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提交 f35aa65b authored 作者: Brandon T. Willard's avatar Brandon T. Willard 提交者： Brandon T. Willard
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Rename PureType to Type and Type to CType

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doc/extending/cop.txt
浏览文件 @ f35aa65b
......@@ -25,7 +25,7 @@ input variables and place the variables in the output variables.
What needs to be defined
========================
There are less methods to define for an `COp` than for a Type:
There are less methods to define for a `COp` than for a `Type`:
.. class:: COp
......@@ -213,9 +213,9 @@ There are less methods to define for an `COp` than for a Type:
Optional. If present this method will be called before doing
constant folding of a node, with that node as a parameter. If
it return True, we will not generate c code when doing constant
it return True, we will not generate C code when doing constant
folding of this node. This is useful when the compilation of
the c code will be longer then the computation in python
the C code will be longer then the computation in python
(e.g. Elemwise of scalars).
In addition, this allow to lower the number of compiled module
......@@ -233,7 +233,7 @@ There are less methods to define for an `COp` than for a Type:
considered the same as if the method was not defined.
If this method is defined and does not return `None`, then the
Op *must* have a `params_type` property with the Type to use
`Op` *must* have a `params_type` property with the `Type` to use
for the params variable.
.. attribute:: _f16_ok
......@@ -252,7 +252,7 @@ There are less methods to define for an `COp` than for a Type:
developpment if a better solution is found.
The ``name`` argument is currently given an invalid value, so steer
away from it. As was the case with Type, ``sub['fail']`` provides
away from it. As was the case with `Type`, ``sub['fail']`` provides
failure code that you *must* use if you want to raise an exception,
after setting the exception message.
......
doc/extending/ctype.txt
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......@@ -6,7 +6,7 @@ Implementing double in C
========================
The previous two sections described how to define a double :ref:`type`
and arithmetic operations on that Type, but all of them were
and arithmetic operations on that `Type`, but all of them were
implemented in pure Python. In this section we will see how to define
the double type in such a way that it can be used by operations
implemented in C (which we will define in the section after that).
......@@ -15,15 +15,15 @@ implemented in C (which we will define in the section after that).
How does it work?
=================
In order to be C-compatible, a Type must provide a C interface to the
In order to be C-compatible, a `Type` must provide a C interface to the
Python data that satisfy the constraints it puts forward. In other
words, it must define C code that can convert a Python reference into
some type suitable for manipulation in C and it must define C code
that can convert some C structure in which the C implementation of an
operation stores its variables into a reference to an object that can be
used from Python and is a valid value for the Type.
used from Python and is a valid value for the `Type`.
For example, in the current example, we have a Type which represents a
For example, in the current example, we have a `Type` which represents a
Python float. First, we will choose a corresponding C type. The
natural choice would be the primitive ``double`` type. Then, we need
to write code that will take a ``PyObject*``, check that it is a
......@@ -42,10 +42,10 @@ find here_.
What needs to be defined
========================
In order to be C-compatible, a Type must define several additional
methods, which all start with the ``c_`` prefix. The complete list can
be found in the documentation for :class:`.gof.type.Type`. Here, we'll focus on
the most important ones:
In order to be C-compatible, the `Type` subclass interface `CType` must be used.
It defines several additional methods, which all start with the ``c_``
prefix. The complete list can be found in the documentation for
:class:`.gof.type.CType`. Here, we'll focus on the most important ones:
.. class:: CLinkerType
......@@ -144,7 +144,7 @@ the most important ones:
Each of these functions take two arguments, ``name`` and ``sub`` which
must be used to parameterize the C code they return. ``name`` is a
string which is chosen by the compiler to represent a :ref:`variable` of
the Type in such a way that there are no name conflicts between
the `CType` in such a way that there are no name conflicts between
different pieces of data. Therefore, all variables declared in
``c_declare`` should have a name which includes ``name``. Furthermore,
the name of the variable containing a pointer to the Python object
......@@ -154,7 +154,7 @@ associated to the Variable is ``py_<name>``.
suitable for use in certain situations. For instance, ``sub['fail']``
contains code that should be inserted wherever an error is identified.
``c_declare`` and ``c_extract`` also accept a third ``check_input``
``c_declare`` and ``c_extract`` also accept a third ``check_input``
optional argument. If you want your type to validate its inputs, it must
only do it when ``check_input`` is True.
......@@ -180,20 +180,19 @@ out:
Defining the methods
====================
.. testsetup::
import theano
double = theano.Type()
**c_declare**
.. testcode::
def c_declare(name, sub):
return """
double %(name)s;
""" % dict(name = name)
double.c_declare = c_declare
from theano.gof.type import Generic
class double(Generic):
def c_declare(self, name, sub, check_input=True):
return """
double %(name)s;
""" % dict(name = name)
Very straightforward. All we need to do is write C code to declare a
double. That double will be named whatever is passed to our function
......@@ -211,7 +210,7 @@ here). Also note that you cannot declare a variable called
them.
What you declare there is basically the C interface you are giving to
your Type. If you wish people to develop operations that make use of
your `CType`. If you wish people to develop operations that make use of
it, it's best to publish it somewhere.
......@@ -219,11 +218,10 @@ it, it's best to publish it somewhere.
.. testcode::
def c_init(name, sub):
return """
%(name)s = 0.0;
""" % dict(name = name)
double.c_init = c_init
def c_init(self, name, sub):
return """
%(name)s = 0.0;
""" % dict(name = name)
This function has to initialize the
double we declared previously to a suitable value. This is useful if
......@@ -245,15 +243,14 @@ called, without knowing for sure which of the two.
