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#if !defined(C10_MOBILE) && !defined(ANDROID)
#include <torch/csrc/inductor/aoti_eager/kernel_meta_info.h>
#include <iostream>
#include <utility>
namespace torch::inductor {
TensorMetadata::TensorMetadata(const at::Tensor& src_tensor)
: is_symbolic_(false),
dtype_(src_tensor.scalar_type()),
device_(src_tensor.device()),
dispatch_key_set_(src_tensor.key_set()),
sizes_(src_tensor.sizes().vec()),
strides_(src_tensor.strides().vec()),
requires_grad_(src_tensor.requires_grad()) {}
TensorMetadata::TensorMetadata(
bool is_symbolic,
c10::ScalarType dtype,
c10::Device device,
c10::DispatchKeySet dispatch_key_set,
std::vector<int64_t> sizes,
std::vector<int64_t> strides,
bool requires_grad)
: is_symbolic_(is_symbolic),
dtype_(dtype),
device_(device),
dispatch_key_set_(dispatch_key_set),
sizes_(std::move(sizes)),
strides_(std::move(strides)),
requires_grad_(requires_grad) {
TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
!is_symbolic_, "Not support symbolic shape now");
}
void TensorMetadata::build_guard(const torch::dynamo::LocalState& local_state) {
TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
!is_symbolic_, "Not support symbolic shape now");
std::vector<std::optional<c10::SymInt>> sym_sizes;
std::vector<std::optional<c10::SymInt>> sym_strides;
std::transform(
sizes_.begin(),
sizes_.end(),
std::back_inserter(sym_sizes),
[](int64_t size) { return std::optional<c10::SymInt>(size); });
std::transform(
strides_.begin(),
strides_.end(),
std::back_inserter(sym_strides),
[](int64_t stride) { return std::optional<c10::SymInt>(stride); });
tensor_check_ = torch::dynamo::TensorCheck(
local_state,
nullptr,
dispatch_key_set_,
dtype_,
device_.index(),
requires_grad_,
sym_sizes,
sym_strides);
}
bool TensorMetadata::operator==(const TensorMetadata& other) const {
TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
!is_symbolic_, "Not support symbolic shape now");
if (tensor_check_.has_value()) {
auto sizes_ = c10::IntArrayRef(other.sizes_);
auto strides_ = c10::IntArrayRef(other.strides_);
auto sym_sizes = c10::SymIntArrayRef(
reinterpret_cast<const c10::SymInt*>(sizes_.data()), sizes_.size());
auto sym_strides = c10::SymIntArrayRef(
reinterpret_cast<const c10::SymInt*>(strides_.data()), strides_.size());
torch::dynamo::LocalState local_state;
local_state.overrideDispatchKeySet(dispatch_key_set_);
auto _tensor_check = tensor_check_.value();
auto res = _tensor_check.check(
local_state,
other.dispatch_key_set_,
other.dtype_,
other.device_,
sym_sizes,
sym_strides,
other.requires_grad_ /* Should we need to care about grad requirement?*/);
return res;
} else {
return this->is_symbolic_ == other.is_symbolic_ &&
this->dtype_ == other.dtype_ && this->device_ == other.device_ &&
this->dispatch_key_set_ == other.dispatch_key_set_ &&
this->requires_grad_ == other.requires_grad_ &&
this->sizes_ == other.sizes_ && this->strides_ == other.strides_;
}
}
std::ostream& operator<<(
std::ostream& stream,
const TensorMetadata& tensor_metadata) {
stream << "is_symbolic_: " << tensor_metadata.is_symbolic_ << '\n';
stream << "dtype_: " << tensor_metadata.dtype_ << '\n';
stream << "device_: " << tensor_metadata.device_ << '\n';
stream << "sizes_: ";
for (const auto& size : tensor_metadata.sizes_) {
stream << size << " ";
}
stream << '\n';
stream << "strides_: ";
for (const auto& stride : tensor_metadata.strides_) {
stream << stride << " ";
}
stream << "requires_grad_: " << tensor_metadata.requires_grad_ << '\n';
stream << "dispatch_key_set_: " << tensor_metadata.dispatch_key_set_ << '\n';
stream << "tensor_check_: " << tensor_metadata.tensor_check_.has_value()
<< '\n';
stream << '\n';
return stream;
}
ParameterMetadata::ParameterMetadata(
TensorMetadata tensor_metadata,
uint64_t input_order)
: tag_(TENSOR), value_(tensor_metadata), order_(input_order) {}
ParameterMetadata::ParameterMetadata(
const at::Tensor& tensor,
uint64_t input_order)
: tag_(TENSOR), order_(input_order) {
value_ = TensorMetadata(tensor);
}
ParameterMetadata::ParameterMetadata(
const std::vector<TensorMetadata>& tensor_metadata_list,
uint64_t input_order)
: tag_(TENSOR_LIST), value_(tensor_metadata_list), order_(input_order) {}
ParameterMetadata::ParameterMetadata(
const std::vector<at::Tensor>& tensor_list,
uint64_t input_order)
: tag_(TENSOR_LIST), order_(input_order) {
std::vector<TensorMetadata> tensor_metadata_list;
tensor_metadata_list.reserve(tensor_list.size());
for (const auto& tensor : tensor_list) {
tensor_metadata_list.emplace_back(tensor);
}
value_ = tensor_metadata_list;
}
ParameterMetadata::ParameterMetadata(
const c10::Scalar& scalar,
uint64_t input_order)
: tag_(SCALAR), value_(scalar), order_(input_order) {}
ParameterMetadata::ParameterMetadata(
const std::string& str,
uint64_t input_order)
: tag_(STRING), value_(str), order_(input_order) {}
ParameterMetadata::ParameterMetadata(
const c10::Device& device,
uint64_t input_order)
: tag_(DEVICE), value_(device), order_(input_order) {}
bool ParameterMetadata::operator==(const ParameterMetadata& other) const {
// Same type
if (tag_ != other.tag_) {
return false;
}
// Same order of the input parameters
if (order_ != other.order_) {
return false;
}
switch (tag_) {
case TENSOR:
return std::get<TensorMetadata>(value_) ==
std::get<TensorMetadata>(other.value_);
case TENSOR_LIST:
return std::get<std::vector<TensorMetadata>>(value_) ==
std::get<std::vector<TensorMetadata>>(other.value_);
case SCALAR:
TORCH_INTERNAL_ASSERT(
std::get<c10::Scalar>(other.value_).isFloatingPoint() ||
std::get<c10::Scalar>(other.value_).isIntegral(true /*includeBool*/));
return equal_to(std::get<c10::Scalar>(other.value_));
case STRING:
return std::get<std::string>(value_) ==
std::get<std::string>(other.value_);
case DEVICE:
return std::get<c10::Device>(value_) ==
std::get<c10::Device>(other.value_);
default:
return false;
}
}
bool ParameterMetadata::equal_to(const c10::Scalar& scalar) const {
TORCH_INTERNAL_ASSERT(scalar.isFloatingPoint() || scalar.isIntegral(true));
if (tag_ != SCALAR) {
return false;
}
const auto& self_scalar = std::get<c10::Scalar>(value_);
if (scalar.isFloatingPoint() && self_scalar.isFloatingPoint()) {
return self_scalar.toDouble() == scalar.toDouble();
} else if (scalar.isIntegral(true) && self_scalar.isIntegral(true)) {
return self_scalar.toInt() == scalar.toInt();
}
return false;
}
} // namespace torch::inductor
#endif
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