[kernel] cached the op kernel and fixed version check (#2886)

* [kernel] cached the op kernel and fixed version check

* polish code

* polish code

op_builder/README.md

@@ -15,17 +15,18 @@ Method 2 is good because it allows the user to only build the kernel they actual
 
 ## PyTorch Extensions in Colossal-AI
 
-As mentioned in the section above, our aim is to make these two methods coherently supported in Colossal-AI, meaning that for a kernel should be either built in `setup.py` or during runtime.
+The project DeepSpeed (https://github.com/microsoft/DeepSpeed) has proposed a [solution](https://github.com/microsoft/DeepSpeed/tree/master/op_builder)) to support kernel-build during either installation or runtime.
-There are mainly two functions used to build extensions.
+We have adapted from DeepSpeed's solution to build extensions. The extension build requries two main functions from PyTorch:
 
 1. `torch.utils.cpp_extension.CUDAExtension`: used to build extensions in `setup.py` during `pip install`.
 2. `torch.utils.cpp_extension.load`: used to build and load extension during runtime
 
 Please note that the extension build by `CUDAExtension` cannot be loaded by the `load` function and `load` will run its own build again (correct me if I am wrong).
 
-We have implemented the following conventions:
+Based on the DeepSpeed's work, we have make several modifications and improvements:
 
 1. All pre-built kernels (those installed with `setup.py`) will be found in `colossalai._C`
 2. All runtime-built kernels will be found in the default torch extension path, i.e. ~/.cache/colossalai/torch_extensions. (If we put the built kernels in the installed site-package directory, this will make pip uninstall incomplete)
+3. Once a kernel is loaded, we will cache it in the builder to avoid repeated kernel loading.
 
 When loading the built kernel, we will first check if the pre-built one exists. If not, the runtime build will be triggered.

op_builder/builder.py

@@ -5,22 +5,7 @@ from abc import ABC, abstractmethod
 from pathlib import Path
 from typing import List
 
-
+from .utils import check_cuda_availability, check_system_pytorch_cuda_match, print_rank_0
-def print_rank_0(message):
-    """
-    Print on only one process to avoid spamming.
-    """
-    try:
-        import torch.distributed as dist
-        if not dist.is_initialized():
-            is_main_rank = True
-        else:
-            is_main_rank = dist.get_rank() == 0
-    except ImportError:
-        is_main_rank = True
-
-    if is_main_rank:
-        print(message)
 
 
 class Builder(ABC):
@@ -37,6 +22,9 @@ class Builder(ABC):
         self.prebuilt_import_path = prebuilt_import_path
         self.version_dependent_macros = ['-DVERSION_GE_1_1', '-DVERSION_GE_1_3', '-DVERSION_GE_1_5']
 
+        # we store the op as an attribute to avoid repeated building and loading
+        self.cached_op_module = None
+
         assert prebuilt_import_path.startswith('colossalai._C'), \
             f'The prebuilt_import_path should start with colossalai._C, but got {self.prebuilt_import_path}'
 
@@ -117,6 +105,35 @@ class Builder(ABC):
         """
         return importlib.import_module(self.prebuilt_import_path)
 
+    def check_runtime_build_environment(self):
+        """
+        Check whether the system environment is ready for extension compilation.
+        """
+        try:
+            import torch
+            from torch.utils.cpp_extension import CUDA_HOME
+            TORCH_AVAILABLE = True
+        except ImportError:
+            TORCH_AVAILABLE = False
+            CUDA_HOME = None
+
+        if not TORCH_AVAILABLE:
+            raise ModuleNotFoundError(
+                "PyTorch is not found. You need to install PyTorch first in order to build CUDA extensions")
+
+        if CUDA_HOME is None:
+            raise RuntimeError(
+                "CUDA_HOME is not found. You need to export CUDA_HOME environment vairable or install CUDA Toolkit first in order to build CUDA extensions"
+            )
+
+        # make sure CUDA is available for compilation during
+        cuda_available = check_cuda_availability()
+        if not cuda_available:
+            raise RuntimeError("CUDA is not available on your system as torch.cuda.is_avaible() returns False.")
+
+        # make sure system CUDA and pytorch CUDA match, an error will raised inside the function if not
+        check_system_pytorch_cuda_match(CUDA_HOME)
+
     def load(self, verbose=True):
         """
         load the kernel during runtime. If the kernel is not built during pip install, it will build the kernel.
@@ -128,16 +145,27 @@ class Builder(ABC):
         Args:
             verbose (bool, optional): show detailed info. Defaults to True.
         """
-        from torch.utils.cpp_extension import load
+        # if the kernel has be compiled and cached, we directly use it
-        start_build = time.time()
+        if self.cached_op_module is not None:
+            return self.cached_op_module
 
