Move TensorImpl / UndefinedTensor(Impl) to core (#11441)

Summary:
Moves TensorImpl to core.
Renames UndefinedTensor to UndefinedTensorImpl and moves to core.
Pull Request resolved: pytorch/pytorch#11441

Differential Revision: D9736620

Pulled By: gchanan

fbshipit-source-id: 0322ae3b903e338de253b35a0d74a9d3e219204b

aten/src/ATen/SparseTensorImpl.h

@@ -1,7 +1,7 @@
 #pragma once
 
 #include "ATen/Tensor.h"
-#include "ATen/TensorImpl.h"
+#include "ATen/core/TensorImpl.h"
 #include "ATen/core/Error.h"
 
 namespace at {

aten/src/ATen/TensorImpl.h

@@ -1,204 +1,2 @@
 #pragma once
-
-#include <atomic>
-#include <memory>
-
-#include "ATen/core/Storage.h"
-#include "ATen/core/optional.h"
-#include "ATen/core/TensorTypeId.h"
-#include "ATen/core/TensorTypeIdRegistration.h"
-#include "ATen/core/LegacyTypeDispatch.h"
-#include "ATen/core/Backend.h"
-
-struct THTensor;
-
-namespace at {
-class Scalar;
-struct Type;
-struct Storage;
-struct Tensor;
-} // namespace at
-
-namespace at {
-struct AT_API TensorImpl : public c10::intrusive_ptr_target {
-  TensorImpl() = delete;
-  TensorImpl(TensorTypeId type_id, ScalarType scalar_type, Allocator *allocator, bool is_variable);
-  TensorImpl(Storage&& storage, TensorTypeId type_id, bool is_variable);
-
-  virtual void release_resources() override;
-
-  Type & type() const {
-    // NB: It's valid to use getTypeRaw here, because the TensorImpl
-    // could not have been created without initializing the Type first.
-    // TODO: This is not actually true via the Caffe2 codepath!  Make
-    // it so.
-    return *globalLegacyTypeDispatch().getTypeRaw(tensorTypeIdToBackend(type_id()), scalar_type(), is_variable());
-  }
-
-  TensorTypeId type_id() const { return type_id_; }
-  virtual IntList sizes() const;
-  virtual IntList strides() const;
-  virtual int64_t dim() const;
-  virtual const Storage& storage() const;
-  friend struct Type;
-
-  virtual int64_t numel() const {
-#ifdef DEBUG
-    AT_ASSERT(compute_numel() == numel_);
-#endif
-    return numel_;
-  }
-
-  virtual bool is_contiguous() const {
-#ifdef DEBUG
-    AT_ASSERT(compute_contiguous() == is_contiguous_);
-#endif
-    return is_contiguous_;
-  }
-
-  // this is called by the generated wrapper code when there are conditions
-  // when this output tensor should be zero dimensional. e.g. when all inputs
-  // to a function 'add' were zero dimensional, then condition_when_zero_dim == true.
-  // we also prevent this from getting marked as a zero dim tensor if it is not
-  // the right shape afterall.
-  virtual TensorImpl* maybe_zero_dim(bool condition_when_zero_dim);
-
-  // True if a tensor was auto-wrapped from a C++ or Python number.
-  // Wrapped numbers do not participate in the result type computation for
-  // mixed-type operations if there are any Tensors that are not wrapped
-  // numbers. Otherwise, they behave like their non-wrapped equivalents.
-  // See [Result type computation] in TensorIterator.h.
-  bool is_wrapped_number() const {
-    return is_wrapped_number_;
-  }
-  void set_wrapped_number(bool value) {
-    AT_ASSERT(dim() == 0);
-    is_wrapped_number_ = value;
-  }
-
-  // ~~~~~ Autograd API ~~~~~
-  // Some methods below are defined in TensorImpl.cpp because Tensor is an
-  // incomplete type.
-
-  virtual void set_requires_grad(bool requires_grad) {
-    AT_ERROR("set_requires_grad is not implemented for Tensor");
-  }
-  virtual bool requires_grad() const {
-    AT_ERROR("requires_grad is not implemented for Tensor");
-  }
-
-  virtual Tensor& grad();
-  virtual const Tensor& grad() const;
-
-  // TODO: make these protected
-  // Note: storage->size() may be greater than the recorded size
-  // of a tensor
-  at::Storage storage_;
-
-  template <typename T>
-  inline T * data() const {
-    return storage_.data<T>() + storage_offset_;
-  }
-
-  template <typename T>
-  inline T * unsafe_data() const {
-    return storage_.unsafe_data<T>() + storage_offset_;
-  }
-
-  inline at::ScalarType scalar_type() const {
-    return scalar_type_;
-  }
-
-  virtual int64_t storage_offset() const {
-    return storage_offset_;
-  }
-
-  // represents that numel() == 0.
-  inline bool is_empty() const {
-    return numel() == 0;
-  }
-
-  virtual void resize_dim(int64_t ndim) {
-    // NB: This is *truly* a resize; calling code (e.g., squeeze)
-    // assumes that old values are preserved
-    sizes_.resize(ndim);
-    strides_.resize(ndim);
-    refresh_numel();
-    refresh_contiguous();
-  }
-
-  virtual void set_size(int64_t dim, int64_t new_size) {
-    sizes_[dim] = new_size;
-    refresh_numel();
-    refresh_contiguous();
-  }
-
-  virtual void set_stride(int64_t dim, int64_t new_stride) {
-    strides_[dim] = new_stride;
-    refresh_numel();
-    refresh_contiguous();
-  }
-
-  virtual void set_storage_offset(int64_t storage_offset) {
-    storage_offset_ = storage_offset;
-    refresh_numel();
-    refresh_contiguous();
-  }
-
-  // WARNING: This function does not check if the requested
-  // sizes/strides are in bounds for the storage that is allocated;
-  // this is the responsibility of the caller
-  void set_sizes_and_strides(at::IntList new_size, at::IntList new_stride) {
-    AT_CHECK(
-        new_size.size() == new_stride.size(),
-        "dimensionality of sizes (",
-        new_size.size(),
-        ") must match dimensionality of strides (",
-        new_stride.size(),
-        ")");
-    sizes_ = new_size.vec();
-    strides_ = new_stride.vec();
-    refresh_numel();
-    refresh_contiguous();
-  }
-
-  virtual int64_t size(int64_t d) const;
-  virtual int64_t stride(int64_t d) const;
-
-  bool is_variable() const { return is_variable_; };
-
- private:
-  int64_t storage_offset_;
-  std::vector<int64_t> sizes_;
-  std::vector<int64_t> strides_;
-
-  bool is_contiguous_;
-  int64_t numel_;
-
-  int64_t compute_numel() const {
-    int64_t n = 1;
-    for (auto s : sizes()) {
-      n *= s;
-    }
-    return n;
-  }
-  bool compute_contiguous() const;
-
- protected:
-  void refresh_numel() {
-    numel_ = compute_numel();
-  }
-  void refresh_contiguous() {
-    is_contiguous_ = compute_contiguous();
-  }
-  TensorTypeId type_id_;
-  // INVARIANT: When storage is non-null, this scalar type must
-  // agree with the scalar type in storage
-  ScalarType scalar_type_;
-  bool is_variable_ = false;
-  bool is_wrapped_number_ = false;
-
- private:
-  TensorImpl(Storage&& storage, TensorTypeId type_id, ScalarType scalar_type, bool is_variable);
-};
-} // namespace at
+#include <ATen/core/TensorImpl.h>

