Merge pull request #76 from tomaarsen/cleanup

Cleanup involving a handful of failures, some optimization and a lot of code quality improvements

bitsandbytes/__init__.py

@@ -3,6 +3,7 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 
+from . import cuda_setup, utils
 from .autograd._functions import (
     MatmulLtState,
     bmm_cublas,
@@ -12,7 +13,6 @@ from .autograd._functions import (
 )
 from .cextension import COMPILED_WITH_CUDA
 from .nn import modules
-from . import cuda_setup, utils
 
 if COMPILED_WITH_CUDA:
     from .optim import adam

bitsandbytes/__main__.py

-# from bitsandbytes.debug_cli import cli
-
-# cli()
 import os
 import sys
 from warnings import warn
@@ -31,8 +28,8 @@ 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 .cuda_setup.main import get_compute_capabilities, get_cuda_lib_handle
 
 print_header("POTENTIALLY LIBRARY-PATH-LIKE ENV VARS")
 for k, v in os.environ.items():

bitsandbytes/autograd/_functions.py

 import operator
 import warnings
+from dataclasses import dataclass
+from functools import reduce  # Required in Python 3
 
 import torch
+
 import bitsandbytes.functional as F
 
-from dataclasses import dataclass
-from functools import reduce  # Required in Python 3
 
 # math.prod not compatible with python < 3.8
 def prod(iterable):
@@ -18,7 +19,7 @@ tensor = torch.Tensor
     This is particularly important for small models where outlier features
     are less systematic and occur with low frequency.
 """
-class GlobalOutlierPooler(object):
+class GlobalOutlierPooler:
     _instance = None
 
     def __init__(self):
@@ -49,8 +50,9 @@ class GlobalOutlierPooler(object):
 
 class MatMul8bit(torch.autograd.Function):
     @staticmethod
-    def forward(ctx, A, B, out=None, quant_type="vector", precision=[8, 8, 8]):
-
+    def forward(ctx, A, B, out=None, quant_type="vector", precision=None):
+        if precision is None:
+            precision = [8, 8, 8]
         if precision[0] != 8:
             with torch.no_grad():
                 output = torch.matmul(A, B)

bitsandbytes/cextension.py

 import ctypes as ct
-import torch
-
 from pathlib import Path
 from warnings import warn
 
+import torch
+
 
-class CUDASetup(object):
+class CUDASetup:
     _instance = None
 
     def __init__(self):
@@ -122,7 +122,7 @@ try:
         CUDASetup.get_instance().generate_instructions()
         CUDASetup.get_instance().print_log_stack()
         raise RuntimeError('''
-        CUDA Setup failed despite GPU being available. Inspect the CUDA SETUP outputs aboveto fix your environment!
+        CUDA Setup failed despite GPU being available. Inspect the CUDA SETUP outputs above to fix your environment!
         If you cannot find any issues and suspect a bug, please open an issue with detals about your environment:
         https://github.com/TimDettmers/bitsandbytes/issues''')
     lib.cadam32bit_g32

bitsandbytes/cuda_setup/__init__.py

-from .paths import CUDA_RUNTIME_LIB, extract_candidate_paths, determine_cuda_runtime_lib_path
 from .main import evaluate_cuda_setup
+from .paths import (
+    CUDA_RUNTIME_LIB,
+    determine_cuda_runtime_lib_path,
+    extract_candidate_paths,
+)

bitsandbytes/cuda_setup/main.py

@@ -17,11 +17,13 @@ evaluation:
 """
 
 import ctypes
+
 import torch
 
-from .paths import determine_cuda_runtime_lib_path
 from bitsandbytes.cextension import CUDASetup
 
+from .paths import determine_cuda_runtime_lib_path
+
 
 def check_cuda_result(cuda, result_val):
     # 3. Check for CUDA errors
@@ -48,7 +50,7 @@ def get_cuda_version(cuda, cudart_path):
     minor = (version-(major*1000))//10
 
     if major < 11:
-       CUDASetup.get_instance().add_log_entry('CUDA SETUP: CUDA version lower than 11 are currenlty not supported for LLM.int8(). You will be only to use 8-bit optimizers and quantization routines!!')
+        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}'
 
@@ -129,7 +131,7 @@ def evaluate_cuda_setup():
         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}"))
+        cuda_setup.add_log_entry(f"CUDA SETUP: CUDA runtime path found: {cudart_path}")
 
     if cc == '' or cc is None:
         failure = True

bitsandbytes/cuda_setup/paths.py

 import errno
 from pathlib import Path
 from typing import Set, Union
+
 from bitsandbytes.cextension import CUDASetup
 
 from .env_vars import get_potentially_lib_path_containing_env_vars

bitsandbytes/debug_cli.py

-import typer
-
-cli = typer.Typer()
-
-
-@cli.callback()
-def callback():
-    """
-    Awesome Portal Gun
-    """
-
-
-@cli.command()
-def shoot():
-    """
-    Shoot the portal gun
-    """
-    typer.echo("Shooting portal gun")
-
-
-@cli.command()
-def load():
-    """
-    Load the portal gun
-    """
-    typer.echo("Loading portal gun")

bitsandbytes/functional.py

@@ -3,17 +3,19 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 import ctypes as ct
+import itertools
 import operator
 import random
 import torch
 import itertools
 import math
 
