensor
   * This is a utility function that combines EmptyTensorFromProto and
   * Deserialize(const TensorProto&, Tensor*);
   */
  Tensor Deserialize(const TensorProto& proto);
};

////////////////////////////////////////////////////////////////////////////////
// Implementations
////////////////////////////////////////////////////////////////////////////////

namespace detail {
// Make space for new elements to be copied to the end of the repeated field.
// The new space is not guaranteed to be initialized.
template <typename T>
void ExtendRepeatedField(
    google::protobuf::RepeatedField<T>* field,
    size_t size) {
  field->Reserve(field->size() + size);
#if GOOGLE_PROTOBUF_VERSION >= 3000000
  field->AddNAlreadyReserved(size);
#else
  // We unfortunately do still need to support old protobuf versions in some
  // build configurations.
  for (size_t i = 0; i < size; ++i) {
    field->Add(0);
  }
#endif
}

template <typename SrcType, typename DstType>
inline void CopyToProtoAsIs(
    const size_t size,
    const SrcType* src,
    google::protobuf::RepeatedField<DstType>* field,
    BaseContext* context) {
  static_assert(
      sizeof(SrcType) == sizeof(DstType),
      "The source type and dest type cannot be copied as-is. Did "
      "you mean CopyToProtoWithCast?");
  ExtendRepeatedField(field, size);
  context->template CopyToCPU<SrcType>(
      size, src, reinterpret_cast<SrcType*>(field->mutable_data()));
  // Make sure that we finish the copy into the protobuf.
  context->FinishDeviceComputation();
}

template <typename SrcType, typename DstType>
inline void CopyToProtoWithCast(
    const size_t size,
    const SrcType* src,
    google::protobuf::RepeatedField<DstType>* field,
    BaseContext* context) {
  // TODO: we are having one unnecessary copy here if the context is already
  // CPUContext. Remove it if it is performance critical.
  unique_ptr<SrcType[]> buffer(new SrcType[size]);
  context->template CopyToCPU<SrcType>(size, src, buffer.get());
  context->FinishDeviceComputation();
  field->Reserve(size);
  for (size_t i = 0; i < size; ++i) {
    field->Add(static_cast<DstType>(buffer[i]));
  }
}

template <typename SrcType, typename DstType>
inline void CopyFromProtoAsIs(
    const size_t size,
    const google::protobuf::RepeatedField<SrcType>& field,
    DstType* dst,
    BaseContext* context) {
  static_assert(
      sizeof(SrcType) == sizeof(DstType),
      "The source type and dest type cannot be copied as-is. Did "
      "you mean CopyFromProtoWithCast?");
  CAFFE_ENFORCE_EQ(size, field.size(), "Incorrect proto field size.");
  context->template CopyFromCPU<DstType>(
      size, reinterpret_cast<const DstType*>(field.data()), dst);
}

template <typename SrcType, typename DstType>
inline void CopyFromProtoWithCast(
    const size_t size,
    const google::protobuf::RepeatedField<SrcType>& field,
    DstType* dst,
    BaseContext* context) {
  CAFFE_ENFORCE_EQ(size, field.size(), "Incorrect proto field size.");
  // TODO: we are having one unnecessary copy here if the context is already
  // CPUContext. Remove it if it is performance critical.
  unique_ptr<DstType[]> buffer(new DstType[size]);
  const SrcType* src = field.data();
  for (size_t i = 0; i < size; ++i) {
    buffer[i] = static_cast<DstType>(src[i]);
  }
  context->template CopyFromCPU<DstType>(size, buffer.get(), dst);
}

}  // namespace detail

////////////////////////////////////////////////////////////////////////////////
// Serialization Helpers
////////////////////////////////////////////////////////////////////////////////

// Converts MessageLite to string while also checking that SerializeAsString
// succeeds. Pass description of class/function of the call if you'd
// like it appended to the error message.
TORCH_API std::string SerializeAsString_EnforceCheck(
    const google::protobuf::MessageLite&,
    const char* error_location = nullptr);

// Convert BlobProto to string with success checks.
inline std::string SerializeBlobProtoAsString_EnforceCheck(
    const BlobProto& blob) {
  return SerializeAsString_EnforceCheck(blob, blob.name().c_str());
}

int64_t NumelFromTensorProto(const TensorProto& tensor_proto);

std::vector<int64_t> DimsFromTensorProto(const TensorProto& proto);

TypeMeta GetDataType(const TensorProto& tensor_proto);

std::unique_ptr<BaseContext> ContextFromProto(const TensorProto& tensor_proto);

}  // namespace caffe2

#endif  // CAFFE2_CORE_BLOB_SERIALIZATION_H_

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