Merge pull request #1 from TimDettmers/main

Update main branch

README.md

@@ -23,12 +23,12 @@ Resources:
 1. Comment out torch.nn.Linear: ``#linear = torch.nn.Linear(...)``
 2. Add bnb 8-bit linear light module: ``linear = bnb.nn.Linear8bitLt(...)`` (base arguments stay the same)
 3. There are two modes:
-   - Mixed 8-bit training with 16-bit main weights. Pass the argument ``use_fp16_weights=True`` (default)
-   - Int8 inference. Pass the argument ``use_fp16_weights=False``
+   - Mixed 8-bit training with 16-bit main weights. Pass the argument ``has_fp16_weights=True`` (default)
+   - Int8 inference. Pass the argument ``has_fp16_weights=False``
 4. To use the full LLM.int8() method, use the ``threshold=k`` argument. We recommend ``k=6.0``.
 ```python
 # LLM.int8()
-linear = bnb.nn.Linear8bitLt(dim1, dim2, bias=True, use_fp16_weights=False, threshold=6.0)
+linear = bnb.nn.Linear8bitLt(dim1, dim2, bias=True, has_fp16_weights=False, threshold=6.0)
 # inputs need to be fp16
 out = linear(x.to(torch.float16))
 ```
@@ -115,7 +115,8 @@ We thank Fabio Cannizzo for his work on [FastBinarySearch](https://github.com/fa
 
 ## How to cite us
 If you found this library and found LLM.int8() useful, please consider citing our work:
-```
+
+```bibtex
 @article{dettmers2022llmint8,
   title={LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale},
   author={Dettmers, Tim and Lewis, Mike and Belkada, Younes and Zettlemoyer, Luke},
@@ -124,8 +125,9 @@ If you found this library and found LLM.int8() useful, please consider citing ou
 }
 ```
 
-For 8-bit optimizers or quantization routines please consider citing the following work.
-```
+For 8-bit optimizers or quantization routines, please consider citing the following work:
+
+```bibtex
 @article{dettmers2022optimizers,
   title={8-bit Optimizers via Block-wise Quantization},
   author={Dettmers, Tim and Lewis, Mike and Shleifer, Sam and Zettlemoyer, Luke},

bitsandbytes/__init__.py

@@ -12,7 +12,7 @@ from .autograd._functions import (
 )
 from .cextension import COMPILED_WITH_CUDA
 from .nn import modules
-from . import cuda_setup
+from . import cuda_setup, utils
 
 if COMPILED_WITH_CUDA:
     from .optim import adam

bitsandbytes/__main__.py

@@ -3,8 +3,9 @@
 # cli()
 import os
 import sys
-import torch
+from warnings import warn
 
+import torch
 
 HEADER_WIDTH = 60
 
@@ -32,8 +33,6 @@ print()
 from . import COMPILED_WITH_CUDA, PACKAGE_GITHUB_URL
 from .cuda_setup.main import get_compute_capabilities, get_cuda_lib_handle
 from .cuda_setup.env_vars import to_be_ignored
-from .utils import print_stderr
-
 
 print_header("POTENTIALLY LIBRARY-PATH-LIKE ENV VARS")
 for k, v in os.environ.items():
@@ -84,7 +83,7 @@ try:
 
 except ImportError:
     print()
-    print_stderr(
+    warn(
         f"WARNING: {__package__} is currently running as CPU-only!\n"
         "Therefore, 8-bit optimizers and GPU quantization are unavailable.\n\n"
         f"If you think that this is so erroneously,\nplease report an issue!"

