def _is_in_xla_context():
"""Returns whether the current context is inside an XLA context."""
outer_graph = ops.get_default_graph()
# The `_control_flow_context` is not copied when building a FuncGraph so
# we look it up from the base graph.
while isinstance(outer_graph, func_graph_module.FuncGraph):
outer_graph = outer_graph.outer_graph
cur_ctxt = outer_graph._get_control_flow_context() # pylint: disable=protected-access
return control_flow_util.GetContainingXLAContext(cur_ctxt) is not None
def is_xla_compiled():
"""Whether we are building graph that will be compiled by XLA.
This checks whether the code is executing within an XLA context.
If True, model authors should ensure the graph they build is compilable by
XLA. Specifically, they should ensure that all ops have XLA implementations
and that all shapes are statically known.
Returns:
bool, whether the current graph will be compiled for XLA.
"""
ctxt = tf.get_default_graph()._get_control_flow_context() # pylint: disable=protected-access
return control_flow_util.GetContainingXLAContext(ctxt) is not None
def _is_in_xla_context():
"""Returns whether the current context is inside an XLA context."""
outer_graph = ops.get_default_graph()
# The `_control_flow_context` is not copied when building a FuncGraph so
# we look it up from the base graph.
while isinstance(outer_graph, func_graph_module.FuncGraph):
outer_graph = outer_graph.outer_graph
cur_ctxt = outer_graph._get_control_flow_context() # pylint: disable=protected-access
return control_flow_util.GetContainingXLAContext(cur_ctxt) is not None
Lowering allows while_v2 to avoid some of the limitations of Functions,
allowing users to specify devices & colocation inside of while_v2
branches, and enabling non-strict evaluation & partial pruning of while_v2
branches. This brings while_v2 closer to feature parity with
tf.while_loop.
However, we do not lower `While` in the XLA context because it is easier
for XLA to apply its own optimizations when dealing with un-lowered
def dynamic_decode(
decoder,
output_time_major=False,
impute_finished=False,
maximum_iterations=None,
parallel_iterations=32,
swap_memory=False,
scope=None,
):
"""Perform dynamic decoding with `decoder`.
Calls initialize() once and step() repeatedly on the Decoder object.
Args:
decoder: A `Decoder` instance.
output_time_major: Python boolean. Default: `False` (batch major). If
`True`, outputs are returned as time major tensors (this mode is faster).
Otherwise, outputs are returned as batch major tensors (this adds extra
time to the computation).
impute_finished: Python boolean. If `True`, then states for batch
entries which are marked as finished get copied through and the
corresponding outputs get zeroed out. This causes some slowdown at
each time step, but ensures that the final state and outputs have
the correct values and that backprop ignores time steps that were
marked as finished.
maximum_iterations: `int32` scalar, maximum allowed number of decoding
steps. Default is `None` (decode until the decoder is fully done).
parallel_iterations: Argument passed to `tf.while_loop`.
swap_memory: Argument passed to `tf.while_loop`.
scope: Optional variable scope to use.
Returns:
`(final_outputs, final_state, final_sequence_lengths)`.
Raises:
TypeError: if `decoder` is not an instance of `Decoder`.
ValueError: if `maximum_iterations` is provided but is not a scalar.
"""
if not isinstance(decoder, Decoder):
raise TypeError(
"Expected decoder to be type Decoder, but saw: %s" % type(decoder)
)
with variable_scope.variable_scope(scope, "decoder") as varscope:
# Determine context types.
ctxt = (
ops.get_default_graph()._get_control_flow_context()
) # pylint: disable=protected-access
is_xla = control_flow_util.GetContainingXLAContext(ctxt) is not None
is_xla = True # XLA detection does not work
in_while_loop = control_flow_util.GetContainingWhileContext(ctxt) is not None
# Properly cache variable values inside the while_loop.
# Don't set a caching device when running in a loop, since it is possible
# that train steps could be wrapped in a tf.while_loop. In that scenario
# caching prevents forward computations in loop iterations from re-reading
# the updated weights.
if not context.executing_eagerly() and not in_while_loop:
if varscope.caching_device is None:
varscope.set_caching_device(lambda op: op.device)
if maximum_iterations is not None:
maximum_iterations = ops.convert_to_tensor(
maximum_iterations, dtype=dtypes.int32, name="maximum_iterations"
)
if maximum_iterations.get_shape().ndims != 0:
raise ValueError("maximum_iterations must be a scalar")
initial_finished, initial_inputs, initial_state = decoder.initialize()
zero_outputs = _create_zero_outputs(
decoder.output_size, decoder.output_dtype, decoder.batch_size
)
if is_xla and maximum_iterations is None:
raise ValueError("maximum_iterations is required for XLA compilation.")
