Misc. style and format updates

tests/gof/test_graph.py

-from itertools import count
 import pickle
 
 import pytest
+
 import numpy as np
 
+from itertools import count
+
 from theano import sparse, shared, tensor
 from theano.gof.graph import (
     Apply,
@@ -58,10 +60,6 @@ class MyOp(Op):
 
 MyOp = MyOp()
 
-##########
-# inputs #
-##########
-
 
 class TestInputs:
     def test_inputs(self):
@@ -77,11 +75,6 @@ class TestInputs:
         assert i == [r1, r2, r5], i
 
 
-#############
-# as_string #
-#############
-
-
 class X:
     def leaf_formatter(self, leaf):
         return str(leaf.type)
@@ -126,11 +119,6 @@ class TestStr(X):
         assert self.str(node2.inputs, node2.outputs) == ["MyOp(R3, R3)"]
 
 
-#########
-# clone #
-#########
-
-
 class TestClone(X):
     def test_accurate(self):
         r1, r2 = MyVariable(1), MyVariable(2)
@@ -186,11 +174,6 @@ class TestClone(X):
         assert i[0] is c1 and o[0] is c1
 
 
-############
-# toposort #
-############
-
-
 def prenode(obj):
     if isinstance(obj, Variable):
         if obj.owner:
@@ -258,11 +241,6 @@ class TestToposort:
         assert all == [o0]
 
 
-#################
-# is_same_graph #
-#################
-
-
 class TestIsSameGraph:
     def check(self, expected, debug=True):
         """
@@ -401,11 +379,6 @@ class TestIsSameGraph:
         )
 
 
-################
-# eval         #
-################
-
-
 class TestEval:
     def setup_method(self):
         self.x, self.y = tensor.scalars("x", "y")
@@ -421,9 +394,6 @@ class TestEval:
         ), "temporary functions must not be serialized"
 
 
-################
-# autoname     #
-################
 class TestAutoName:
     def test_auto_name(self):
         # Get counter value

tests/scan_module/test_scan_utils.py

 import itertools
-import numpy as np
+
 import pytest
+
+import numpy as np
+
 import theano
+
 from theano import tensor
 from theano.scan_module.scan_utils import equal_computations, map_variables
 from theano.tensor.type_other import NoneConst
@@ -17,9 +21,6 @@ def test_equal_compuations():
     assert equal_computations(max_argmax1, max_argmax2)
 
 
-#################
-# map_variables #
-#################
 
 
 class TestMapVariables:

tests/tensor/test_subtensor.py

@@ -53,7 +53,7 @@ from tests.tensor.test_basic import inplace_func, rand, randint_ranged
 
 class TestSubtensor(utt.OptimizationTestMixin):
     """
-    This is build in a way that allow to reuse it to test the equivalent gpu op.
+    This is designed to be sub-classed (e.g. by the GPU tests).
     """
 
     def setup_method(self):

theano/__init__.py

@@ -197,7 +197,7 @@ def dot(l, r):
 
 
 def get_scalar_constant_value(v):
-    """return the constant scalar(0-D) value underlying variable `v`
+    """Return the constant scalar (i.e. 0-D) value underlying variable `v`.
 
     If v is the output of dimshuffles, fills, allocs, rebroadcasts, cast
     this function digs through them.
@@ -208,7 +208,8 @@ def get_scalar_constant_value(v):
     tensor.basic.NotScalarConstantError.
     """
     # Is it necessary to test for presence of theano.sparse at runtime?
-    if "sparse" in globals() and isinstance(v.type, sparse.SparseType):
+    sparse = globals().get("sparse")
+    if sparse and isinstance(v.type, sparse.SparseType):
         if v.owner is not None and isinstance(v.owner.op, sparse.CSM):
             data = v.owner.inputs[0]
             return tensor.get_scalar_constant_value(data)

theano/compile/ops.py

@@ -18,7 +18,7 @@ from collections import OrderedDict
 
 from six import integer_types
 
-from theano import gof
+from theano.gof import Op, Apply, ParamsType, Variable
 
 
 def register_view_op_c_code(type, code, version=()):
@@ -39,7 +39,7 @@ def register_view_op_c_code(type, code, version=()):
     ViewOp.c_code_and_version[type] = (code, version)
 
 
-class ViewOp(gof.Op):
+class ViewOp(Op):
     """
     Returns an inplace view of the input. Used internally by Theano.
 