         try:
+            # if the kernel has been pre-built during installation
+            # we just directly import it
             op_module = self.import_op()
             if verbose:
-                print_rank_0(f"OP {self.prebuilt_import_path} already exists, skip building.")
+                print_rank_0(
+                    f"[extension] OP {self.prebuilt_import_path} has been compileed ahead of time, skip building.")
         except ImportError:
+            # check environment
+            self.check_runtime_build_environment()
+
+            # time the kernel compilation
+            start_build = time.time()
+
             # construct the build directory
             import torch
+            from torch.utils.cpp_extension import load
             torch_version_major = torch.__version__.split('.')[0]
             torch_version_minor = torch.__version__.split('.')[1]
             torch_cuda_version = torch.version.cuda
@@ -147,11 +175,7 @@ class Builder(ABC):
             Path(build_directory).mkdir(parents=True, exist_ok=True)
 
             if verbose:
-                print_rank_0(
+                print_rank_0(f"[extension] Compiling or loading the JIT-built {self.name} kernel during runtime now")
-                    "=========================================================================================")
-                print_rank_0(f"No pre-built kernel is found, build and load the {self.name} kernel during runtime now")
-                print_rank_0(
-                    "=========================================================================================")
 
             # load the kernel
             op_module = load(name=self.name,
@@ -163,9 +187,14 @@ class Builder(ABC):
                              build_directory=build_directory,
                              verbose=verbose)
 
-        build_duration = time.time() - start_build
+            build_duration = time.time() - start_build
-        if verbose:
+
-            print_rank_0(f"Time to load {self.name} op: {build_duration} seconds")
+            # log jit compilation time
+            if verbose:
+                print_rank_0(f"[extension] Time to compile or load {self.name} op: {build_duration} seconds")
+
+        # cache the built/loaded kernel
+        self.cached_op_module = op_module
 
         return op_module
 

op_builder/utils.py

@@ -1,29 +1,203 @@
+import os
 import re
 import subprocess
+import warnings
 from typing import List
 
 
-def get_cuda_bare_metal_version(cuda_dir):
+def print_rank_0(message: str) -> None:
-    raw_output = subprocess.check_output([cuda_dir + "/bin/nvcc", "-V"], universal_newlines=True)
+    """
-    output = raw_output.split()
+    Print on only one process to avoid spamming.
-    release_idx = output.index("release") + 1
+    """
-    release = output[release_idx].split(".")
+    try:
-    bare_metal_major = release[0]
+        import torch.distributed as dist
-    bare_metal_minor = release[1][0]
+        if not dist.is_initialized():
+            is_main_rank = True
+        else:
+            is_main_rank = dist.get_rank() == 0
+    except ImportError:
+        is_main_rank = True
 
-    return raw_output, bare_metal_major, bare_metal_minor
+    if is_main_rank:
+        print(message)
 
