[NPU] Support npu op log, log_grad, sqrt, sqrt_grad, square, tanh and tanh_grad (#31600)
oyjxer
4 years ago
committed by
GitHub
parent
de65486c19
commit
47860ce20d
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import numpy as np
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import unittest
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import sys
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sys.path.append("..")
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from op_test import OpTest
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import paddle
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import paddle.fluid as fluid
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paddle.enable_static()
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SEED = 2021
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@unittest.skipIf(not paddle.is_compiled_with_npu(),
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"core is not compiled with NPU")
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class TestLog(OpTest):
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def setUp(self):
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self.set_npu()
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self.op_type = "log"
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self.place = paddle.NPUPlace(0)
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self.init_dtype()
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np.random.seed(SEED)
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x = np.random.uniform(1, 2, [11, 17]).astype(self.dtype)
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out = np.log(x)
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self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
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self.attrs = {}
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self.outputs = {'Out': out}
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def set_npu(self):
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self.__class__.use_npu = True
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def init_dtype(self):
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self.dtype = np.float32
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def test_check_output(self):
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self.check_output_with_place(self.place, check_dygraph=False)
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# TODO(ascendrc): Add grad test
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# def test_check_grad(self):
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# if self.dtype == np.float16:
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# return
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# self.check_grad(['X'], 'Out')
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#
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@unittest.skipIf(not paddle.is_compiled_with_npu(),
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"core is not compiled with NPU")
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class TestLogFp16(OpTest):
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def setUp(self):
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self.set_npu()
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self.op_type = "log"
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self.place = paddle.NPUPlace(0)
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self.init_dtype()
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np.random.seed(SEED)
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x = np.random.uniform(1, 2, [3, 4]).astype(self.dtype)
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out = np.log(x)
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self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
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self.attrs = {}
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self.outputs = {'Out': out}
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def set_npu(self):
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self.__class__.use_npu = True
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self.__class__.no_need_check_grad = True
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def init_dtype(self):
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self.dtype = np.float16
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def test_check_output(self):
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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(),
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"core is not compiled with NPU")
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class TestLogNet(unittest.TestCase):
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def _test(self, run_npu=True):
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main_prog = paddle.static.Program()
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startup_prog = paddle.static.Program()
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main_prog.random_seed = SEED
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startup_prog.random_seed = SEED
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np.random.seed(SEED)
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a_np = np.random.random(size=(32, 32)).astype('float32')
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b_np = np.random.random(size=(32, 32)).astype('float32')
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label_np = np.random.randint(2, size=(32, 1)).astype('int64')
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with paddle.static.program_guard(main_prog, startup_prog):
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a = paddle.static.data(name="a", shape=[32, 32], dtype='float32')
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b = paddle.static.data(name="b", shape=[32, 32], dtype='float32')
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label = paddle.static.data(
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name="label", shape=[32, 1], dtype='int64')
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c = paddle.multiply(a, b)
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d = paddle.log(c)
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fc_1 = fluid.layers.fc(input=d, size=128)
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prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
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cost = fluid.layers.cross_entropy(input=prediction, label=label)
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loss = fluid.layers.reduce_mean(cost)
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sgd = fluid.optimizer.SGD(learning_rate=0.01)
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sgd.minimize(loss)
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if run_npu:
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place = paddle.NPUPlace(0)
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else:
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place = paddle.CPUPlace()
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exe = paddle.static.Executor(place)
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exe.run(startup_prog)
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print("Start run on {}".format(place))
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for epoch in range(100):
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pred_res, loss_res = exe.run(
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main_prog,
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feed={"a": a_np,
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"b": b_np,
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"label": label_np},
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fetch_list=[prediction, loss])
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if epoch % 10 == 0:
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print("Epoch {} | Prediction[0]: {}, Loss: {}".format(
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epoch, pred_res[0], loss_res))
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return pred_res, loss_res
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def test_npu(self):
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cpu_pred, cpu_loss = self._test(False)
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npu_pred, npu_loss = self._test(True)
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self.assertTrue(np.allclose(npu_pred, cpu_pred, atol=1e-4))
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self.assertTrue(np.allclose(npu_loss, cpu_loss, atol=1e-4))
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if __name__ == '__main__':
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unittest.main()
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import numpy as np
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import unittest
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import sys
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sys.path.append("..")