bitsandbytes/autograd/_functions.py

 import operator
 import torch
-import bitsandbytes as bnb
 import bitsandbytes.functional as F
 
 from dataclasses import dataclass
@@ -368,9 +367,6 @@ class MatMul8bitLt(torch.autograd.Function):
         return grad_A, grad_B, None, grad_bias, None
 
 
-matmul = MatMul8bitLt.apply
-
-
 def matmul(
     A: tensor,
     B: tensor,

bitsandbytes/cuda_setup/compute_capability.py

-import ctypes
-from dataclasses import dataclass, field
-
-
-@dataclass
-class CudaLibVals:
-    # code bits taken from
-    # https://gist.github.com/f0k/63a664160d016a491b2cbea15913d549
-
-    nGpus: ctypes.c_int = field(default=ctypes.c_int())
-    cc_major: ctypes.c_int = field(default=ctypes.c_int())
-    cc_minor: ctypes.c_int = field(default=ctypes.c_int())
-    device: ctypes.c_int = field(default=ctypes.c_int())
-    error_str: ctypes.c_char_p = field(default=ctypes.c_char_p())
-    cuda: ctypes.CDLL = field(init=False, repr=False)
-    ccs: List[str, ...] = field(init=False)
-
-    def _initialize_driver_API(self):
-        self.check_cuda_result(self.cuda.cuInit(0))
-
-    def _load_cuda_lib(self):
-        """
-        1. find libcuda.so library (GPU driver) (/usr/lib)
-           init_device -> init variables -> call function by reference
-        """
-        libnames = "libcuda.so"
-        for libname in libnames:
-            try:
-                self.cuda = ctypes.CDLL(libname)
-            except OSError:
-                continue
-            else:
-                break
-        else:
-            raise OSError("could not load any of: " + " ".join(libnames))
-
-    def call_cuda_func(self, function_obj, **kwargs):
-        CUDA_SUCCESS = 0  # constant taken from cuda.h
-        pass
-        # if (CUDA_SUCCESS := function_obj(
-
-    def _error_handle(cuda_lib_call_return_value):
-        """
-        2. call extern C function to determine CC
-           (see https://docs.nvidia.com/cuda/cuda-driver-api/group__CUDA__DEVICE__DEPRECATED.html)
-        """
-        CUDA_SUCCESS = 0  # constant taken from cuda.h
-
-        if cuda_lib_call_return_value != CUDA_SUCCESS:
-            self.cuda.cuGetErrorString(
-                cuda_lib_call_return_value,
-                ctypes.byref(self.error_str),
-            )
-            print("Count not initialize CUDA - failure!")
-            raise Exception("CUDA exception!")
-        return cuda_lib_call_return_value
-
-    def __post_init__(self):
-        self._load_cuda_lib()
-        self._initialize_driver_API()
-        self.check_cuda_result(
-            self.cuda, self.cuda.cuDeviceGetCount(ctypes.byref(self.nGpus))
-        )
-        tmp_ccs = []
-        for gpu_index in range(self.nGpus.value):
-            check_cuda_result(
-                self.cuda,
-                self.cuda.cuDeviceGet(ctypes.byref(self.device), gpu_index),
-            )
-            check_cuda_result(
-                self.cuda,
-                self.cuda.cuDeviceComputeCapability(
-                    ctypes.byref(self.cc_major),
-                    ctypes.byref(self.cc_minor),
-                    self.device,
-                ),
-            )
-            tmp_ccs.append(f"{self.cc_major.value}.{self.cc_minor.value}")
-        self.ccs = sorted(tmp_ccs, reverse=True)

bitsandbytes/cuda_setup/main.py

@@ -17,9 +17,7 @@ evaluation:
 """
 
 import ctypes
-from pathlib import Path
 
-from ..utils import execute_and_return
 from .paths import determine_cuda_runtime_lib_path
 
 
@@ -28,7 +26,7 @@ def check_cuda_result(cuda, result_val):
     if result_val != 0:
         error_str = ctypes.c_char_p()
         cuda.cuGetErrorString(result_val, ctypes.byref(error_str))
-        raise Exception(f"CUDA exception! Error code: {error_str.value.decode()}")
+        print(f"CUDA exception! Error code: {error_str.value.decode()}")
 
 def get_cuda_version(cuda, cudart_path):
     # https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART____VERSION.html#group__CUDART____VERSION
@@ -57,7 +55,7 @@ def get_cuda_lib_handle():
         cuda = ctypes.CDLL("libcuda.so")
     except OSError:
         # TODO: shouldn't we error or at least warn here?
-        raise Exception('CUDA SETUP: ERROR! 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!')
+        print('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))
 
@@ -80,7 +78,6 @@ def get_compute_capabilities(cuda):
     cc_major = ctypes.c_int()
     cc_minor = ctypes.c_int()
 
-    result = ctypes.c_int()
     device = ctypes.c_int()
 
     check_cuda_result(cuda, cuda.cuDeviceGetCount(ctypes.byref(nGpus)))
@@ -119,6 +116,10 @@ def evaluate_cuda_setup():
     print('For effortless bug reporting copy-paste your error into this form: https://docs.google.com/forms/d/e/1FAIpQLScPB8emS3Thkp66nvqwmjTEgxp8Y9ufuWTzFyr9kJ5AoI47dQ/viewform?usp=sf_link')
     print('='*80)
     binary_name = "libbitsandbytes_cpu.so"
+    #if not torch.cuda.is_available():
+        #print('No GPU detected. Loading CPU library...')
