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.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+libbitsandbytes_cuda116.dll filter=lfs diff=lfs merge=lfs -text

libbitsandbytes_cuda116.dll

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:88f7bd2916ca3effc43f88492f1e1b9088d13cb5be3b4a3a4aede6aa3bf8d412
+size 4724224

main.py

@@ -0,0 +1,412 @@
+"""
+extract factors the build is dependent on:
+[X] compute capability
+    [ ] TODO: Q - What if we have multiple GPUs of different makes?
+- CUDA version
+- Software:
+    - CPU-only: only CPU quantization functions (no optimizer, no matrix multipl)
+    - CuBLAS-LT: full-build 8-bit optimizer
+    - no CuBLAS-LT: no 8-bit matrix multiplication (`nomatmul`)
+
+evaluation:
+    - if paths faulty, return meaningful error
+    - else:
+        - determine CUDA version
+        - determine capabilities
+        - based on that set the default path
+"""
+
+import ctypes as ct
+import os
+import errno
+import torch
+from warnings import warn
+
+from pathlib import Path
+from typing import Set, Union
+from .env_vars import get_potentially_lib_path_containing_env_vars
+
+CUDA_RUNTIME_LIB: str = "libcudart.so"
+
+class CUDASetup:
+    _instance = None
+
+    def __init__(self):
+        raise RuntimeError("Call get_instance() instead")
+
+    def generate_instructions(self):
+        if self.cuda is None:
+            self.add_log_entry('CUDA SETUP: Problem: The main issue seems to be that the main CUDA library was not detected.')
+            self.add_log_entry('CUDA SETUP: Solution 1): Your paths are probably not up-to-date. You can update them via: sudo ldconfig.')
+            self.add_log_entry('CUDA SETUP: Solution 2): If you do not have sudo rights, you can do the following:')
+            self.add_log_entry('CUDA SETUP: Solution 2a): Find the cuda library via: find / -name libcuda.so 2>/dev/null')
+            self.add_log_entry('CUDA SETUP: Solution 2b): Once the library is found add it to the LD_LIBRARY_PATH: export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:FOUND_PATH_FROM_2a')
+            self.add_log_entry('CUDA SETUP: Solution 2c): For a permanent solution add the export from 2b into your .bashrc file, located at ~/.bashrc')
+            return
+
+        if self.cudart_path is None:
+            self.add_log_entry('CUDA SETUP: Problem: The main issue seems to be that the main CUDA runtime library was not detected.')
+            self.add_log_entry('CUDA SETUP: Solution 1: To solve the issue the libcudart.so location needs to be added to the LD_LIBRARY_PATH variable')
+            self.add_log_entry('CUDA SETUP: Solution 1a): Find the cuda runtime library via: find / -name libcudart.so 2>/dev/null')
+            self.add_log_entry('CUDA SETUP: Solution 1b): Once the library is found add it to the LD_LIBRARY_PATH: export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:FOUND_PATH_FROM_1a')
+            self.add_log_entry('CUDA SETUP: Solution 1c): For a permanent solution add the export from 1b into your .bashrc file, located at ~/.bashrc')
+            self.add_log_entry('CUDA SETUP: Solution 2: If no library was found in step 1a) you need to install CUDA.')
+            self.add_log_entry('CUDA SETUP: Solution 2a): Download CUDA install script: wget https://github.com/TimDettmers/bitsandbytes/blob/main/cuda_install.sh')
+            self.add_log_entry('CUDA SETUP: Solution 2b): Install desired CUDA version to desired location. The syntax is bash cuda_install.sh CUDA_VERSION PATH_TO_INSTALL_INTO.')
+            self.add_log_entry('CUDA SETUP: Solution 2b): For example, "bash cuda_install.sh 113 ~/local/" will download CUDA 11.3 and install into the folder ~/local')
+            return
+
+        make_cmd = f'CUDA_VERSION={self.cuda_version_string}'
+        if len(self.cuda_version_string) < 3:
+            make_cmd += ' make cuda92'
+        elif self.cuda_version_string == '110':
+            make_cmd += ' make cuda110'
+        elif self.cuda_version_string[:2] == '11' and int(self.cuda_version_string[2]) > 0:
+            make_cmd += ' make cuda11x'
+        elif self.cuda_version_string == '100':
+            self.add_log_entry('CUDA SETUP: CUDA 10.0 not supported. Please use a different CUDA version.')