.. testcode::
def c_extract(name, sub):
return """
if (!PyFloat_Check(py_%(name)s)) {
PyErr_SetString(PyExc_TypeError, "expected a float");
%(fail)s
}
%(name)s = PyFloat_AsDouble(py_%(name)s);
""" % dict(name = name, fail = sub['fail'])
double.c_extract = c_extract
def c_extract(self, name, sub, check_input=True):
return """
if (!PyFloat_Check(py_%(name)s)) {
PyErr_SetString(PyExc_TypeError, "expected a float");
%(fail)s
}
%(name)s = PyFloat_AsDouble(py_%(name)s);
""" % dict(name = name, fail = sub['fail'])
This method is slightly more sophisticated. What happens here is that
we have a reference to a Python object which Theano has placed in
......@@ -385,7 +382,7 @@ that Variable. For instance, imagine you define the following function
and call it:
.. code-block:: python
x, y, z = double('x'), double('y'), double('z')
a = add(x, y)
b = mul(a, z)
......@@ -469,9 +466,9 @@ Final version
.. testcode::
from theano import gof
from theano.gof.type import
class Double(gof.Type):
class Double(Type):
def filter(self, x, strict=False, allow_downcast=None):
if strict and not isinstance(x, float):
......
doc/extending/extending_theano.txt
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......@@ -17,7 +17,7 @@ write a new one. Don't worry, Theano was designed to make it easy to add new
Ops, Types, and Optimizations.
.. These first few pages will walk you through the definition of a new :ref:`type`,
.. ``double``, and a basic arithmetic :ref:`operations <op>` on that Type.
.. ``double``, and a basic arithmetic :ref:`operations <op>` on that `Type`.
As an illustration, this tutorial shows how to write a simple Python-based
:ref:`operations <op>` which performs operations on
......@@ -134,7 +134,7 @@ or :func:`make_thunk`.
- it operates on the Variables found in
``*inputs`` in Theano's symbolic language to infer the type of
the symbolic output Variables. It creates output Variables of a suitable
symbolic Type to serve as the outputs of this op's
symbolic `Type` to serve as the outputs of this op's
application.
- it creates an Apply instance with the input and output Variable, and
return the Apply instance.
......@@ -397,7 +397,7 @@ A common and easy way to ensure inputs are variables is to run them through
``as_tensor_variable``. This function leaves TensorType variables alone, raises
an error for non-TensorType variables, and copies any ``numpy.ndarray`` into
the storage for a TensorType Constant. The ``make_node`` method dictates the
appropriate Type for all output variables.
appropriate `Type` for all output variables.
The ``perform`` method implements the Op's mathematical logic in Python.
The inputs (here ``x``) are passed by value, but a single output is returned
......
doc/extending/op.txt
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......@@ -50,7 +50,7 @@ define the following methods.
.. function:: make_node(*inputs)
This method is responsible for creating output Variables of a
suitable symbolic Type to serve as the outputs of this Op's
suitable symbolic `Type` to serve as the outputs of this Op's
application. The Variables found in ``*inputs`` must be operated on
using Theano's symbolic language to compute the symbolic output
Variables. This method should put these outputs into an Apply
......@@ -769,7 +769,7 @@ as first argument to Apply. We define ``perform`` using the function
``fn`` passed in the constructor.
This design is a flexible way to define basic operations without
duplicating code. The same way a Type subclass represents a set of
duplicating code. The same way a `Type` subclass represents a set of
structurally similar types (see previous section), an `Op` subclass
represents a set of structurally similar operations: operations that
have the same input/output types, operations that only differ in one
......
doc/extending/other_ops.txt
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......@@ -266,8 +266,8 @@ along with pointers to the relevant documentation.
primitive type. The C type associated with this Theano type is the
represented C primitive itself.
* :ref:`SparseType <sparse_ops>` : Theano type used to represent sparse
tensors. There is no equivalent C type for this Theano Type but you
* :ref:`SparseType <sparse_ops>` : Theano `Type` used to represent sparse
tensors. There is no equivalent C type for this Theano `Type` but you
can split a sparse variable into its parts as TensorVariables. Those
can then be used as inputs to an op with C code.
......
doc/extending/type.txt
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......@@ -10,7 +10,7 @@ Making the double type
Type's contract
===============
In Theano's framework, a ``Type`` (:class:`.gof.type.Type`)
In Theano's framework, a ``Type`` (:class:`Type`)
is any object which defines the following
methods. To obtain the default methods described below, the Type should
be an instance of ``Type`` or should be an instance of a
......@@ -22,7 +22,7 @@ i.e. the same default argument names and values. If you wish to add
extra arguments to any of these methods, these extra arguments must have
default values.
.. class:: PureType
.. class:: Type
.. method:: filter(value, strict=False, allow_downcast=None)
......@@ -265,21 +265,21 @@ the Type is to instantiate a plain Type and set the needed fields:
.. testcode::
from theano import gof
from theano.gof.type import Type
double = gof.Type()
double = Type()
double.filter = filter
double.values_eq_approx = values_eq_approx
Another way to make this Type is to make a subclass of ``gof.Type``
Another way to make this Type is to make a subclass of ``Type``
and define ``filter`` and ``values_eq_approx`` in the subclass:
.. code-block:: python
from theano import gof
from theano.gof.type import Type
class Double(gof.Type):
class Double(Type):
def filter(self, x, strict=False, allow_downcast=None):
# See code above.
......@@ -300,9 +300,9 @@ instances of ``Double`` are technically the same Type. However, different
.. testsetup::
from theano import gof
from theano.gof.type import Type
class Double(gof.Type):
class Double(Type):
def filter(self, x, strict=False, allow_downcast=None):
if strict:
......@@ -399,9 +399,9 @@ Final version
.. testcode::
from theano import gof
from theano.gof.type import Type
class Double(gof.Type):
class Double(Type):
def filter(self, x, strict=False, allow_downcast=None):
if strict:
......@@ -432,4 +432,3 @@ Final version
We add one utility function, ``__str__``. That way, when we print
``double``, it will print out something intelligible.
