apply black 20.8b1 formatting update

Summary:
allow-large-files

black_any_style

Reviewed By: zertosh

Differential Revision: D24325133

fbshipit-source-id: b4afe80d1e8b2bc993f4b8e3822c02964df47462

crypten/__init__.py

@@ -172,16 +172,16 @@ def is_encrypted_tensor(obj):
 
 def _setup_przs(device=None):
     """
-        Generate shared random seeds to generate pseudo-random sharings of
-        zero. The random seeds are shared such that each process shares
-        one seed with the previous rank process and one with the next rank.
-        This allows for the generation of `n` random values, each known to
-        exactly two of the `n` parties.
-
-        For arithmetic sharing, one of these parties will add the number
-        while the other subtracts it, allowing for the generation of a
-        pseudo-random sharing of zero. (This can be done for binary
-        sharing using bitwise-xor rather than addition / subtraction)
+    Generate shared random seeds to generate pseudo-random sharings of
+    zero. The random seeds are shared such that each process shares
+    one seed with the previous rank process and one with the next rank.
+    This allows for the generation of `n` random values, each known to
+    exactly two of the `n` parties.
+
+    For arithmetic sharing, one of these parties will add the number
+    while the other subtracts it, allowing for the generation of a
+    pseudo-random sharing of zero. (This can be done for binary
+    sharing using bitwise-xor rather than addition / subtraction)
     """
     # Initialize RNG Generators
     comm.get().g0 = torch.Generator()

crypten/communicator/communicator.py

@@ -150,11 +150,11 @@ def _log_communication_time(self, comm_time):
 
     def get_generator(self, idx, device=None):
         """
-            Get the corresponding RNG generator, as specified by its index and device
+        Get the corresponding RNG generator, as specified by its index and device
 
-            Args:
-                idx: The index of the generator, can be either 0 or 1
-                device: The device that the generator lives in.
+        Args:
+            idx: The index of the generator, can be either 0 or 1
+            device: The device that the generator lives in.
         """
 
         if device is None:

crypten/cryptensor.py

@@ -787,24 +787,23 @@ def where(self, condition, y):
 
     def sigmoid(self, reciprocal_method="log"):
         """Computes the sigmoid function on the input value
-                sigmoid(x) = (1 + exp(-x))^{-1}
+        sigmoid(x) = (1 + exp(-x))^{-1}
         """
         raise NotImplementedError("sigmoid is not implemented")
 
     def tanh(self, reciprocal_method="log"):
         """Computes tanh from the sigmoid function:
-            tanh(x) = 2 * sigmoid(2 * x) - 1
+        tanh(x) = 2 * sigmoid(2 * x) - 1
         """
         raise NotImplementedError("tanh is not implemented")
 
     def softmax(self, dim, **kwargs):
-        """Compute the softmax of a tensor's elements along a given dimension
-        """
+        """Compute the softmax of a tensor's elements along a given dimension"""
         raise NotImplementedError("softmax is not implemented")
 
     def log_softmax(self, dim, **kwargs):
         """Applies a softmax of a tensor's elements along a given dimension,
-           followed by a logarithm.
+        followed by a logarithm.
         """
         raise NotImplementedError("log_softmax is not implemented")
 

crypten/cuda/cuda_tensor.py

@@ -27,11 +27,11 @@ def decorator(func):
 
 class CUDALongTensor(object):
     """
-        A wrapper class for `torch.cuda.LongTensor`. When performing operations that are
-        currently not supported for `torch.cuda.LongTensor` (e.g `matmul`, `conv2d`), it will
-        convert the underlying LongTensor into DoubleTensor and convert the computed
-        result back to a LongTensor. The computed result will be the same as the original
-        expected result.
+    A wrapper class for `torch.cuda.LongTensor`. When performing operations that are
+    currently not supported for `torch.cuda.LongTensor` (e.g `matmul`, `conv2d`), it will
+    convert the underlying LongTensor into DoubleTensor and convert the computed
+    result back to a LongTensor. The computed result will be the same as the original
+    expected result.
     """
 
     __BITS = torch.iinfo(torch.long).bits

crypten/mpc/mpc.py

@@ -982,8 +982,7 @@ def _truncate_tanh(self):
 
     @mode(Ptype.arithmetic)
     def softmax(self, dim, **kwargs):
-        """Compute the softmax of a tensor's elements along a given dimension
-        """
+        """Compute the softmax of a tensor's elements along a given dimension"""
         # 0-d case
         if self.dim() == 0:
             assert dim == 0, "Improper dim argument"

crypten/mpc/primitives/arithmetic.py

@@ -28,11 +28,11 @@
 # MPC tensor where shares additive-sharings.
 class ArithmeticSharedTensor(object):
     """
-        Encrypted tensor object that uses additive sharing to perform computations.
+    Encrypted tensor object that uses additive sharing to perform computations.
 
