Convert directory fbcode/deeplearning to use the Ruff Formatter

Summary:
X-link: flashlight/wav2letter#1024

X-link: flashlight/text#88

X-link: flashlight/flashlight#1176

X-link: pytorch/FBGEMM#3242

Converts the directory specified to use the Ruff formatter in pyfmt

ruff_dog

If this diff causes merge conflicts when rebasing, please run
`hg status -n -0 --change . -I '**/*.{py,pyi}' | xargs -0 arc pyfmt`
on your diff, and amend any changes before rebasing onto latest.
That should help reduce or eliminate any merge conflicts.

allow-large-files
bypass-github-export-checks

Differential Revision: D63766623

Author: Thomas Polasek

benchmarks/models.py

@@ -8,7 +8,6 @@
 Contains models used for benchmarking
 """
 
-
 from dataclasses import dataclass
 from typing import Any
 

benchmarks/profiler.py

@@ -10,7 +10,6 @@
 Run via Jupyter
 """
 
-
 from benchmark import ModelBenchmarks
 
 

crypten/communicator/communicator.py

@@ -162,7 +162,6 @@ def _logging(func):
 
     @wraps(func)
     def logging_wrapper(self, *args, **kwargs):
-
         # TODO: Replace this
         # - hacks the inputs into some of the functions for world_size 1:
         world_size = self.get_world_size()

crypten/communicator/in_process_communicator.py

@@ -17,7 +17,6 @@
 
 
 class InProcessCommunicator(Communicator):
-
     BYTES_PER_ELEMENT = 8
     tls = threading.local()
     mailbox = None

crypten/cryptensor.py

@@ -78,16 +78,12 @@ def register_cryptensor_cls(cls):
             if name in CrypTensor.__CRYPTENSOR_TYPES__:
                 raise ValueError(
                     "Cannot register duplicate CrypTensor type: \
-                    tensor type {} already exists.".format(
-                        name
-                    )
+                    tensor type {} already exists.".format(name)
                 )
             if not issubclass(cls, CrypTensor):
                 raise ValueError(
                     "Registered tensor ({}: {}) must extend \
-                    CrypTensor".format(
-                        name, cls.__name__
-                    )
+                    CrypTensor".format(name, cls.__name__)
                 )
             CrypTensor.__CRYPTENSOR_TYPES__[name] = cls
             return cls
@@ -222,7 +218,6 @@ def backward(self, grad_input=None, top_node=True):
         """
         if self.requires_grad:
             with CrypTensor.no_grad():  # disable autograd for backward pass
-
                 # in initial backward call, identify all required nodes:
                 if top_node:
                     self._identify_required_grads()
@@ -302,7 +297,6 @@ def __torch_function__(self, func, types, args=(), kwargs=None):
             )
 
     def _get_forward_function_no_ctx(self, grad_fn):
-
         # determine if self is a dummy object (the case for staticmethods):
         is_dummy = getattr(self, "__IS_DUMMY__", False)
 
@@ -320,7 +314,6 @@ def autograd_forward_no_ctx(*args, **kwargs):
         return autograd_forward_no_ctx
 
     def _get_autograd_forward_function(self, name, grad_fn, in_place):
-
         # determine if self is a dummy object (the case for staticmethods):
         is_dummy = getattr(self, "__IS_DUMMY__", False)
 

crypten/gradients.py

@@ -408,7 +408,6 @@ def backward(ctx, grad_output):
 class AutogradSqueeze(AutogradFunction):
     @staticmethod
     def forward(ctx, *args, **kwargs):
-
         # preprocess inputs:
         assert len(args) >= 1
         if len(args) == 1:
@@ -497,7 +496,6 @@ def backward(ctx, grad_output):
 class AutogradDropout(AutogradFunction):
     @staticmethod
     def forward(ctx, input, p=0.5, training=True, inplace=False):
-
         if training and inplace:
             logging.warning(
                 "CrypTen dropout does not support inplace computation during training."
@@ -534,7 +532,6 @@ def backward(ctx, grad_output):
 class AutogradFeatureDropout(AutogradFunction):
     @staticmethod
     def forward(ctx, input, p=0.5, training=True, inplace=False):
-
         if training and inplace:
             logging.warning(
                 "CrypTen _feature_dropout does not support inplace computation during training."
@@ -1133,7 +1130,6 @@ def backward(ctx, grad_output):
 class AutogradSum(AutogradFunction):
     @staticmethod
     def forward(ctx, *args, **kwargs):
-
         # preprocess inputs:
         assert len(args) >= 1
         if len(args) == 1:
@@ -1192,7 +1188,6 @@ def backward(ctx, grad_output):
 class AutogradMean(AutogradFunction):
     @staticmethod
     def forward(ctx, *args, **kwargs):
-
         # preprocess inputs:
         assert len(args) >= 1
         if len(args) == 1:
@@ -1230,7 +1225,6 @@ def backward(ctx, grad_output):
 class AutogradVariance(AutogradFunction):
     @staticmethod
     def forward(ctx, self, *args, **kwargs):
-
         # preprocess inputs:
         if len(args) == 0:
             dim = None
@@ -1287,7 +1281,6 @@ def backward(ctx, grad_output):
 class AutogradMin(AutogradFunction):
     @staticmethod
     def forward(ctx, *args, **kwargs):
-
         # preprocess inputs:
         assert len(args) >= 1
         if len(args) == 1:
@@ -1335,7 +1328,6 @@ def backward(ctx, grad_output):
 class AutogradMax(AutogradFunction):
     @staticmethod
     def forward(ctx, *args, **kwargs):
-
         # preprocess inputs:
         assert len(args) >= 1
         if len(args) == 1:
@@ -1453,7 +1445,6 @@ def backward(ctx, grad_output):
 class AutogradAvgPool2D(AutogradFunction):
     @staticmethod
     def forward(ctx, input, kernel_size, stride=None, padding=0, ceil_mode=False):
-
         # preprocess inputs:
         if stride is None:
             stride = kernel_size
@@ -1528,7 +1519,6 @@ def forward(
         ceil_mode=False,
         return_indices=False,
     ):
-
         # preprocess inputs:
         if stride is None:
             stride = kernel_size
@@ -1887,7 +1877,6 @@ def backward(ctx, grad_output):
         grad_output = grad_output.mul(weight)
         grad_input = grad_output.mul(inv_var)
         if training:
-
             # compute gradient term that is due to the mean:
             num_element = reduce(
                 lambda x, y: x * y, [grad_output.size(d) for d in stats_dimensions]