+        #return binary_name
+
     cudart_path = determine_cuda_runtime_lib_path()
     if cudart_path is None:
         print(

bitsandbytes/cuda_setup/paths.py

@@ -2,23 +2,11 @@ from pathlib import Path
 from typing import Set, Union
 from warnings import warn
 
-from ..utils import print_stderr
 from .env_vars import get_potentially_lib_path_containing_env_vars
 
-
 CUDA_RUNTIME_LIB: str = "libcudart.so"
 
 
-def purge_unwanted_semicolon(tentative_path: Path) -> Path:
-    """
-    Special function to handle the following exception:
-    __LMOD_REF_COUNT_PATH=/sw/cuda/11.6.2/bin:2;/mmfs1/home/dettmers/git/sched/bin:1;/mmfs1/home/dettmers/data/anaconda3/bin:1;/mmfs1/home/dettmers/data/anaconda3/condabin:1;/mmfs1/home/dettmers/.local/bin:1;/mmfs1/home/dettmers/bin:1;/usr/local/bin:1;/usr/bin:1;/usr/local/sbin:1;/usr/sbin:1;/mmfs1/home/dettmers/.fzf/bin:1;/mmfs1/home/dettmers/data/local/cuda-11.4/bin:1
-    """
-    # if ';' in str(tentative_path):
-    #     path_as_str, _ = str(tentative_path).split(';')
-    pass
-
-
 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}
 
@@ -29,7 +17,7 @@ def remove_non_existent_dirs(candidate_paths: Set[Path]) -> Set[Path]:
     }
 
     if non_existent_directories:
-        print_stderr(
+        warn(
             "WARNING: The following directories listed in your path were found to "
             f"be non-existent: {non_existent_directories}"
         )
@@ -117,8 +105,6 @@ def determine_cuda_runtime_lib_path() -> Union[Path, None]:
         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))

bitsandbytes/functional.py

@@ -5,7 +5,6 @@
 import ctypes as ct
 import operator
 import random
-import math
 import torch
 
 from typing import Tuple
@@ -185,14 +184,9 @@ def create_dynamic_map(signed=True, n=7):
 
 
 def get_special_format_str():
+    if not torch.cuda.is_available(): return 'col_turing'
     major, minor = torch.cuda.get_device_capability()
-    if major < 7:
-        print(
-            f"Device with CUDA capability of {major} not supported for 8-bit matmul. Device has no tensor cores!"
-        )
-        assert major >= 7
-
-    if major == 7:
+    if major <= 7:
         return "col_turing"
     elif major == 8:
         return "col_ampere"
@@ -248,23 +242,6 @@ def get_transform_func(dtype, orderA, orderOut, transpose=False):
         return getattr(lib, name)
 
 
-class GlobalData(object):
-    _instance = None
-
-    def __init__(self):
-        raise RuntimeError("Call get_instance() instead")
-
-    def initialize(self):
-        self.data = {}
-
-    @classmethod
-    def get_instance(cls):
-        if cls._instance is None:
-            cls._instance = cls.__new__(cls)
-            cls._instance.initialize()
-        return cls._instance
-
-
 def get_transform_buffer(
     shape, dtype, device, to_order, from_order="row", transpose=False
 ):
@@ -1685,21 +1662,6 @@ def double_quant(
     return out_row, out_col, row_stats, col_stats, coo_tensor
 
 
-def get_special_format_str():
-    major, minor = torch.cuda.get_device_capability()
-    if major < 7:
-        print(
-            f"Device with CUDA capability of {major} not supported for 8-bit matmul. Device has no tensor cores!"
