def test_force_backprop_mode():
with chainerx.backprop_scope('bp1') as bp1, \
chainerx.backprop_scope('bp2') as bp2:
with chainerx.no_backprop_mode():
assert not chainerx.is_backprop_required()
assert not chainerx.is_backprop_required(bp1)
assert not chainerx.is_backprop_required(bp2)
with chainerx.force_backprop_mode():
assert chainerx.is_backprop_required()
assert chainerx.is_backprop_required(bp1)
assert chainerx.is_backprop_required(bp2)
assert not chainerx.is_backprop_required()
assert not chainerx.is_backprop_required(bp1)
assert not chainerx.is_backprop_required(bp2)
with chainerx.force_backprop_mode(chainerx.get_default_context()):
assert chainerx.is_backprop_required()
assert chainerx.is_backprop_required(bp1)
assert chainerx.is_backprop_required(bp2)
assert not chainerx.is_backprop_required()
assert not chainerx.is_backprop_required(bp1)
assert not chainerx.is_backprop_required(bp2)
with chainerx.force_backprop_mode(bp1):
assert not chainerx.is_backprop_required()
assert chainerx.is_backprop_required(bp1)
assert not chainerx.is_backprop_required(bp2)
assert not chainerx.is_backprop_required()
assert not chainerx.is_backprop_required(bp1)
assert not chainerx.is_backprop_required(bp2)
with chainerx.force_backprop_mode((bp1, bp2)):
assert not chainerx.is_backprop_required()
assert chainerx.is_backprop_required(bp1)
assert chainerx.is_backprop_required(bp2)
assert not chainerx.is_backprop_required()
assert not chainerx.is_backprop_required(bp1)
assert not chainerx.is_backprop_required(bp2)
with chainerx.force_backprop_mode():
assert chainerx.is_backprop_required()
assert chainerx.is_backprop_required(bp1)
assert chainerx.is_backprop_required(bp2)
def test_is_backprop_required():
current_context = chainerx.get_default_context()
another_context = chainerx.Context()
with chainerx.backprop_scope('bp1') as bp1, \
chainerx.backprop_scope('bp2') as bp2:
with chainerx.no_backprop_mode():
with chainerx.force_backprop_mode(bp1):
assert not chainerx.is_backprop_required()
assert chainerx.is_backprop_required(bp1)
assert not chainerx.is_backprop_required(bp2)
assert not chainerx.is_backprop_required(
context=current_context)
assert chainerx.is_backprop_required(context=another_context)
with pytest.raises(TypeError):
chainerx.is_backprop_required(context='foo')
def force_backprop_mode():
"""Make a context manager which enables back-propagation.
When you want to enable back-propagation in :func:`no_backprop_mode`, call
this method. A :class:`~chainer.Variable` created in this context always
has a computational graph unless overridden by deeper contexts. If you call
this method outside of :func:`no_backprop_mode` context, it changes
nothing.
In the following example, ``y`` has a computational graph and calling
:func:`~chainer.Variable.backward` on ``y`` will compute and accumulate the
gradients of the variables in the graph, in this case only ``x``.
>>> x = chainer.Variable(np.array([1,], np.float32))
>>> with chainer.no_backprop_mode():
... with chainer.force_backprop_mode():
... y = x + 1
>>> y.backward()
>>> x.grad
array([1.], dtype=float32)
.. note::
``chainer.force_backprop_mode()`` implicitly applies ChainerX's
counterpart :func:`chainerx.force_backprop_mode()`, but not vice versa.
Also, setting ``enable_backprop`` :ref:`configuration <configuration>`
does not affect ChainerX.
.. seealso::
See :func:`chainer.no_backprop_mode` for details on disabled
back-propagation mode.
"""
c = configuration.using_config('enable_backprop', True)
if chainerx.is_available():
return _BackpropModeContext((c, chainerx.force_backprop_mode()))
return _BackpropModeContext((c,))
def force_backprop_mode():
"""Make a context manager which enables back-propagation.
When you want to enable back-propagation in :func:`no_backprop_mode`, call
this method. A :class:`~chainer.Variable` created in this context always
has a computational graph unless overridden by deeper contexts. If you call
this method outside of :func:`no_backprop_mode` context, it changes
nothing.
In the following example, ``y`` has a computational graph and calling
:func:`~chainer.Variable.backward` on ``y`` will compute and accumulate the
gradients of the variables in the graph, in this case only ``x``.
>>> x = chainer.Variable(np.array([1,], np.float32))
>>> with chainer.no_backprop_mode():
... with chainer.force_backprop_mode():
... y = x + 1
>>> y.backward()
>>> x.grad
array([1.], dtype=float32)
.. note::
``chainer.force_backprop_mode()`` implicitly applies ChainerX's
counterpart :func:`chainerx.force_backprop_mode()`, but not vice versa.
Also, setting ``enable_backprop`` :ref:`configuration <configuration>`
does not affect ChainerX.
.. seealso::
See :func:`chainer.no_backprop_mode` for details on disabled
back-propagation mode.
"""
c = configuration.using_config('enable_backprop', True)
if chainerx.is_available():
return _BackpropModeContext((c, chainerx.force_backprop_mode()))
return _BackpropModeContext((c, ))