Merge pull request #2 from TimDettmers/fix_imports

Remove unused imports, fix NotImplementedError

bitsandbytes/functional.py

@@ -2,13 +2,13 @@
 #   
 # 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 os
 import random
-import math
-import ctypes as ct
+from typing import Tuple
+
 import torch
 from torch import Tensor
-from typing import Tuple
 
 lib = ct.cdll.LoadLibrary(os.path.dirname(__file__) + '/libbitsandbytes.so')
 name2qmap = {}

bitsandbytes/nn/modules.py

@@ -7,7 +7,6 @@ import torch
 from typing import Optional
 
 from torch import Tensor
-from torch.nn.parameter import Parameter
 import torch.nn.functional as F
 
 from bitsandbytes.optim import GlobalOptimManager

bitsandbytes/optim/adagrad.py

@@ -2,11 +2,8 @@
 #   
 # This source code is licensed under the MIT license found in the 
 # LICENSE file in the root directory of this source tree.
-import torch
 from bitsandbytes.optim.optimizer import Optimizer1State
 
-torch.optim.Adagrad
-
 class Adagrad(Optimizer1State):
     def __init__(self, params, lr=1e-2, lr_decay=0, weight_decay=0, initial_accumulator_value=0, eps=1e-10,
             optim_bits=32, args=None, min_8bit_size=4096, percentile_clipping=100, block_wise=True):

bitsandbytes/optim/adamw.py

@@ -2,9 +2,7 @@
 #   
 # This source code is licensed under the MIT license found in the 
 # LICENSE file in the root directory of this source tree.
-import torch
 from bitsandbytes.optim.optimizer import Optimizer2State
-import bitsandbytes.functional as F
 
 class AdamW(Optimizer2State):
     def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,

bitsandbytes/optim/lars.py

@@ -12,7 +12,7 @@ class LARS(Optimizer1State):
                  weight_decay=0, nesterov=False, optim_bits=32, args=None,
                  min_8bit_size=4096, percentile_clipping=100, max_unorm=0.02):
         if momentum == 0:
-            raise NotImplementError(f'LARS without momentum is not supported!')
+            raise NotImplementedError(f'LARS without momentum is not supported!')
         super(LARS, self).__init__('lars', params, lr, (momentum, dampening), 0.0,
                 weight_decay, optim_bits, args, min_8bit_size, percentile_clipping, max_unorm=max_unorm, block_wise=False)
 
@@ -21,7 +21,7 @@ class LARS8bit(Optimizer1State):
                  weight_decay=0, nesterov=False, args=None,
                  min_8bit_size=4096, percentile_clipping=100, max_unorm=0.02):
         if momentum == 0:
-            raise NotImplementError(f'LARS without momentum is not supported!')
+            raise NotImplementedError(f'LARS without momentum is not supported!')
         super(LARS8bit, self).__init__('lars', params, lr, (momentum, dampening), 0.0,
                 weight_decay, 8, args, min_8bit_size, percentile_clipping, max_unorm=max_unorm, block_wise=False)
 
@@ -30,7 +30,7 @@ class LARS32bit(Optimizer1State):
                  weight_decay=0, nesterov=False, args=None,
                  min_8bit_size=4096, percentile_clipping=100, max_unorm=0.02):
         if momentum == 0:
-            raise NotImplementError(f'LARS without momentum is not supported!')
+            raise NotImplementedError(f'LARS without momentum is not supported!')
         super(LARS32bit, self).__init__('lars', params, lr, (momentum, dampening), 0.0,
                 weight_decay, 32, args, min_8bit_size, percentile_clipping, max_unorm=max_unorm, block_wise=False)
 

bitsandbytes/optim/rmsprop.py

@@ -2,16 +2,15 @@
 #   
 # This source code is licensed under the MIT license found in the 
 # LICENSE file in the root directory of this source tree.
-import torch
 from bitsandbytes.optim.optimizer import Optimizer1State
 
 class RMSprop(Optimizer1State):
     def __init__(self, params, lr=1e-2, alpha=0.99, eps=1e-8, weight_decay=0, momentum=0, centered=False, optim_bits=32, args=None,
             min_8bit_size=4096, percentile_clipping=100, block_wise=True):
         if alpha == 0:
-            raise NotImplementError(f'RMSprop with alpha==0.0 is not supported!')
+            raise NotImplementedError(f'RMSprop with alpha==0.0 is not supported!')
         if centered:
-            raise NotImplementError(f'Centered RMSprop is not supported!')
+            raise NotImplementedError(f'Centered RMSprop is not supported!')
         super(RMSprop, self).__init__('rmsprop', params, lr, (alpha, momentum), eps,
                 weight_decay, optim_bits, args, min_8bit_size, percentile_clipping, block_wise)
 
