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未验证 提交 be4ea174 编写于 作者: L liu zhengxi 提交者： GitHub
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[Migrate Fluid] Migrate dynamic_decode and tensor_array_to_tensor (#48876)

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Showing with 887 addition and 611 deletion
python/paddle/fluid/layers/__init__.py
浏览文件 @ be4ea174
......@@ -27,7 +27,6 @@ from .loss import *
from .learning_rate_scheduler import *
from .collective import *
from .sequence_lod import *
from . import rnn
__all__ = []
__all__ += nn.__all__
......@@ -37,6 +36,3 @@ __all__ += control_flow.__all__
__all__ += learning_rate_scheduler.__all__
__all__ += sequence_lod.__all__
__all__ += loss.__all__
__all__ += rnn.__all__
from .rnn import *
python/paddle/fluid/layers/nn.py
浏览文件 @ be4ea174
......@@ -45,7 +45,7 @@ from .layer_function_generator import (
templatedoc,
_generate_doc_string_,
)
from .tensor import concat, assign, fill_constant, zeros, tensor_array_to_tensor
from .tensor import concat, assign, fill_constant, zeros
from . import utils
from .. import unique_name
from functools import reduce
......
python/paddle/fluid/layers/rnn.py 已删除 100644 → 0
浏览文件 @ d4f43ad4
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
from functools import partial, reduce
import warnings
import paddle
from paddle.utils import deprecated
from . import nn
from . import tensor
from . import control_flow
from . import utils
from . import sequence_lod
from .utils import *
from .. import core
from ..framework import default_main_program
from ..data_feeder import convert_dtype
from ..layer_helper import LayerHelper
from ..framework import _non_static_mode
from ..param_attr import ParamAttr
from ..data_feeder import check_variable_and_dtype, check_type, check_dtype
from collections.abc import Sequence
__all__ = [
'dynamic_decode',
]
class ArrayWrapper:
def __init__(self, x):
self.array = [x]
def append(self, x):
self.array.append(x)
return self
def __getitem__(self, item):
return self.array.__getitem__(item)
def _dynamic_decode_imperative(
decoder,
inits=None,
max_step_num=None,
output_time_major=False,
impute_finished=False,
is_test=False,
return_length=False,
**kwargs
):
def _maybe_copy(state, new_state, step_mask):
# TODO: use where_op
state_dtype = state.dtype
if convert_dtype(state_dtype) in ["bool"]:
state = tensor.cast(state, dtype="float32")
new_state = tensor.cast(new_state, dtype="float32")
if step_mask.dtype != state.dtype:
step_mask = tensor.cast(step_mask, dtype=state.dtype)
# otherwise, renamed bool gradients of would be summed up leading
# to sum(bool) error.
step_mask.stop_gradient = True
new_state = paddle.tensor.math._multiply_with_axis(
state, step_mask, axis=0
) - paddle.tensor.math._multiply_with_axis(
new_state, (step_mask - 1), axis=0
)
if convert_dtype(state_dtype) in ["bool"]:
new_state = tensor.cast(new_state, dtype=state_dtype)
return new_state
initial_inputs, initial_states, initial_finished = decoder.initialize(inits)
inputs, states, finished = (
initial_inputs,
initial_states,
initial_finished,
)
cond = paddle.logical_not((paddle.all(initial_finished)))
sequence_lengths = tensor.cast(paddle.zeros_like(initial_finished), "int64")
outputs = None
step_idx = 0
step_idx_tensor = tensor.fill_constant(
shape=[1], dtype="int64", value=step_idx
)
while cond.numpy():
(step_outputs, next_states, next_inputs, next_finished) = decoder.step(
step_idx_tensor, inputs, states, **kwargs
)
if not decoder.tracks_own_finished:
# BeamSearchDecoder would track it own finished, since
# beams would be reordered and the finished status of each
# entry might change. Otherwise, perform logical OR which
# would not change the already finished.
next_finished = paddle.logical_or(next_finished, finished)
# To confirm states.finished/finished be consistent with
# next_finished.
tensor.assign(next_finished, finished)
next_sequence_lengths = paddle.add(
sequence_lengths,
tensor.cast(
paddle.logical_not(finished), sequence_lengths.dtype
),
)
if impute_finished: # rectify the states for the finished.
next_states = map_structure(
lambda x, y: _maybe_copy(x, y, finished),
states,
next_states,
)
else:
warnings.warn(
"`next_states` has no `lengths` attribute, the returned `sequence_lengths` would be all zeros."
) if not hasattr(next_states, "lengths") else None
next_sequence_lengths = getattr(
next_states, "lengths", sequence_lengths
)
outputs = (
map_structure(lambda x: ArrayWrapper(x), step_outputs)
if step_idx == 0
else map_structure(
lambda x, x_array: x_array.append(x), step_outputs, outputs
)
)
inputs, states, finished, sequence_lengths = (
next_inputs,
next_states,
next_finished,
next_sequence_lengths,
)
paddle.increment(x=step_idx_tensor, value=1.0)
step_idx += 1
cond = paddle.logical_not(paddle.all(finished))
if max_step_num is not None and step_idx > max_step_num:
break
final_outputs = map_structure(
lambda x: paddle.stack(x.array, axis=0), outputs
)
final_states = states
try:
final_outputs, final_states = decoder.finalize(
final_outputs, final_states, sequence_lengths
)
except NotImplementedError:
pass
if not output_time_major:
final_outputs = map_structure(
lambda x: paddle.transpose(
x, [1, 0] + list(range(2, len(x.shape)))
),
final_outputs,
)
return (
(final_outputs, final_states, sequence_lengths)
if return_length
else (final_outputs, final_states)
)
def _dynamic_decode_declarative(
decoder,
inits=None,
max_step_num=None,
output_time_major=False,
impute_finished=False,
is_test=False,
return_length=False,
**kwargs
):
initial_inputs, initial_states, initial_finished = decoder.initialize(inits)
global_inputs, global_states, global_finished = (
initial_inputs,
initial_states,
initial_finished,
)
global_finished.stop_gradient = True
step_idx = tensor.fill_constant(shape=[1], dtype="int64", value=0)
cond = paddle.logical_not((paddle.all(initial_finished)))
if max_step_num is not None:
max_step_num = tensor.fill_constant(
shape=[1], dtype="int64", value=max_step_num
)
while_op = paddle.static.nn.control_flow.While(cond, is_test=is_test)
sequence_lengths = tensor.cast(paddle.zeros_like(initial_finished), "int64")
sequence_lengths.stop_gradient = True
if is_test:
# for test, reuse inputs and states variables to save memory
inputs = map_structure(lambda x: x, initial_inputs)
states = map_structure(lambda x: x, initial_states)
else:
# inputs and states of all steps must be saved for backward and training
inputs_arrays = map_structure(
lambda x: paddle.tensor.array_write(x, step_idx), initial_inputs
)
states_arrays = map_structure(
lambda x: paddle.tensor.array_write(x, step_idx), initial_states
)
def _maybe_copy(state, new_state, step_mask):
# TODO: use where_op
state_dtype = state.dtype
if convert_dtype(state_dtype) in ["bool"]:
state = tensor.cast(state, dtype="float32")
new_state = tensor.cast(new_state, dtype="float32")
if step_mask.dtype != state.dtype:
step_mask = tensor.cast(step_mask, dtype=state.dtype)
