Calculating Element-wise Tangent in PyTorch: Alternatives to `torch._foreach_tan`

Functionality

torch._foreach_tan is an internal function within PyTorch that applies the tangent (tan) operation element-wise to each tensor in a list of tensors. In simpler terms, it calculates the tangent of every value within each tensor in the list.

Use Case

This function is likely used for internal computations within PyTorch and is not typically intended for direct use in user code. PyTorch provides a more user-friendly function, torch.tan, to achieve the same result.

import torch

tensor_list = [torch.tensor([1.0, 2.0, 3.0]), torch.tensor([4.0, 5.0, 6.0])]

result_list = [torch.tan(tensor) for tensor in tensor_list]

print(result_list)

This code will print a list containing tensors with the tangent of each element from the original tensors.

• The function applies the tangent operation to each element within each tensor.
• It operates on a list of tensors.
• torch._foreach_tan is an internal function, use torch.tan for element-wise tangent calculations.

---

import torch

# This is for illustrative purposes only, avoid using `torch._foreach_tan` directly
def _foreach_tan_example(tensor_list):
  """Simulates the behavior of `torch._foreach_tan` (don't use this in practice)"""
  result_list = []
  for tensor in tensor_list:
    result_list.append(torch.tan(tensor))  # This is the actual calculation
  return result_list

# Sample tensors
tensor1 = torch.tensor([1.0, 2.0, 3.0])
tensor2 = torch.tensor([4.0, 5.0, 6.0])
tensor_list = [tensor1, tensor2]

# Using the internal function (not recommended)
# This is for demonstration only, avoid using this in your code
try:
  # Simulate the behavior because the actual function is internal
  internal_result = _foreach_tan_example(tensor_list)
  print("Internal result (not recommended):", internal_result)
except AttributeError:
  print("`torch._foreach_tan` is an internal function, avoid using it directly.")

# Using the recommended `torch.tan` function
recommended_result = [torch.tan(tensor) for tensor in tensor_list]
print("Recommended approach:", recommended_result)

This code defines a function _foreach_tan_example that mimics the behavior of torch._foreach_tan by iterating through the list and applying torch.tan to each tensor. However, it's important to remember that you shouldn't use this function in your actual code as torch._foreach_tan is internal.

---

torch.tan

This function applies the tangent operation element-wise to a single tensor. You can use it with a list comprehension or other loop to achieve the same result as torch._foreach_tan (which operates on a list of tensors).

import torch

tensor_list = [torch.tensor([1.0, 2.0, 3.0]), torch.tensor([4.0, 5.0, 6.0])]

result_list = [torch.tan(tensor) for tensor in tensor_list]

print(result_list)