Merge pull request #76 from tomaarsen/cleanup

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

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 groups)),
+                chain.from_iterable(g["params"] for g in saved_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!")
-        super(RMSprop, self).__init__(
+            raise NotImplementedError("Centered RMSprop is not supported!")
+        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!")
-        super(RMSprop8bit, self).__init__(
+            raise NotImplementedError("Centered RMSprop is not supported!")
+        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!")
-        super(RMSprop32bit, self).__init__(
+            raise NotImplementedError("Centered RMSprop is not supported!")
+        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!")
-        super(SGD, self).__init__(
+            raise NotImplementedError("SGD without momentum is not supported!")
+        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!")
-        super(SGD8bit, self).__init__(
+            raise NotImplementedError("SGD without momentum is not supported!")
+        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!")
-        super(SGD32bit, self).__init__(
+            raise NotImplementedError("SGD without momentum is not supported!")
+        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

-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

 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
 ]