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/tensor.h (18668B)

#ifndef CAFFE2_CORE_TENSOR_H_
#define CAFFE2_CORE_TENSOR_H_

#include <c10/macros/Macros.h>
#include "caffe2/core/storage.h"

#include <ATen/core/UndefinedTensorImpl.h>
#include <c10/core/TensorOptions.h>
#include <c10/util/intrusive_ptr.h>

#if defined(EXPOSE_C2_OPS) || \
    !defined(CAFFE2_IS_XPLAT_BUILD) && !defined(C10_MOBILE)
namespace at {
class Tensor;
};
#endif
namespace caffe2 {

using at::UndefinedTensorImpl;

/**
 * @brief Tensor class holds a shared pointer to the implementation TensorImpl,
 * redirects API calls to TensorImpl;
 * Copying of Tensor results in sharing the same underlying implementation
 * object
 *
 * NB: See TensorImpl for documentation on these methods.
 */
class TORCH_API Tensor final {
 private:
  enum Unsafe { IDoWantAliasing };
  Tensor(const Tensor& other, Unsafe _) : impl_(other.getIntrusivePtr()) {}

protected:
  using TensorImplPtr = c10::intrusive_ptr<TensorImpl, UndefinedTensorImpl>;
  TensorImplPtr impl_;

void enforce_invariants();

public:
  Tensor() : impl_() {}

Tensor(const Tensor& t) : impl_(t.impl_) {}
  Tensor& operator=(const Tensor& t) {
    impl_ = t.impl_;
    return *this;
  }

Tensor(Tensor&&) = default;
  Tensor& operator=(Tensor&&) = default;

operator bool() const {
    return impl_.defined();
  }

TensorImpl* unsafeGetTensorImpl() const {
    return impl_.get();
  }

Tensor UnsafeSharedInstance() const {
    return Tensor(*this, IDoWantAliasing);
  }

/**
   * @brief Creates a tensor of the given device type.
   *
   * Note that the actual data allocation is not going to be carried out until
   * you resize the tensor and then call mutable_data().
   */
  explicit Tensor(at::Device device)
      : impl_(c10::make_intrusive<TensorImpl, UndefinedTensorImpl>(
            Storage::create_legacy(device),
            c10::computeDispatchKey(c10::nullopt, at::kStrided, device),
            TypeMeta())) {}

/**
   * @brief Creates a tensor of the given dimension.
   *
   * Note that the actual data allocation is not going to be carried out until
   * the first time mutable_data() is called.
   */
  explicit Tensor(at::IntArrayRef dims, DeviceType type) : Tensor(type) {
    // TODO: here, we create a Storage
    // and immediately discard it in Resize() since
    // reset_tensor will be true and FreeMemory will be called,
    // we might want to avoid creating Storage twice?
    Resize(dims);
  }

// we want to preserve index information
  explicit Tensor(at::IntArrayRef dims, at::Device device) : Tensor(device) {
    Resize(dims);
  }

// TODO: remove?
  explicit Tensor(const vector<int>& dims, DeviceType type) : Tensor(type) {
    Resize(dims);
  }

/**
   * @brief: Create a Tensor of at::DeviceType `type` and initialize it with
   * src Tensor
   */
  Tensor(const Tensor& src, DeviceType type) : Tensor(type) {
    CopyFrom(src);
  }

/**
   * @brief Mutual conversion with at::Tensor
   *
   * The tensor will share the same instance (data, strides, sizes, etc) but
   * a different subset of APIs would be available
   */
#if defined(EXPOSE_C2_OPS) || \
    !defined(CAFFE2_IS_XPLAT_BUILD) && !defined(C10_MOBILE)
  explicit Tensor(at::Tensor tensor);

explicit operator at::Tensor() const&;

explicit operator at::Tensor() &&;
#endif

bool is_same(const Tensor& other) const noexcept {
    return impl_ == other.impl_;
  }

Tensor Clone() const {
    Tensor x(GetDevice());
    x.CopyFrom(*this);
    return x;
  }

