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未验证 提交 a646e75f 编写于 作者: J JYChen 提交者： GitHub
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[Fluid Clean] remove module fluid.layers.control_flow (#55661) 

* remove api staticrnn

* move select_input/output to static/controw flow

* delete some func, only remain Switch

* clean fluid.layers.controw_flow

* remove fluid.layers.controlflow

* fix conditional_block ut
上级 40cf6e64
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python/paddle/fluid/layers/__init__.py
浏览文件 @ a646e75f
......@@ -18,8 +18,6 @@ from . import io
from .io import *
from . import tensor
from .tensor import *
from . import control_flow
from .control_flow import *
from . import math_op_patch
from .math_op_patch import *
from .learning_rate_scheduler import *
......@@ -30,5 +28,4 @@ __all__ = []
__all__ += nn.__all__
__all__ += io.__all__
__all__ += tensor.__all__
__all__ += control_flow.__all__
__all__ += learning_rate_scheduler.__all__
python/paddle/fluid/layers/control_flow.py 已删除 100755 → 0
浏览文件 @ 40cf6e64
# Copyright (c) 2018 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.
from ..wrapped_decorator import signature_safe_contextmanager
from .layer_function_generator import templatedoc
from .. import core
from ..framework import (
Program,
Variable,
Operator,
static_only,
in_dygraph_mode,
)
from ..layer_helper import LayerHelper, unique_name
from ...utils import (
assert_same_structure,
map_structure,
hold_mutable_vars,
copy_mutable_vars,
is_sequence,
pack_sequence_as,
flatten,
to_sequence,
)
import numpy
import warnings
from functools import reduce, partial
from ..data_feeder import (
convert_dtype,
check_variable_and_dtype,
check_type,
check_dtype,
)
from ..backward import _infer_var_data_type_shape_
import paddle
from paddle import _C_ops, _legacy_C_ops
__all__ = [
'Switch',
'StaticRNN',
'while_loop',
]
def select_output(input, outputs, mask):
"""
**select_output**
This API takes in one input and multiple outputs and an integer mask. It
selects the output specified by the mask and copy the input to selected
output. It is useful in control flow.
Args:
input(Variable): The input variable
outputs(tuple|list): The output variables
mask(Variable): A tensor containing 1 integer number selecting which
output to be copied with input
Returns:
Variable: The outputs variables
"""
helper = LayerHelper('select_output', **locals())
check_type(input, 'input', (Variable), 'select_output')
check_variable_and_dtype(mask, 'mask', ['int32'], 'select_output')
check_type(outputs, 'outputs', (list, tuple), 'select_output')
helper.append_op(
type='select_output',
inputs={'X': input, 'Mask': mask},
outputs={'Out': outputs},
)
return outputs
def _select_input_infer_shape(first_shape, second_shape):
"""
This function infer the output shape by following algorithm:
1. if the dims is different, raise a error.
2. compare axis one by one:
if a == b: we set axis to a
if a != b: we set axis to -1
for compatibility, non declarative mode, we just return second_shape.
"""
if len(first_shape) != len(second_shape):
warnings.warn(
f"the input shapes of select_input should have the same rank, but get {first_shape}, {second_shape}"
)
return second_shape
out_shape = list(
map(lambda a, b: a if a == b else -1, first_shape, second_shape)
)
return out_shape
def select_input(inputs, mask):
"""
**select_input**
This API takes in multiple inputs and uses an integer mask to select one
input to output. It is useful in control flow.
Args:
inputs(tuple|list): The input variables
mask(Variable): A tensor containing 1 integer number selecting which
input to output
Returns:
Variable: The selected input variable
"""
helper = LayerHelper('select_input', **locals())
check_type(inputs, 'inputs', (list, tuple), 'select_input')
check_variable_and_dtype(mask, 'mask', ['int32'], 'select_input')
# Select input should expand the shape. If it is - 1 and valid number, use - 1 first. If the dim is different, an error will be reported directly
# assert inputs[0].dtype == inputs[1].dtype, f"Expect the inputs should have the same dtype, but get {inputs[0].dtype} and {inputs[1].dtype}"
output_shape = _select_input_infer_shape(inputs[0].shape, inputs[1].shape)
output_dtype = inputs[1].dtype
output_type = inputs[1].type
out = helper.create_variable(
dtype=output_dtype, shape=output_shape, type=output_type
)
helper.append_op(
type='select_input',
inputs={'X': inputs, 'Mask': mask},
outputs={'Out': out},
)
return out
# (TODO: Mine) There exists dependency. It will be removed later.
class BlockGuard:
"""
BlockGuard class.
BlockGuard class is used to create a sub-block in a program by
using the Python `with` keyword.
"""
def __init__(self, main_program):
if not isinstance(main_program, Program):
raise TypeError("BlockGuard takes a program")
self.main_program = main_program
def __enter__(self):
self.main_program._create_block()
def __exit__(self, exc_type, exc_val, exc_tb):
self.main_program._rollback()
if exc_type is not None:
return False # re-raise exception
return True
# (TODO: Mine) There exists dependency. It will be removed later.
class BlockGuardWithCompletion(BlockGuard):
"""
BlockGuardWithCompletion class.
BlockGuardWithCompletion class is used to create an op with a block in a program.
"""
def __init__(self, rnn):
if not isinstance(rnn, StaticRNN):
raise TypeError("BlockGuardWithCompletion takes a StaticRNN")
super().__init__(rnn.helper.main_program)
self.rnn = rnn
def __enter__(self):
self.rnn.status = StaticRNN.IN_RNN_BLOCK
return super().__enter__()
def __exit__(self, exc_type, exc_val, exc_tb):
if exc_type is not None:
return False
self.rnn.status = StaticRNN.AFTER_RNN_BLOCK
self.rnn._complete_op()
return super().__exit__(exc_type, exc_val, exc_tb)
class StaticRNNMemoryLink:
"""
StaticRNNMemoryLink class.
StaticRNNMemoryLink class is used to create a link between two
memory cells of a StaticRNN.
NOTE: This is a internal data structure of a very low-level API.
Please use StaticRNN instead.
Args:
init(Variable): the initial variable for Memory.
pre_mem(Variable): the memory variable in previous time step.
mem(Variable): the memory variable in current time step.
"""
def __init__(self, init, pre_mem, mem=None):
self.init = init
self.pre_mem = pre_mem
self.mem = mem
class StaticRNN:
"""
:api_attr: Static Graph
StaticRNN class.
The StaticRNN can process a batch of sequence data. The first dimension of inputs
represents sequence length, the length of each input sequence must be equal.
StaticRNN will unfold sequence into time steps, user needs to define how to process
each time step during the :code:`with` step.
Args:
name (str, optional): Please refer to :ref:`api_guide_Name`, Default None.
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
import paddle.fluid.layers as layers
vocab_size, hidden_size=10000, 200
paddle.enable_static()
x = paddle.static.data(name="x", shape=[None, 1, 1], dtype='int64')
# create word sequence
x_emb = layers.embedding(
input=x,
size=[vocab_size, hidden_size],
dtype='float32',
is_sparse=False)
# transform batch size to dim 1
x_emb = paddle.transpose(x_emb, perm=[1, 0, 2])
rnn = fluid.layers.StaticRNN()
with rnn.step():
# mark created x_emb as input, each step process a word
word = rnn.step_input(x_emb)
# create prev memory parameter, batch size comes from word
prev = rnn.memory(shape=[-1, hidden_size], batch_ref = word)
hidden = paddle.static.nn.fc(x=[word, prev], size=hidden_size, activation='relu')
# use hidden to update prev
rnn.update_memory(prev, hidden)
# mark hidden as output
rnn.step_output(hidden)
# get StaticrNN final output
result = rnn()
"""
BEFORE_RNN_BLOCK = 0
IN_RNN_BLOCK = 1
AFTER_RNN_BLOCK = 2
def __init__(self, name=None):
check_type(name, "name", (str, type(None)), "fluid.layers.StaticRNN")
self.helper = LayerHelper("static_rnn", name=name)
self.memories = {} # memory map, from pre_mem.name --> MemoryLink
self.inputs = [] # input variable list in current block
self.outputs = [] # output variable list in parent block
self.status = StaticRNN.BEFORE_RNN_BLOCK # status flag.
# sequence length, since it is a static RNN, sequence length are fixed.
self.seq_len = None
def step(self):
"""
Define operators in each step. step is used in :code:`with` block, OP in :code:`with` block
will be executed sequence_len times (sequence_len is the length of input)
"""
return BlockGuardWithCompletion(self)
def _assert_in_rnn_block_(self, method):
if self.status != StaticRNN.IN_RNN_BLOCK:
raise ValueError("You must invoke {0} in rnn block".format(method))
def memory(
self,
init=None,
shape=None,
batch_ref=None,
init_value=0.0,
init_batch_dim_idx=0,
ref_batch_dim_idx=1,
):
"""
Create a memory variable for static rnn.
If the :code:`init` is not None, :code:`memory` will be initialized by
this Variable. If the :code:`init` is None, :code:`shape` and :code:`batch_ref`
must be set, and this function will create a new variable with shape and batch_ref
to initialize :code:`init` Variable.
Args:
init(Variable, optional): Tensor used to init memory. If it is not set,
:code:`shape` and :code:`batch_ref` must be provided.
Default: None.
shape(list|tuple): When :code:`init` is None use this arg to initialize memory shape.
NOTE the shape does not contain batch_size. Default: None.
batch_ref(Variable, optional): When :code:`init` is None, memory's batch size will
be set as batch_ref's ref_batch_dim_idx value. Default: None.
init_value(float, optional): When :code:`init` is None, used to init memory's value. Default: 0.0.
init_batch_dim_idx(int, optional): the batch_size axis of the :code:`init` Variable. Default: 0.
ref_batch_dim_idx(int, optional): the batch_size axis of the :code:`batch_ref` Variable. Default: 1.
Returns:
Variable: The memory variable.
Examples 1:
.. code-block:: python
import paddle
import paddle.fluid as fluid
import paddle.fluid.layers as layers
vocab_size, hidden_size=10000, 200
paddle.enable_static()
x = paddle.static.data(name="x", shape=[None, 1, 1], dtype='int64')
# create word sequence
x_emb = layers.embedding(
input=x,
size=[vocab_size, hidden_size],
dtype='float32',
is_sparse=False)
# transform batch size to dim 1
x_emb = paddle.transpose(x_emb, perm=[1, 0, 2])
rnn = fluid.layers.StaticRNN()
with rnn.step():
# mark created x_emb as input, each step process a word
word = rnn.step_input(x_emb)
# create prev memory parameter, batch size comes from word
prev = rnn.memory(shape=[-1, hidden_size], batch_ref = word)
hidden = paddle.static.nn.fc(x=[word, prev], size=hidden_size, activation='relu')
# use hidden to update prev
rnn.update_memory(prev, hidden)
Examples 2:
.. code-block:: python
import paddle
import paddle.fluid as fluid
import paddle.fluid.layers as layers
vocab_size, hidden_size=10000, 200
paddle.enable_static()
x = paddle.static.data(name="x", shape=[None, 1, 1], dtype='int64')
# create word sequence
x_emb = layers.embedding(
input=x,
size=[vocab_size, hidden_size],
dtype='float32',
is_sparse=False)
# transform batch size to dim 1
x_emb = paddle.transpose(x_emb, perm=[1, 0, 2])
boot_memory = paddle.static.data(name='boot', shape=[-1, hidden_size], dtype='float32', lod_level=1)
rnn = fluid.layers.StaticRNN()
with rnn.step():
# mark created x_emb as input, each step process a word
word = rnn.step_input(x_emb)
# init memory
prev = rnn.memory(init=boot_memory)
hidden = paddle.static.nn.fc(x=[word, prev], size=hidden_size, activation='relu')
# update hidden with prev
rnn.update_memory(prev, hidden)
"""
self._assert_in_rnn_block_('memory')
check_type(
init,
"init",
(Variable, type(None)),
"fluid.layers.StaticRNN.memory",
)
check_type(
shape,
"shape",
(list, tuple, type(None)),
"fluid.layers.StaticRNN.memory",
)
check_type(
batch_ref,
"batch_ref",
(Variable, type(None)),
"fluid.layers.StaticRNN.memory",
)
if init is None:
if shape is None or batch_ref is None:
raise ValueError(
"if init is None, memory at least need shape and batch_ref"
)
parent_block = self._parent_block()
var_name = unique_name.generate_with_ignorable_key(
"@".join([self.helper.name, "memory_boot"])
)
boot_var = parent_block.create_var(
name=var_name,
shape=shape,
dtype=batch_ref.dtype,
persistable=False,
)
parent_block.append_op(
type="fill_constant_batch_size_like",
inputs={'Input': [batch_ref]},
outputs={'Out': [boot_var]},
attrs={
'value': init_value,
'shape': boot_var.shape,
'dtype': boot_var.dtype,
'input_dim_idx': ref_batch_dim_idx,
'output_dim_idx': init_batch_dim_idx,
},
)
return self.memory(init=boot_var)
else:
pre_mem = self.helper.create_variable(
name=unique_name.generate_with_ignorable_key(
"@".join([self.helper.name, "mem"])
),
dtype=init.dtype,
shape=init.shape,
)
self.memories[pre_mem.name] = StaticRNNMemoryLink(
init=init, pre_mem=pre_mem
)
return pre_mem
def step_input(self, x):
"""
Mark a sequence as a StaticRNN input.
Args:
x(Variable): The input sequence, the shape of x
should be [seq_len, ...].
Returns:
Variable: The current time step data in the input sequence.