+from functools import reduce  # Required in Python 3
 from typing import Tuple
 from torch import Tensor
 
 from .cextension import COMPILED_WITH_CUDA, lib
-from functools import reduce  # Required in Python 3
+
 
 # math.prod not compatible with python < 3.8
 def prod(iterable):
@@ -84,7 +86,7 @@ if COMPILED_WITH_CUDA:
     )
 
 
-class CUBLAS_Context(object):
+class CUBLAS_Context:
     _instance = None
 
     def __init__(self):
@@ -114,7 +116,7 @@ class CUBLAS_Context(object):
         return self.context[device.index]
 
 
-class Cusparse_Context(object):
+class Cusparse_Context:
     _instance = None
 
     def __init__(self):
@@ -264,12 +266,11 @@ def create_quantile_map(A, total_bits=8):
 
 def get_special_format_str():
     if not torch.cuda.is_available(): return 'col_turing'
-    major, minor = torch.cuda.get_device_capability()
+    major, _minor = torch.cuda.get_device_capability()
     if major <= 7:
         return "col_turing"
-    elif major == 8:
+    if major == 8:
         return "col_ampere"
-    else:
     return "col_turing"
 
 
@@ -397,8 +398,6 @@ def nvidia_transform(
         dim2 = ct.c_int32(shape[2])
 
     ptr = CUBLAS_Context.get_instance().get_context(A.device)
-    ptrA = get_ptr(A)
-    ptrOut = get_ptr(out)
     func(ptr, get_ptr(A), get_ptr(out), dim1, dim2)
 
     return out, new_state
@@ -1053,7 +1052,7 @@ def histogram_scatter_add_2d(
 
     maxdim1 = ct.c_int32(histogram.shape[0])
     n = ct.c_int32(index1.numel())
-    is_on_gpu([histogram, index1, index2d, source])
+    is_on_gpu([histogram, index1, index2, source])
     lib.chistogram_scatter_add_2d(get_ptr(histogram), get_ptr(index1), get_ptr(index2), get_ptr(source), maxdim1, n)
 
 def check_matmul(A, B, out, transposed_A, transposed_B, expected_type=torch.int8):
@@ -1512,7 +1511,7 @@ def get_colrow_absmax(
     return row_stats, col_stats, nnz_block_ptr
 
 
-class COOSparseTensor(object):
+class COOSparseTensor:
     def __init__(self, rows, cols, nnz, rowidx, colidx, values):
         assert rowidx.dtype == torch.int32
         assert colidx.dtype == torch.int32
@@ -1529,7 +1528,7 @@ class COOSparseTensor(object):
         self.values = values
 
 
-class CSRSparseTensor(object):
+class CSRSparseTensor:
     def __init__(self, rows, cols, nnz, rowptr, colidx, values):
         assert rowptr.dtype == torch.int32
         assert colidx.dtype == torch.int32
@@ -1546,7 +1545,7 @@ class CSRSparseTensor(object):
         self.values = values
 
 
-class CSCSparseTensor(object):
+class CSCSparseTensor:
     def __init__(self, rows, cols, nnz, colptr, rowidx, values):
         assert colptr.dtype == torch.int32
         assert rowidx.dtype == torch.int32
@@ -1710,8 +1709,6 @@ def transform(A, to_order, from_order='row', out=None, transpose=False, state=No
         dim1 = ct.c_int32(shape[0] * shape[1])
         dim2 = ct.c_int32(shape[2])
 
-    ptrA = get_ptr(A)
-    ptrOut = get_ptr(out)
     is_on_gpu([A, out])
     if to_order == 'col32':
         if transpose:

bitsandbytes/nn/modules.py

@@ -2,24 +2,11 @@
 #
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
-from typing import (
-    Any,
-    Callable,
-    Dict,
-    Iterator,
-    Mapping,
-    Optional,
-    Set,
-    Tuple,
-    TypeVar,
-    Union,
-    overload,
-)
+from typing import Optional, TypeVar, Union, overload
 
 import torch
 import torch.nn.functional as F
 from torch import Tensor, device, dtype, nn
-from torch.nn.parameter import Parameter
 
