wehub-resource-sync
158 lines
4.8 KiB
Python
158 lines
4.8 KiB
Python
# 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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import os
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import random
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import numpy as np
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from legacy_test.test_dist_base import (
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TestParallelDyGraphRunnerBase,
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dump_output,
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print_to_err,
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runtime_main,
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)
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import paddle
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import paddle.distributed as dist
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from paddle import base
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from paddle.nn import Linear
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seed = 90
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RUN_STEP = 20
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batch_size = 4
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batch_num = 1000
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class SimpleNet(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self.net_a = Linear(10, 20)
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self.net_b = Linear(20, 5)
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self.net_c = Linear(5, 10)
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def forward(self, x):
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x = self.net_a(x)
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x = self.net_b(x)
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x = self.net_c(x)
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return x
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class TestNoSync(TestParallelDyGraphRunnerBase):
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def get_model(self):
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model = SimpleNet()
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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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)
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optimizer = paddle.optimizer.SGD(
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learning_rate=0.001, parameters=model.parameters()
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)
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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(list(batch))
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x_data = x_data.reshape((-1, 10))
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x = paddle.to_tensor(x_data)
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out = model(x)
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loss = out.sum() / len(batch)
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return loss
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def run_trainer_func(self, args):
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if base.core.is_compiled_with_cuda():
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device_id = int(os.getenv("FLAGS_selected_gpus", "0"))
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place = base.CUDAPlace(device_id)
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else:
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assert "Only support CUDAPlace for now."
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with base.dygraph.guard(place):
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paddle.seed(seed)
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np.random.seed(seed)
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random.seed(seed)
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model, train_reader, opt = self.get_model()
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if args.update_method == "nccl2":
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dist.init_parallel_env()
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print_to_err(
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type(self).__name__,
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"begin to prepare context in dygraph with nccl2",
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)
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model = paddle.DataParallel(
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model, find_unused_parameters=args.find_unused_parameters
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)
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print_to_err(type(self).__name__, "model built in dygraph")
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out_losses = self.model_train(args, model, opt, train_reader)
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dump_output(out_losses)
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return out_losses
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def run_trainer_with_spawn_func(self, args):
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# 1. enable dygraph
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paddle.disable_static()
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# 2. init seed
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seed = 90
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paddle.seed(seed)
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np.random.seed(seed)
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random.seed(seed)
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# get trainer id
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args.trainer_id = paddle.distributed.get_rank()
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# 3. init parallel env
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if args.update_method in ["nccl2", "gloo"]:
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paddle.distributed.init_parallel_env()
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# 4. train model
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model, train_reader, opt = self.get_model()
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if args.update_method in ["nccl2", "gloo"]:
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model = paddle.DataParallel(
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model, find_unused_parameters=args.find_unused_parameters
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)
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out_losses = self.model_train(args, model, opt, train_reader)
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dump_output(out_losses)
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return out_losses
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def model_train(self, args, model, opt, train_reader):
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out_losses = []
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for step_id, data in enumerate(train_reader()):
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data = self._get_data(data, args)
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if step_id == RUN_STEP:
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break
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if step_id % 3 != 0:
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if args.update_method == "nccl2":
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with model.no_sync():
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loss = self.run_one_loop(model, opt, data)
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loss.backward()
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else:
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loss = self.run_one_loop(model, opt, data)
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loss.backward()
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else:
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loss = self.run_one_loop(model, opt, data)
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loss.backward()
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opt.minimize(loss)
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out_losses.append(loss.numpy())
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model.clear_gradients()
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return out_losses
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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.random.random_sample((10,)).astype('float32')
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yield x_data
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return __reader__
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if __name__ == "__main__":
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runtime_main(TestNoSync)
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