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
import paddle.fluid.layers as layers
vocab_size, hidden_size=10000, 200
paddle.enable_static()
x = paddle.static.data(name="x", shape=[None, 1, 1], dtype='int64')
# create word sequence
x_emb = layers.embedding(
input=x,
size=[vocab_size, hidden_size],
dtype='float32',
is_sparse=False)
# transform batch size to dim 1
x_emb = paddle.transpose(x_emb, perm=[1, 0, 2])
rnn = fluid.layers.StaticRNN()
with rnn.step():
# mark created x_emb as input, each step process a word
word = rnn.step_input(x_emb)
# create prev memory parameter, batch size comes from word
prev = rnn.memory(shape=[-1, hidden_size], batch_ref = word)
hidden = paddle.static.nn.fc(x=[word, prev], size=hidden_size, activation='relu')
# use hidden to update prev
rnn.update_memory(prev, hidden)
"""
self._assert_in_rnn_block_('step_input')
check_type(x, "x", Variable, "fluid.layers.StaticRNN.step_input")
if self.seq_len is None:
self.seq_len = x.shape[0]
elif x.shape[0] != -1 and self.seq_len != x.shape[0]:
raise ValueError("Static RNN only take fix seq_len input")
ipt = self.helper.create_variable(
name=x.name, dtype=x.dtype, shape=list(x.shape[1:]), type=x.type
)
self.inputs.append(ipt)
return ipt
def step_output(self, o):
"""
Mark a sequence as a StaticRNN output.
Args:
o(Variable): The output sequence.
Returns:
None.
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
import paddle.fluid.layers as layers
vocab_size, hidden_size=10000, 200
paddle.enable_static()
x = paddle.static.data(name="x", shape=[None, 1, 1], dtype='int64')
# create word sequence
x_emb = layers.embedding(
input=x,
size=[vocab_size, hidden_size],
dtype='float32',
is_sparse=False)
# transform batch size to dim 1
x_emb = paddle.transpose(x_emb, perm=[1, 0, 2])
rnn = fluid.layers.StaticRNN()
with rnn.step():
# mark created x_emb as input, each step process a word
word = rnn.step_input(x_emb)
# create prev memory parameter, batch size comes from word
prev = rnn.memory(shape=[-1, hidden_size], batch_ref = word)
hidden = paddle.static.nn.fc(x=[word, prev], size=hidden_size, activation='relu')
# use hidden to update prev
rnn.update_memory(prev, hidden)
rnn.step_output(hidden)
result = rnn()
"""
self._assert_in_rnn_block_('step_output')
check_type(o, "o", Variable, "fluid.layers.StaticRNN.step_output")
tmp_o = self.helper.create_variable_for_type_inference(dtype=o.dtype)
self.helper.append_op(
type='rnn_memory_helper',
inputs={'X': [o]},
outputs={'Out': tmp_o},
attrs={'dtype': o.dtype},
)
out_var = self._parent_block().create_var(
name=tmp_o.name,
shape=[self.seq_len] + list(tmp_o.shape),
dtype=tmp_o.dtype,
)
self.outputs.append(out_var)
def output(self, *outputs):
"""
Mark the StaticRNN output variables.
Args:
outputs: The output Tensor, can mark multiple variables as output
Returns:
None
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
import paddle.fluid.layers as layers
vocab_size, hidden_size=10000, 200
paddle.enable_static()
x = paddle.static.data(name="x", shape=[None, 1, 1], dtype='int64')
# create word sequence
x_emb = layers.embedding(
input=x,
size=[vocab_size, hidden_size],
dtype='float32',
is_sparse=False)
# transform batch size to dim 1
x_emb = paddle.transpose(x_emb, perm=[1, 0, 2])
rnn = fluid.layers.StaticRNN()
with rnn.step():
# mark created x_emb as input, each step process a word
word = rnn.step_input(x_emb)
# create prev memory parameter, batch size comes from word
prev = rnn.memory(shape=[-1, hidden_size], batch_ref = word)
hidden = paddle.static.nn.fc(x=[word, prev], size=hidden_size, activation='relu')
# use hidden to update prev
rnn.update_memory(prev, hidden)
# mark each step's hidden and word as output
rnn.output(hidden, word)
result = rnn()
"""
for each in outputs:
self.step_output(each)
def update_memory(self, mem, var):
"""
Update the memory from :code:`mem` to :code:`var`.
Args:
mem(Variable): the memory variable.
var(Variable): the plain variable generated in RNN block, used to update memory.
var and mem should have same dims and data type.
Returns:
None
"""
check_type(mem, "mem", Variable, "fluid.layers.StaticRNN.update_memory")
check_type(var, "var", Variable, "fluid.layers.StaticRNN.update_memory")
self.memories[mem.name].mem = var
def _parent_block(self):
prog = self.helper.main_program
parent_idx = prog.current_block().parent_idx
assert parent_idx >= 0
parent_block = prog.block(parent_idx)
return parent_block
def __call__(self, *args, **kwargs):
if self.status != StaticRNN.AFTER_RNN_BLOCK:
raise ValueError("RNN output can only be retrieved after rnn block")
if len(self.outputs) == 0:
raise ValueError("RNN has no output")
elif len(self.outputs) == 1:
return self.outputs[0]
else:
return self.outputs
def _complete_op(self):
main_program = self.helper.main_program
rnn_block = main_program.current_block()
parent_block = self._parent_block()
local_inputs = set()
for op in rnn_block.ops:
assert isinstance(op, Operator)
for oname in op.output_names:
for out_var_name in op.output(oname):
local_inputs.add(out_var_name)
for var in self.inputs:
local_inputs.add(var.name)
for m in self.memories:
local_inputs.add(m)
# NOTE(zcd): the params have two categories of variables.
# - the variables that are the out of StaticRnn.
# - the variables that are the parameters of some layers, for example, conv2d.
params = list()
for op in rnn_block.ops:
assert isinstance(op, Operator)
for iname in op.input_names:
for in_var_name in op.input(iname):
if in_var_name not in local_inputs:
params.append(in_var_name)
parameters = [
parent_block._find_var_recursive(name) for name in set(params)
]
step_scope = parent_block.create_var(
type=core.VarDesc.VarType.STEP_SCOPES
)
inlinks = [parent_block.var(i.name) for i in self.inputs]
outlinks = self.outputs
# NOTE(zcd): the states maybe empty in some case.
boot_memories = []
pre_memories = []
memories = []
for _, mem in self.memories.items():
boot_memories.append(mem.init)
pre_memories.append(mem.pre_mem.name)
assert (
mem.mem is not None
), "%s should be updated in every step." % (mem.init.name)
mem_var = rnn_block.var(mem.mem.name)
assert isinstance(mem_var, Variable)
new_mem = self.helper.create_variable_for_type_inference(
dtype=mem_var.dtype
)
rnn_block.append_op(
type='rnn_memory_helper',
inputs={'X': [mem_var]},
outputs={'Out': [new_mem]},
attrs={'dtype': mem_var.dtype},
)
memories.append(new_mem.name)
parent_block.append_op(
type='recurrent',
inputs={
'inputs': inlinks,
'initial_states': boot_memories,
'parameters': parameters,
},
outputs={'outputs': outlinks, 'step_scopes': [step_scope]},
attrs={
'has_states': len(pre_memories) > 0,
'ex_states': pre_memories,
'states': memories,
'sub_block': rnn_block,
},
)
# (TODO: Mine) There exists dependency. It will be removed later.
class WhileGuard(BlockGuard):
def __init__(self, while_op):
if not isinstance(while_op, While):
raise TypeError("WhileGuard takes a while op")
super().__init__(while_op.helper.main_program)
self.while_op = while_op
def __enter__(self):
self.while_op.status = While.IN_WHILE_BLOCK
return super().__enter__()
def __exit__(self, exc_type, exc_val, exc_tb):
if exc_type is not None:
return False
self.while_op.status = While.AFTER_WHILE_BLOCK
self.while_op._complete()
return super().__exit__(exc_type, exc_val, exc_tb)
# (TODO: Mine) There exists dependency. It will be removed later.
def get_inputs_outputs_in_block(
current_block, inner_inputs, inner_outputs, helper
):
"""
Find inputs and outputs in current control flow block.
:param current_block: Current control flow block.
:param inner_inputs: Input var name of ops in current block.
:param inner_outputs: Output var name of ops in current block.
:return: inner_inputs, inner_outputs
"""
def is_ignore_vars(op, var_name):
# NOTE(dev): There are some persistable var created in some non-standard API
# such as "contrib.layers.shuffle_batch". It create a "Seed" used both in
# Input and Output. This var shall not be considered as a loop_var in
# control_flow.
IGNORE_VAR_NAMES = {"shuffle_batch": ["shuffle_batch_seed"]}
if op.type in IGNORE_VAR_NAMES:
var_names = IGNORE_VAR_NAMES[op.type]
for name in var_names:
if name in var_name:
return True
return False
# Step1: update inner_inputs and inner_outputs
# NOTE: Here assumes that all variables are input or output of Ops,
# but some variables are created without appendding a real op.
# For example, in `arr = create_array(dtype)`, `arr` is not a output of a op.
for op in current_block.ops:
assert isinstance(op, Operator)
for iname in op.input_names:
for in_var_name in op.input(iname):
if in_var_name not in inner_outputs and not is_ignore_vars(
op, in_var_name
):
inner_inputs.add(in_var_name)
for oname in op.output_names:
for out_var_name in op.output(oname):
inner_outputs.add(out_var_name)
# Step2: Remove LOD_TENSOR_ARRAY created in current control flow block.
remove_inner_inputs = set()
parent_block = helper.main_program.block(current_block.parent_idx)
for in_var_name in inner_inputs:
parent_block_var = parent_block._find_var_recursive(in_var_name)
current_block_var = None
if current_block.has_var(in_var_name):
current_block_var = current_block.var(in_var_name)
if (
not parent_block_var
and current_block_var
and current_block_var.type == core.VarDesc.VarType.LOD_TENSOR_ARRAY
):
remove_inner_inputs.add(in_var_name)
inner_inputs = inner_inputs - remove_inner_inputs
return inner_inputs, inner_outputs
# (TODO: Mine) There exists dependency. It will be removed later.
class While:
"""
:api_attr: Static Graph
while loop control flow. Repeat while body until cond is False.
Note:
A new OP :ref:`api_fluid_layers_while_loop` is highly recommended instead of ``While`` if the shape of parameter ``cond`` is [1].
OP :ref:`api_fluid_layers_while_loop` is easier to use and is called with less code but does the same thing as ``While`` .
Notice:
Local variables created in ``While`` are similar to that created in while of C++, and cannot be referenced externally.
As a result, they cannot be obtained through ``fetch_list`` of ``Executor``. If you would like to access the variable
out of ``while`` , PaddlePaddle provides ``assign`` API to assign local variables to external. Please refer to example
code 2 or refer to `issue#22724 <https://github.com/PaddlePaddle/Paddle/issues/22724>`_.
Args:
cond(Variable): A Tensor whose data type is bool controlling whether to continue looping.
is_test(bool, optional): A flag indicating whether execution is in test phase. Default value is False.
name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` .
Examples 1:
.. code-block:: python
import paddle.fluid as fluid
import paddle
import numpy as np
i = paddle.full(shape=[1], dtype='int64', fill_value=0) # loop counter
loop_len = paddle.full(shape=[1],dtype='int64', fill_value=10) # loop length
cond = paddle.less_than(x=i, y=loop_len)
while_op = fluid.layers.While(cond=cond)
with while_op.block():
i = paddle.increment(x=i, value=1)
paddle.assign(paddle.less_than(x=i, y=loop_len), cond)
exe = fluid.Executor(fluid.CPUPlace())
exe.run(fluid.default_startup_program())
res = exe.run(fluid.default_main_program(), feed={}, fetch_list=[i])
print(res) # [array([10])]
Examples 2:
.. code-block:: python
import paddle
import paddle.fluid as fluid
import numpy as np
paddle.enable_static()
i = paddle.full(shape=[1], dtype='int64', fill_value=0)
loop_len = paddle.full(shape=[1], dtype='int64', fill_value=10)
one = paddle.full(shape=[1], dtype='float32', fill_value=1)
data = paddle.static.data(name='data', shape=[1], dtype='float32')
sums = paddle.full(shape=[1], dtype='float32', fill_value=0) # Define the variable to be obtained ouside of While, which name should be different from the variable inside the While to be obtained
cond = paddle.less_than(x=i, y=loop_len)
while_op = fluid.layers.While(cond=cond)
with while_op.block():
sums_tensor = paddle.add(x=data, y=data)
fluid.layers.assign(sums_tensor, sums) # Update the value of sums_tensor defined in While to the sums which defined outside of While through layers.assign
i = paddle.increment(x=i, value=1)
data = paddle.add(x=data, y=one)
paddle.assign(paddle.less_than(x=i, y=loop_len), cond)
feed_data = np.ones(1).astype('float32')
exe = fluid.Executor(fluid.CPUPlace())
exe.run(fluid.default_startup_program())
res = exe.run(fluid.default_main_program(), feed={'data': feed_data}, fetch_list=sums)
print(res[0]) # [2.] # Because the data in While does not update the value outside the While, the value of sums is [2.] after the loop
"""
BEFORE_WHILE_BLOCK = 0
IN_WHILE_BLOCK = 1
AFTER_WHILE_BLOCK = 2
def __init__(self, cond, is_test=False, name=None):
self.helper = LayerHelper("while", name=name)
self.status = While.BEFORE_WHILE_BLOCK
check_variable_and_dtype(cond, 'cond', ['bool'], 'fluid.layers.While')
if reduce(lambda a, b: a * b, cond.shape, 1) != 1:
raise TypeError(
"condition expected shape as [1], but given shape as {0}.".format(
list(cond.shape)
)
)
self.cond_var = cond
self.is_test = is_test
def block(self):
return WhileGuard(self)
def _complete(self):
main_program = self.helper.main_program
while_block = main_program.current_block()
parent_block = main_program.block(
main_program.current_block().parent_idx
)
inner_outputs = {self.cond_var.name}
x_name_list = set()
x_name_list, inner_outputs = get_inputs_outputs_in_block(
while_block, x_name_list, inner_outputs, self.helper
)
out_vars = []
for inner_out_name in inner_outputs:
inner_var = parent_block._find_var_recursive(inner_out_name)
if inner_var:
out_vars.append(inner_var)
x_name_list |= set(map(lambda x: x.name, out_vars))
# NOTE(dev): cond_var has been contained in Input('Condition'), so
# we remove it from Input('X')
x_name_list -= {self.cond_var.name}
step_scope = parent_block.create_var(
type=core.VarDesc.VarType.STEP_SCOPES
)
parent_block.append_op(
type='while',
inputs={
'X': [
parent_block._var_recursive(x_name)
for x_name in x_name_list
],
'Condition': [self.cond_var],
},
outputs={'Out': out_vars, 'StepScopes': [step_scope]},
attrs={'sub_block': while_block, "is_test": self.is_test},
)
support_ret_buildin_type = (bool, float, int)
# (TODO: Mine) There exists dependency. It will be removed later.
def assign_skip_lod_tensor_array(input, output):
"""
Assign input to output, but skip the process of copying LoDTensorArray unless it's created in while_block.