-def get_cuda_cc_flag() -> List:
-    """get_cuda_cc_flag
 
-    cc flag for your GPU arch
+def get_cuda_version_in_pytorch() -> List[int]:
+    """
+    This function returns the CUDA version in the PyTorch build.
+
+    Returns:
+        The CUDA version required by PyTorch, in the form of tuple (major, minor).
+    """
+    import torch
+
+    try:
+        torch_cuda_major = torch.version.cuda.split(".")[0]
+        torch_cuda_minor = torch.version.cuda.split(".")[1]
+    except:
+        raise ValueError(
+            "[extension] Cannot retrive the CUDA version in the PyTorch binary given by torch.version.cuda")
+    return torch_cuda_major, torch_cuda_minor
+
+
+def get_cuda_bare_metal_version(cuda_dir) -> List[int]:
+    """
+    Get the System CUDA version from nvcc.
+
+    Args:
+        cuda_dir (str): the directory for CUDA Toolkit.
+
+    Returns:
+        The CUDA version required by PyTorch, in the form of tuple (major, minor).
+    """
+    nvcc_path = os.path.join(cuda_dir, 'bin/nvcc')
+
+    if cuda_dir is None:
+        raise ValueError(
+            f"[extension] The argument cuda_dir is None, but expected to be a string. Please make sure your have exported the environment variable CUDA_HOME correctly."
+        )
+
+    # check for nvcc path
+    if not os.path.exists(nvcc_path):
+        raise FileNotFoundError(
+            f"[extension] The nvcc compiler is not found in {nvcc_path}, please make sure you have set the correct value for CUDA_HOME."
+        )
+
+    # parse the nvcc -v output to obtain the system cuda version
+    try:
+        raw_output = subprocess.check_output([cuda_dir + "/bin/nvcc", "-V"], universal_newlines=True)
+        output = raw_output.split()
+        release_idx = output.index("release") + 1
+        release = output[release_idx].split(".")
+        bare_metal_major = release[0]
+        bare_metal_minor = release[1][0]
+    except:
+        raise ValueError(
+            f"[extension] Failed to parse the nvcc output to obtain the system CUDA bare metal version. The output for 'nvcc -v' is \n{raw_output}"
+        )
+
+    return bare_metal_major, bare_metal_minor
+
+
+def check_system_pytorch_cuda_match(cuda_dir):
+    bare_metal_major, bare_metal_minor = get_cuda_bare_metal_version(cuda_dir)
+    torch_cuda_major, torch_cuda_minor = get_cuda_version_in_pytorch()
+
+    if bare_metal_major != torch_cuda_major:
+        raise Exception(
+            f'[extension] Failed to build PyTorch extension because the detected CUDA version ({bare_metal_major}.{bare_metal_minor}) '
+            f'mismatches the version that was used to compile PyTorch ({torch_cuda_major}.{torch_cuda_minor}).'
+            'Please make sure you have set the CUDA_HOME correctly and installed the correct PyTorch in https://pytorch.org/get-started/locally/ .'
+        )
+
+    print(bare_metal_minor != torch_cuda_minor)
+    if bare_metal_minor != torch_cuda_minor:
+        warnings.warn(
+            f"[extension] The CUDA version on the system ({bare_metal_major}.{bare_metal_minor}) does not match with the version ({torch_cuda_major}.{torch_cuda_minor}) torch was compiled with. "
+            "The mismatch is found in the minor version. As the APIs are compatible, we will allow compilation to proceed. "
+            "If you encounter any issue when using the built kernel, please try to build it again with fully matched CUDA versions"
+        )
+    return True
+
+
+def get_pytorch_version() -> List[int]:
+    """
+    This functions finds the PyTorch version.
+
+    Returns:
+        A tuple of integers in the form of (major, minor, patch).
+    """
+    import torch
+    torch_version = torch.__version__.split('+')[0]
+    TORCH_MAJOR = int(torch_version.split('.')[0])
+    TORCH_MINOR = int(torch_version.split('.')[1])
+    TORCH_PATCH = int(torch_version.split('.')[2])
+    return TORCH_MAJOR, TORCH_MINOR, TORCH_PATCH
+
+
+def check_pytorch_version(min_major_version, min_minor_version) -> bool:
+    """
+    Compare the current PyTorch version with the minium required version.
+
+    Args:
+        min_major_version (int): the minimum major version of PyTorch required
+        min_minor_version (int): the minimum minor version of PyTorch required
+
+    Returns:
+        A boolean value. The value is True if the current pytorch version is acceptable and False otherwise.
+    """
+    # get pytorch version
+    torch_major, torch_minor, _ = get_pytorch_version()
+
+    # if the
+    if torch_major < min_major_version or (torch_major == min_major_version and torch_minor < min_minor_version):
+        raise RuntimeError(
+            f"[extension] Colossal-AI requires Pytorch {min_major_version}.{min_minor_version} or newer.\n"
+            "The latest stable release can be obtained from https://pytorch.org/get-started/locally/")
+
+
+def check_cuda_availability():
+    """
+    Check if CUDA is available on the system.
+
+    Returns:
+        A boolean value. True if CUDA is available and False otherwise.
+    """
+    import torch
+    return torch.cuda.is_available()
+
+
+def set_cuda_arch_list(cuda_dir):
+    """
+    This function sets the PyTorch TORCH_CUDA_ARCH_LIST variable for ahead-of-time extension compilation.
+    Ahead-of-time compilation occurs when CUDA_EXT=1 is set when running 'pip install'.
+    """
+    cuda_available = check_cuda_availability()
+
+    # we only need to set this when CUDA is not available for cross-compilation
+    if not cuda_available:
+        warnings.warn(
+            '\n[extension]  PyTorch did not find available GPUs on this system.\n',
+            'If your intention is to cross-compile, this is not an error.\n'
+            'By default, Colossal-AI will cross-compile for \n'
+            '1. Pascal (compute capabilities 6.0, 6.1, 6.2),\n'
+            '2. Volta (compute capability 7.0)\n'
+            '3. Turing (compute capability 7.5),\n'
+            '4. Ampere (compute capability 8.0, 8.6)if the CUDA version is >= 11.0\n'
+            '\nIf you wish to cross-compile for a single specific architecture,\n'
+            'export TORCH_CUDA_ARCH_LIST="compute capability" before running setup.py.\n')
+
+        if os.environ.get("TORCH_CUDA_ARCH_LIST", None) is None:
+            bare_metal_major, bare_metal_minor = get_cuda_bare_metal_version(cuda_dir)
+
+            arch_list = ['6.0', '6.1', '6.2', '7.0', '7.5']
+
+            if int(bare_metal_major) == 11:
+                if int(bare_metal_minor) == 0:
+                    arch_list.append('8.0')
+                else:
+                    arch_list.append('8.0')
+                    arch_list.append('8.6')
+
+            arch_list_str = ';'.join(arch_list)
+            os.environ["TORCH_CUDA_ARCH_LIST"] = arch_list_str
+        return False
+    return True
+
+
+def get_cuda_cc_flag() -> List[str]:
+    """
+    This function produces the cc flags for your GPU arch
+
+    Returns:
+        The CUDA cc flags for compilation.
     """
 
