#pragma once
#include <c10/util/Optional.h>
#include <ATen/core/TensorBody.h>
#include <ATen/ExpandUtils.h>
#include <ATen/Functions.h>
#include <ATen/ScalarOps.h>
// TODO: try to remove this
// There is some back story, see https://github.com/pytorch/pytorch/issues/48684
#include <ATen/NativeFunctions.h>
#include <ATen/core/List.h>
namespace at {
namespace indexing {
const int64_t INDEX_MAX = std::numeric_limits<int64_t>::max();
const int64_t INDEX_MIN = std::numeric_limits<int64_t>::min();
enum class TensorIndexType { None, Ellipsis, Integer, Boolean, Slice, Tensor };
constexpr c10::nullopt_t None = c10::nullopt;
struct TORCH_API EllipsisIndexType final { EllipsisIndexType() {} };
TORCH_API extern const EllipsisIndexType Ellipsis;
struct TORCH_API Slice final {
public:
// This mirrors `__PySlice_Unpack` in torch/csrc/utils/python_compat.h
Slice(
c10::optional<int64_t> start_index = c10::nullopt,
c10::optional<int64_t> stop_index = c10::nullopt,
c10::optional<int64_t> step_index = c10::nullopt) {
if (!step_index.has_value()) {
step_ = 1;
} else {
step_ = step_index.value();
TORCH_CHECK_VALUE(step_ != 0, "slice step cannot be zero");
// Here step might be -INDEX_MAX-1; in this case we replace it
// with -INDEX_MAX. This doesn't affect the semantics, and it
// guards against later undefined behaviour resulting from code that
// does "step = -step" as part of a slice reversal.
if (step_ < -INDEX_MAX)
step_ = -INDEX_MAX;
}
if (!start_index.has_value()) {
start_ = step_ < 0 ? INDEX_MAX : 0;
} else {
start_ = start_index.value();
}
if (!stop_index.has_value()) {
stop_ = step_ < 0 ? INDEX_MIN : INDEX_MAX;
} else {
stop_ = stop_index.value();
}
}
inline int64_t start() const {
return start_;
}
inline int64_t stop() const {
return stop_;
}
inline int64_t step() const {
return step_;
}
private:
int64_t start_;
int64_t stop_;
int64_t step_;
};
TORCH_API std::ostream& operator<<(std::ostream& stream, const Slice& slice);
// `at::indexing::TensorIndex` is used for converting C++ tensor indices such as
// `{None, "...", Ellipsis, 0, true, Slice(1, None, 2), torch::tensor({1, 2})}`
// into its equivalent `std::vector<TensorIndex>`, so that further tensor indexing
// operations can be performed using the supplied indices.
//
// There is one-to-one correspondence between Python and C++ tensor index types:
// Python | C++
// -----------------------------------------------------
// `None` | `at::indexing::None`
// `Ellipsis` | `at::indexing::Ellipsis`
// `...` | `"..."`
// `123` | `123`
// `True` / `False` | `true` / `false`
// `:` | `Slice()` / `Slice(None, None)`
// `::` | `Slice()` / `Slice(None, None, None)`
// `1:` | `Slice(1, None)`
// `1::` | `Slice(1, None, None)`
// `:3` | `Slice(None, 3)`
// `:3:` | `Slice(None, 3, None)`
// `::2` | `Slice(None, None, 2)`
// `1:3` | `Slice(1, 3)`
// `1::2` | `Slice(1, None, 2)`
// `:3:2` | `Slice(None, 3, 2)`
// `1:3:2` | `Slice(1, 3, 2)`
// `torch.tensor([1, 2])`) | `torch::tensor({1, 2})`
struct TORCH_API TensorIndex final {
// Case 1: `at::indexing::None`
TensorIndex(c10::nullopt_t) : type_(TensorIndexType::None) {}
// Case 2: "..." / `at::indexing::Ellipsis`
TensorIndex(at::indexing::EllipsisIndexType) : type_(TensorIndexType::Ellipsis) {}
TensorIndex(const char *str) : TensorIndex(at::indexing::Ellipsis) {
TORCH_CHECK_VALUE(
strcmp(str, "...") == 0,
"Expected \"...\" to represent an ellipsis index, but got \"", str, "\"");
}
// Case 3: Integer value
TensorIndex(int64_t integer) : integer_(integer), type_(TensorIndexType::Integer) {}