"""
def has_shape_diff(x_var, y_var):
if len(x_var.shape) != len(y_var.shape):
return True
for x_dim, y_dim in zip(x_var.shape, y_var.shape):
if x_dim != y_dim and -1 not in [x_dim, y_dim]:
return True
return False
if not isinstance(input, (Variable, core.eager.Tensor)):
if isinstance(output, Variable) and isinstance(
input, support_ret_buildin_type
):
paddle.assign(input, output)
else:
output = input
return
if input.type == core.VarDesc.VarType.LOD_TENSOR_ARRAY:
main_program = input.block.program
parent_block = main_program.block(
main_program.current_block().parent_idx
)
if parent_block and not parent_block._find_var_recursive(input.name):
paddle.assign(input, output)
else:
if (
isinstance(output, Variable)
and isinstance(input, Variable)
and has_shape_diff(input, output)
):
warnings.warn(
"In dy2static mode, we attemp to assign a variable with shape {} into a variable with shape{}, which is not always right.".format(
input.shape, output.shape
)
)
paddle.assign(input, output)
# (TODO: Mine) There exists dependency (jit.dy2static.convert_operators). It will be removed later.
def while_loop(cond, body, loop_vars, is_test=False, name=None):
"""
:api_attr: Static Graph
while_loop is one of the control flows. Repeats while_loop `body` until `cond` returns False.
Notice:
Local variables defined in ``body`` cannot be obtained through ``fetch_list`` of ``Executor`` , variables should
be defined outside ``body`` and placed in ``loop_vars`` for looping, then these variables can be fetched by ``fetch_list`` .
Args:
cond(Callable): A callable returning a boolean tensor controlling whether to continue looping. And ``cond`` takes
as many arguments as ``loop_vars`` .
body(Callable): A callable returning a tuple or list of tensors or LoDTensorArrays of the same arity
(length and structure) and types as ``loops_vars`` . And ``body`` takes as many arguments as ``loop_vars`` .
loop_vars(list|tuple): A list or tuple of tensors or LoDTensorArrays that is passed to both ``cond`` and ``body`` .
is_test(bool, optional): A flag indicating whether execution is in test phase. Default value is False.
name(str, optional): Normally there is no need for users to set this property. For more information, please
refer to :ref:`api_guide_Name`. Default is None.
Returns:
A list or tuple of Tensors or LoDTensorArrays which returned by ``body`` .
Examples:
.. code-block:: python
import paddle
paddle.enable_static()
def cond(i, ten):
return i < ten
def body(i, ten):
i = i + 1
return [i, ten]
main_program = paddle.static.default_main_program()
startup_program = paddle.static.default_startup_program()
with paddle.static.program_guard(main_program, startup_program):
i = paddle.full(shape=[1], fill_value=0, dtype='int64') # loop counter
ten = paddle.full(shape=[1], fill_value=10, dtype='int64') # loop length
i, ten = paddle.static.nn.while_loop(cond, body, [i, ten])
exe = paddle.static.Executor(paddle.CPUPlace())
res = exe.run(main_program, feed={}, fetch_list=[i])
print(res) # [array([10])]
"""
helper = LayerHelper('while_loop', **locals())
if not callable(cond):
raise TypeError("cond in while_loop should be callable")
if not callable(body):
raise TypeError("body in while_loop should be callable")
check_type(loop_vars, 'loop_vars', (list, tuple), 'fluid.layers.while_loop')
if len(loop_vars) == 0:
raise ValueError("loop_vars in while_loop should not be empty")
pre_cond = cond(*loop_vars)
if reduce(lambda a, b: a * b, pre_cond.shape, 1) != 1:
raise TypeError(
"the shape of the variable returned by cond should be [1],"
"but given shape as {0}.".format(list(pre_cond.shape))
)
if in_dygraph_mode():
now_cond = pre_cond.item()
while now_cond:
output_vars = body(*loop_vars)
if not isinstance(output_vars, (list, tuple)):
output_vars = [output_vars]
if len(output_vars) != len(loop_vars):
raise ValueError(
"body in while_loop should return the same arity "
"(length and structure) and types as loop_vars"
)
now_cond = cond(*output_vars).item()
map_structure(assign_skip_lod_tensor_array, output_vars, loop_vars)
return loop_vars
else:
check_variable_and_dtype(
pre_cond,
'var of cond returned',
['bool'],
'fluid.layers.while_loop',
)
while_loop_block = While(pre_cond, is_test, name)
has_mutable_vars_in_loop = hold_mutable_vars(loop_vars)
with while_loop_block.block():
# If a variable with mutable type is included in loop_vars, like `dict/list`,
# modifying it in the body function will cause origin variable to be modified
# synchronously. This will raise an assignment error out of while block.
# Here we make a copy of the mutable vars to avoid this problem.
if has_mutable_vars_in_loop:
new_loop_vars = copy_mutable_vars(loop_vars)
output_vars = body(*new_loop_vars)
else:
output_vars = body(*loop_vars)
if not isinstance(output_vars, (list, tuple)):
output_vars = [output_vars]
try:
loop_vars = _deal_with_undefined_var(output_vars, loop_vars)
assert_same_structure(output_vars, loop_vars, check_types=False)
except ValueError as e:
raise ValueError(
"body in while_loop should return the same arity "
"(length and structure) as loop_vars: {0}".format(e)
)
now_cond = cond(*output_vars)
map_structure(assign_skip_lod_tensor_array, output_vars, loop_vars)
paddle.assign(now_cond, pre_cond)
return loop_vars
# (TODO: Mine) There exists dependency. It will be removed later.
def _deal_with_undefined_var(output_vars, loop_vars):
"""Deal with undefined var cases, We create undefined variable based on the results of body().
In Dy2Static, we use undefined var to represent the var created in control flow. This function
expand the loop_vars and replace original loop_vars.
1. UndefinedVar = Variable # create a variable
2. UndefinedVar = None # create a undefined var with RETURN_NO_VALUE_MAGIC_NUM
3. UndefinedVar = List(int) # create a list of variable
4. UndefinedVar = value # create a variable
"""
from paddle.jit.dy2static.utils import (
UndefinedVar,
create_undefined_variable,
)
def create_var_like(o_var):
if (
isinstance(o_var, (Variable,) + support_ret_buildin_type)
or o_var is None
):
return create_undefined_variable()
if is_sequence(o_var):
"""
Create a complex container class inside the body of while, including Python list and python Dict
"""
return map_structure(lambda x: create_undefined_variable(), o_var)
if len(output_vars) != len(loop_vars):
raise ValueError("The length of loop_vars should be the same.")
results = []
for o_var, l_var in zip(output_vars, loop_vars):
if isinstance(l_var, UndefinedVar) or l_var is None:
results.append(create_var_like(o_var))
else:
results.append(l_var)
return results
class ConditionalBlockGuard(BlockGuard):
"""
ConditionalBlockGuard is derived from BlockGuard. It is dedicated for
holding a ConditionalBlock, and helping users entering and exiting the
ConditionalBlock via Python's 'with' keyword. However, ConditionalBlockGuard
is generally an internal component of IfElse, users should not use it directly.
"""
def __init__(self, block):
check_type(block, "block", ConditionalBlock, "ConditionalBlockGuard")
super().__init__(block.helper.main_program)
self.block = block
def __enter__(self):
return super().__enter__()
def __exit__(self, exc_type, exc_val, exc_tb):
self.block.complete()
return super().__exit__(exc_type, exc_val, exc_tb)
class ConditionalBlock:
'''
**ConditionalBlock**
ConditionalBlock is an operator that bind a block to a specific condition,
if the condition matches, the corresponding block will be executed.
Args:
inputs (Variable): bool conditions.
is_scalar_condition (bool): whether the branch is controlled by a scalar.
name(str): name of this ConditionalBlock.
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
cond = paddle.less_than(x=label, y=limit)
true_image, false_image = layers.split_lod_tensor(
input=image, mask=cond)
true_cond = layers.ConditionalBlock([true_image])
with true_cond.block():
...
with false_cond.block():
...
'''
def __init__(self, inputs, is_scalar_condition=False, name=None):
for each_input in inputs:
check_type(each_input, "input", Variable, "ConditionalBlock")
self.inputs = inputs
self.is_scalar_condition = is_scalar_condition
self.helper = LayerHelper('conditional_block', name=name)
def block(self):
return ConditionalBlockGuard(self)
def complete(self):
inside_block = self.helper.main_program.current_block()
parent_block = self.helper.main_program.block(inside_block.parent_idx)
intermediate = set()
params = set()
params, intermediate = get_inputs_outputs_in_block(
inside_block, params, intermediate, helper=self.helper
)
# Todo(liym27) Here assume that all params are in recursive parent block
# but when minimize() called in control flow, some params may be in
# conditional grad block
param_list = [
parent_block._var_recursive(each_name) for each_name in params
]
out_list = []
for inner_out_name in intermediate:
inner_var = parent_block._find_var_recursive(inner_out_name)
if inner_var:
out_list.append(inner_var)
step_scope = parent_block.create_var(
type=core.VarDesc.VarType.STEP_SCOPES
)
conditional_block_op = parent_block.append_op(
type='conditional_block',
inputs={
'Cond': self.inputs,
'Input': param_list,
},
outputs={'Out': out_list, 'Scope': [step_scope]},
attrs={
'sub_block': inside_block,
'is_scalar_condition': self.is_scalar_condition,
},
)
if self.need_append_conditional_block_grad(inside_block):
self.append_conditional_block_grad(
parent_block, inside_block, conditional_block_op
)
def need_append_conditional_block_grad(self, inside_block):
grad_sub_block_idx = inside_block.backward_block_idx
inside_block_idx = inside_block.idx
# if inside_block have grad_block and grad_block is not itself,
# we will append conditional block grad.
return (
grad_sub_block_idx != -1 and grad_sub_block_idx != inside_block_idx
)
def append_conditional_block_grad(
self, parent_block, inside_block, conditional_block_op
):
'''
Append op `conditional_block_grad` manually.
When `optimizer.minimize/append_backward` is called in Paddle control flow,
grad ops will be appended before appending op `conditional_block` so that
op `conditional_block_grad` can't be appended when calling
`optimizer.minimize/append_backward`. After appending op `conditional_block`,
`conditional_block_grad` is appended manually.
Args:
parent_block (Block): The block that `conditional_block_op` blongs to.
inside_block (Block): The sub block of `conditional_block_op`.
conditional_block_op (Operator): The forward op conditional_block.
'''
grad_sub_block_idx = inside_block.backward_block_idx
grad_sub_block = self.helper.main_program.block(grad_sub_block_idx)
intermediate = set()
params = set()
for each_op in grad_sub_block.ops:
assert isinstance(each_op, Operator)
for iname in each_op.input_names:
for in_var_name in each_op.input(iname):
if in_var_name not in intermediate:
params.add(in_var_name)
for oname in each_op.output_names:
for out_var_name in each_op.output(oname):
intermediate.add(out_var_name)
param_list = []
for inner_input_name in params:
inner_var = parent_block._find_var_recursive(inner_input_name)
if inner_var:
param_list.append(inner_var.name)
grad_op_desc, op_grad_to_var = core.get_grad_op_desc(
conditional_block_op.desc, set(), [grad_sub_block.desc]
)
# append op_desc in grad_op_descs to target_block
op_role_attr_name = core.op_proto_and_checker_maker.kOpRoleAttrName()
backward = core.op_proto_and_checker_maker.OpRole.Backward
new_op_desc = parent_block.desc.append_op()
new_op_desc.copy_from(grad_op_desc[0])
new_op_desc._set_attr(op_role_attr_name, backward)
# set input and output manually
new_op_desc.set_input('Input', param_list)
new_op_desc.set_output(
'Input@GRAD', [param + "@GRAD" for param in param_list]
)
new_vars = set()
for grad_var_name in new_op_desc.output_arg_names():
if (
grad_sub_block.desc.has_var_recursive(grad_var_name.encode())
or grad_var_name == core.empty_var_name()
):
continue
grad_sub_block.desc.var(grad_var_name.encode())
new_vars.add(grad_var_name)
if grad_var_name not in op_grad_to_var:
continue
# infer_shape and infer_type
new_op_desc.infer_var_type(grad_sub_block.desc)
new_op_desc.infer_shape(grad_sub_block.desc)
for arg in new_op_desc.output_arg_names():
if arg in new_vars:
_infer_var_data_type_shape_(arg, grad_sub_block)
self.helper.main_program._sync_with_cpp()
def _to_sequence_except_dict(x):
"""
In this function, dict is not viewed as sequence.