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from op_test import OpTest
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import paddle
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import paddle.fluid as fluid
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paddle.enable_static()
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SEED = 2021
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@unittest.skipIf(not paddle.is_compiled_with_npu(),
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"core is not compiled with NPU")
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class TestSqrt(OpTest):
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def setUp(self):
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self.set_npu()
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self.op_type = "sqrt"
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self.place = paddle.NPUPlace(0)
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self.init_dtype()
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np.random.seed(SEED)
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x = np.random.uniform(1, 2, [11, 17]).astype(self.dtype)
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out = np.sqrt(x)
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self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
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self.attrs = {}
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self.outputs = {'Out': out}
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def set_npu(self):
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self.__class__.use_npu = True
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def init_dtype(self):
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self.dtype = np.float32
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def test_check_output(self):
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self.check_output_with_place(self.place, check_dygraph=False)
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# TODO(ascendrc): Add grad test
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# def test_check_grad(self):
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# if self.dtype == np.float16:
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# return
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# self.check_grad(['X'], 'Out')
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#
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@unittest.skipIf(not paddle.is_compiled_with_npu(),
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"core is not compiled with NPU")
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class TestSqrtFp16(OpTest):
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def setUp(self):
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self.set_npu()
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self.op_type = "sqrt"
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self.place = paddle.NPUPlace(0)
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self.init_dtype()
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np.random.seed(SEED)
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x = np.random.uniform(1, 2, [3, 4]).astype(self.dtype)
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out = np.sqrt(x)
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self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
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self.attrs = {}
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self.outputs = {'Out': out}
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def set_npu(self):
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self.__class__.use_npu = True
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self.__class__.no_need_check_grad = True
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def init_dtype(self):
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self.dtype = np.float16
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def test_check_output(self):
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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(),
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"core is not compiled with NPU")
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class TestSqrtNet(unittest.TestCase):
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def _test(self, run_npu=True):
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main_prog = paddle.static.Program()
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startup_prog = paddle.static.Program()
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main_prog.random_seed = SEED
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startup_prog.random_seed = SEED
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np.random.seed(SEED)
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a_np = np.random.random(size=(32, 32)).astype('float32')
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b_np = np.random.random(size=(32, 32)).astype('float32')
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label_np = np.random.randint(2, size=(32, 1)).astype('int64')
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with paddle.static.program_guard(main_prog, startup_prog):
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a = paddle.static.data(name="a", shape=[32, 32], dtype='float32')
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b = paddle.static.data(name="b", shape=[32, 32], dtype='float32')
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label = paddle.static.data(
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name="label", shape=[32, 1], dtype='int64')
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c = paddle.multiply(a, b)
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d = paddle.sqrt(c)
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fc_1 = fluid.layers.fc(input=d, size=128)
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prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
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cost = fluid.layers.cross_entropy(input=prediction, label=label)
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loss = fluid.layers.reduce_mean(cost)
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sgd = fluid.optimizer.SGD(learning_rate=0.01)
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sgd.minimize(loss)
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if run_npu:
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place = paddle.NPUPlace(0)
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else:
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place = paddle.CPUPlace()
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exe = paddle.static.Executor(place)
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exe.run(startup_prog)
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print("Start run on {}".format(place))
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for epoch in range(100):
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pred_res, loss_res = exe.run(
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main_prog,
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feed={"a": a_np,
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"b": b_np,
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"label": label_np},
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fetch_list=[prediction, loss])
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if epoch % 10 == 0:
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print("Epoch {} | Prediction[0]: {}, Loss: {}".format(
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epoch, pred_res[0], loss_res))
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return pred_res, loss_res
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def test_npu(self):
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cpu_pred, cpu_loss = self._test(False)
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npu_pred, npu_loss = self._test(True)
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self.assertTrue(np.allclose(npu_pred, cpu_pred))
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self.assertTrue(np.allclose(npu_loss, cpu_loss))
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if __name__ == '__main__':
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unittest.main()
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import numpy as np
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import unittest
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import sys
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sys.path.append("..")
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from op_test import OpTest
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import paddle
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import paddle.fluid as fluid
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paddle.enable_static()
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SEED = 2021
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@unittest.skipIf(not paddle.is_compiled_with_npu(),
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"core is not compiled with NPU")
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class TestSquare(OpTest):
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def setUp(self):
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self.set_npu()
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self.op_type = "square"
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self.place = paddle.NPUPlace(0)
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self.init_dtype()
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np.random.seed(SEED)
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x = np.random.uniform(1, 2, [11, 17]).astype(self.dtype)
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out = np.square(x)
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self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
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self.attrs = {}
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self.outputs = {'Out': out}
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def set_npu(self):
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self.__class__.use_npu = True
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def init_dtype(self):
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self.dtype = np.float32
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def test_check_output(self):
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self.check_output_with_place(self.place, check_dygraph=False)
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# TODO(ascendrc): Add grad test
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# def test_check_grad(self):
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# if self.dtype == np.float16:
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# return
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# self.check_grad(['X'], 'Out')
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#
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@unittest.skipIf(not paddle.is_compiled_with_npu(),
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"core is not compiled with NPU")
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class TestSquareFp16(OpTest):
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def setUp(self):
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self.set_npu()
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self.op_type = "square"
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self.place = paddle.NPUPlace(0)
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self.init_dtype()
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np.random.seed(SEED)
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x = np.random.uniform(1, 2, [3, 4]).astype(self.dtype)
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out = np.square(x)
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self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
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self.attrs = {}
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self.outputs = {'Out': out}
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def set_npu(self):
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self.__class__.use_npu = True
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self.__class__.no_need_check_grad = True
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def init_dtype(self):
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self.dtype = np.float16
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def test_check_output(self):
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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(),
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"core is not compiled with NPU")
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class TestSquareNet(unittest.TestCase):
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def _test(self, run_npu=True):
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main_prog = paddle.static.Program()
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startup_prog = paddle.static.Program()
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main_prog.random_seed = SEED
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startup_prog.random_seed = SEED
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np.random.seed(SEED)
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a_np = np.random.random(size=(32, 32)).astype('float32')
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b_np = np.random.random(size=(32, 32)).astype('float32')
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label_np = np.random.randint(2, size=(32, 1)).astype('int64')
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with paddle.static.program_guard(main_prog, startup_prog):
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a = paddle.static.data(name="a", shape=[32, 32], dtype='float32')
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b = paddle.static.data(name="b", shape=[32, 32], dtype='float32')
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label = paddle.static.data(
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name="label", shape=[32, 1], dtype='int64')
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c = paddle.multiply(a, b)
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d = paddle.square(c)
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fc_1 = fluid.layers.fc(input=d, size=128)
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prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
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cost = fluid.layers.cross_entropy(input=prediction, label=label)
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loss = fluid.layers.reduce_mean(cost)
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sgd = fluid.optimizer.SGD(learning_rate=0.01)
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sgd.minimize(loss)
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if run_npu:
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place = paddle.NPUPlace(0)
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else:
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place = paddle.CPUPlace()
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exe = paddle.static.Executor(place)
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exe.run(startup_prog)
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print("Start run on {}".format(place))
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for epoch in range(100):
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pred_res, loss_res = exe.run(
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main_prog,
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feed={"a": a_np,
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"b": b_np,
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"label": label_np},
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fetch_list=[prediction, loss])
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if epoch % 10 == 0:
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print("Epoch {} | Prediction[0]: {}, Loss: {}".format(
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epoch, pred_res[0], loss_res))
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return pred_res, loss_res
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def test_npu(self):
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cpu_pred, cpu_loss = self._test(False)
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npu_pred, npu_loss = self._test(True)
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self.assertTrue(np.allclose(npu_pred, cpu_pred))
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self.assertTrue(np.allclose(npu_loss, cpu_loss))
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if __name__ == '__main__':
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unittest.main()
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@ -0,0 +1,154 @@
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# 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 __future__ import print_function
|
||||
|
||||
import numpy as np
|
||||
import unittest
|
||||
import sys
|
||||
sys.path.append("..")
|
||||
from op_test import OpTest
|
||||
import paddle
|
||||
import paddle.fluid as fluid
|
||||
|
||||
paddle.enable_static()
|
||||
SEED = 2021
|
||||
|
||||
|
||||
@unittest.skipIf(not paddle.is_compiled_with_npu(),
|
||||
"core is not compiled with NPU")
|
||||
class TestTanh(OpTest):
|
||||
def setUp(self):
|
||||
self.set_npu()
|
||||
self.op_type = "tanh"
|
||||
self.place = paddle.NPUPlace(0)
|
||||
|
||||
self.init_dtype()
|
||||
np.random.seed(SEED)
|
||||
x = np.random.uniform(1, 2, [11, 17]).astype(self.dtype)
|
||||
out = np.tanh(x)
|
||||
|
||||
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
|
||||
self.attrs = {}
|
||||
self.outputs = {'Out': out}
|
||||
|
||||
def set_npu(self):
|
||||
self.__class__.use_npu = True
|
||||
|
||||
def init_dtype(self):
|
||||
self.dtype = np.float32
|
||||
|
||||
def test_check_output(self):
|
||||
self.check_output_with_place(self.place, check_dygraph=False)
|
||||
|
||||
# TODO(ascendrc): Add grad test
|
||||
# def test_check_grad(self):
|
||||
# if self.dtype == np.float16:
|
||||
# return
|
||||
# self.check_grad(['X'], 'Out')
|
||||
#
|
||||
|
||||
|
||||
@unittest.skipIf(not paddle.is_compiled_with_npu(),
|
||||
"core is not compiled with NPU")
|
||||
class TestTanhFp16(OpTest):
|
||||
def setUp(self):
|
||||
self.set_npu()
|
||||
self.op_type = "tanh"
|
||||
self.place = paddle.NPUPlace(0)
|
||||
|
||||
self.init_dtype()
|
||||
np.random.seed(SEED)
|
||||
x = np.random.uniform(1, 2, [3, 4]).astype(self.dtype)
|
||||
out = np.tanh(x)
|
||||
|
||||
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
|
||||
self.attrs = {}
|
||||
self.outputs = {'Out': out}
|
||||
|
||||
def set_npu(self):
|
||||
self.__class__.use_npu = True
|
||||
self.__class__.no_need_check_grad = True
|
||||
|
||||
def init_dtype(self):
|
||||
self.dtype = np.float16
|
||||
|
||||
def test_check_output(self):
|
||||
self.check_output_with_place(self.place, check_dygraph=False, atol=1e-3)
|
||||
|
||||
|
||||
@unittest.skipIf(not paddle.is_compiled_with_npu(),
|
||||
"core is not compiled with NPU")
|
||||
class TestTanhNet(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)
|
||||
|
||||
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')
|
||||
|
||||
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')
|
||||
|
||||
c = paddle.multiply(a, b)
|
||||
d = paddle.tanh(c)
|
||||
|
||||
fc_1 = fluid.layers.fc(input=d, size=128)
|
||||
prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
|
||||
|
||||
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)
|
||||
|
||||
if run_npu:
|
||||
place = paddle.NPUPlace(0)
|
||||
else:
|
||||
place = paddle.CPUPlace()
|
||||
|
||||
exe = paddle.static.Executor(place)
|
||||
exe.run(startup_prog)
|
||||
|
||||
print("Start run on {}".format(place))
|
||||
for epoch in range(100):
|
||||
|
||||
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))
|
||||
|
||||
return pred_res, loss_res
|
||||
|
||||
def test_npu(self):
|
||||
cpu_pred, cpu_loss = self._test(False)
|
||||
npu_pred, npu_loss = self._test(True)
|
||||
|
||||
self.assertTrue(np.allclose(npu_pred, cpu_pred))
|
||||
self.assertTrue(np.allclose(npu_loss, cpu_loss))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
Loading…
Reference in new issue