-        )
-        assert major >= 7
-
-    if major == 7: return 'col_turing'
-    elif major == 8: return 'col_ampere'
-    else: return 'col_turing'
-
-
-
-
 def transform(A, to_order, from_order='row', out=None, transpose=False, state=None, ld=None):
     prev_device = pre_call(A.device)
     if state is None: state = (A.shape, from_order)

bitsandbytes/optim/__init__.py

@@ -5,13 +5,12 @@
 
 from bitsandbytes.cextension import COMPILED_WITH_CUDA
 
-if COMPILED_WITH_CUDA:
-    from .adam import Adam, Adam8bit, Adam32bit
-    from .adamw import AdamW, AdamW8bit, AdamW32bit
-    from .sgd import SGD, SGD8bit, SGD32bit
-    from .lars import LARS, LARS8bit, LARS32bit, PytorchLARS
-    from .lamb import LAMB, LAMB8bit, LAMB32bit
-    from .rmsprop import RMSprop, RMSprop8bit, RMSprop32bit
-    from .adagrad import Adagrad, Adagrad8bit, Adagrad32bit
+from .adam import Adam, Adam8bit, Adam32bit
+from .adamw import AdamW, AdamW8bit, AdamW32bit
+from .sgd import SGD, SGD8bit, SGD32bit
+from .lars import LARS, LARS8bit, LARS32bit, PytorchLARS
+from .lamb import LAMB, LAMB8bit, LAMB32bit
+from .rmsprop import RMSprop, RMSprop8bit, RMSprop32bit
+from .adagrad import Adagrad, Adagrad8bit, Adagrad32bit
 
 from .optimizer import GlobalOptimManager

bitsandbytes/utils.py

 import shlex
 import subprocess
-import sys
 from typing import Tuple
 
 
@@ -22,11 +21,3 @@ def execute_and_return(command_string: str) -> Tuple[str, str]:
 
     std_out, std_err = execute_and_return_decoded_std_streams(command_string)
     return std_out, std_err
-
-
-def print_stderr(s: str) -> None:
-    print(s, file=sys.stderr)
-
-
-def warn_of_missing_prerequisite(s: str) -> None:
-    print_stderr("WARNING, missing pre-requisite: " + s)

csrc/ops.cu

@@ -371,7 +371,11 @@ template void transform<int32_t, COL32, ROW, false, 32>(cublasLtHandle_t ltHandl
 template <int FORMATB, int DTYPE_OUT, int SCALE_ROWS> int igemmlt(cublasLtHandle_t ltHandle, int m, int n, int k, const int8_t *A, const int8_t *B, void *C, float *row_scale, int lda, int ldb, int ldc) 
 {
 #ifdef NO_CUBLASLT
-  printf("ERROR: Your GPU does not support Int8 Matmul!");
+  cout << "" << endl;
+  cout << "=============================================" << endl;
+  cout << "ERROR: Your GPU does not support Int8 Matmul!" << endl;
+  cout << "=============================================" << endl;
+  cout << "" << endl;
   assert(false);
 
 	return 0;

setup.py

@@ -18,7 +18,7 @@ def read(fname):
 
 setup(
     name=f"bitsandbytes",
-    version=f"0.32.1",
+    version=f"0.32.3",
     author="Tim Dettmers",
     author_email="dettmers@cs.washington.edu",
     description="8-bit optimizers and matrix multiplication routines.",

tests/test_autograd.py

@@ -40,6 +40,7 @@ names = [
     ids=names,
 )
 def test_matmul(dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose):
+    if not torch.cuda.is_available(): pytest.skip('No GPU found.')
     if dim2 > 0:
         dim2 = dim2 - (dim2 % 16)
     dim3 = dim3 - (dim3 % 16)
@@ -306,6 +307,7 @@ def test_matmullt(
     has_fp16_weights,
     has_bias
 ):
+    if not torch.cuda.is_available(): pytest.skip('No GPU found.')
     dimA = (dim2, dim3) if not transpose[0] else (dim3, dim2)
     dimB = (dim3, dim4) if not transpose[1] else (dim4, dim3)
     outlier_dim = torch.randint(0, dimA[1], size=(dimA[1] // 8,), device="cuda")

tests/test_functional.py

@@ -1813,16 +1813,16 @@ def test_spmm_coo_dequant(dim1, dim2, dtype):
 
 
 batch_size = 1
-seqdim = 2048
+seqdim = 1
 values = []
-values.append((batch_size, seqdim, 768, 4 * 768))
+#values.append((batch_size, seqdim, 768, 4 * 768))
 # values.append((batch_size, seqdim, 1024, 4*1024))
 # values.append((batch_size, seqdim, 1536, 4*1536))
 # values.append((batch_size, seqdim, 2048, 4*2048))
 # values.append((batch_size, seqdim, 2560, 4*2560))
 # values.append((batch_size, seqdim, 4096, 4*4096))
 # values.append((batch_size, seqdim, 5140, 4*5140))
-# values.append((batch_size, seqdim, 12288, 4*12288))
+values.append((batch_size, seqdim, 12288, 4*12288))
 names = [
     "batch_{0}_seq_{1}_model_{2}_hidden_{3}".format(*vals) for vals in values
 ]
@@ -1830,6 +1830,7 @@ names = [
 