"""
if isinstance(x, dict):
return [x]
return to_sequence(x)
def _is_sequence_except_dict(x):
"""
In this function, dict is not viewed as sequence.
"""
if isinstance(x, dict):
return False
return is_sequence(x)
def expand_undefined_var(nest1, nest2, names):
"""TODO: make this function recursively.
nest1: Var1, (UndefinedVar, [1,2,3])
nest2: Var2, ([1,2,3,4], UndefinedVar)
In this case, we should not expand recursively.
"""
from paddle.jit.dy2static.utils import UndefinedVar
from paddle.jit.dy2static.return_transformer import (
RETURN_VALUE_PREFIX,
)
def pack_undefined_var_as(seq):
return pack_sequence_as(
seq, [UndefinedVar("padding") for i in flatten(seq)]
)
def map_fn(n1, n2, name, order):
if not name.startswith(RETURN_VALUE_PREFIX) and (
isinstance(n1, UndefinedVar) or n1 is None
):
if n1 is None and n2 is not None:
if order == 0:
warnings.warn(
"In cond : Var '{}' or part of it is set differently in ifelse branchs, "
"<{}, {}> in true branch and <{}, {}> in false branch. Set var to "
"'None' in ifelse block might lead to error.".format(
name, type(n1), n1, type(n2), n2
)
)
else:
warnings.warn(
"In cond : Var '{}' or part of it is set differently in ifelse branchs, "
"<{}, {}> in true branch and <{}, {}> in false branch. Set var to "
"'None' in ifelse block might lead to error.".format(
name, type(n2), n2, type(n1), n1
)
)
return pack_undefined_var_as(n2)
return n1
nest1_out = list(
map(
map_fn,
_to_sequence_except_dict(nest1),
_to_sequence_except_dict(nest2),
_to_sequence_except_dict(names),
[0 for i in _to_sequence_except_dict(names)],
)
)
nest2_out = list(
map(
map_fn,
_to_sequence_except_dict(nest2),
_to_sequence_except_dict(nest1),
_to_sequence_except_dict(names),
[1 for i in _to_sequence_except_dict(names)],
)
)
if not _is_sequence_except_dict(nest1):
nest1_out = nest1_out[0]
if not _is_sequence_except_dict(nest2):
nest2_out = nest2_out[0]
return nest1_out, nest2_out
# TODO: It will be deleted later.
class Switch:
"""
:api_attr: Static Graph
This class is used to implement Switch branch control function.
Switch branch contains several case branches and one default branch.
Switch control flow checks whether the case branch conditions are satisfied in turn,
and only executes the statement after the first case branch that satisfies the conditions.
If there is no case branch that satisfies the condition,
only the statement following the default branch is executed.
Note:
A new OP :ref:`api_fluid_layers_case` is highly recommended instead of ``Switch`` if the shape of parameter ``cond`` is [1].
OP :ref:`api_fluid_layers_case` is easier to use and is called with less code but does the same thing as ``Switch`` .
Member Functions:
case(condition): The case branch of Switch whose parameter cond is a scalar Variable of bool type. Only if the cond of the current case branch is True and the cond of the previous case branch is False, the statement after the case branch will be executed, and the statement after the case branch will not be executed.
default(): The default branch of Switch. When cond of all case branches is False, the statement after default branch is executed.
Case and default functions can only be used inside the scope of Switch, as shown below:
.. code-block:: python
'''
import paddle
import paddle.fluid as fluid
with fluid.layers.Switch() as switch:
with switch.case(cond1):
i = paddle.full(shape=[1], dtype='int64', fill_value=1)
with switch.case(cond2):
i = paddle.full(shape=[1], dtype='int64', fill_value=2)
with switch.default():
i = paddle.full(shape=[1], dtype='int64', fill_value=0)
'''
Args:
name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` .
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
lr = paddle.static.create_global_var(
shape=[1],
value=0.0,
dtype='float32',
persistable=True,
name="learning_rate")
zero_var = paddle.full(
shape=[1], dtype='float32', fill_value=0.0)
one_var = paddle.full(
shape=[1], dtype='float32', fill_value=1.0)
two_var = paddle.full(
shape=[1], dtype='float32', fill_value=2.0)
global_step = fluid.layers.autoincreased_step_counter(counter_name='@LR_DECAY_COUNTER@', begin=0, step=1)
with fluid.layers.control_flow.Switch() as switch:
with switch.case(global_step == zero_var):
paddle.assign(input=one_var, output=lr)
with switch.default():
paddle.assign(input=two_var, output=lr)
exe = fluid.Executor(fluid.CPUPlace())
exe.run(fluid.default_startup_program())
res = exe.run(fluid.default_main_program(), feed={}, fetch_list=[lr])
print(res) # [array([1.], dtype=float32)]
"""
def __init__(self, name=None):
self.helper = LayerHelper('switch', name=name)
self.inside_scope = False
self.pre_not_conditions = []
def case(self, condition):
if not self.inside_scope:
raise ValueError("case should be called inside with")
check_variable_and_dtype(
condition,
'condition',
['bool'],
'the member function case of fluid.layers.Switch',
)
if len(self.pre_not_conditions) == 0:
cond_block = ConditionalBlock([condition], is_scalar_condition=True)
not_cond = paddle.logical_not(x=condition)
self.pre_not_conditions.append(not_cond)
else:
pre_cond_num = len(self.pre_not_conditions)
pre_not_cond = self.pre_not_conditions[pre_cond_num - 1]
new_not_cond = paddle.logical_and(
x=pre_not_cond, y=paddle.logical_not(x=condition)
)
self.pre_not_conditions.append(new_not_cond)
cond_block = ConditionalBlock(
[paddle.logical_and(x=pre_not_cond, y=condition)],
is_scalar_condition=True,
)
return ConditionalBlockGuard(cond_block)
def default(self):
pre_cond_num = len(self.pre_not_conditions)
if pre_cond_num == 0:
raise ValueError("there should be at least one condition")
cond_block = ConditionalBlock(
[self.pre_not_conditions[pre_cond_num - 1]],
is_scalar_condition=True,
)
return ConditionalBlockGuard(cond_block)
def __enter__(self):
"""
set flag that now is inside switch.block {}
:return:
"""
self.inside_scope = True
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.inside_scope = False
if exc_type is not None:
return False # re-raise exception
return True
python/paddle/fluid/layers/learning_rate_scheduler.py
浏览文件 @ a646e75f
......@@ -24,7 +24,6 @@ import math
import numbers
import paddle
from . import control_flow
from . import nn
from . import tensor
from ..framework import (
......@@ -434,8 +433,7 @@ def piecewise_decay(boundaries, values):
persistable=True,
name="learning_rate",
)
# TODO: fluid.layers.control_flow.Switch should be replaced by paddle.static.nn.case(or cond) if possible
with control_flow.Switch() as switch:
with paddle.static.nn.control_flow.Switch() as switch:
for i in range(len(boundaries)):
boundary_val = paddle.tensor.fill_constant(
shape=[1],
......
python/paddle/incubate/nn/layer/io.py
浏览文件 @ a646e75f
......@@ -11,13 +11,13 @@
# 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 paddle
from ....fluid.framework import Variable
from ....fluid.layers.control_flow import BlockGuard
from ....framework import LayerHelper, core
class BlockGuardServ(BlockGuard):
class BlockGuardServ(paddle.static.nn.control_flow.BlockGuard):
"""
BlockGuardServ class.
......
python/paddle/jit/dy2static/convert_operators.py
浏览文件 @ a646e75f
......@@ -21,8 +21,6 @@ from paddle.fluid.dygraph.base import (
in_declarative_mode,
)
from paddle.fluid.framework import Variable, core, default_main_program
from paddle.fluid.layers import control_flow
from paddle.fluid.layers.control_flow import while_loop
from .utils import (
RETURN_NO_VALUE_VAR_NAME,
......@@ -181,7 +179,9 @@ def _run_paddle_while(
return_name_ids, loop_vars
) # change the non-local var to variable
# variable maybe modified to inner var. change it into
loop_vars = control_flow.while_loop(new_cond_fn, new_body_fn, loop_vars)
from paddle.static.nn import while_loop
loop_vars = while_loop(new_cond_fn, new_body_fn, loop_vars)
helper.set(return_name_ids, loop_vars)
return loop_vars
......@@ -820,6 +820,8 @@ def _run_paddle_pop(array, *args):
new_array = _slice_tensor_array(array, 0, idx)
i = idx + 1
from paddle.static.nn import while_loop
_, new_array = while_loop(cond, body, [i, new_array])
paddle.assign(new_array, output=array)
......
python/paddle/nn/layer/rnn.py
浏览文件 @ a646e75f
......@@ -28,7 +28,6 @@ from paddle.fluid.framework import (
in_dygraph_mode,
program_guard,
)
from paddle.fluid.layers import control_flow
from paddle.framework import core
from paddle.nn import functional as F
from paddle.nn import initializer as I
......@@ -275,7 +274,7 @@ def _rnn_static_graph(
end = paddle.cast(end, "int64")
cond = start_i < end
while_op = control_flow.While(cond)
while_op = paddle.static.nn.control_flow.While(cond)
out_array = paddle.tensor.create_array(
dtype=paddle.utils.flatten(inputs)[0].dtype
......
python/paddle/static/nn/__init__.py
浏览文件 @ a646e75f
......@@ -40,7 +40,6 @@ from .common import layer_norm # noqa: F401
from .common import embedding # noqa: F401
from .common import sparse_embedding # noqa: F401
from ...fluid.layers import StaticRNN # noqa: F401
from .sequence_lod import sequence_conv # noqa: F401
from .sequence_lod import sequence_softmax # noqa: F401
......@@ -99,6 +98,5 @@ __all__ = [ # noqa
'sequence_scatter',
'sequence_enumerate',
'sequence_reverse',
'StaticRNN',
'prelu',
]
python/paddle/static/nn/control_flow.py
浏览文件 @ a646e75f
......@@ -24,10 +24,8 @@ from paddle.common_ops_import import (
in_dygraph_mode,
)
from paddle.fluid import core
from paddle.fluid.backward import _infer_var_data_type_shape_
from paddle.fluid.framework import Operator, Program, Variable, static_only
# Temporary solution, it will be deleted later
from paddle.fluid.layers.control_flow import ConditionalBlock, select_input
from paddle.utils import (
assert_same_structure,
copy_mutable_vars,
......@@ -152,6 +150,198 @@ class WhileGuard(BlockGuard):
return super().__exit__(exc_type, exc_val, exc_tb)
class ConditionalBlock:
'''
**ConditionalBlock**
ConditionalBlock is an operator that bind a block to a specific condition,
if the condition matches, the corresponding block will be executed.
Args:
inputs (Variable): bool conditions.
is_scalar_condition (bool): whether the branch is controlled by a scalar.
name(str): name of this ConditionalBlock.
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
cond = paddle.less_than(x=label, y=limit)
true_image, false_image = layers.split_lod_tensor(
input=image, mask=cond)
true_cond = layers.ConditionalBlock([true_image])
with true_cond.block():
...
with false_cond.block():
...
'''
def __init__(self, inputs, is_scalar_condition=False, name=None):
for each_input in inputs:
check_type(each_input, "input", Variable, "ConditionalBlock")
self.inputs = inputs
self.is_scalar_condition = is_scalar_condition
self.helper = LayerHelper('conditional_block', name=name)
def block(self):
return ConditionalBlockGuard(self)
def complete(self):
inside_block = self.helper.main_program.current_block()
parent_block = self.helper.main_program.block(inside_block.parent_idx)
intermediate = set()
params = set()
params, intermediate = get_inputs_outputs_in_block(
inside_block, params, intermediate, helper=self.helper
)
# Todo(liym27) Here assume that all params are in recursive parent block
# but when minimize() called in control flow, some params may be in
# conditional grad block
param_list = [
parent_block._var_recursive(each_name) for each_name in params
]
out_list = []
for inner_out_name in intermediate:
inner_var = parent_block._find_var_recursive(inner_out_name)
if inner_var:
out_list.append(inner_var)
step_scope = parent_block.create_var(
type=core.VarDesc.VarType.STEP_SCOPES
)
conditional_block_op = parent_block.append_op(
type='conditional_block',
inputs={
'Cond': self.inputs,
'Input': param_list,
},
outputs={'Out': out_list, 'Scope': [step_scope]},
attrs={
'sub_block': inside_block,
'is_scalar_condition': self.is_scalar_condition,
},
)
if self.need_append_conditional_block_grad(inside_block):
self.append_conditional_block_grad(
parent_block, inside_block, conditional_block_op
)
def need_append_conditional_block_grad(self, inside_block):
grad_sub_block_idx = inside_block.backward_block_idx
inside_block_idx = inside_block.idx
# if inside_block have grad_block and grad_block is not itself,
# we will append conditional block grad.
return (
grad_sub_block_idx != -1 and grad_sub_block_idx != inside_block_idx
)
def append_conditional_block_grad(
self, parent_block, inside_block, conditional_block_op
):
'''
Append op `conditional_block_grad` manually.
