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未验证 提交 93acef3b 编写于 作者: L lugimzzz 提交者： GitHub
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【fluid clean】remove fluid.dygraph.rnn.lstmcell and fluid.dygraph.rnn.grucell (#48719)

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python/paddle/fluid/dygraph/__init__.py
浏览文件 @ 93acef3b
......@@ -43,9 +43,6 @@ from .io import *
from . import static_runner
from .static_runner import StaticModelRunner
from . import rnn
from .rnn import *
from . import amp
from .amp import *
......@@ -60,5 +57,4 @@ __all__ += parallel.__all__
__all__ += checkpoint.__all__
__all__ += learning_rate_scheduler.__all__
__all__ += io.__all__
__all__ += rnn.__all__
__all__ += amp.__all__
python/paddle/fluid/dygraph/rnn.py 已删除 100644 → 0
浏览文件 @ 2bba3e18
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# 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 paddle
from . import Layer
from ..layers import (
concat,
fill_constant,
elementwise_mul,
split,
)
import copy
import paddle
__all__ = ['LSTMCell', 'GRUCell']
class LSTMCell(Layer):
r"""
LSTMCell implementation using basic operators.
There are two LSTMCell version, the default one is compatible with CUDNN LSTM implementation.
The algorithm can be described as the equations below.
.. math::
i_t &= sigmoid(W_{ix}x_{t} + W_{ih}h_{t-1} + bx_i + bh_i)
f_t &= sigmoid(W_{fx}x_{t} + W_{fh}h_{t-1} + bx_f + bh_f)
o_t &= sigmoid(W_{ox}x_{t} + W_{oh}h_{t-1} + bx_o + bh_o)
\\tilde{c_t} &= tanh(W_{cx}x_t + W_{ch}h_{t-1} + bx_c + bh_c)
c_t &= f_t \\odot c_{t-1} + i_t \\odot \\tilde{c_t}
h_t &= o_t \\odot tanh(c_t)
The other LSTMCell version is compatible with the BasicLSTMUnit used in static graph.
The algorithm can be described as the equations below.
.. math::
i_t &= sigmoid(W_{ix}x_{t} + W_{ih}h_{t-1} + b_i)
f_t &= sigmoid(W_{fx}x_{t} + W_{fh}h_{t-1} + b_f + forget_bias )
o_t &= sigmoid(W_{ox}x_{t} + W_{oh}h_{t-1} + b_o)
\\tilde{c_t} &= tanh(W_{cx}x_t + W_{ch}h_{t-1} + b_c)
c_t &= f_t \\odot c_{t-1} + i_t \\odot \\tilde{c_t}
h_t &= o_t \\odot tanh(c_t)
Args:
hidden_size (integer): The hidden size used in the Cell.
input_size (integer): The input size used in the Cell.
param_attr(ParamAttr|None): The parameter attribute for the learnable
weight matrix. Note:
If it is set to None or one attribute of ParamAttr, LSTMCell will
create ParamAttr as param_attr. If the Initializer of the param_attr
is not set, the parameter is initialized with Xavier. Default: None.
bias_attr (ParamAttr|None): The parameter attribute for the bias
of LSTMCell.
If it is set to None or one attribute of ParamAttr, LSTMCell will
create ParamAttr as bias_attr. If the Initializer of the bias_attr
is not set, the bias is initialized as zero. Default: None.
gate_activation (function|None): The activation function for gates (actGate).
Default: 'fluid.layers.sigmoid'
activation (function|None): The activation function for cells (actNode).