 @pytest.mark.parametrize("batch, seq, model, hidden", values, ids=names)
 def test_bench_matmul(batch, seq, model, hidden):
+    iters = 128
     formatB = F.get_special_format_str()
 
     A = torch.randn(batch, seq, model, device="cuda").half()
@@ -1848,28 +1849,33 @@ def test_bench_matmul(batch, seq, model, hidden):
     linearMixedBit.eval()
 
     # warmup
-    for i in range(100):
+    for i in range(iters):
         torch.matmul(A, B.t())
     torch.cuda.synchronize()
     print("")
 
     torch.cuda.synchronize()
     t0 = time.time()
-    for i in range(100):
+    for i in range(iters):
         torch.matmul(A, B.t())
     torch.cuda.synchronize()
     print(
-        f"pytorch: [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s"
+        f"pytorch fp16: [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s"
     )
 
     torch.cuda.synchronize()
     t0 = time.time()
-    for i in range(100):
+    for i in range(iters):
         bnb.matmul(A, B)
     torch.cuda.synchronize()
-    print(
-        f"bnb lt: [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s"
-    )
+    print(f"CB -> CxB conversion (each iteration): [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s")
+
+    torch.cuda.synchronize()
+    t0 = time.time()
+    for i in range(iters):
+        bnb.matmul(A, B, threshold=6.0)
+    torch.cuda.synchronize()
+    print(f"CB -> CxB conversion + threshold: [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s")
 
     CA, CAt, SCA, SCAt, coo_tensorA = F.double_quant(A, threshold=0.0)
     C32A, SA = F.transform(CA, "col32")
@@ -1877,18 +1883,16 @@ def test_bench_matmul(batch, seq, model, hidden):
     CxB, SB = F.transform(CB, to_order=formatB)
     torch.cuda.synchronize()
     t0 = time.time()
-    for i in range(100):
+    for i in range(iters):
         out32, Sout32 = F.igemmlt(C32A, CxB, SA, SB)
     torch.cuda.synchronize()
-    print(
-        f"igemmlt: [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s"
-    )
+    print(f"no overhead matmul-lt: [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s")
 
     BA, statsB = F.vectorwise_quant(B, dim=1)
     CxB, SB = F.nvidia_transform(CB, to_order=formatB)
     torch.cuda.synchronize()
     t0 = time.time()
-    for i in range(100):
+    for i in range(iters):
         A2 = A.view(-1, A.shape[-1]).contiguous()
         CA, statsA = F.vectorwise_quant(A2, dim=1)
         C32A, SA = F.nvidia_transform(CA, "col32")
@@ -1896,15 +1900,13 @@ def test_bench_matmul(batch, seq, model, hidden):
         Cout, Sout = F.nvidia_transform(out32, "row", state=Sout32)
         F.vectorwise_mm_dequant(Cout, statsA, statsB.t())
     torch.cuda.synchronize()
-    print(
-        f"vector pytorch + nvidia: [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s"
-    )
+    #print(f"vector pytorch + nvidia: [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s")
 
     BA, statsB = F.vectorwise_quant(B, dim=1, quant_type="linear")
     CxB, SB = F.nvidia_transform(CB, to_order=formatB)
     torch.cuda.synchronize()
     t0 = time.time()
-    for i in range(100):
+    for i in range(iters):
         A2 = A.view(-1, A.shape[-1]).contiguous()
         CA, statsA = F.vectorwise_quant(A2, dim=1, quant_type="linear")
         C32A, SA = F.nvidia_transform(CA, "col32")
@@ -1912,14 +1914,12 @@ def test_bench_matmul(batch, seq, model, hidden):
         Cout, Sout = F.nvidia_transform(out32, "row", state=Sout32)
         out = Cout * statsB * statsA * (1.0 / (127 * 127))
     torch.cuda.synchronize()
-    print(
-        f"linear pytorch + nvidia: [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s"
-    )
+    #print(f"linear pytorch + nvidia: [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s")
 
     linear8bit(A)
     torch.cuda.synchronize()
     t0 = time.time()
-    for i in range(100):
+    for i in range(iters):
         linear8bit(A)
     torch.cuda.synchronize()
     print(
@@ -1929,7 +1929,7 @@ def test_bench_matmul(batch, seq, model, hidden):
     linearMixedBit(A)
     torch.cuda.synchronize()
     t0 = time.time()
-    for i in range(100):
+    for i in range(iters):
         linearMixedBit(A)
     torch.cuda.synchronize()
     print(