wehub-resource-sync
269 lines
7.4 KiB
Python
269 lines
7.4 KiB
Python
# 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
|
|
from dygraph_to_static_utils import (
|
|
Dy2StTestBase,
|
|
enable_to_static_guard,
|
|
)
|
|
|
|
import paddle
|
|
from paddle import base
|
|
|
|
PLACE = base.CUDAPlace(0) if base.is_compiled_with_cuda() else base.CPUPlace()
|
|
|
|
|
|
class SubNetWithDict(paddle.nn.Layer):
|
|
def __init__(self, hidden_size=16, output_size=16):
|
|
super().__init__()
|
|
|
|
init_weight = lambda x: paddle.ParamAttr(
|
|
initializer=paddle.nn.initializer.Constant(x)
|
|
)
|
|
|
|
self.q_fc = paddle.nn.Linear(
|
|
in_features=hidden_size,
|
|
out_features=output_size,
|
|
bias_attr=False,
|
|
weight_attr=init_weight(0.6),
|
|
)
|
|
self.k_fc = paddle.nn.Linear(
|
|
in_features=hidden_size,
|
|
out_features=output_size,
|
|
bias_attr=False,
|
|
weight_attr=init_weight(0.5),
|
|
)
|
|
self.v_fc = paddle.nn.Linear(
|
|
in_features=hidden_size,
|
|
out_features=output_size,
|
|
bias_attr=False,
|
|
weight_attr=init_weight(0.2),
|
|
)
|
|
|
|
def forward(self, input, cache=None):
|
|
input = paddle.to_tensor(input)
|
|
|
|
q = self.q_fc(input)
|
|
k = self.k_fc(input)
|
|
v = self.v_fc(input)
|
|
|
|
if cache is not None:
|
|
cache_k, cache_v = cache["k"], cache["v"]
|
|
k = 0.1 * cache_k + k
|
|
v = 0.2 * cache_v + v
|
|
cache["k"], cache["v"] = k, v
|
|
|
|
weight = paddle.matmul(x=q, y=k, transpose_y=True)
|
|
weight = paddle.nn.functional.softmax(weight)
|
|
out = paddle.matmul(weight, v)
|
|
|
|
return out
|
|
|
|
|
|
class MainNetWithDict(paddle.nn.Layer):
|
|
def __init__(self, batch_size=64, hidden_size=16, output_size=16):
|
|
super().__init__()
|
|
self.batch_size = batch_size
|
|
self.hidden_size = hidden_size
|
|
self.output_size = output_size
|
|
self.sub_net = SubNetWithDict(hidden_size, output_size)
|
|
|
|
def forward(self, input, max_len=4):
|
|
input = paddle.to_tensor(input)
|
|
cache = {
|
|
"k": paddle.tensor.fill_constant(
|
|
shape=[self.batch_size, self.output_size],
|
|
dtype='float32',
|
|
value=0,
|
|
),
|
|
"v": paddle.tensor.fill_constant(
|
|
shape=[self.batch_size, self.output_size],
|
|
dtype='float32',
|
|
value=0,
|
|
),
|
|
}
|
|
# TODO(Aurelius84): The following code will be converted into:
|
|
# max_len = paddle.static.nn.cond(paddle.shape(input)[0] != max_len,
|
|
# lambda: paddle.shape(input)[0], lambda: max_len)
|
|
# But max_len should be wrapped into tensor, which is not supported.
|
|
|
|
# Comment out this line of code for now.
|
|
# max_len = input.shape[0] if input.shape[0] != max_len else max_len
|
|
out = input
|
|
for i in range(max_len):
|
|
out = self.sub_net(out, cache)
|
|
cache = update_cache(cache)
|
|
return out
|
|
|
|
|
|
# Test to call function defined outside of class.
|
|
def update_cache(cache):
|
|
for k, val in cache.items():
|
|
cache[k] = paddle.nn.functional.softmax(val)
|
|
|
|
return cache
|
|
|
|
|
|
class TestNetWithDict(Dy2StTestBase):
|
|
def setUp(self):
|
|
self.x = np.random.random([10, 16]).astype('float32')
|
|
self.batch_size = self.x.shape[0]
|
|
|
|
def _run_static(self):
|
|
with enable_to_static_guard(True):
|
|
return self.train()
|
|
|
|
def _run_dygraph(self):
|
|
with enable_to_static_guard(False):
|
|
return self.train()
|
|
|
|
def train(self):
|
|
with base.dygraph.guard(PLACE):
|
|
net = paddle.jit.to_static(
|
|
MainNetWithDict(batch_size=self.batch_size)
|
|
)
|
|
ret = net(self.x)
|
|
return ret.numpy()
|
|
|
|
def test_ast_to_func(self):
|
|
np.testing.assert_allclose(self._run_dygraph(), self._run_static())
|
|
|
|
|
|
# Tests for dict pop
|
|
def test_dict_pop(x):
|
|
x = paddle.to_tensor(x)
|
|
dict_a = {"red": 0, "green": 1, "blue": 2}
|
|
|
|
m = dict_a.pop("red")
|
|
n = dict_a.pop("black", 3)
|
|
|
|
out = x + m + n
|
|
return out
|
|
|
|
|
|
def test_dict_pop_2(x):
|
|
x = paddle.to_tensor(x)
|
|
dict_a = {"red": x, "green": x + 1, "blue": x + 3}
|
|
|
|
m = dict_a.pop("red")
|
|
n = dict_a.pop("black", 3)
|
|
|
|
out = x + m + n
|
|
return out
|
|
|
|
|
|
class TestDictPop(Dy2StTestBase):
|
|
def setUp(self):
|
|
self.input = np.random.random(3).astype('int32')
|
|
self.place = (
|
|
paddle.CUDAPlace(0)
|
|
if paddle.is_compiled_with_cuda()
|
|
else paddle.CPUPlace()
|
|
)
|
|
|
|
def get_test_func(self):
|
|
return test_dict_pop
|
|
|
|
def _run_static(self):
|
|
static_fn = paddle.jit.to_static(self.get_test_func())
|
|
return static_fn(self.input)
|
|
|
|
def _run_dygraph(self):
|
|
fn = self.get_test_func()
|
|
return fn(self.input)
|
|
|
|
def test_transformed_result(self):
|
|
dygraph_res = self._run_dygraph()
|
|
static_res = self._run_static()
|
|
np.testing.assert_allclose(
|
|
dygraph_res,
|
|
static_res,
|
|
rtol=1e-05,
|
|
err_msg=f'dygraph result is {dygraph_res}\nstatic result is {static_res}',
|
|
)
|
|
|
|
|
|
class TestDictPop2(TestDictPop):
|
|
def get_test_func(self):
|
|
return test_dict_pop_2
|
|
|
|
|
|
class NetWithDictPop(paddle.nn.Layer):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
def forward(self, x, **kwargs):
|
|
x = paddle.to_tensor(x)
|
|
y = kwargs.pop('y', None)
|
|
if y:
|
|
y = paddle.to_tensor(x)
|
|
x += y
|
|
|
|
x.mean()
|
|
return x
|
|
|
|
|
|
class TestDictPop3(TestNetWithDict):
|
|
def setUp(self):
|
|
self.x = np.array([2, 2]).astype('float32')
|
|
|
|
def train(self):
|
|
with base.dygraph.guard(PLACE):
|
|
net = paddle.jit.to_static(NetWithDictPop())
|
|
ret = net(z=0, x=self.x, y=True)
|
|
return ret.numpy()
|
|
|
|
def test_ast_to_func(self):
|
|
np.testing.assert_allclose(self._run_dygraph(), self._run_static())
|
|
|
|
|
|
class TestDictCmpInFor(Dy2StTestBase):
|
|
def test_with_for(self):
|
|
def func():
|
|
pos = [1, 3]
|
|
neg = [-1, -3]
|
|
dict_val = {'minus': 0}
|
|
# test `zip` with `for`
|
|
for x, y in zip(pos, neg):
|
|
val = x - y
|
|
dict_val.update(
|
|
{k: val + dict_val[k] for k, v in dict_val.items()}
|
|
)
|
|
|
|
return dict_val
|
|
|
|
self.assertEqual(paddle.jit.to_static(func)()['minus'], 8)
|
|
|
|
def test_with_for_enumerate(self):
|
|
def func():
|
|
pos = [1, 3]
|
|
neg = [-1, -3]
|
|
dict_val = {'minus': 0}
|
|
# test `zip` with `for`
|
|
for i, (x, y) in enumerate(zip(pos, neg)):
|
|
val = x - y
|
|
dict_val.update(
|
|
{k: val + dict_val[k] for k, v in dict_val.items()}
|
|
)
|
|
|
|
return dict_val
|
|
|
|
self.assertEqual(paddle.jit.to_static(func)()['minus'], 8)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|