@@ -54,7 +54,7 @@ class ViewOp(gof.Op):
     _f16_ok = True
 
     def make_node(self, x):
-        return gof.Apply(self, [x], [x.type()])
+        return Apply(self, [x], [x.type()])
 
     def perform(self, node, inp, out):
         (x,) = inp
@@ -151,7 +151,7 @@ def register_deep_copy_op_c_code(typ, code, version=()):
     DeepCopyOp.c_code_and_version[typ] = (code, version)
 
 
-class DeepCopyOp(gof.Op):
+class DeepCopyOp(Op):
     # Mapping from Type to C code (and version) to use.
     # In the C code, the name of the input variable is %(iname)s,
     # the output variable is %(oname)s.
@@ -165,7 +165,7 @@ class DeepCopyOp(gof.Op):
         pass
 
     def make_node(self, x):
-        return gof.Apply(self, [x], [x.type()])
+        return Apply(self, [x], [x.type()])
 
     def perform(self, node, args, outs):
         if hasattr(args[0], "copy"):
@@ -235,7 +235,7 @@ def register_shape_c_code(type, code, version=()):
     Shape.c_code_and_version[type] = (code, version)
 
 
-class Shape(gof.Op):
+class Shape(Op):
     """
     L{Op} to return the shape of a matrix.
 
@@ -260,7 +260,7 @@ class Shape(gof.Op):
         # This will fail at execution time.
         if not isinstance(x, theano.Variable):
             x = theano.tensor.as_tensor_variable(x)
-        return gof.Apply(self, [x], [theano.tensor.lvector()])
+        return Apply(self, [x], [theano.tensor.lvector()])
 
     def perform(self, node, inp, out_):
         (x,) = inp
@@ -329,7 +329,7 @@ shape = Shape()
 _shape = shape  # was used in the past, now use shape directly.
 
 
-class Shape_i(gof.Op):
+class Shape_i(Op):
     """
     L{Op} to return the shape of a matrix.
 
@@ -369,7 +369,7 @@ class Shape_i(gof.Op):
     #    using params.
     @property
     def params_type(self):
-        return gof.ParamsType(i=theano.scalar.basic.int64)
+        return ParamsType(i=theano.scalar.basic.int64)
 
     def __str__(self):
         return "%s{%i}" % (self.__class__.__name__, self.i)
@@ -540,7 +540,7 @@ def load_back(mod, name):
     return obj
 
 
-class FromFunctionOp(gof.Op):
+class FromFunctionOp(Op):
     """
     Build a basic Theano Op around a function.
 
@@ -666,7 +666,7 @@ def register_rebroadcast_c_code(typ, code, version=()):
     Rebroadcast.c_code_and_version[typ] = (code, version)
 
 
-class Rebroadcast(gof.Op):
+class Rebroadcast(Op):
     """
     Change the input's broadcastable fields in some predetermined way.
 
@@ -737,7 +737,7 @@ class Rebroadcast(gof.Op):
                 self.axis.get(i, b) for i, b in enumerate(x.type.broadcastable)
             ]
         )
-        return gof.Apply(self, [x], [t()])
+        return Apply(self, [x], [t()])
 
     def perform(self, node, inp, out_):
         (x,) = inp
@@ -848,7 +848,7 @@ def register_specify_shape_c_code(typ, code, version=(), c_support_code_apply=No
     SpecifyShape.c_code_and_version[typ] = (code, version, c_support_code_apply)
 
 
-class SpecifyShape(gof.Op):
+class SpecifyShape(Op):
     """
     L{Op} that puts into the graph the user-provided shape.
 
@@ -876,14 +876,14 @@ class SpecifyShape(gof.Op):
     _f16_ok = True
 
     def make_node(self, x, shape):
-        if not isinstance(x, gof.Variable):
+        if not isinstance(x, Variable):
             x = theano.tensor.as_tensor_variable(x)
         shape = theano.tensor.as_tensor_variable(shape)
         assert shape.ndim == 1
         assert shape.dtype in theano.tensor.integer_dtypes
         if isinstance(shape, theano.tensor.TensorConstant):
             assert shape.data.size == x.ndim
-        return gof.Apply(self, [x, shape], [x.type()])
+        return Apply(self, [x, shape], [x.type()])
 
     def perform(self, node, inp, out_):
         x, shape = inp

theano/gof/toolbox.py

-from functools import partial
-from collections import OrderedDict
-
 import sys
+import copy
 import time
 import inspect
 
 import numpy as np
+import theano
+
+from functools import partial
+from collections import OrderedDict
+
 from six.moves import StringIO
 