TensorIndex(int integer) : TensorIndex((int64_t)integer) {}
// Case 4: Boolean value
template <class T,
class = typename std::enable_if<std::is_same<bool, T>::value>::type >
TensorIndex(T boolean) : boolean_(boolean), type_(TensorIndexType::Boolean) {}
// Case 5: Slice represented in `at::indexing::Slice` form
TensorIndex(Slice slice) : slice_(std::move(slice)), type_(TensorIndexType::Slice) {}
// Case 6: Tensor value
TensorIndex(Tensor tensor) : tensor_(std::move(tensor)), type_(TensorIndexType::Tensor) {}
inline bool is_none() const {
return type_ == TensorIndexType::None;
}
inline bool is_ellipsis() const {
return type_ == TensorIndexType::Ellipsis;
}
inline bool is_integer() const {
return type_ == TensorIndexType::Integer;
}
inline int64_t integer() const {
return integer_;
}
inline bool is_boolean() const {
return type_ == TensorIndexType::Boolean;
}
inline bool boolean() const {
return boolean_;
}
inline bool is_slice() const {
return type_ == TensorIndexType::Slice;
}
inline const Slice& slice() const {
return slice_;
}
inline bool is_tensor() const {
return type_ == TensorIndexType::Tensor;
}
inline const Tensor& tensor() const {
return tensor_;
}
private:
int64_t integer_;
bool boolean_;
Slice slice_;
Tensor tensor_;
TensorIndexType type_;
};
TORCH_API std::ostream& operator<<(std::ostream& stream, const TensorIndex& tensor_index);
TORCH_API std::ostream& operator<<(std::ostream& stream, const std::vector<TensorIndex>& tensor_indices);
namespace impl {
static inline Tensor applySlice(
const Tensor& self,
int64_t dim,
int64_t start,
int64_t stop,
int64_t step,
bool disable_slice_optimization,
const at::Device& self_device,
const IntArrayRef& self_sizes) {
// TODO: implement negative step
TORCH_CHECK_VALUE(step > 0, "step must be greater than zero");
// Skip this optimization if we are tracing, as the trace may be polymorphic
// over the shape of the `self` tensor, and we still want to record
// the slice.
int64_t length = (self_device == at::kCPU || self_device == at::kCUDA) ? self_sizes[dim] : self.size(dim);
if (!disable_slice_optimization && start == 0 && stop == length && step == 1) {
return self;
}
return self.slice(dim, start, stop, step);
}
static inline Tensor applySelect(
const Tensor& self,
int64_t dim,
int64_t index,
int64_t real_dim,
const at::Device& self_device,
const IntArrayRef& self_sizes) {
TORCH_CHECK_INDEX(
!(index == 0 && dim == 0 && self_sizes.size() == 0),
"invalid index of a 0-dim tensor. ",
"Use `tensor.item()` in Python or `tensor.item<T>()` in C++ to convert a 0-dim tensor to a number");
int64_t size = self_sizes[dim];
TORCH_CHECK_INDEX(
index >= -size && index < size,
"index ", index, " is out of bounds for dimension ", real_dim, " with size ", size);
// if the index is negative, do not normalize it because that would fix the index
// on the current tensor size in the tracer.
// aten::select also works on negative indices
return self.select(dim, index);
}
static inline Tensor boolToIndexingTensorCPUOrCUDA(const Tensor& self, bool value) {
// booleans add a dimension of size 1. true indexes this dimension as if 0:, false as empty.
if (value) {
return at::empty({1}, {}, self.options().dtype(kLong)).fill_(0.);
} else {
return at::empty({0}, {}, self.options().dtype(kLong));
}
}
static inline Tensor boolToIndexingTensorNonNativeDeviceType(const Tensor& self, bool value) {
// booleans add a dimension of size 1. true indexes this dimension as if 0:, false as empty.