# otherwise, renamed bool gradients of would be summed up leading
# to sum(bool) error.
step_mask.stop_gradient = True
new_state = paddle.tensor.math._multiply_with_axis(
state, step_mask, axis=0
) - paddle.tensor.math._multiply_with_axis(
new_state, (step_mask - 1), axis=0
)
if convert_dtype(state_dtype) in ["bool"]:
new_state = tensor.cast(new_state, dtype=state_dtype)
return new_state
def _transpose_batch_time(x):
return paddle.transpose(x, [1, 0] + list(range(2, len(x.shape))))
def _create_array_out_of_while(dtype):
current_block_idx = default_main_program().current_block_idx
default_main_program().current_block_idx = (
default_main_program().current_block().parent_idx
)
tensor_array = paddle.tensor.create_array(dtype)
default_main_program().current_block_idx = current_block_idx
return tensor_array
# While
with while_op.block():
if not is_test:
inputs = map_structure(
lambda array: paddle.tensor.array_read(array, step_idx),
inputs_arrays,
)
states = map_structure(
lambda array: paddle.tensor.array_read(array, step_idx),
states_arrays,
)
(outputs, next_states, next_inputs, next_finished) = decoder.step(
step_idx, inputs, states, **kwargs
)
if not decoder.tracks_own_finished:
# BeamSearchDecoder would track it own finished, since beams would
# be reordered and the finished status of each entry might change.
# Otherwise, perform logical OR which would not change the already
# finished.
next_finished = paddle.logical_or(next_finished, global_finished)
next_sequence_lengths = paddle.add(
sequence_lengths,
tensor.cast(
paddle.logical_not(global_finished),
sequence_lengths.dtype,
),
)
if impute_finished: # rectify the states for the finished.
next_states = map_structure(
lambda x, y: _maybe_copy(x, y, global_finished),
states,
next_states,
)
else:
warnings.warn(
"`next_states` has no `lengths` attribute, the returned `sequence_lengths` would be all zeros."
) if not hasattr(next_states, "lengths") else None
next_sequence_lengths = getattr(
next_states, "lengths", sequence_lengths
)
# create tensor array in global block after dtype[s] of outputs can be got
outputs_arrays = map_structure(
lambda x: _create_array_out_of_while(x.dtype), outputs
)
map_structure(
lambda x, x_array: paddle.tensor.array_write(
x, i=step_idx, array=x_array
),
outputs,
outputs_arrays,
)
paddle.increment(x=step_idx, value=1.0)
# update the global_finished first, since it might be also in states of
# decoder, which otherwise would write a stale finished status to array
tensor.assign(next_finished, global_finished)
tensor.assign(next_sequence_lengths, sequence_lengths)
if is_test:
map_structure(tensor.assign, next_inputs, global_inputs)
map_structure(tensor.assign, next_states, global_states)
else:
map_structure(
lambda x, x_array: paddle.tensor.array_write(
x, i=step_idx, array=x_array
),
next_inputs,
inputs_arrays,
)
map_structure(
lambda x, x_array: paddle.tensor.array_write(
x, i=step_idx, array=x_array
),
next_states,
states_arrays,
)
if max_step_num is not None:
paddle.logical_and(
paddle.logical_not(paddle.all(global_finished)),
paddle.less_equal(step_idx, max_step_num),
cond,
)
else:
paddle.logical_not(paddle.all(global_finished), cond)
final_outputs = map_structure(
lambda array: tensor.tensor_array_to_tensor(
array, axis=0, use_stack=True
)[0],
outputs_arrays,
)
if is_test:
final_states = global_states
else:
final_states = map_structure(
lambda array: paddle.tensor.array_read(array, step_idx),
states_arrays,
)
try:
final_outputs, final_states = decoder.finalize(
final_outputs, final_states, sequence_lengths
)
except NotImplementedError:
pass
if not output_time_major:
final_outputs = map_structure(_transpose_batch_time, final_outputs)
return (
(final_outputs, final_states, sequence_lengths)
if return_length
else (final_outputs, final_states)
)
def dynamic_decode(
decoder,
inits=None,
max_step_num=None,
output_time_major=False,
impute_finished=False,
is_test=False,
return_length=False,
**kwargs
):
r"""
Dynamic decoding performs :code:`decoder.step()` repeatedly until the returned
Tensor indicating finished status contains all True values or the number of
decoding step reaches to :attr:`max_step_num`.
:code:`decoder.initialize()` would be called once before the decoding loop.
If the `decoder` has implemented `finalize` method, :code:`decoder.finalize()`
would be called once after the decoding loop.
Parameters:
decoder(Decoder): An instance of `Decoder`.
inits(object, optional): Argument passed to `decoder.initialize`.
