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#pragma once
// ${generated_comment}
#include <ATen/core/Tensor.h>
#include <ATen/TracerMode.h>
#include <ATen/core/grad_mode.h>
#include <c10/util/ArrayRef.h>
#include <c10/core/MemoryFormat.h>
#include <torch/csrc/api/include/torch/detail/TensorDataContainer.h>
#include <torch/csrc/autograd/variable.h>
#ifndef AT_PER_OPERATOR_HEADERS
#include <ATen/Functions.h>
#else
#include <ATen/ops/from_blob.h>
$ops_headers
#endif
#include <functional>
#include <initializer_list>
#include <utility>
namespace torch {
/// NOTE: Currently `torch::tensor(...)` doesn't support mixed data types
/// (i.e. `torch::tensor({{bool, 2.0}})` doesn't work). We might be able to
/// support it in the future by iterating over all sub-lists to find
/// the largest data type that can represent all of the elements, or by using
/// variadic templates.
///
/// NOTE: C++ `torch::tensor` with a floating-point type or an `at::ArrayRef` / `std::vector` /
/// (nested) braced-init-list of floating-point types always produces a tensor of dtype
/// `torch::get_default_dtype()`, matching Python `torch.tensor` behavior.
///
/// NOTE: C++ `torch::tensor` with an integer type or an `at::ArrayRef` / `std::vector` /
/// (nested) braced-init-list of integer types always produces a tensor of dtype `at::kLong`
/// (aka. int64_t), matching Python `torch.tensor` behavior.
///
/// NOTE: The following dtypes are not supported by `torch::tensor` currently:
/// - `unsigned int`
/// - `unsigned long int`
/// - `unsigned long long int`
/// - `long long int`
inline at::Tensor tensor(detail::TensorDataContainer tensor_data_container, const at::TensorOptions& options = {}) {
return autograd::make_variable(
// note: we remove the requires_grad setting from the TensorOptions because
// it is ignored anyways (and we actually have an assertion that it isn't set
// which would fail otherwise). We handle requires_grad explicitly here
// instead of passing it through to the kernel.
tensor_data_container.convert_to_tensor(options.requires_grad(c10::nullopt)),
options.requires_grad());
}
/// A generic deleter function.
using Deleter = std::function<void(void*)>;
using at::MemoryFormat;
/// Exposes the given `data` as a `Tensor` without taking ownership of the
/// original data. `sizes` should specify the shape of the tensor, `strides` the
/// stride in each dimension. The `deleter` function (a
/// `std::function<void(void*)>`) will be called on the `data` when the Tensor
/// data would normally be deallocated. The `TensorOptions` specify additional
/// configuration options for the returned tensor, such as what type to
/// interpret the `data` as.
inline at::Tensor from_blob(
void* data,
at::IntArrayRef sizes,
at::IntArrayRef strides,
const Deleter& deleter,
const at::TensorOptions& options = at::TensorOptions()) {
at::Tensor tensor = ([&]() {
at::AutoDispatchBelowAutograd guard; // TODO: remove
at::tracer::impl::NoTracerDispatchMode tracer_guard;
return at::from_blob(data, sizes, strides, deleter, options.requires_grad(c10::nullopt));
})();
return autograd::make_variable(tensor, options.requires_grad());
}
/// Exposes the given `data` as a `Tensor` without taking ownership of the
/// original data. `sizes` should specify the shape of the tensor, `strides` the
/// stride in each dimension. The `TensorOptions`
/// specify additional configuration options for the returned tensor, such as
/// what type to interpret the `data` as.
inline at::Tensor from_blob(
void* data,
at::IntArrayRef sizes,
at::IntArrayRef strides,
const at::TensorOptions& options = at::TensorOptions()) {
at::Tensor tensor = ([&]() {
at::AutoDispatchBelowAutograd guard; // TODO: remove
at::tracer::impl::NoTracerDispatchMode tracer_guard;
return at::from_blob(data, sizes, strides, options.requires_grad(c10::nullopt));
})();
return autograd::make_variable(tensor, options.requires_grad());
}
/// Exposes the given `data` as a `Tensor` without taking ownership of the
/// original data. `sizes` should specify the shape of the tensor. The `deleter`
/// (a `std::function<void(void*)>`) function will be called on the `data` when
/// the Tensor data would normally be deallocated. The `TensorOptions` specify
/// additional configuration options for the returned tensor, such as what type
/// to interpret the `data` as.
inline at::Tensor from_blob(
void* data,
at::IntArrayRef sizes,
const Deleter& deleter,
const at::TensorOptions& options = at::TensorOptions()) {
at::Tensor tensor = ([&]() {
at::AutoDispatchBelowAutograd guard; // TODO: remove
at::tracer::impl::NoTracerDispatchMode tracer_guard;
return at::from_blob(data, sizes, deleter, options.requires_grad(c10::nullopt));
})();
return autograd::make_variable(tensor, options.requires_grad());
}
/// Exposes the given `data` as a `Tensor` without taking ownership of the
/// original data. `sizes` should specify the shape of the tensor. The
/// `TensorOptions` specify additional configuration options for the returned
/// tensor, such as what type to interpret the `data` as.
inline at::Tensor from_blob(
void* data,
at::IntArrayRef sizes,
const at::TensorOptions& options = at::TensorOptions()) {
at::Tensor tensor = ([&]() {
at::AutoDispatchBelowAutograd guard; // TODO: remove
at::tracer::impl::NoTracerDispatchMode tracer_guard;
return at::from_blob(data, sizes, options.requires_grad(c10::nullopt));
})();
return autograd::make_variable(tensor, options.requires_grad());
}
${function_definitions}
} // namespace torch
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