if (value) {
return at::zeros({1}, {}, self.options().dtype(kLong));
} else {
return at::empty({0}, {}, self.options().dtype(kLong));
}
}
static inline Tensor boolToIndexingTensor(const Tensor& self, bool value, const at::Device& self_device) {
if (self_device == at::kCPU || self_device == at::kCUDA) {
return boolToIndexingTensorCPUOrCUDA(self, value);
} else {
return boolToIndexingTensorNonNativeDeviceType(self, value);
}
}
static inline Tensor scalarToTensorNonNativeDeviceType(Scalar v, const TensorOptions& options) {
return at::scalar_tensor(v, options);
}
static inline void recordTensorIndex(const Tensor& tensor, std::vector<Tensor>& outIndices, int64_t* dim_ptr) {
// TODO: check scalarType
outIndices.resize(*dim_ptr + 1);
outIndices[*dim_ptr] = tensor;
(*dim_ptr)++;
};
static inline c10::List<c10::optional<Tensor>> typeConvertIndices(const Tensor& self, std::vector<Tensor>&& indices) {
c10::List<c10::optional<Tensor>> converted_inds;
converted_inds.reserve(indices.size());
for (size_t i = 0; i < indices.size(); ++i) {
const auto &ind = indices[i];
if (ind.defined()) {
converted_inds.push_back(ind.to(ind.options().device(self.device())));
} else {
converted_inds.push_back(std::move(indices[i]));
}
}
return converted_inds;
}
// NOTE: Why do we mirror instead of replace the `count_specified_dimensions` function
// in torch/csrc/autograd/python_variable_indexing.cpp? It's because
// `count_specified_dimensions` is on the hot path of Python tensor multi-dim indexing
// (i.e. it's called by `applySlicing` which is called by `THPVariable_getitem` /
// `THPVariable_setitem` when handling indexing of more than one dimension). If we were
// to merge the Python/C++ `count_specified_dimensions` function, on the Python side
// we would have to construct a `std::vector` container to be consumed by the C++
// `count_specified_dimensions` function, which adds 100s of nanoseconds overhead and
// is undesirable.
static inline int64_t count_specified_dimensions(const ArrayRef<TensorIndex>& indices) {
// Count the number of indexed dimensions (everything but ellipsis and None)
int64_t count = 0;
for (auto& obj : indices) {
if (obj.is_tensor()) {
auto& tensor = obj.tensor();
if (tensor.scalar_type() == kByte || tensor.scalar_type() == kBool) {
count += tensor.dim();
} else {
count++;
}
} else if (!obj.is_none() && !obj.is_ellipsis() && !obj.is_boolean()) {
count++;
}
}
return count;
}
} // namespace impl
// NOTE: Many functions below are only for consumption from Python indexing
// implementation, they include:
//
// - `Tensor scalarToTensor(...)`
// - `IntArrayRef slicePrefix1sSize(...)`
// - `void copy_to(...)`
// - `Tensor handleDimInMultiDimIndexing(...)`
// - `Tensor dispatch_index(...)`
// - `Tensor dispatch_index_put_(...)`
// - `Tensor get_item(...)`
// - `void set_item(...)`
//
// The rest of the functions are in `at::indexing::impl` namespace, signifying
// that they shouldn't be used from Python indexing implementation.
static inline Tensor scalarToTensor(Scalar v, const TensorOptions& options, const at::Device& self_device) {
if (self_device == at::kCPU) {
return at::detail::scalar_tensor_static(v, options.dtype_opt()->toScalarType(), self_device);
} else {
return impl::scalarToTensorNonNativeDeviceType(v, options);
}
}
// To match numpy semantics:
// As a special case for backwards compatibility,
// strip away unit dimensions from the left of 'src'
static inline IntArrayRef slicePrefix1sSize(const IntArrayRef& sizes) {
size_t first_non1_src = sizes.size();
for (size_t i = 0; i < sizes.size(); ++i) {
if (sizes[i] != 1) {
first_non1_src = i;
break;
}
}
return sizes.slice(first_non1_src);
}
static inline void copy_to(const Tensor& dst, const Tensor& src) {
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