Default `None`.
max_step_num(int, optional): The maximum number of steps. If not provided,
decode until the decoder is fully done, or in other words, the returned
Tensor by :code:`decoder.step()` indicating finished status contains
all True. Default `None`.
output_time_major(bool, optional): Indicate the data layout of Tensor included
in the final outputs(the first returned value of this method). If
attr:`False`, the data layout would be batch major with shape
`[batch_size, seq_len, ...]`. If attr:`True`, the data layout would
be time major with shape `[seq_len, batch_size, ...]`. Default: `False`.
impute_finished(bool, optional): If `True` and `decoder.tracks_own_finished`
is False, then states get copied through for batch entries which are
marked as finished, which differs with the unfinished using the new states
returned by :code:`decoder.step()` and ensures that the final states have
the correct values. Otherwise, states wouldn't be copied through when
finished. If the returned `final_states` is needed, it should be set as
True, which causes some slowdown. Default `False`.
is_test(bool, optional): A flag indicating whether to use test mode. In
test mode, it is more memory saving. Default `False`.
return_length(bool, optional): A flag indicating whether to return an
extra Tensor variable in the output tuple, which stores the actual
lengths of all decoded sequences. Default `False`.
**kwargs: Additional keyword arguments. Arguments passed to `decoder.step`.
Returns:
- final_outputs (Tensor, nested structure of Tensor), each Tensor in :code:`final_outputs` is the stacked of all decoding steps' outputs, which might be revised
by :code:`decoder.finalize()` if the decoder has implemented finalize.
And :code:`final_outputs` has the same structure and data types as the :code:`outputs`
returned by :code:`decoder.step()`
- final_states (Tensor, nested structure of Tensor), :code:`final_states` is the counterpart at last time step of initial states \
returned by :code:`decoder.initialize()` , thus has the same structure
with it and has tensors with same shapes and data types.
- sequence_lengths (Tensor), stores the actual lengths of all decoded sequences.
sequence_lengths is provided only if :code:`return_length` is True.
Examples:
.. code-block:: python
import paddle
from paddle.nn import BeamSearchDecoder, dynamic_decode
from paddle.nn import GRUCell, Linear, Embedding
trg_embeder = Embedding(100, 32)
output_layer = Linear(32, 32)
decoder_cell = GRUCell(input_size=32, hidden_size=32)
decoder = BeamSearchDecoder(decoder_cell,
start_token=0,
end_token=1,
beam_size=4,
embedding_fn=trg_embeder,
output_fn=output_layer)
encoder_output = paddle.ones((4, 8, 32), dtype=paddle.get_default_dtype())
outputs = dynamic_decode(decoder=decoder,
inits=decoder_cell.get_initial_states(encoder_output),
max_step_num=10)
"""
if _non_static_mode():
return _dynamic_decode_imperative(
decoder,
inits,
max_step_num,
output_time_major,
impute_finished,
is_test,
return_length,
**kwargs
)
else:
return _dynamic_decode_declarative(
decoder,
inits,
max_step_num,
output_time_major,
impute_finished,
is_test,
return_length,
**kwargs
)
python/paddle/fluid/layers/tensor.py
浏览文件 @ be4ea174
......@@ -42,7 +42,6 @@ from paddle import _C_ops, _legacy_C_ops
__all__ = [
'cast',
'tensor_array_to_tensor',
'concat',
'sums',
'assign',
......@@ -261,123 +260,6 @@ def concat(input, axis=0, name=None):
return out
def tensor_array_to_tensor(input, axis=1, name=None, use_stack=False):
r"""
This function concatenates or stacks all tensors in the input LoDTensorArray
along the axis mentioned and returns that as the output.
For Example:
.. code-block:: text
Case 1:
Given:
input.data = {[[0.6, 0.1, 0.3],
[0.5, 0.3, 0.2]],
[[1.3],
[1.8]],
[[2.3, 2.1],
[2.5, 2.4]]}
axis = 1, use_stack = False
Then:
output.data = [[0.6, 0.1, 0.3, 1.3, 2.3, 2.1],
[0.5, 0.3, 0.2, 1.8, 2.5, 2.4]]
output_index.data = [3, 1, 2]
Case 2:
Given:
input.data = {[[0.6, 0.1],
[0.5, 0.3]],
[[0.3, 1.3],
[0.2, 1.8]],
[[2.3, 2.1],
[2.5, 2.4]]}
axis = 1, use_stack = True
Then:
output.data = [[[0.6, 0.1]
[0.3, 1.3]
[2.3, 2.1],
[[0.5, 0.3]
[0.2, 1.8]
[2.5, 2.4]]]
output_index.data = [2, 2, 2]
Args:
input(Variable): A LodTensorArray variable.
axis(int): The axis along which the tensors in attr::`input` will be
concatenated or stacked.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
use_stack(bool): Act as concat_op or stack_op. For stack mode, all
tensors in the tensor array must have the same shape.
Returns:
Variable: The concatenated or stacked tensor variable.
Variable: A 1-D tensor variable with int32 data type. The data in this \
tensor contains all input including tensors' sizes along the axis.
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
import numpy as np
x0 = fluid.layers.assign(np.random.rand(2, 2).astype("float32"))
x1 = fluid.layers.assign(np.random.rand(2, 2).astype("float32"))
i = fluid.layers.fill_constant(shape=[1], dtype="int64", value=0)
array = paddle.tensor.create_array(dtype='float32')
paddle.tensor.array_write(x0, i, array)
paddle.tensor.array_write(x1, i + 1, array)
output, output_index = fluid.layers.tensor_array_to_tensor(input=array)
"""
if _non_static_mode():
assert isinstance(
input, list
), "The 'input' in tensor_array_to_tensor must be list"
from .nn import concat
from ..dygraph import to_variable
from paddle import stack
op = stack if use_stack else concat
res = op(input, axis=axis)
sizes = to_variable(
numpy.array(list(map(lambda x: int(x.shape[axis]), input)))
)
return res, sizes
check_type(input, 'input', (list, Variable), 'tensor_array_to_tensor')
if isinstance(input, list):
for i, input_x in enumerate(input):
check_type(
input_x,
'input[' + str(i) + ']',
Variable,
'tensor_array_to_tensor',
)
helper = LayerHelper('tensor_array_to_tensor', **locals())
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
out_index = helper.create_variable_for_type_inference(dtype="int32")
helper.append_op(
type='tensor_array_to_tensor',
inputs={'X': input},
outputs={'Out': [out], 'OutIndex': [out_index]},
attrs={'axis': axis, 'use_stack': use_stack},
)
return out, out_index
def sums(input, out=None):
r"""
This function computes the sum of multiple input Tensors elementwisely.
......
python/paddle/fluid/tests/unittests/test_dynamic_rnn_stop_gradient.py
浏览文件 @ be4ea174
......@@ -19,6 +19,7 @@ import numpy as np
import paddle
import paddle.fluid as fluid
import paddle.fluid.layers as layers
from paddle.tensor.manipulation import tensor_array_to_tensor
paddle.enable_static()
......@@ -58,7 +59,7 @@ def build_and_run_program(place, batch_size, beam_size, stop_gradient=False):
length_cond = paddle.less_than(x=step_idx, y=max_len)
layers.assign(length_cond, cond)
out = layers.tensor_array_to_tensor(scores, axis=0, use_stack=True)[0]
out = tensor_array_to_tensor(scores, axis=0, use_stack=True)[0]
loss = paddle.mean(out)
opt = fluid.optimizer.Adam(0.01)
opt.minimize(loss)
......
python/paddle/fluid/tests/unittests/test_rnn_decode_api.py
浏览文件 @ be4ea174
......@@ -12,6 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import collections
import random
import unittest
......@@ -22,9 +23,19 @@ import paddle.fluid as fluid
import paddle.fluid.layers as layers
import paddle.nn as nn
from paddle import Model, set_device
from paddle.fluid.dygraph import Layer
from paddle.fluid.data_feeder import convert_dtype
from paddle.fluid.framework import _test_eager_guard
from paddle.nn import BeamSearchDecoder, dynamic_decode
from paddle.fluid.layers.utils import map_structure
from paddle.nn import (
RNN,
BeamSearchDecoder,
Embedding,
Layer,
Linear,
LSTMCell,
SimpleRNNCell,
dynamic_decode,
)
from paddle.static import InputSpec as Input
paddle.enable_static()
......@@ -351,7 +362,7 @@ class TestBeamSearch(ModuleApiTest):
beam_size=4,
max_step_num=20,
):
embedder = paddle.nn.Embedding(vocab_size, embed_dim)
embedder = Embedding(vocab_size, embed_dim)
output_layer = nn.Linear(hidden_size, vocab_size)
cell = nn.LSTMCell(embed_dim, hidden_size)
self.max_step_num = max_step_num
......@@ -392,5 +403,312 @@ class TestBeamSearch(ModuleApiTest):
self.func_check_output()
class EncoderCell(SimpleRNNCell):
def __init__(
self,
num_layers,
input_size,
hidden_size,
dropout_prob=0.0,
init_scale=0.1,
):
super(EncoderCell, self).__init__(input_size, hidden_size)
self.dropout_prob = dropout_prob
# use add_sublayer to add multi-layers
self.lstm_cells = []
for i in range(num_layers):
self.lstm_cells.append(
self.add_sublayer(
"lstm_%d" % i,
LSTMCell(
input_size=input_size if i == 0 else hidden_size,
hidden_size=hidden_size,
),
)
)
def forward(self, step_input, states):
new_states = []
for i, lstm_cell in enumerate(self.lstm_cells):
out, new_state = lstm_cell(step_input, states[i])
step_input = (
layers.dropout(
out,
self.dropout_prob,
dropout_implementation='upscale_in_train',
)
if self.dropout_prob > 0
else out
)
new_states.append(new_state)
return step_input, new_states
@property
def state_shape(self):
return [cell.state_shape for cell in self.lstm_cells]
class Encoder(Layer):
def __init__(
self,
vocab_size,
embed_dim,
hidden_size,
num_layers,
dropout_prob=0.0,
init_scale=0.1,
):
super(Encoder, self).__init__()
self.embedder = Embedding(vocab_size, embed_dim)
self.stack_lstm = RNN(
EncoderCell(
num_layers, embed_dim, hidden_size, dropout_prob, init_scale
),
is_reverse=False,
time_major=False,
)
def forward(self, sequence, sequence_length):
inputs = self.embedder(sequence)
encoder_output, encoder_state = self.stack_lstm(
inputs, sequence_length=sequence_length
)
return encoder_output, encoder_state
DecoderCell = EncoderCell
class Decoder(Layer):
def __init__(
self,
vocab_size,
embed_dim,
hidden_size,
num_layers,
dropout_prob=0.0,
init_scale=0.1,
):
super(Decoder, self).__init__()
self.embedder = Embedding(vocab_size, embed_dim)
self.stack_lstm = RNN(
DecoderCell(
num_layers, embed_dim, hidden_size, dropout_prob, init_scale
),
is_reverse=False,
time_major=False,
)
self.output_layer = Linear(hidden_size, vocab_size, bias_attr=False)
def forward(self, target, decoder_initial_states):
inputs = self.embedder(target)
decoder_output, _ = self.stack_lstm(
inputs, initial_states=decoder_initial_states
)
predict = self.output_layer(decoder_output)
return predict
class TrainingHelper:
def __init__(self, inputs, sequence_length, time_major=False):
self.inputs = inputs
self.sequence_length = sequence_length
self.time_major = time_major
self.inputs_ = map_structure(
lambda x: paddle.nn.functional.pad(
x,
pad=([0, 1] + [0, 0] * (len(x.shape) - 1))
if time_major
else ([0, 0, 0, 1] + [0, 0] * (len(x.shape) - 2)),
),
self.inputs,
)
def initialize(self):
init_finished = paddle.equal(
self.sequence_length,
paddle.full(
shape=[1], dtype=self.sequence_length.dtype, fill_value=0
),
)
init_inputs = map_structure(
lambda x: x[0] if self.time_major else x[:, 0], self.inputs
)
return init_inputs, init_finished
def sample(self, time, outputs, states):
sample_ids = paddle.argmax(outputs, axis=-1)
return sample_ids
