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

@@ -1,6 +1,3 @@
-# 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

@@ -1,12 +1,13 @@
 import operator
 import warnings
-
-import torch
-import bitsandbytes.functional as F
-
 from dataclasses import dataclass
 from functools import reduce  # Required in Python 3
 
+import torch
+
+import bitsandbytes.functional as F
+
+
 # math.prod not compatible with python < 3.8
 def prod(iterable):
     return reduce(operator.mul, iterable, 1)
@@ -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

@@ -1,11 +1,11 @@
 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):

bitsandbytes/cuda_setup/__init__.py

@@ -1,2 +1,6 @@
-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

@@ -1,6 +1,7 @@
 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

@@ -1,26 +0,0 @@
-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 (
+from typing import Optional, TypeVar, Union, overload
-    Any,
-    Callable,
-    Dict,
-    Iterator,
-    Mapping,
-    Optional,
-    Set,
-    Tuple,
-    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 .lars import LARS, LARS8bit, LARS32bit, PytorchLARS
-from .adagrad import Adagrad, Adagrad8bit, Adagrad32bit
-
 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)

bitsandbytes/optim/optimizer.py

@@ -12,13 +12,13 @@ import torch
 import bitsandbytes.functional as F
 
 
-class MockArgs(object):
+class MockArgs:
     def __init__(self, initial_data):
         for key in initial_data:
             setattr(self, key, initial_data[key])
 
 
-class GlobalOptimManager(object):
+class GlobalOptimManager:
     _instance = None
 
     def __init__(self):
@@ -56,9 +56,9 @@ class GlobalOptimManager(object):
         """
         Overrides initial optimizer config for specific parameters.
 
-        The key-values of the optimizer config for the input parameters are overidden
+        The key-values of the optimizer config for the input parameters are overridden
         This can be both, optimizer parameters like "betas", or "lr" or it can be
-        8-bit specific paramters like "optim_bits", "percentile_clipping".
+        8-bit specific parameters like "optim_bits", "percentile_clipping".
 
         Parameters
         ----------
@@ -93,13 +93,12 @@ class GlobalOptimManager(object):
 
 class Optimizer8bit(torch.optim.Optimizer):
     def __init__(self, params, defaults, optim_bits=32):
-        super(Optimizer8bit, self).__init__(params, defaults)
+        super().__init__(params, defaults)
         self.initialized = False
         self.name2qmap = {}
 
         self.mng = GlobalOptimManager.get_instance()
-        self.non_castable_tensor_keys = set(
+        self.non_castable_tensor_keys = {
-            [
                 "qmap1",
                 "qmap2",
                 "max1",
@@ -112,8 +111,7 @@ class Optimizer8bit(torch.optim.Optimizer):
                 "absmax1",
                 "absmax2",
                 "unorm_vec",
-            ]
+        }
-        )
 
         if optim_bits == 8:
             self.fill_qmap()
@@ -123,7 +121,7 @@ class Optimizer8bit(torch.optim.Optimizer):
         self.name2qmap["udynamic"] = F.create_dynamic_map(signed=False)
 
     def __setstate__(self, state):
-        super(Optimizer8bit, self).__setstate__(state)
+        super().__setstate__(state)
 
     def load_state_dict(self, state_dict):
         r"""Loads the optimizer state.
@@ -155,8 +153,8 @@ class Optimizer8bit(torch.optim.Optimizer):
         id_map = {
             old_id: p
             for old_id, p in zip(
-                chain.from_iterable((g["params"] for g in saved_groups)),
+                chain.from_iterable(g["params"] for g in saved_groups),
-                chain.from_iterable((g["params"] for g in groups)),
+                chain.from_iterable(g["params"] for g in groups),
             )
         }
 
@@ -284,11 +282,11 @@ class Optimizer8bit(torch.optim.Optimizer):
         return config
 
     def init_state(self, group, p, gindex, pindex):
-        raise NotImplementedError(f"init_state method needs to be overidden")
+        raise NotImplementedError("init_state method needs to be overridden")
 
     def update_step(self, group, p, gindex, pindex):
         raise NotImplementedError(
-            f"The update_step method needs to be overidden"
+            "The update_step method needs to be overridden"
         )
 
 
@@ -310,9 +308,9 @@ class Optimizer2State(Optimizer8bit):
         skip_zeros=False,
     ):
         if not 0.0 <= lr:
-            raise ValueError("Invalid learning rate: {}".format(lr))
+            raise ValueError(f"Invalid learning rate: {lr}")
         if not 0.0 <= eps:
-            raise ValueError("Invalid epsilon value: {}".format(eps))
+            raise ValueError(f"Invalid epsilon value: {eps}")
         if isinstance(betas, str):
             # format: '(beta1, beta2)'
             betas = betas.replace("(", "").replace(")", "").strip().split(",")
@@ -324,10 +322,10 @@ class Optimizer2State(Optimizer8bit):
                 )
         if not 0.0 <= weight_decay:
             raise ValueError(
-                "Invalid weight_decay value: {}".format(weight_decay)
+                f"Invalid weight_decay value: {weight_decay}"
             )
         defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
-        super(Optimizer2State, self).__init__(params, defaults, optim_bits)
+        super().__init__(params, defaults, optim_bits)
 
