Add cuda_runtime in include_dirs

setup.py

 import os
 import torch
 from setuptools import setup, find_packages
+from distutils.sysconfig import get_python_lib
 from torch.utils.cpp_extension import BuildExtension, CUDAExtension, CUDA_HOME
 
-if CUDA_HOME is None:
-    raise RuntimeError(
-        f"Cannot find CUDA_HOME. CUDA must be available to build the package.")
+def check_dependencies():
+    if CUDA_HOME is None:
+        raise RuntimeError(
+            f"Cannot find CUDA_HOME. CUDA must be available to build the package.")
 
-# Collect the compute capabilities of all available GPUs.
-compute_capabilities = set()
-for i in range(torch.cuda.device_count()):
-    major, minor = torch.cuda.get_device_capability(i)
-    if major < 8:
-        raise RuntimeError("GPUs with compute capability less than 8.0 are not supported.")
-    compute_capabilities.add(major * 10 + minor)
-
-# Get environment variables
-build_cuda_extension = os.environ.get('BUILD_CUDA_EXT', '1') == '1'
-torch_is_prebuilt = os.environ.get('TORCH_IS_PREBUILT', '0') == '1'
+def get_compute_capabilities():
+    # Collect the compute capabilities of all available GPUs.
+    compute_capabilities = set()
+    for i in range(torch.cuda.device_count()):
+        major, minor = torch.cuda.get_device_capability(i)
+        if major < 8:
+            raise RuntimeError("GPUs with compute capability less than 8.0 are not supported.")
+        compute_capabilities.add(major * 10 + minor)
+    
+    # figure out compute capability
+    compute_capabilities = {80, 86, 89, 90}
+    
+    capability_flags = []
+    for cap in compute_capabilities:
+        capability_flags += ["-gencode", f"arch=compute_{cap},code=sm_{cap}"]
+    
+    return capability_flags
 
 # Define dependencies
 dependencies = [
@@ -25,29 +33,22 @@ dependencies = [
     "transformers>=4.32.0", 
     "lm_eval", "texttable",
     "toml", "attributedict",
-    "protobuf"
+    "protobuf",
+    "torch>=2.0.0", "torchvision"
 ]
 
-if not torch_is_prebuilt:
-    dependencies.extend(["torch>=2.0.0", "torchvision"])
+# Get environment variables
+build_cuda_extension = os.environ.get('BUILD_CUDA_EXT', '1') == '1'
 
 # Setup CUDA extension
 ext_modules = []
 
 if build_cuda_extension:
-    # figure out compute capability
-    compute_capabilities = {80, 86, 89, 90}
-    if torch_is_prebuilt:
-        compute_capabilities.update({87})
-    
-    capability_flags = []
-    for cap in compute_capabilities:
-        capability_flags += ["-gencode", f"arch=compute_{cap},code=sm_{cap}"]
-
     # num threads
     n_threads = str(min(os.cpu_count(), 8))
 
     # final args
+    capability_flags = get_compute_capabilities()
     cxx_args = ["-g", "-O3", "-fopenmp", "-lgomp", "-std=c++17"]
     nvcc_args = ["-O3", "-std=c++17", "--threads", n_threads] + capability_flags
 
@@ -67,6 +68,13 @@ if build_cuda_extension:
         )
     )
 
+# Find directories to be included in setup
+include_dirs = []
+conda_cuda_include_dir = os.path.join(get_python_lib(), "nvidia/cuda_runtime/include")
+
+if os.path.isdir(conda_cuda_include_dir):
+    include_dirs.append(conda_cuda_include_dir)
+
 setup(
     name="autoawq",
     version="0.1.0",
@@ -90,6 +98,7 @@ setup(
         "Programming Language :: C++",
     ],
     install_requires=dependencies,
+    include_dirs=include_dirs,
     packages=find_packages(exclude=["examples*"]),
     ext_modules=ext_modules,
     cmdclass={"build_ext": BuildExtension}