def next_inputs(self, time, outputs, states, sample_ids):
time = (
paddle.cast(time, "int32")
if convert_dtype(time.dtype) not in ["int32"]
else time
)
if self.sequence_length.dtype != time.dtype:
self.sequence_length = paddle.cast(self.sequence_length, time.dtype)
next_time = time + 1
finished = paddle.less_equal(self.sequence_length, next_time)
def _slice(x):
axes = [0 if self.time_major else 1]
return paddle.squeeze(
paddle.slice(
x, axes=axes, starts=[next_time], ends=[next_time + 1]
),
axis=axes,
)
next_inputs = map_structure(_slice, self.inputs_)
return finished, next_inputs, states
class BasicDecoder(paddle.nn.decode.Decoder):
def __init__(self, cell, helper, output_fn=None):
super().__init__()
self.cell = cell
self.helper = helper
self.output_fn = output_fn
def initialize(self, initial_cell_states):
(initial_inputs, initial_finished) = self.helper.initialize()
return initial_inputs, initial_cell_states, initial_finished
class OutputWrapper(
collections.namedtuple("OutputWrapper", ("cell_outputs", "sample_ids"))
):
pass
def step(self, time, inputs, states, **kwargs):
cell_outputs, cell_states = self.cell(inputs, states, **kwargs)
if self.output_fn is not None:
cell_outputs = self.output_fn(cell_outputs)
sample_ids = self.helper.sample(
time=time, outputs=cell_outputs, states=cell_states
)
sample_ids.stop_gradient = True
(finished, next_inputs, next_states) = self.helper.next_inputs(
time=time,
outputs=cell_outputs,
states=cell_states,
sample_ids=sample_ids,
)
outputs = self.OutputWrapper(cell_outputs, sample_ids)
return (outputs, next_states, next_inputs, finished)
class BaseModel(Layer):
def __init__(
self,
vocab_size=10,
embed_dim=32,
hidden_size=32,
num_layers=1,
dropout_prob=0.0,
init_scale=0.1,
):
super(BaseModel, self).__init__()
self.hidden_size = hidden_size
self.word_embedding = Embedding(vocab_size, embed_dim)
self.encoder = Encoder(
vocab_size,
embed_dim,
hidden_size,
num_layers,
dropout_prob,
init_scale,
)
self.decoder = Decoder(
vocab_size,
embed_dim,
hidden_size,
num_layers,
dropout_prob,
init_scale,
)
def forward(self, src, src_length, trg, trg_length):
encoder_output = self.encoder(src, src_length)
trg_emb = self.decoder.embedder(trg)
helper = TrainingHelper(inputs=trg_emb, sequence_length=trg_length)
decoder = BasicDecoder(self.decoder.stack_lstm.cell, helper)
(
decoder_output,
decoder_final_state,
dec_seq_lengths,
) = dynamic_decode(
decoder,
inits=self.decoder.stack_lstm.cell.get_initial_states(
encoder_output
),
impute_finished=True,
is_test=False,
return_length=True,
)
logits, samples, sample_length = (
decoder_output.cell_outputs,
decoder_output.sample_ids,
dec_seq_lengths,
)
return logits
class TestDynamicDecode(ModuleApiTest):
def setUp(self):
paddle.set_default_dtype("float64")
shape = (1, 10)
bs_shape = 1
self.inputs = [
np.random.randint(0, 10, size=shape).astype("int64"),
np.random.randint(0, 10, size=bs_shape).astype("int64"),
np.random.randint(0, 10, size=shape).astype("int64"),
np.random.randint(0, 10, size=bs_shape).astype("int64"),
]
self.outputs = None
self.attrs = {
"vocab_size": 10,
"embed_dim": 32,
"hidden_size": 32,
}
self.param_states = {}
@staticmethod
def model_init(
self,
vocab_size,
embed_dim,
hidden_size,
bos_id=0,
eos_id=1,
):
self.model = BaseModel(
vocab_size=vocab_size, embed_dim=embed_dim, hidden_size=hidden_size
)
@staticmethod
def model_forward(model, src, src_length, trg, trg_length):
return model.model(src, src_length, trg, trg_length)
def make_inputs(self):
inputs = [
Input([None, None], "int64", "src"),
Input([None], "int64", "src_length"),
Input([None, None], "int64", "trg"),
Input([None], "int64", "trg_length"),
]
return inputs
def func_check_output(self):
self.setUp()
self.make_inputs()
self.check_output()
def test_check_output(self):
with _test_eager_guard():
self.func_check_output()
self.func_check_output()
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_slice_op.py
浏览文件 @ be4ea174
......@@ -23,6 +23,7 @@ import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.fluid.layers as layers
from paddle.tensor.manipulation import tensor_array_to_tensor
paddle.enable_static()
......@@ -703,7 +704,7 @@ class TestSliceApiWithLoDTensorArray(unittest.TestCase):
paddle.tensor.array_length(arr) - 1
) # dtype of end is int64
self.sliced_arr = slice_arr = arr[self.start : end]
output, _ = fluid.layers.tensor_array_to_tensor(
output, _ = tensor_array_to_tensor(
slice_arr, axis=self.axis, use_stack=True
)
elif case_num == 3:
......@@ -711,7 +712,7 @@ class TestSliceApiWithLoDTensorArray(unittest.TestCase):
[1], "int64", 2147483648
)
self.sliced_arr = slice_arr = arr[self.start : value_int64]
output, _ = fluid.layers.tensor_array_to_tensor(
output, _ = tensor_array_to_tensor(
slice_arr, axis=self.axis, use_stack=True
)
......
python/paddle/fluid/tests/unittests/test_tensor_array_to_tensor.py
浏览文件 @ be4ea174
......@@ -20,6 +20,7 @@ import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid import Program, program_guard
from paddle.tensor.manipulation import tensor_array_to_tensor
paddle.enable_static()
......@@ -32,12 +33,12 @@ class TestTensorArrayToTensorError(unittest.TestCase):
input_data = np.random.random((2, 4)).astype("float32")
def test_Variable():
fluid.layers.tensor_array_to_tensor(input=input_data)
tensor_array_to_tensor(input=input_data)
self.assertRaises(TypeError, test_Variable)
def test_list_Variable():
fluid.layers.tensor_array_to_tensor(input=[input_data])
tensor_array_to_tensor(input=[input_data])
self.assertRaises(TypeError, test_list_Variable)
......@@ -198,7 +199,7 @@ class TestLoDTensorArrayStack(unittest.TestCase):
for i, x in enumerate(self.inputs):
x = fluid.layers.assign(x)
paddle.tensor.array_write(x, idx + i, array)
output, output_index = fluid.layers.tensor_array_to_tensor(
output, output_index = tensor_array_to_tensor(
input=array, **self.attrs
)
loss = paddle.sum(output)
......@@ -241,15 +242,13 @@ class TestTensorArrayToTensorAPI(unittest.TestCase):
array = paddle.tensor.create_array(dtype='float32')
paddle.tensor.array_write(x0, i, array)
paddle.tensor.array_write(x1, i + 1, array)
output_stack, output_index_stack = fluid.layers.tensor_array_to_tensor(
output_stack, output_index_stack = tensor_array_to_tensor(
input=array, axis=1, use_stack=True
)
(
output_concat,
output_index_concat,
) = fluid.layers.tensor_array_to_tensor(
input=array, axis=1, use_stack=False
)
) = tensor_array_to_tensor(input=array, axis=1, use_stack=False)
return (
output_stack,
output_index_stack,
......
python/paddle/nn/decode.py
浏览文件 @ be4ea174
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
......@@ -12,18 +12,34 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import collections
import warnings
import numpy as np
import paddle
from paddle.framework import _non_static_mode
from paddle.static import default_main_program
from ..fluid.layers import dynamic_decode # noqa: F401
from ..fluid.data_feeder import convert_dtype
from ..fluid.layers.utils import flatten, map_structure
__all__ = []
class ArrayWrapper:
def __init__(self, x):
self.array = [x]
def append(self, x):
self.array.append(x)
return self
def __getitem__(self, item):
return self.array.__getitem__(item)
class Decoder:
"""
Decoder is the base class for any decoder instance used in `dynamic_decode`.
......@@ -650,3 +666,425 @@ class BeamSearchDecoder(Decoder):
bool: A python bool `True`.
"""
return True
def _dynamic_decode_imperative(
decoder,
inits=None,
max_step_num=None,
output_time_major=False,
impute_finished=False,
is_test=False,
return_length=False,
**kwargs
):
def _maybe_copy(state, new_state, step_mask):
# TODO: use where_op
state_dtype = state.dtype
if convert_dtype(state_dtype) in ["bool"]:
state = paddle.cast(state, dtype="float32")
new_state = paddle.cast(new_state, dtype="float32")
if step_mask.dtype != state.dtype:
step_mask = paddle.cast(step_mask, dtype=state.dtype)
# otherwise, renamed bool gradients of would be summed up leading
# to sum(bool) error.
step_mask = step_mask.unsqueeze([1])
step_mask.stop_gradient = True
new_state = paddle.multiply(state, step_mask) - paddle.multiply(
new_state, (step_mask - 1)
)
if convert_dtype(state_dtype) in ["bool"]:
new_state = paddle.cast(new_state, dtype=state_dtype)
return new_state
initial_inputs, initial_states, initial_finished = decoder.initialize(inits)
inputs, states, finished = (
initial_inputs,
initial_states,
initial_finished,
)
cond = paddle.logical_not((paddle.all(initial_finished)))
sequence_lengths = paddle.cast(paddle.zeros_like(initial_finished), "int64")
outputs = None
step_idx = 0
step_idx_tensor = paddle.full(shape=[1], fill_value=step_idx, dtype="int64")
while cond.numpy():
(step_outputs, next_states, next_inputs, next_finished) = decoder.step(
step_idx_tensor, inputs, states, **kwargs
)
if not decoder.tracks_own_finished:
# BeamSearchDecoder would track it own finished, since
# beams would be reordered and the finished status of each
# entry might change. Otherwise, perform logical OR which
# would not change the already finished.
next_finished = paddle.logical_or(next_finished, finished)
# To confirm states.finished/finished be consistent with
# next_finished.
paddle.assign(next_finished, finished)
next_sequence_lengths = paddle.add(
sequence_lengths,
paddle.cast(
paddle.logical_not(finished), sequence_lengths.dtype
),
)
if impute_finished: # rectify the states for the finished.
next_states = map_structure(
lambda x, y: _maybe_copy(x, y, finished),
states,
next_states,
)
else:
warnings.warn(
"`next_states` has no `lengths` attribute, the returned `sequence_lengths` would be all zeros."
) if not hasattr(next_states, "lengths") else None
next_sequence_lengths = getattr(
next_states, "lengths", sequence_lengths
)
outputs = (
map_structure(lambda x: ArrayWrapper(x), step_outputs)
if step_idx == 0
else map_structure(
lambda x, x_array: x_array.append(x), step_outputs, outputs
)
)
inputs, states, finished, sequence_lengths = (
next_inputs,
next_states,
next_finished,
next_sequence_lengths,
)
step_idx_tensor = paddle.increment(x=step_idx_tensor, value=1.0)
step_idx += 1
cond = paddle.logical_not(paddle.all(finished))
if max_step_num is not None and step_idx > max_step_num:
break
final_outputs = map_structure(
lambda x: paddle.stack(x.array, axis=0), outputs
)
final_states = states
try:
final_outputs, final_states = decoder.finalize(
final_outputs, final_states, sequence_lengths
)
except NotImplementedError:
pass
if not output_time_major:
final_outputs = map_structure(
lambda x: paddle.transpose(
x, [1, 0] + list(range(2, len(x.shape)))
),
final_outputs,
)
return (
(final_outputs, final_states, sequence_lengths)
if return_length
else (final_outputs, final_states)
)
def _dynamic_decode_declarative(
decoder,
inits=None,
max_step_num=None,
output_time_major=False,
impute_finished=False,
is_test=False,
return_length=False,
**kwargs
):
initial_inputs, initial_states, initial_finished = decoder.initialize(inits)
global_inputs, global_states, global_finished = (
initial_inputs,
initial_states,
initial_finished,
)
global_finished.stop_gradient = True
step_idx = paddle.full(shape=[1], fill_value=0, dtype="int64")
cond = paddle.logical_not((paddle.all(initial_finished)))
if max_step_num is not None:
max_step_num = paddle.full(
shape=[1], fill_value=max_step_num, dtype="int64"
)
while_op = paddle.static.nn.control_flow.While(cond, is_test=is_test)
sequence_lengths = paddle.cast(paddle.zeros_like(initial_finished), "int64")
sequence_lengths.stop_gradient = True
if is_test:
# for test, reuse inputs and states variables to save memory
inputs = map_structure(lambda x: x, initial_inputs)
states = map_structure(lambda x: x, initial_states)
else:
# inputs and states of all steps must be saved for backward and training
inputs_arrays = map_structure(
lambda x: paddle.tensor.array.array_write(x, step_idx),
initial_inputs,
)
states_arrays = map_structure(
lambda x: paddle.tensor.array.array_write(x, step_idx),
initial_states,
)
def _maybe_copy(state, new_state, step_mask):
# TODO: use where_op
state_dtype = state.dtype
if convert_dtype(state_dtype) in ["bool"]:
state = paddle.cast(state, dtype="float32")
new_state = paddle.cast(new_state, dtype="float32")
if step_mask.dtype != state.dtype:
step_mask = paddle.cast(step_mask, dtype=state.dtype)