/**
   * Clone self as a Tensor that share the same Storage,
   * that is, both Tensors are views on the same Storage.
   * If we change the sizes or strides of one Tensor, it
   * does not affect the other Tensor that it shares Storage
   * with.
   * A similar yet different usage is `Tensor x = y;`, this
   * will make x and y pointing to the same Tensor and resizing
   * one of them will resize the other as well.
   *
   * TODO: Deduplicate this with THTensor_(newWithTensor)
   * (exposed in ATen as at::alias but not otherwise available)
   */
  Tensor Alias() const {
    Tensor x(sizes(), GetDevice());
    if (!dtype_initialized()) {
      C10_LOG_EVERY_MS(WARNING, 1000)
          << "Cloning a tensor that don't have a data type (did you call mutable_data<T> on the tensor?)";
    }
    AT_ASSERTM(
        storage_initialized(),
        "Cloning a tensor that has no content and has size > 0");
    // set_storage already sets data_type_ of TensorImpl
    x.impl_->set_storage_and_dtype(storage(), impl_->dtype());
    x.impl_->set_storage_offset(impl_->storage_offset());
    x.impl_->set_sizes_and_strides(sizes(), strides());
    return x;
  }

DeviceType GetDeviceType() const {
    return impl_->device_type();
  }

at::Device GetDevice() const {
    return impl_.get()->device();
  }

/**
   * @brief Copies the data from a source tensor, with a context provided to
   * carry out the underlying memcpy operation.  This method respects
   * caffe2_keep_on_shrink.
   *
   * After CopyFrom, this function guarantees that the destination tensor will
   * have the same initialization state and dtype as src.  This function
   * preserves the DeviceType of the source tensor (so, e.g., if you allocate
   * a tensor on CPU and then CopyFrom a CUDA tensor, that will to a
   * CUDA-to-CPU transfer).
   *
   * 'async' parameter triggers async copy for CUDA tensors
   */
  void CopyFrom(const Tensor& src, bool async = false);

/**
   * @brief Extend the outer-most dimension of this tensor
   *        to dimension of `num`.
   */
  void ExtendTo(int64_t num, float growthPct) const {
    CAFFE_ENFORCE_GE_WITH_CALLER(impl_->dim(), 1);
    CAFFE_ENFORCE_GE_WITH_CALLER(growthPct, 0);
    Extend(num - impl_->size(0), growthPct);
  }

void Extend(int64_t num, float growthPct) const {
    impl_.get()->Extend(num, growthPct);
  }

/**
   * @brief Shrinks the outer-most dimension to given size, keeping the data.
   *
   * This method guarantees that no re-allocations are carried out, which means
   * that the extra capacity after the end of the shrunk tensor is maintained.
   * Notably, this function does NOT respect caffe2_keep_on_shrink.
   */
  void ShrinkTo(int64_t outer_dim) const {
    CAFFE_ENFORCE_WITH_CALLER(
        impl_->is_contiguous(),
        "Right now ShrinkTo is only supported on contiguous Tensor.");
    CAFFE_ENFORCE_WITH_CALLER(impl_->dim() >= 1, "Tensor must be at least 1D");
    CAFFE_ENFORCE_WITH_CALLER(
        outer_dim <= impl_->size(0),
        "New outer dimension must be smaller than current.");
    CAFFE_ENFORCE(
        impl_->storage().unique(),
        "Can't call ShrinkTo on shared storage, please call Resize instead.");
    impl_.get()->set_size(0, outer_dim);
  }

template <class T>
  void ReserveSpace(const T& outer_dim) const {
    impl_.get()->ReserveSpace(outer_dim);
  }

template <typename... Ts>
  void Resize(Ts... dim_source) const {
    impl_.get()->Resize(dim_source...);
  }

template <typename T>
  void Resize(const std::vector<T>& dim_source) const {
    impl_.get()->Resize(ArrayRef<T>(dim_source));
  }

