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[NPU] Support npu op log, log_grad, sqrt, sqrt_grad, square, tanh and…
… tanh_grad (#31600)
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python/paddle/fluid/tests/unittests/npu/test_log_op_npu.py
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# Copyright (c) 2021 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. | ||
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from __future__ import print_function | ||
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import numpy as np | ||
import unittest | ||
import sys | ||
sys.path.append("..") | ||
from op_test import OpTest | ||
import paddle | ||
import paddle.fluid as fluid | ||
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paddle.enable_static() | ||
SEED = 2021 | ||
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@unittest.skipIf(not paddle.is_compiled_with_npu(), | ||
"core is not compiled with NPU") | ||
class TestLog(OpTest): | ||
def setUp(self): | ||
self.set_npu() | ||
self.op_type = "log" | ||
self.place = paddle.NPUPlace(0) | ||
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self.init_dtype() | ||
np.random.seed(SEED) | ||
x = np.random.uniform(1, 2, [11, 17]).astype(self.dtype) | ||
out = np.log(x) | ||
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self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)} | ||
self.attrs = {} | ||
self.outputs = {'Out': out} | ||
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def set_npu(self): | ||
self.__class__.use_npu = True | ||
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def init_dtype(self): | ||
self.dtype = np.float32 | ||
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def test_check_output(self): | ||
self.check_output_with_place(self.place, check_dygraph=False) | ||
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# TODO(ascendrc): Add grad test | ||
# def test_check_grad(self): | ||
# if self.dtype == np.float16: | ||
# return | ||
# self.check_grad(['X'], 'Out') | ||
# | ||
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@unittest.skipIf(not paddle.is_compiled_with_npu(), | ||
"core is not compiled with NPU") | ||
class TestLogFp16(OpTest): | ||
def setUp(self): | ||
self.set_npu() | ||
self.op_type = "log" | ||
self.place = paddle.NPUPlace(0) | ||
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self.init_dtype() | ||
np.random.seed(SEED) | ||
x = np.random.uniform(1, 2, [3, 4]).astype(self.dtype) | ||
out = np.log(x) | ||
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self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)} | ||
self.attrs = {} | ||
self.outputs = {'Out': out} | ||
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def set_npu(self): | ||
self.__class__.use_npu = True | ||
self.__class__.no_need_check_grad = True | ||
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def init_dtype(self): | ||
self.dtype = np.float16 | ||
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def test_check_output(self): | ||
self.check_output_with_place(self.place, check_dygraph=False, atol=1e-5) | ||
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@unittest.skipIf(not paddle.is_compiled_with_npu(), | ||
"core is not compiled with NPU") | ||
class TestLogNet(unittest.TestCase): | ||
def _test(self, run_npu=True): | ||
main_prog = paddle.static.Program() | ||
startup_prog = paddle.static.Program() | ||
main_prog.random_seed = SEED | ||
startup_prog.random_seed = SEED | ||
np.random.seed(SEED) | ||
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a_np = np.random.random(size=(32, 32)).astype('float32') | ||
b_np = np.random.random(size=(32, 32)).astype('float32') | ||
label_np = np.random.randint(2, size=(32, 1)).astype('int64') | ||
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with paddle.static.program_guard(main_prog, startup_prog): | ||
a = paddle.static.data(name="a", shape=[32, 32], dtype='float32') | ||
b = paddle.static.data(name="b", shape=[32, 32], dtype='float32') | ||
label = paddle.static.data( | ||
name="label", shape=[32, 1], dtype='int64') | ||
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c = paddle.multiply(a, b) | ||
d = paddle.log(c) | ||
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fc_1 = fluid.layers.fc(input=d, size=128) | ||
prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax') | ||
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cost = fluid.layers.cross_entropy(input=prediction, label=label) | ||
loss = fluid.layers.reduce_mean(cost) | ||
sgd = fluid.optimizer.SGD(learning_rate=0.01) | ||
sgd.minimize(loss) | ||
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if run_npu: | ||
place = paddle.NPUPlace(0) | ||
else: | ||
place = paddle.CPUPlace() | ||
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exe = paddle.static.Executor(place) | ||
exe.run(startup_prog) | ||
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print("Start run on {}".format(place)) | ||
for epoch in range(100): | ||
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pred_res, loss_res = exe.run( | ||
main_prog, | ||
feed={"a": a_np, | ||
"b": b_np, | ||
"label": label_np}, | ||
fetch_list=[prediction, loss]) | ||
if epoch % 10 == 0: | ||
print("Epoch {} | Prediction[0]: {}, Loss: {}".format( | ||
epoch, pred_res[0], loss_res)) | ||
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return pred_res, loss_res | ||
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def test_npu(self): | ||
cpu_pred, cpu_loss = self._test(False) | ||
npu_pred, npu_loss = self._test(True) | ||
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self.assertTrue(np.allclose(npu_pred, cpu_pred, atol=1e-4)) | ||
self.assertTrue(np.allclose(npu_loss, cpu_loss, atol=1e-4)) | ||
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if __name__ == '__main__': | ||
unittest.main() |
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