如何查找torch._C导入函数及追踪torch.nn.NLLLoss源码实现

Hey there! Let's walk through how to trace down the actual implementation of torch.nn.NLLLoss and demystify how to find functions under torch._C.

First, what is torch._C? #

torch._C isn't a regular Python module you'll find in .py files—it's the Python binding layer for PyTorch's C++ core. PyTorch uses tools like PyBind11 and Cython to expose its high-performance C++/CUDA code to Python, and torch._C is where those low-level bindings live. You won't find a "definition" in Python source because it's directly linked to PyTorch's compiled backend.

Tracing torch._C.nll_loss to its core implementation #

Here's a step-by-step way to find the actual code behind torch._C.nll_loss:

1. Start with the ATen library
   Most of PyTorch's tensor operations (including loss functions) are implemented in the ATen library, which is PyTorch's foundational tensor computation engine. For nll_loss, head to aten/src/ATen/native/Loss.cpp in the PyTorch repo—this is where the core CPU/CUDA logic for negative log-likelihood loss lives.
2. Find the binding code
   To see how this C++ function gets exposed to torch._C, look in the torch/csrc directory. The bindings for loss functions are often in auto-generated files (like torch/csrc/autograd/generated/python_functions.cpp) or manually registered in files like torch/csrc/nn/functional.cpp. Search for nll_loss in these directories to find the exact line where the C++ function is bound to the Python torch._C module.
3. Connect the Python dots
   The torch.nn.functional.nll_loss function you found is a thin Python wrapper that handles input validation, device handling, and then calls torch._C.nll_loss to trigger the actual C++ computation.

General tips for finding functions in torch._C #

If you need to track down other functions under torch._C in the future, try these tricks:

• Search the PyTorch repo
  Head to the PyTorch GitHub repo and use the search bar to look for the function name (e.g., nll_loss). Filter results to C++ files—this will lead you straight to the implementation and binding code.
• Use Python's introspection
  Run quick commands in your Python shell to get clues:

  print(torch._C.nll_loss.__doc__)  # Shows parameter info and basic docs
  print(torch._C.nll_loss.__module__)  # Confirms it's from the C backend
  

• Follow the module structure
  torch._C maps to PyTorch's C++ modules:
  • Functions related to neural nets are often bound in torch/csrc/nn
  • Autograd-related functions are in torch/csrc/autograd
  • Core tensor ops are tied to ATen's code in aten/src/ATen

Hope this helps you navigate PyTorch's codebase with more confidence!

内容的提问来源于stack exchange，提问作者Sam Bobel