When `optimizer.minimize/append_backward` is called in Paddle control flow,
grad ops will be appended before appending op `conditional_block` so that
op `conditional_block_grad` can't be appended when calling
`optimizer.minimize/append_backward`. After appending op `conditional_block`,
`conditional_block_grad` is appended manually.
Args:
parent_block (Block): The block that `conditional_block_op` blongs to.
inside_block (Block): The sub block of `conditional_block_op`.
conditional_block_op (Operator): The forward op conditional_block.
'''
grad_sub_block_idx = inside_block.backward_block_idx
grad_sub_block = self.helper.main_program.block(grad_sub_block_idx)
intermediate = set()
params = set()
for each_op in grad_sub_block.ops:
assert isinstance(each_op, Operator)
for iname in each_op.input_names:
for in_var_name in each_op.input(iname):
if in_var_name not in intermediate:
params.add(in_var_name)
for oname in each_op.output_names:
for out_var_name in each_op.output(oname):
intermediate.add(out_var_name)
param_list = []
for inner_input_name in params:
inner_var = parent_block._find_var_recursive(inner_input_name)
if inner_var:
param_list.append(inner_var.name)
grad_op_desc, op_grad_to_var = core.get_grad_op_desc(
conditional_block_op.desc, set(), [grad_sub_block.desc]
)
# append op_desc in grad_op_descs to target_block
op_role_attr_name = core.op_proto_and_checker_maker.kOpRoleAttrName()
backward = core.op_proto_and_checker_maker.OpRole.Backward
new_op_desc = parent_block.desc.append_op()
new_op_desc.copy_from(grad_op_desc[0])
new_op_desc._set_attr(op_role_attr_name, backward)
# set input and output manually
new_op_desc.set_input('Input', param_list)
new_op_desc.set_output(
'Input@GRAD', [param + "@GRAD" for param in param_list]
)
new_vars = set()
for grad_var_name in new_op_desc.output_arg_names():
if (
grad_sub_block.desc.has_var_recursive(grad_var_name.encode())
or grad_var_name == core.empty_var_name()
):
continue
grad_sub_block.desc.var(grad_var_name.encode())
new_vars.add(grad_var_name)
if grad_var_name not in op_grad_to_var:
continue
# infer_shape and infer_type
new_op_desc.infer_var_type(grad_sub_block.desc)
new_op_desc.infer_shape(grad_sub_block.desc)
for arg in new_op_desc.output_arg_names():
if arg in new_vars:
_infer_var_data_type_shape_(arg, grad_sub_block)
self.helper.main_program._sync_with_cpp()
class ConditionalBlockGuard(BlockGuard):
"""
ConditionalBlockGuard is derived from BlockGuard. It is dedicated for
holding a ConditionalBlock, and helping users entering and exiting the
ConditionalBlock via Python's 'with' keyword. However, ConditionalBlockGuard
is generally an internal component of IfElse, users should not use it directly.
"""
def __init__(self, block):
check_type(block, "block", ConditionalBlock, "ConditionalBlockGuard")
super().__init__(block.helper.main_program)
self.block = block
def __enter__(self):
return super().__enter__()
def __exit__(self, exc_type, exc_val, exc_tb):
self.block.complete()
return super().__exit__(exc_type, exc_val, exc_tb)
def get_inputs_outputs_in_block(
current_block, inner_inputs, inner_outputs, helper
):
......@@ -1167,6 +1357,92 @@ def copy_var_to_parent_block(var, layer_helper):
return parent_block_var
def select_output(input, outputs, mask):
"""
**select_output**
This API takes in one input and multiple outputs and an integer mask. It
selects the output specified by the mask and copy the input to selected
output. It is useful in control flow.
Args:
input(Variable): The input variable
outputs(tuple|list): The output variables
mask(Variable): A tensor containing 1 integer number selecting which
output to be copied with input
Returns:
Variable: The outputs variables
"""
helper = LayerHelper('select_output', **locals())
check_type(input, 'input', (Variable), 'select_output')
check_variable_and_dtype(mask, 'mask', ['int32'], 'select_output')
check_type(outputs, 'outputs', (list, tuple), 'select_output')
helper.append_op(
type='select_output',
inputs={'X': input, 'Mask': mask},
outputs={'Out': outputs},
)
return outputs
def _select_input_infer_shape(first_shape, second_shape):
"""
This function infer the output shape by following algorithm:
1. if the dims is different, raise a error.
2. compare axis one by one:
if a == b: we set axis to a
if a != b: we set axis to -1
for compatibility, non declarative mode, we just return second_shape.
"""
if len(first_shape) != len(second_shape):
warnings.warn(
f"the input shapes of select_input should have the same rank, but get {first_shape}, {second_shape}"
)
return second_shape
out_shape = list(
map(lambda a, b: a if a == b else -1, first_shape, second_shape)
)
return out_shape
def select_input(inputs, mask):
"""
**select_input**
This API takes in multiple inputs and uses an integer mask to select one
input to output. It is useful in control flow.
Args:
inputs(tuple|list): The input variables
mask(Tensor): A tensor containing 1 integer number selecting which
input to output
Returns:
Variable: The selected input variable
"""
helper = LayerHelper('select_input', **locals())
check_type(inputs, 'inputs', (list, tuple), 'select_input')
check_variable_and_dtype(mask, 'mask', ['int32'], 'select_input')
# Select input should expand the shape. If it is - 1 and valid number, use - 1 first. If the dim is different, an error will be reported directly
# assert inputs[0].dtype == inputs[1].dtype, f"Expect the inputs should have the same dtype, but get {inputs[0].dtype} and {inputs[1].dtype}"
output_shape = _select_input_infer_shape(inputs[0].shape, inputs[1].shape)
output_dtype = inputs[1].dtype
output_type = inputs[1].type
out = helper.create_variable(
dtype=output_dtype, shape=output_shape, type=output_type
)
helper.append_op(
type='select_input',
inputs={'X': inputs, 'Mask': mask},
outputs={'Out': out},
)
return out
def select_input_with_buildin_type(inputs, mask, name):
from paddle.jit.dy2static.utils import UndefinedVar
from paddle.jit.dy2static.variable_trans_func import to_static_variable
......@@ -1433,3 +1709,64 @@ def Print(
},
)
return output
class Switch:
def __init__(self, name=None):
self.helper = LayerHelper('switch', name=name)
self.inside_scope = False
self.pre_not_conditions = []
def case(self, condition):
if not self.inside_scope:
raise ValueError("case should be called inside with")
check_variable_and_dtype(
condition,
'condition',
['bool'],
'the member function case of fluid.layers.Switch',
)
if len(self.pre_not_conditions) == 0:
cond_block = ConditionalBlock([condition], is_scalar_condition=True)
not_cond = paddle.logical_not(x=condition)
self.pre_not_conditions.append(not_cond)
else:
pre_cond_num = len(self.pre_not_conditions)
pre_not_cond = self.pre_not_conditions[pre_cond_num - 1]
new_not_cond = paddle.logical_and(
x=pre_not_cond, y=paddle.logical_not(x=condition)
)
self.pre_not_conditions.append(new_not_cond)
cond_block = ConditionalBlock(
[paddle.logical_and(x=pre_not_cond, y=condition)],
is_scalar_condition=True,
)
return ConditionalBlockGuard(cond_block)
def default(self):
pre_cond_num = len(self.pre_not_conditions)
if pre_cond_num == 0:
raise ValueError("there should be at least one condition")
cond_block = ConditionalBlock(
[self.pre_not_conditions[pre_cond_num - 1]],
is_scalar_condition=True,
)
return ConditionalBlockGuard(cond_block)
def __enter__(self):
"""
set flag that now is inside switch.block {}
:return:
"""
self.inside_scope = True
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.inside_scope = False
if exc_type is not None:
return False # re-raise exception
return True
test/legacy_test/test_build_strategy_fusion_group_pass.py
浏览文件 @ a646e75f
......@@ -16,6 +16,7 @@ import unittest
from test_eager_deletion_padding_rnn import PaddingRNNTestBase, RNNConfig
import paddle
from paddle import fluid
from paddle.fluid import core
......@@ -36,4 +37,5 @@ class FusionGroupPaddingRNNTest(PaddingRNNTestBase):
if __name__ == '__main__':
paddle.enable_static()
unittest.main()
test/legacy_test/test_conditional_block.py
浏览文件 @ a646e75f
......@@ -19,9 +19,8 @@ import numpy as np
import paddle
from paddle import fluid
from paddle.fluid import core
from paddle.fluid.backward import append_backward
from paddle.fluid.executor import Executor
from paddle.fluid.layers.control_flow import ConditionalBlock
from paddle.static import Executor, append_backward
from paddle.static.nn.control_flow import ConditionalBlock
class ConditionalBlockTest(unittest.TestCase):
......@@ -83,4 +82,5 @@ class TestConditionalBlockOpInferShape(unittest.TestCase):
if __name__ == '__main__':
paddle.enable_static()
unittest.main()
test/legacy_test/test_eager_deletion_padding_rnn.py
浏览文件 @ a646e75f
......@@ -21,7 +21,6 @@ import paddle
from paddle import fluid
from paddle.fluid import layers
from paddle.fluid.executor import Executor
from paddle.fluid.layers.control_flow import StaticRNN as PaddingRNN
os.environ["CPU_NUM"] = "1"
......@@ -83,7 +82,7 @@ class RNNConfig:
else:
raise ValueError('Unsupported model_type.')
if rnn_model not in ('static', 'padding', 'cudnn'):
if rnn_model not in ('static', 'cudnn'):
raise ValueError('Unsupported rnn_model.')
self.batch_size = 12
......@@ -117,124 +116,6 @@ def lm_model(
dropout=None,
rnn_model='static',
):
def padding_rnn(input_embedding, len=3, init_hidden=None, init_cell=None):
weight_1_arr = []
weight_2_arr = []
bias_arr = []
hidden_array = []
cell_array = []
mask_array = []
for i in range(num_layers):
weight_1 = paddle.create_parameter(
[hidden_size * 2, hidden_size * 4],
dtype="float32",
name="fc_weight1_" + str(i),
default_initializer=paddle.nn.initializer.Uniform(
low=-init_scale, high=init_scale
),
)
weight_1_arr.append(weight_1)
bias_1 = paddle.create_parameter(
[hidden_size * 4],
dtype="float32",
name="fc_bias1_" + str(i),
default_initializer=paddle.nn.initializer.Constant(0.0),
)
bias_arr.append(bias_1)
pre_hidden = paddle.slice(
init_hidden, axes=[0], starts=[i], ends=[i + 1]
)
pre_cell = paddle.slice(
init_cell, axes=[0], starts=[i], ends=[i + 1]
)
pre_hidden = paddle.reshape(pre_hidden, shape=[-1, hidden_size])
pre_cell = paddle.reshape(pre_cell, shape=[-1, hidden_size])
hidden_array.append(pre_hidden)
cell_array.append(pre_cell)
input_embedding = paddle.transpose(input_embedding, perm=[1, 0, 2])
rnn = PaddingRNN()
with rnn.step():
input = rnn.step_input(input_embedding)
for k in range(num_layers):
pre_hidden = rnn.memory(init=hidden_array[k])
pre_cell = rnn.memory(init=cell_array[k])
weight_1 = weight_1_arr[k]
bias = bias_arr[k]
nn = paddle.concat([input, pre_hidden], 1)
gate_input = paddle.matmul(x=nn, y=weight_1)
gate_input = paddle.add(gate_input, bias)
i = paddle.slice(
gate_input, axes=[1], starts=[0], ends=[hidden_size]
)
j = paddle.slice(
gate_input,
axes=[1],
starts=[hidden_size],
ends=[hidden_size * 2],
)
f = paddle.slice(
gate_input,
axes=[1],
starts=[hidden_size * 2],
ends=[hidden_size * 3],
)
o = paddle.slice(
gate_input,
axes=[1],
starts=[hidden_size * 3],
ends=[hidden_size * 4],
)
c = pre_cell * paddle.nn.functional.sigmoid(
f
) + paddle.nn.functional.sigmoid(i) * paddle.tanh(j)
m = paddle.tanh(c) * paddle.nn.functional.sigmoid(o)
rnn.update_memory(pre_hidden, m)
rnn.update_memory(pre_cell, c)
rnn.step_output(m)
rnn.step_output(c)
input = m
if dropout is not None and dropout > 0.0:
input = paddle.nn.functional.dropout(
input,
p=dropout,
mode='upscale_in_train',
)
rnn.step_output(input)
rnnout = rnn()
last_hidden_array = []
last_cell_array = []
real_res = rnnout[-1]
for i in range(num_layers):
m = rnnout[i * 2]
c = rnnout[i * 2 + 1]
m.stop_gradient = True
c.stop_gradient = True
last_h = paddle.slice(
m, axes=[0], starts=[num_steps - 1], ends=[num_steps]
)
last_hidden_array.append(last_h)
last_c = paddle.slice(
c, axes=[0], starts=[num_steps - 1], ends=[num_steps]
)
last_cell_array.append(last_c)
real_res = paddle.transpose(x=real_res, perm=[1, 0, 2])
last_hidden = paddle.concat(last_hidden_array, 0)
last_cell = paddle.concat(last_cell_array, 0)
return real_res, last_hidden, last_cell
def encoder_static(
input_embedding, len=3, init_hidden=None, init_cell=None
):
......@@ -381,14 +262,7 @@ def lm_model(
mode='upscale_in_train',
)
if rnn_model == "padding":
rnn_out, last_hidden, last_cell = padding_rnn(
x_emb,
len=num_steps,
init_hidden=init_hidden_reshape,
init_cell=init_cell_reshape,
)
elif rnn_model == "static":
if rnn_model == "static":
rnn_out, last_hidden, last_cell = encoder_static(
x_emb,
len=num_steps,
......@@ -622,36 +496,6 @@ class PaddingRNNTestBase(unittest.TestCase):
ppl = np.append(ppl, train_ppl)
return ppl
def compare_padding_static_mode(self, use_program_cache=True):
'''
Test that train ppl of padding mode is same to that of static graph mode
'''
config = RNNConfig('test', 'padding')
with fluid.scope_guard(fluid.Scope()):
padding_rnn_ppl = self.train(config, use_program_cache)
config = RNNConfig('test', 'static')
with fluid.scope_guard(fluid.Scope()):
static_rnn_ppl = self.train(config, use_program_cache)
np.testing.assert_allclose(padding_rnn_ppl, static_rnn_ppl, rtol=0.001)
class EagerDeletionPaddingRNNTest(PaddingRNNTestBase):
def test_padding_mode_no_eager_deletion(self):
'''
Test that train ppl of padding mode is same to that of static graph mode without eager deletion
'''
fluid.core._set_eager_deletion_mode(-1.0, 1.0, True)