Default: 'fluid.layers.tanh'
forget_bias(float|1.0): forget bias used when computing forget gate. This
is not used in default LSTMCell implementation (CUDNN compatiable)
use_cudnn_impl(bool|True): whether to use CUDNN compatible LSTMCell
dtype(string): data type used in this cell
Returns:
None
Examples:
.. code-block:: python
from paddle import fluid
import paddle.fluid.core as core
from paddle.fluid.dygraph import LSTMCell
import numpy as np
batch_size = 64
input_size = 128
hidden_size = 256
step_input_np = np.random.uniform(-0.1, 0.1, (
batch_size, input_size)).astype('float64')
pre_hidden_np = np.random.uniform(-0.1, 0.1, (
batch_size, hidden_size)).astype('float64')
pre_cell_np = np.random.uniform(-0.1, 0.1, (
batch_size, hidden_size)).astype('float64')
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
else:
place = core.CPUPlace()
with fluid.dygraph.guard(place):
cudnn_lstm = LSTMCell(hidden_size, input_size)
step_input_var = fluid.dygraph.to_variable(step_input_np)
pre_hidden_var = fluid.dygraph.to_variable(pre_hidden_np)
pre_cell_var = fluid.dygraph.to_variable(pre_cell_np)
new_hidden, new_cell = cudnn_lstm(step_input_var, pre_hidden_var, pre_cell_var)
"""
def __init__(
self,
hidden_size,
input_size,
param_attr=None,
bias_attr=None,
gate_activation=None,
activation=None,
forget_bias=1.0,
use_cudnn_impl=True,
dtype='float64',
):
super().__init__(dtype)
self._hidden_size = hidden_size
self._input_size = input_size
self._param_attr = param_attr
self._bias_attr = bias_attr
self._dtype = dtype
self._gate_activation = gate_activation or paddle.nn.functional.sigmoid
self._activation = activation or paddle.tanh
self._use_cudnn_impl = use_cudnn_impl
if self._use_cudnn_impl:
if (
self._param_attr is not None
and self._param_attr.name is not None
):
weight_ih_param_attr = copy.deepcopy(self._param_attr)
weight_hh_param_attr = copy.deepcopy(self._param_attr)
weight_ih_param_attr.name += "_weight_ih"
weight_hh_param_attr.name += "_weight_hh"
else:
weight_ih_param_attr = self._param_attr
weight_hh_param_attr = self._param_attr
if self._bias_attr is not None and self._bias_attr.name is not None:
bias_ih_param_attr = copy.deepcopy(self._bias_attr)
bias_hh_param_attr = copy.deepcopy(self._bias_attr)
bias_ih_param_attr.name += "_bias_ih"
bias_hh_param_attr.name += "_bias_hh"
else:
bias_ih_param_attr = self._bias_attr
bias_hh_param_attr = self._bias_attr
self._weight_ih = self.create_parameter(
attr=weight_ih_param_attr,
shape=[4 * self._hidden_size, self._input_size],
dtype=self._dtype,
)
self._weight_hh = self.create_parameter(
attr=weight_hh_param_attr,
shape=[4 * self._hidden_size, self._hidden_size],
dtype=self._dtype,
)
self._bias_ih = self.create_parameter(
attr=bias_ih_param_attr,
shape=[4 * self._hidden_size],
dtype=self._dtype,
is_bias=True,
)
self._bias_hh = self.create_parameter(
attr=bias_hh_param_attr,
shape=[4 * self._hidden_size],
dtype=self._dtype,
is_bias=True,
)
else:
self._forget_bias = fill_constant(
[1], dtype=dtype, value=forget_bias
)
self._forget_bias.stop_gradient = False
self._weight = self.create_parameter(
attr=self._param_attr,
shape=[
self._input_size + self._hidden_size,
4 * self._hidden_size,
],
dtype=dtype,
)
self._bias = self.create_parameter(
attr=self._bias_attr,
shape=[4 * self._hidden_size],
dtype=dtype,
is_bias=True,
)
def forward(self, input, pre_hidden, pre_cell):
if self._use_cudnn_impl:
igates = paddle.matmul(input, y=self._weight_ih, transpose_y=True)
igates = paddle.add(igates, self._bias_ih)
hgates = paddle.matmul(
pre_hidden, self._weight_hh, transpose_y=True
)
hgates = paddle.add(hgates, self._bias_hh)
chunked_igates = split(igates, num_or_sections=4, dim=1)
chunked_hgates = split(hgates, num_or_sections=4, dim=1)
ingate = paddle.add(chunked_igates[0], chunked_hgates[0])
ingate = self._gate_activation(ingate)
forgetgate = paddle.add(chunked_igates[1], chunked_hgates[1])
forgetgate = self._gate_activation(forgetgate)
cellgate = paddle.add(chunked_igates[2], chunked_hgates[2])
cellgate = self._activation(cellgate)
outgate = paddle.add(chunked_igates[3], chunked_hgates[3])
outgate = self._gate_activation(outgate)
new_cell = (forgetgate * pre_cell) + (ingate * cellgate)
new_hidden = outgate * self._activation(new_cell)
else:
concat_input_hidden = concat([input, pre_hidden], 1)
gate_input = paddle.matmul(x=concat_input_hidden, y=self._weight)
gate_input = paddle.add(gate_input, self._bias)
i, j, f, o = split(gate_input, num_or_sections=4, dim=-1)
new_cell = paddle.add(
paddle.multiply(
pre_cell,
self._gate_activation(paddle.add(f, self._forget_bias)),
),
paddle.multiply(
paddle.nn.functional.sigmoid(i), paddle.tanh(j)
),
)
new_hidden = self._activation(new_cell) * self._gate_activation(o)
return new_hidden, new_cell
class GRUCell(Layer):
r"""
GRU implementation using basic operators.