         if args is None:
             args = {}
@@ -542,9 +540,9 @@ class Optimizer1State(Optimizer8bit):
         skip_zeros=False,
     ):
         if not 0.0 <= lr:
-            raise ValueError("Invalid learning rate: {}".format(lr))
+            raise ValueError(f"Invalid learning rate: {lr}")
         if not 0.0 <= eps:
-            raise ValueError("Invalid epsilon value: {}".format(eps))
+            raise ValueError(f"Invalid epsilon value: {eps}")
         for i in range(len(betas)):
             if not 0.0 <= betas[i] < 1.0:
                 raise ValueError(
@@ -552,10 +550,10 @@ class Optimizer1State(Optimizer8bit):
                 )
         if not 0.0 <= weight_decay:
             raise ValueError(
-                "Invalid weight_decay value: {}".format(weight_decay)
+                f"Invalid weight_decay value: {weight_decay}"
             )
         defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
-        super(Optimizer1State, self).__init__(params, defaults, optim_bits)
+        super().__init__(params, defaults, optim_bits)
 
         if args is None:
             args = {}

bitsandbytes/optim/rmsprop.py

@@ -23,11 +23,11 @@ class RMSprop(Optimizer1State):
     ):
         if alpha == 0:
             raise NotImplementedError(
-                f"RMSprop with alpha==0.0 is not supported!"
+                "RMSprop with alpha==0.0 is not supported!"
             )
         if centered:
-            raise NotImplementedError(f"Centered RMSprop is not supported!")
+            raise NotImplementedError("Centered RMSprop is not supported!")
-        super(RMSprop, self).__init__(
+        super().__init__(
             "rmsprop",
             params,
             lr,
@@ -59,11 +59,11 @@ class RMSprop8bit(Optimizer1State):
     ):
         if alpha == 0:
             raise NotImplementedError(
-                f"RMSprop with alpha==0.0 is not supported!"
+                "RMSprop with alpha==0.0 is not supported!"
             )
         if centered:
-            raise NotImplementedError(f"Centered RMSprop is not supported!")
+            raise NotImplementedError("Centered RMSprop is not supported!")
-        super(RMSprop8bit, self).__init__(
+        super().__init__(
             "rmsprop",
             params,
             lr,
@@ -96,11 +96,11 @@ class RMSprop32bit(Optimizer1State):
 
         if alpha == 0:
             raise NotImplementedError(
-                f"RMSprop with alpha==0.0 is not supported!"
+                "RMSprop with alpha==0.0 is not supported!"
             )
         if centered:
-            raise NotImplementedError(f"Centered RMSprop is not supported!")
+            raise NotImplementedError("Centered RMSprop is not supported!")
-        super(RMSprop32bit, self).__init__(
+        super().__init__(
             "rmsprop",
             params,
             lr,

bitsandbytes/optim/sgd.py

@@ -21,8 +21,8 @@ class SGD(Optimizer1State):
         block_wise=True,
     ):
         if momentum == 0:
-            raise NotImplementedError(f"SGD without momentum is not supported!")
+            raise NotImplementedError("SGD without momentum is not supported!")
-        super(SGD, self).__init__(
+        super().__init__(
             "momentum",
             params,
             lr,
@@ -52,8 +52,8 @@ class SGD8bit(Optimizer1State):
         block_wise=True,
     ):
         if momentum == 0:
-            raise NotImplementedError(f"SGD without momentum is not supported!")
+            raise NotImplementedError("SGD without momentum is not supported!")
-        super(SGD8bit, self).__init__(
+        super().__init__(
             "momentum",
             params,
             lr,
@@ -83,8 +83,8 @@ class SGD32bit(Optimizer1State):
         block_wise=True,
     ):
         if momentum == 0:
-            raise NotImplementedError(f"SGD without momentum is not supported!")
+            raise NotImplementedError("SGD without momentum is not supported!")
-        super(SGD32bit, self).__init__(
+        super().__init__(
             "momentum",
             params,
             lr,

csrc/kernels.cuh

@@ -121,5 +121,3 @@ template <int THREADS, int ITEMS_PER_THREAD, int TILE_ROWS, int TILE_COLS, int T
 template <int FORMAT> __global__ void kExtractOutliers(char *A, int *idx, char *out, int idx_size, int rowsA, int colsA, int tiledRowsA, int tiledColsA);
 
 #endif
-
-

csrc/pythonInterface.c

@@ -290,4 +290,3 @@ extern "C"
 	void cquantize_blockwise_cpu_fp32(float *code, float *A, float *absmax, unsigned char *out, long long blocksize, long long n){ quantize_cpu(code, A, absmax, out, blocksize, n); }
 	void cdequantize_blockwise_cpu_fp32(float *code, unsigned char *A, float *absmax, float *out, long long blocksize, long long n){ dequantize_cpu(code, A, absmax, out, blocksize, n); }
 }
-

cuda_install.sh

@@ -76,6 +76,3 @@ if [[ -n "$CUDA_VERSION" ]]; then
 else
   echo ""
 fi
-
-
-

setup.py

@@ -26,9 +26,6 @@ setup(
     keywords="gpu optimizers optimization 8-bit quantization compression",
     url="https://github.com/TimDettmers/bitsandbytes",
     packages=find_packages(),
-    entry_points={
-        "console_scripts": ["debug_cuda = bitsandbytes.debug_cli:cli"],
-    },
     package_data={"": libs},
     long_description=read("README.md"),
     long_description_content_type="text/markdown",

tests/test_autograd.py

@@ -1,4 +1,4 @@
-from itertools import product, permutations
+from itertools import permutations, product
 
 import pytest
 import torch
@@ -27,7 +27,7 @@ str_values = list(
     )
 )
 names = [
-    "dim1_{0}_dim2_{1}_dim3_{2}_dim4_{3}_func_{4}_dtype_{5}_requires_grad_{6}_transpose_{7}".format(
+    "dim1_{}_dim2_{}_dim3_{}_dim4_{}_func_{}_dtype_{}_requires_grad_{}_transpose_{}".format(
         *vals
     )
     for vals in str_values
@@ -286,7 +286,7 @@ str_values = list(
         has_bias
     )
 )
-names = ["dim1_{0}_dim2_{1}_dim3_{2}_dim4_{3}_func_{4}_dtype_{5}_requires_grad_{6}_transpose_{7}_decomp_{8}_has_fp16_weights_{9}_has_bias_{10}".format(*vals) for vals in str_values]
+names = ["dim1_{}_dim2_{}_dim3_{}_dim4_{}_func_{}_dtype_{}_requires_grad_{}_transpose_{}_decomp_{}_has_fp16_weights_{}_has_bias_{}".format(*vals) for vals in str_values]
 
