wehub-resource-sync
135 lines
3.8 KiB
Python
135 lines
3.8 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 random
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import unittest
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import numpy as np
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from hybrid_parallel_mp_model import (
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SimpleDPNet,
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SimpleMPNet,
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TestDistMPTraining,
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parallel_matmul,
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set_random_seed,
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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.distributed import fleet
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vocab_size = 20
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hidden_size = 10
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inner_size = 8
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output_size = 10
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seq_length = 2
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batch_size = 4
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class SimpleMPMultimodalNet(SimpleMPNet):
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def forward(self, x, **kwargs):
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x = paddle.to_tensor(x)
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x = self.embedding(x)
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x = self.linear1(x)
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x = self.linear2(x)
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x = self.linear3(x)
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x = parallel_matmul(x, self.embedding.weight, False)
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return x
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class SimpleDPMultimodalNet(SimpleDPNet):
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def forward(self, x, **kwargs):
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x = paddle.to_tensor(x)
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x = self.embedding(x)
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x = self.linear1(x)
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x = self.linear2(x)
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x = self.linear3(x)
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x = paddle.matmul(x, self.embedding.weight, transpose_y=True)
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return x
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class TestMPBroadcastObj(TestDistMPTraining):
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def build_model_optimizer(self):
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hcg = fleet.get_hybrid_communicate_group()
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word_size = hcg.get_model_parallel_world_size()
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mp_id = hcg.get_model_parallel_rank()
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dp_id = hcg.get_data_parallel_rank()
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rank_id = dist.get_rank()
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set_random_seed(1024, dp_id, rank_id)
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np_fc1 = np.random.random_sample((hidden_size, inner_size))
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np_fc2 = np.random.random_sample((inner_size, hidden_size))
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model_a = SimpleMPMultimodalNet(
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vocab_size,
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hidden_size,
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inner_size,
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output_size,
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np_fc1,
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np_fc2,
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mp_id,
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)
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optimizer_a = self.build_optimizer(model_a)
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model_a = fleet.distributed_model(model_a)
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optimizer_a = fleet.distributed_optimizer(optimizer_a)
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model_b = SimpleDPMultimodalNet(
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vocab_size, hidden_size, inner_size, output_size, np_fc1, np_fc2
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)
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optimizer_b = self.build_optimizer(model_b)
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return model_a, optimizer_a, model_b, optimizer_b
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def train_batch(self, batch, model, optimizer, is_mp):
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img, text = batch
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output = model(img, text=text)
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loss = output.mean()
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loss.backward() # do backward
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optimizer.step() # update parameters
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optimizer.clear_grad()
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return loss
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def test_mp_model(self):
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(
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model_a,
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optimizer_a,
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model_b,
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optimizer_b,
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) = self.build_model_optimizer()
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for _ in range(5):
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img = np.random.randint(
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0,
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vocab_size,
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(
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batch_size,
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seq_length,
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),
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)
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text = [
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random.sample('zyxwvutsrqponmlkjihgfedcba', 5)
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for i in range(batch_size)
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]
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batch = (img, text)
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loss_a = self.train_batch(batch, model_a, optimizer_a, True)
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loss_b = self.train_batch(batch, model_b, optimizer_b, False)
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np.testing.assert_allclose(
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loss_a.numpy(), loss_b.numpy(), rtol=1e-6
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)
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if __name__ == "__main__":
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unittest.main()
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