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提交 b1f7979f authored 作者: Arnaud Bergeron's avatar Arnaud Bergeron
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Fixup extending/* and delete associated tests.

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doc/extending/cop.txt
浏览文件 @ b1f7979f
......@@ -253,8 +253,10 @@ We will be defining C code for the multiplication Op on doubles.
**c_code**
.. If you modify this code, also change :
.. theano/tests/test_tutorial.py:T_extending.test_extending_2
.. testsetup::
from theano import Op
mul = Op()
.. testcode::
......@@ -298,10 +300,6 @@ As before, I tried to organize the code in order to minimize
repetition. You can check that mul produces the same C code in this
version that it produces in the code I gave above.
.. If you modify this code, also change :
.. theano/tests/test_tutorial.py:T_extending.test_extending_2
.. testcode::
from theano import gof
......
doc/extending/ctype.txt
浏览文件 @ b1f7979f
......@@ -159,9 +159,7 @@ Defining the methods
.. testsetup::
import theano
.. If you modify this code, also change :
.. theano/tests/test_tutorial.py:T_extending.test_extending_2
double = theano.Type()
**c_declare**
......@@ -193,9 +191,6 @@ your Type. If you wish people to develop operations that make use of
it, it's best to publish it somewhere.
.. If you modify this code, also change :
.. theano/tests/test_tutorial.py:T_extending.test_extending_2
**c_init**
.. testcode::
......@@ -222,9 +217,6 @@ you should only assume that either ``c_init`` or ``c_extract`` has been
called, without knowing for sure which of the two.
.. If you modify this code, also change :
.. theano/tests/test_tutorial.py:T_extending.test_extending_2
**c_extract**
.. testcode::
......@@ -261,9 +253,6 @@ using the ``PyFloat_AsDouble`` function (yet again provided by CPython's C
API) and we put it in our double variable that we declared previously.
.. If you modify this code, also change :
.. theano/tests/test_tutorial.py:T_extending.test_extending_2
**c_sync**
.. testcode::
......@@ -323,9 +312,6 @@ than sorry.
do *NOT* decrease its reference count!
.. If you modify this code, also change :
.. theano/tests/test_tutorial.py:T_extending.test_extending_2
**c_cleanup**
.. testcode::
......@@ -374,14 +360,8 @@ depends on the the relationship between Python and C with respect to
that Variable. For instance, imagine you define the following function
and call it:
.. If you modify this code, also change :
.. theano/tests/test_tutorial.py:T_extending.test_extending_2
.. testcode::
.. code-block:: python
from theano import function
from theano.tensor import double
x, y, z = double('x'), double('y'), double('z')
a = add(x, y)
b = mul(a, z)
......@@ -463,9 +443,6 @@ multiplication block.
Final version
=============
.. If you modify this code, also change :
.. theano/tests/test_tutorial.py:T_extending.test_extending_2
.. testcode::
from theano import gof
......@@ -530,7 +507,7 @@ know how to generate C code.
You can implement c_code for this op. You register it like this:
.. testcode::
.. code-block:: python
theano.compile.ops.register_deep_copy_op_c_code(YOUR_TYPE_CLASS, THE_C_CODE, version=())
......@@ -552,7 +529,7 @@ ViewOp to generate C code when working with this type, as
otherwise it will use Python code instead. This is achieved by
calling:
.. testcode::
.. code-block:: python
theano.compile.ops.register_view_op_c_code(YOUR_TYPE_CLASS, THE_C_CODE, version=())
......@@ -572,7 +549,7 @@ Theano Variable that has a shape attribute (Shape_i returns only one of
the elements of the shape).
.. testcode::
.. code-block:: python
theano.compile.ops.register_shape_c_code(YOUR_TYPE_CLASS, THE_C_CODE, version=())
theano.compile.ops.register_shape_i_c_code(YOUR_TYPE_CLASS, THE_C_CODE, CHECK_INPUT, version=())
......
doc/extending/fibby.txt
浏览文件 @ b1f7979f
差异被折叠。
doc/extending/graphstructures.txt
浏览文件 @ b1f7979f
......@@ -20,11 +20,11 @@ should help you understand how these pieces fit together:
.. testcode::
import theano.tensor as T
import theano.tensor as T
x = T.dmatrix('x')
y = T.dmatrix('y')
z = x + y
x = T.dmatrix('x')
y = T.dmatrix('y')
z = x + y
**Diagram**
......@@ -71,73 +71,67 @@ without any shortcuts, that will make the graph construction very explicit.