 
 @pytest.mark.parametrize(

tests/test_cuda_setup_evaluator.py

@@ -1,13 +1,13 @@
 import os
-import pytest
-import bitsandbytes as bnb
-
 from typing import List, NamedTuple
 
+import pytest
+
+import bitsandbytes as bnb
 from bitsandbytes.cuda_setup import (
     CUDA_RUNTIME_LIB,
-    evaluate_cuda_setup,
     determine_cuda_runtime_lib_path,
+    evaluate_cuda_setup,
     extract_candidate_paths,
 )
 

tests/test_functional.py

@@ -28,7 +28,7 @@ def assert_all_approx_close(a, b, rtol=1e-3, atol=1e-3, count=0):
 
 class FFN(torch.nn.Module):
     def __init__(self, input_features, hidden_size, bias=True):
-        super(FFN, self).__init__()
+        super().__init__()
         self.fc1 = torch.nn.Linear(input_features, hidden_size, bias=bias)
         self.fc2 = torch.nn.Linear(hidden_size, input_features, bias=bias)
 
@@ -42,7 +42,7 @@ class FFN(torch.nn.Module):
         return x
 
 
-class Timer(object):
+class Timer:
     def __init__(self):
         self.starts = {}
         self.ends = {}
@@ -69,7 +69,7 @@ class Timer(object):
                 self.ends.pop(name)
 
         if print_ms and name in self.agg:
-            print("{0} took: {1:.5f}s".format(name, self.agg[name] / 1000.0))
+            print(f"{name} took: {self.agg[name] / 1000.0:.5f}s")
 
         return self.agg[name]
 
@@ -302,7 +302,7 @@ batched = [False, True]
 values = list(product(dim1, dim2, methods, batched))
 values_names = list(product(dim1, dim2, method_names, batched))
 names = [
-    "dim1_{0}_dim2_{1}_quant_{2}_batched_{3}".format(*vals)
+    "dim1_{}_dim2_{}_quant_{}_batched_{}".format(*vals)
     for vals in values_names
 ]
 
@@ -360,7 +360,7 @@ seq_dim = torch.randint(16, 256, size=(n,)).tolist()
 transpose = [(False, False), (False, True), (True, False), (True, True)]
 values = list(product(hidden_dim, batch_dim, transpose, seq_dim))
 names = [
-    "hidden_dim_{0}_batch_dim_{1},transpose_{2}_seq_dim_{3}".format(*vals)
+    "hidden_dim_{}_batch_dim_{},transpose_{}_seq_dim_{}".format(*vals)
     for vals in values
 ]
 
@@ -425,7 +425,7 @@ hidden_dim = torch.randint(32, 1024 * 4, size=(n,)).tolist()
 batch_dim = torch.randint(2, 16, size=(n,)).tolist()
 values = list(product(seq_dim, hidden_dim, batch_dim))
 names = [
-    "seq_dim{0}_hidden_dim{1}_batch_dim{2}".format(*vals) for vals in values
+    "seq_dim{}_hidden_dim{}_batch_dim{}".format(*vals) for vals in values
 ]
 
 
@@ -457,7 +457,7 @@ batch_dim = torch.randint(2, 16, size=(n,)).tolist()
 transpose = [False, True]
 values = list(product(seq_dim, hidden_dim, batch_dim, transpose))
 names = [
-    "seq_dim={0}_hidden_dim={1}_batch_dim={2}_transpose{3}".format(*vals)
+    "seq_dim={}_hidden_dim={}_batch_dim={}_transpose{}".format(*vals)
     for vals in values
 ]
 
@@ -542,7 +542,7 @@ dim4 = torch.randint(32, 256, size=(n,)).tolist()
 transpose = [(False, False), (True, False), (False, True), (True, True)]
 values = list(product(dim1, dim2, dim3, dim4, transpose))
 names = [
-    "dim1_{0}_dim2_{1}_dim3_{2}_dim4_{3}_transpose_{4}".format(*vals)
+    "dim1_{}_dim2_{}_dim3_{}_dim4_{}_transpose_{}".format(*vals)
     for vals in values
 ]
 
@@ -580,7 +580,7 @@ dim1 = torch.randint(1, 64, size=(n,)).tolist()
 dim2 = torch.randint(32, 128, size=(n,)).tolist()
 dim3 = torch.randint(32, 256, size=(n,)).tolist()
 values = list(product(dim1, dim2, dim3))
-names = ["dim1_{0}_dim2_{1}_dim3_{2}".format(*vals) for vals in values]
+names = ["dim1_{}_dim2_{}_dim3_{}".format(*vals) for vals in values]
 
 
 @pytest.mark.parametrize("dim1, dim2, dim3", values, ids=names)
@@ -609,7 +609,7 @@ transpose = [False]
 dims = [2, 3]
 values = list(product(dim1, dim2, dim3, dims, dtype, a_order, out_order, transpose))
 