     # only import torch when needed
     # this is to avoid importing torch when building on a machine without torch pre-installed
     # one case is to build wheel for pypi release
     import torch
-    
+
     cc_flag = []
     for arch in torch.cuda.get_arch_list():
         res = re.search(r'sm_(\d+)', arch)
@@ -31,12 +205,19 @@ def get_cuda_cc_flag() -> List:
             arch_cap = res[1]
             if int(arch_cap) >= 60:
                 cc_flag.extend(['-gencode', f'arch=compute_{arch_cap},code={arch}'])
-
     return cc_flag
 
-def append_nvcc_threads(nvcc_extra_args):
+
+def append_nvcc_threads(nvcc_extra_args: List[str]) -> List[str]:
+    """
+    This function appends the threads flag to your nvcc args.
+
+    Returns:
+        The nvcc compilation flags including the threads flag.
+    """
     from torch.utils.cpp_extension import CUDA_HOME
-    _, bare_metal_major, bare_metal_minor = get_cuda_bare_metal_version(CUDA_HOME)
+
+    bare_metal_major, bare_metal_minor = get_cuda_bare_metal_version(CUDA_HOME)
     if int(bare_metal_major) >= 11 and int(bare_metal_minor) >= 2:
         return nvcc_extra_args + ["--threads", "4"]
     return nvcc_extra_args

setup.py

@@ -1,115 +1,87 @@
 import os
-import re
 from datetime import datetime
+from typing import List
 
 from setuptools import find_packages, setup
 
-from op_builder.utils import get_cuda_bare_metal_version
+from op_builder.utils import (
+    check_cuda_availability,
+    check_pytorch_version,
+    check_system_pytorch_cuda_match,
+    get_cuda_bare_metal_version,
+    get_pytorch_version,
+    set_cuda_arch_list,
+)
 
 try:
     import torch
-    from torch.utils.cpp_extension import CUDA_HOME, BuildExtension, CUDAExtension
+    from torch.utils.cpp_extension import CUDA_HOME, BuildExtension
-    print("\n\ntorch.__version__  = {}\n\n".format(torch.__version__))
-    TORCH_MAJOR = int(torch.__version__.split('.')[0])
-    TORCH_MINOR = int(torch.__version__.split('.')[1])
-
-    if TORCH_MAJOR < 1 or (TORCH_MAJOR == 1 and TORCH_MINOR < 10):
-        raise RuntimeError("Colossal-AI requires Pytorch 1.10 or newer.\n"
-                           "The latest stable release can be obtained from https://pytorch.org/")
     TORCH_AVAILABLE = True
 except ImportError:
     TORCH_AVAILABLE = False
     CUDA_HOME = None
 
