def Fwd(*args):
(theta, state0, inputs) = _Pack(args, fwd_sig)
state1, extras = self._cell_fn(theta, state0, inputs)
assert not function.get_extra_args(), (
'cell_fn is not pure with extra args: %s.' %
(function.get_extra_args()))
_AssertIsCompatible(state1, self._state)
_AssertIsCompatible(extras, self._extras)
return _Flatten([state1, extras])
def Fwd(*args):
(theta, state0, inputs) = _Pack(args, fwd_sig)
state1, extras = self._cell_fn(theta, state0, inputs)
assert not function.get_extra_args(), (
'cell_fn is not pure with extra args: %s.' %
(function.get_extra_args()))
_AssertIsCompatible(state1, self._state)
_AssertIsCompatible(extras, self._extras)
return _Flatten([state1, extras])
def Bak(*args):
"""Backward step."""
(theta, state0, inputs, extras, d_state1) = _Pack(args, bak_sig)
(dtheta, dstate0, dinputs) = self._cell_grad(theta, state0, inputs,
extras, d_state1)
assert not function.get_extra_args(), (
'cell_grad is not pure with extra args: %s.' %
(function.get_extra_args()))
_AssertIsCompatible(dtheta, self._theta)
_AssertIsCompatible(dstate0, self._state)
_AssertIsCompatible(dinputs, self._inputs)
return _Flatten(
_ConvertNoneGradientToZeros([theta, state0, inputs],
[dtheta, dstate0, dinputs]))
def CellGrad(theta, state0, inputs, extras, dstate1):
"""Default gradient function for cell_fn."""
state1, extras = cell_fn(theta, state0, inputs)
assert isinstance(state1, py_utils.NestedMap), ('%s' % state1)
assert isinstance(extras, py_utils.NestedMap), ('%s' % extras)
# NOTE: The default grad function recomputes the forward
# function and does not take advantage of 'extras' returned by
# the forward function.
# Assert that if captured inputs were given, they match the actual
# tensors passed to the function we are compiled into. Must come after
# the call to cell_fn, which does the capture.
_AssertSameTensors(function.get_extra_inputs(),
implicit_captures.Flatten())
# Extract the internal captured tensor placeholders within the Defun
# we are running in.
(captured, ) = _Pack(function.get_extra_args(), [implicit_captures])
ys = _Flatten([state1])
xs = _Flatten([theta, state0, inputs, captured])
grad_ys = _Flatten([dstate1])
grads = tf.gradients(ys=ys, xs=xs, grad_ys=grad_ys)
return _ConvertNoneGradientToZeros(
[theta, state0, inputs, captured],
_Pack(grads, [theta, state0, inputs, captured]))
def Bak(*args):
"""Backward step."""
(theta, state0, inputs, extras, d_state1) = _Pack(args, bak_sig)
(dtheta, dstate0, dinputs,
dcaptures) = self._cell_grad(theta, state0, inputs, extras,
d_state1)
_AssertIsCompatible(dtheta, self._theta)
_AssertIsCompatible(dstate0, self._state)
_AssertIsCompatible(dinputs, self._inputs)
if dcaptures is None:
# NOTE: Custom gradient fns can return None if they do not support
# captured tensors. The return value is reserved for the future when
# that may be supported.
dcaptures = _EmptyLike(self._implicit_captures)
_AssertIsCompatible(dcaptures, self._implicit_captures)
# Make sure this function didn't capture anything different than the
# cell_fn when reflected on at the beginning. Must come after the call
# to cell_grad() which adds to the captured list.
_AssertSameTensors(function.get_extra_inputs(),
self._implicit_captures.Flatten())
(captured, ) = _Pack(function.get_extra_args(),
[self._implicit_captures])
return _Flatten(
_ConvertNoneGradientToZeros(
[theta, state0, inputs, captured],
[dtheta, dstate0, dinputs, dcaptures]))
def _FlatOutputProcessor(inputs):
"""Returns a flattened list of 'processor(inputs)'."""
outputs = processor(inputs)
tf.logging.debug('Processor outputs=%s', outputs)
assert len(outputs) > 1, outputs
# Add 'outputs' as a list so that each element will be flattened.
output_tmpl.values = list(outputs)
flat_outputs = output_tmpl.Flatten()
tf.logging.debug('Processor flat outputs=%s', flat_outputs)
tf.logging.debug('extra_inputs=%s extra_args=%s extra_vars=%s',
function.get_extra_inputs(),
function.get_extra_args(), function.get_extra_vars())
assert not function.get_extra_args(), (
'fns {} is not pure: extra_args={}'.format(
processor, function.get_extra_args()))
return flat_outputs
def Grad(x, y0):
if use_forward_func:
y = Model(x)
else:
y = _Model(x)
loss = tf.reduce_mean(tf.reduce_sum(y0 * tf.log(y), 1), 0)
dw, db = tf.gradients(loss, function.get_extra_args())
cvars.extend(function.get_extra_vars())
return loss, dw, db
def WhileBody(i, n, start, delta, *args):
"""A While wrapper for forbody that handles loop-carried captured inputs."""