-names = ["dim1_{0}_dim2_{1}_dim3_{2}_dims_{3}_dtype_{4}_orderA_{5}_orderOut_{6}_transpose_{7}".format(*vals)for vals in values]
+names = ["dim1_{}_dim2_{}_dim3_{}_dims_{}_dtype_{}_orderA_{}_orderOut_{}_transpose_{}".format(*vals)for vals in values]
 
 
 @pytest.mark.parametrize("dim1, dim2, dim3, dims, dtype, orderA, orderOut, transpose",values,ids=names)
@@ -691,7 +691,7 @@ ldb = [0]
 # ldb = list(range(256, 1*1024, 256))
 values = list(product(dim1, dim2, dim3, dim4, dims, ldb))
 names = [
-    "dim1_{0}_dim2_{1}_dim3_{2}_dim4_{3}_dims_{4}_ldb_{5}".format(*vals)
+    "dim1_{}_dim2_{}_dim3_{}_dim4_{}_dims_{}_ldb_{}".format(*vals)
     for vals in values
 ]
 
@@ -739,7 +739,7 @@ dims = (2,)
 # ldb = list(range(256, 1*1024, 256))
 values = list(product(dim1, dim2, dim3, dim4, dims))
 names = [
-    "dim1_{0}_dim2_{1}_dim3_{2}_dim4_{3}_dims_{4}".format(*vals)
+    "dim1_{}_dim2_{}_dim3_{}_dim4_{}_dims_{}".format(*vals)
     for vals in values
 ]
 
@@ -797,7 +797,7 @@ values = [
 
 # values = list(product(batch, seq, model, hidden))
 names = [
-    "batch_{0}_seq_{1}_model_{2}_hidden_{3}".format(*vals) for vals in values
+    "batch_{}_seq_{}_model_{}_hidden_{}".format(*vals) for vals in values
 ]
 
 
@@ -965,7 +965,7 @@ dims = (2,)
 formatB = ["col_turing", "col_ampere"]
 has_bias = [True, False]
 values = list(product(dim1, dim4, dims, formatB, has_bias))
-names = ["dim1_{0}_dim4_{1}_dims_{2}_formatB_{3}_has_bias_{4}".format(*vals) for vals in values]
+names = ["dim1_{}_dim4_{}_dims_{}_formatB_{}_has_bias_{}".format(*vals) for vals in values]
 
 
 @pytest.mark.parametrize("dim1, dim4, dims, formatB, has_bias", values, ids=names)
@@ -1015,7 +1015,7 @@ dim2 = [1 * 1024]
 dims = (2,)
 # ldb = list(range(256, 1*1024, 256))
 values = list(product(dim1, dim2, dims))
-names = ["dim1_{0}_dim2_{1}_dims_{2}".format(*vals) for vals in values]
+names = ["dim1_{}_dim2_{}_dims_{}".format(*vals) for vals in values]
 
 
 @pytest.mark.parametrize("dim1, dim2, dims", values, ids=names)
@@ -1071,7 +1071,7 @@ dim1 = torch.randint(1, 4 * 1024, size=(n,)).tolist()
 dim2 = torch.randint(1, 4 * 1024, size=(n,)).tolist()
 
 values = list(product(dim1, dim2))
-names = ["dim1_{0}_dim2_{1}".format(*vals) for vals in values]
+names = ["dim1_{}_dim2_{}".format(*vals) for vals in values]
 
 
 @pytest.mark.parametrize("dim1, dim2", values, ids=names)
@@ -1118,7 +1118,7 @@ dim4 = torch.randint(1, 4 * 1024, size=(n,)).tolist()
 inner = torch.randint(1, 4 * 1024, size=(n,)).tolist()
 
 values = list(zip(dim1, dim4, inner))
-names = ["dim1_{0}_dim4_{1}_inner_{2}".format(*vals) for vals in values]
+names = ["dim1_{}_dim4_{}_inner_{}".format(*vals) for vals in values]
 
 
 @pytest.mark.parametrize("dim1, dim4, inner", values, ids=names)
@@ -1162,7 +1162,7 @@ dim4 = torch.randint(1, 4 * 1024, size=(n,)).tolist()
 inner = torch.randint(1, 4 * 1024, size=(n,)).tolist()
 
 values = list(zip(dim1, dim4, inner))
-names = ["dim1_{0}_dim4_{1}_inner_{2}".format(*vals) for vals in values]
+names = ["dim1_{}_dim4_{}_inner_{}".format(*vals) for vals in values]
 
 
 @pytest.mark.parametrize("dim1, dim4, inner", values, ids=names)
@@ -1237,7 +1237,7 @@ inner = [12288 * 4, 4096 * 4]
 dim4 = [12288, 4096]
 
 values = list(zip(dim1, dim4, inner))
-names = ["dim1_{0}_dim4_{1}_inner_{2}".format(*vals) for vals in values]
+names = ["dim1_{}_dim4_{}_inner_{}".format(*vals) for vals in values]
 
 
 @pytest.mark.parametrize("dim1, dim4, inner", values, ids=names)
@@ -1303,7 +1303,7 @@ values = list(
     product(dim1, dim2, dim3, dims, dtype, a_order, out_order, transpose)
 )
 names = [
-    "dim1_{0}_dim2_{1}_dim3_{2}_dims_{3}_dtype_{4}_orderA_{5}_orderOut_{6}_{7}".format(
+    "dim1_{}_dim2_{}_dim3_{}_dims_{}_dtype_{}_orderA_{}_orderOut_{}_{}".format(
         *vals
     )
     for vals in values
@@ -1354,7 +1354,7 @@ a_order = ["col_turing"]
 out_order = ["row"]
 values = list(product(dim1, dim2, dtype, a_order, out_order))
 names = [
-    "dim1_{0}_dim2_{1}_dtype_{2}_orderA_{3}_orderOut_{4}".format(*vals)
+    "dim1_{}_dim2_{}_dtype_{}_orderA_{}_orderOut_{}".format(*vals)
     for vals in values
 ]
 