doc/extending/using_params.txt
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......@@ -48,10 +48,10 @@ The first thing you need to do is to define a Theano Type for your
params object. It doesn't have to be complete type because only the
following methods will be used for the type:
- :meth:`filter <PureType.filter>`
- :meth:`__eq__ <PureType.__eq__>`
- :meth:`__hash__ <PureType.__hash__>`
- :meth:`values_eq <PureType.values_eq>`
- :meth:`filter <Type.filter>`
- :meth:`__eq__ <Type.__eq__>`
- :meth:`__hash__ <Type.__hash__>`
- :meth:`values_eq <Type.values_eq>`
Additionaly if you want to use your params with C code, you need to extend `COp`
and implement the following methods:
......
tests/link/test_cc.py
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......@@ -5,7 +5,7 @@ import theano
from theano.gof import fg
from theano.gof.graph import Apply, Constant, Variable
from theano.gof.op import COp
from theano.gof.type import Type
from theano.gof.type import CType
from theano.link.basic import PerformLinker
from theano.link.c.basic import CLinker, DualLinker, OpWiseCLinker
......@@ -15,7 +15,7 @@ def as_variable(x):
return x
class TDouble(Type):
class TDouble(CType):
def filter(self, data, strict=False, allow_downcast=False):
return float(data)
......
theano/__init__.py
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......@@ -97,12 +97,12 @@ from theano.compile.function.types import FunctionMaker
from theano.gof import (
Apply,
Constant,
CType,
FunctionGraph,
Generic,
InconsistencyError,
Op,
OpenMPOp,
Type,
Variable,
generic,
opt,
......
theano/compile/compiledir.py
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......@@ -55,7 +55,7 @@ def cleanup():
elif obj.startswith("c_compiler_str="):
have_c_compiler = True
elif isinstance(
obj, (theano.gof.Op, theano.gof.Type)
obj, (theano.gof.Op, theano.gof.CType)
) and hasattr(obj, "c_code_cache_version"):
v = obj.c_code_cache_version()
if v not in [(), None] and v not in key[0]:
......@@ -139,7 +139,7 @@ def print_compiledir_content():
{
x
for x in flatten(keydata.keys)
if isinstance(x, theano.gof.Type)
if isinstance(x, theano.gof.CType)
}
)
compile_start = compile_end = float("nan")
......
theano/compile/ops.py
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......@@ -16,6 +16,7 @@ import theano
from theano.gof import ParamsType
from theano.gof.graph import Apply, Variable
from theano.gof.op import COp, Op
from theano.gof.type import CType
from theano.misc.safe_asarray import _asarray
......@@ -619,11 +620,11 @@ def as_op(itypes, otypes, infer_shape=None):
"""
if not isinstance(itypes, (list, tuple)):
itypes = [itypes]
if any(not isinstance(t, theano.Type) for t in itypes):
if any(not isinstance(t, CType) for t in itypes):
raise TypeError("itypes has to be a list of Theano types")
if not isinstance(otypes, (list, tuple)):
otypes = [otypes]
if any(not isinstance(t, theano.Type) for t in otypes):
if any(not isinstance(t, CType) for t in otypes):
raise TypeError("otypes has to be a list of Theano types")
# make sure they are lists and not tuples
......
theano/gof/__init__.py
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......@@ -42,5 +42,5 @@ from theano.gof.toolbox import (
ReplaceValidate,
Validator,
)
from theano.gof.type import CEnumType, EnumList, EnumType, Generic, Type, generic
from theano.gof.type import CEnumType, CType, EnumList, EnumType, Generic, generic
from theano.gof.utils import MetaObject, MethodNotDefined
theano/gof/graph.py
浏览文件 @ f35aa65b
......@@ -223,27 +223,26 @@ class Apply(Node):
return cp
def clone_with_new_inputs(self, inputs, strict=True):
"""
Duplicate this Apply instance in a new graph.
"""Duplicate this `Apply` instance in a new graph.
Parameters
----------
inputs
List of Variable instances to use as inputs.
inputs : list of Variables
List of `Variable` instances to use as inputs.
strict : bool
If True, the type fields of all the inputs must be equal
to the current ones (or compatible, for instance Tensor /
GpuArray of the same dtype and broadcastable patterns,
in which case they will be converted into current Type), and
If ``True``, the type fields of all the inputs must be equal
to the current ones (or compatible, for instance `Tensor` /
`GpuArray` of the same dtype and broadcastable patterns,
in which case they will be converted into current `Type`), and
returned outputs are guaranteed to have the same types as
self.outputs. If False, then there's no guarantee that the
clone's outputs will have the same types as self.outputs,
and cloning may not even be possible (it depends on the Op).
``self.outputs``. If ``False``, then there's no guarantee that the
clone's outputs will have the same types as ``self.outputs``,
and cloning may not even be possible (it depends on the `Op`).
Returns
-------
object
An Apply instance with the same op but different outputs.
An `Apply` instance with the same `Op` but different outputs.
"""
assert isinstance(inputs, (list, tuple))
......@@ -672,18 +671,18 @@ def walk(
Parameters
----------
nodes: deque
nodes : deque
The nodes from which to start walking.
expand: callable
expand : callable
A callable that is applied to each node in `nodes`, the results of
which are either new nodes to visit or ``None``.
bfs: bool
bfs : bool
If ``True``, breath first search is used; otherwise, depth first
search.
return_children: bool
return_children : bool
If ``True``, each output node will be accompanied by the output of
`expand` (i.e. the corresponding child nodes).
hash_fn: callable
hash_fn : callable
The function used to produce hashes of the elements in `nodes`.
The default is ``id``.