# When parallel is True, use_program_cache does not make a difference.
self.compare_padding_static_mode(use_program_cache=True)
def test_padding_mode_eager_deletion(self):
'''
Test that train ppl of padding mode is same to that of static graph mode under eager deletion
'''
fluid.core._set_eager_deletion_mode(0.0, 1.0, True)
# When parallel is True, use_program_cache does not make a difference.
self.compare_padding_static_mode(use_program_cache=True)
if __name__ == '__main__':
unittest.main()
test/legacy_test/test_eager_deletion_recurrent_op.py
浏览文件 @ a646e75f
......@@ -19,10 +19,6 @@ import numpy as np
import paddle
from paddle import fluid
from paddle.fluid import ParamAttr, core, layers
from paddle.fluid.backward import append_backward
from paddle.fluid.executor import Executor
from paddle.fluid.framework import Program, grad_var_name
paddle.enable_static()
......@@ -31,646 +27,6 @@ os.environ["CPU_NUM"] = "1"
fluid.core._set_eager_deletion_mode(0.0, 1.0, True)
class PyRNNBase:
def __init__(self, input_shape, output_shape):
self.x = np.ones(shape=input_shape).astype("float32")
self.y = np.zeros(shape=output_shape).astype("float32")
def step(self, step_id, x):
raise NotImplementedError
def forward(self):
for step_id in range(self.x.shape[0]):
self.step(step_id, self.x[step_id])
return np.mean(self.y)
def segment_inputs(self):
return [self.x[i] for i in range(self.x.shape[0])]
class PySimpleRNN1(PyRNNBase):
def __init__(self, input_shape, output_shape):
super().__init__(input_shape, output_shape)
seq_len, batch_size, input_dim = input_shape
self.h_boot = np.random.normal(size=(batch_size, input_dim)).astype(
"float32"
)
self.scale = 1.0 / 2.0
men_dim = (seq_len, batch_size, input_dim)
self.mems = np.zeros(shape=men_dim).astype("float32")
def step(self, step_id, x):
if step_id == 0:
pre_mem = self.h_boot
else:
pre_mem = self.mems[step_id - 1]
self.mems[step_id] = (pre_mem + x) * self.scale
self.y[step_id] = self.mems[step_id]
class PySimpleRNN2(PyRNNBase):
def __init__(self, input_shape, output_shape):
super().__init__(input_shape, output_shape)
seq_len, batch_size, input_dim = input_shape
self.W = np.ones(shape=(input_dim, input_dim)).astype("float32")
self.U = np.zeros(shape=(input_dim, input_dim)).astype("float32")
self.h_boot = np.ones(shape=(batch_size, input_dim)).astype("float32")
men_dim = (seq_len, batch_size, input_dim)
self.mems = np.zeros(shape=men_dim).astype("float32")
def step(self, step_id, x):
if step_id > 0:
pre_mem = self.mems[step_id - 1]
else:
pre_mem = self.h_boot
xW = np.matmul(x, self.W).astype("float32")
hU = np.matmul(pre_mem, self.U).astype("float32")
def py_sigmoid(x):
return 1.0 / (1.0 + np.exp(-x))
self.mems[step_id] = py_sigmoid(xW + hU)
self.y[step_id] = self.mems[step_id]
def create_tensor(np_data, place):
tensor = core.LoDTensor()
tensor.set(np_data, place)
return tensor
class EagerDeletionRecurrentOpTest1(unittest.TestCase):
'''
Test RNNOp
equation:
h_t = ( x_t + h_{t-1} ) / scale
vars:
- x
memories:
- h
outputs:
- h
'''
input_dim = 2
batch_size = 1
sent_len = 1
def setup_program(self):
self.main_program = Program()
self.startup_program = Program()
self.place = core.CPUPlace()
def setUp(self):
self.setup_program()
self.data_field = {"x", "h_boot"}
self.input_shape = (self.sent_len, self.batch_size, self.input_dim)
self.output_shape = (self.sent_len, self.batch_size, self.input_dim)
self.py_rnn = PySimpleRNN1(self.input_shape, self.output_shape)
with fluid.program_guard(self.main_program, self.startup_program):
self.output = paddle.mean(self.create_rnn_op())
def create_rnn_op(self):
x = paddle.static.data(
shape=[self.sent_len, self.batch_size, self.input_dim],
dtype='float32',
name='x',
)
x.stop_gradient = False
h_boot = paddle.static.data(
shape=[-1, self.input_dim], dtype='float32', name='h_boot'
)
h_boot.stop_gradient = False
rnn = layers.StaticRNN()
with rnn.step():
h_pre = rnn.memory(init=h_boot)
x_t = rnn.step_input(x)
h = paddle.scale(
x=paddle.add(x=h_pre, y=x_t),
scale=self.py_rnn.scale,
)
rnn.update_memory(h_pre, h)
rnn.output(h)
return rnn()
def forward(self):
gc_vars = core._get_eager_deletion_vars(
self.main_program.desc, [self.output.name]
)
self.assertEqual(len(gc_vars), self.main_program.num_blocks)
self.feed_map = {
x: create_tensor(getattr(self.py_rnn, x), self.place)
for x in self.data_field
}
exe = Executor(self.place)
out = exe.run(
self.main_program, feed=self.feed_map, fetch_list=[self.output]
)
return out[0]
def backward(self):
self.feed_map = {
x: create_tensor(getattr(self.py_rnn, x), self.place)
for x in self.data_field
}
fetch_list = [
self.main_program.global_block().var(grad_var_name(x))
for x in self.data_field
]
gc_vars = core._get_eager_deletion_vars(
self.main_program.desc, [var.name for var in fetch_list]
)
self.assertEqual(len(gc_vars), self.main_program.num_blocks)
exe = Executor(self.place)
return exe.run(
self.main_program,
feed=self.feed_map,
fetch_list=fetch_list,
return_numpy=False,
)
def test_backward(self, rtol=0.01):
self.check_forward()
num_grad = self.get_numerical_gradient()
with fluid.program_guard(self.main_program, self.startup_program):
append_backward(self.output)
ana_grad = [np.array(x) for x in self.backward()]
for idx, name in enumerate(self.data_field):
self.assertEqual(num_grad[idx].shape, ana_grad[idx].shape)
np.testing.assert_allclose(
num_grad[idx],
ana_grad[idx],
rtol=rtol,
err_msg='num_grad ('
+ name
+ ') has diff at '
+ str(self.place)
+ '\nExpect '
+ str(num_grad[idx])
+ '\n'
+ 'But Got'
+ str(ana_grad[idx])
+ ' in class '
+ self.__class__.__name__,
)
def check_forward(self):
pd_output = self.forward()
py_output = self.py_rnn.forward()
self.assertEqual(pd_output.shape, py_output.shape)
np.testing.assert_allclose(pd_output, py_output, rtol=0.01)
def get_numerical_gradient(self, delta=0.005):
dloss_dout = 1.0
feed_list = [getattr(self.py_rnn, x) for x in self.data_field]
grad_list = [np.zeros_like(x) for x in feed_list]
for feed, grad in zip(feed_list, grad_list):
for f, g in np.nditer([feed, grad], op_flags=['readwrite']):
o = float(f)
f[...] = o + delta
y_pos = self.forward()
f[...] = o - delta
y_neg = self.forward()
f[...] = o
dout_dfeed = (y_pos - y_neg) / (delta * 2)
g[...] = dout_dfeed
return grad_list
class EagerDeletionRecurrentOpTest2(EagerDeletionRecurrentOpTest1):
r'''
Test RNNOp
equation:
h_t = \sigma (W x_t + U h_{t-1})
weights:
- W
- U
vars:
- x
memories:
- h
outputs:
- h
'''
input_dim = 2
batch_size = 10
sent_len = 2
def setUp(self):
self.setup_program()
self.data_field = {"x", "h_boot", "W", "U"}
self.input_shape = (self.sent_len, self.batch_size, self.input_dim)
self.output_shape = (self.sent_len, self.batch_size, self.input_dim)
self.py_rnn = PySimpleRNN2(self.input_shape, self.output_shape)
with fluid.program_guard(self.main_program, self.startup_program):
self.output = paddle.mean(self.create_rnn_op())
def create_rnn_op(self):
x = paddle.static.data(
shape=[self.sent_len, self.batch_size, self.input_dim],
dtype='float32',
name='x',
)
x.stop_gradient = False
h_boot = paddle.static.data(
shape=[-1, self.input_dim], dtype='float32', name='h_boot'
)
h_boot.stop_gradient = False
rnn = layers.StaticRNN()
with rnn.step():
h_pre = rnn.memory(init=h_boot)
x_t = rnn.step_input(x)
temp_l = paddle.static.nn.fc(
x=x_t,
size=self.input_dim,
weight_attr=ParamAttr(
name='W',
initializer=paddle.nn.initializer.Constant(1.0),
),
bias_attr=False,
)
temp_r = paddle.static.nn.fc(
x=h_pre,
size=self.input_dim,
weight_attr=ParamAttr(
name='U',
initializer=paddle.nn.initializer.Constant(0.0),
),
bias_attr=False,
)
h = paddle.nn.functional.sigmoid(x=paddle.add(x=temp_l, y=temp_r))
rnn.update_memory(h_pre, h)
rnn.output(h)
return rnn()
def test_backward(self):
super().test_backward(rtol=0.01)
class EagerDeletionRecurrentOpMultipleMemoryTest(EagerDeletionRecurrentOpTest1):
'''
Test RNNOp with two memories
equation:
h_1 = h_pre_1
h_2 = h_pre_2
y = h_1 + h_2
vars:
- x
memories:
- h_1, h_2
outputs:
- y
'''
class PySimpleRNN3(PyRNNBase):
def __init__(self, input_shape, output_shape):
super(
EagerDeletionRecurrentOpMultipleMemoryTest.PySimpleRNN3, self
).__init__(input_shape, output_shape)
seq_len, batch_size, input_dim = input_shape
self.h_boot1 = np.random.normal(
size=(batch_size, input_dim)
).astype("float32")
self.h_boot2 = np.random.normal(
size=(batch_size, input_dim)
).astype("float32")
men_dim = (seq_len, batch_size, input_dim)
self.mems1 = np.zeros(shape=men_dim).astype("float32")
self.mems2 = np.zeros(shape=men_dim).astype("float32")
def step(self, step_id, x):
if step_id == 0:
pre_mem1 = self.h_boot1
pre_mem2 = self.h_boot2
else:
pre_mem1 = self.mems1[step_id - 1]
pre_mem2 = self.mems2[step_id - 1]
self.mems1[step_id] = pre_mem1
self.mems2[step_id] = pre_mem2
self.y[step_id] = self.mems1[step_id] + self.mems2[step_id] + x
input_dim = 1
batch_size = 1
sent_len = 2
def setUp(self):
self.setup_program()
self.data_field = {"x", "h_boot1", "h_boot2"}
self.input_shape = (self.sent_len, self.batch_size, self.input_dim)
self.output_shape = (self.sent_len, self.batch_size, self.input_dim)
self.py_rnn = EagerDeletionRecurrentOpMultipleMemoryTest.PySimpleRNN3(
self.input_shape, self.output_shape
)
with fluid.program_guard(self.main_program, self.startup_program):
self.output = paddle.mean(self.create_rnn_op())
def create_rnn_op(self):
x = paddle.static.data(
shape=[self.sent_len, self.batch_size, self.input_dim],
dtype='float32',
name='x',
)
x.stop_gradient = False
h_boot1 = paddle.static.data(
shape=[self.batch_size, self.input_dim],
dtype='float32',
name='h_boot1',
)
h_boot1.stop_gradient = False
h_boot2 = paddle.static.data(
shape=[self.batch_size, self.input_dim],
dtype='float32',
name='h_boot2',
)
h_boot2.stop_gradient = False
rnn = layers.StaticRNN()
with rnn.step():
h_pre1 = rnn.memory(init=h_boot1)
h_pre2 = rnn.memory(init=h_boot2)
x_t = rnn.step_input(x)
mem1 = paddle.scale(x=h_pre1, scale=1.0)
mem2 = paddle.scale(x=h_pre2, scale=1.0)
out = paddle.add_n([mem1, x_t, mem2])
rnn.update_memory(h_pre1, mem1)
rnn.update_memory(h_pre2, mem2)
rnn.output(out)
return rnn()
class EagerDeletionRecurrentOpNoMemBootTest(EagerDeletionRecurrentOpTest1):
'''
Test RNNOp without memory boot
equation:
mem = x + mem_pre
y = mem
vars:
- x
memories:
- mem
outputs:
- y
'''
class PySimpleRNN4(PyRNNBase):
def __init__(self, input_shape, output_shape):
super(
EagerDeletionRecurrentOpNoMemBootTest.PySimpleRNN4, self
).__init__(input_shape, output_shape)
men_dim = input_shape
self.mems = np.zeros(shape=men_dim).astype("float32")
def step(self, step_id, x):
if step_id == 0:
pre_mem = np.zeros_like(x)
else:
pre_mem = self.mems[step_id - 1]
self.mems[step_id] = pre_mem + x
self.y[step_id] = self.mems[step_id]
input_dim = 1
batch_size = 1
sent_len = 2
def setUp(self):
self.setup_program()
self.data_field = {"x"}