# otherwise, renamed bool gradients of would be summed up leading
# to sum(bool) error.
step_mask = step_mask.unsqueeze([1])
step_mask.stop_gradient = True
new_state = paddle.multiply(state, step_mask) - paddle.multiply(
new_state, (step_mask - 1)
)
if convert_dtype(state_dtype) in ["bool"]:
new_state = paddle.cast(new_state, dtype=state_dtype)
return new_state
def _transpose_batch_time(x):
return paddle.transpose(x, [1, 0] + list(range(2, len(x.shape))))
def _create_array_out_of_while(dtype):
current_block_idx = default_main_program().current_block_idx
default_main_program().current_block_idx = (
default_main_program().current_block().parent_idx
)
tensor_array = paddle.tensor.array.create_array(dtype)
default_main_program().current_block_idx = current_block_idx
return tensor_array
# While
with while_op.block():
if not is_test:
inputs = map_structure(
lambda array: paddle.tensor.array.array_read(array, step_idx),
inputs_arrays,
)
states = map_structure(
lambda array: paddle.tensor.array.array_read(array, step_idx),
states_arrays,
)
(outputs, next_states, next_inputs, next_finished) = decoder.step(
step_idx, inputs, states, **kwargs
)
if not decoder.tracks_own_finished:
# BeamSearchDecoder would track it own finished, since beams would
# be reordered and the finished status of each entry might change.
# Otherwise, perform logical OR which would not change the already
# finished.
next_finished = paddle.logical_or(next_finished, global_finished)
next_sequence_lengths = paddle.add(
sequence_lengths,
paddle.cast(
paddle.logical_not(global_finished),
sequence_lengths.dtype,
),
)
if impute_finished: # rectify the states for the finished.
next_states = map_structure(
lambda x, y: _maybe_copy(x, y, global_finished),
states,
next_states,
)
else:
warnings.warn(
"`next_states` has no `lengths` attribute, the returned `sequence_lengths` would be all zeros."
) if not hasattr(next_states, "lengths") else None
next_sequence_lengths = getattr(
next_states, "lengths", sequence_lengths
)
# create tensor array in global block after dtype[s] of outputs can be got
outputs_arrays = map_structure(
lambda x: _create_array_out_of_while(x.dtype), outputs
)
map_structure(
lambda x, x_array: paddle.tensor.array.array_write(
x, i=step_idx, array=x_array
),
outputs,
outputs_arrays,
)
step_idx = paddle.increment(x=step_idx, value=1.0)
# update the global_finished first, since it might be also in states of
# decoder, which otherwise would write a stale finished status to array
paddle.assign(next_finished, global_finished)
paddle.assign(next_sequence_lengths, sequence_lengths)
if is_test:
map_structure(paddle.assign, next_inputs, global_inputs)
map_structure(paddle.assign, next_states, global_states)
else:
map_structure(
lambda x, x_array: paddle.tensor.array.array_write(
x, i=step_idx, array=x_array
),
next_inputs,
inputs_arrays,
)
map_structure(
lambda x, x_array: paddle.tensor.array.array_write(
x, i=step_idx, array=x_array
),
next_states,
states_arrays,
)
if max_step_num is not None:
paddle.logical_and(
paddle.logical_not(paddle.all(global_finished)),
paddle.less_equal(step_idx, max_step_num),
cond,
)
else:
paddle.logical_not(paddle.all(global_finished), cond)
final_outputs = map_structure(
lambda array: paddle.tensor.manipulation.tensor_array_to_tensor(
array, axis=0, use_stack=True
)[0],
outputs_arrays,
)
if is_test:
final_states = global_states
else:
final_states = map_structure(
lambda array: paddle.tensor.array.array_read(array, step_idx),
states_arrays,
)
try:
final_outputs, final_states = decoder.finalize(
final_outputs, final_states, sequence_lengths
)
except NotImplementedError:
pass
if not output_time_major:
final_outputs = map_structure(_transpose_batch_time, final_outputs)
return (
(final_outputs, final_states, sequence_lengths)
if return_length
else (final_outputs, final_states)
)
def dynamic_decode(
decoder,
inits=None,
max_step_num=None,
output_time_major=False,
impute_finished=False,
is_test=False,
return_length=False,
**kwargs
):
r"""
Dynamic decoding performs :code:`decoder.step()` repeatedly until the returned
Tensor indicating finished status contains all True values or the number of
decoding step reaches to :attr:`max_step_num`.
:code:`decoder.initialize()` would be called once before the decoding loop.
If the `decoder` has implemented `finalize` method, :code:`decoder.finalize()`
would be called once after the decoding loop.
Parameters:
decoder(Decoder): An instance of `Decoder`.
inits(object, optional): Argument passed to `decoder.initialize`.