 import bitsandbytes as bnb
 from bitsandbytes.optim import GlobalOptimManager
@@ -39,7 +26,7 @@ class StableEmbedding(torch.nn.Embedding):
         sparse: bool = False,
         _weight: Optional[Tensor] = None,
     ) -> None:
-        super(StableEmbedding, self).__init__(
+        super().__init__(
             num_embeddings,
             embedding_dim,
             padding_idx,
@@ -96,7 +83,7 @@ class Embedding(torch.nn.Embedding):
         sparse: bool = False,
         _weight: Optional[Tensor] = None,
     ) -> None:
-        super(Embedding, self).__init__(
+        super().__init__(
             num_embeddings,
             embedding_dim,
             padding_idx,
@@ -225,7 +212,7 @@ class Linear8bitLt(nn.Linear):
         threshold=0.0,
         index=None,
     ):
-        super(Linear8bitLt, self).__init__(
+        super().__init__(
             input_features, output_features, bias
         )
         self.state = bnb.MatmulLtState()

bitsandbytes/optim/__init__.py

@@ -5,12 +5,11 @@
 
 from bitsandbytes.cextension import COMPILED_WITH_CUDA
 
+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 .lars import LARS, LARS8bit, LARS32bit, PytorchLARS
 from .optimizer import GlobalOptimManager
+from .rmsprop import RMSprop, RMSprop8bit, RMSprop32bit
+from .sgd import SGD, SGD8bit, SGD32bit

bitsandbytes/optim/adagrad.py

@@ -21,18 +21,18 @@ class Adagrad(Optimizer1State):
         block_wise=True,
     ):
         if not 0.0 <= lr:
-            raise ValueError("Invalid learning rate: {}".format(lr))
+            raise ValueError(f"Invalid learning rate: {lr}")
         if not 0.0 <= weight_decay:
             raise ValueError(
-                "Invalid weight_decay value: {}".format(weight_decay)
+                f"Invalid weight_decay value: {weight_decay}"
             )
         if not 0.0 <= eps:
-            raise ValueError("Invalid epsilon value: {}".format(eps))
+            raise ValueError(f"Invalid epsilon value: {eps}")
         if initial_accumulator_value != 0.0:
             raise ValueError("Initial accumulator value != 0.0 not supported!")
         if lr_decay != 0.0:
             raise ValueError("Lr Decay != 0.0 not supported!")
-        super(Adagrad, self).__init__(
+        super().__init__(
             "adagrad",
             params,
             lr,
@@ -63,19 +63,19 @@ class Adagrad8bit(Optimizer1State):
         block_wise=True,
     ):
         if not 0.0 <= lr:
-            raise ValueError("Invalid learning rate: {}".format(lr))
+            raise ValueError(f"Invalid learning rate: {lr}")
         if not 0.0 <= weight_decay:
             raise ValueError(
-                "Invalid weight_decay value: {}".format(weight_decay)
+                f"Invalid weight_decay value: {weight_decay}"
             )
         if not 0.0 <= eps:
-            raise ValueError("Invalid epsilon value: {}".format(eps))
+            raise ValueError(f"Invalid epsilon value: {eps}")
         if initial_accumulator_value != 0.0:
             raise ValueError("Initial accumulator value != 0.0 not supported!")
         if lr_decay != 0.0:
             raise ValueError("Lr Decay != 0.0 not supported!")
         assert block_wise
-        super(Adagrad8bit, self).__init__(
+        super().__init__(
             "adagrad",
             params,
             lr,
@@ -106,18 +106,18 @@ class Adagrad32bit(Optimizer1State):
         block_wise=True,
     ):
         if not 0.0 <= lr:
-            raise ValueError("Invalid learning rate: {}".format(lr))
+            raise ValueError(f"Invalid learning rate: {lr}")
         if not 0.0 <= weight_decay:
             raise ValueError(
-                "Invalid weight_decay value: {}".format(weight_decay)
+                f"Invalid weight_decay value: {weight_decay}"
             )
         if not 0.0 <= eps:
-            raise ValueError("Invalid epsilon value: {}".format(eps))
+            raise ValueError(f"Invalid epsilon value: {eps}")
         if initial_accumulator_value != 0.0:
             raise ValueError("Initial accumulator value != 0.0 not supported!")
         if lr_decay != 0.0:
             raise ValueError("Lr Decay != 0.0 not supported!")
-        super(Adagrad32bit, self).__init__(
+        super().__init__(
             "adagrad",
             params,
             lr,

