wehub-resource-sync
146 lines
4.9 KiB
Python
146 lines
4.9 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 copy
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import random
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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.distributed.auto_parallel.static.dist_input_spec import (
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DistributedInputSpec,
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)
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from paddle.io import BatchSampler, DataLoader, Dataset
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np.random.seed(1127)
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paddle.seed(1127)
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random.seed(1127)
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mesh = dist.ProcessMesh([0, 1], dim_names=["dp"])
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class RandomDataset(Dataset):
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def __init__(self, seq_len, hidden, num_samples=100):
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super().__init__()
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self.seq_len = seq_len
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self.hidden = hidden
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self.num_samples = num_samples
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self.mode = "A"
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def __getitem__(self, index):
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if self.mode == "A":
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input = np.random.uniform(size=[self.seq_len, self.hidden]).astype(
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"float32"
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)
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else:
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input = np.random.normal(
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size=[self.seq_len * 2, self.hidden]
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).astype("float32")
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label = np.random.randint(0, 2, size=[128]).astype("int64")
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return input, label
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def __len__(self):
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return self.num_samples
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class MlpModel(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self.w0 = self.create_parameter(shape=[1024, 4096])
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self.w1 = self.create_parameter(shape=[1024, 4096])
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def forward(self, x):
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y1 = paddle.matmul(x, self.w0)
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y2 = paddle.matmul(x, self.w1)
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z = y1 + y2
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return z
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class TestCustomSpec:
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def get_input_spec(self, dist_dataloader):
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dist_dataloader._dataloader.mode = "A"
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input1, label1 = next(dist_dataloader())
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dist_dataloader._dataloader.mode = "B"
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input2, label2 = next(dist_dataloader())
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inputs_spec1 = [DistributedInputSpec.from_dtensor(input1, "input0")]
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inputs_spec2 = [DistributedInputSpec.from_dtensor(input2, "input0")]
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labels_spec = [DistributedInputSpec.from_dtensor(label1, "label0")]
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return [inputs_spec1, labels_spec], [inputs_spec2, labels_spec]
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def run_test(self):
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model = MlpModel()
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loss_func = paddle.nn.CrossEntropyLoss()
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dataset = RandomDataset(128, 1024, 40)
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sampler = BatchSampler(
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dataset,
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batch_size=4,
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)
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dataloader = DataLoader(
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dataset,
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batch_sampler=sampler,
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)
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dist_dataloader = dist.shard_dataloader(
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dataloader=dataloader, meshes=mesh, shard_dims="dp"
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)
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opt = paddle.optimizer.AdamW(
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learning_rate=0.001, parameters=model.parameters()
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)
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opt2 = copy.deepcopy(opt)
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input_spec1, input_spec2 = self.get_input_spec(dist_dataloader)
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model.w0.stop_gradient = True
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model.w1.stop_gradient = False
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dist_model1 = dist.to_static(
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model, dist_dataloader, loss_func, opt, input_spec=input_spec1
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)
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dist_model1.train()
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model.w0.stop_gradient = False
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model.w1.stop_gradient = True
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dist_model2 = dist.to_static(
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model, dist_dataloader, loss_func, opt2, input_spec=input_spec2
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)
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dist_model2.train()
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datasets_modes = ["A", "A", "B", "A", "B"]
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for mode in datasets_modes:
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dist_dataloader._dataloader.mode = mode
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input, label = next(iter(dist_dataloader))
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if mode == "A":
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before_w0 = dist_model1.state_dict("param")['w0'].mean().numpy()
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before_w1 = dist_model1.state_dict("param")['w1'].mean().numpy()
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loss = dist_model1(input, label)
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after_w0 = dist_model1.state_dict("param")['w0'].mean().numpy()
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after_w1 = dist_model1.state_dict("param")['w1'].mean().numpy()
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assert np.equal(before_w0, after_w0).all()
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assert not np.equal(before_w1, after_w1).all()
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else:
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before_w0 = dist_model2.state_dict("param")['w0'].mean().numpy()
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before_w1 = dist_model2.state_dict("param")['w1'].mean().numpy()
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loss = dist_model2(input, label)
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after_w0 = dist_model2.state_dict("param")['w0'].mean().numpy()
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after_w1 = dist_model2.state_dict("param")['w1'].mean().numpy()
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assert not np.equal(before_w0, after_w0).all()
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assert np.equal(before_w1, after_w1).all()
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if __name__ == '__main__':
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TestCustomSpec().run_test()
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