@@ -1380,7 +1380,7 @@ dim2 = torch.randint(1, 4 * 1024, size=(n,)).tolist()
 # dim2 = [5]
 
 values = list(product(dim1, dim2))
-names = ["dim1_{0}_dim2_{1}".format(*vals) for vals in values]
+names = ["dim1_{}_dim2_{}".format(*vals) for vals in values]
 
 
 @pytest.mark.parametrize("dim1, dim2", values, ids=names)
@@ -1417,7 +1417,7 @@ dim2 = torch.randint(1, 1 * 1024, size=(n,)).tolist()
 # dim2 = [11]
 transposed_B = [False, True]
 values = list(product(dim1, dim2, transposed_B))
-names = ["dim1_{0}_dim2_{1}_transposed_B_{2}".format(*vals) for vals in values]
+names = ["dim1_{}_dim2_{}_transposed_B_{}".format(*vals) for vals in values]
 
 
 @pytest.mark.parametrize("dim1, dim2, transposed_B", values, ids=names)
@@ -1498,7 +1498,7 @@ n = 2
 dim1 = torch.randint(256, 1 * 1024, size=(n,)).tolist()
 dim2 = torch.randint(256, 1 * 1024, size=(n,)).tolist()
 values = list(product(dim1, dim2))
-names = ["dim1_{0}_dim2_{1}".format(*vals) for vals in values]
+names = ["dim1_{}_dim2_{}".format(*vals) for vals in values]
 
 
 @pytest.mark.parametrize("dim1, dim2", values, ids=names)
@@ -1563,7 +1563,7 @@ dtype = [torch.float16]
 out_function = ["zeros", "ones"]
 values = list(product(dim1, dim2, dtype, out_function))
 names = [
-    "dim1_{0}_dim2_{1}_dtype_{2}_out_func_{3}".format(*vals) for vals in values
+    "dim1_{}_dim2_{}_dtype_{}_out_func_{}".format(*vals) for vals in values
 ]
 
 
@@ -1680,7 +1680,7 @@ dim2 = [2048]
 # dim2 = [2]
 dtype = [torch.int8]
 values = list(product(dim1, dim2, dtype))
-names = ["dim1_{0}_dim2_{1}_dtype_{2}".format(*vals) for vals in values]
+names = ["dim1_{}_dim2_{}_dtype_{}".format(*vals) for vals in values]
 
 
 @pytest.mark.parametrize("dim1, dim2, dtype", values, ids=names)
@@ -1796,7 +1796,7 @@ values.append((batch_size, seqdim, 768, 4 * 768))
 # values.append((batch_size, seqdim, 5140, 4*5140))
 #values.append((batch_size, seqdim, 12288, 4*12288))
 names = [
-    "batch_{0}_seq_{1}_model_{2}_hidden_{3}".format(*vals) for vals in values
+    "batch_{}_seq_{}_model_{}_hidden_{}".format(*vals) for vals in values
 ]
 
 

tests/test_modules.py

@@ -7,7 +7,7 @@ from torch import nn
 import bitsandbytes as bnb
 
 
-class MockArgs(object):
+class MockArgs:
     def __init__(self, initial_data):
         for key in initial_data:
             setattr(self, key, initial_data[key])
@@ -15,7 +15,7 @@ class MockArgs(object):
 
 class MLP8bit(torch.nn.Module):
     def __init__(self, dim1, dim2, has_fp16_weights=True, memory_efficient_backward=False, threshold=0.0):
-        super(MLP8bit, self).__init__()
+        super().__init__()
         self.fc1 = bnb.nn.Linear8bitLt(
             dim1, dim2, has_fp16_weights=has_fp16_weights, memory_efficient_backward=memory_efficient_backward,
             threshold=threshold
@@ -289,7 +289,7 @@ class LinearFunction(torch.autograd.Function):
 
 class Linear8bit(nn.Module):
     def __init__(self, input_features, output_features, bias=True, args=None):
-        super(Linear8bit, self).__init__()
+        super().__init__()
         self.input_features = input_features
         self.output_features = output_features
         self.args = args
@@ -312,7 +312,7 @@ class Linear8bit(nn.Module):
 
 threshold = [0.0, 3.0]
 values = threshold
-names = ["threshold_{0}".format(vals) for vals in values]
+names = [f"threshold_{vals}" for vals in values]
 
 
 @pytest.mark.parametrize("threshold", values, ids=names)
@@ -378,7 +378,7 @@ def test_linear8bitlt_accumulated_gradient():
 
 threshold = [0.0, 2.0]
 values = threshold
-names = ["threshold_{0}".format(vals) for vals in values]
+names = [f"threshold_{vals}" for vals in values]
 
 
 @pytest.mark.parametrize("threshold", values, ids=names)

tests/test_optim.py

@@ -18,7 +18,7 @@ k = 20
 
 
 def get_temp_dir():
-    path = "/tmp/autoswap/{0}".format(str(uuid.uuid4()))
+    path = f"/tmp/autoswap/{str(uuid.uuid4())}"
     os.makedirs(path, exist_ok=True)
     return path
 