This is what you would normally type:
.. If you modify this code, also change :
.. theano/tests/test_tutorial.py:T_graphstructures.test_graphstructures_1
.. testcode::
# create 3 Variables with owner = None
x = T.matrix('x')
y = T.matrix('y')
z = T.matrix('z')
# create 3 Variables with owner = None
x = T.matrix('x')
y = T.matrix('y')
z = T.matrix('z')
# create 2 Variables (one for 'e', one intermediate for y*z)
# create 2 Apply instances (one for '+', one for '*')
e = x + y * z
# create 2 Variables (one for 'e', one intermediate for y*z)
# create 2 Apply instances (one for '+', one for '*')
e = x + y * z
**Long example**
This is what you would type to build the graph explicitly:
.. If you modify this code, also change :
.. theano/tests/test_tutorial.py:T_graphstructures.test_graphstructures_1
.. testcode::
from theano.tensor import add, mul, Apply, Variable, TensorType
from theano.tensor import add, mul, Apply, Variable, Constant, TensorType
# Instantiate a type that represents a matrix of doubles
float64_matrix = TensorType(dtype = 'float64', # double
broadcastable = (False, False)) # matrix
# Instantiate a type that represents a matrix of doubles
float64_matrix = TensorType(dtype='float64', # double
broadcastable=(False, False)) # matrix
# We make the Variable instances we need.
x = Variable(type = float64_matrix, name = 'x')
y = Variable(type = float64_matrix, name = 'y')
z = Variable(type = float64_matrix, name = 'z')
# This is the Variable that we want to symbolically represents y*z
mul_variable = Variable(type = float64_matrix)
assert mul_variable.owner is None
# Instantiate a symbolic multiplication
node_mul = Apply(op = mul,
inputs = [y, z],
outputs = [mul_variable])
# Fields 'owner' and 'index' are set by Apply
assert mul_variable.owner is node_mul
# 'index' is the position of mul_variable in mode_mul's outputs
assert mul_variable.index == 0
# This is the Variable that we want to symbolically represents x+(y*z)
add_variable = Variable(type = float64_matrix)
assert add_variable.owner is None
# Instantiate a symbolic addition
node_add = Apply(op = add,
inputs = [x, mul_variable],
outputs = [add_variable])
# Fields 'owner' and 'index' are set by Apply
assert add_variable.owner is node_add
assert add_variable.index == 0
e = add_variable
# We have access to x, y and z through pointers
assert e.owner.inputs[0] is x
assert e.owner.inputs[1] is mul_variable
assert e.owner.inputs[1].owner.inputs[0] is y
assert e.owner.inputs[1].owner.inputs[1] is z
x = Variable(type=float64_matrix, name='x')
y = Variable(type=float64_matrix, name='y')
z = Variable(type=float64_matrix, name='z')
# This is the Variable that we want to symbolically represents y*z
mul_variable = Variable(type=float64_matrix)
assert mul_variable.owner is None
# Instantiate a symbolic multiplication
node_mul = Apply(op=mul,
inputs=[y, z],
outputs=[mul_variable])
# Fields 'owner' and 'index' are set by Apply
assert mul_variable.owner is node_mul
# 'index' is the position of mul_variable in mode_mul's outputs
assert mul_variable.index == 0
# This is the Variable that we want to symbolically represents x+(y*z)
add_variable = Variable(type=float64_matrix)
assert add_variable.owner is None
# Instantiate a symbolic addition
node_add = Apply(op=add,
inputs=[x, mul_variable],
outputs=[add_variable])
# Fields 'owner' and 'index' are set by Apply
assert add_variable.owner is node_add
assert add_variable.index == 0
e = add_variable
# We have access to x, y and z through pointers
assert e.owner.inputs[0] is x
assert e.owner.inputs[1] is mul_variable
assert e.owner.inputs[1].owner.inputs[0] is y
assert e.owner.inputs[1].owner.inputs[1] is z
Note how the call to ``Apply`` modifies the ``owner`` and ``index``
......@@ -163,12 +157,11 @@ builds the following graph:
.. testcode::
node = Apply(op = add,
inputs = [Variable(type = dscalar, name = 'x'),
Constant(type = lscalar, data = 1)],
outputs = [Variable(type = dscalar)])
e = node.outputs[0]
node = Apply(op=add,
inputs=[Variable(type=T.dscalar, name='x'),
Constant(type=T.lscalar, data=1)],
outputs=[Variable(type=T.dscalar)])
e = node.outputs[0]
Graph Structures
......@@ -402,39 +395,34 @@ In both types of pairs, the second element of the tuple is an index,
such that: ``var.clients[*][0].inputs[index]`` or
``fgraph.outputs[index]`` is that variable.
