wehub-resource-sync
94 lines
2.9 KiB
Python
94 lines
2.9 KiB
Python
# Copyright (c) 2023 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 paddle
|
|
from paddle import nn
|
|
|
|
|
|
class Net_Cond(nn.Layer):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
def forward(self):
|
|
cond_input_x = paddle.ones(shape=[32, 32], dtype="float32")
|
|
cond_input_y = paddle.zeros(shape=[32, 32], dtype="float32")
|
|
if paddle.shape(cond_input_x)[0] <= paddle.shape(cond_input_y)[0]:
|
|
cond_input_y = paddle.matmul(
|
|
cond_input_x,
|
|
cond_input_x.T,
|
|
)
|
|
return cond_input_y.mean()
|
|
|
|
|
|
class Net_While(nn.Layer):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
def forward(self):
|
|
while_input_x = paddle.ones(shape=[64, 32], dtype="float32")
|
|
while_input_y = paddle.zeros(shape=[32, 32], dtype="float32")
|
|
while paddle.shape(while_input_x)[1] >= paddle.shape(while_input_y)[1]:
|
|
while_input_y = paddle.matmul(
|
|
while_input_x,
|
|
while_input_x.T,
|
|
)
|
|
return while_input_y.mean()
|
|
|
|
|
|
class Net_Sub_Block_FP32(nn.Layer):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
def forward(self):
|
|
cond_input_x = paddle.ones(shape=[32, 32], dtype="float32")
|
|
cond_input_y = paddle.zeros(shape=[32, 32], dtype="float32")
|
|
if paddle.shape(cond_input_x)[0] <= paddle.shape(cond_input_y)[0]:
|
|
cond_input_y = paddle.log(cond_input_x)
|
|
return cond_input_y.mean()
|
|
|
|
|
|
class TestD2SAmpWithControlFlowOp(unittest.TestCase):
|
|
def test_cond_op(self):
|
|
model = Net_Cond()
|
|
model = paddle.jit.to_static(model, full_graph=True)
|
|
model = paddle.amp.decorate(
|
|
models=model, level='O2', save_dtype="float32"
|
|
)
|
|
with paddle.amp.auto_cast(level='O2'):
|
|
model()
|
|
|
|
def test_while_op(self):
|
|
model = Net_While()
|
|
model = paddle.jit.to_static(model, full_graph=True)
|
|
model = paddle.amp.decorate(
|
|
models=model, level='O2', save_dtype="float32"
|
|
)
|
|
with paddle.amp.auto_cast(level='O2'):
|
|
model()
|
|
|
|
def test_sub_block_fp32_op(self):
|
|
model = Net_Sub_Block_FP32()
|
|
model = paddle.jit.to_static(model, full_graph=True)
|
|
model = paddle.amp.decorate(
|
|
models=model, level='O2', save_dtype="float32"
|
|
)
|
|
with paddle.amp.auto_cast(level='O2'):
|
|
model()
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|