bitsandbytes/optim/adam.py

@@ -28,7 +28,7 @@ class Adam(Optimizer2State):
         percentile_clipping=100,
         block_wise=True,
     ):
-        super(Adam, self).__init__(
+        super().__init__(
             "adam",
             params,
             lr,
@@ -57,7 +57,7 @@ class Adam8bit(Optimizer2State):
         percentile_clipping=100,
         block_wise=True,
     ):
-        super(Adam8bit, self).__init__(
+        super().__init__(
             "adam",
             params,
             lr,
@@ -86,7 +86,7 @@ class Adam32bit(Optimizer2State):
         percentile_clipping=100,
         block_wise=True,
     ):
-        super(Adam32bit, self).__init__(
+        super().__init__(
             "adam",
             params,
             lr,
@@ -146,7 +146,7 @@ class AnalysisAdam(torch.optim.Optimizer):
             weight_decay=weight_decay,
             amsgrad=amsgrad,
         )
-        super(AnalysisAdam, self).__init__(params, defaults)
+        super().__init__(params, defaults)
         self.analysis = bnb_analysis
         self.savedir = savedir
 

bitsandbytes/optim/adamw.py

@@ -20,7 +20,7 @@ class AdamW(Optimizer2State):
         percentile_clipping=100,
         block_wise=True,
     ):
-        super(AdamW, self).__init__(
+        super().__init__(
             "adam",
             params,
             lr,
@@ -49,7 +49,7 @@ class AdamW8bit(Optimizer2State):
         percentile_clipping=100,
         block_wise=True,
     ):
-        super(AdamW8bit, self).__init__(
+        super().__init__(
             "adam",
             params,
             lr,
@@ -78,7 +78,7 @@ class AdamW32bit(Optimizer2State):
         percentile_clipping=100,
         block_wise=True,
     ):
-        super(AdamW32bit, self).__init__(
+        super().__init__(
             "adam",
             params,
             lr,

bitsandbytes/optim/lamb.py

@@ -23,7 +23,7 @@ class LAMB(Optimizer2State):
         block_wise=False,
         max_unorm=1.0,
     ):
-        super(LAMB, self).__init__(
+        super().__init__(
             "lamb",
             params,
             lr,
@@ -56,7 +56,7 @@ class LAMB8bit(Optimizer2State):
         block_wise=False,
         max_unorm=1.0,
     ):
-        super(LAMB8bit, self).__init__(
+        super().__init__(
             "lamb",
             params,
             lr,
@@ -89,7 +89,7 @@ class LAMB32bit(Optimizer2State):
         block_wise=False,
         max_unorm=1.0,
     ):
-        super(LAMB32bit, self).__init__(
+        super().__init__(
             "lamb",
             params,
             lr,

bitsandbytes/optim/lars.py

@@ -25,9 +25,9 @@ class LARS(Optimizer1State):
     ):
         if momentum == 0:
             raise NotImplementedError(
-                f"LARS without momentum is not supported!"
+                "LARS without momentum is not supported!"
             )
-        super(LARS, self).__init__(
+        super().__init__(
             "lars",
             params,
             lr,
@@ -59,9 +59,9 @@ class LARS8bit(Optimizer1State):
     ):
         if momentum == 0:
             raise NotImplementedError(
-                f"LARS without momentum is not supported!"
+                "LARS without momentum is not supported!"
             )
-        super(LARS8bit, self).__init__(
+        super().__init__(
             "lars",
             params,
             lr,
@@ -93,9 +93,9 @@ class LARS32bit(Optimizer1State):
     ):
         if momentum == 0:
             raise NotImplementedError(
-                f"LARS without momentum is not supported!"
+                "LARS without momentum is not supported!"
             )
-        super(LARS32bit, self).__init__(
+        super().__init__(
             "lars",
             params,
             lr,
@@ -123,12 +123,12 @@ class PytorchLARS(Optimizer):
         max_unorm=0.02,
     ):
         if lr < 0.0:
-            raise ValueError("Invalid learning rate: {}".format(lr))
+            raise ValueError(f"Invalid learning rate: {lr}")
         if momentum < 0.0:
-            raise ValueError("Invalid momentum value: {}".format(momentum))
+            raise ValueError(f"Invalid momentum value: {momentum}")
         if weight_decay < 0.0:
             raise ValueError(
-                "Invalid weight_decay value: {}".format(weight_decay)
+                f"Invalid weight_decay value: {weight_decay}"
             )
 
         defaults = dict(
@@ -143,10 +143,10 @@ class PytorchLARS(Optimizer):
             raise ValueError(
                 "Nesterov momentum requires a momentum and zero dampening"
             )
-        super(PytorchLARS, self).__init__(params, defaults)
+        super().__init__(params, defaults)
 
     def __setstate__(self, state):
-        super(PytorchLARS, self).__setstate__(state)
+        super().__setstate__(state)
         for group in self.param_groups:
             group.setdefault("nesterov", False)
 
@@ -181,7 +181,7 @@ class PytorchLARS(Optimizer):
                 state = self.state[p]
                 d_p = p.grad
                 if weight_decay != 0:
-                    d_p = d_p.add(param, alpha=weight_decay)
+                    d_p = d_p.add(p, alpha=weight_decay)
 
                 if momentum != 0:
                     buf = state.get("momentum_buffer", None)