if maximum_iterations is not None:
initial_finished = math_ops.logical_or(
initial_finished, 0 >= maximum_iterations
)
initial_sequence_lengths = array_ops.zeros_like(
initial_finished, dtype=dtypes.int32
)
initial_time = constant_op.constant(0, dtype=dtypes.int32)
def _shape(batch_size, from_shape):
if (not isinstance(from_shape, tensor_shape.TensorShape) or from_shape.ndims == 0):
return tensor_shape.TensorShape(None)
else:
batch_size = tensor_util.constant_value(
ops.convert_to_tensor(batch_size, name="batch_size")
)
return tensor_shape.TensorShape([batch_size]).concatenate(from_shape)
dynamic_size = maximum_iterations is None or not is_xla
def _create_ta(s, d):
return tensor_array_ops.TensorArray(
dtype=d,
size=0 if dynamic_size else maximum_iterations,
dynamic_size=dynamic_size,
element_shape=_shape(decoder.batch_size, s),
)
initial_outputs_ta = nest.map_structure(
_create_ta, decoder.output_size, decoder.output_dtype
)
def condition(
unused_time,
unused_outputs_ta,
unused_state,
unused_inputs,
finished,
unused_sequence_lengths,
):
# return math_ops.logical_not(math_ops.reduce_all(finished)) #Remove this cond
return True
def body(time, outputs_ta, state, inputs, finished, sequence_lengths):
"""Internal while_loop body.
Args:
time: scalar int32 tensor.
outputs_ta: structure of TensorArray.
state: (structure of) state tensors and TensorArrays.
inputs: (structure of) input tensors.
finished: bool tensor (keeping track of what's finished).
sequence_lengths: int32 tensor (keeping track of time of finish).
Returns:
`(time + 1, outputs_ta, next_state, next_inputs, next_finished,
next_sequence_lengths)`.
```
"""
(next_outputs, decoder_state, next_inputs, decoder_finished) = decoder.step(
time, inputs, state
)
if decoder.tracks_own_finished:
next_finished = decoder_finished
else:
next_finished = math_ops.logical_or(decoder_finished, finished)
next_sequence_lengths = array_ops.where(
math_ops.logical_not(finished),
array_ops.fill(array_ops.shape(sequence_lengths), time + 1),
sequence_lengths,
)
nest.assert_same_structure(state, decoder_state)
nest.assert_same_structure(outputs_ta, next_outputs)
nest.assert_same_structure(inputs, next_inputs)
# Zero out output values past finish
if impute_finished:
emit = nest.map_structure(
lambda out, zero: array_ops.where(finished, zero, out),
next_outputs,
zero_outputs,
)
else:
emit = next_outputs
# Copy through states past finish
def _maybe_copy_state(new, cur):
# TensorArrays and scalar states get passed through.
if isinstance(cur, tensor_array_ops.TensorArray):
pass_through = True
else:
new.set_shape(cur.shape)
pass_through = new.shape.ndims == 0
return new if pass_through else array_ops.where(finished, cur, new)
if impute_finished:
next_state = nest.map_structure(_maybe_copy_state, decoder_state, state)
else:
next_state = decoder_state
outputs_ta = nest.map_structure(
lambda ta, out: ta.write(time, out), outputs_ta, emit
)
return (
time + 1,
outputs_ta,
next_state,
next_inputs,
next_finished,
next_sequence_lengths,
)
res = control_flow_ops.while_loop(
condition,
body,
loop_vars=(
initial_time,
initial_outputs_ta,
initial_state,
initial_inputs,
initial_finished,
initial_sequence_lengths,
),
parallel_iterations=parallel_iterations,
maximum_iterations=maximum_iterations,
swap_memory=swap_memory,
)
final_outputs_ta = res[1]
final_state = res[2]
final_sequence_lengths = res[5]
final_outputs = nest.map_structure(lambda ta: ta.stack(), final_outputs_ta)
try:
final_outputs, final_state = decoder.finalize(
final_outputs, final_state, final_sequence_lengths
)
except NotImplementedError:
pass
if not output_time_major:
final_outputs = nest.map_structure(_transpose_batch_time, final_outputs)
return final_outputs, final_state, final_sequence_lengths
def dynamic_decode(decoder,
output_time_major: bool = False,
impute_finished: bool = False,
maximum_iterations=None,
parallel_iterations: int = 32,
swap_memory: bool = False,
training=None,
scope=None,
**kwargs):
"""Perform dynamic decoding with `decoder`.
