add ruff lint rule to remove unused imports via ruff (pytorch#969)

remove unused imports via ruff

.github/workflows/ruff_linter.yml

@@ -25,13 +25,18 @@ jobs:
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
-        pip install ruff
+        pip install ruff==0.6.8
     - name: Analyzing the code with ruff
       run: |
         ruff check .
-    - name: Check all Python files for syntax errors (E999) and undefined vars (F821)
+    - name: Check *all* Python files for F821, F823, and W191
       run: |
-        ruff check --isolated --select E999,F821
-    - name: Check well formatted code
+        # --isolated is used to skip the allowlist at all so this applies to all files
+        # please be careful when using this large changes means everyone needs to rebase
+        ruff check --isolated --select F821,F823,W191
+    - name: Check the allow-listed files for F,I
+      run: |
+        ruff check --select F,I
+    - name: Check the allow-listed files for well formatted code
       run: |
         ruff format --check

dev-requirements.txt

@@ -19,5 +19,5 @@ tabulate  # QOL for printing tables to stdout
 ninja
 
 # Linting
-ruff
+ruff==0.6.8
 pre-commit

test/dtypes/test_affine_quantized_float.py

@@ -1,35 +1,36 @@
+import pytest
+
 from torchao.utils import (
     TORCH_VERSION_AT_LEAST_2_5,
 )
-import pytest
 
 if not TORCH_VERSION_AT_LEAST_2_5:
     pytest.skip("Unsupported PyTorch version", allow_module_level=True)
 
+import copy
+import io
+import random
+import unittest
+from contextlib import nullcontext
+from functools import partial
+from typing import Tuple
+
+import pytest
+import torch
 from torch._inductor.test_case import TestCase as InductorTestCase
 from torch.testing._internal import common_utils
 
+from torchao.float8.float8_utils import compute_error
 from torchao.quantization import (
-    quantize_,
-    float8_weight_only,
     float8_dynamic_activation_float8_weight,
+    float8_weight_only,
+    quantize_,
 )
+from torchao.quantization.observer import PerRow, PerTensor
 from torchao.quantization.quant_api import (
     float8_static_activation_float8_weight,
 )
-from torchao.quantization.quant_primitives import choose_qparams_affine, MappingType
-from torchao.quantization.observer import PerTensor, PerRow
-from torchao.float8.float8_utils import compute_error
-import torch
-import unittest
-import pytest
-import copy
-import random
-from functools import partial
-from typing import Tuple
-from contextlib import nullcontext
-import io
-
+from torchao.quantization.quant_primitives import MappingType, choose_qparams_affine
 
 random.seed(0)
 torch.manual_seed(0)

test/dtypes/test_nf4.py

@@ -1,14 +1,18 @@
 import copy
+import io
 import logging
-import unittest
-from packaging import version
 import math
+import unittest
+from collections import OrderedDict
+from typing import Tuple, Union
+
 import pytest
 import torch
+import torch.nn.functional as F
 from torch import nn
 from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import (
-    apply_activation_checkpointing,
     CheckpointWrapper,
+    apply_activation_checkpointing,
 )
 from torch.distributed.fsdp.wrap import ModuleWrapPolicy
 from torch.testing._internal.common_distributed import skip_if_lt_x_gpu
@@ -19,18 +23,15 @@
     parametrize,
     run_tests,
 )
+
+import torchao
+from packaging import version
 from torchao.dtypes.nf4tensor import (
+    _INNER_TENSOR_NAMES_FOR_SHARDING,
     NF4Tensor,
     linear_nf4,
     to_nf4,
-    _INNER_TENSOR_NAMES_FOR_SHARDING,
 )
-import torch.nn.functional as F
-import io
-from collections import OrderedDict
-import torchao
-from typing import Tuple, Union
-
 
