wehub-resource-sync
82 lines
3.0 KiB
Python
82 lines
3.0 KiB
Python
# 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 os
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import unittest
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import numpy as np
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from op_test import get_device_place
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import paddle
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from paddle import base
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from paddle.distributed import fleet
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class TestRawProgramOptimizer(unittest.TestCase):
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def setUp(self):
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os.environ["PADDLE_TRAINER_ID"] = "0"
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os.environ["PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36001"
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def mlp(self, input_x, input_y, hid_dim=128, label_dim=2):
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fc_1 = paddle.static.nn.fc(x=input_x, size=hid_dim, activation='tanh')
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fc_2 = paddle.static.nn.fc(x=fc_1, size=hid_dim, activation='tanh')
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prediction = paddle.static.nn.fc(
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x=[fc_2], size=label_dim, activation='softmax'
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)
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cost = paddle.nn.functional.cross_entropy(
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input=prediction, label=input_y
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)
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avg_cost = paddle.mean(x=cost)
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return avg_cost
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def gen_data(self):
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return {
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"x": np.random.random(size=(128, 32)).astype('float32'),
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"y": np.random.randint(2, size=(128, 1)).astype('int64'),
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}
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def test_single_gpu(self):
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paddle.enable_static()
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with paddle.pir_utils.OldIrGuard():
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fleet.init(is_collective=True)
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sharding_program = paddle.static.Program()
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sharding_startup_program = paddle.static.Program()
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strategy = fleet.DistributedStrategy()
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strategy.without_graph_optimization = True
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with (
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base.program_guard(sharding_program, sharding_startup_program),
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base.unique_name.guard(),
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):
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input_x = paddle.static.data(
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name="x", shape=[None, 32], dtype='float32'
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)
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input_y = paddle.static.data(
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name="y", shape=[None, 1], dtype='int64'
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)
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cost = self.mlp(input_x=input_x, input_y=input_y)
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output_name = cost.name
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optimizer = fleet.distributed_optimizer(
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paddle.optimizer.Adam(), strategy
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)
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optimizer.minimize(cost)
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trainer_id = fleet.worker_index()
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exe = paddle.static.Executor(get_device_place(trainer_id))
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rank = fleet.worker_index()
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exe.run(sharding_startup_program)
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exe.run(program=sharding_program, feed=self.gen_data())
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if __name__ == "__main__":
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unittest.main()
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