Calls initialize() once and step() repeatedly on the Decoder object.
Args:
decoder: A `Decoder` instance.
output_time_major: Python boolean. Default: `False` (batch major). If
`True`, outputs are returned as time major tensors (this mode is
faster). Otherwise, outputs are returned as batch major tensors (this
adds extra time to the computation).
impute_finished: Python boolean. If `True`, then states for batch
entries which are marked as finished get copied through and the
corresponding outputs get zeroed out. This causes some slowdown at
each time step, but ensures that the final state and outputs have
the correct values and that backprop ignores time steps that were
marked as finished.
maximum_iterations: A strictly positive `int32` scalar, the maximum
allowed number of decoding steps. Default is `None` (decode until the
decoder is fully done).
parallel_iterations: Argument passed to `tf.while_loop`.
swap_memory: Argument passed to `tf.while_loop`.
training: Python boolean. Indicates whether the layer should behave
in training mode or in inference mode. Only relevant
when `dropout` or `recurrent_dropout` is used.
scope: Optional name scope to use.
**kwargs: dict, other keyword arguments for dynamic_decode. It might
contain arguments for `BaseDecoder` to initialize, which takes all
tensor inputs during call().
Returns:
`(final_outputs, final_state, final_sequence_lengths)`.
Raises:
ValueError: if `maximum_iterations` is provided but is not a scalar.
"""
with variable_scope.variable_scope(scope, 'decoder') as varscope:
ctxt = ops.get_default_graph()._get_control_flow_context()
is_xla = control_flow_util.GetContainingXLAContext(ctxt) is not None
in_while_loop = (control_flow_util.GetContainingWhileContext(ctxt)
is not None)
if not context.executing_eagerly() and not in_while_loop:
if varscope.caching_device is None:
varscope.set_caching_device(lambda op: op.device)
is_xla = not tf.executing_eagerly(
) and control_flow_util.GraphOrParentsInXlaContext(
tf.compat.v1.get_default_graph())
if maximum_iterations is not None:
maximum_iterations = tf.convert_to_tensor(
maximum_iterations,
dtype=tf.int32,
name='maximum_iterations',
)
if maximum_iterations.shape.ndims != 0:
raise ValueError('maximum_iterations must be a scalar')
tf.debugging.assert_greater(
maximum_iterations,
0,
message='maximum_iterations should be greater than 0',
)
elif is_xla:
raise ValueError(
'maximum_iterations is required for XLA compilation.')
if isinstance(decoder, Decoder):
initial_finished, initial_inputs, initial_state = (
decoder.initialize())
else:
# For BaseDecoder that takes tensor inputs during call.
decoder_init_input = kwargs.pop('decoder_init_input', None)
decoder_init_kwargs = kwargs.pop('decoder_init_kwargs', {})
initial_finished, initial_inputs, initial_state = decoder.initialize(
decoder_init_input, **decoder_init_kwargs)
zero_outputs = tf.nest.map_structure(
lambda shape, dtype: tf.zeros(
_prepend_batch(decoder.batch_size, shape), dtype=dtype),
decoder.output_size,
decoder.output_dtype,
)
if maximum_iterations is not None:
initial_finished = tf.logical_or(initial_finished,
0 >= maximum_iterations)
initial_sequence_lengths = tf.zeros_like(initial_finished,
dtype=tf.int32)
initial_time = tf.constant(0, dtype=tf.int32)
def _shape(batch_size, from_shape):
if (not isinstance(from_shape, tf.TensorShape)
or from_shape.ndims == 0):
return None
else:
batch_size = tf.get_static_value(
tf.convert_to_tensor(batch_size, name='batch_size'))
return tf.TensorShape([batch_size]).concatenate(from_shape)
dynamic_size = maximum_iterations is None or not is_xla
# The dynamic shape `TensoArray` is not allowed in TFLite yet.
dynamic_size = dynamic_size
def _create_ta(s, d):
return tf.TensorArray(
dtype=d,
size=0 if dynamic_size else maximum_iterations,
dynamic_size=dynamic_size,
element_shape=_shape(decoder.batch_size, s),
)
initial_outputs_ta = tf.nest.map_structure(_create_ta,
decoder.output_size,
decoder.output_dtype)
def condition(
unused_time,
unused_outputs_ta,
unused_state,
unused_inputs,
finished,
unused_sequence_lengths,
):
return tf.logical_not(tf.reduce_all(finished))
def body(time, outputs_ta, state, inputs, finished, sequence_lengths):
"""Internal while_loop body.