self.input_shape = (self.sent_len, self.batch_size, self.input_dim)
self.output_shape = (self.sent_len, self.batch_size, self.input_dim)
self.py_rnn = EagerDeletionRecurrentOpNoMemBootTest.PySimpleRNN4(
self.input_shape, self.output_shape
)
with fluid.program_guard(self.main_program, self.startup_program):
self.output = paddle.mean(self.create_rnn_op())
def create_rnn_op(self):
x = paddle.static.data(
shape=[self.sent_len, self.batch_size, self.input_dim],
dtype='float32',
name='x',
)
x.stop_gradient = False
rnn = layers.StaticRNN()
with rnn.step():
mem_pre = rnn.memory(shape=[-1, self.input_dim], batch_ref=x)
x_t = rnn.step_input(x)
mem = paddle.add(x=mem_pre, y=x_t)
rnn.update_memory(mem_pre, mem)
rnn.output(mem)
return rnn()
class EagerDeletionTwoRecurrentOpsTest(EagerDeletionRecurrentOpTest1):
'''
Test RNNOp with two recurrent ops
equation:
first_rnn:
mem_inside = x + mem_pre_inside
first_inside_out = mem_inside
second_rnn:
mem = x + reduce_sum(rnn_inside_out)
y = mem + mem_pre
vars:
- x
memories:
- mem_inside
- mem
outputs:
- y
'''
class PySimpleRNN5(PyRNNBase):
def __init__(self, input_shape, output_shape):
super().__init__(input_shape, output_shape)
self.mem_0 = np.zeros(shape=input_shape).astype("float32")
self.mem_1 = np.zeros(shape=input_shape).astype("float32")
self.rnn_0_output = np.zeros(shape=input_shape).astype("float32")
def step(self, step_id, x):
# First Rnn
for step in range(self.x.shape[0]):
x_t = self.x[step]
pre_mem = (
np.zeros_like(x_t) if step == 0 else self.mem_0[step - 1]
)
self.mem_0[step] = x_t + pre_mem
self.rnn_0_output[step] = self.mem_0[step]
# Second RNN
pre_mem = (
np.zeros_like(x) if step_id == 0 else self.mem_1[step_id - 1]
)
self.mem_1[step_id] = x + np.sum(self.rnn_0_output)
self.y[step_id] = self.mem_1[step_id] + pre_mem
input_dim = 1
batch_size = 1
sent_len = 1
def setUp(self):
self.setup_program()
self.data_field = {"x"}
self.input_shape = (self.sent_len, self.batch_size, self.input_dim)
self.output_shape = (self.sent_len, self.batch_size, self.input_dim)
self.py_rnn = EagerDeletionTwoRecurrentOpsTest.PySimpleRNN5(
self.input_shape, self.output_shape
)
with fluid.program_guard(self.main_program, self.startup_program):
self.output = paddle.mean(self.create_rnn_op())
def create_rnn_op(self):
x = paddle.static.data(
shape=[self.sent_len, self.batch_size, self.input_dim],
dtype='float32',
name='x',
)
x.stop_gradient = False
rnn_0 = layers.StaticRNN()
with rnn_0.step():
x_t = rnn_0.step_input(x)
mem_pre = rnn_0.memory(shape=[-1, self.input_dim], batch_ref=x)
mem = paddle.add(x=mem_pre, y=x_t)
rnn_0.update_memory(mem_pre, mem)
rnn_0.output(mem)
rnn_1 = layers.StaticRNN()
with rnn_1.step():
mem_pre = rnn_1.memory(shape=[-1, self.input_dim], batch_ref=x)
x_t = rnn_1.step_input(x)
last_rnn_output = rnn_0()
last_rnn_sum = paddle.sum(last_rnn_output)
mem = paddle.add(x=x_t, y=last_rnn_sum)
y = paddle.add(x=mem_pre, y=mem)
rnn_1.update_memory(mem_pre, mem)
rnn_1.output(y)
return rnn_1()
class EagerDeletionFarwardOnlyRnnAndBackwardRnnTest(
EagerDeletionRecurrentOpTest1
):
'''
Test one forward only RNN and one backward RNN in one program
'''
def setUp(self):
self.setup_program()
self.data_field = {"x", "h_boot"}
self.input_shape = (self.sent_len, self.batch_size, self.input_dim)
self.output_shape = (self.sent_len, self.batch_size, self.input_dim)
self.py_rnn = PySimpleRNN1(self.input_shape, self.output_shape)
with fluid.program_guard(self.main_program, self.startup_program):
x = paddle.static.data(
shape=[self.sent_len, self.batch_size, self.input_dim],
dtype='float32',
name='x',
)
x.stop_gradient = False
h_boot = paddle.static.data(
shape=[-1, self.input_dim], dtype='float32', name='h_boot'
)
h_boot.stop_gradient = False
forward_only_rnn = layers.StaticRNN()
with forward_only_rnn.step():
h_pre = forward_only_rnn.memory(init=h_boot)
x_t = forward_only_rnn.step_input(x)
h = paddle.scale(
x=paddle.add(x=h_pre, y=x_t),
scale=self.py_rnn.scale,
)
forward_only_rnn.update_memory(h_pre, h)
forward_only_rnn.output(h)
forward_only_output = forward_only_rnn()
forward_only_output.stop_gradient = True
self.forward_only_output = paddle.mean(forward_only_output)
rnn = layers.StaticRNN()
with rnn.step():
h_pre = rnn.memory(init=h_boot)
x_t = rnn.step_input(x)
h = paddle.scale(
x=paddle.add(x=h_pre, y=x_t),
scale=self.py_rnn.scale,
)
rnn.update_memory(h_pre, h)
rnn.output(h)
self.output = paddle.mean(rnn())
def forward_two_rnn(self):
self.feed_map = {
x: create_tensor(getattr(self.py_rnn, x), self.place)
for x in self.data_field
}
exe = Executor(self.place)
out = exe.run(
self.main_program,
feed=self.feed_map,
fetch_list=[self.forward_only_output, self.output],
)
return out[0], out[1]
def check_forward(self):
forward_only_output, pd_output = self.forward_two_rnn()
py_output = self.py_rnn.forward()
self.assertEqual(forward_only_output.shape, py_output.shape)
self.assertEqual(pd_output.shape, py_output.shape)
np.testing.assert_allclose(forward_only_output, py_output, rtol=0.01)
np.testing.assert_allclose(pd_output, py_output, rtol=0.01)
class RecurrentNet(paddle.nn.Layer):
def __init__(self):
super().__init__()
......
test/legacy_test/test_executor_and_use_program_cache.py
浏览文件 @ a646e75f
......@@ -98,7 +98,7 @@ class ExecutorPaddingRNNTest(PaddingRNNTestBase):
)
def test_inference_output(self):
for rnn_model in ["static", "padding"]:
for rnn_model in ["static"]:
# Set parallel to False to use the default executor.
self.train_and_save_inference_program(
rnn_model=rnn_model, use_program_cache=True
......@@ -166,4 +166,5 @@ class ExecutorPaddingRNNTest(PaddingRNNTestBase):
if __name__ == '__main__':
paddle.enable_static()
unittest.main()
test/legacy_test/test_recurrent_op.py 已删除 100644 → 0
浏览文件 @ 40cf6e64
# Copyright (c) 2018 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 unittest
import numpy as np
import paddle
from paddle import fluid
from paddle.fluid import ParamAttr, core, layers
from paddle.fluid.backward import append_backward
from paddle.fluid.executor import Executor
from paddle.fluid.framework import Program, grad_var_name
np.random.seed(123)
class PyRNNBase:
def __init__(self, input_shape, output_shape):
self.x = np.ones(shape=input_shape).astype("float32")
self.y = np.zeros(shape=output_shape).astype("float32")
def step(self, step_id, x):
raise NotImplementedError
def forward(self):
for step_id in range(self.x.shape[0]):
self.step(step_id, self.x[step_id])
return np.mean(self.y)
def segment_inputs(self):
return [self.x[i] for i in range(self.x.shape[0])]
class PySimpleRNN1(PyRNNBase):
def __init__(self, input_shape, output_shape):
super().__init__(input_shape, output_shape)
seq_len, batch_size, input_dim = input_shape
self.h_boot = np.random.normal(size=(batch_size, input_dim)).astype(
"float32"
)
self.scale = 1.0 / 2.0
men_dim = (seq_len, batch_size, input_dim)
self.mems = np.zeros(shape=men_dim).astype("float32")
def step(self, step_id, x):
if step_id == 0:
pre_mem = self.h_boot
else:
pre_mem = self.mems[step_id - 1]
self.mems[step_id] = (pre_mem + x) * self.scale
self.y[step_id] = self.mems[step_id]
class PySimpleRNN2(PyRNNBase):
def __init__(self, input_shape, output_shape):
super().__init__(input_shape, output_shape)
seq_len, batch_size, input_dim = input_shape
self.W = np.ones(shape=(input_dim, input_dim)).astype("float32")
self.U = np.zeros(shape=(input_dim, input_dim)).astype("float32")
self.h_boot = np.ones(shape=(batch_size, input_dim)).astype("float32")
men_dim = (seq_len, batch_size, input_dim)
self.mems = np.zeros(shape=men_dim).astype("float32")
def step(self, step_id, x):
if step_id > 0:
pre_mem = self.mems[step_id - 1]
else:
pre_mem = self.h_boot
xW = np.matmul(x, self.W).astype("float32")
hU = np.matmul(pre_mem, self.U).astype("float32")
def py_sigmoid(x):
return 1.0 / (1.0 + np.exp(-x))
self.mems[step_id] = py_sigmoid(xW + hU)
self.y[step_id] = self.mems[step_id]
def create_tensor(np_data, place):
tensor = core.LoDTensor()
tensor.set(np_data, place)
return tensor
class RecurrentOpTest1(unittest.TestCase):
'''
Test RNNOp
equation:
h_t = ( x_t + h_{t-1} ) / scale
vars:
- x
memories:
- h
outputs:
- h
'''
input_dim = 2
batch_size = 1
sent_len = 1
def setup_program(self):
self.main_program = Program()
self.startup_program = Program()
self.place = core.CPUPlace()
def setUp(self):
self.setup_program()
self.feed_data_field = {"x", "h_boot"}
self.grad_data_field = self.feed_data_field
self.input_shape = (self.sent_len, self.batch_size, self.input_dim)
self.output_shape = (self.sent_len, self.batch_size, self.input_dim)
self.py_rnn = PySimpleRNN1(self.input_shape, self.output_shape)
with fluid.program_guard(self.main_program, self.startup_program):
self.output = paddle.mean(self.create_rnn_op())
def create_rnn_op(self):
x = paddle.static.data(
shape=[self.sent_len, self.batch_size, self.input_dim],
dtype='float32',
name='x',
)
x.stop_gradient = False
h_boot = paddle.static.data(
shape=[-1, self.input_dim], dtype='float32', name='h_boot'
)
h_boot.stop_gradient = False
rnn = layers.StaticRNN()
with rnn.step():
h_pre = rnn.memory(init=h_boot)
x_t = rnn.step_input(x)
h = paddle.scale(
x=paddle.add(x=h_pre, y=x_t),
scale=self.py_rnn.scale,
)
rnn.update_memory(h_pre, h)
rnn.output(h)
return rnn()
def forward(self):
self.feed_map = {
x: create_tensor(getattr(self.py_rnn, x), self.place)
for x in self.feed_data_field
}
exe = Executor(self.place)
out = exe.run(
self.main_program, feed=self.feed_map, fetch_list=[self.output]
)
return out[0]
def backward(self):
self.feed_map = {
x: create_tensor(getattr(self.py_rnn, x), self.place)
for x in self.feed_data_field
}
fetch_list = [
self.main_program.global_block().var(grad_var_name(x))
for x in self.grad_data_field
]
exe = Executor(self.place)
return exe.run(
self.main_program,
feed=self.feed_map,
fetch_list=fetch_list,
return_numpy=False,
)
def test_backward(self, rtol=0.01):
self.check_forward()
with fluid.program_guard(self.main_program, self.startup_program):
append_backward(self.output)
ana_grad = [np.array(x) for x in self.backward()]
num_grad = self.get_numerical_gradient()
for idx, name in enumerate(self.grad_data_field):
self.assertEqual(num_grad[idx].shape, ana_grad[idx].shape)
np.testing.assert_allclose(
num_grad[idx],
ana_grad[idx],
rtol=rtol,
atol=1e-8,
err_msg='num_grad ('
+ name
+ ') has diff at '
+ str(self.place)
+ '\nExpect '
+ str(num_grad[idx])
+ '\n'
+ 'But Got'
+ str(ana_grad[idx])
+ ' in class '
+ self.__class__.__name__,
)
def check_forward(self):
pd_output = self.forward()
py_output = self.py_rnn.forward()
self.assertEqual(pd_output.shape, py_output.shape)
np.testing.assert_allclose(pd_output, py_output, rtol=0.01)
def get_numerical_gradient(self, delta=0.005):
dloss_dout = 1.0
feed_list = [getattr(self.py_rnn, x) for x in self.grad_data_field]
grad_list = [np.zeros_like(x) for x in feed_list]
