wehub-resource-sync
141 lines
4.7 KiB
Python
141 lines
4.7 KiB
Python
# Copyright (c) 2024 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 import nn
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from paddle.distributed.auto_parallel.static.mix_to_dist_pass import (
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apply_mix2dist_pass,
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)
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from paddle.io import DataLoader
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BATCH_SIZE = 4
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BATCH_NUM = 40
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IMAGE_SIZE = 16
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CLASS_NUM = 8
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np.random.seed(2024)
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paddle.seed(2024)
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class RandomDataset(paddle.io.Dataset):
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def __init__(self, images, labels, num_samples):
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self.images = images
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self.labels = labels
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self.num_samples = num_samples
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def __getitem__(self, idx):
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return self.images[idx], self.labels[idx]
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def __len__(self):
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return self.num_samples
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class SimpleDemoNet(nn.Layer):
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def __init__(self, mesh1, mesh2):
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super().__init__()
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self._mesh1 = mesh1
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self._mesh2 = mesh2
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self.linear_0 = nn.Linear(IMAGE_SIZE, IMAGE_SIZE, bias_attr=False)
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self.linear_1 = nn.Linear(IMAGE_SIZE, CLASS_NUM, bias_attr=False)
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self.relu_0 = nn.ReLU()
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self.relu_1 = nn.ReLU()
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self.relu_2 = nn.ReLU()
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# shard the weights of this layer
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self.linear_0.weight = dist.shard_tensor(
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self.linear_0.weight,
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self._mesh1,
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[dist.Replicate()],
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stop_gradient=False,
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)
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self.linear_1.weight = dist.shard_tensor(
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self.linear_1.weight,
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self._mesh2,
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[dist.Replicate()],
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stop_gradient=False,
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)
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def forward(self, x):
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x.stop_gradient = False
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out = self.relu_0(x) # trigger backward partial allreduce
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out = self.linear_0(out)
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out = self.relu_1(out)
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out = dist.reshard(out, self._mesh2, [dist.Replicate()])
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out = self.linear_1(out)
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out = self.relu_2(out) # trigger forward partial allreduce
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return out
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def create_data_loader():
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images = np.random.rand(BATCH_NUM, IMAGE_SIZE).astype('float32')
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labels = np.random.rand(BATCH_NUM, CLASS_NUM).astype('float32')
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dataset = RandomDataset(images, labels, BATCH_NUM)
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loader = DataLoader(dataset, batch_size=BATCH_SIZE)
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return loader
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class TestLearningRate(unittest.TestCase):
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def test_copy_between_mesh(self):
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mesh1 = dist.ProcessMesh([0], dim_names=["x"])
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mesh2 = dist.ProcessMesh([1], dim_names=["y"])
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layer = SimpleDemoNet(mesh1, mesh2)
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opt = paddle.optimizer.SGD(
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learning_rate=0.1, parameters=layer.parameters()
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)
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loss_fn = nn.MSELoss()
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loader = create_data_loader()
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dist_loader = dist.shard_dataloader(loader, meshes=[mesh1, mesh2])
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dist_model = dist.to_static(layer, dist_loader, loss_fn, opt)
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engine = dist_model._engine
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engine._build("train")
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dist_program = engine._fwd_main_progs["train"]
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apply_mix2dist_pass(dist_program)
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loss = dist_program.get_output_value_by_name(engine._loss_names[0])
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with paddle.static.program_guard(dist_program):
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params_grads = paddle.autograd.ir_backward.append_backward(loss)
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engine._optimizer._apply_optimize(
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loss, startup_program=None, params_grads=params_grads
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)
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apply_mix2dist_pass(dist_program)
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sgd_idx = 0
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ops = dist_program.global_block().ops
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for op in ops:
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if op.name() != 'pd_op.sgd_':
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continue
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param = op.operand_source(0)
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learning_rate = op.operand_source(1)
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op_dist_attr = learning_rate.get_defining_op().dist_attr
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self.assertEqual(
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learning_rate.dist_attr().process_mesh,
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param.dist_attr().process_mesh,
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)
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self.assertEqual(
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learning_rate.dist_attr().process_mesh,
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op_dist_attr.process_mesh,
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)
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if sgd_idx == 0:
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self.assertEqual(param.dist_attr().process_mesh, mesh2)
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elif sgd_idx == 1:
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self.assertEqual(param.dist_attr().process_mesh, mesh1)
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sgd_idx += 1
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self.assertEqual(sgd_idx, 2)
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if __name__ == "__main__":
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unittest.main()
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