-# ninja build does not work unless include_dirs are abs path
+# Some constants for installation checks
-this_dir = os.path.dirname(os.path.abspath(__file__))
+MIN_PYTORCH_VERSION_MAJOR = 1
-build_cuda_ext = False
+MIN_PYTORCH_VERSION_MINOR = 10
-ext_modules = []
+THIS_DIR = os.path.dirname(os.path.abspath(__file__))
-is_nightly = int(os.environ.get('NIGHTLY', '0')) == 1
+BUILD_CUDA_EXT = int(os.environ.get('CUDA_EXT', '0')) == 1
+IS_NIGHTLY = int(os.environ.get('NIGHTLY', '0')) == 1
 
-if int(os.environ.get('CUDA_EXT', '0')) == 1:
+# a variable to store the op builder
+ext_modules = []
+
+
+# check for CUDA extension dependencies
+def environment_check_for_cuda_extension_build():
     if not TORCH_AVAILABLE:
         raise ModuleNotFoundError(
-            "PyTorch is not found while CUDA_EXT=1. You need to install PyTorch first in order to build CUDA extensions"
+            "[extension] PyTorch is not found while CUDA_EXT=1. You need to install PyTorch first in order to build CUDA extensions"
         )
 
     if not CUDA_HOME:
         raise RuntimeError(
-            "CUDA_HOME is not found while CUDA_EXT=1. You need to export CUDA_HOME environment vairable or install CUDA Toolkit first in order to build CUDA extensions"
+            "[extension] CUDA_HOME is not found while CUDA_EXT=1. You need to export CUDA_HOME environment vairable or install CUDA Toolkit first in order to build CUDA extensions"
         )
 
-    build_cuda_ext = True
+    check_system_pytorch_cuda_match(CUDA_HOME)
+    check_pytorch_version(MIN_PYTORCH_VERSION_MAJOR, MIN_PYTORCH_VERSION_MINOR)
+    check_cuda_availability()
 
 
-def check_cuda_torch_binary_vs_bare_metal(cuda_dir):
+def fetch_requirements(path) -> List[str]:
-    raw_output, bare_metal_major, bare_metal_minor = get_cuda_bare_metal_version(cuda_dir)
+    """
-    torch_binary_major = torch.version.cuda.split(".")[0]
+    This function reads the requirements file.
-    torch_binary_minor = torch.version.cuda.split(".")[1]
 
-    print("\nCompiling cuda extensions with")
+    Args:
-    print(raw_output + "from " + cuda_dir + "/bin\n")
+        path (str): the path to the requirements file.
 
-    if bare_metal_major != torch_binary_major:
+    Returns:
-        print(f'The detected CUDA version ({raw_output}) mismatches the version that was used to compile PyTorch '
+        The lines in the requirements file.
-              f'({torch.version.cuda}). CUDA extension will not be installed.')
+    """
-        return False
-
-    if bare_metal_minor != torch_binary_minor:
-        print("\nWarning: Cuda extensions are being compiled with a version of Cuda that does "
-              "not match the version used to compile Pytorch binaries.  "
-              f"Pytorch binaries were compiled with Cuda {torch.version.cuda}.\n"
-              "In some cases, a minor-version mismatch will not cause later errors:  "
-              "https://github.com/NVIDIA/apex/pull/323#discussion_r287021798. ")
-    return True
-
-
-def check_cuda_availability(cuda_dir):
-    if not torch.cuda.is_available():
-        # https://github.com/NVIDIA/apex/issues/486
-        # Extension builds after https://github.com/pytorch/pytorch/pull/23408 attempt to query
-        # torch.cuda.get_device_capability(), which will fail if you are compiling in an environment
-        # without visible GPUs (e.g. during an nvidia-docker build command).
-        print(
-            '\nWarning: Torch did not find available GPUs on this system.\n',
-            'If your intention is to cross-compile, this is not an error.\n'
-            'By default, Colossal-AI will cross-compile for Pascal (compute capabilities 6.0, 6.1, 6.2),\n'
-            'Volta (compute capability 7.0), Turing (compute capability 7.5),\n'
-            'and, if the CUDA version is >= 11.0, Ampere (compute capability 8.0).\n'
-            'If you wish to cross-compile for a single specific architecture,\n'
-            'export TORCH_CUDA_ARCH_LIST="compute capability" before running setup.py.\n')
-        if os.environ.get("TORCH_CUDA_ARCH_LIST", None) is None:
-            _, bare_metal_major, _ = get_cuda_bare_metal_version(cuda_dir)
-            if int(bare_metal_major) == 11:
-                os.environ["TORCH_CUDA_ARCH_LIST"] = "6.0;6.1;6.2;7.0;7.5;8.0"
-            else:
-                os.environ["TORCH_CUDA_ARCH_LIST"] = "6.0;6.1;6.2;7.0;7.5"
-        return False
-
-    if cuda_dir is None:
-        print("nvcc was not found. CUDA extension will not be installed. If you're installing within a container from "
-              "https://hub.docker.com/r/pytorch/pytorch, only images whose names contain 'devel' will provide nvcc.")
-        return False
-    return True
-
-
-def append_nvcc_threads(nvcc_extra_args):
-    _, bare_metal_major, bare_metal_minor = get_cuda_bare_metal_version(CUDA_HOME)
-    if int(bare_metal_major) >= 11 and int(bare_metal_minor) >= 2:
-        return nvcc_extra_args + ["--threads", "4"]
-    return nvcc_extra_args
-
-
-def fetch_requirements(path):
     with open(path, 'r') as fd:
         return [r.strip() for r in fd.readlines()]
 
 
-def fetch_readme():
+def fetch_readme() -> str:
+    """
+    This function reads the README.md file in the current directory.
+
+    Returns:
+        The lines in the README file.
+    """
     with open('README.md', encoding='utf-8') as f:
         return f.read()
 
 
-def get_version():
+def get_version() -> str:
+    """
+    This function reads the version.txt and generates the colossalai/version.py file.
+
+    Returns:
+        The library version stored in version.txt.
+    """
+
     setup_file_path = os.path.abspath(__file__)
     project_path = os.path.dirname(setup_file_path)
     version_txt_path = os.path.join(project_path, 'version.txt')
@@ -121,13 +93,17 @@ def get_version():
     # write version into version.py
     with open(version_py_path, 'w') as f:
         f.write(f"__version__ = '{version}'\n")
-        if build_cuda_ext:
+
-            torch_version = '.'.join(torch.__version__.split('.')[:2])
+        # look for pytorch and cuda version
-            cuda_version = '.'.join(get_cuda_bare_metal_version(CUDA_HOME)[1:])
+        if BUILD_CUDA_EXT:
+            torch_major, torch_minor, _ = get_pytorch_version()
+            torch_version = f'{torch_major}.{torch_minor}'
+            cuda_version = '.'.join(get_cuda_bare_metal_version(CUDA_HOME))
         else:
             torch_version = None
             cuda_version = None
 
+        # write the version into the python file
         if torch_version:
             f.write(f'torch = "{torch_version}"\n')
         else:
@@ -141,25 +117,26 @@ def get_version():
     return version
 
 
-if build_cuda_ext:
+if BUILD_CUDA_EXT:
-    build_cuda_ext = check_cuda_availability(CUDA_HOME) and check_cuda_torch_binary_vs_bare_metal(CUDA_HOME)
+    environment_check_for_cuda_extension_build()
-
+    set_cuda_arch_list(CUDA_HOME)
-if build_cuda_ext:
-    # Set up macros for forward/backward compatibility hack around
-    # https://github.com/pytorch/pytorch/commit/4404762d7dd955383acee92e6f06b48144a0742e
-    # and
-    # https://github.com/NVIDIA/apex/issues/456
-    # https://github.com/pytorch/pytorch/commit/eb7b39e02f7d75c26d8a795ea8c7fd911334da7e#diff-4632522f237f1e4e728cb824300403ac
 
     from op_builder import ALL_OPS
+    op_names = []
+
+    # load all builders
     for name, builder_cls in ALL_OPS.items():
-        print(f'===== Building Extension {name} =====')
+        op_names.append(name)
         ext_modules.append(builder_cls().builder())
 
+    # show log
+    op_name_list = ', '.join(op_names)
+    print(f"[extension]  loaded builders for {op_name_list}")
+
 # always put not nightly branch as the if branch
 # otherwise github will treat colossalai-nightly as the project name
 # and it will mess up with the dependency graph insights
-if not is_nightly:
+if not IS_NIGHTLY:
     version = get_version()
     package_name = 'colossalai'
 else: