def _dynamic_rnn_time_input_loop(cell,
inputs,
initial_state,
parallel_iterations,
swap_memory,
sequence_length=None,
dtype=None):
"""Internal implementation of Dynamic RNN, add current time to input of cell.
Args:
cell: An instance of RNNCell.
inputs: A `Tensor` of shape [time, batch_size, input_size], or a nested
tuple of such elements.
initial_state: A `Tensor` of shape `[batch_size, state_size]`, or if
`cell.state_size` is a tuple, then this should be a tuple of
tensors having shapes `[batch_size, s] for s in cell.state_size`.
parallel_iterations: Positive Python int.
swap_memory: A Python boolean
sequence_length: (optional) An `int32` `Tensor` of shape [batch_size].
dtype: (optional) Expected dtype of output. If not specified, inferred from
initial_state.
Returns:
Tuple `(final_outputs, final_state)`.
final_outputs:
A `Tensor` of shape `[time, batch_size, cell.output_size]`. If
`cell.output_size` is a (possibly nested) tuple of ints or `TensorShape`
objects, then this returns a (possibly nested) tuple of Tensors matching
the corresponding shapes.
final_state:
A `Tensor`, or possibly nested tuple of Tensors, matching in length
and shapes to `initial_state`.
Raises:
ValueError: If the input depth cannot be inferred via shape inference
from the inputs.
"""
state = initial_state
assert isinstance(parallel_iterations,
int), "parallel_iterations must be int"
state_size = cell.state_size
flat_input = nest.flatten(inputs)
flat_output_size = nest.flatten(cell.output_size)
# Construct an initial output
input_shape = array_ops.shape(flat_input[0])
time_steps = input_shape[0]
batch_size = rnn._best_effort_input_batch_size(flat_input)
inputs_got_shape = tuple(input_.get_shape().with_rank_at_least(3)
for input_ in flat_input)
const_time_steps, const_batch_size = inputs_got_shape[0].as_list()[:2]
for shape in inputs_got_shape:
if not shape[2:].is_fully_defined():
raise ValueError(
"Input size (depth of inputs) must be accessible via shape inference,"
" but saw value None.")
got_time_steps = shape[0].value
got_batch_size = shape[1].value
if const_time_steps != got_time_steps:
raise ValueError(
"Time steps is not the same for all the elements in the input in a "
"batch.")
if const_batch_size != got_batch_size:
raise ValueError(
"Batch_size is not the same for all the elements in the input."
)
# Prepare dynamic conditional copying of state & output
def _create_zero_arrays(size):
size = _concat(batch_size, size)
return array_ops.zeros(array_ops.stack(size),
rnn._infer_state_dtype(dtype, state))
flat_zero_output = tuple(
_create_zero_arrays(output) for output in flat_output_size)
zero_output = nest.pack_sequence_as(structure=cell.output_size,
flat_sequence=flat_zero_output)
if sequence_length is not None:
min_sequence_length = math_ops.reduce_min(sequence_length)
max_sequence_length = math_ops.reduce_max(sequence_length)
else:
max_sequence_length = time_steps
time = array_ops.constant(0, dtype=dtypes.int32, name="time")
with ops.name_scope("dynamic_rnn") as scope:
base_name = scope
def _create_ta(name, element_shape, dtype):
return tensor_array_ops.TensorArray(dtype=dtype,
size=time_steps,
element_shape=element_shape,
tensor_array_name=base_name + name)
in_graph_mode = not context.executing_eagerly()
if in_graph_mode:
output_ta = tuple(
_create_ta("output_%d" % i,
element_shape=(tensor_shape.TensorShape(
[const_batch_size]).concatenate(
rnn._maybe_tensor_shape_from_tensor(out_size))),
dtype=rnn._infer_state_dtype(dtype, state))
for i, out_size in enumerate(flat_output_size))
input_ta = tuple(
_create_ta("input_%d" % i,
element_shape=flat_input_i.shape[1:],
dtype=flat_input_i.dtype)
for i, flat_input_i in enumerate(flat_input))
input_ta = tuple(
ta.unstack(input_) for ta, input_ in zip(input_ta, flat_input))
else:
output_ta = tuple([0 for _ in range(time_steps.numpy())]
for i in range(len(flat_output_size)))
input_ta = flat_input
def _time_step(time, output_ta_t, state):
"""Take a time step of the dynamic RNN.
Args:
time: int32 scalar Tensor.
output_ta_t: List of `TensorArray`s that represent the output.
state: nested tuple of vector tensors that represent the state.
Returns:
The tuple (time + 1, output_ta_t with updated flow, new_state).
"""
if in_graph_mode:
input_t = tuple(ta.read(time) for ta in input_ta)
# Restore some shape information
for input_, shape in zip(input_t, inputs_got_shape):
input_.set_shape(shape[1:])
else:
input_t = tuple(ta[time.numpy()] for ta in input_ta)
input_t = nest.pack_sequence_as(structure=inputs,
flat_sequence=input_t)
#Here, we make the change to add 'time' as input when calling the cell.
call_cell = lambda: cell(input_t, state, time)
if sequence_length is not None:
(output, new_state) = rnn._rnn_step(
time=time,
sequence_length=sequence_length,
min_sequence_length=min_sequence_length,
max_sequence_length=max_sequence_length,
zero_output=zero_output,
state=state,
call_cell=call_cell,
state_size=state_size,
skip_conditionals=True)
else:
(output, new_state) = call_cell()
# Pack state if using state tuples
output = nest.flatten(output)
if in_graph_mode:
output_ta_t = tuple(
ta.write(time, out) for ta, out in zip(output_ta_t, output))
else:
for ta, out in zip(output_ta_t, output):
ta[time.numpy()] = out
return (time + 1, output_ta_t, new_state)
if in_graph_mode:
# Make sure that we run at least 1 step, if necessary, to ensure
# the TensorArrays pick up the dynamic shape.
loop_bound = math_ops.minimum(time_steps,
math_ops.maximum(1, max_sequence_length))
else:
# Using max_sequence_length isn't currently supported in the Eager branch.
loop_bound = time_steps
_, output_final_ta, final_state = control_flow_ops.while_loop(
cond=lambda time, *_: time < loop_bound,
body=_time_step,
loop_vars=(time, output_ta, state),
parallel_iterations=parallel_iterations,
maximum_iterations=time_steps,
swap_memory=swap_memory)
# Unpack final output if not using output tuples.
if in_graph_mode:
final_outputs = tuple(ta.stack() for ta in output_final_ta)
# Restore some shape information
for output, output_size in zip(final_outputs, flat_output_size):
shape = _concat([const_time_steps, const_batch_size],
output_size,
static=True)
output.set_shape(shape)
else:
final_outputs = output_final_ta
final_outputs = nest.pack_sequence_as(structure=cell.output_size,
flat_sequence=final_outputs)
if not in_graph_mode:
final_outputs = nest.map_structure_up_to(
cell.output_size, lambda x: array_ops.stack(x, axis=0),
final_outputs)
return (final_outputs, final_state)
def raw_rnn(cell,
loop_fn,
parallel_iterations=None,
swap_memory=False,
scope=None):
"""
raw_rnn adapted from the original tensorflow implementation
(https://github.com/tensorflow/tensorflow/blob/r1.4/tensorflow/python/ops/rnn.py)
to emit arbitrarily nested states for each time step (concatenated along the time axis)
in addition to the outputs at each timestep and the final state
returns (
states for all timesteps,
outputs for all timesteps,
final cell state,
)
"""
if not _like_rnncell(cell):
raise TypeError("cell must be an instance of RNNCell")
if not callable(loop_fn):
raise TypeError("loop_fn must be a callable")
parallel_iterations = parallel_iterations or 32
# Create a new scope in which the caching device is either
# determined by the parent scope, or is set to place the cached
# Variable using the same placement as for the rest of the RNN.
with vs.variable_scope(scope or "rnn") as varscope:
if not context.executing_eagerly():
if varscope.caching_device is None:
varscope.set_caching_device(lambda op: op.device)
time = constant_op.constant(0, dtype=dtypes.int32)
(elements_finished, next_input, initial_state, emit_structure,
init_loop_state) = loop_fn(time, None, None, None)
flat_input = nest.flatten(next_input)
# Need a surrogate loop state for the while_loop if none is available.
loop_state = (init_loop_state if init_loop_state is not None else
constant_op.constant(0, dtype=dtypes.int32))
input_shape = [input_.get_shape() for input_ in flat_input]
static_batch_size = input_shape[0][0]
for input_shape_i in input_shape:
# Static verification that batch sizes all match
static_batch_size.merge_with(input_shape_i[0])
batch_size = static_batch_size.value
const_batch_size = batch_size
if batch_size is None:
batch_size = array_ops.shape(flat_input[0])[0]
nest.assert_same_structure(initial_state, cell.state_size)
state = initial_state
flat_state = nest.flatten(state)
flat_state = [ops.convert_to_tensor(s) for s in flat_state]
state = nest.pack_sequence_as(structure=state,
flat_sequence=flat_state)
if emit_structure is not None:
flat_emit_structure = nest.flatten(emit_structure)
flat_emit_size = [
emit.shape
if emit.shape.is_fully_defined() else array_ops.shape(emit)
for emit in flat_emit_structure
]
flat_emit_dtypes = [emit.dtype for emit in flat_emit_structure]
else:
emit_structure = cell.output_size
flat_emit_size = nest.flatten(emit_structure)
flat_emit_dtypes = [flat_state[0].dtype] * len(flat_emit_size)
flat_state_size = [
s.shape if s.shape.is_fully_defined() else array_ops.shape(s)
for s in flat_state
]
flat_state_dtypes = [s.dtype for s in flat_state]
flat_emit_ta = [
tensor_array_ops.TensorArray(
dtype=dtype_i,
dynamic_size=True,
element_shape=(tensor_shape.TensorShape([
const_batch_size
]).concatenate(_maybe_tensor_shape_from_tensor(size_i))),
size=0,
name="rnn_output_%d" % i)
for i, (dtype_i,
size_i) in enumerate(zip(flat_emit_dtypes, flat_emit_size))
]
emit_ta = nest.pack_sequence_as(structure=emit_structure,
flat_sequence=flat_emit_ta)
flat_zero_emit = [
array_ops.zeros(_concat(batch_size, size_i), dtype_i)
for size_i, dtype_i in zip(flat_emit_size, flat_emit_dtypes)
]
zero_emit = nest.pack_sequence_as(structure=emit_structure,
flat_sequence=flat_zero_emit)
flat_state_ta = [
tensor_array_ops.TensorArray(
dtype=dtype_i,
dynamic_size=True,
element_shape=(tensor_shape.TensorShape([
const_batch_size
]).concatenate(_maybe_tensor_shape_from_tensor(size_i))),
size=0,
name="rnn_state_%d" % i)
for i, (
dtype_i,
size_i) in enumerate(zip(flat_state_dtypes, flat_state_size))
]
state_ta = nest.pack_sequence_as(structure=state,
flat_sequence=flat_state_ta)
def condition(unused_time, elements_finished, *_):
return math_ops.logical_not(math_ops.reduce_all(elements_finished))
def body(time, elements_finished, current_input, state_ta, emit_ta,
state, loop_state):
(next_output, cell_state) = cell(current_input, state)
nest.assert_same_structure(state, cell_state)
nest.assert_same_structure(cell.output_size, next_output)
next_time = time + 1
(next_finished, next_input, next_state, emit_output,
next_loop_state) = loop_fn(next_time, next_output, cell_state,
loop_state)
nest.assert_same_structure(state, next_state)
nest.assert_same_structure(current_input, next_input)
nest.assert_same_structure(emit_ta, emit_output)
# If loop_fn returns None for next_loop_state, just reuse the previous one.
loop_state = loop_state if next_loop_state is None else next_loop_state
def _copy_some_through(current, candidate):
"""Copy some tensors through via array_ops.where."""
def copy_fn(cur_i, cand_i):
# TensorArray and scalar get passed through.
if isinstance(cur_i, tensor_array_ops.TensorArray):
return cand_i
if cur_i.shape.ndims == 0:
return cand_i
# Otherwise propagate the old or the new value.
with ops.colocate_with(cand_i):
return array_ops.where(elements_finished, cur_i,
cand_i)
return nest.map_structure(copy_fn, current, candidate)
emit_output = _copy_some_through(zero_emit, emit_output)
next_state = _copy_some_through(state, next_state)
emit_ta = nest.map_structure(lambda ta, emit: ta.write(time, emit),
emit_ta, emit_output)
state_ta = nest.map_structure(
lambda ta, state: ta.write(time, state), state_ta, next_state)
elements_finished = math_ops.logical_or(elements_finished,
next_finished)
return (next_time, elements_finished, next_input, state_ta,
emit_ta, next_state, loop_state)
returned = control_flow_ops.while_loop(
condition,
body,
loop_vars=[
time, elements_finished, next_input, state_ta, emit_ta, state,
loop_state
],
parallel_iterations=parallel_iterations,
swap_memory=swap_memory)
(state_ta, emit_ta, final_state, final_loop_state) = returned[-4:]
flat_states = nest.flatten(state_ta)
flat_states = [
array_ops.transpose(ta.stack(), (1, 0, 2)) for ta in flat_states
]
states = nest.pack_sequence_as(structure=state_ta,
flat_sequence=flat_states)
flat_outputs = nest.flatten(emit_ta)
flat_outputs = [
array_ops.transpose(ta.stack(), (1, 0, 2)) for ta in flat_outputs
]
outputs = nest.pack_sequence_as(structure=emit_ta,
flat_sequence=flat_outputs)
return (states, outputs, final_state)
def raw_rnn(cell, loop_fn, parallel_iterations=None, swap_memory=False, scope=None):
"""
raw_rnn adapted from the original tensorflow implementation
(https://github.com/tensorflow/tensorflow/blob/r1.4/tensorflow/python/ops/rnn.py)
to emit arbitrarily nested states for each time step (concatenated along the time axis)
in addition to the outputs at each timestep and the final state
returns (
states for all timesteps,
outputs for all timesteps,
final cell state,
)
"""
if not _like_rnncell(cell):
raise TypeError("cell must be an instance of RNNCell")
if not callable(loop_fn):
raise TypeError("loop_fn must be a callable")
parallel_iterations = parallel_iterations or 32
# Create a new scope in which the caching device is either
# determined by the parent scope, or is set to place the cached
# Variable using the same placement as for the rest of the RNN.
with vs.variable_scope(scope or "rnn") as varscope:
if context.in_graph_mode():
if varscope.caching_device is None:
varscope.set_caching_device(lambda op: op.device)
time = constant_op.constant(0, dtype=dtypes.int32)
(elements_finished, next_input, initial_state, emit_structure,
init_loop_state) = loop_fn(time, None, None, None)
flat_input = nest.flatten(next_input)
# Need a surrogate loop state for the while_loop if none is available.
loop_state = (init_loop_state if init_loop_state is not None
else constant_op.constant(0, dtype=dtypes.int32))
input_shape = [input_.get_shape() for input_ in flat_input]
static_batch_size = input_shape[0][0]
for input_shape_i in input_shape:
# Static verification that batch sizes all match
static_batch_size.merge_with(input_shape_i[0])
batch_size = static_batch_size.value
const_batch_size = batch_size
if batch_size is None:
batch_size = array_ops.shape(flat_input[0])[0]
nest.assert_same_structure(initial_state, cell.state_size)
state = initial_state
flat_state = nest.flatten(state)
flat_state = [ops.convert_to_tensor(s) for s in flat_state]
state = nest.pack_sequence_as(structure=state,
flat_sequence=flat_state)
if emit_structure is not None:
flat_emit_structure = nest.flatten(emit_structure)
flat_emit_size = [emit.shape if emit.shape.is_fully_defined() else
array_ops.shape(emit) for emit in flat_emit_structure]
flat_emit_dtypes = [emit.dtype for emit in flat_emit_structure]
else:
emit_structure = cell.output_size
flat_emit_size = nest.flatten(emit_structure)
flat_emit_dtypes = [flat_state[0].dtype] * len(flat_emit_size)
flat_state_size = [s.shape if s.shape.is_fully_defined() else
array_ops.shape(s) for s in flat_state]
flat_state_dtypes = [s.dtype for s in flat_state]
flat_emit_ta = [
tensor_array_ops.TensorArray(
dtype=dtype_i,
dynamic_size=True,
element_shape=(tensor_shape.TensorShape([const_batch_size])
.concatenate(_maybe_tensor_shape_from_tensor(size_i))),
size=0,
name="rnn_output_%d" % i
)
for i, (dtype_i, size_i) in enumerate(zip(flat_emit_dtypes, flat_emit_size))
]
emit_ta = nest.pack_sequence_as(structure=emit_structure, flat_sequence=flat_emit_ta)
flat_zero_emit = [
array_ops.zeros(_concat(batch_size, size_i), dtype_i)
for size_i, dtype_i in zip(flat_emit_size, flat_emit_dtypes)]
zero_emit = nest.pack_sequence_as(structure=emit_structure, flat_sequence=flat_zero_emit)
flat_state_ta = [
tensor_array_ops.TensorArray(
dtype=dtype_i,
dynamic_size=True,
element_shape=(tensor_shape.TensorShape([const_batch_size])
.concatenate(_maybe_tensor_shape_from_tensor(size_i))),
size=0,
name="rnn_state_%d" % i
)
for i, (dtype_i, size_i) in enumerate(zip(flat_state_dtypes, flat_state_size))
]
state_ta = nest.pack_sequence_as(structure=state, flat_sequence=flat_state_ta)
def condition(unused_time, elements_finished, *_):
return math_ops.logical_not(math_ops.reduce_all(elements_finished))
def body(time, elements_finished, current_input, state_ta, emit_ta, state, loop_state):
(next_output, cell_state) = cell(current_input, state)
nest.assert_same_structure(state, cell_state)
nest.assert_same_structure(cell.output_size, next_output)
next_time = time + 1
(next_finished, next_input, next_state, emit_output,
next_loop_state) = loop_fn(next_time, next_output, cell_state, loop_state)
nest.assert_same_structure(state, next_state)
nest.assert_same_structure(current_input, next_input)
nest.assert_same_structure(emit_ta, emit_output)
# If loop_fn returns None for next_loop_state, just reuse the previous one.
loop_state = loop_state if next_loop_state is None else next_loop_state
def _copy_some_through(current, candidate):
"""Copy some tensors through via array_ops.where."""
def copy_fn(cur_i, cand_i):
# TensorArray and scalar get passed through.
if isinstance(cur_i, tensor_array_ops.TensorArray):
return cand_i
if cur_i.shape.ndims == 0:
return cand_i
# Otherwise propagate the old or the new value.
with ops.colocate_with(cand_i):
return array_ops.where(elements_finished, cur_i, cand_i)
return nest.map_structure(copy_fn, current, candidate)
emit_output = _copy_some_through(zero_emit, emit_output)
next_state = _copy_some_through(state, next_state)
emit_ta = nest.map_structure(lambda ta, emit: ta.write(time, emit), emit_ta, emit_output)
state_ta = nest.map_structure(lambda ta, state: ta.write(time, state), state_ta, next_state)
elements_finished = math_ops.logical_or(elements_finished, next_finished)
return (next_time, elements_finished, next_input, state_ta,
emit_ta, next_state, loop_state)
returned = control_flow_ops.while_loop(
condition, body, loop_vars=[
time, elements_finished, next_input, state_ta,
emit_ta, state, loop_state],
parallel_iterations=parallel_iterations,
swap_memory=swap_memory
)
(state_ta, emit_ta, final_state, final_loop_state) = returned[-4:]
flat_states = nest.flatten(state_ta)
flat_states = [array_ops.transpose(ta.stack(), (1, 0, 2)) for ta in flat_states]
states = nest.pack_sequence_as(structure=state_ta, flat_sequence=flat_states)
flat_outputs = nest.flatten(emit_ta)
flat_outputs = [array_ops.transpose(ta.stack(), (1, 0, 2)) for ta in flat_outputs]
outputs = nest.pack_sequence_as(structure=emit_ta, flat_sequence=flat_outputs)
return (states, outputs, final_state)