Document that load_inline requires having a compiler installed (#137521)

janeyx99 · pytorchmergebot · commit 47a571e166dd · 2024-12-11T03:47:54.000Z
Prompted by this forum q: https://discuss.pytorch.org/t/are-the-requirements-for-using-torch-utils-cpp-extension-with-cuda-documented-anywhere/211222 Would be curious to know if we could get more precise. Pull Request resolved: #137521 Approved by: https://github.com/zou3519
diff --git a/torch/utils/cpp_extension.py b/torch/utils/cpp_extension.py
@@ -1592,7 +1592,7 @@ def load_inline(name,
     file and  prepended with ``torch/types.h``, ``cuda.h`` and
     ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled
     separately, but ultimately linked into a single library. Note that no
-    bindings are generated for functions in ``cuda_sources`` per  se. To bind
+    bindings are generated for functions in ``cuda_sources`` per se. To bind
     to a CUDA kernel, you must create a C++ function that calls it, and either
     declare or define this C++ function in one of the ``cpp_sources`` (and
     include its name in ``functions``).
@@ -1630,8 +1630,15 @@ def load_inline(name,
         ...                      functions=['sin_add'])
 
     .. note::
-        By default, the Ninja backend uses #CPUS + 2 workers to build the
-        extension. This may use up too many resources on some systems. One
+        Since load_inline will just-in-time compile the source code, please ensure
+        that you have the right toolchains installed in the runtime. For example,
+        when loading C++, make sure a C++ compiler is available. If you're loading
+        a CUDA extension, you will need to additionally install the corresponding CUDA
+        toolkit (nvcc and any other dependencies your code has). Compiling toolchains
+        are not included when you install torch and must be additionally installed.
+
+        During compiling, by default, the Ninja backend uses #CPUS + 2 workers to build
+        the extension. This may use up too many resources on some systems. One
         can control the number of workers by setting the `MAX_JOBS` environment
         variable to a non-negative number.
     '''