+            self.add_log_entry('CUDA SETUP: Before you try again running bitsandbytes, make sure old CUDA 10.0 versions are uninstalled and removed from $LD_LIBRARY_PATH variables.')
+            return
+
+
+        has_cublaslt = is_cublasLt_compatible(self.cc)
+        if not has_cublaslt:
+            make_cmd += '_nomatmul'
+
+        self.add_log_entry('CUDA SETUP: Something unexpected happened. Please compile from source:')
+        self.add_log_entry('git clone git@github.com:TimDettmers/bitsandbytes.git')
+        self.add_log_entry('cd bitsandbytes')
+        self.add_log_entry(make_cmd)
+        self.add_log_entry('python setup.py install')
+
+    def initialize(self):
+        if not getattr(self, 'initialized', False):
+            self.has_printed = False
+            self.lib = None
+            self.initialized = False
+
+    def run_cuda_setup(self):
+        self.initialized = True
+        self.cuda_setup_log = []
+
+        binary_name, cudart_path, cuda, cc, cuda_version_string = evaluate_cuda_setup()
+        self.cudart_path = cudart_path
+        self.cuda = cuda
+        self.cc = cc
+        self.cuda_version_string = cuda_version_string
+
+        package_dir = Path(__file__).parent.parent
+        binary_path = package_dir / binary_name
+
+        try:
+            if not binary_path.exists():
+                self.add_log_entry(f"CUDA SETUP: Required library version not found: {binary_name}. Maybe you need to compile it from source?")
+                legacy_binary_name = "libbitsandbytes_cpu.so"
+                self.add_log_entry(f"CUDA SETUP: Defaulting to {legacy_binary_name}...")
+                binary_path = package_dir / legacy_binary_name
+                if not binary_path.exists() or torch.cuda.is_available():
+                    self.add_log_entry('')
+                    self.add_log_entry('='*48 + 'ERROR' + '='*37)
+                    self.add_log_entry('CUDA SETUP: CUDA detection failed! Possible reasons:')
+                    self.add_log_entry('1. CUDA driver not installed')
+                    self.add_log_entry('2. CUDA not installed')
+                    self.add_log_entry('3. You have multiple conflicting CUDA libraries')
+                    self.add_log_entry('4. Required library not pre-compiled for this bitsandbytes release!')
+                    self.add_log_entry('CUDA SETUP: If you compiled from source, try again with `make CUDA_VERSION=DETECTED_CUDA_VERSION` for example, `make CUDA_VERSION=113`.')
+                    self.add_log_entry('CUDA SETUP: The CUDA version for the compile might depend on your conda install. Inspect CUDA version via `conda list | grep cuda`.')
+                    self.add_log_entry('='*80)
+                    self.add_log_entry('')
+                    self.generate_instructions()
+                    self.print_log_stack()
+                    raise Exception('CUDA SETUP: Setup Failed!')
+                self.lib = ct.cdll.LoadLibrary(str(binary_path))
+            else:
+                self.add_log_entry(f"CUDA SETUP: Loading binary {binary_path}...")
+                self.lib = ct.cdll.LoadLibrary(str(binary_path))
+        except Exception as ex:
+            self.add_log_entry(str(ex))
+            self.print_log_stack()
+
+    def add_log_entry(self, msg, is_warning=False):
+        self.cuda_setup_log.append((msg, is_warning))
+
+    def print_log_stack(self):
+        for msg, is_warning in self.cuda_setup_log:
+            if is_warning:
+                warn(msg)
+            else:
+                print(msg)
+
+    @classmethod
+    def get_instance(cls):
+        if cls._instance is None:
+            cls._instance = cls.__new__(cls)
+            cls._instance.initialize()
+        return cls._instance
+
+
+def is_cublasLt_compatible(cc):
+    has_cublaslt = False
+    if cc is not None:
+        cc_major, cc_minor = cc.split('.')