@@ -116,7 +116,7 @@ gtype = [torch.float32, torch.float16]
 optimizer_names = ["adam", "momentum", "rmsprop", "lars"]
 values = list(product(dim1, dim2, gtype, optimizer_names))
 names = [
-    "dim1_{0}_dim2_{1}_gtype_{2}_optim_{3}".format(*vals) for vals in values
+    "dim1_{}_dim2_{}_gtype_{}_optim_{}".format(*vals) for vals in values
 ]
 
 
@@ -187,7 +187,7 @@ dim1 = [1024]
 dim2 = [32, 1024, 4097]
 gtype = [torch.float32, torch.float16]
 values = list(product(dim1, dim2, gtype))
-names = ["dim1_{0}_dim2_{1}_gtype_{2}".format(*vals) for vals in values]
+names = ["dim1_{}_dim2_{}_gtype_{}".format(*vals) for vals in values]
 
 
 @pytest.mark.parametrize("dim1, dim2, gtype", values, ids=names)
@@ -250,7 +250,7 @@ optimizer_names = [
 ]
 values = list(product(dim1, dim2, gtype, optimizer_names))
 names = [
-    "dim1_{0}_dim2_{1}_gtype_{2}_optim_{3}".format(*vals) for vals in values
+    "dim1_{}_dim2_{}_gtype_{}_optim_{}".format(*vals) for vals in values
 ]
 
 
@@ -391,7 +391,7 @@ gtype = [torch.float32]
 optim_bits = [32, 8]
 values = list(product(dim1, dim2, gtype, optim_bits))
 names = [
-    "dim1_{0}_dim2_{1}_gtype_{2}_optim_bits_{3}".format(*vals)
+    "dim1_{}_dim2_{}_gtype_{}_optim_bits_{}".format(*vals)
     for vals in values
 ]
 
@@ -495,7 +495,7 @@ gtype = [torch.float32, torch.float16]
 optimizer_names = ["adam8bit_blockwise"]
 values = list(product(dim1, dim2, gtype, optimizer_names))
 names = [
-    "dim1_{0}_dim2_{1}_gtype_{2}_optim_{3}".format(*vals) for vals in values
+    "dim1_{}_dim2_{}_gtype_{}_optim_{}".format(*vals) for vals in values
 ]
 
 