......@@ -735,10 +734,10 @@ def ancestors(
Parameters
----------
graphs: list of `Variable` instances
graphs : list of `Variable` instances
Output `Variable` instances from which to search backward through
owners.
blockers: list of `Variable` instances
blockers : list of `Variable` instances
A collection of `Variable`s that, when found, prevent the graph search
from preceding from that point.
......@@ -764,10 +763,10 @@ def graph_inputs(
Parameters
----------
graphs: list of `Variable` instances
graphs : list of `Variable` instances
Output `Variable` instances from which to search backward through
owners.
blockers: list of `Variable` instances
blockers : list of `Variable` instances
A collection of `Variable`s that, when found, prevent the graph search
from preceding from that point.
......@@ -788,9 +787,9 @@ def vars_between(
Parameters
----------
ins: list
ins : list
Input `Variable`s.
outs: list
outs : list
Output `Variable`s.
Yields
......@@ -817,9 +816,9 @@ def orphans_between(
Parameters
----------
ins: list
ins : list
Input `Variable`s.
outs: list
outs : list
Output `Variable`s.
Yields
......@@ -845,9 +844,9 @@ def applys_between(
Parameters
----------
ins: list
ins : list
Input `Variable`s.
outs: list
outs : list
Output `Variable`s.
Yields
......@@ -972,15 +971,15 @@ def general_toposort(
Parameters
----------
deps: callable
deps : callable
A python function that takes a node as input and returns its dependence.
compute_deps_cache: optional
compute_deps_cache : optional
If provided deps_cache should also be provided. This is a function like
deps, but that also cache its results in a dict passed as deps_cache.
deps_cache: dict
deps_cache : dict
A dict mapping nodes to their children. This is populated by
`compute_deps_cache`.
clients: dict
clients : dict
If a dict is passed it will be filled with a mapping of
nodes-to-clients for each node in the subgraph.
......@@ -1357,9 +1356,9 @@ def list_of_nodes(
Parameters
----------
inputs: list of Variable
inputs : list of Variable
Input `Variable`s.
outputs: list of Variable
outputs : list of Variable
Output `Variable`s.
"""
......@@ -1380,9 +1379,9 @@ def is_in_ancestors(l_apply: Apply, f_node: Apply) -> bool:
Parameters
----------
l_apply: Apply
l_apply : Apply
The node to walk.
f_apply: Apply
f_apply : Apply
The node to find in `l_apply`.
Returns
......
theano/gof/null_type.py
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from theano.gof.type import Type
from theano.gof.type import CType
class NullType(Type):
class NullType(CType):
"""
A type that allows no values.
......
theano/gof/params_type.py
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......@@ -116,7 +116,7 @@ for more info about enumeration aliases).
import hashlib
import re
from theano.gof.type import EnumType, Type
from theano.gof.type import CType, EnumType
from theano.gof.utils import MethodNotDefined
......@@ -315,14 +315,15 @@ class Params(dict):
return not self.__eq__(other)
class ParamsType(Type):
class ParamsType(CType):
"""
This class can create a struct of Theano types (like TensorType, GpuArrayType, etc.)
to be used as a convenience op parameter wrapping many data.
This class can create a struct of Theano types (like `TensorType`,
`GpuArrayType`, etc.) to be used as a convenience op parameter wrapping
many data.
ParamsType constructor takes key-value args.
Key will be the name of the attribute in the struct.
Value is the Theano type of this attribute, ie. an instance of (a subclass of) :class:`Type`
`ParamsType` constructor takes key-value args. Key will be the name of the
attribute in the struct. Value is the Theano type of this attribute,
ie. an instance of (a subclass of) :class:`CType`
(eg. ``TensorType('int64', (False,))``).
In a Python code any attribute named ``key`` will be available via::
......@@ -337,7 +338,8 @@ class ParamsType(Type):
.. note::
This Type is not complete and should never be used for regular graph operations.
This `Type` is not complete and should never be used for regular graph
operations.
"""
......@@ -358,9 +360,9 @@ class ParamsType(Type):
)
type_instance = kwargs[attribute_name]
type_name = type_instance.__class__.__name__
if not isinstance(type_instance, Type):
if not isinstance(type_instance, CType):
raise TypeError(
'ParamsType: attribute "%s" should inherit from Theano Type, got "%s".'
'ParamsType: attribute "%s" should inherit from Theano CType, got "%s".'
% (attribute_name, type_name)
)
......
theano/gof/type.py
浏览文件 @ f35aa65b
"""
WRITEME
Defines the `Type` class.
"""
"""The `Type` classes."""
import ctypes
......@@ -16,12 +11,13 @@ from theano.gof import graph, utils
from theano.gof.op import COp
from theano.gof.utils import MetaObject, MethodNotDefined
from theano.link.c.interface import CLinkerType
from theano.utils import Singleton
__docformat__ = "restructuredtext en"
class PureType:
class Type:
"""
Interface specification for variable type instances.
......@@ -35,10 +31,10 @@ class PureType:
"""
# the type that will be created by call to make_variable.
# the type that will be created by a call to make_variable.
Variable = graph.Variable
# the type that will be created by call to make_constant
# the type that will be created by a call to make_constant
Constant = graph.Constant
def filter(self, data, strict=False, allow_downcast=None):
......@@ -217,13 +213,9 @@ class PureType:
"""
_nothing = """
"""
class Type(MetaObject, PureType, CLinkerType):
class CType(MetaObject, Type, CLinkerType):
"""
Convenience wrapper combining `PureType` and `CLinkerType`.
Convenience wrapper combining `Type` and `CLinkerType`.
Theano comes with several subclasses of such as:
......@@ -265,48 +257,11 @@ class Type(MetaObject, PureType, CLinkerType):
"""
class SingletonType(Type):
"""
Convenient Base class for a Type subclass with no attributes.