 bnb_available = False
 

test/quantization/test_observer.py

@@ -1,23 +1,25 @@
 import re
+import unittest
+
 import torch
 import torch.nn as nn
+
+# NOTE: we can copy paste these here if we decide to deprecate them in torch.ao
+from torch.ao.quantization.observer import MinMaxObserver, PerChannelMinMaxObserver
+from torch.testing._internal import common_utils
 from torch.testing._internal.common_utils import TestCase
+
 from torchao.quantization.observer import (
     AffineQuantizedMinMaxObserver,
-    PerTensor,
     PerAxis,
-)
-from torchao.quantization.quant_primitives import (
-    MappingType,
+    PerTensor,
 )
 from torchao.quantization.quant_api import (
     insert_observers_,
 )
-from torch.testing._internal import common_utils
-import unittest
-
-# NOTE: we can copy paste these here if we decide to deprecate them in torch.ao
-from torch.ao.quantization.observer import MinMaxObserver, PerChannelMinMaxObserver
+from torchao.quantization.quant_primitives import (
+    MappingType,
+)
 
 
 class TestQuantFlow(TestCase):

torchao/dtypes/nf4tensor.py

@@ -1,15 +1,14 @@
 import functools
-from dataclasses import dataclass, replace
 import math
-from typing import Dict, Tuple, Any, Optional, Union
 import sys
+from dataclasses import dataclass, replace
 from enum import Enum, auto
+from typing import Any, Dict, Optional, Tuple, Union
 
 import torch
 import torch.nn.functional as F
-from torch.distributed.device_mesh import DeviceMesh
 from torch._prims_common import make_contiguous_strides_for
-
+from torch.distributed.device_mesh import DeviceMesh
 
 aten = torch.ops.aten
 

torchao/float8/float8_tensor.py

@@ -4,16 +4,16 @@
 # This source code is licensed under the BSD 3-Clause license found in the
 # LICENSE file in the root directory of this source tree.
 import enum
-from typing import Dict, Optional, NamedTuple
+from typing import Dict, NamedTuple, Optional
 
 import torch
-
 import torch.distributed._functional_collectives as funcol
+from torch.distributed._tensor import DTensor
+
 from torchao.float8.float8_utils import (
     e4m3_dtype,
     to_fp8_saturated,
 )
-from torch.distributed._tensor import DTensor
 
 aten = torch.ops.aten
 

torchao/float8/float8_utils.py

@@ -6,11 +6,11 @@
 
 from typing import Iterable, Literal, Tuple, Union
 
-import torchao.float8.config as config
-
 import torch
 import torch.distributed as dist
 
+import torchao.float8.config as config
+
 # Helpful visualizer for debugging (only supports fp32):
 # https://www.h-schmidt.net/FloatConverter/IEEE754.html
 

torchao/float8/inference.py

@@ -7,9 +7,10 @@
 Defines an nn module designed to be used during inference
 """
 
-from typing import Optional, Tuple, NamedTuple
+from typing import NamedTuple, Optional, Tuple
 
 import torch
+
 from torchao.float8.float8_utils import is_row_major, pad_tensor_for_matmul
 
 Tensor = torch.Tensor

torchao/quantization/linear_activation_weight_observer.py

@@ -1,13 +1,14 @@
+from typing import Callable, Dict, Optional
+
 import torch
-from typing import Callable, Optional, Dict
 from torch.utils._python_dispatch import return_and_correct_aliasing
+
+from torchao.quantization.observer import AffineQuantizedObserverBase
 from torchao.utils import (
-    TorchAOBaseTensor,
     TORCH_VERSION_AT_LEAST_2_5,
+    TorchAOBaseTensor,
 )
 
-from torchao.quantization.observer import AffineQuantizedObserverBase
-
 __all__ = [
     "LinearActivationWeightObservedTensor",
 ]

torchao/quantization/weight_tensor_linear_activation_quantization.py

@@ -1,9 +1,11 @@
+from typing import Any, Callable, Dict, Optional
+
 import torch
-from typing import Callable, Optional, Dict, Any
 from torch.utils._python_dispatch import return_and_correct_aliasing
+
 from torchao.utils import (
-    TorchAOBaseTensor,
     TORCH_VERSION_AT_LEAST_2_5,
+    TorchAOBaseTensor,
 )
 
 __all__ = [