Simplify statements into equivalent, modern variants

via pyupgrade --py37-plus. The changes e.g. are subclassing from object, calling super() with super(ThisClass, self), or old-style syntax formatting.

bitsandbytes/autograd/_functions.py

@@ -18,7 +18,7 @@ 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):

bitsandbytes/cextension.py

@@ -5,7 +5,7 @@ from pathlib import Path
 from warnings import warn
 
 
-class CUDASetup(object):
+class CUDASetup:
     _instance = None
 
     def __init__(self):

bitsandbytes/cuda_setup/main.py

@@ -127,7 +127,7 @@ def evaluate_cuda_setup():
         cuda_setup.add_log_entry("WARNING: No libcudart.so found! Install CUDA or the cudatoolkit package (anaconda)!", is_warning=True)
         return binary_name
 
-    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}")
     cuda = get_cuda_lib_handle()
     cc = get_compute_capability(cuda)
     cuda_setup.add_log_entry(f"CUDA SETUP: Highest compute capability among GPUs detected: {cc}")

bitsandbytes/functional.py

@@ -82,7 +82,7 @@ if COMPILED_WITH_CUDA:
     )
 
 
-class CUBLAS_Context(object):
+class CUBLAS_Context:
     _instance = None
 
     def __init__(self):
@@ -112,7 +112,7 @@ class CUBLAS_Context(object):
         return self.context[device.index]
 
 
-class Cusparse_Context(object):
+class Cusparse_Context:
     _instance = None
 
     def __init__(self):
@@ -1417,7 +1417,7 @@ 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
@@ -1434,7 +1434,7 @@ 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
@@ -1451,7 +1451,7 @@ 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

bitsandbytes/nn/modules.py

@@ -39,7 +39,7 @@ 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 +96,7 @@ 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 +225,7 @@ 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/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

@@ -27,7 +27,7 @@ class LARS(Optimizer1State):
             raise NotImplementedError(
                 f"LARS without momentum is not supported!"
             )
-        super(LARS, self).__init__(
+        super().__init__(
             "lars",
             params,
             lr,
@@ -61,7 +61,7 @@ class LARS8bit(Optimizer1State):
             raise NotImplementedError(
                 f"LARS without momentum is not supported!"
             )
-        super(LARS8bit, self).__init__(
+        super().__init__(
             "lars",
             params,
             lr,
@@ -95,7 +95,7 @@ class LARS32bit(Optimizer1State):
             raise NotImplementedError(
                 f"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)
 

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):
@@ -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 groups)),
+                chain.from_iterable(g["params"] for g in saved_groups),
+                chain.from_iterable(g["params"] for g in groups),
             )
         }
 
@@ -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

@@ -27,7 +27,7 @@ class RMSprop(Optimizer1State):
             )
         if centered:
             raise NotImplementedError(f"Centered RMSprop is not supported!")
-        super(RMSprop, self).__init__(
+        super().__init__(
             "rmsprop",
             params,
             lr,
@@ -63,7 +63,7 @@ class RMSprop8bit(Optimizer1State):
             )
         if centered:
             raise NotImplementedError(f"Centered RMSprop is not supported!")
-        super(RMSprop8bit, self).__init__(
+        super().__init__(
             "rmsprop",
             params,
             lr,
@@ -100,7 +100,7 @@ class RMSprop32bit(Optimizer1State):
             )
         if centered:
             raise NotImplementedError(f"Centered RMSprop is not supported!")
-        super(RMSprop32bit, self).__init__(
+        super().__init__(
             "rmsprop",
             params,
             lr,

bitsandbytes/optim/sgd.py

@@ -22,7 +22,7 @@ class SGD(Optimizer1State):
     ):
         if momentum == 0:
             raise NotImplementedError(f"SGD without momentum is not supported!")
-        super(SGD, self).__init__(
+        super().__init__(
             "momentum",
             params,
             lr,
@@ -53,7 +53,7 @@ class SGD8bit(Optimizer1State):
     ):
         if momentum == 0:
             raise NotImplementedError(f"SGD without momentum is not supported!")
-        super(SGD8bit, self).__init__(
+        super().__init__(
             "momentum",
             params,
             lr,
@@ -84,7 +84,7 @@ class SGD32bit(Optimizer1State):
     ):
         if momentum == 0:
             raise NotImplementedError(f"SGD without momentum is not supported!")
-        super(SGD32bit, self).__init__(
+        super().__init__(
             "momentum",
             params,
             lr,

tests/test_autograd.py

@@ -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_functional.py

@@ -26,7 +26,7 @@ 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)
 
@@ -40,7 +40,7 @@ class FFN(torch.nn.Module):
         return x
 
 
-class Timer(object):
+class Timer:
     def __init__(self):
         self.starts = {}
         self.ends = {}
@@ -67,7 +67,7 @@ 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]
 
@@ -289,7 +289,7 @@ 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
 ]
 
@@ -347,7 +347,7 @@ 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
 ]
 
@@ -412,7 +412,7 @@ 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
 ]
 
 
@@ -444,7 +444,7 @@ 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
 ]
 
@@ -529,7 +529,7 @@ 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
 ]
 
@@ -567,7 +567,7 @@ 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)
@@ -596,7 +596,7 @@ 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)
@@ -678,7 +678,7 @@ 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
 ]
 
@@ -726,7 +726,7 @@ 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
 ]
 
@@ -784,7 +784,7 @@ 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
 ]
 
 
@@ -952,7 +952,7 @@ 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)
@@ -1002,7 +1002,7 @@ 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)
@@ -1058,7 +1058,7 @@ 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)
@@ -1105,7 +1105,7 @@ 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)
@@ -1149,7 +1149,7 @@ 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)
@@ -1224,7 +1224,7 @@ 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)
@@ -1290,7 +1290,7 @@ 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
@@ -1341,7 +1341,7 @@ 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
 ]
 
@@ -1367,7 +1367,7 @@ 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)
@@ -1404,7 +1404,7 @@ 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)
@@ -1485,7 +1485,7 @@ 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)
@@ -1550,7 +1550,7 @@ 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
 ]
 
 
@@ -1678,7 +1678,7 @@ 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)
@@ -1794,7 +1794,7 @@ 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
 ]