Manually specify flags if no arch set

stack-info: PR: #2219, branch: drisspg/stack/55

Author: drisspg

CUDA_ARCH_NOTES.md

@@ -291,6 +291,37 @@ def get_extensions():
     use_cuda = torch.version.cuda and (CUDA_HOME is not None or ROCM_HOME is not None)
     extension = CUDAExtension if use_cuda else CppExtension
 
+    # =====================================================================================
+    # CUDA Architecture Settings
+    # =====================================================================================
+    # If TORCH_CUDA_ARCH_LIST is not set during compilation, PyTorch tries to automatically
+    # detect architectures from available GPUs. This can fail when:
+    #   1. No GPU is visible to PyTorch
+    #   2. CUDA is available but no device is detected
+    #
+    # To resolve this, you can manually set CUDA architecture targets:
+    #   export TORCH_CUDA_ARCH_LIST="7.0;7.5;8.0;8.6+PTX"
+    #
+    # Adding "+PTX" to the last architecture enables JIT compilation for future GPUs.
+    # =====================================================================================
+    if use_cuda and "TORCH_CUDA_ARCH_LIST" not in os.environ and torch.version.cuda:
+        # Set to common architectures for CUDA 12.x compatibility
+        cuda_arch_list = "7.0;7.5;8.0;8.6;8.9;9.0"
+
+        # Only add SM10.0 (Blackwell) flags when using CUDA 12.8 or newer
+        cuda_version = torch.version.cuda
+        if cuda_version and cuda_version.startswith("12.8"):
+            print("Detected CUDA 12.8 - adding SM10.0 architectures to build list")
+            cuda_arch_list += ";10.0"
+
+        # Add PTX to the last architecture for future compatibility
+        cuda_arch_list += "+PTX"
+
+        os.environ["TORCH_CUDA_ARCH_LIST"] = cuda_arch_list
+        print(
+            f"Setting default TORCH_CUDA_ARCH_LIST={os.environ['TORCH_CUDA_ARCH_LIST']}"
+        )
+
     extra_link_args = []
     extra_compile_args = {
         "cxx": [f"-DPy_LIMITED_API={PY3_9_HEXCODE}"],