Args:
time: scalar int32 tensor.
outputs_ta: structure of TensorArray.
state: (structure of) state tensors and TensorArrays.
inputs: (structure of) input tensors.
finished: bool tensor (keeping track of what's finished).
sequence_lengths: int32 tensor (keeping track of time of finish).
Returns:
`(time + 1, outputs_ta, next_state, next_inputs, next_finished,
next_sequence_lengths)`.
```
"""
(
next_outputs,
decoder_state,
next_inputs,
decoder_finished,
) = decoder.step(time, inputs, state, training)
decoder_state_sequence_lengths = False
if decoder.tracks_own_finished:
next_finished = decoder_finished
lengths = getattr(decoder_state, 'lengths', None)
if lengths is not None:
# sequence lengths are provided by decoder_state.lengths;
# overwrite our sequence lengths.
decoder_state_sequence_lengths = True
sequence_lengths = tf.cast(lengths, tf.int32)
else:
next_finished = tf.logical_or(decoder_finished, finished)
if decoder_state_sequence_lengths:
# Just pass something through the loop; at the next iteration
# we'll pull the sequence lengths from the decoder_state again.
next_sequence_lengths = sequence_lengths
else:
next_sequence_lengths = tf.where(
tf.logical_not(finished),
tf.fill(tf.shape(sequence_lengths), time + 1),
sequence_lengths,
)
tf.nest.assert_same_structure(state, decoder_state)
tf.nest.assert_same_structure(outputs_ta, next_outputs)
tf.nest.assert_same_structure(inputs, next_inputs)
# Zero out output values past finish
if impute_finished:
def zero_out_finished(out, zero):
if finished.shape.rank < zero.shape.rank:
broadcast_finished = tf.broadcast_to(
tf.expand_dims(finished, axis=-1), zero.shape)
return tf.where(broadcast_finished, zero, out)
else:
return tf.where(finished, zero, out)
emit = tf.nest.map_structure(zero_out_finished, next_outputs,
zero_outputs)
else:
emit = next_outputs
# Copy through states past finish
def _maybe_copy_state(new, cur):
# TensorArrays and scalar states get passed through.
if isinstance(cur, tf.TensorArray):
pass_through = True
else:
new.set_shape(cur.shape)
pass_through = new.shape.ndims == 0
if not pass_through:
broadcast_finished = tf.broadcast_to(
tf.expand_dims(finished, axis=-1), new.shape)
return tf.where(broadcast_finished, cur, new)
else:
return new
if impute_finished:
next_state = tf.nest.map_structure(_maybe_copy_state,
decoder_state, state)
else:
next_state = decoder_state
outputs_ta = tf.nest.map_structure(
lambda ta, out: ta.write(time, out), outputs_ta, emit)
return (
time + 1,
outputs_ta,
next_state,
next_inputs,
next_finished,
next_sequence_lengths,
)
res = tf.while_loop(
condition,
body,
loop_vars=(
initial_time,
initial_outputs_ta,
initial_state,
initial_inputs,
initial_finished,
initial_sequence_lengths,
),
parallel_iterations=parallel_iterations,
maximum_iterations=maximum_iterations,
swap_memory=swap_memory,
)
final_outputs_ta = res[1]
final_state = res[2]
final_sequence_lengths = res[5]
final_outputs = tf.nest.map_structure(lambda ta: ta.stack(),
final_outputs_ta)
try:
final_outputs, final_state = decoder.finalize(
final_outputs, final_state, final_sequence_lengths)
except NotImplementedError:
pass
if not output_time_major:
final_outputs = tf.nest.map_structure(_transpose_batch_time,
final_outputs)
return final_outputs, final_state, final_sequence_lengths
def Decoder_Dynamic_Decode(
decoder,
output_time_major= False,
impute_finished= False,
maximum_iterations= None,
parallel_iterations= 32,
swap_memory= False,
scope= None
):
if not isinstance(decoder, Decoder):
raise TypeError("Expected decoder to be type Decoder, but saw: %s" % type(decoder))
with variable_scope.variable_scope(scope, "decoder") as varscope:
ctxt = tf.get_default_graph()._get_control_flow_context() # pylint: disable=protected-access
is_xla = control_flow_util.GetContainingXLAContext(ctxt) is not None
in_while_loop = control_flow_util.GetContainingWhileContext(ctxt) is not None
if not context.executing_eagerly() and not in_while_loop:
if varscope.caching_device is None:
varscope.set_caching_device(lambda op: op.device)
if maximum_iterations is not None:
maximum_iterations = tf.convert_to_tensor(
maximum_iterations,
dtype=tf.int32,
name="maximum_iterations"
)
if maximum_iterations.get_shape().ndims != 0:
raise ValueError("maximum_iterations must be a scalar")
elif is_xla:
raise ValueError("maximum_iterations is required for XLA compilation.")