aten/src/ATen/Utils.h

@@ -2,7 +2,7 @@
 
 #include "ATen/core/ATenGeneral.h"
 #include "ATen/StorageImpl.h"
-#include "ATen/UndefinedTensor.h"
+#include "ATen/core/UndefinedTensorImpl.h"
 
 #include <ATen/core/ScalarType.h>
 #include "ATen/Formatting.h"

aten/src/ATen/WrapDimUtils.h

@@ -1,7 +1,7 @@
 #pragma once
 
 #include "ATen/core/WrapDimMinimal.h"
-#include "ATen/TensorImpl.h"
+#include "ATen/core/TensorImpl.h"
 
 namespace at {
 

aten/src/ATen/WrapDimUtilsMulti.h

@@ -1,6 +1,6 @@
 #pragma once
 
-#include "ATen/TensorImpl.h"
+#include "ATen/core/TensorImpl.h"
 #include "ATen/WrapDimUtils.h"
 #include <sstream>
 #include <bitset>

aten/src/ATen/copy_wrapper.py

@@ -238,7 +238,7 @@ def create(all_types, backend):
         top_env['copy_includes'].append(
             '#include "ATen/{}.h"'.format(the_type['Type']))
     top_env['copy_includes'].append(
-        '#include "ATen/TensorImpl.h"')
+        '#include "ATen/core/TensorImpl.h"')
 
     # Code generation
     for the_type in all_types:

aten/src/ATen/TensorImpl.cpp → aten/src/ATen/core/TensorImpl.cpp

@@ -1,4 +1,4 @@
-#include <ATen/TensorImpl.h>
+#include <ATen/core/TensorImpl.h>
 
 #include <ATen/core/optional.h>
 #include <ATen/core/Backend.h>