xref: /aosp_15_r20/external/pytorch/aten/src/ATen/native/nested/NestedTensorUnaryOps.cpp (revision da0073e96a02ea20f0ac840b70461e3646d07c45)

#include <ATen/native/nested/NestedTensorMath.h>

#include <ATen/AccumulateType.h>
#include <ATen/Dispatch.h>
#include <ATen/Functions.h>
#include <ATen/NativeFunctions.h>
#include <ATen/NestedTensorImpl.h>
#include <ATen/ScalarOps.h>
#include <ATen/TensorIndexing.h>
#include <ATen/TensorOperators.h>
#include <ATen/TensorUtils.h>
#include <ATen/core/Tensor.h>
#include <ATen/native/layer_norm.h>
#include <ATen/native/nested/NestedTensorUtils.h>

namespace at::native {

Tensor NestedTensor_abs(const Tensor& self) {
  return map_nt(self, at::abs);
}

Tensor& NestedTensor_abs_(Tensor& self) {
  auto self_ptr = get_nested_tensor_impl(self);
  check_numel_equals_buffer_size(self_ptr);
  auto buffer = self_ptr->get_buffer();
  at::abs_(buffer);
  return self;
}

Tensor NestedTensor_where(const Tensor& condition, const Tensor& self, const Tensor& other) {
  TORCH_CHECK(condition.is_nested(), "condition must be nested");
  TORCH_CHECK(other.is_nested(), "other must be nested");
  TORCH_CHECK(!self.is_nested(), "self must not be nested");

  auto condition_ptr = get_nested_tensor_impl(condition);
  auto other_ptr = get_nested_tensor_impl(other);

  int64_t ntensors = condition_ptr->size(0);
  TORCH_CHECK(other_ptr->size(0) == ntensors, "condition and other must have the same number of tensors");

  // Get the buffer and sizes of the 'other' tensor to use for the output
  const Tensor& other_buffer = other_ptr->get_unsafe_storage_as_tensor();
  const Tensor& other_sizes = other_ptr->get_nested_sizes();

  // Create output buffer with the same size as other_buffer
  Tensor output_buffer = other_buffer.new_empty(other_buffer.sizes());

  // Create the output nested tensor
  Tensor output = wrap_buffer(output_buffer, other_sizes.clone());

  // Unbind condition, other, and output into lists of tensors
  std::vector<Tensor> condition_unbind = condition.unbind();
  std::vector<Tensor> other_unbind = other.unbind();
  std::vector<Tensor> output_unbind = output.unbind();

  // Apply at::where operation on each triplet of condition, self, and other tensors
  for (int64_t i = 0; i < ntensors; i++) {
    at::where_out(
      output_unbind[i],
      condition_unbind[i],
      self,  // Note: self is not nested, so we use it directly
      other_unbind[i]);
  }

  return output;
}

Tensor NestedTensor_sgn(const Tensor& self) {
  return map_nt(self, at::sgn);
}

Tensor& NestedTensor_sgn_(Tensor& self) {
  auto self_ptr = get_nested_tensor_impl(self);
  check_numel_equals_buffer_size(self_ptr);
  auto buffer = self_ptr->get_buffer();
  buffer.sgn_();
  return self;
}

Tensor& NestedTensor_logical_not_(Tensor& self){
  auto self_ptr = get_nested_tensor_impl(self);
  check_numel_equals_buffer_size(self_ptr);
  auto buffer = self_ptr->get_buffer();
  buffer.logical_not_();
  return self;
}

Tensor NestedTensor_logical_not(const Tensor& self) {
  return map_nt(self, at::logical_not);
}

Tensor& NestedTensor_relu_(Tensor& self) {
  auto self_ptr = get_nested_tensor_impl(self);
  check_numel_equals_buffer_size(self_ptr);
  auto buffer = self_ptr->get_buffer();
  at::relu_(buffer);
  return self;
}

Tensor NestedTensor_relu(const Tensor& self) {
  return map_nt(self, at::relu);
}

Tensor& NestedTensor_gelu_(Tensor& self, c10::string_view approximate) {
  auto self_ptr = get_nested_tensor_impl(self);
  check_numel_equals_buffer_size(self_ptr);
  auto buffer = self_ptr->get_buffer();
  at::gelu_(buffer, approximate);
  return self;
}

Tensor NestedTensor_gelu(const Tensor& self, c10::string_view approximate) {
  return map_nt(
      self,
      [approximate](const Tensor& buffer) {
        return at::gelu(buffer, approximate);
      });
}

Tensor& NestedTensor_tanh_(Tensor& self) {
  auto self_ptr = get_nested_tensor_impl(self);
  check_numel_equals_buffer_size(self_ptr);
  auto buffer = self_ptr->get_buffer();
  at::tanh_(buffer);
  return self;
}

Tensor NestedTensor_tanh(const Tensor& self) {
  return map_nt(self, at::tanh);
}

Tensor& NestedTensor_neg_(Tensor& self) {
  auto self_ptr = get_nested_tensor_impl(self);
  check_numel_equals_buffer_size(self_ptr);
  auto buffer = self_ptr->get_buffer();
  at::neg_(buffer);
  return self;
}

Tensor NestedTensor_neg(const Tensor& self) {
  return map_nt(self, at::neg);
}

Tensor& zero_nested_(Tensor& self) {
  const auto& self_buf = get_nested_tensor_impl(self)->get_buffer();
  self_buf.fill_(0);
  return self;
}

Tensor NestedTensor_silu(const Tensor& self){
  return map_nt(self, at::silu);
}

Tensor& NestedTensor_silu_(Tensor& self){
  auto self_ptr = get_nested_tensor_impl(self);
  check_numel_equals_buffer_size(self_ptr);
  auto buffer = self_ptr->get_buffer();
  at::silu_(buffer);
  return self;
}

Tensor sin_nested(const Tensor& self) {
  return map_nt(self, at::sin);
}

Tensor cos_nested(const Tensor& self) {
  return map_nt(self, at::cos);
}

Tensor _pin_memory_nested(const Tensor& self, std::optional<Device> device) {
  auto* nt_input = get_nested_tensor_impl(self);
  const auto& input_buffer = nt_input->get_unsafe_storage_as_tensor();
  return wrap_buffer(
      at::_pin_memory(input_buffer, device),
      nt_input->get_nested_sizes(),
      nt_input->get_nested_strides(),
      nt_input->get_storage_offsets());
}

} // namespace at::native