.. testcode::
import theano
v = theano.tensor.vector()
f = theano.function([v], (v+1).sum())
theano.printing.debugprint(f)
# Sorted list of all nodes in the compiled graph.
topo = f.maker.fgraph.toposort()
topo[0].outputs[0].clients
# [(Sum(Elemwise{add,no_inplace}.0), 0)]
topo[1].outputs[0].clients
# [('output', 0)]
# An internal variable
var = topo[0].outputs[0]
client = var.clients[0]
client
# (Sum(Elemwise{add,no_inplace}.0), 0)
type(client[0])
# <class 'theano.gof.graph.Apply'>
assert client[0].inputs[client[1]] is var
# An output of the graph
var = topo[1].outputs[0]
client = var.clients[0]
client
# ('output', 0)
assert f.maker.fgraph.outputs[client[1]] is var
.. testoutput::
Sum{acc_dtype=float64} [@A] '' 1
|Elemwise{add,no_inplace} [@B] '' 0
|TensorConstant{(1,) of 1.0} [@C]
|<TensorType(float64, vector)> [@D]
\ No newline at end of file
>>> import theano
>>> v = theano.tensor.vector()
>>> f = theano.function([v], (v+1).sum())
>>> theano.printing.debugprint(f)
Sum{acc_dtype=float64} [@A] '' 1
|Elemwise{add,no_inplace} [@B] '' 0
|TensorConstant{(1,) of 1.0} [@C]
|<TensorType(float64, vector)> [@D]
>>> # Sorted list of all nodes in the compiled graph.
>>> topo = f.maker.fgraph.toposort()
>>> topo[0].outputs[0].clients
[(Sum{acc_dtype=float64}(Elemwise{add,no_inplace}.0), 0)]
>>> topo[1].outputs[0].clients
[('output', 0)]
>>> # An internal variable
>>> var = topo[0].outputs[0]
>>> client = var.clients[0]
>>> client
(Sum{acc_dtype=float64}(Elemwise{add,no_inplace}.0), 0)
>>> type(client[0])
<class 'theano.gof.graph.Apply'>
>>> assert client[0].inputs[client[1]] is var
>>> # An output of the graph
>>> var = topo[1].outputs[0]
>>> client = var.clients[0]
>>> client
('output', 0)
>>> assert f.maker.fgraph.outputs[client[1]] is var
doc/extending/inplace.txt
浏览文件 @ b1f7979f
......@@ -55,6 +55,11 @@ Suppose you had an Op which took ``x`` as input and returned
purpose, you would set the ``view_map`` field as follows:
.. testsetup::
from theano import Op
myop = Op()
.. testcode::
myop.view_map = {0: [0]}
......
doc/extending/op.txt
浏览文件 @ b1f7979f
......@@ -541,9 +541,6 @@ multiplication Op could take an arbitrary number of arguments.
First, we'll instantiate a ``mul`` Op:
.. If you modify this code, also change :
.. theano/tests/test_tutorial.py:T_extending.test_extending_1
.. testcode:: mul
from theano import gof
......@@ -558,9 +555,6 @@ two. This function ensures that both inputs have the ``double`` type.
Since multiplying two doubles yields a double, this function makes an
Apply node with an output Variable of type ``double``.
.. If you modify this code, also change :
.. theano/tests/test_tutorial.py:T_extending.test_extending_1
.. testcode:: mul
def make_node(x, y):
......@@ -594,8 +588,6 @@ built-in type ``float`` because this is the type that ``double.filter()``
will always return, per our own definition. ``output_storage`` will
contain a single storage cell for the multiplication's variable.
.. If you modify this code, also change :
.. theano/tests/test_tutorial.py:T_extending.test_extending_1
.. testcode:: mul
def perform(node, inputs, output_storage):
......@@ -626,31 +618,32 @@ Here, ``z`` is a list of one element. By default, ``z == [None]``.
Trying out our new Op
=====================
.. If you modify this code, also change :
.. theano/tests/test_tutorial.py:T_extending.test_extending_1
In the following code, we use our new Op:
>>> import theano
>>> x, y = double('x'), double('y')
>>> z = mul(x, y)
>>> f = theano.function([x, y], z)
>>> f(5, 6)
30.0
>>> f(5.6, 6.7)
37.519999999999996
.. doctest:: mul
>>> import theano
>>> x, y = double('x'), double('y')
>>> z = mul(x, y)
>>> f = theano.function([x, y], z)
>>> f(5, 6)
30.0
>>> f(5.6, 6.7)
37.519999999999996
Note that there is an implicit call to
``double.filter()`` on each argument, so if we give integers as inputs
they are magically cast to the right type. Now, what if we try this?