It saves having to implement __eq__ and __hash__.
"""
__instance = None
def __new__(cls):
# If sub-subclass of SingletonType don't redeclare __instance
# when we look for it, we will find it in the subclass. We
# don't want that, so we check the class. When we add one, we
# add one only to the current class, so all is working
# correctly.
if cls.__instance is None or not isinstance(cls.__instance, cls):
cls.__instance = Type.__new__(cls)
return cls.__instance
def __str__(self):
return self.__class__.__name__
# even if we try to make a singleton, this do not always work. So
# we compare the type. See test_type_other.test_none_Constant for
# an exmple. So we need to implement __eq__ and __hash__
def __eq__(self, other):
if self is other:
return True
if type(self) is type(other):
return True
return False
def __hash__(self):
return hash(type(self))
class Generic(SingletonType):
class Generic(CType, Singleton):
"""
Represents a generic Python object.
This class implements the `PureType` and `CLinkerType` interfaces
This class implements the `CType` and `CLinkerType` interfaces
for generic PyObject instances.
EXAMPLE of what this means, or when you would use this type.
......@@ -400,7 +355,7 @@ class _make_cdata(COp):
return (0, self.rtype.version)
class CDataType(Type):
class CDataType(CType):
"""
Represents opaque C data to be passed around. The intent is to
ease passing arbitrary data between ops C code.
......@@ -613,7 +568,7 @@ class CDataTypeConstant(graph.Constant):
CDataType.Constant = CDataTypeConstant
class EnumType(Type, dict):
class EnumType(CType, dict):
"""
Main subclasses:
- :class:`EnumList`
......@@ -804,12 +759,12 @@ class EnumType(Type, dict):
def __getattr__(self, key):
if key in self:
return self[key]
return Type.__getattr__(self, key)
return CType.__getattr__(self, key)
def __setattr__(self, key, value):
if key in self:
raise NotImplementedError("constant values are immutable.")
Type.__setattr__(self, key, value)
CType.__setattr__(self, key, value)
def __setitem__(self, key, value):
raise NotImplementedError("constant values are immutable.")
......
theano/gpuarray/basic_ops.py
浏览文件 @ f35aa65b
......@@ -12,7 +12,7 @@ from theano.gof.graph import Apply, Variable
from theano.gof.op import COp, ExternalCOp, Op
from theano.gof.opt import copy_stack_trace
from theano.gof.params_type import ParamsType
from theano.gof.type import Type
from theano.gof.type import CType
from theano.gof.utils import MethodNotDefined
from theano.gradient import grad_undefined
from theano.link.c.interface import HideC
......@@ -220,7 +220,7 @@ class Kernel:
def get_dtype(t):
if isinstance(t, str):
return np.dtype(t)
elif isinstance(t, Type):
elif isinstance(t, CType):
return t.dtype
elif isinstance(t, Variable):
return t.type.dtype
......
theano/gpuarray/subtensor.py
浏览文件 @ f35aa65b
......@@ -6,6 +6,7 @@ import theano.tensor as tt
from theano import gof
from theano.gof.op import COp, Op
from theano.gof.params_type import ParamsType
from theano.gof.type import CType
from theano.gradient import grad_not_implemented
from theano.link.c.interface import HideC
from theano.scalar import bool as bool_t
......@@ -160,7 +161,7 @@ class GpuSubtensor(HideC, Subtensor):
return "0", 1
elif isinstance(idx, (np.integer, int)):
return str(idx), 0
elif isinstance(idx, gof.Type):
elif isinstance(idx, CType):
return indices.pop(0), 0
else:
assert 0, idx
......@@ -195,7 +196,7 @@ class GpuSubtensor(HideC, Subtensor):
file=sio,
)
else:
if isinstance(idx, gof.Type):
if isinstance(idx, CType):
start = indices.pop(0)
elif isinstance(idx, (np.integer, int)):
start = idx
......@@ -263,7 +264,7 @@ class GpuIncSubtensor(IncSubtensor):
indices = list(reversed(inputs[2:]))
def convert(entry):
if isinstance(entry, gof.Type):
if isinstance(entry, CType):
rval = indices.pop()
return rval
elif isinstance(entry, slice):
......
theano/gpuarray/type.py
浏览文件 @ f35aa65b
......@@ -6,7 +6,7 @@ import warnings
import numpy as np
import theano
from theano import Constant, Type, Variable, config, scalar, tensor
from theano import Constant, CType, Variable, config, scalar, tensor
from theano.compile import SharedVariable
from theano.misc.safe_asarray import _asarray
from theano.tensor.type import TensorType
......@@ -127,7 +127,7 @@ def _unreg_context(name):
del _context_reg[name]
class GpuArrayType(Type):
class GpuArrayType(CType):
"""
The type that represents an array on a gpu.
......@@ -173,7 +173,7 @@ class GpuArrayType(Type):
See Also
--------
theano.gof.type.PureType
theano.gof.type.Type
"""
......@@ -883,7 +883,7 @@ theano.compile.register_specify_shape_c_code(
)
class GpuContextType(Type):
class GpuContextType(CType):
"""
Minimal type used for passing contexts to nodes.