for_result = forbody(start + i * delta, *args)
# Nullary functions return an Operation. Normal functions can't do this
# because their return values are converted to Tensors.
if isinstance(for_result, ops.Operation):
for_result = ()
# Unary functions return a single Tensor value.
elif isinstance(for_result, ops.Tensor):
for_result = (for_result, )
extra_args = tuple(function.get_extra_args())
return (i + 1, n, start, delta) + tuple(for_result) + extra_args
def Grad(x, y0):
if use_forward_func:
y = Model(x)
else:
y = _Model(x)
loss = tf.reduce_mean(tf.reduce_sum(y0 * tf.log(y), 1), 0)
arg_w, arg_b = function.get_extra_args()
self.assertEqual(arg_w.get_shape(), tf.TensorShape([64, 64]))
self.assertEqual(arg_b.get_shape(), tf.TensorShape([64]))
dw, db = tf.gradients(loss, [arg_w, arg_b])
cvars.extend(function.get_extra_vars())
return loss, dw, db
def WhileBody(i, n, start, delta, *args):
"""A While wrapper for forbody that handles loop-carried captured inputs."""
for_result = forbody(start + i * delta, *args)
# Nullary functions return an Operation. Normal functions can't do this
# because their return values are converted to Tensors.
if isinstance(for_result, ops.Operation):
for_result = ()
# Unary functions return a single Tensor value.
elif isinstance(for_result, ops.Tensor):
for_result = (for_result,)
extra_args = tuple(function.get_extra_args())
return (i + 1, n, start, delta) + tuple(for_result) + extra_args
def Grad(x, y0):
if use_forward_func:
y = Model(x)
else:
y = _Model(x)
loss = tf.reduce_mean(tf.reduce_sum(y0 * tf.log(y), 1), 0)
arg_w, arg_b = function.get_extra_args()
self.assertEqual(arg_w.get_shape(), tf.TensorShape([64, 64]))
self.assertEqual(arg_b.get_shape(), tf.TensorShape([64]))
dw, db = tf.gradients(loss, [arg_w, arg_b])
cvars.extend(function.get_extra_vars())
return loss, dw, db
def _FlatOutputProcessor(source_id, record):
"""Returns a flattened list of 'processor(inputs)'."""
processor_spec = tf_inspect.getargspec(processor)
tf.logging.debug('GenericInput.processor.argspec=%s', processor_spec)
processor_args = set(processor_spec.args) - set(['self'])
if len(processor_args) == 1:
output, bucketing_key = processor(record)
elif processor_args == set(['source_id', 'record']):
output, bucketing_key = processor(source_id=source_id,
record=record)
else:
raise ValueError(
'GenericInput: processor should take either a single arg '
'or two args named as "source_id" and "record". '
'Actual: %s' % processor_args)
if isinstance(output, list):
assert output
assert all(isinstance(x, tf.Tensor)
for x in output), '{}'.format(output)
else:
assert isinstance(output, py_utils.NestedMap), '{}'.format(output)
assert output
assert all(isinstance(x, tf.Tensor)
for x in output.Flatten()), '{}'.format(
output.DebugString())
bucketing_key = tf.cast(bucketing_key, tf.int32)
tf.logging.debug('Processor outputs=%s bucketing_key=%s', output,
bucketing_key)
output_tmpl.out_values = output
flat_output_tmpl = output_tmpl.Flatten()
tf.logging.debug('Processor flat outputs=%s', flat_output_tmpl)
tf.logging.debug('extra_inputs=%s extra_args=%s extra_vars=%s',
function.get_extra_inputs(),
function.get_extra_args(), function.get_extra_vars())
assert not function.get_extra_args(), (
'fns {} is not pure: extra_args={}'.format(
processor, function.get_extra_args()))
return flat_output_tmpl + [bucketing_key]
def BodyWrapper(*args):
"""A wrapper for body that handles loop-carried captured inputs."""
body_result = body(*args)
extra_args = tuple(function.get_extra_args())
# Nullary functions return an Operation. Normal functions can't do this
# because their return values are converted to Tensors.
if isinstance(body_result, ops.Operation):
return extra_args
# Unary functions return a single Tensor value.
elif not isinstance(body_result, tuple):
return (body_result, ) + extra_args
# N-ary functions return a tuple of Tensors.
else:
return body_result + extra_args
def Wrapper(*args):
"""A wrapper that handles loop-carried captured inputs."""
result = func(*args)
extra_args = tuple(function.get_extra_args())
# Nullary functions return an Operation. Normal functions can't do this
# because their return values are converted to Tensors.
if isinstance(result, ops.Operation):
return extra_args
# Unary functions return a single Tensor value.
elif not isinstance(result, tuple):
return (result,) + extra_args
# N-ary functions return a tuple of Tensors.
else:
return result + extra_args