-import theano
 from theano import config
-from theano.gof import graph
+from theano.gof.graph import (
+    io_toposort,
+)
 
 
 class AlreadyThere(Exception):
@@ -312,7 +316,7 @@ class Bookkeeper(Feature):
         FunctionGraph is initially populated, this is where you should
         run checks on the initial contents of the FunctionGraph.
         """
-        for node in graph.io_toposort(fgraph.inputs, fgraph.outputs):
+        for node in io_toposort(fgraph.inputs, fgraph.outputs):
             self.on_import(fgraph, node, "on_attach")
 
     def on_detach(self, fgraph):
@@ -320,7 +324,7 @@ class Bookkeeper(Feature):
         Should remove any dynamically added functionality
         that it installed into the function_graph
         """
-        for node in graph.io_toposort(fgraph.inputs, fgraph.outputs):
+        for node in io_toposort(fgraph.inputs, fgraph.outputs):
             self.on_prune(fgraph, node, "Bookkeeper.detach")
 
 

theano/scan_module/scan_opt.py

@@ -62,19 +62,16 @@ from collections import OrderedDict
 
 from six import integer_types
 
-from theano import tensor, scalar
+from theano import gof, tensor, scalar
 from theano.tensor import opt, get_scalar_constant_value, Alloc, AllocEmpty
-from theano import gof
 
 from theano.compile import optdb
 from theano.compile.function_module import deep_copy_op
 from theano.gof import toolbox, DestroyHandler, InconsistencyError
-from theano.gof.opt import Optimizer
-from theano.gof.opt import pre_constant_merge, pre_greedy_local_optimizer
+from theano.gof.opt import Optimizer, pre_constant_merge, pre_greedy_local_optimizer
 
-from theano.scan_module import scan_op
-from theano.scan_module import scan_utils
 from theano.scan_module.scan_utils import equal_computations, scan_args
+from theano.scan_module import scan_op, scan_utils
 
 __docformat__ = "restructedtext en"
 __authors__ = (

theano/tensor/extra_ops.py

@@ -4,15 +4,15 @@ import theano
 
 from collections.abc import Collection
 
-from theano.tensor import basic
-from theano.tensor import nlinalg  # noqa
-from theano import gof, scalar
-from theano.gof import Generic, ParamsType, EnumList
-from theano import gradient
-from theano.gradient import DisconnectedType, disconnected_type
-from theano.scalar import int32 as int_t
-
-tensor = basic
+from theano.gof import Op, Apply, Generic, ParamsType, EnumList
+from theano.tensor import basic, nlinalg
+from theano.scalar import int32 as int_t, upcast
+from theano.gradient import (
+    DisconnectedType,
+    disconnected_type,
+    _float_zeros_like,
+    grad_undefined,
+)
 
 
 class CpuContiguous(theano.Op):
@@ -197,8 +197,8 @@ class SearchsortedOp(theano.Op):
         else:
             x, v = inputs
 
-        x_grad = gradient._float_zeros_like(x)
-        v_grad = gradient._float_zeros_like(v)
+        x_grad = _float_zeros_like(x)
+        v_grad = _float_zeros_like(v)
         if num_ins == 3:
             return [x_grad, v_grad, disconnected_type()]
         else:
@@ -332,7 +332,7 @@ class CumOp(theano.Op):
 
     def infer_shape(self, node, shapes):
         if self.axis is None:
-            return [(tensor.prod(shapes[0]),)]  # Flatten
+            return [(basic.prod(shapes[0]),)]  # Flatten
 
         return shapes
 
@@ -658,7 +658,7 @@ class RepeatOp(theano.Op):
         x = basic.as_tensor_variable(x)
         repeats = basic.as_tensor_variable(repeats)
 
-        if repeats.dtype not in tensor.integer_dtypes:
+        if repeats.dtype not in basic.integer_dtypes:
             raise TypeError("repeats.dtype must be an integer.")
 