for feed, grad in zip(feed_list, grad_list):
for f, g in np.nditer([feed, grad], op_flags=['readwrite']):
o = float(f)
f[...] = o + delta
y_pos = self.forward()
f[...] = o - delta
y_neg = self.forward()
f[...] = o
dout_dfeed = (y_pos - y_neg) / (delta * 2)
g[...] = dout_dfeed
return grad_list
class RecurrentOpTest2(RecurrentOpTest1):
r'''
Test RNNOp
equation:
h_t = \sigma (W x_t + U h_{t-1})
weights:
- W
- U
vars:
- x
memories:
- h
outputs:
- h
'''
input_dim = 2
batch_size = 10
sent_len = 2
def setUp(self):
self.setup_program()
self.feed_data_field = {"x", "h_boot", "W", "U"}
self.grad_data_field = self.feed_data_field
self.input_shape = (self.sent_len, self.batch_size, self.input_dim)
self.output_shape = (self.sent_len, self.batch_size, self.input_dim)
self.py_rnn = PySimpleRNN2(self.input_shape, self.output_shape)
with fluid.program_guard(self.main_program, self.startup_program):
self.output = paddle.mean(self.create_rnn_op())
def create_rnn_op(self):
x = paddle.static.data(
shape=[self.sent_len, self.batch_size, self.input_dim],
dtype='float32',
name='x',
)
x.stop_gradient = False
h_boot = paddle.static.data(
shape=[-1, self.input_dim], dtype='float32', name='h_boot'
)
h_boot.stop_gradient = False
rnn = layers.StaticRNN()
with rnn.step():
h_pre = rnn.memory(init=h_boot)
x_t = rnn.step_input(x)
temp_l = paddle.static.nn.fc(
x=x_t,
size=self.input_dim,
weight_attr=ParamAttr(
name='W',
initializer=paddle.nn.initializer.Constant(1.0),
),
bias_attr=False,
)
temp_r = paddle.static.nn.fc(
x=h_pre,
size=self.input_dim,
weight_attr=ParamAttr(
name='U',
initializer=paddle.nn.initializer.Constant(0.0),
),
bias_attr=False,
)
h = paddle.nn.functional.sigmoid(x=paddle.add(x=temp_l, y=temp_r))
rnn.update_memory(h_pre, h)
rnn.output(h)
return rnn()
def test_backward(self):
super().test_backward(rtol=0.01)
class RecurrentOpMultipleMemoryTest(RecurrentOpTest1):
'''
Test RNNOp with two memories
equation:
h_1 = h_pre_1
h_2 = h_pre_2
y = h_1 + h_2
vars:
- x
memories:
- h_1, h_2
outputs:
- y
'''
class PySimpleRNN3(PyRNNBase):
def __init__(self, input_shape, output_shape):
super().__init__(input_shape, output_shape)
seq_len, batch_size, input_dim = input_shape
self.h_boot1 = np.random.normal(
size=(batch_size, input_dim)
).astype("float32")
self.h_boot2 = np.random.normal(
size=(batch_size, input_dim)
).astype("float32")
men_dim = (seq_len, batch_size, input_dim)
self.mems1 = np.zeros(shape=men_dim).astype("float32")
self.mems2 = np.zeros(shape=men_dim).astype("float32")
def step(self, step_id, x):
if step_id == 0:
pre_mem1 = self.h_boot1
pre_mem2 = self.h_boot2
else:
pre_mem1 = self.mems1[step_id - 1]
pre_mem2 = self.mems2[step_id - 1]
self.mems1[step_id] = pre_mem1
self.mems2[step_id] = pre_mem2
self.y[step_id] = self.mems1[step_id] + self.mems2[step_id] + x
input_dim = 1
batch_size = 1
sent_len = 2
def setUp(self):
self.setup_program()
self.feed_data_field = {"x", "h_boot1", "h_boot2"}
self.grad_data_field = self.feed_data_field
self.input_shape = (self.sent_len, self.batch_size, self.input_dim)
self.output_shape = (self.sent_len, self.batch_size, self.input_dim)
self.py_rnn = RecurrentOpMultipleMemoryTest.PySimpleRNN3(
self.input_shape, self.output_shape
)
with fluid.program_guard(self.main_program, self.startup_program):
self.output = paddle.mean(self.create_rnn_op())
def create_rnn_op(self):
x = paddle.static.data(
shape=[self.sent_len, self.batch_size, self.input_dim],
dtype='float32',
name='x',
)
x.stop_gradient = False
h_boot1 = paddle.static.data(
shape=[self.batch_size, self.input_dim],
dtype='float32',
name='h_boot1',
)
h_boot1.stop_gradient = False
h_boot2 = paddle.static.data(
shape=[self.batch_size, self.input_dim],
dtype='float32',
name='h_boot2',
)
h_boot2.stop_gradient = False
rnn = layers.StaticRNN()
with rnn.step():
h_pre1 = rnn.memory(init=h_boot1)
h_pre2 = rnn.memory(init=h_boot2)
x_t = rnn.step_input(x)
mem1 = paddle.scale(x=h_pre1, scale=1.0)
mem2 = paddle.scale(x=h_pre2, scale=1.0)
out = paddle.add_n([mem1, x_t, mem2])
rnn.update_memory(h_pre1, mem1)
rnn.update_memory(h_pre2, mem2)
rnn.output(out)
return rnn()
class RecurrentOpNoMemBootTest(RecurrentOpTest1):
'''
Test RNNOp with two memories
equation:
mem = x + mem_pre
y = mem
vars:
- x
memories:
- mem
outputs:
- y
'''
class PySimpleRNN4(PyRNNBase):
def __init__(self, input_shape, output_shape):
super().__init__(input_shape, output_shape)
men_dim = input_shape
self.mems = np.zeros(shape=men_dim).astype("float32")
def step(self, step_id, x):
if step_id == 0:
pre_mem = np.zeros_like(x)
else:
pre_mem = self.mems[step_id - 1]
self.mems[step_id] = pre_mem + x
self.y[step_id] = self.mems[step_id]
input_dim = 1
batch_size = 1
sent_len = 2
def setUp(self):
self.setup_program()
self.feed_data_field = {"x"}
self.grad_data_field = self.feed_data_field
self.input_shape = (self.sent_len, self.batch_size, self.input_dim)
self.output_shape = (self.sent_len, self.batch_size, self.input_dim)
self.py_rnn = RecurrentOpNoMemBootTest.PySimpleRNN4(
self.input_shape, self.output_shape
)
with fluid.program_guard(self.main_program, self.startup_program):
self.output = paddle.mean(self.create_rnn_op())
def create_rnn_op(self):
x = paddle.static.data(
shape=[self.sent_len, self.batch_size, self.input_dim],
dtype='float32',
name='x',
)
x.stop_gradient = False
rnn = layers.StaticRNN()
with rnn.step():
mem_pre = rnn.memory(shape=[-1, self.input_dim], batch_ref=x)
x_t = rnn.step_input(x)
mem = paddle.add(x=mem_pre, y=x_t)
rnn.update_memory(mem_pre, mem)
rnn.output(mem)
return rnn()
class RecurrentOpSubBlockTest(RecurrentOpTest1):
r'''
Test RNNOp with subblock variable
equation:
y_ = emb * w1
h_t = \concat([x, h_{t-1}])
h_t = h_t * w2
h_t = \\unsqueeze(h_t, 1)
h_t = \dot_attention(h_t, y_)
h_t = \squeeze(h_t, 1)
y = h_t
vars:
- x
- w1
- w2
memories:
- h
outputs:
- y
'''
class PySimpleRNN5(PyRNNBase):
def __init__(self, input_shape, output_shape):
super().__init__(input_shape, output_shape)
seq_len, batch_size, input_dim = input_shape
self.w1 = np.random.uniform(
-0.1, 0.1, size=(input_dim, input_dim)
).astype("float32")
self.w2 = np.random.uniform(
-0.1, 0.1, size=(input_dim * 2, input_dim)
).astype("float32")
self.emb = np.random.uniform(
-0.1, 0.1, size=(seq_len, batch_size, input_dim)
).astype("float32")
men_dim = (seq_len, batch_size, input_dim)
self.mems = np.zeros(shape=men_dim).astype("float32")
self.oy = np.matmul(self.emb, self.w1)
def step(self, step_id, x):
def dot_attention(query, memory):
attn = np.matmul(query, memory.transpose((0, 2, 1)))
weight = softmax(attn)
weight_memory = np.matmul(weight, memory)
return weight_memory, weight
def softmax(x):
return np.exp(x) / sum(np.exp(x))
if step_id == 0:
pre_mem = np.zeros_like(x)
else:
pre_mem = self.mems[step_id - 1]
concat_in = np.concatenate([x, pre_mem], 1)
new_mem = np.matmul(concat_in, self.w2)
new_mem = np.expand_dims(new_mem, 1)
new_mem, _ = dot_attention(new_mem, self.oy)
new_mem = np.squeeze(new_mem, 1)
self.mems[step_id] = new_mem
self.y[step_id] = self.mems[step_id]
input_dim = 2
batch_size = 3
sent_len = 3
def setUp(self):
self.setup_program()
self.feed_data_field = {"x", "emb", "w1", "w2"}
self.grad_data_field = self.feed_data_field
self.input_shape = (self.sent_len, self.batch_size, self.input_dim)
self.output_shape = (self.sent_len, self.batch_size, self.input_dim)
self.py_rnn = RecurrentOpSubBlockTest.PySimpleRNN5(
self.input_shape, self.output_shape
)
with fluid.program_guard(self.main_program, self.startup_program):
rnn_out = self.create_rnn_op()
self.output = paddle.mean(rnn_out)
def create_rnn_op(self):
x = paddle.static.data(
shape=[self.sent_len, self.batch_size, self.input_dim],
dtype='float32',
name='x',
)
x.stop_gradient = False
emb = paddle.static.data(
name='emb',
shape=[self.sent_len, self.batch_size, self.input_dim],
dtype='float32',
)
emb.stop_gradient = False
w1 = paddle.static.data(
shape=[self.input_dim, self.input_dim],
dtype='float32',
name='w1',
)
w1.stop_gradient = False
w2 = paddle.static.data(
shape=[self.input_dim * 2, self.input_dim],
dtype='float32',
name='w2',
)
w2.stop_gradient = False
rnn = layers.StaticRNN()
def dot_attention(query, memory):
attn = paddle.matmul(query, memory, transpose_y=True)
weight = paddle.nn.functional.softmax(attn)
weight_memory = paddle.matmul(weight, memory)
return weight_memory, weight
y = paddle.matmul(emb, w1)
with rnn.step():
pre_h = rnn.memory(
shape=(self.sent_len, self.input_dim),
batch_ref=x,
init_value=0.0,
)
step_in = rnn.step_input(x)
concat_in = paddle.concat([step_in, pre_h], 1)
new_h = paddle.matmul(concat_in, w2)
new_h = paddle.unsqueeze(new_h, [1])
new_h, _ = dot_attention(new_h, y)
new_h = paddle.squeeze(new_h, [1])
rnn.update_memory(pre_h, new_h)
rnn.step_output(new_h)
return rnn()
class RecurrentOpStopGradientTest(RecurrentOpTest1):
r"""
Test RNNOp with stop_gradient = True
equation:
h_t = \sigma (W x_t + U h_{t-1})
weights:
- W
- U
vars:
- x
memories:
- h
output:
- h
"""
input_dim = 2
batch_size = 10
sent_len = 2
def setUp(self):
self.setup_program()
self.feed_data_field = {"x", "h_boot", "W", "U"}
self.grad_data_field = {"x", "W", "U"}
self.input_shape = (self.sent_len, self.batch_size, self.input_dim)
self.output_shape = (self.sent_len, self.batch_size, self.input_dim)
self.py_rnn = PySimpleRNN2(self.input_shape, self.output_shape)
with fluid.program_guard(self.main_program, self.startup_program):
self.output = paddle.mean(self.create_rnn_op())
def create_rnn_op(self):
x = paddle.static.data(
shape=[self.sent_len, self.batch_size, self.input_dim],
dtype="float32",
name="x",
)
x.stop_gradient = False
h_boot = paddle.static.data(
shape=[-1, self.input_dim], dtype="float32", name="h_boot"
)
h_boot.stop_gradient = True
rnn = layers.StaticRNN()
with rnn.step():
h_pre = rnn.memory(init=h_boot) # init doesn't have gradient
x_t = rnn.step_input(x)
temp_l = paddle.static.nn.fc(
x=x_t,
size=self.input_dim,
weight_attr=ParamAttr(
name="W",
initializer=paddle.nn.initializer.Constant(1.0),
),
bias_attr=False,
)
temp_r = paddle.static.nn.fc(
x=h_pre,
size=self.input_dim,
weight_attr=ParamAttr(
name="U",
initializer=paddle.nn.initializer.Constant(0.0),
),
bias_attr=False,
)
h = paddle.nn.functional.sigmoid(x=paddle.add(temp_l, temp_r))
rnn.update_memory(h_pre, h)
rnn.output(h)
return rnn()
if __name__ == '__main__':
unittest.main()
test/legacy_test/test_select_input_output_op.py
浏览文件 @ a646e75f
......@@ -22,7 +22,7 @@ from paddle.fluid import core
from paddle.fluid.backward import append_backward
from paddle.fluid.executor import Executor
from paddle.fluid.framework import Program, program_guard
from paddle.fluid.layers.control_flow import select_input, select_output
from paddle.static.nn.control_flow import select_input, select_output
paddle.enable_static()
......
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