+        if int(cc_major) < 7 or (int(cc_major) == 7 and int(cc_minor) < 5):
+            CUDASetup.get_instance().add_log_entry("WARNING: Compute capability < 7.5 detected! Only slow 8-bit matmul is supported for your GPU!", is_warning=True)
+        else:
+            has_cublaslt = True
+    return has_cublaslt
+
+def extract_candidate_paths(paths_list_candidate: str) -> Set[Path]:
+    return {Path(ld_path) for ld_path in paths_list_candidate.split(":") if ld_path}
+
+
+def remove_non_existent_dirs(candidate_paths: Set[Path]) -> Set[Path]:
+    existent_directories: Set[Path] = set()
+    for path in candidate_paths:
+        try:
+            if path.exists():
+                existent_directories.add(path)
+        except OSError as exc:
+            if exc.errno != errno.ENAMETOOLONG:
+                raise exc
+
+    non_existent_directories: Set[Path] = candidate_paths - existent_directories
+    if non_existent_directories:
+        CUDASetup.get_instance().add_log_entry("WARNING: The following directories listed in your path were found to "
+            f"be non-existent: {non_existent_directories}", is_warning=True)
+
+    return existent_directories
+
+
+def get_cuda_runtime_lib_paths(candidate_paths: Set[Path]) -> Set[Path]:
+    return {
+        path / CUDA_RUNTIME_LIB
+        for path in candidate_paths
+        if (path / CUDA_RUNTIME_LIB).is_file()
+    }
+
+
+def resolve_paths_list(paths_list_candidate: str) -> Set[Path]:
+    """
+    Searches a given environmental var for the CUDA runtime library,
+    i.e. `libcudart.so`.
+    """
+    return remove_non_existent_dirs(extract_candidate_paths(paths_list_candidate))
+
+
+def find_cuda_lib_in(paths_list_candidate: str) -> Set[Path]:
+    return get_cuda_runtime_lib_paths(
+        resolve_paths_list(paths_list_candidate)
+    )
+
+
+def warn_in_case_of_duplicates(results_paths: Set[Path]) -> None:
+    if len(results_paths) > 1:
+        warning_msg = (
+            f"Found duplicate {CUDA_RUNTIME_LIB} files: {results_paths}.. "
+            "We'll flip a coin and try one of these, in order to fail forward.\n"
+            "Either way, this might cause trouble in the future:\n"
+            "If you get `CUDA error: invalid device function` errors, the above "
+            "might be the cause and the solution is to make sure only one "
+            f"{CUDA_RUNTIME_LIB} in the paths that we search based on your env.")
+        CUDASetup.get_instance().add_log_entry(warning_msg, is_warning=True)
+
+
+def determine_cuda_runtime_lib_path() -> Union[Path, None]:
+    """
+        Searches for a cuda installations, in the following order of priority:
+            1. active conda env
+            2. LD_LIBRARY_PATH
+            3. any other env vars, while ignoring those that
+                - are known to be unrelated (see `bnb.cuda_setup.env_vars.to_be_ignored`)
+                - don't contain the path separator `/`
+
+        If multiple libraries are found in part 3, we optimistically try one,
+        while giving a warning message.