>>> x = double('x')
>>> z = mul(x, 2)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/u/breuleuo/hg/theano/theano/gof/op.py", line 207, in __call__
File "<stdin>", line 2, in make_node
AttributeError: 'int' object has no attribute 'type'
.. doctest:: mul
>>> x = double('x')
>>> z = mul(x, 2)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/u/breuleuo/hg/theano/theano/gof/op.py", line 207, in __call__
File "<stdin>", line 2, in make_node
AttributeError: 'int' object has no attribute 'type'
Automatic Constant Wrapping
---------------------------
......@@ -659,8 +652,6 @@ Well, OK. We'd like our Op to be a bit more flexible. This can be done
by modifying ``make_node`` to accept Python ``int`` or ``float`` as
``x`` and/or ``y``:
.. If you modify this code, also change :
.. theano/tests/test_tutorial.py:T_extending.test_extending_1
.. testcode:: mul
def make_node(x, y):
......@@ -677,16 +668,15 @@ Whenever we pass a Python int or float instead of a Variable as ``x`` or
``y``, ``make_node`` will convert it to :ref:`constant` for us. ``gof.Constant``
is a :ref:`variable` we statically know the value of.
.. If you modify this code, also change :
.. theano/tests/test_tutorial.py:T_op.test_op_1
.. doctest:: mul
>>> x = double('x')
>>> z = mul(x, 2)
>>> f = theano.function([x], z)
>>> f(10)
20.0
>>> f(3.4)
6.7999999999999998
>>> x = double('x')
>>> z = mul(x, 2)
>>> f = theano.function([x], z)
>>> f(10)
20.0
>>> f(3.4)
6.8
Now the code works the way we want it to.
......@@ -707,9 +697,6 @@ operations ``add``, ``sub`` and ``div``, code for ``make_node`` can be
shared between these Ops. Here is revised implementation of these four
arithmetic operators:
.. If you modify this code, also change :
.. theano/tests/test_tutorial.py:T_extending.test_extending_1
.. testcode::
from theano import gof
......
doc/extending/optimization.txt
浏览文件 @ b1f7979f
......@@ -119,9 +119,6 @@ Global optimization
Here is the code for a global optimization implementing the
simplification described above:
.. If you modify this code, also change :
.. theano/tests/test_tutorial.py:T_extending.test_extending_2
.. testcode::
import theano
......@@ -182,9 +179,6 @@ pointer-following game you need to get ahold of the nodes of interest
for the simplification (``x``, ``y``, ``z``, ``a``, ``b``, etc.).
.. If you modify this code, also change :
.. theano/tests/test_tutorial.py:T_extending.test_extending_2
Test time:
>>> from theano.scalar import float64, add, mul, true_div
......@@ -222,8 +216,8 @@ computation, using the ``merge_optimizer`` defined in
``theano.gof.opt``.
>>> from theano.gof.opt import merge_optimizer
>>> merge_optimizer.optimize(e)
(0, 0.0001430511474609375, None, None, {}, 1, 0)
>>> merge_optimizer.optimize(e) # doctest: +ELLIPSIS
(0, ..., None, None, {}, 1, 0)
>>> e
[true_div(mul(*1 -> add(y, z), x), *1)]
>>> simplify.optimize(e)
......@@ -254,9 +248,6 @@ Local optimization
The local version of the above code would be the following:
.. If you modify this code, also change :
.. theano/tests/test_tutorial.py:T_extending.test_extending_2
.. testcode::
......@@ -295,9 +286,6 @@ with a :ref:`navigator`. Basically, a :ref:`navigator` is a global
optimizer that loops through all nodes in the graph (or a well-defined
subset of them) and applies one or several local optimizers on them.
.. If you modify this code, also change :
.. theano/tests/test_tutorial.py:T_extending.test_extending_2
>>> x = float64('x')
>>> y = float64('y')
>>> z = float64('z')
......@@ -307,7 +295,7 @@ subset of them) and applies one or several local optimizers on them.
[add(z, mul(true_div(mul(y, x), y), true_div(z, x)))]
>>> simplify = gof.TopoOptimizer(local_simplify)
>>> simplify.optimize(e)
(<theano.gof.opt.TopoOptimizer object at 0x7f3219787f90>, 1, 5, 3, 0.00017309188842773438, 0.00020599365234375, 6.4849853515625e-05)
(<theano.gof.opt.TopoOptimizer object at 0x...>, 1, 5, 3, ..., ..., ...)
>>> e
[add(z, mul(x, true_div(z, x)))]
......@@ -334,6 +322,9 @@ Theano defines some shortcuts to make LocalOptimizers:
Replaces all occurrences of the first pattern by the second pattern.