@@ -19,9 +18,9 @@ class RMSprop8bit(Optimizer1State):
     def __init__(self, params, lr=1e-2, alpha=0.99, eps=1e-8, weight_decay=0, momentum=0, centered=False, args=None,
             min_8bit_size=4096, percentile_clipping=100, block_wise=True):
         if alpha == 0:
-            raise NotImplementError(f'RMSprop with alpha==0.0 is not supported!')
+            raise NotImplementedError(f'RMSprop with alpha==0.0 is not supported!')
         if centered:
-            raise NotImplementError(f'Centered RMSprop is not supported!')
+            raise NotImplementedError(f'Centered RMSprop is not supported!')
         super(RMSprop8bit, self).__init__('rmsprop', params, lr, (alpha, momentum), eps,
                 weight_decay, 8, args, min_8bit_size, percentile_clipping, block_wise)
 
@@ -30,7 +29,7 @@ class RMSprop32bit(Optimizer1State):
             min_8bit_size=4096, percentile_clipping=100, block_wise=True):
 
         if alpha == 0:
-            raise NotImplementError(f'RMSprop with alpha==0.0 is not supported!')
+            raise NotImplementedError(f'RMSprop with alpha==0.0 is not supported!')
         if centered:
             raise NotImplementError(f'Centered RMSprop is not supported!')
         super(RMSprop32bit, self).__init__('rmsprop', params, lr, (alpha, momentum), eps,

bitsandbytes/optim/sgd.py

@@ -9,7 +9,7 @@ class SGD(Optimizer1State):
                  weight_decay=0, nesterov=False, optim_bits=32, args=None,
             min_8bit_size=4096, percentile_clipping=100, block_wise=True):
         if momentum == 0:
-            raise NotImplementError(f'SGD without momentum is not supported!')
+            raise NotImplementedError(f'SGD without momentum is not supported!')
         super(SGD, self).__init__('momentum', params, lr, (momentum, dampening), 0.0,
                 weight_decay, optim_bits, args, min_8bit_size, percentile_clipping, block_wise)
 
@@ -18,7 +18,7 @@ class SGD8bit(Optimizer1State):
                  weight_decay=0, nesterov=False, args=None,
             min_8bit_size=4096, percentile_clipping=100, block_wise=True):
         if momentum == 0:
-            raise NotImplementError(f'SGD without momentum is not supported!')
+            raise NotImplementedError(f'SGD without momentum is not supported!')
         super(SGD8bit, self).__init__('momentum', params, lr, (momentum, dampening), 0.0,
                 weight_decay, 8, args, min_8bit_size, percentile_clipping, block_wise)
 
@@ -27,6 +27,6 @@ class SGD32bit(Optimizer1State):
                  weight_decay=0, nesterov=False, args=None,
             min_8bit_size=4096, percentile_clipping=100, block_wise=True):
         if momentum == 0:
-            raise NotImplementError(f'SGD without momentum is not supported!')
+            raise NotImplementedError(f'SGD without momentum is not supported!')
         super(SGD32bit, self).__init__('momentum', params, lr, (momentum, dampening), 0.0,
                 weight_decay, 32, args, min_8bit_size, percentile_clipping, block_wise)

tests/test_modules.py

@@ -6,10 +6,6 @@ import pytest
 import torch
 import bitsandbytes as bnb
 
-from itertools import product
-
-from bitsandbytes import functional as F
-
 
 @pytest.mark.parametrize("embcls", [bnb.nn.Embedding, bnb.nn.StableEmbedding], ids=['Embedding', 'StableEmbedding'])
 def test_embeddings(embcls):

tests/test_optim.py

@@ -7,7 +7,6 @@ import time
 import shutil
 import uuid
 import pytest
-import ctypes
 import torch
 import bitsandbytes as bnb
 import bitsandbytes.functional as F