crypten/mpc/primitives/arithmetic.py

@@ -223,9 +223,9 @@ def __setitem__(self, index, value):
         """Set tensor values by index"""
         if isinstance(value, (int, float)) or is_tensor(value):
             value = ArithmeticSharedTensor(value)
-        assert isinstance(
-            value, ArithmeticSharedTensor
-        ), "Unsupported input type %s for __setitem__" % type(value)
+        assert isinstance(value, ArithmeticSharedTensor), (
+            "Unsupported input type %s for __setitem__" % type(value)
+        )
         self.share.__setitem__(index, value.share)
 
     def pad(self, pad, mode="constant", value=0):
@@ -268,9 +268,9 @@ def stack(tensors, *args, **kwargs):
         for i, tensor in enumerate(tensors):
             if is_tensor(tensor):
                 tensors[i] = ArithmeticSharedTensor(tensor)
-            assert isinstance(
-                tensors[i], ArithmeticSharedTensor
-            ), "Can't stack %s with ArithmeticSharedTensor" % type(tensor)
+            assert isinstance(tensors[i], ArithmeticSharedTensor), (
+                "Can't stack %s with ArithmeticSharedTensor" % type(tensor)
+            )
 
         result = tensors[0].shallow_copy()
         result.share = torch_stack(
@@ -630,9 +630,9 @@ def scatter_(self, dim, index, src):
         """
         if is_tensor(src):
             src = ArithmeticSharedTensor(src)
-        assert isinstance(
-            src, ArithmeticSharedTensor
-        ), "Unrecognized scatter src type: %s" % type(src)
+        assert isinstance(src, ArithmeticSharedTensor), (
+            "Unrecognized scatter src type: %s" % type(src)
+        )
         self.share.scatter_(dim, index, src.share)
         return self
 

crypten/mpc/primitives/binary.py

@@ -318,9 +318,9 @@ def __setitem__(self, index, value):
         """Set tensor values by index"""
         if is_tensor(value) or isinstance(value, list):
             value = BinarySharedTensor(value)
-        assert isinstance(
-            value, BinarySharedTensor
-        ), "Unsupported input type %s for __setitem__" % type(value)
+        assert isinstance(value, BinarySharedTensor), (
+            "Unsupported input type %s for __setitem__" % type(value)
+        )
         self.share.__setitem__(index, value.share)
 
     @staticmethod
@@ -436,9 +436,9 @@ def scatter_(self, dim, index, src):
         """
         if is_tensor(src):
             src = BinarySharedTensor(src)
-        assert isinstance(
-            src, BinarySharedTensor
-        ), "Unrecognized scatter src type: %s" % type(src)
+        assert isinstance(src, BinarySharedTensor), (
+            "Unrecognized scatter src type: %s" % type(src)
+        )
         self.share.scatter_(dim, index, src.share)
         return self
 

crypten/mpc/primitives/converters.py

@@ -16,7 +16,6 @@
 
 
 def _A2B(arithmetic_tensor):
-
     # first try memory-inefficient implementation that takes O(log P) rounds:
     try:
         binary_tensor = BinarySharedTensor.stack(

crypten/mpc/provider/ttp_provider.py

@@ -309,7 +309,6 @@ def _get_binary_PRSS(self, size, bitlength=None, remove_rank=None):
         return result
 
     def additive(self, size0, size1, op, *args, **kwargs):
-
         # Add all shares of `a` and `b` to get plaintext `a` and `b`
         a = self._get_additive_PRSS(size0)
         b = self._get_additive_PRSS(size1)