/**
   * Resize the tensor like the source tensor. Note that this is just a
   * sugar wrapper that essentially calls Resize(src_tensor.dims()).
   * This method respects caffe2_keep_on_shrink.
   */
  inline void ResizeLike(const Tensor& src_tensor) const {
    CAFFE_ENFORCE_WITH_CALLER(
        src_tensor.is_contiguous(),
        "Right now ResizeLike is only supported for contiguous Tensor.");
    if (impl_ != src_tensor.impl_) {
      impl_.get()->Resize(src_tensor.sizes());
    }
  }

inline void Reshape(const vector<int64_t>& dims) const {
    impl_.get()->Reshape(dims);
  }

inline void Reshape(const vector<int>& dims) const {
    impl_.get()->Reshape(ToVectorint64_t(dims));
  }

inline void FreeMemory() const {
    impl_.get()->FreeMemory();
  }

/**
   * A utility function to print the debug string for the tensor. Note that this
   * is very slow since it involves quite some string operations, so do not use
   * it in your performance-critical code.
   */
  string DebugString() const {
    std::stringstream ss;
    ss << "A Tensor of item size " << impl_->dtype().itemsize() << " and type "
       << impl_->dtype().name() << " and dimension (";
    for (int d : impl_->sizes()) {
      ss << d << ",";
    }
    ss << ").";
    return ss.str();
  }

// To be deprecated
  void ShareData(const Tensor& src) const {
    impl_.get()->ShareData(*src.impl_.get());
  }

/**
   * @brief Shares the data with an externally managed pointer.
   *
   * This is similar to ShareData() but the source is a pointer with an advanced
   * deleter option. In default, no deletion takes place, and one needs to make
   * sure that the external memory is deallocated only after the tensor finishes
   * using it. If a Deleter object is passed in, when this tensor is reallocated
   * or freed, the deleter function is going to be called.
   */
  template <typename T>
  void ShareExternalPointer(
      T* src,
      size_t nbytes = 0,
      MemoryDeleter d = nullptr) const {
    ShareExternalPointer((void*)src, caffe2::TypeMeta::Make<T>(), nbytes, d);
  }

template <typename T>
  void ShareExternalPointer(at::DataPtr&& data_ptr, size_t nbytes = 0) const {
    ShareExternalPointer(
        std::move(data_ptr), caffe2::TypeMeta::Make<T>(), nbytes);
  }

void ShareExternalPointer(
      void* src,
      const TypeMeta data_type,
      size_t nbytes = 0,
      MemoryDeleter d = nullptr) const {
    CAFFE_ENFORCE_WITH_CALLER(
        impl_->is_contiguous(),
        "Right now ShareExternalPointer is only supported for contiguous Tensor.");
    CAFFE_ENFORCE_WITH_CALLER(
        data_type != ScalarType::Undefined,
        "To share with a raw external pointer you need to pass in an "
        "initialized data_type(TypeMeta).");
    impl_.get()->ShareExternalPointer(
        at::DataPtr(src, src, d, impl_->device_type()), data_type, nbytes);
  }

void ShareExternalPointer(
      at::DataPtr&& data_ptr,
      const TypeMeta data_type,
      size_t nbytes) {
    impl_.get()->ShareExternalPointer(std::move(data_ptr), data_type, nbytes);
  }

const c10::intrusive_ptr<TensorImpl, UndefinedTensorImpl>& getIntrusivePtr()
      const {
    return impl_;
  }

bool defined() const {
    return impl_;
  }

/**
   * Returns a raw void* pointer of the underlying storage. mutable_data()
   * or raw_mutable_data() must have been called prior to this function call.
   */
  inline void* raw_data() const {
    return impl_->data();
  }

template <typename T>
  inline T* data() const {
    return impl_.get()->data<T>();
  }

inline void* raw_mutable_data(const TypeMeta meta) const {
    return impl_.get()->raw_mutable_data(meta);
  }