Default `None`.
max_step_num(int, optional): The maximum number of steps. If not provided,
decode until the decoder is fully done, or in other words, the returned
Tensor by :code:`decoder.step()` indicating finished status contains
all True. Default `None`.
output_time_major(bool, optional): Indicate the data layout of Tensor included
in the final outputs(the first returned value of this method). If
attr:`False`, the data layout would be batch major with shape
`[batch_size, seq_len, ...]`. If attr:`True`, the data layout would
be time major with shape `[seq_len, batch_size, ...]`. Default: `False`.
impute_finished(bool, optional): If `True` and `decoder.tracks_own_finished`
is False, then states get copied through for batch entries which are
marked as finished, which differs with the unfinished using the new states
returned by :code:`decoder.step()` and ensures that the final states have
the correct values. Otherwise, states wouldn't be copied through when
finished. If the returned `final_states` is needed, it should be set as
True, which causes some slowdown. Default `False`.
is_test(bool, optional): A flag indicating whether to use test mode. In
test mode, it is more memory saving. Default `False`.
return_length(bool, optional): A flag indicating whether to return an
extra Tensor variable in the output tuple, which stores the actual
lengths of all decoded sequences. Default `False`.
**kwargs: Additional keyword arguments. Arguments passed to `decoder.step`.
Returns:
tuple: A tuple( :code:`(final_outputs, final_states, sequence_lengths)` ) \
when `return_length` is True, otherwise a tuple( :code:`(final_outputs, final_states)` ). \
The final outputs and states, both are Tensor or nested structure of Tensor. \
`final_outputs` has the same structure and data types as the :code:`outputs` \
returned by :code:`decoder.step()` , and each Tenser in `final_outputs` \
is the stacked of all decoding steps' outputs, which might be revised \
by :code:`decoder.finalize()` if the decoder has implemented `finalize`. \
`final_states` is the counterpart at last time step of initial states \
returned by :code:`decoder.initialize()` , thus has the same structure \
with it and has tensors with same shapes and data types. `sequence_lengths` \
is an `int64` tensor with the same shape as `finished` returned \
by :code:`decoder.initialize()` , and it stores the actual lengths of \
all decoded sequences.
Examples:
.. code-block:: python
import numpy as np
import paddle
from paddle.nn import BeamSearchDecoder, dynamic_decode
from paddle.nn import GRUCell, Linear, Embedding
trg_embeder = Embedding(100, 32)
output_layer = Linear(32, 32)
decoder_cell = GRUCell(input_size=32, hidden_size=32)
decoder = BeamSearchDecoder(decoder_cell,
start_token=0,
end_token=1,
beam_size=4,
embedding_fn=trg_embeder,
output_fn=output_layer)
encoder_output = paddle.ones((4, 8, 32), dtype=paddle.get_default_dtype())
outputs = dynamic_decode(decoder=decoder,
inits=decoder_cell.get_initial_states(encoder_output),
max_step_num=10)
"""
if _non_static_mode():
return _dynamic_decode_imperative(
decoder,
inits,
max_step_num,
output_time_major,
impute_finished,
is_test,
return_length,
**kwargs
)
else:
return _dynamic_decode_declarative(
decoder,
inits,
max_step_num,
output_time_major,
impute_finished,
is_test,
return_length,
**kwargs
)
python/paddle/tensor/manipulation.py
浏览文件 @ be4ea174
......@@ -44,6 +44,120 @@ from .creation import _complex_to_real_dtype, _real_to_complex_dtype, zeros
__all__ = []
def tensor_array_to_tensor(input, axis=1, use_stack=False, name=None):
r"""
This function concatenates or stacks all tensors in the input LoDTensorArray
along the axis mentioned and returns that as the output.
For Example:
.. code-block:: text
Case 1:
Given:
input.data = {[[0.6, 0.1, 0.3],
[0.5, 0.3, 0.2]],
[[1.3],
[1.8]],
[[2.3, 2.1],
[2.5, 2.4]]}
axis = 1, use_stack = False
Then:
output.data = [[0.6, 0.1, 0.3, 1.3, 2.3, 2.1],
[0.5, 0.3, 0.2, 1.8, 2.5, 2.4]]
output_index.data = [3, 1, 2]
Case 2:
Given:
input.data = {[[0.6, 0.1],
[0.5, 0.3]],
[[0.3, 1.3],
[0.2, 1.8]],
[[2.3, 2.1],
[2.5, 2.4]]}
axis = 1, use_stack = True
Then:
output.data = [[[0.6, 0.1]
[0.3, 1.3]
[2.3, 2.1],
[[0.5, 0.3]
[0.2, 1.8]
[2.5, 2.4]]]
output_index.data = [2, 2, 2]
Args:
input(TensorArray): A TensorArray variable.
axis(int): The axis along which the tensors in attr::`input` will be
concatenated or stacked.
use_stack(bool): Act as concat_op or stack_op. For stack mode, all
tensors in the tensor array must have the same shape.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Tensor: The concatenated or stacked tensor variable.
Tensor: A 1-D tensor variable with int32 data type. The data in this \
tensor contains all input including tensors' sizes along the axis.
Examples:
.. code-block:: python
import numpy
import paddle
x0 = paddle.assign(numpy.random.rand(2, 2).astype("float32"))
x1 = paddle.assign(numpy.random.rand(2, 2).astype("float32"))
i = paddle.full(shape=[1], dtype="int64", fill_value=0)
array = paddle.tensor.array.create_array(dtype='float32')
paddle.tensor.array.array_write(x0, i, array)
paddle.tensor.array.array_write(x1, i + 1, array)
output, output_index = paddle.tensor.manipulation.tensor_array_to_tensor(input=array)
"""
if _non_static_mode():
assert isinstance(
input, list
), "The 'input' in tensor_array_to_tensor must be list"
from paddle import concat, stack
op = stack if use_stack else concat
res = op(input, axis=axis)
sizes = paddle.to_tensor(
np.array(list(map(lambda x: int(x.shape[axis]), input)))
)
return res, sizes
check_type(input, 'input', (list, Variable), 'tensor_array_to_tensor')
if isinstance(input, list):
for i, input_x in enumerate(input):
check_type(
input_x,
'input[' + str(i) + ']',
Variable,
'tensor_array_to_tensor',
)
helper = LayerHelper('tensor_array_to_tensor', **locals())
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
out_index = helper.create_variable_for_type_inference(dtype="int32")
helper.append_op(
type='tensor_array_to_tensor',
inputs={'X': input},
outputs={'Out': [out], 'OutIndex': [out_index]},
attrs={'axis': axis, 'use_stack': use_stack},
)
return out, out_index
def cast(x, dtype):
"""
......
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