wehub-resource-sync
143 lines
4.4 KiB
Python
143 lines
4.4 KiB
Python
# Copyright (c) 2022 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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import paddle
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import paddle.distributed as dist
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from paddle.nn import Linear
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paddle.seed(1024)
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np.random.seed(2021)
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batch = 5
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in_dim = 10
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out_dim = 20
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class SimpleNet(paddle.nn.Layer):
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def __init__(self, train_id):
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super().__init__()
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self.w1 = self.create_parameter(
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shape=[in_dim, out_dim], dtype="float32"
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)
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self.w2 = self.create_parameter(
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shape=[in_dim, out_dim], dtype="float32"
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)
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self.share_net = Linear(out_dim, 10)
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self.unused_param = self.create_parameter(
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shape=[out_dim, in_dim], dtype="float64"
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)
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# just for test sync_params_buffers
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self.register_buffer("queue", paddle.randn([10, 5]))
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self.queue = paddle.nn.functional.normalize(self.queue, axis=0)
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self.register_buffer("queue_ptr", paddle.zeros([1], 'int64'))
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self.trainer_id = train_id
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def forward(self, x):
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is_use = (
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paddle.equal_all(x, paddle.ones(shape=(batch, in_dim))).item()
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and self.trainer_id == 1
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)
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if is_use:
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tmp = paddle.matmul(x, self.w1)
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else:
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tmp = paddle.matmul(x, self.w2)
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return self.share_net(tmp)
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class TestDistTraining(unittest.TestCase):
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def test_multiple_xpus(self):
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dist.init_parallel_env()
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self.trainer_id = dist.get_rank()
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model_a = SimpleNet(self.trainer_id)
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model_b = SimpleNet(self.trainer_id)
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state_dict = model_a.state_dict()
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model_b.set_state_dict(state_dict)
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model_a = paddle.DataParallel(model_a, find_unused_parameters=True)
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model_b = paddle.DataParallel(model_b, find_unused_parameters=True)
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ones_input = paddle.ones(shape=(batch, in_dim))
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ones_input.stop_gradient = True
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w1_grad_sum = np.zeros((in_dim, out_dim), dtype='float32')
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w2_grad_sum = np.zeros((in_dim, out_dim), dtype='float32')
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for step_id in range(5):
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random_input = paddle.rand(shape=(batch, in_dim))
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random_input.stop_gradient = True
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if step_id % 2 == 0:
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out_a = model_a(random_input)
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out_b = model_b(random_input)
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else:
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out_a = model_a(ones_input)
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out_b = model_b(ones_input)
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out_a.sum().backward()
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out_b.sum().backward()
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self.check_gradient(model_a.parameters())
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self.check_gradient(model_b.parameters())
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# test acc gradient
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w1_grad_sum = self.check_acc(
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model_a._layers.w1.grad, w1_grad_sum, model_b._layers.w1.grad
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)
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w2_grad_sum = self.check_acc(
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model_a._layers.w2.grad, w2_grad_sum, model_b._layers.w2.grad
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)
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model_a.clear_gradients()
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def check_acc(self, grad, grad_sum, acc_grad):
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if grad is not None:
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grad_sum = grad_sum + grad.numpy(False)
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acc_grad = acc_grad.numpy(False) if acc_grad is not None else None
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np.testing.assert_allclose(grad_sum, acc_grad, rtol=1e-6)
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return grad_sum
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def print_trainer_0(self, *args):
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if self.trainer_id == 0:
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print(*args)
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def broadcast_param(self, param, root):
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paddle.distributed.broadcast(param, root)
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return param
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def check_gradient(self, params):
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other_param = []
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for param in params:
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if param.trainable and (param._grad_ivar() is not None):
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grad = param._grad_ivar()
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other_grad = self.broadcast_param(grad.clone(), root=1)
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if self.trainer_id == 0:
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np.testing.assert_allclose(
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other_grad.numpy(False), grad.numpy(False)
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)
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if __name__ == '__main__':
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unittest.main()
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