/**
   * Returns a mutable raw pointer of the underlying storage. This can only be
   * used when you know for sure that the underlying storage of the tensor is
   * already created via an earlier raw_mutable_data(meta) call or a
   * mutable_data<T>() call.
   *
   * If the existing data does not match the desired type, it will be deleted
   * and a new storage will be created.
   */
  inline void* raw_mutable_data() const {
    const auto& data_type = impl_->dtype();
    CAFFE_ENFORCE_WITH_CALLER(
        data_type != ScalarType::Undefined,
        "Calling raw_mutable_data() without meta, but the current meta is "
        "of unknown type.");
    return raw_mutable_data(data_type);
  }

template <typename T>
  inline T* mutable_data() const {
    return impl_.get()->mutable_data<T>();
  }

/**
   * Returns the number of dimensions of the data.
   */
  inline int dim() const {
    return impl_->dim();
  }

/**
   * (To be deprecated) Returns the number of dimensions of the data.
   */
  inline int ndim() const {
    return impl_->dim();
  }

/**
   * (To be deprecated) Returns the size (i.e. the number of items) of the
   * tensor.
   */
  inline int64_t size() const {
    return impl_->numel();
  }

/**
   * Returns the number of items of the tensor.
   */
  inline int64_t numel() const {
    return impl_->numel();
  }

/**
   * Return the number of bytes each item takes in the tensor.
   */
  inline size_t itemsize() const {
    return impl_->dtype().itemsize();
  }

/**
   * Returns the total number of bytes of the storage.
   *
   * This is equivalent to calling size() * itemsize().
   */
  inline size_t nbytes() const {
    return impl_->numel() * itemsize();
  }

inline at::IntArrayRef sizes() const {
    return impl_.get()->sizes();
  }

inline int64_t size_from_dim(int k) const {
    return size_from_dim_(k, impl_->sizes());
  }

inline int64_t size_to_dim(int k) const {
    return size_to_dim_(k, impl_->sizes());
  }

inline int64_t size_between_dim(int k, int l) const {
    return size_between_dim_(k, l, impl_->sizes());
  }

/**
   * Returns the 'canonical' version of a (usually)  user-specified axis,
   * allowing for negative indexing (e.g., -1 for the last axis).
   *
   * @param axis_index the axis index.
   *        If 0 <= index < dim(), return index.
   *        If -ndim <= index <= -1, return (dim() - (-index)),
   *        e.g., the last axis index (dim() - 1) if index == -1,
   *        the second to last if index == -2, etc.
   *        Dies on out of range index.
   */
  inline int canonical_axis_index(int axis_index) const {
    return canonical_axis_index_(axis_index, impl_->dim());
  }

inline int64_t stride(int64_t dim) const {
    return impl_.get()->stride(dim);
  }

inline at::IntArrayRef strides() const {
    return impl_.get()->strides();
  }

inline bool is_contiguous(
      at::MemoryFormat memory_format = at::MemoryFormat::Contiguous) const {
    return impl_.get()->is_contiguous(memory_format);
  }

/**
   * Checks if the tensor content is of the given data type.
   */
  template <typename T>
  inline bool IsType() const {
    return impl_->dtype().Match<T>();
  }

/**
   * Returns the TypeMeta object associated with the current data type.
   */
  inline const TypeMeta dtype() const {
    return impl_->dtype();
  }

/**
   * (To be deprecated) Returns the TypeMeta object associated with the current
   * data type.
   */
  inline const TypeMeta meta() const {
    return impl_->dtype();
  }

/**
   * Returns the i-th dimension of the tensor in int.
   *
   * This function returns an int value instead of int64_t, which depending on
   * the typedef could be int64. If you want int64 dim values, make sure you
   * call dim() instead.
   */
  inline int dim32(const int i) const {
#ifndef NDEBUG
    CAFFE_ENFORCE_LT_WITH_CALLER(
        i, static_cast<int>(impl_->dim()), "Exceeding ndim limit");
    CAFFE_ENFORCE_GE_WITH_CALLER(i, 0, "Cannot have negative dimension index");
#endif
    // Avoid TensorImpl::size() because it is a virtual call that
    // supports out-of-range indexing like Python.
    auto s = impl_->sizes()[i];
    CAFFE_ENFORCE_LT_WITH_CALLER(s, std::numeric_limits<int>::max());
    return static_cast<int>(s);
  }

inline int64_t size(const int i) const {
    return impl_->size(i);
  }

// To be deprecated
  inline int64_t dim(const int i) const {
    return impl_->size(i);
  }

const Storage& storage() {
    return impl_->storage();
  }

const Storage& storage() const {
    return impl_->storage();
  }

bool storage_initialized() const {
    return impl_->storage_initialized();
  }

bool dtype_initialized() const {
    return impl_->dtype_initialized();
  }
};