to_be_fixed__complaints_by_linter.log

@@ -1,149 +0,0 @@
-./setup.py:20:10: F541 f-string is missing placeholders
-./setup.py:21:13: F541 f-string is missing placeholders
-./quicktest.py:5:1: F401 'bitsandbytes as bnb' imported but unused
-./bitsandbytes/cuda_setup.py:42:56: F821 undefined name 'error_str'
-./bitsandbytes/cuda_setup.py:43:15: F541 f-string is missing placeholders
-./bitsandbytes/cuda_setup.py:67:5: F841 local variable 'context' is assigned to but never used
-./bitsandbytes/cuda_setup.py:68:5: F841 local variable 'error_str' is assigned to but never used
-./bitsandbytes/cuda_setup.py:76:9: F841 local variable 'result' is assigned to but never used
-./bitsandbytes/cuda_setup.py:144:13: F841 local variable 'has_gpu' is assigned to but never used
-./bitsandbytes/functional.py:294:13: F821 undefined name 'math'
-./bitsandbytes/functional.py:295:16: F821 undefined name 'math'
-./bitsandbytes/functional.py:303:5: F841 local variable 'ptrA' is assigned to but never used
-./bitsandbytes/functional.py:304:5: F841 local variable 'ptrOut' is assigned to but never used
-./bitsandbytes/functional.py:1057:17: W503 line break before binary operator
-./bitsandbytes/functional.py:1058:17: W503 line break before binary operator
-./bitsandbytes/functional.py:1059:17: W503 line break before binary operator
-./bitsandbytes/functional.py:1649:1: F811 redefinition of unused 'get_special_format_str' from line 160
-./bitsandbytes/functional.py:1687:5: F841 local variable 'ptrA' is assigned to but never used
-./bitsandbytes/functional.py:1688:5: F841 local variable 'ptrOut' is assigned to but never used
-./bitsandbytes/functional.py:1802:5: F841 local variable 'ccolsA' is assigned to but never used
-./bitsandbytes/functional.py:1805:5: F841 local variable 'cldb' is assigned to but never used
-./bitsandbytes/functional.py:1806:5: F841 local variable 'cldc' is assigned to but never used
-./bitsandbytes/functional.py:1873:9: F841 local variable 'dtype' is assigned to but never used
-./bitsandbytes/__init__.py:6:1: F401 '.autograd._functions.MatmulLtState' imported but unused
-./bitsandbytes/__init__.py:6:1: F401 '.autograd._functions.bmm_cublas' imported but unused
-./bitsandbytes/__init__.py:6:1: F401 '.autograd._functions.matmul' imported but unused
-./bitsandbytes/__init__.py:6:1: F401 '.autograd._functions.matmul_cublas' imported but unused
-./bitsandbytes/__init__.py:6:1: F401 '.autograd._functions.mm_cublas' imported but unused
-./bitsandbytes/__init__.py:9:1: F401 '.nn.modules' imported but unused
-./bitsandbytes/__init__.py:12:5: F401 '.optim.adam' imported but unused
-./bitsandbytes/autograd/_functions.py:5:1: F401 'bitsandbytes as bnb' imported but unused
-./bitsandbytes/autograd/_functions.py:12:75: W291 trailing whitespace
-./bitsandbytes/nn/__init__.py:5:1: F401 '.modules.Int8Params' imported but unused
-./bitsandbytes/nn/__init__.py:5:1: F401 '.modules.Linear8bit' imported but unused
-./bitsandbytes/nn/__init__.py:5:1: F401 '.modules.Linear8bitLt' imported but unused
-./bitsandbytes/nn/__init__.py:5:1: F401 '.modules.StableEmbedding' imported but unused
-./bitsandbytes/nn/modules.py:5:1: F401 'typing.Any' imported but unused
-./bitsandbytes/nn/modules.py:5:1: F401 'typing.Callable' imported but unused
-./bitsandbytes/nn/modules.py:5:1: F401 'typing.Dict' imported but unused
-./bitsandbytes/nn/modules.py:5:1: F401 'typing.Iterator' imported but unused
-./bitsandbytes/nn/modules.py:5:1: F401 'typing.Mapping' imported but unused
-./bitsandbytes/nn/modules.py:5:1: F401 'typing.Set' imported but unused
-./bitsandbytes/nn/modules.py:5:1: F401 'typing.Tuple' imported but unused
-./bitsandbytes/nn/modules.py:11:1: F401 'torch.nn.parameter.Parameter' imported but unused
-./bitsandbytes/nn/modules.py:183:13: W503 line break before binary operator
-./bitsandbytes/nn/modules.py:184:13: W503 line break before binary operator
-./bitsandbytes/nn/modules.py:272:24: F821 undefined name 'dist'
-./bitsandbytes/nn/modules.py:272:49: F821 undefined name 'dist'
-./bitsandbytes/optim/optimizer.py:243:9: F841 local variable 'overflows' is assigned to but never used
-./bitsandbytes/optim/optimizer.py:280:35: F541 f-string is missing placeholders
-./bitsandbytes/optim/optimizer.py:283:35: F541 f-string is missing placeholders
-./bitsandbytes/optim/lars.py:27:39: F541 f-string is missing placeholders
-./bitsandbytes/optim/lars.py:59:39: F541 f-string is missing placeholders
-./bitsandbytes/optim/lars.py:91:39: F541 f-string is missing placeholders
-./bitsandbytes/optim/lars.py:157:13: F841 local variable 'params_with_grad' is assigned to but never used
-./bitsandbytes/optim/lars.py:158:13: F841 local variable 'd_p_list' is assigned to but never used
-./bitsandbytes/optim/lars.py:159:13: F841 local variable 'momentum_buffer_list' is assigned to but never used
-./bitsandbytes/optim/lars.py:174:35: F821 undefined name 'param'
-./bitsandbytes/optim/__init__.py:9:5: F401 '.adam.Adam' imported but unused
-./bitsandbytes/optim/__init__.py:9:5: F401 '.adam.Adam8bit' imported but unused
-./bitsandbytes/optim/__init__.py:9:5: F401 '.adam.Adam32bit' imported but unused
-./bitsandbytes/optim/__init__.py:10:5: F401 '.adamw.AdamW' imported but unused
-./bitsandbytes/optim/__init__.py:10:5: F401 '.adamw.AdamW8bit' imported but unused
-./bitsandbytes/optim/__init__.py:10:5: F401 '.adamw.AdamW32bit' imported but unused
-./bitsandbytes/optim/__init__.py:11:5: F401 '.sgd.SGD' imported but unused
-./bitsandbytes/optim/__init__.py:11:5: F401 '.sgd.SGD8bit' imported but unused
-./bitsandbytes/optim/__init__.py:11:5: F401 '.sgd.SGD32bit' imported but unused
-./bitsandbytes/optim/__init__.py:12:5: F401 '.lars.LARS' imported but unused
-./bitsandbytes/optim/__init__.py:12:5: F401 '.lars.LARS8bit' imported but unused
-./bitsandbytes/optim/__init__.py:12:5: F401 '.lars.LARS32bit' imported but unused
-./bitsandbytes/optim/__init__.py:12:5: F401 '.lars.PytorchLARS' imported but unused
-./bitsandbytes/optim/__init__.py:13:5: F401 '.lamb.LAMB' imported but unused
-./bitsandbytes/optim/__init__.py:13:5: F401 '.lamb.LAMB8bit' imported but unused
-./bitsandbytes/optim/__init__.py:13:5: F401 '.lamb.LAMB32bit' imported but unused
-./bitsandbytes/optim/__init__.py:14:5: F401 '.rmsprop.RMSprop' imported but unused