......
theano/gradient.py
浏览文件 @ f35aa65b
......@@ -119,7 +119,7 @@ def grad_undefined(op, x_pos, x, comment=""):
)()
class DisconnectedType(theano.gof.type.Type):
class DisconnectedType(theano.gof.type.CType):
"""A type indicating that a variable is a result
of taking the gradient of c with respect to x
......
theano/link/basic.py
浏览文件 @ f35aa65b
......@@ -4,7 +4,7 @@ from copy import copy, deepcopy
from theano.configdefaults import config
from theano.gof.fg import FunctionGraph
from theano.gof.graph import Apply
from theano.gof.type import Type
from theano.gof.type import CType
from theano.link.utils import gc_helper, map_storage, raise_with_op, streamline
from theano.utils import deprecated, difference, to_return_values
......@@ -45,7 +45,7 @@ class Container:
):
if not isinstance(storage, list) or not len(storage) >= 1:
raise TypeError("storage must be a list of length at least one")
if isinstance(r, Type):
if isinstance(r, CType):
self.type = r
else:
self.type = r.type
......
theano/link/c/basic.py
浏览文件 @ f35aa65b
差异被折叠。
theano/link/jax/jax_dispatch.py
浏览文件 @ f35aa65b
......@@ -6,7 +6,6 @@ import jax
import jax.numpy as jnp
import jax.scipy as jsp
import theano
from theano.compile.ops import (
DeepCopyOp,
Rebroadcast,
......@@ -17,6 +16,7 @@ from theano.compile.ops import (
)
from theano.configdefaults import config
from theano.gof import FunctionGraph
from theano.gof.type import CType
from theano.ifelse import IfElse
from theano.scalar.basic import Cast, Clip, Composite, Identity, ScalarOp, Second
from theano.scan.op import Scan
......@@ -589,7 +589,7 @@ def jax_funcify_IfElse(op):
def convert_indices(indices, entry):
if indices and isinstance(entry, theano.gof.Type):
if indices and isinstance(entry, CType):
rval = indices.pop(0)
return rval
elif isinstance(entry, slice):
......
theano/scalar/basic.py
浏览文件 @ f35aa65b
......@@ -25,7 +25,7 @@ from theano.gof.fg import FunctionGraph
from theano.gof.graph import Apply, Constant, Variable, clone, list_of_nodes
from theano.gof.op import COp
from theano.gof.opt import MergeOptimizer
from theano.gof.type import Type
from theano.gof.type import CType
from theano.gof.utils import MetaObject, MethodNotDefined
from theano.gradient import DisconnectedType, grad_undefined
from theano.misc.safe_asarray import _asarray
......@@ -313,7 +313,7 @@ def constant(x, name=None, dtype=None):
return ScalarConstant(get_scalar_type(str(x.dtype)), x, name=name)
class Scalar(Type):
class Scalar(CType):
"""
Internal class, should not be used by clients.
......@@ -1096,7 +1096,7 @@ class ScalarOp(COp):
if hasattr(self, "output_types_preference"):
variables = self.output_types_preference(*types)
if not isinstance(variables, (list, tuple)) or any(
not isinstance(x, Type) for x in variables
not isinstance(x, CType) for x in variables
):
raise TypeError(
"output_types_preference should return a list or a tuple of types",
......
theano/sparse/type.py
浏览文件 @ f35aa65b
......@@ -32,7 +32,7 @@ def _is_sparse(x):
return isinstance(x, scipy.sparse.spmatrix)
class SparseType(gof.Type):
class SparseType(gof.CType):
"""
Fundamental way to create a sparse node.
......
theano/tensor/basic.py
浏览文件 @ f35aa65b
......@@ -565,7 +565,7 @@ def get_scalar_constant_value(
var.ndim == 0 for var in v.owner.inputs[0].owner.inputs[1:]
):
idx = v.owner.op.idx_list[0]
if isinstance(idx, gof.Type):
if isinstance(idx, gof.CType):
idx = get_scalar_constant_value(
v.owner.inputs[1], max_recur=max_recur
)
......@@ -579,7 +579,7 @@ def get_scalar_constant_value(
var.ndim == 1 for var in v.owner.inputs[0].owner.inputs[1:]
):
idx = v.owner.op.idx_list[0]
if isinstance(idx, gof.Type):
if isinstance(idx, gof.CType):
idx = get_scalar_constant_value(
v.owner.inputs[1], max_recur=max_recur
)
......@@ -616,7 +616,7 @@ def get_scalar_constant_value(
):
idx = v.owner.op.idx_list[0]
if isinstance(idx, gof.Type):
if isinstance(idx, gof.CType):
idx = get_scalar_constant_value(
v.owner.inputs[1], max_recur=max_recur
)
......@@ -638,7 +638,7 @@ def get_scalar_constant_value(
op = owner.op
idx_list = op.idx_list
idx = idx_list[0]
if isinstance(idx, gof.Type):
if isinstance(idx, gof.CType):
idx = get_scalar_constant_value(
owner.inputs[1], max_recur=max_recur
)
......
theano/tensor/random/type.py
浏览文件 @ f35aa65b
......@@ -3,10 +3,10 @@ import sys
import numpy as np
import theano
from theano.gof.type import Type
from theano.gof.type import CType
class RandomStateType(Type):
class RandomStateType(CType):
"""A Type wrapper for `numpy.random.RandomState`.
The reason this exists (and `Generic` doesn't suffice) is that
......