/**
 * Reinitialize a Tensor to given dims and options if necessary, note that
 * this will not do anything if the
 * Tensor already has correct size and data type
 */
TORCH_API void
ReinitializeTensor(Tensor* t, at::IntArrayRef dims, at::TensorOptions options);

TORCH_API void ReinitializeAndCopyFrom(
    Tensor* t,
    at::TensorOptions options,
    const Tensor& src,
    bool async = false);

using TensorCPU = Tensor;

constexpr int k_limit_default_ = 1000;

// TODO: the following logic can be merged into regular Tensor class methods
// after MKLMemory starts to implement Tensor interface

// Type call registry
typedef TypeMeta (*TypeCall)(const void*);
TypeCall GetTypeCallFunction(TypeIdentifier id);
void RegisterTypeCallFunction(TypeIdentifier id, TypeCall c);

// Shape call registry
typedef vector<int64_t> (
    *TensorInfoCall)(const void*, size_t* capacity, DeviceOption* device);
TensorInfoCall GetTensorInfoFunction(TypeIdentifier id);
void RegisterTensorInfoFunction(TypeIdentifier id, TensorInfoCall c);

// resize helper function
void TensorVectorResize(
    std::vector<Tensor>& tensors,
    int size,
    DeviceType type);

// Tensor factory function
TORCH_API Tensor empty(at::IntArrayRef dims, at::TensorOptions options);

/**
 * @brief Creates a CPU tensor, and fills its contents with the given values.
 * Values are copied in
 */
// TODO: can be unified with at::from_blob when Tensor is merged and string
// types are supported
template <typename T>
Tensor TensorCPUFromValues(at::IntArrayRef dims, at::ArrayRef<T> values) {
  Tensor r = empty(dims, at::device(CPU).dtype<T>());
  CAFFE_ENFORCE_EQ(values.size(), r.numel());
  CPUContext context;
  context.CopyItemsFromCPU(
      r.dtype(), values.size(), values.data(), r.mutable_data<T>());
  return r;
}

vector<int64_t>
GetTensorInfo(const void* c, size_t* capacity, DeviceOption* device);

class TORCH_API TensorPrinter {
 public:
  explicit TensorPrinter(
      const std::string& tensor_name = "",
      const std::string& file_name = "",
      int limit = k_limit_default_);
  ~TensorPrinter();

template <class T>
  void Print(const Tensor& tensor);

void PrintMeta(const Tensor& tensor);

string MetaStr(const Tensor& tensor);

private:
  bool to_file_;
  int limit_;
  std::unique_ptr<std::ofstream> log_file_;
  std::string tensor_name_;
};

template <class T>
void TensorPrinter::Print(const Tensor& tensor) {
  std::stringstream values_stream;
  // One most likely doesn't want to print int64-number of items for visual
  // inspection, so we cast down to int here.
  int total_count = static_cast<int>(std::min(tensor.numel(), int64_t(limit_)));

const T* tensor_data = tensor.template data<T>();
  for (int i = 0; i < total_count - 1; ++i) {
    values_stream << tensor_data[i] << ",";
  }
  if (total_count) {
    // We do not add a comma after the last item.
    values_stream << tensor_data[total_count - 1];
  }

if (to_file_) {
    (*log_file_) << MetaStr(tensor) << values_stream.str() << std::endl;
  } else {
    // Log to console.
    LOG(INFO) << MetaStr(tensor) << values_stream.str();
  }
}

} // namespace caffe2
#endif // CAFFE2_CORE_TENSOR_H_

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