-./bitsandbytes/optim/__init__.py:14:5: F401 '.rmsprop.RMSprop8bit' imported but unused
-./bitsandbytes/optim/__init__.py:14:5: F401 '.rmsprop.RMSprop32bit' imported but unused
-./bitsandbytes/optim/__init__.py:15:5: F401 '.adagrad.Adagrad' imported but unused
-./bitsandbytes/optim/__init__.py:15:5: F401 '.adagrad.Adagrad8bit' imported but unused
-./bitsandbytes/optim/__init__.py:15:5: F401 '.adagrad.Adagrad32bit' imported but unused
-./bitsandbytes/optim/__init__.py:17:1: F401 '.optimizer.GlobalOptimManager' imported but unused
-./bitsandbytes/optim/adam.py:229:21: F841 local variable 'max_exp_avg_sq' is assigned to but never used
-./bitsandbytes/optim/rmsprop.py:25:39: F541 f-string is missing placeholders
-./bitsandbytes/optim/rmsprop.py:27:39: F541 f-string is missing placeholders
-./bitsandbytes/optim/rmsprop.py:59:39: F541 f-string is missing placeholders
-./bitsandbytes/optim/rmsprop.py:61:39: F541 f-string is missing placeholders
-./bitsandbytes/optim/rmsprop.py:94:39: F541 f-string is missing placeholders
-./bitsandbytes/optim/rmsprop.py:96:39: F541 f-string is missing placeholders
-./bitsandbytes/optim/sgd.py:24:39: F541 f-string is missing placeholders
-./bitsandbytes/optim/sgd.py:55:39: F541 f-string is missing placeholders
-./bitsandbytes/optim/sgd.py:86:39: F541 f-string is missing placeholders
-./tests/test_optim.py:1:1: F401 'ctypes' imported but unused
-./tests/test_optim.py:199:5: F841 local variable 'mask' is assigned to but never used
-./tests/test_optim.py:218:9: F841 local variable 'atol' is assigned to but never used
-./tests/test_optim.py:218:15: F841 local variable 'rtol' is assigned to but never used
-./tests/test_optim.py:304:17: W503 line break before binary operator
-./tests/test_optim.py:354:21: W503 line break before binary operator
-./tests/test_autograd.py:309:13: F841 local variable 'err' is assigned to but never used
-./tests/test_cuda_setup_evaluator.py:31:9: F821 undefined name 'test_dir'
-./tests/test_cuda_setup_evaluator.py:33:14: F821 undefined name 'test_input'
-./tests/test_cuda_setup_evaluator.py:81:32: E203 whitespace before ':'
-./tests/test_functional.py:55:13: F841 local variable 'ms' is assigned to but never used
-./tests/test_functional.py:177:5: F841 local variable 'diffs' is assigned to but never used
-./tests/test_functional.py:178:5: F841 local variable 'reldiffs' is assigned to but never used
-./tests/test_functional.py:260:5: F841 local variable 'minA' is assigned to but never used
-./tests/test_functional.py:261:5: F841 local variable 'maxA' is assigned to but never used
-./tests/test_functional.py:584:5: F841 local variable 'func' is assigned to but never used
-./tests/test_functional.py:617:17: F841 local variable 'offset' is assigned to but never used
-./tests/test_functional.py:618:17: F841 local variable 'col2' is assigned to but never used
-./tests/test_functional.py:619:17: F841 local variable 'row2' is assigned to but never used
-./tests/test_functional.py:705:9: F841 local variable 'C1' is assigned to but never used
-./tests/test_functional.py:706:9: F841 local variable 'C2' is assigned to but never used
-./tests/test_functional.py:715:9: F841 local variable 'output' is assigned to but never used
-./tests/test_functional.py:750:5: F841 local variable 'formatB' is assigned to but never used
-./tests/test_functional.py:754:5: F841 local variable 'w2' is assigned to but never used
-./tests/test_functional.py:763:5: F841 local variable 'dtype' is assigned to but never used
-./tests/test_functional.py:770:9: F841 local variable 'out1' is assigned to but never used
-./tests/test_functional.py:1108:5: F841 local variable 'relerr1' is assigned to but never used
-./tests/test_functional.py:1108:14: F841 local variable 'relerr2' is assigned to but never used
-./tests/test_functional.py:1114:9: F841 local variable 'C1' is assigned to but never used
-./tests/test_functional.py:1135:9: F841 local variable 'C4' is assigned to but never used
-./tests/test_functional.py:1179:5: F841 local variable 'err1' is assigned to but never used
-./tests/test_functional.py:1179:11: F841 local variable 'err2' is assigned to but never used
-./tests/test_functional.py:1179:17: F841 local variable 'err3' is assigned to but never used
-./tests/test_functional.py:1180:5: F841 local variable 'relerr1' is assigned to but never used
-./tests/test_functional.py:1180:14: F841 local variable 'relerr2' is assigned to but never used
-./tests/test_functional.py:1192:9: F841 local variable 'C1' is assigned to but never used
-./tests/test_functional.py:1313:9: F841 local variable 'c' is assigned to but never used
-./tests/test_functional.py:1314:9: F841 local variable 'c2' is assigned to but never used
-./tests/test_functional.py:1406:9: F841 local variable 'C1' is assigned to but never used
-./tests/test_functional.py:1425:9: F841 local variable 'out2' is assigned to but never used
-./tests/test_functional.py:1542:5: F841 local variable 'idx_col' is assigned to but never used
-./tests/test_functional.py:1566:30: E203 whitespace before ':'
-./tests/test_functional.py:1568:38: E203 whitespace before ':'
-./tests/test_functional.py:1655:5: F841 local variable 'offset' is assigned to but never used
-./tests/test_functional.py:1706:9: F841 local variable 'out' is assigned to but never used
-./tests/test_functional.py:1822:9: F841 local variable 'out' is assigned to but never used
-./tests/test_functional.py:1882:5: F841 local variable 'out2' is assigned to but never used
-./tests/test_functional.py:1928:9: F841 local variable 'dtype' is assigned to but never used
-./tests/test_functional.py:1934:9: F841 local variable 'minx' is assigned to but never used
-./tests/test_functional.py:1948:5: F841 local variable 'C0' is assigned to but never used
-./tests/test_modules.py:1:1: F401 'itertools.product' imported but unused
-./tests/test_modules.py:52:9: F841 local variable 'norm' is assigned to but never used
-./tests/test_modules.py:52:16: F821 undefined name 'math'
-./tests/test_modules.py:52:26: F821 undefined name 'math'
-./tests/test_modules.py:52:37: F821 undefined name 'math'
-./tests/test_modules.py:177:21: F821 undefined name 'einops'
-./tests/test_modules.py:233:9: F841 local variable 'stochastic' is assigned to but never used
-./tests/test_modules.py:382:9: F841 local variable 'o1' is assigned to but never used