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- # 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 get_test_cover_info import (
- XPUOpTestWrapper,
- create_test_class,
- get_xpu_op_support_types,
- )
- from op import Operator
- from op_test_xpu import XPUOpTest
-
- import paddle
- from paddle import base
- from paddle.base import core
-
-
- class XPUTestSgdOp(XPUOpTestWrapper):
- def __init__(self):
- self.op_name = 'sgd'
- self.use_dynamic_create_class = False
-
- class TestSGDOp(XPUOpTest):
- def setUp(self):
- self.op_type = "sgd"
- self.dtype = self.in_type
- self.conf()
- w = np.random.random((self.h, self.w)).astype(self.dtype)
- g = np.random.random((self.h, self.w)).astype(self.dtype)
- lr = np.array([0.1]).astype(self.dtype)
-
- self.inputs = {'Param': w, 'Grad': g, 'LearningRate': lr}
- self.outputs = {'ParamOut': w - lr * g}
-
- def conf(self):
- self.h = 102
- self.w = 105
-
- def test_check_output_with_place(self):
- self.check_output_with_place(paddle.XPUPlace(0))
-
- class TestSGDOpCase8X(TestSGDOp):
- def conf(self):
- self.h = 10
- self.w = 64
-
-
- support_types = get_xpu_op_support_types('sgd')
- for stype in support_types:
- create_test_class(globals(), XPUTestSgdOp, stype)
-
-
- class TestSGDOpWithLargeInput(unittest.TestCase):
- def runTest(self):
- data = paddle.tensor.fill_constant(shape=[1], value=128, dtype='int64')
- label = paddle.tensor.fill_constant(
- shape=[1, 150], value=0.5, dtype='float32'
- )
- emb = paddle.static.nn.embedding(
- input=data, size=(10000, 150), dtype='float32'
- )
- out = paddle.nn.functional.normalize(x=emb, axis=-1)
-
- cost = paddle.nn.functional.square_error_cost(input=out, label=label)
- avg_cost = paddle.mean(cost)
- sgd_optimizer = paddle.optimizer.SGD(learning_rate=0.001)
- sgd_optimizer.minimize(avg_cost)
-
- place = paddle.XPUPlace(0)
- exe = base.Executor(place)
- exe.run(base.default_startup_program())
- result = exe.run(base.default_main_program(), fetch_list=[avg_cost])
-
-
- class TestSparseSGDOp(unittest.TestCase):
- def check_with_place(self, place):
- scope = core.Scope()
-
- # create and initialize Grad Variable
- height = 10
- rows = [0, 4, 7]
- self.conf()
-
- grad_selected_rows = scope.var('Grad').get_selected_rows()
- grad_selected_rows.set_height(height)
- grad_selected_rows.set_rows(rows)
- np_array = np.ones((len(rows), self.row_numel)).astype("float32")
- np_array[0, 0] = 2.0
- np_array[2, 8] = 4.0
-
- grad_tensor = grad_selected_rows.get_tensor()
- grad_tensor.set(np_array, place)
-
- # create and initialize Param Variable
- param = scope.var('Param').get_tensor()
- param_array = np.full((height, self.row_numel), 5.0).astype("float32")
- param.set(param_array, place)
-
- # create and initialize LearningRate Variable
- lr = scope.var('LearningRate').get_tensor()
- lr_array = np.full((1), 2.0).astype("float32")
- lr.set(lr_array, place)
-
- # create and run sgd operator
- sgd_op = Operator(
- "sgd",
- Param='Param',
- Grad='Grad',
- ParamOut='Param',
- LearningRate='LearningRate',
- )
- sgd_op.run(scope, place)
-
- # get and compare result
- result_array = np.array(param)
-
- # rows[0] = 0, 5.0 - 2.0 * 2.0
- self.assertAlmostEqual(1.0, result_array[rows[0], 0])
- # rows[0] = 0, 5.0 - 2.0 * 1.0
- self.assertAlmostEqual(3.0, result_array[rows[0], 2])
- # 5.0 - 2.0 * 0.0
- self.assertAlmostEqual(5.0, result_array[1, 0])
- # rows[1] = 4, 5.0 - 2.0 * 1.0
- self.assertAlmostEqual(3.0, result_array[rows[1], 10])
- # 5.0 - 2.0 * 0.0
- self.assertAlmostEqual(5.0, result_array[5, 8])
- # rows[2] = 7, 5.0 - 2.0 * 1.0
- self.assertAlmostEqual(3.0, result_array[rows[2], 1])
- # rows[2] = 7, 5.0 - 2.0 * 4.0
- self.assertAlmostEqual(-3.0, result_array[rows[2], 8])
-
- def test_sparse_sgd(self):
- places = [core.XPUPlace(0)]
- for place in places:
- self.check_with_place(place)
-
- def conf(self):
- self.row_numel = 12
-
-
- class TestSparseSGDOpCase8X(TestSparseSGDOp):
- def conf(self):
- self.row_numel = 16
-
-
- if __name__ == "__main__":
- paddle.enable_static()
- unittest.main()
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