initial_finished, initial_inputs, initial_state = decoder.initialize()
if maximum_iterations is not None:
initial_finished = tf.logical_or(
initial_finished,
0 >= maximum_iterations
)
initial_sequence_lengths = tf.zeros_like(initial_finished, dtype=tf.int32)
initial_time = tf.constant(0, dtype=tf.int32)
def _shape(batch_size, from_shape):
if (not isinstance(from_shape, TensorShape) or from_shape.ndims == 0):
return TensorShape(None)
else:
batch_size = tensor_util.constant_value(tf.convert_to_tensor(batch_size, name="batch_size"))
return TensorShape([batch_size]).concatenate(from_shape)
dynamic_size = maximum_iterations is None or not is_xla
def _create_ta(s, d):
return tf.TensorArray(
dtype=d,
size= 0 if dynamic_size else maximum_iterations,
dynamic_size= dynamic_size,
element_shape= _shape(decoder.batch_size, s)
)
initial_outputs_ta = nest.map_structure(
_create_ta,
decoder.output_size,
decoder.output_dtype
)
def condition(
unused_time,
unused_outputs_ta,
unused_state,
unused_inputs,
finished,
unused_sequence_lengths
):
return tf.logical_not(tf.reduce_all(finished))
def body(
time,
outputs_ta,
state,
inputs,
finished,
sequence_lengths
):
next_outputs, next_state, next_inputs, decoder_finished = decoder.step(time, inputs, state)
if decoder.tracks_own_finished:
next_finished = decoder_finished
else:
next_finished = tf.logical_or(decoder_finished, finished)
next_finished = tf.reshape(next_finished, [-1]) #reshape이유 1: helper에서 cond에 들어가면 merge가 됨, 2: inference시에 2차원 값이 나옴
next_sequence_lengths = tf.where(
tf.logical_not(finished),
x= tf.fill(tf.shape(sequence_lengths), time + 1),
y= sequence_lengths
)
nest.assert_same_structure(state, next_state)
nest.assert_same_structure(outputs_ta, next_outputs)
nest.assert_same_structure(inputs, next_inputs)
if impute_finished:
new_linear = nest.map_structure(
lambda out, zero: tf.where(finished, zero, out),
next_outputs.linear,
tf.zeros_like(next_outputs.linear)
)
next_outputs._replace(linear= new_linear)
def _maybe_copy_state(new, cur):
if isinstance(cur, tf.TensorArray):
pass_through = True
else:
new.set_shape(cur.shape)
pass_through = (new.shape.ndims == 0)
return new if pass_through else tf.where(finished, cur, new)
next_state = nest.map_structure(_maybe_copy_state, next_state, state)
outputs_ta = nest.map_structure(lambda ta, out: ta.write(time, out), outputs_ta, next_outputs)
return time + 1, outputs_ta, next_state, next_inputs, next_finished, next_sequence_lengths
res = tf.while_loop(
cond= condition,
body= body,
loop_vars=[
initial_time,
initial_outputs_ta,
initial_state,
initial_inputs,
initial_finished,
initial_sequence_lengths
],
parallel_iterations=parallel_iterations,
maximum_iterations=maximum_iterations,
swap_memory=swap_memory
)
final_outputs_ta, final_state, final_sequence_lengths = res[1], res[2], res[5]
final_outputs = nest.map_structure(lambda ta: ta.stack(), final_outputs_ta)
try:
final_outputs, final_state = decoder.finalize(final_outputs, final_state, final_sequence_lengths)
except NotImplementedError:
pass
if not output_time_major:
final_outputs = nest.map_structure(rnn._transpose_batch_time, final_outputs)
return final_outputs, final_state, final_sequence_lengths
def _is_on_tpu():
ctxt = framework_ops.get_default_graph()._get_control_flow_context() # pylint: disable=protected-access
return control_flow_util.GetContainingXLAContext(ctxt) is not None
def _is_in_xla_context():
"""Returns whether the current context is inside an XLA context."""
cur_ctxt = ops.get_default_graph()._get_control_flow_context() # pylint: disable=protected-access
return control_flow_util.GetContainingXLAContext(cur_ctxt) is not None