         # Some dtypes are not supported by numpy's implementation of repeat.
@@ -784,7 +784,7 @@ def repeat(x, repeats, axis=None):
     .. versionadded:: 0.6
 
     """
-    repeats = tensor.as_tensor_variable(repeats)
+    repeats = basic.as_tensor_variable(repeats)
 
     if repeats.ndim > 1:
         raise ValueError("The dimension of repeats should not exceed 1.")
@@ -827,22 +827,22 @@ def repeat(x, repeats, axis=None):
         # After the original tensor is duplicated along the additional
         # dimension, we reshape it to the expected output shape, and
         # return the output z.
-        z = tensor.alloc(x.dimshuffle(*dims_), *shape_).reshape(shape)
+        z = basic.alloc(x.dimshuffle(*dims_), *shape_).reshape(shape)
         return z
 
 
-class Bartlett(gof.Op):
+class Bartlett(Op):
     # See function bartlett for docstring
     __props__ = ()
 
     def make_node(self, M):
-        M = tensor.as_tensor_variable(M)
+        M = basic.as_tensor_variable(M)
         if M.ndim != 0:
             raise TypeError("%s only works on scalar input" % self.__class__.__name__)
         elif M.dtype not in theano.tensor.integer_dtypes:
             # dtype is a theano attribute here
             raise TypeError("%s only works on integer input" % self.__class__.__name__)
-        return gof.Apply(self, [M], [tensor.dvector()])
+        return Apply(self, [M], [basic.dvector()])
 
     def perform(self, node, inputs, out_):
         M = inputs[0]
@@ -851,7 +851,7 @@ class Bartlett(gof.Op):
 
     def infer_shape(self, node, in_shapes):
         temp = node.inputs[0]
-        M = tensor.switch(tensor.lt(temp, 0), tensor.cast(0, temp.dtype), temp)
+        M = basic.switch(basic.lt(temp, 0), basic.cast(0, temp.dtype), temp)
         return [[M]]
 
     def grad(self, inputs, output_grads):
@@ -889,7 +889,7 @@ def bartlett(M):
     return bartlett_(M)
 
 
-class FillDiagonal(gof.Op):
+class FillDiagonal(Op):
     # See function fill_diagonal for docstring
     __props__ = ()
 
@@ -897,8 +897,8 @@ class FillDiagonal(gof.Op):
         return [in_shapes[0]]
 
     def make_node(self, a, val):
-        a = tensor.as_tensor_variable(a)
-        val = tensor.as_tensor_variable(val)
+        a = basic.as_tensor_variable(a)
+        val = basic.as_tensor_variable(val)
         if a.ndim < 2:
             raise TypeError(
                 "%s: first parameter must have at least"
@@ -908,13 +908,13 @@ class FillDiagonal(gof.Op):
             raise TypeError(
                 "%s: second parameter must be a scalar" % self.__class__.__name__
             )
-        val = tensor.cast(val, dtype=scalar.upcast(a.dtype, val.dtype))
+        val = basic.cast(val, dtype=upcast(a.dtype, val.dtype))
         if val.dtype != a.dtype:
             raise TypeError(
                 "%s: type of second parameter must be the same as"
                 " the first's" % self.__class__.__name__
             )
-        return gof.Apply(self, [a, val], [a.type()])
+        return Apply(self, [a, val], [a.type()])
 
     def perform(self, node, inputs, output_storage):
         a = inputs[0].copy()
@@ -950,7 +950,7 @@ class FillDiagonal(gof.Op):
             )
         wr_a = fill_diagonal(grad, 0)  # valid for any number of dimensions
         # diag is only valid for matrices
-        wr_val = theano.tensor.nlinalg.diag(grad).sum()
+        wr_val = nlinalg.diag(grad).sum()
         return [wr_a, wr_val]
 
 
@@ -991,7 +991,7 @@ def fill_diagonal(a, val):
     return fill_diagonal_(a, val)
 
 
-class FillDiagonalOffset(gof.Op):
+class FillDiagonalOffset(Op):
     # See function fill_diagonal_offset for docstring
     __props__ = ()
 
@@ -999,9 +999,9 @@ class FillDiagonalOffset(gof.Op):
         return [in_shapes[0]]
 
     def make_node(self, a, val, offset):
-        a = tensor.as_tensor_variable(a)
-        val = tensor.as_tensor_variable(val)
-        offset = tensor.as_tensor_variable(offset)
+        a = basic.as_tensor_variable(a)
+        val = basic.as_tensor_variable(val)
+        offset = basic.as_tensor_variable(offset)
         if a.ndim != 2:
             raise TypeError(
                 "%s: first parameter must have exactly"
@@ -1015,7 +1015,7 @@ class FillDiagonalOffset(gof.Op):
             raise TypeError(
                 "%s: third parameter must be a scalar" % self.__class__.__name__
             )
-        val = tensor.cast(val, dtype=scalar.upcast(a.dtype, val.dtype))
+        val = basic.cast(val, dtype=upcast(a.dtype, val.dtype))
         if val.dtype != a.dtype:
             raise TypeError(
                 "%s: type of second parameter must be the same"
@@ -1028,7 +1028,7 @@ class FillDiagonalOffset(gof.Op):
                 % self.__class__.__name__
             )
 