+    """
+    candidate_env_vars = get_potentially_lib_path_containing_env_vars()
+
+    if "CONDA_PREFIX" in candidate_env_vars:
+        conda_libs_path = Path(candidate_env_vars["CONDA_PREFIX"]) / "lib"
+
+        conda_cuda_libs = find_cuda_lib_in(str(conda_libs_path))
+        warn_in_case_of_duplicates(conda_cuda_libs)
+
+        if conda_cuda_libs:
+            return next(iter(conda_cuda_libs))
+
+        CUDASetup.get_instance().add_log_entry(f'{candidate_env_vars["CONDA_PREFIX"]} did not contain '
+            f'{CUDA_RUNTIME_LIB} as expected! Searching further paths...', is_warning=True)
+
+    if "LD_LIBRARY_PATH" in candidate_env_vars:
+        lib_ld_cuda_libs = find_cuda_lib_in(candidate_env_vars["LD_LIBRARY_PATH"])
+
+        if lib_ld_cuda_libs:
+            return next(iter(lib_ld_cuda_libs))
+        warn_in_case_of_duplicates(lib_ld_cuda_libs)
+
+        CUDASetup.get_instance().add_log_entry(f'{candidate_env_vars["LD_LIBRARY_PATH"]} did not contain '
+            f'{CUDA_RUNTIME_LIB} as expected! Searching further paths...', is_warning=True)
+
+    remaining_candidate_env_vars = {
+        env_var: value for env_var, value in candidate_env_vars.items()
+        if env_var not in {"CONDA_PREFIX", "LD_LIBRARY_PATH"}
+    }
+
+    cuda_runtime_libs = set()
+    for env_var, value in remaining_candidate_env_vars.items():
+        cuda_runtime_libs.update(find_cuda_lib_in(value))
+
+    if len(cuda_runtime_libs) == 0:
+        CUDASetup.get_instance().add_log_entry('CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching /usr/local/cuda/lib64...')
+        cuda_runtime_libs.update(find_cuda_lib_in('/usr/local/cuda/lib64'))
+
+    warn_in_case_of_duplicates(cuda_runtime_libs)
+
+    return next(iter(cuda_runtime_libs)) if cuda_runtime_libs else None
+
+
+def check_cuda_result(cuda, result_val):
+    # 3. Check for CUDA errors
+    if result_val != 0:
+        error_str = ct.c_char_p()
+        cuda.cuGetErrorString(result_val, ct.byref(error_str))
+        if error_str.value is not None:
+            CUDASetup.get_instance().add_log_entry(f"CUDA exception! Error code: {error_str.value.decode()}")
+        else:
+            CUDASetup.get_instance().add_log_entry(f"Unknown CUDA exception! Please check your CUDA install. It might also be that your GPU is too old.")
+
+
+# https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART____VERSION.html#group__CUDART____VERSION
+def get_cuda_version(cuda, cudart_path):
+    if cuda is None: return None
+
+    try:
+        cudart = ct.CDLL(cudart_path)
+    except OSError:
+        CUDASetup.get_instance().add_log_entry(f'ERROR: libcudart.so could not be read from path: {cudart_path}!')
+        return None
+
+    version = ct.c_int()
+    try:
+        check_cuda_result(cuda, cudart.cudaRuntimeGetVersion(ct.byref(version)))
+    except AttributeError as e:
+        CUDASetup.get_instance().add_log_entry(f'ERROR: {str(e)}')
+        CUDASetup.get_instance().add_log_entry(f'CUDA SETUP: libcudart.so path is {cudart_path}')
+        CUDASetup.get_instance().add_log_entry(f'CUDA SETUP: Is seems that your cuda installation is not in your path. See https://github.com/TimDettmers/bitsandbytes/issues/85 for more information.')
+    version = int(version.value)
+    major = version//1000
+    minor = (version-(major*1000))//10
+
+    if major < 11:
+        CUDASetup.get_instance().add_log_entry('CUDA SETUP: CUDA version lower than 11 are currently not supported for LLM.int8(). You will be only to use 8-bit optimizers and quantization routines!!')
+
+    return f'{major}{minor}'
+
+
+def get_cuda_lib_handle():
+    # 1. find libcuda.so library (GPU driver) (/usr/lib)
+    try:
+        cuda = ct.CDLL("libcuda.so")
+    except OSError:
+        CUDASetup.get_instance().add_log_entry('CUDA SETUP: WARNING! libcuda.so not found! Do you have a CUDA driver installed? If you are on a cluster, make sure you are on a CUDA machine!')