theano/tensor/subtensor.py
浏览文件 @ f35aa65b
......@@ -7,13 +7,13 @@ from textwrap import dedent
import numpy as np
import theano
from theano import gof
from theano import scalar as scal
from theano.configdefaults import config
from theano.gof import MethodNotDefined, ParamsType
from theano.gof.graph import Apply
from theano.gof.graph import Apply, Variable
from theano.gof.op import COp, Op
from theano.gof.type import Type
from theano.gof.params_type import ParamsType
from theano.gof.type import CType
from theano.gof.utils import MethodNotDefined
from theano.gradient import DisconnectedType
from theano.misc.safe_asarray import _asarray
from theano.printing import pprint
......@@ -56,7 +56,7 @@ def as_index_constant(a):
)
elif isinstance(a, (int, np.integer)):
return scal.ScalarConstant(scal.int64, a)
elif not isinstance(a, theano.tensor.Variable):
elif not isinstance(a, Variable):
return theano.tensor.as_tensor(a)
else:
return a
......@@ -82,7 +82,7 @@ def get_idx_list(inputs, idx_list, get_count=False):
# General case
def convert(entry):
if isinstance(entry, gof.Type):
if isinstance(entry, CType):
return indices.pop()
elif isinstance(entry, slice):
return slice(convert(entry.start), convert(entry.stop), convert(entry.step))
......@@ -115,7 +115,7 @@ def get_canonical_form_slice(theslice, length):
try:
x_constant = get_scalar_constant_value(x)
is_constant = True
except theano.tensor.NotScalarConstantError:
except NotScalarConstantError:
x_constant = theano.tensor.extract_constant(x)
is_constant = False
return x_constant, is_constant
......@@ -487,30 +487,30 @@ class Subtensor(COp):
)
if (
isinstance(entry, (np.ndarray, theano.tensor.Variable))
isinstance(entry, (np.ndarray, Variable))
and hasattr(entry, "dtype")
and entry.dtype == "bool"
):
raise AdvancedIndexingError("Invalid index type or slice for Subtensor")
if isinstance(entry, gof.Variable) and (
if isinstance(entry, Variable) and (
entry.type in invalid_scal_types or entry.type in invalid_tensor_types
):
raise TypeError("Expected an integer")
if isinstance(entry, gof.Variable) and entry.type in scal_types:
if isinstance(entry, Variable) and entry.type in scal_types:
return entry.type
elif isinstance(entry, gof.Type) and entry in scal_types:
elif isinstance(entry, CType) and entry in scal_types:
return entry
if (
isinstance(entry, gof.Variable)
isinstance(entry, Variable)
and entry.type in tensor_types
and np.all(entry.type.broadcastable)
):
return scal.get_scalar_type(entry.type.dtype)
elif (
isinstance(entry, gof.Type)
isinstance(entry, CType)
and entry in tensor_types
and np.all(entry.broadcastable)
):
......@@ -553,7 +553,7 @@ class Subtensor(COp):
"""
Return the idx_list with constant inputs replaced by their
python scalar equivalent.
May raise `theano.tensor.NotScalarConstantError` if the idx contains
May raise `NotScalarConstantError` if the idx contains
non-constant entries.
If allow_partial is True, then entries that are not constant will
......@@ -594,7 +594,7 @@ class Subtensor(COp):
only_process_constants=only_process_constants,
elemwise=elemwise,
)
except theano.tensor.NotScalarConstantError:
except NotScalarConstantError:
if allow_partial:
return val
else:
......@@ -610,7 +610,7 @@ class Subtensor(COp):
# Since scal.as_scalar does not know about tensor types (it would
# create a circular import) , this method converts either a
# TensorVariable or a ScalarVariable to a scalar.
if isinstance(a, gof.Variable) and isinstance(a.type, TensorType):
if isinstance(a, Variable) and isinstance(a.type, TensorType):
return theano.tensor.scalar_from_tensor(a)
else:
return scal.as_scalar(a)
......@@ -633,7 +633,7 @@ class Subtensor(COp):
raise IndexError("too many indices for array")
input_types = Subtensor.collapse(
idx_list, lambda entry: isinstance(entry, gof.Type)
idx_list, lambda entry: isinstance(entry, CType)
)
if len(inputs) != len(input_types):
raise IndexError(
......@@ -672,7 +672,7 @@ class Subtensor(COp):
broadcastable.append(False)
return gof.Apply(
return Apply(
self,
(x,) + inputs,
[theano.tensor.tensor(dtype=x.type.dtype, broadcastable=broadcastable)],
......@@ -851,7 +851,7 @@ class Subtensor(COp):
inc_spec_pos(1)
if depth == 0:
is_slice.append(0)
elif isinstance(entry, Type):
elif isinstance(entry, CType):
init_cmds.append(
"subtensor_spec[%i] = %s;" % (spec_pos(), inputs[input_pos()])
)
......@@ -1050,7 +1050,7 @@ class Subtensor(COp):
return (9,)
def c_code(self, node, name, inputs, outputs, sub): # DEBUG
if not isinstance(node.inputs[0].type, theano.tensor.TensorType):
if not isinstance(node.inputs[0].type, TensorType):
raise NotImplementedError()
x = inputs[0]
......@@ -1469,7 +1469,7 @@ class IncSubtensor(COp):
raise IndexError("too many indices for array")
input_types = Subtensor.collapse(
idx_list, lambda entry: isinstance(entry, gof.Type)
idx_list, lambda entry: isinstance(entry, CType)
)
if len(inputs) != len(input_types):
raise IndexError(
......@@ -1482,7 +1482,7 @@ class IncSubtensor(COp):
% (input.type, expected_type)
)
return gof.Apply(self, (x, y) + inputs, [x.type()])
return Apply(self, (x, y) + inputs, [x.type()])
def decl_view(self):
return "PyArrayObject * zview = NULL;"
......@@ -1493,7 +1493,7 @@ class IncSubtensor(COp):
indices = list(reversed(inputs[2:]))
def convert(entry):
if isinstance(entry, gof.Type):
if isinstance(entry, CType):
return indices.pop()
elif isinstance(entry, slice):
return slice(
......@@ -1645,7 +1645,7 @@ class IncSubtensor(COp):
"""
if not isinstance(node.inputs[0].type, theano.tensor.TensorType):
if not isinstance(node.inputs[0].type, TensorType):
raise NotImplementedError()
def c_code_cache_version(self):
......@@ -2239,9 +2239,9 @@ def as_index_variable(idx):
return NoneConst.clone()
if isinstance(idx, slice):
return make_slice(idx)
if isinstance(idx, gof.Variable) and isinstance(idx.type, SliceType):
if isinstance(idx, Variable) and isinstance(idx.type, SliceType):
return idx
if isinstance(idx, gof.Variable) and isinstance(idx.type, NoneTypeT):
if isinstance(idx, Variable) and isinstance(idx.type, NoneTypeT):
return idx
idx = theano.tensor.as_tensor_variable(idx)
if idx.type.dtype not in theano.tensor.discrete_dtypes:
......@@ -2312,7 +2312,7 @@ class AdvancedSubtensor(Op):
for i in indexed_result_shape(fake_shape, bcast_index)
]
return gof.Apply(
return Apply(
self,
(x,) + index,
[theano.tensor.tensor(dtype=x.type.dtype, broadcastable=bcast)],
......@@ -2415,7 +2415,7 @@ class AdvancedIncSubtensor(Op):
if isinstance(inp, (list, tuple)):
inp = theano.tensor.as_tensor_variable(inp)
new_inputs.append(inp)
return gof.Apply(
return Apply(
self,
(x, y) + tuple(new_inputs),
[
......
theano/tensor/type.py
浏览文件 @ f35aa65b
......@@ -7,14 +7,14 @@ import theano
from theano import scalar as scal
from theano.configdefaults import config
from theano.gof.graph import Variable
from theano.gof.type import Type
from theano.gof.type import CType
from theano.misc.safe_asarray import _asarray
_logger = logging.getLogger("theano.tensor.type")
class TensorType(Type):
class TensorType(CType):
"""
Symbolic `Type` representing a numpy.ndarray value.
......
theano/tensor/type_other.py
浏览文件 @ f35aa65b
......@@ -7,7 +7,7 @@ import numpy as np
import theano
from theano.gof.graph import Apply, Constant
from theano.gof.op import Op
from theano.gof.type import Generic, Type
from theano.gof.type import CType, Generic
from theano.gradient import DisconnectedType
......@@ -49,7 +49,7 @@ class MakeSlice(Op):
make_slice = MakeSlice()
class SliceType(Type):
class SliceType(CType):
def filter(self, x, strict=False, allow_downcast=None):
if isinstance(x, slice):
return x
......
theano/typed_list/type.py
浏览文件 @ f35aa65b
from theano.gof.type import Type
from theano.gof.type import CType, Type
class TypedListType(Type):
class TypedListType(CType):
"""
Parameters
......
theano/utils.py
浏览文件 @ f35aa65b
......@@ -119,39 +119,6 @@ def get_unbound_function(unbound):
return unbound
class DefaultOrderedDict(OrderedDict):
def __init__(self, default_factory=None, *a, **kw):
if default_factory is not None and not isinstance(default_factory, Callable):
raise TypeError("first argument must be callable")
OrderedDict.__init__(self, *a, **kw)
self.default_factory = default_factory
def __getitem__(self, key):
try:
return OrderedDict.__getitem__(self, key)
except KeyError:
return self.__missing__(key)
def __missing__(self, key):
if self.default_factory is None:
raise KeyError(key)
self[key] = value = self.default_factory()
return value
def __reduce__(self):
if self.default_factory is None:
args = tuple()
else:
args = (self.default_factory,)
return type(self), args, None, None, list(self.items())
def copy(self):
return self.__copy__()
def __copy__(self):
return type(self)(self.default_factory, self)
def maybe_add_to_os_environ_pathlist(var, newpath):
"""Unfortunately, Conda offers to make itself the default Python
and those who use it that way will probably not activate envs
......@@ -377,22 +344,6 @@ def flatten(a):
return [a]
class NoDuplicateOptWarningFilter(logging.Filter):
"""Filter to avoid duplicating optimization warnings."""
prev_msgs = set()
def filter(self, record):
msg = record.getMessage()
if msg.startswith("Optimization Warning: "):
if msg in self.prev_msgs:
return False
else:
self.prev_msgs.add(msg)
return True
return True
def apply_across_args(*fns):
"""Create new functions that distributes the wrapped functions across iterable arguments.
......@@ -418,3 +369,85 @@ def apply_across_args(*fns):
return partial(f2, fns[0])
else:
return [partial(f2, f) for f in fns]
class NoDuplicateOptWarningFilter(logging.Filter):
"""Filter to avoid duplicating optimization warnings."""
prev_msgs = set()
def filter(self, record):
msg = record.getMessage()
if msg.startswith("Optimization Warning: "):
if msg in self.prev_msgs:
return False
else:
self.prev_msgs.add(msg)
return True
return True
class Singleton:
"""Convenient base class for a singleton.
It saves having to implement __eq__ and __hash__.
"""
__instance = None
def __new__(cls):
# If sub-subclass of SingletonType don't redeclare __instance
# when we look for it, we will find it in the subclass. We
# don't want that, so we check the class. When we add one, we
# add one only to the current class, so all is working
# correctly.
if cls.__instance is None or not isinstance(cls.__instance, cls):
cls.__instance = super().__new__(cls)
return cls.__instance
def __str__(self):
return self.__class__.__name__
def __eq__(self, other):
if self is other:
return True
if type(self) is type(other):
return True
return False
def __hash__(self):
return hash(type(self))
class DefaultOrderedDict(OrderedDict):
def __init__(self, default_factory=None, *a, **kw):
if default_factory is not None and not isinstance(default_factory, Callable):
raise TypeError("first argument must be callable")
OrderedDict.__init__(self, *a, **kw)
self.default_factory = default_factory
def __getitem__(self, key):
try:
return OrderedDict.__getitem__(self, key)
except KeyError:
return self.__missing__(key)
def __missing__(self, key):
if self.default_factory is None:
raise KeyError(key)
self[key] = value = self.default_factory()
return value
def __reduce__(self):
if self.default_factory is None:
args = tuple()
else:
args = (self.default_factory,)
return type(self), args, None, None, list(self.items())
def copy(self):
return self.__copy__()
def __copy__(self):
return type(self)(self.default_factory, self)
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