wehub-resource-sync
160 lines
4.6 KiB
Python
160 lines
4.6 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 os
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import tempfile
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import numpy as np
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import paddle
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import paddle.nn.functional as F
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from paddle import nn
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from paddle.distributed.fleet import auto
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paddle.enable_static()
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global_process_mesh = auto.ProcessMesh(mesh=[0, 1])
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PP_MESH_0 = auto.ProcessMesh([0])
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PP_MESH_1 = auto.ProcessMesh([1])
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batch_size = 2
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batch_num = 10
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hidden_size = 1024
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sequence_len = 512
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image_size = hidden_size
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class_num = 10
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paddle.seed(44)
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class MyDataset(paddle.io.IterableDataset):
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def __init__(self, num_samples):
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self.num_samples = num_samples
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def __iter__(self):
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for i in range(self.num_samples):
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input = np.random.uniform(size=image_size).astype("float32")
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label = np.random.randint(0, class_num - 1, dtype="int64")
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yield input, label
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class MyDataset1(paddle.io.Dataset):
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def __init__(self, num_samples):
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self.num_samples = num_samples
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self.data = []
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for i in range(self.num_samples):
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input1 = np.random.uniform(size=image_size).astype("float32")
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label1 = np.array(
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np.random.randint(0, class_num - 1, dtype="int64")
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)
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input2 = np.random.uniform(size=image_size).astype("float32")
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label2 = np.array(
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np.random.randint(0, class_num - 1, dtype="int64")
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)
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input = np.stack((input1, input2))
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label = np.stack((label1, label2))
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self.data.append((input, label))
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def __getitem__(self, idx):
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return self.data[idx]
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def __len__(self):
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return len(self.data)
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class MLPLayer(nn.Layer):
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def __init__(
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self,
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hidden_size=1024,
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intermediate_size=4 * 1024,
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dropout_ratio=0.1,
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initializer_range=0.02,
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):
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super().__init__()
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d_model = hidden_size
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dim_feedforward = intermediate_size
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weight_attr = paddle.ParamAttr(
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initializer=nn.initializer.Normal(mean=0.0, std=initializer_range)
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)
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bias_attr = None
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self.linear0 = nn.Linear(
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d_model, dim_feedforward, weight_attr, bias_attr=bias_attr
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)
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self.linear1 = nn.Linear(
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dim_feedforward, d_model, weight_attr, bias_attr=bias_attr
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)
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self.linear2 = nn.Linear(d_model, 1, weight_attr, bias_attr=bias_attr)
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self.norm = nn.LayerNorm(d_model, epsilon=1e-5)
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self.dropout = nn.Dropout(dropout_ratio, mode="upscale_in_train")
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def forward(self, input):
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out = auto.shard_op(self.norm, PP_MESH_0)(input)
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out = self.linear0(out)
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out = F.gelu(out, approximate=True)
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out = auto.shard_op(self.linear1, PP_MESH_1)(out)
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out = self.dropout(out)
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out = self.linear2(out)
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self.out = out
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return out
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def train(fetch):
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mlp = MLPLayer(
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hidden_size=hidden_size,
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intermediate_size=4 * hidden_size,
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dropout_ratio=0.1,
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initializer_range=0.02,
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)
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loss = paddle.nn.CrossEntropyLoss()
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optimizer = paddle.optimizer.Adam(
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learning_rate=0.00001,
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beta1=0.9,
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beta2=0.999,
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epsilon=1e-08,
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grad_clip=None,
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)
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dist_strategy = auto.Strategy()
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dist_strategy.auto_mode = "semi"
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dist_strategy.split_data = True
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# init engine
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engine = auto.Engine(
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mlp, loss, optimizer, paddle.metric.Accuracy(), strategy=dist_strategy
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)
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# train
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train_dataset = MyDataset(batch_num * batch_size)
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engine.fit(train_dataset, epochs=2, batch_size=batch_size)
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train_dataset1 = MyDataset1(batch_size * batch_num)
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engine.fit(train_dataset1, epochs=2, batch_size=None)
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# eval
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eval_dataset = MyDataset(batch_size)
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engine.evaluate(eval_dataset, batch_size=batch_size)
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# predict
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test_dataset = MyDataset(batch_size)
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engine.predict(test_dataset, batch_size=batch_size)
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# save
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temp_dir = tempfile.TemporaryDirectory()
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model_filename = os.path.join(temp_dir.name, 'mlp_inf')
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engine.save(model_filename, training=False)
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temp_dir.cleanup()
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if __name__ == "__main__":
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train(fetch=True)
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