wehub-resource-sync
125 lines
3.7 KiB
Python
125 lines
3.7 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.autograd import PyLayer
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from paddle.distributed.fleet.utils.hybrid_parallel_util import (
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fused_allreduce_gradients,
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)
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batch = 5
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in_dim = 20
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out_dim = 10
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class cus_tanh(PyLayer):
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@staticmethod
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def forward(ctx, x):
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y = paddle.tanh(x)
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ctx.save_for_backward(y)
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return y
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@staticmethod
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def backward(ctx, dy):
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(y,) = ctx.saved_tensor()
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grad = dy * (1 - paddle.square(y))
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return grad
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class SimpleNet(paddle.nn.Layer):
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def __init__(self, train_id, model_id):
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super().__init__()
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self.w = self.create_parameter(shape=[in_dim, batch], dtype="float32")
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self.linear = paddle.nn.Linear(in_dim, out_dim)
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self.tanh = paddle.tanh
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self.trainer_id = train_id
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self.model_id = model_id
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def forward(self, inputs):
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if self.model_id == 0:
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inputs = cus_tanh.apply(inputs)
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else:
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inputs = self.tanh(inputs)
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inputs = paddle.matmul(self.w, inputs)
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return self.linear(inputs)
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class TestDistTraining(unittest.TestCase):
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def test_multiple_xpus(self):
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self.trainer_id = dist.get_rank()
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dist.init_parallel_env()
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model_a = SimpleNet(self.trainer_id, 0)
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model_b = SimpleNet(self.trainer_id, 1)
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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)
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model_b = paddle.DataParallel(model_b)
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for step in range(10):
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x_data = np.random.randn(batch, in_dim).astype(np.float32)
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x = paddle.to_tensor(x_data)
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x.stop_gradient = False
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with model_a.no_sync():
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y_pred_a = model_a(x)
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loss_a = y_pred_a.mean()
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loss_a.backward()
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fused_allreduce_gradients(list(model_a.parameters()), None)
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y_pred_b = model_b(x)
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loss_b = y_pred_b.mean()
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loss_b.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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self.check_acc(model_a._layers.w.grad, model_b._layers.w.grad)
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model_a.clear_gradients()
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model_b.clear_gradients()
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def check_acc(self, grad, acc_grad):
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grad = grad.numpy(False) if grad is not None else None
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acc_grad = acc_grad.numpy(False) if acc_grad is not None else None
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return np.testing.assert_allclose(grad, acc_grad, rtol=1e-6)
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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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