-        return gof.Apply(self, [a, val, offset], [a.type()])
+        return Apply(self, [a, val, offset], [a.type()])
 
     def perform(self, node, inputs, output_storage):
         a = inputs[0].copy()
@@ -1097,7 +1097,7 @@ class FillDiagonalOffset(gof.Op):
 
         wr_val = grad.flatten()[start:end:step].sum()
 
-        wr_offset = theano.gradient.grad_undefined(
+        wr_offset = grad_undefined(
             self,
             2,
             offset,
@@ -1291,7 +1291,7 @@ class Unique(theano.Op):
             self.axis = None
 
 
-class UnravelIndex(gof.Op):
+class UnravelIndex(Op):
     __props__ = ("order",)
 
     def __init__(self, order="C"):
@@ -1313,7 +1313,7 @@ class UnravelIndex(gof.Op):
         if dims.ndim != 1:
             raise TypeError("dims must be a 1D array")
 
-        return gof.Apply(
+        return Apply(
             self,
             [indices, dims],
             [
@@ -1372,7 +1372,7 @@ def unravel_index(indices, dims, order="C"):
         return tuple(res)
 
 
-class RavelMultiIndex(gof.Op):
+class RavelMultiIndex(Op):
     __props__ = ("mode", "order")
 
     def __init__(self, mode="raise", order="C"):
@@ -1397,7 +1397,7 @@ class RavelMultiIndex(gof.Op):
         if dims.ndim != 1:
             raise TypeError("dims must be a 1D array")
 
-        return gof.Apply(
+        return Apply(
             self,
             multi_index + [dims],
             [

theano/tensor/opt.py

@@ -18,16 +18,32 @@ import theano
 from functools import reduce
 from collections import defaultdict
 
-from six import integer_types
-
-from theano import gof
-
-from theano.gof import opt, InconsistencyError, TopoOptimizer, graph
-from theano.gof import Variable, Constant
-from theano.gof.opt import copy_stack_trace, in2out
+from six import integer_types, StringIO
+
+from theano import (
+    gof,
+    config,
+    scalar,
+    compile,
+)  # to register the optimizer built by this file
+from theano.gof import (
+    opt,
+    InconsistencyError,
+    TopoOptimizer,
+    graph,
+    Variable,
+    Constant,
+    toolbox,
+)
+from theano.gof.opt import (
+    copy_stack_trace,
+    in2out,
+    Optimizer,
+    pre_constant_merge,
+    pre_greedy_local_optimizer,
+)
 from theano.gof.utils import MethodNotDefined
 from theano.gradient import DisconnectedType
-from theano import config
 from theano.tensor.elemwise import Elemwise, DimShuffle
 from theano.tensor.subtensor import (
     get_idx_list,
@@ -43,10 +59,8 @@ from theano.tensor.subtensor import (
     advanced_inc_subtensor1,
 )
 from theano.tensor.sort import TopKOp
-from theano import scalar
 from theano.scalar import basic
 from theano.tensor import basic as T
-from theano import compile  # to register the optimizer built by this file
 from theano.compile.ops import Shape, Shape_i
 from theano.tensor.type import (
     values_eq_approx_remove_inf,
@@ -54,8 +68,6 @@ from theano.tensor.type import (
     values_eq_approx_remove_inf_nan,
 )
 
-from theano.gof.opt import Optimizer, pre_constant_merge, pre_greedy_local_optimizer
-from theano.gof import toolbox
 from theano.tensor.basic import (
     Alloc,
     get_scalar_constant_value,
@@ -64,12 +76,9 @@ from theano.tensor.basic import (
     NotScalarConstantError,
     Reshape,
 )
-from six import StringIO
 
 _logger = logging.getLogger("theano.tensor.opt")
 
-# Utilities
-
 
 def _fill_chain(new_out, orig_inputs):
     for i in orig_inputs: