wehub-resource-sync
147 lines
4.2 KiB
Python
Executable File
147 lines
4.2 KiB
Python
Executable File
# Copyright (c) 2018 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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import unittest
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import numpy as np
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from numpy import linalg as LA
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from op_test import OpTest, get_device_place, is_custom_device
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import paddle
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import paddle.distributed as dist
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from paddle import _C_ops
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def squared_l2_norm(x):
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return _C_ops.squared_l2_norm(x)
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class TestSquaredL2NormF16Op(unittest.TestCase):
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def init_test_case(self):
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X = np.random.uniform(-0.1, 0.1, (8, 5, 10)).astype('float32')
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return X
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def check_main(self, x_np, dtype):
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paddle.disable_static()
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x = paddle.to_tensor(x_np)
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x.stop_gradient = False
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y = squared_l2_norm(x)
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x_g = paddle.grad(y, [x])
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paddle.enable_static()
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return y, x_g
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def test_main(self):
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x_np = self.init_test_case()
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y_np_1, x_g_np_1 = self.check_main(x_np, 'float32')
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y_np_2, x_g_np_2 = self.check_main(x_np, 'float16')
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def assert_equal(x, y):
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np.testing.assert_allclose(x, y, rtol=1e-05, atol=0.0)
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assert_equal(y_np_1, y_np_2)
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assert_equal(x_g_np_1, x_g_np_2)
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class TestSquaredL2NormF16Op1(TestSquaredL2NormF16Op):
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def init_test_case(self):
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X = np.random.uniform(-2.0, 2.0, (30, 10)).astype('float32')
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return X
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class TestSquaredL2NormF16Op2(TestSquaredL2NormF16Op):
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def init_test_case(self):
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X = np.random.uniform(-5.0, 5.0, (20, 10, 20)).astype('float32')
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return X
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class TestL2LossOp(OpTest):
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"""Test squared_l2_norm"""
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def config(self):
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self.x_shape = (13, 19)
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self.check_auto_parallel = False
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def setUp(self):
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self.config()
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self.python_api = squared_l2_norm
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self.public_python_api = squared_l2_norm
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self.op_type = "squared_l2_norm"
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self.prim_op_type = "comp"
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self.max_relative_error = 0.05
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X = np.random.uniform(-1, 1, self.x_shape).astype("float32")
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X[np.abs(X) < self.max_relative_error] = 0.1
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self.inputs = {'X': X}
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self.outputs = {'Out': np.array([np.square(LA.norm(X))])}
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def test_check_output(self):
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self.check_output(check_prim_pir=True)
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def test_check_grad(self):
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self.check_grad(
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['X'],
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'Out',
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max_relative_error=self.max_relative_error,
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check_auto_parallel=self.check_auto_parallel,
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)
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class TestSquaredL2NormAutoParallel_1(TestL2LossOp):
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def config(self):
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self.x_shape = (14, 18)
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self.check_auto_parallel = True
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self.placements = {
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'X': [dist.Replicate()],
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}
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class TestSquaredL2NormAutoParallel_2(TestL2LossOp):
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def config(self):
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self.x_shape = (14, 18)
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self.check_auto_parallel = True
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self.placements = {
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'X': [dist.Shard(0)],
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}
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class TestSquaredL2NormAutoParallel_3(TestL2LossOp):
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def config(self):
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self.x_shape = (14, 18)
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self.check_auto_parallel = True
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self.placements = {
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'X': [dist.Shard(1)],
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}
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class TestL2LossDeterministic(unittest.TestCase):
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def check_place(self, place):
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with paddle.base.dygraph.guard(place):
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x_np = np.random.rand(5, 11, 13).astype('float32')
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x = paddle.to_tensor(x_np)
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y1 = _C_ops.squared_l2_norm(x)
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y2 = _C_ops.squared_l2_norm(x)
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np.testing.assert_array_equal(y1.numpy(), y2.numpy())
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def test_main(self):
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self.check_place(paddle.CPUPlace())
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if paddle.is_compiled_with_cuda() or is_custom_device():
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self.check_place(get_device_place())
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if __name__ == "__main__":
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paddle.enable_static()
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unittest.main()
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