See :class:`PatternSub`.
.. testsetup::
from theano.scalar import identity
.. testcode::
......@@ -438,9 +429,9 @@ Query
A Query is built by the following call:
.. testcode::
.. code-block:: python
theano.gof.Query(include, require = None, exclude = None, subquery = None)
theano.gof.Query(include, require=None, exclude=None, subquery=None)
.. class:: Query
......@@ -481,20 +472,21 @@ Optimizer:
.. testcode::
from theano.gof import Query
from theano.compile import optdb
# This is how the optimizer for the fast_run mode is defined
fast_run = optdb.query(Query(include = ['fast_run']))
fast_run = optdb.query(Query(include=['fast_run']))
# This is how the optimizer for the fast_compile mode is defined
fast_compile = optdb.query(Query(include = ['fast_compile']))
fast_compile = optdb.query(Query(include=['fast_compile']))
# This is the same as fast_run but no optimizations will replace
# any operation by an inplace version. This assumes, of course,
# that all inplace operations are tagged as 'inplace' (as they
# should!)
fast_run_no_inplace = optdb.query(Query(include = ['fast_run'], exclude = ['inplace']))
fast_run_no_inplace = fast_run.excluding('inplace')
fast_run_no_inplace = optdb.query(Query(include=['fast_run'],
exclude=['inplace']))
Registering an Optimizer
......
doc/extending/other_ops.txt
浏览文件 @ b1f7979f
......@@ -90,7 +90,7 @@ and (like in SciPy) they do not support broadcasting operations by default
formats for sparse type: ``csr`` and ``csc``. So in ``make_mode()``,
you can create output variables like this:
.. testcode::
.. code-block:: python
out_format = inputs[0].format # or 'csr' or 'csc' if the output format is fixed
SparseType(dtype=inputs[0].dtype, format=out_format).make_variable()
......
doc/extending/type.txt
浏览文件 @ b1f7979f
......@@ -176,8 +176,6 @@ must define ``filter`` and shall override ``values_eq_approx``.
**filter**
.. If you modify this code, also change :
.. theano/tests/test_tutorial.py:T_extending.test_extending_1
.. testcode::
# Note that we shadow Python's function ``filter`` with this
......@@ -246,8 +244,6 @@ contract. Recall that Type defines default implementations for all
required methods of the interface, except ``filter``. One way to make
the Type is to instantiate a plain Type and set the needed fields:
.. If you modify this code, also change :
.. theano/tests/test_tutorial.py:T_extending.test_extending_1
.. testcode::
from theano import gof
......@@ -260,8 +256,6 @@ the Type is to instantiate a plain Type and set the needed fields:
Another way to make this Type is to make a subclass of ``gof.Type``
and define ``filter`` and ``values_eq_approx`` in the subclass:
.. If you modify this code, also change :
.. theano/tests/test_tutorial.py:T_extending.test_extending_1
.. code-block:: python
from theano import gof
......@@ -331,9 +325,6 @@ There are several ways to make sure that equality testing works properly:
#. Define ``Double.__eq__`` so that instances of type Double
are equal. For example:
.. If you modify this code, also change :
.. theano/tests/test_tutorial.py:T_extending.test_extending_1
.. testcode::
def __eq__(self, other):
......@@ -387,8 +378,6 @@ attempt to clear up the confusion:
Final version
=============
.. If you modify this code, also change :
.. theano/tests/test_tutorial.py:T_extending.test_extending_1
.. testcode::
from theano import gof
......
doc/extending/unittest.txt
浏览文件 @ b1f7979f
......@@ -236,16 +236,16 @@ Example:
def test_validity(self):
a = T.dmatrix('a')
b = T.dmatrix('b')
c = T.dot(a,b)
f = theano.function([a,b],[c])
cmp = f(self.avals,self.bvals) == numpy.dot(self.avals,self.bvals)
c = T.dot(a, b)
f = theano.function([a, b], [c])
cmp = f(self.avals, self.bvals) == numpy.dot(self.avals, self.bvals)
self.assertTrue(numpy.all(cmp))
Avoid hard-coding variables, as in the following case:
.. testcode:: writeUnitest
.. code-block:: python
self.assertTrue(numpy.all(f(self.avals,self.bvals)==numpy.array([[25,25,30,28],[21,18,14,25]])))
self.assertTrue(numpy.all(f(self.avals, self.bvals) == numpy.array([[25, 25, 30, 28], [21, 18, 14, 25]])))
This makes the test case less manageable and forces the user to update
the variables each time the input is changed or possibly when the
......
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