There are two GRUCell version, the default one is compatible with CUDNN GRU implementation.
The algorithm can be described as the equations below.
.. math::
u_t & = sigmoid(W_{ux} x_{t} + b_ux + W_{uh} h_{t-1} + b_uh)
r_t & = sigmoid(W_{rx} x_{t} + b_rx + W_{rh} h_{t-1} + b_rh)
\\tilde{h_{t}} & = tanh(W_{cx} x_{t} + b_cx + r_t \\odot (W_{ch} h_{t-1} + b_ch))
h_t & = u_t h_{t-1} + (1-u_t) \\tilde{h_{t}}
The other LSTMCell version is compatible with the BasicGRUUnit used in static graph.
The algorithm can be described as the equations below.
.. math::
u_t & = sigmoid(W_{ux} x_{t} + W_{uh} h_{t-1} + b_u)
r_t & = sigmoid(W_{rx} x_{t} + W_{rh} h_{t-1} + b_r)
\\tilde{h_{t}} & = tanh(W_{cx} x_{t} + W_{ch} \\odot(r_t, h_{t-1}) + b_m)
h_t & = u_t h_{t-1} + (1-u_t) \\tilde{h_{t}}
Args:
hidden_size (integer): The hidden size used in the Cell.
input_size (integer): The input size used in the Cell.
param_attr(ParamAttr|None): The parameter attribute for the learnable
weight matrix. Note:
If it is set to None or one attribute of ParamAttr, GRUCell will
create ParamAttr as param_attr. If the Initializer of the param_attr
is not set, the parameter is initialized with Xavier. Default: None.
bias_attr (ParamAttr|None): The parameter attribute for the bias
of GRUCell.
If it is set to None or one attribute of ParamAttr, GRUCell will
create ParamAttr as bias_attr. If the Initializer of the bias_attr
is not set, the bias is initialized zero. Default: None.
gate_activation (function|None): The activation function for gates (actGate).
Default: 'fluid.layers.sigmoid'
activation (function|None): The activation function for cell (actNode).
Default: 'fluid.layers.tanh'
use_cudnn_impl(bool|True): whether to use CUDNN compatible LSTMCell
dtype(string): data type used in this cell
Returns:
None
Examples:
.. code-block:: python
from paddle import fluid
import paddle.fluid.core as core
from paddle.fluid.dygraph import GRUCell
import numpy as np
batch_size = 64
input_size = 128
hidden_size = 256
step_input_np = np.random.uniform(-0.1, 0.1, (
batch_size, input_size)).astype('float64')
pre_hidden_np = np.random.uniform(-0.1, 0.1, (
batch_size, hidden_size)).astype('float64')
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
else:
place = core.CPUPlace()
with fluid.dygraph.guard(place):
cudnn_gru = GRUCell(hidden_size, input_size)
step_input_var = fluid.dygraph.to_variable(step_input_np)
pre_hidden_var = fluid.dygraph.to_variable(pre_hidden_np)
"""
def __init__(
self,
hidden_size,
input_size,
param_attr=None,
bias_attr=None,
gate_activation=None,
activation=None,
use_cudnn_impl=True,
dtype='float64',
):
super().__init__()
self._hidden_size = hidden_size
self._input_size = input_size
self._param_attr = param_attr
self._bias_attr = bias_attr
self._dtype = dtype
self._gate_activation = gate_activation or paddle.nn.functional.sigmoid
self._activation = activation or paddle.tanh
self._use_cudnn_impl = use_cudnn_impl
if self._use_cudnn_impl:
if (
self._param_attr is not None
and self._param_attr.name is not None
):
weight_ih_param_attr = copy.deepcopy(self._param_attr)
weight_hh_param_attr = copy.deepcopy(self._param_attr)
weight_ih_param_attr.name += "_weight_ih"
weight_hh_param_attr.name += "_weight_hh"
else:
weight_ih_param_attr = self._param_attr
weight_hh_param_attr = self._param_attr
if self._bias_attr is not None and self._bias_attr.name is not None:
bias_ih_param_attr = copy.deepcopy(self._bias_attr)
bias_hh_param_attr = copy.deepcopy(self._bias_attr)
bias_ih_param_attr.name += "_bias_ih"
bias_hh_param_attr.name += "_bias_hh"
else:
bias_ih_param_attr = self._bias_attr
bias_hh_param_attr = self._bias_attr
self._weight_ih = self.create_parameter(
attr=weight_ih_param_attr,
shape=[3 * self._hidden_size, self._input_size],
dtype=self._dtype,
)
self._weight_hh = self.create_parameter(
attr=weight_hh_param_attr,
shape=[3 * self._hidden_size, self._hidden_size],
dtype=self._dtype,
)
self._bias_ih = self.create_parameter(
attr=bias_ih_param_attr,
shape=[3 * self._hidden_size],
dtype=self._dtype,
is_bias=True,
)
self._bias_hh = self.create_parameter(
attr=bias_hh_param_attr,
shape=[3 * self._hidden_size],
dtype=self._dtype,
is_bias=True,
)
else:
if (
self._param_attr is not None
and self._param_attr.name is not None
):
gate_weight_param_attr = copy.deepcopy(self._param_attr)
candidate_weight_param_attr = copy.deepcopy(self._param_attr)
gate_weight_param_attr.name += "_gate_weight"
candidate_weight_param_attr.name += "_candidate_weight"
else:
gate_weight_param_attr = self._param_attr
candidate_weight_param_attr = self._param_attr
if self._bias_attr is not None and self._bias_attr.name is not None:
gate_bias_param_attr = copy.deepcopy(self._bias_attr)
candidate_bias_param_attr = copy.deepcopy(self._bias_attr)
gate_bias_param_attr.name += "_gate_bias"
candidate_bias_param_attr.name += "_candidate_bias"
else:
gate_bias_param_attr = self._bias_attr
candidate_bias_param_attr = self._bias_attr
self._gate_weight = self.create_parameter(
attr=gate_weight_param_attr,
shape=[
self._input_size + self._hidden_size,
2 * self._hidden_size,
],
dtype=dtype,
)
self._candidate_weight = self.create_parameter(
attr=candidate_weight_param_attr,
shape=[self._input_size + self._hidden_size, self._hidden_size],
dtype=dtype,
)
self._gate_bias = self.create_parameter(
attr=gate_bias_param_attr,
shape=[2 * self._hidden_size],
dtype=dtype,
is_bias=True,
)
self._candidate_bias = self.create_parameter(
attr=candidate_bias_param_attr,
shape=[self._hidden_size],
dtype=dtype,
is_bias=True,
)
def forward(self, input, pre_hidden):
if self._use_cudnn_impl:
igates = paddle.matmul(input, y=self._weight_ih, transpose_y=True)
igates = paddle.add(igates, self._bias_ih)
hgates = paddle.matmul(
pre_hidden, self._weight_hh, transpose_y=True
)
hgates = paddle.add(hgates, self._bias_hh)
chunked_igates = split(igates, num_or_sections=3, dim=1)
chunked_hgates = split(hgates, num_or_sections=3, dim=1)
reset_gate = paddle.add(chunked_igates[0], chunked_hgates[0])
reset_gate = self._gate_activation(reset_gate)
input_gate = paddle.add(chunked_igates[1], chunked_hgates[1])
input_gate = self._gate_activation(input_gate)
_temp = reset_gate * chunked_hgates[2]
new_gate = paddle.add(chunked_igates[2], _temp)
new_gate = self._activation(new_gate)
new_hidden = (pre_hidden - new_gate) * input_gate + new_gate
else:
concat_input_hidden = concat([input, pre_hidden], 1)
gate_input = paddle.matmul(
x=concat_input_hidden, y=self._gate_weight
)
gate_input = paddle.add(gate_input, self._gate_bias)
gate_input = self._gate_activation(gate_input)
r, u = split(gate_input, num_or_sections=2, dim=1)
r_hidden = r * pre_hidden
candidate = paddle.matmul(
concat([input, r_hidden], 1), self._candidate_weight
)
candidate = paddle.add(candidate, self._candidate_bias)
c = self._activation(candidate)
new_hidden = u * pre_hidden + (1 - u) * c
return new_hidden
python/paddle/fluid/tests/unittests/test_cudnn_grucell.py 已删除 100644 → 0
浏览文件 @ 2bba3e18
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# 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.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid.dygraph import GRUCell
np.random.seed = 123
def sigmoid(x):
return 1.0 / (1.0 + np.exp(-x))
def tanh(x):
return 2.0 * sigmoid(2.0 * x) - 1.0
def cudnn_step(
step_input_np, pre_hidden_np, weight_ih, bias_ih, weight_hh, bias_hh
):
igates = np.matmul(step_input_np, weight_ih.transpose(1, 0))
igates += bias_ih
hgates = np.matmul(pre_hidden_np, weight_hh.transpose(1, 0))
hgates += bias_hh
chunked_igates = np.split(igates, indices_or_sections=3, axis=1)
chunked_hgates = np.split(hgates, indices_or_sections=3, axis=1)
reset_gate = chunked_igates[0] + chunked_hgates[0]
reset_gate = sigmoid(reset_gate)
input_gate = chunked_igates[1] + chunked_hgates[1]
input_gate = sigmoid(input_gate)
_temp = reset_gate * chunked_hgates[2]
new_gate = chunked_igates[2] + _temp
new_gate = tanh(new_gate)
new_hidden = (pre_hidden_np - new_gate) * input_gate + new_gate
return new_hidden
def non_cudnn_step(
step_in, pre_hidden, gate_w, gate_b, candidate_w, candidate_b
):
concat_1 = np.concatenate([step_in, pre_hidden], 1)
gate_input = np.matmul(concat_1, gate_w)
gate_input += gate_b
gate_input = sigmoid(gate_input)
r, u = np.split(gate_input, indices_or_sections=2, axis=1)
r_hidden = r * pre_hidden
candidate = np.matmul(np.concatenate([step_in, r_hidden], 1), candidate_w)
candidate += candidate_b
c = tanh(candidate)
new_hidden = u * pre_hidden + (1 - u) * c
return new_hidden
class TestCudnnGRU(unittest.TestCase):
def setUp(self):
self.input_size = 100
self.hidden_size = 200
self.batch_size = 64
def test_run(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
else:
place = core.CPUPlace()
with fluid.dygraph.guard(place):
param_attr = fluid.ParamAttr(name="param_attr")
bias_attr = fluid.ParamAttr(name="bias_attr")
named_cudnn_gru = GRUCell(
self.hidden_size, self.input_size, param_attr, bias_attr
)
cudnn_gru = GRUCell(self.hidden_size, self.input_size)
param_list = cudnn_gru.state_dict()
named_param_list = named_cudnn_gru.state_dict()
# process weight and bias
weight_ih_name = "_weight_ih"
bias_ih_name = "_bias_ih"
weight_hh_name = "_weight_hh"
bias_hh_name = "_bias_hh"
weight_ih = param_list[weight_ih_name].numpy()
weight_ih = np.random.uniform(
-0.1, 0.1, size=weight_ih.shape
).astype('float64')
param_list[weight_ih_name].set_value(weight_ih)
named_param_list[weight_ih_name].set_value(weight_ih)
bias_ih = param_list[bias_ih_name].numpy()
bias_ih = np.random.uniform(-0.1, 0.1, size=bias_ih.shape).astype(
'float64'
)
param_list[bias_ih_name].set_value(bias_ih)
named_param_list[bias_ih_name].set_value(bias_ih)
weight_hh = param_list[weight_hh_name].numpy()
weight_hh = np.random.uniform(
-0.1, 0.1, size=weight_hh.shape
).astype('float64')
param_list[weight_hh_name].set_value(weight_hh)
named_param_list[weight_hh_name].set_value(weight_hh)
bias_hh = param_list[bias_hh_name].numpy()
bias_hh = np.random.uniform(-0.1, 0.1, size=bias_hh.shape).astype(
'float64'
)
param_list[bias_hh_name].set_value(bias_hh)
named_param_list[bias_hh_name].set_value(bias_hh)
step_input_np = np.random.uniform(
-0.1, 0.1, (self.batch_size, self.input_size)
).astype('float64')
pre_hidden_np = np.random.uniform(
-0.1, 0.1, (self.batch_size, self.hidden_size)
).astype('float64')
step_input_var = fluid.dygraph.to_variable(step_input_np)
pre_hidden_var = fluid.dygraph.to_variable(pre_hidden_np)
api_out = cudnn_gru(step_input_var, pre_hidden_var)
named_api_out = named_cudnn_gru(step_input_var, pre_hidden_var)
np_out = cudnn_step(
step_input_np, pre_hidden_np, weight_ih, bias_ih, weight_hh, bias_hh
)
np.testing.assert_allclose(api_out.numpy(), np_out, rtol=1e-05, atol=0)
np.testing.assert_allclose(
named_api_out.numpy(), np_out, rtol=1e-05, atol=0
)
class TestNonCudnnGRU(unittest.TestCase):
def setUp(self):
self.input_size = 100
self.hidden_size = 200
self.batch_size = 64
def test_run(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
else:
place = core.CPUPlace()
with fluid.dygraph.guard(place):
param_attr = fluid.ParamAttr(name="param_attr")
bias_attr = fluid.ParamAttr(name="bias_attr")
named_non_cudnn_gru = GRUCell(
self.hidden_size,
self.input_size,
param_attr,
bias_attr,
use_cudnn_impl=False,
)
non_cudnn_gru = GRUCell(
self.hidden_size, self.input_size, use_cudnn_impl=False
)
param_list = non_cudnn_gru.state_dict()
named_param_list = named_non_cudnn_gru.state_dict()
# process weight and bias
gate_w_name = "_gate_weight"
gate_b_name = "_gate_bias"
candidate_w_name = "_candidate_weight"
candidate_b_name = "_candidate_bias"
gate_w = param_list[gate_w_name].numpy()
gate_w = np.random.uniform(-0.1, 0.1, size=gate_w.shape).astype(
'float64'
)
param_list[gate_w_name].set_value(gate_w)
named_param_list[gate_w_name].set_value(gate_w)
gate_b = param_list[gate_b_name].numpy()
gate_b = np.random.uniform(-0.1, 0.1, size=gate_b.shape).astype(
'float64'
)
param_list[gate_b_name].set_value(gate_b)
named_param_list[gate_b_name].set_value(gate_b)
candidate_w = param_list[candidate_w_name].numpy()
candidate_w = np.random.uniform(
-0.1, 0.1, size=candidate_w.shape
).astype('float64')
param_list[candidate_w_name].set_value(candidate_w)
named_param_list[candidate_w_name].set_value(candidate_w)
candidate_b = param_list[candidate_b_name].numpy()
candidate_b = np.random.uniform(
-0.1, 0.1, size=candidate_b.shape
).astype('float64')
param_list[candidate_b_name].set_value(candidate_b)
named_param_list[candidate_b_name].set_value(candidate_b)
step_input_np = np.random.uniform(
-0.1, 0.1, (self.batch_size, self.input_size)
).astype('float64')
pre_hidden_np = np.random.uniform(
-0.1, 0.1, (self.batch_size, self.hidden_size)
).astype('float64')
step_input_var = fluid.dygraph.to_variable(step_input_np)
pre_hidden_var = fluid.dygraph.to_variable(pre_hidden_np)
api_out = non_cudnn_gru(step_input_var, pre_hidden_var)
named_api_out = named_non_cudnn_gru(step_input_var, pre_hidden_var)
np_out = non_cudnn_step(
step_input_np,
pre_hidden_np,
gate_w,
gate_b,
candidate_w,
candidate_b,
)
np.testing.assert_allclose(api_out.numpy(), np_out, rtol=1e-05, atol=0)
np.testing.assert_allclose(
named_api_out.numpy(), np_out, rtol=1e-05, atol=0
)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_cudnn_lstmcell.py 已删除 100644 → 0
浏览文件 @ 2bba3e18
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# 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.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid.dygraph import LSTMCell
np.random.seed = 123
def sigmoid(x):
return 1.0 / (1.0 + np.exp(-x))
def tanh(x):
return 2.0 * sigmoid(2.0 * x) - 1.0
def non_cudnn_step(
step_in, pre_hidden, pre_cell, gate_w, gate_b, forget_bias=1.0
):
concat_1 = np.concatenate([step_in, pre_hidden], 1)
gate_input = np.matmul(concat_1, gate_w)
gate_input += gate_b
i, j, f, o = np.split(gate_input, indices_or_sections=4, axis=1)
new_cell = pre_cell * sigmoid(f + forget_bias) + sigmoid(i) * tanh(j)
new_hidden = tanh(new_cell) * sigmoid(o)
return new_hidden, new_cell
def cudnn_step(
step_input_np,
pre_hidden_np,
pre_cell_np,
weight_ih,
bias_ih,
weight_hh,
bias_hh,
):
igates = np.matmul(step_input_np, weight_ih.transpose(1, 0))
igates = igates + bias_ih
hgates = np.matmul(pre_hidden_np, weight_hh.transpose(1, 0))
hgates = hgates + bias_hh
chunked_igates = np.split(igates, indices_or_sections=4, axis=1)
chunked_hgates = np.split(hgates, indices_or_sections=4, axis=1)
ingate = chunked_igates[0] + chunked_hgates[0]
ingate = sigmoid(ingate)
forgetgate = chunked_igates[1] + chunked_hgates[1]
forgetgate = sigmoid(forgetgate)
cellgate = chunked_igates[2] + chunked_hgates[2]
cellgate = tanh(cellgate)
outgate = chunked_igates[3] + chunked_hgates[3]
outgate = sigmoid(outgate)
new_cell = (forgetgate * pre_cell_np) + (ingate * cellgate)
new_hidden = outgate * tanh(new_cell)
return new_hidden, new_cell
class TestCudnnLSTM(unittest.TestCase):
def setUp(self):
self.input_size = 100
self.hidden_size = 200
self.batch_size = 128
def test_run(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
else:
place = core.CPUPlace()
with fluid.dygraph.guard(place):
param_attr = fluid.ParamAttr(name="param_attr")
bias_attr = fluid.ParamAttr(name="bias_attr")
named_cudnn_lstm = LSTMCell(
self.hidden_size, self.input_size, param_attr, bias_attr
)
cudnn_lstm = LSTMCell(self.hidden_size, self.input_size)
param_list = cudnn_lstm.state_dict()
named_param_list = named_cudnn_lstm.state_dict()
# process weight and bias
weight_ih_name = "_weight_ih"
bias_ih_name = "_bias_ih"
weight_hh_name = "_weight_hh"
bias_hh_name = "_bias_hh"
weight_ih = param_list[weight_ih_name].numpy()
weight_ih = np.random.uniform(
-0.1, 0.1, size=weight_ih.shape
).astype('float64')
param_list[weight_ih_name].set_value(weight_ih)
named_param_list[weight_ih_name].set_value(weight_ih)
bias_ih = param_list[bias_ih_name].numpy()
bias_ih = np.random.uniform(-0.1, 0.1, size=bias_ih.shape).astype(
'float64'
)
param_list[bias_ih_name].set_value(bias_ih)
named_param_list[bias_ih_name].set_value(bias_ih)
weight_hh = param_list[weight_hh_name].numpy()
weight_hh = np.random.uniform(
-0.1, 0.1, size=weight_hh.shape
).astype('float64')
param_list[weight_hh_name].set_value(weight_hh)
named_param_list[weight_hh_name].set_value(weight_hh)
bias_hh = param_list[bias_hh_name].numpy()
bias_hh = np.random.uniform(-0.1, 0.1, size=bias_hh.shape).astype(
'float64'
)
param_list[bias_hh_name].set_value(bias_hh)
named_param_list[bias_hh_name].set_value(bias_hh)
step_input_np = np.random.uniform(
-0.1, 0.1, (self.batch_size, self.input_size)
).astype('float64')
pre_hidden_np = np.random.uniform(
-0.1, 0.1, (self.batch_size, self.hidden_size)
).astype('float64')
pre_cell_np = np.random.uniform(
-0.1, 0.1, (self.batch_size, self.hidden_size)
).astype('float64')
step_input_var = fluid.dygraph.to_variable(step_input_np)
pre_hidden_var = fluid.dygraph.to_variable(pre_hidden_np)
pre_cell_var = fluid.dygraph.to_variable(pre_cell_np)
api_out = cudnn_lstm(step_input_var, pre_hidden_var, pre_cell_var)
named_api_out = named_cudnn_lstm(
step_input_var, pre_hidden_var, pre_cell_var
)
api_hidden_out = api_out[0]
api_cell_out = api_out[1]
named_api_hidden_out = named_api_out[0]
named_api_cell_out = named_api_out[1]
np_hidden_out, np_cell_out = cudnn_step(
step_input_np,
pre_hidden_np,
pre_cell_np,
weight_ih,
bias_ih,
weight_hh,
bias_hh,
)
np.testing.assert_allclose(
api_hidden_out.numpy(), np_hidden_out, rtol=1e-05, atol=0
)
np.testing.assert_allclose(
api_cell_out.numpy(), np_cell_out, rtol=1e-05, atol=0
)
np.testing.assert_allclose(
named_api_hidden_out.numpy(), np_hidden_out, rtol=1e-05, atol=0
)
np.testing.assert_allclose(
named_api_cell_out.numpy(), np_cell_out, rtol=1e-05, atol=0
)
class TestNonCudnnLSTM(unittest.TestCase):
def setUp(self):
self.input_size = 100
self.hidden_size = 200
self.batch_size = 128
def test_run(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
else:
place = core.CPUPlace()
with fluid.dygraph.guard(place):
param_attr = fluid.ParamAttr(name="param_attr")
bias_attr = fluid.ParamAttr(name="bias_attr")
named_cudnn_lstm = LSTMCell(
self.hidden_size,
self.input_size,
param_attr,
bias_attr,
use_cudnn_impl=False,
)
cudnn_lstm = LSTMCell(
self.hidden_size, self.input_size, use_cudnn_impl=False
)
param_list = cudnn_lstm.state_dict()
named_param_list = named_cudnn_lstm.state_dict()
# process weight and bias
gate_w_name = "_weight"
gate_b_name = "_bias"
gate_w = param_list[gate_w_name].numpy()
gate_w = np.random.uniform(-0.1, 0.1, size=gate_w.shape).astype(
'float64'
)
param_list[gate_w_name].set_value(gate_w)
named_param_list[gate_w_name].set_value(gate_w)
gate_b = param_list[gate_b_name].numpy()
gate_b = np.random.uniform(-0.1, 0.1, size=gate_b.shape).astype(
'float64'
)
param_list[gate_b_name].set_value(gate_b)
named_param_list[gate_b_name].set_value(gate_b)
step_input_np = np.random.uniform(
-0.1, 0.1, (self.batch_size, self.input_size)
).astype('float64')
pre_hidden_np = np.random.uniform(
-0.1, 0.1, (self.batch_size, self.hidden_size)
).astype('float64')
pre_cell_np = np.random.uniform(
-0.1, 0.1, (self.batch_size, self.hidden_size)
).astype('float64')
step_input_var = fluid.dygraph.to_variable(step_input_np)
pre_hidden_var = fluid.dygraph.to_variable(pre_hidden_np)
pre_cell_var = fluid.dygraph.to_variable(pre_cell_np)
api_out = cudnn_lstm(step_input_var, pre_hidden_var, pre_cell_var)
named_api_out = named_cudnn_lstm(
step_input_var, pre_hidden_var, pre_cell_var
)
api_hidden_out = api_out[0]
api_cell_out = api_out[1]
named_api_hidden_out = named_api_out[0]
named_api_cell_out = named_api_out[1]
np_hidden_out, np_cell_out = non_cudnn_step(
step_input_np, pre_hidden_np, pre_cell_np, gate_w, gate_b
)
np.testing.assert_allclose(
api_hidden_out.numpy(), np_hidden_out, rtol=1e-05, atol=0
)
np.testing.assert_allclose(
api_cell_out.numpy(), np_cell_out, rtol=1e-05, atol=0
)
np.testing.assert_allclose(
named_api_hidden_out.numpy(), np_hidden_out, rtol=1e-05, atol=0
)
np.testing.assert_allclose(
named_api_cell_out.numpy(), np_cell_out, rtol=1e-05, atol=0
)
if __name__ == '__main__':
unittest.main()
tools/parallel_UT_rule.py
浏览文件 @ 93acef3b
......@@ -1103,7 +1103,6 @@ FOURTH_HIGH_PARALLEL_JOB_NEW = [
'test_trt_reduce_sum_op',
'data_type_transform_test',
'test_gru_rnn_op',
'test_cudnn_grucell',
'test_argsort_op',
'test_batch_norm_op',
'test_inplace',
......@@ -1190,7 +1189,6 @@ FOURTH_HIGH_PARALLEL_JOB_NEW = [
'test_nn_functional_embedding_dygraph',
'test_reshape_op',
'test_maxout_op',
'test_cudnn_lstmcell',
'test_sigmoid_focal_loss',
'test_manual_seed',
'test_lrn_op',
......@@ -2727,7 +2725,6 @@ TWO_PARALLEL_JOB = [
'test_polygon_box_transform',
'test_sequence_pad_op',
'test_sequence_expand',
'test_cudnn_grucell',
'test_pool2d_bf16_mkldnn_op',
'test_bilinear_api',
'test_parallel_executor_inference_feed_partial_data',
......@@ -2783,7 +2780,6 @@ TWO_PARALLEL_JOB = [
'test_cos_sim_op',
'test_sequence_enumerate_op',
'test_sequence_concat',
'test_cudnn_lstmcell',
'test_data_norm_op',
'test_decoupled_py_reader_data_check',
'test_deformable_conv_v1_op',
......
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