Support unused parameters in dynamic graph distributed (#30224)
ShenLiang
4 years ago
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# Copyright (c) 2020 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 paddle
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from test_dist_base import runtime_main, TestParallelDyGraphRunnerBase
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from paddle.nn import Layer, Embedding
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class SimpleNet(Layer):
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def __init__(self,
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hidden_size,
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vocab_size,
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num_steps=20,
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init_scale=0.1,
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is_sparse=False,
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dtype="float32"):
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super(SimpleNet, self).__init__()
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self.hidden_size = hidden_size
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self.vocab_size = vocab_size
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self.init_scale = init_scale
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self.num_steps = num_steps
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self.embedding = Embedding(
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self.vocab_size,
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self.hidden_size,
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sparse=True,
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weight_attr=paddle.ParamAttr(
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name='embedding_param',
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initializer=paddle.nn.initializer.Uniform(
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low=-init_scale, high=init_scale)))
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self.softmax_weight = self.create_parameter(
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attr=paddle.ParamAttr(),
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shape=[self.hidden_size, self.vocab_size],
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dtype=dtype,
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default_initializer=paddle.nn.initializer.Uniform(
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low=-self.init_scale, high=self.init_scale))
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self.softmax_bias = self.create_parameter(
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attr=paddle.ParamAttr(),
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shape=[self.vocab_size],
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dtype=dtype,
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default_initializer=paddle.nn.initializer.Uniform(
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low=-self.init_scale, high=self.init_scale))
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# add tmp var
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self.tmp = self.create_parameter(
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attr=paddle.ParamAttr(),
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shape=[self.vocab_size],
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dtype=dtype,
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default_initializer=paddle.nn.initializer.Uniform(
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low=-self.init_scale, high=self.init_scale))
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def forward(self, input, label):
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x_emb = self.embedding(input)
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fc = paddle.matmul(x_emb, self.softmax_weight)
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# it use stop gradient to block gradient return
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fc.stop_gradient = True
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fc = paddle.add(fc, self.softmax_bias)
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projection = paddle.reshape(fc, shape=[-1, self.vocab_size])
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loss = paddle.nn.functional.softmax_with_cross_entropy(
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logits=projection, label=label, soft_label=False)
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loss = paddle.reshape(loss, shape=[-1, self.num_steps])
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loss = paddle.mean(loss, axis=[0])
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loss = paddle.sum(loss)
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return {"loss": loss}
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# global configs
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batch_size = 4
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batch_num = 200
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hidden_size = 10
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vocab_size = 1000
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num_steps = 3
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init_scale = 0.1
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def fake_sample_reader():
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def __reader__():
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for i in range(batch_num):
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x_data = np.arange(num_steps).astype('int64')
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y_data = np.arange(1, 1 + num_steps).astype('int64')
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yield x_data, y_data
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return __reader__
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class TestSparseEmbeddingUnusedVars(TestParallelDyGraphRunnerBase):
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def get_model(self):
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model = SimpleNet(
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hidden_size=hidden_size,
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vocab_size=vocab_size,
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num_steps=num_steps,
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init_scale=init_scale,
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is_sparse=True)
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train_reader = paddle.batch(
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fake_sample_reader(), batch_size=batch_size, drop_last=True)
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optimizer = paddle.optimizer.SGD(learning_rate=0.001,
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parameters=model.parameters())
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return model, train_reader, optimizer
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def run_one_loop(self, model, optimizer, batch):
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x_data = np.array([x[0].reshape(3) for x in batch]).astype('int64')
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y_data = np.array([x[1].reshape(3) for x in batch]).astype('int64')
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x_data = x_data.reshape((-1, num_steps, 1))
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y_data = y_data.reshape((-1, 1))
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x = paddle.to_tensor(x_data)
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y = paddle.to_tensor(y_data)
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dy_loss = model(x, y)
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return dy_loss["loss"]
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if __name__ == "__main__":
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runtime_main(TestSparseEmbeddingUnusedVars)
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# 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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from __future__ import print_function
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import os
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import sys
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import unittest
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import paddle.fluid as fluid
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from test_dist_base import TestDistBase
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from spawn_runner_base import TestDistSpawnRunner
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from parallel_dygraph_unused_variables import TestSparseEmbeddingUnusedVars
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flag_name = os.path.splitext(__file__)[0]
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class TestParallelDygraphMnist(TestDistBase):
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def _setup_config(self):
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self._sync_mode = False
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self._nccl2_mode = True
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self._dygraph = True
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def test_mnist(self):
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if fluid.core.is_compiled_with_cuda():
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self.check_with_place(
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"parallel_dygraph_unused_variables.py",
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delta=1e-5,
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check_error_log=True,
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log_name=flag_name)
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class TestSparseEmbeddingUnusedVarsSpawn(TestDistSpawnRunner):
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def test_mnist_with_spawn(self):
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if fluid.core.is_compiled_with_cuda() and sys.version_info >= (3, 4):
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self.check_dist_result_with_spawn(
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test_class=TestSparseEmbeddingUnusedVars, delta=1e-5)
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class TestFleetDygraphMnist(TestDistBase):
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def _setup_config(self):
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self._sync_mode = False
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self._nccl2_mode = True
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self._dygraph = True
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self._gpu_fleet_api = True
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def test_mnist(self):
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if fluid.core.is_compiled_with_cuda():
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self.check_with_place(
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"parallel_dygraph_unused_variables.py",
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delta=1e-5,
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check_error_log=True,
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log_name=flag_name)
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if __name__ == "__main__":
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unittest.main()
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