+        return None
+    check_cuda_result(cuda, cuda.cuInit(0))
+
+    return cuda
+
+
+def get_compute_capabilities(cuda):
+    """
+    1. find libcuda.so library (GPU driver) (/usr/lib)
+       init_device -> init variables -> call function by reference
+    2. call extern C function to determine CC
+       (https://docs.nvidia.com/cuda/cuda-driver-api/group__CUDA__DEVICE__DEPRECATED.html)
+    3. Check for CUDA errors
+       https://stackoverflow.com/questions/14038589/what-is-the-canonical-way-to-check-for-errors-using-the-cuda-runtime-api
+    # bits taken from https://gist.github.com/f0k/63a664160d016a491b2cbea15913d549
+    """
+
+    nGpus = ct.c_int()
+    cc_major = ct.c_int()
+    cc_minor = ct.c_int()
+
+    device = ct.c_int()
+
+    check_cuda_result(cuda, cuda.cuDeviceGetCount(ct.byref(nGpus)))
+    ccs = []
+    for i in range(nGpus.value):
+        check_cuda_result(cuda, cuda.cuDeviceGet(ct.byref(device), i))
+        ref_major = ct.byref(cc_major)
+        ref_minor = ct.byref(cc_minor)
+        # 2. call extern C function to determine CC
+        check_cuda_result(cuda, cuda.cuDeviceComputeCapability(ref_major, ref_minor, device))
+        ccs.append(f"{cc_major.value}.{cc_minor.value}")
+
+    return ccs
+
+
+# def get_compute_capability()-> Union[List[str, ...], None]: # FIXME: error
+def get_compute_capability(cuda):
+    """
+    Extracts the highest compute capbility from all available GPUs, as compute
+    capabilities are downwards compatible. If no GPUs are detected, it returns
+    None.
+    """
+    if cuda is None: return None
+
+    # TODO: handle different compute capabilities; for now, take the max
+    ccs = get_compute_capabilities(cuda)
+    if ccs: return ccs[-1]
+
+
+def evaluate_cuda_setup():
+    if 'BITSANDBYTES_NOWELCOME' not in os.environ or str(os.environ['BITSANDBYTES_NOWELCOME']) == '0':
+        print('')
+        print('='*35 + 'BUG REPORT' + '='*35)
+        print('Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues')
+        print('='*80)
+    if torch.cuda.is_available(): return 'libbitsandbytes_cuda116.dll', None, None, None, None
+
+    cuda_setup = CUDASetup.get_instance()
+    cudart_path = determine_cuda_runtime_lib_path()
+    cuda = get_cuda_lib_handle()
+    cc = get_compute_capability(cuda)
+    cuda_version_string = get_cuda_version(cuda, cudart_path)
+
+    failure = False
+    if cudart_path is None:
+        failure = True
+        cuda_setup.add_log_entry("WARNING: No libcudart.so found! Install CUDA or the cudatoolkit package (anaconda)!", is_warning=True)
+    else:
+        cuda_setup.add_log_entry(f"CUDA SETUP: CUDA runtime path found: {cudart_path}")
+
+    if cc == '' or cc is None:
+        failure = True
+        cuda_setup.add_log_entry("WARNING: No GPU detected! Check your CUDA paths. Proceeding to load CPU-only library...", is_warning=True)
+    else:
+        cuda_setup.add_log_entry(f"CUDA SETUP: Highest compute capability among GPUs detected: {cc}")
+
+    if cuda is None:
+        failure = True
+    else:
+        cuda_setup.add_log_entry(f'CUDA SETUP: Detected CUDA version {cuda_version_string}')
+
+    # 7.5 is the minimum CC vor cublaslt
+    has_cublaslt = is_cublasLt_compatible(cc)
+
+    # TODO:
+    # (1) CUDA missing cases (no CUDA installed by CUDA driver (nvidia-smi accessible)
+    # (2) Multiple CUDA versions installed
+
+    # we use ls -l instead of nvcc to determine the cuda version
+    # since most installations will have the libcudart.so installed, but not the compiler
+
+    if failure:
+        binary_name = "libbitsandbytes_cpu.so"
+    elif has_cublaslt:
+        binary_name = f"libbitsandbytes_cuda{cuda_version_string}.so"
+    else:
+        "if not has_cublaslt (CC < 7.5), then we have to choose  _nocublaslt.so"
+        binary_name = f"libbitsandbytes_cuda{cuda_version_string}_nocublaslt.so"
+
+    return binary_name, cudart_path, cuda, cc, cuda_version_string