-        Additive shares are computed by splitting each value of the input tensor
-        into n separate random values that add to the input tensor, where n is
-        the number of parties present in the protocol (world_size).
+    Additive shares are computed by splitting each value of the input tensor
+    into n separate random values that add to the input tensor, where n is
+    the number of parties present in the protocol (world_size).
     """
 
     # constructors:
@@ -165,7 +165,7 @@ def __setitem__(self, index, value):
 
     def pad(self, pad, mode="constant", value=0):
         """
-            Pads the input tensor with values provided in `value`.
+        Pads the input tensor with values provided in `value`.
         """
         assert mode == "constant", (
             "Padding with mode %s is currently unsupported" % mode
@@ -473,13 +473,13 @@ def conv_transpose2d(self, kernel, **kwargs):
 
     def index_add(self, dim, index, tensor):
         """Perform out-of-place index_add: Accumulate the elements of tensor into the
-        self tensor by adding to the indices in the order given in index. """
+        self tensor by adding to the indices in the order given in index."""
         result = self.clone()
         return result.index_add_(dim, index, tensor)
 
     def index_add_(self, dim, index, tensor):
         """Perform in-place index_add: Accumulate the elements of tensor into the
-        self tensor by adding to the indices in the order given in index. """
+        self tensor by adding to the indices in the order given in index."""
         public = isinstance(tensor, (int, float)) or is_tensor(tensor)
         private = isinstance(tensor, ArithmeticSharedTensor)
         if public:
@@ -543,8 +543,8 @@ def _sum_pool2d(self, kernel_size, stride=None, padding=0):
 
     def take(self, index, dimension=None):
         """Take entries of tensor along a dimension according to the index.
-            This function is identical to torch.take() when dimension=None,
-            otherwise, it is identical to ONNX gather() function.
+        This function is identical to torch.take() when dimension=None,
+        otherwise, it is identical to ONNX gather() function.
         """
         result = self.shallow_copy()
         index = index.long()

crypten/mpc/primitives/binary.py

@@ -24,11 +24,11 @@
 # MPC tensor where shares are XOR-sharings.
 class BinarySharedTensor(object):
     """
-        Encrypted tensor object that uses binary sharing to perform computations.
+    Encrypted tensor object that uses binary sharing to perform computations.
 
-        Binary shares are computed by splitting each value of the input tensor
-        into n separate random values that xor together to the input tensor value,
-        where n is the number of parties present in the protocol (world_size).
+    Binary shares are computed by splitting each value of the input tensor
+    into n separate random values that xor together to the input tensor value,
+    where n is the number of parties present in the protocol (world_size).
     """
 
     def __init__(self, tensor=None, size=None, src=0, device=None):

crypten/mpc/primitives/ot/baseOT.py

@@ -20,10 +20,10 @@
 
 class BaseOT:
     """
-        hardcoded public parameter
-        log2(__prime) > 128
+    hardcoded public parameter
+    log2(__prime) > 128
 
-        __generator is a primitive root of __prime
+    __generator is a primitive root of __prime
     """
 
     __prime = 631276824160446938136046282957027762913

crypten/nn/module.py

@@ -462,7 +462,7 @@ def forward_function(*args, **kwargs):
 
     def __getattr__(self, name):
         """Redefine __getattr__ so that any parameters, modules or buffers
-           inside the Module object can be accessed as attributes
+        inside the Module object can be accessed as attributes
         """
         if "_parameters" in self.__dict__:
             parameters = self.__dict__["_parameters"]
@@ -482,8 +482,8 @@ def __getattr__(self, name):
 
     def __setattr__(self, name, value):
         """Redefine __setattr__ so that any submodules created
-           inside the Module object are registered with _modules
-           OrderedDict.
+        inside the Module object are registered with _modules
+        OrderedDict.
         """
 
         def remove_from(*dicts):
@@ -2056,9 +2056,9 @@ def _identify_bool_attributes_with_defaults(
     attributes, attr_name, attr_value, default=True
 ):
     """For boolean attributes that have default values in the ONNX specification
-        checks to see if they are present in `attributes`, and assigns the
-        default if not present and appropriate value if present. Note `attr_value`
-        must be the value `attributes[attr_name]` if the default is to be kept.
+    checks to see if they are present in `attributes`, and assigns the
+    default if not present and appropriate value if present. Note `attr_value`
+    must be the value `attributes[attr_name]` if the default is to be kept.
     """
     output = default
     if attr_name in attributes and attributes[attr_name] != attr_value:

test/test_arithmetic.py

@@ -23,7 +23,7 @@
 
 class TestArithmetic(MultiProcessTestCase):
     """
-        This class tests all functions of the ArithmeticSharedTensor.
+    This class tests all functions of the ArithmeticSharedTensor.
     """
 
     def setUp(self):
@@ -72,8 +72,8 @@ def test_share_attr(self):
 
     def test_encrypt_decrypt(self):
         """
-            Tests tensor encryption and decryption for both positive
-            and negative values.
+        Tests tensor encryption and decryption for both positive
+        and negative values.
         """
         sizes = [
             (),

test/test_binary.py

@@ -18,7 +18,7 @@
 
 class TestBinary(MultiProcessTestCase):
     """
-        This class tests all functions of BinarySharedTensor.
+    This class tests all functions of BinarySharedTensor.
     """
 
     def setUp(self):
@@ -48,8 +48,8 @@ def _check(self, encrypted_tensor, reference, msg, dst=None, tolerance=None):
 
     def test_encrypt_decrypt(self):
         """
-            Tests tensor encryption and decryption for both positive
-            and negative values.
+        Tests tensor encryption and decryption for both positive
+        and negative values.
         """
         sizes = [
             (),

test/test_communicator.py

@@ -33,7 +33,7 @@
 
 class TestCommunicator:
     """
-        This class tests all member functions of crypten package
+    This class tests all member functions of crypten package
     """
 
     def test_przs_generators(self):

test/test_crypten.py

@@ -22,7 +22,7 @@
 
 class TestCrypten(MultiProcessTestCase):
     """
-        This class tests all member functions of crypten package
+    This class tests all member functions of crypten package
     """
 
     def setUp(self):

test/test_cuda.py

@@ -41,7 +41,7 @@ def forward(self, x):
 @unittest.skipIf(torch.cuda.is_available() is False, "requires CUDA")
 class TestCUDA(TestMPC):
     """
-        This class tests all functions of CUDALongTensor as well as its integration with MPCTensor.
+    This class tests all functions of CUDALongTensor as well as its integration with MPCTensor.
     """
 
     def _check_int(self, result, reference, msg):
@@ -476,11 +476,11 @@ def test_torch_gather(self):
 
     @unittest.skip("torch.scatter behaves inconsistently on CUDA")
     def test_torch_scatter(self):
-        """ Test scatter/scatter_add function of CUDALongTensor
+        """Test scatter/scatter_add function of CUDALongTensor
 
-            This test will be skipped for now since torch.scatter provides
-            inconsistent result given the same input on CUDA. This is likely
-            due to a potential bug on pytorch's implementation of scatter
+        This test will be skipped for now since torch.scatter provides
+        inconsistent result given the same input on CUDA. This is likely
+        due to a potential bug on pytorch's implementation of scatter
         """
 
         funcs = ["scatter", "scatter_add"]
@@ -537,9 +537,9 @@ def test_torch_nonzero(self):
 
     @unittest.skip("torch.scatter behaves inconsistently on CUDA")
     def test_scatter(self):
-        """ This test will be skipped for now since torch.scatter provides
-            inconsistent result given the same input on CUDA. This is likely
-            due to a potential bug on pytorch's implementation of scatter
+        """This test will be skipped for now since torch.scatter provides
+        inconsistent result given the same input on CUDA. This is likely
+        due to a potential bug on pytorch's implementation of scatter
         """
         pass
 

test/test_distributions.py

@@ -15,7 +15,7 @@
 
 class TestDistributions(object):
     """
-        This class tests accuracy of distributions provided by random sampling in crypten.
+    This class tests accuracy of distributions provided by random sampling in crypten.
     """
 
     def _check_distribution(

test/test_mpc.py

@@ -24,7 +24,7 @@
 
 class TestMPC(object):
     """
-        This class tests all functions of MPCTensor.
+    This class tests all functions of MPCTensor.
     """
 
     def _get_random_test_tensor(self, *args, **kwargs):
@@ -142,8 +142,8 @@ def test_share_attr(self):
 
     def test_encrypt_decrypt(self):
         """
-            Tests tensor encryption and decryption for both positive
-            and negative values.
+        Tests tensor encryption and decryption for both positive
+        and negative values.
         """
         sizes = [
             (),

test/test_nn.py

@@ -25,7 +25,7 @@
 
 class TestNN(object):
     """
-        This class tests the crypten.nn package.
+    This class tests the crypten.nn package.
     """
 
     def _check(self, encrypted_tensor, reference, msg, tolerance=None):

test/test_optim.py

@@ -16,7 +16,7 @@
 
 class TestOptim(object):
     """
-        This class tests the crypten.optim package.
+    This class tests the crypten.optim package.
     """
 
     def _check(self, encrypted_tensor, reference, msg, tolerance=None):