chore: import upstream snapshot with attribution

test/auto_parallel/iterable_dataset.py

@@ -0,0 +1,159 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import tempfile
+
+import numpy as np
+
+import paddle
+import paddle.nn.functional as F
+from paddle import nn
+from paddle.distributed.fleet import auto
+
+paddle.enable_static()
+global_process_mesh = auto.ProcessMesh(mesh=[0, 1])
+PP_MESH_0 = auto.ProcessMesh([0])
+PP_MESH_1 = auto.ProcessMesh([1])
+batch_size = 2
+batch_num = 10
+hidden_size = 1024
+sequence_len = 512
+image_size = hidden_size
+class_num = 10
+
+paddle.seed(44)
+
+
+class MyDataset(paddle.io.IterableDataset):
+    def __init__(self, num_samples):
+        self.num_samples = num_samples
+
+    def __iter__(self):
+        for i in range(self.num_samples):
+            input = np.random.uniform(size=image_size).astype("float32")
+            label = np.random.randint(0, class_num - 1, dtype="int64")
+            yield input, label
+
+
+class MyDataset1(paddle.io.Dataset):
+    def __init__(self, num_samples):
+        self.num_samples = num_samples
+        self.data = []
+        for i in range(self.num_samples):
+            input1 = np.random.uniform(size=image_size).astype("float32")
+            label1 = np.array(
+                np.random.randint(0, class_num - 1, dtype="int64")
+            )
+            input2 = np.random.uniform(size=image_size).astype("float32")
+            label2 = np.array(
+                np.random.randint(0, class_num - 1, dtype="int64")
+            )
+            input = np.stack((input1, input2))
+            label = np.stack((label1, label2))
+            self.data.append((input, label))
+
+    def __getitem__(self, idx):
+        return self.data[idx]
+
+    def __len__(self):
+        return len(self.data)
+
+
+class MLPLayer(nn.Layer):
+    def __init__(
+        self,
+        hidden_size=1024,
+        intermediate_size=4 * 1024,
+        dropout_ratio=0.1,
+        initializer_range=0.02,
+    ):
+        super().__init__()
+        d_model = hidden_size
+        dim_feedforward = intermediate_size
+        weight_attr = paddle.ParamAttr(
+            initializer=nn.initializer.Normal(mean=0.0, std=initializer_range)
+        )
+        bias_attr = None
+
+        self.linear0 = nn.Linear(
+            d_model, dim_feedforward, weight_attr, bias_attr=bias_attr
+        )
+        self.linear1 = nn.Linear(
+            dim_feedforward, d_model, weight_attr, bias_attr=bias_attr
+        )
+        self.linear2 = nn.Linear(d_model, 1, weight_attr, bias_attr=bias_attr)
+        self.norm = nn.LayerNorm(d_model, epsilon=1e-5)
+        self.dropout = nn.Dropout(dropout_ratio, mode="upscale_in_train")
+
+    def forward(self, input):
+        out = auto.shard_op(self.norm, PP_MESH_0)(input)
+        out = self.linear0(out)
+        out = F.gelu(out, approximate=True)
+        out = auto.shard_op(self.linear1, PP_MESH_1)(out)
+        out = self.dropout(out)
+        out = self.linear2(out)
+        self.out = out
+        return out
+
+
+def train(fetch):
+    mlp = MLPLayer(
+        hidden_size=hidden_size,
+        intermediate_size=4 * hidden_size,
+        dropout_ratio=0.1,
+        initializer_range=0.02,
+    )
+    loss = paddle.nn.CrossEntropyLoss()
+    optimizer = paddle.optimizer.Adam(
+        learning_rate=0.00001,
+        beta1=0.9,
+        beta2=0.999,
+        epsilon=1e-08,
+        grad_clip=None,
+    )
+
+    dist_strategy = auto.Strategy()
+    dist_strategy.auto_mode = "semi"
+    dist_strategy.split_data = True
+
+    # init engine
+    engine = auto.Engine(
+        mlp, loss, optimizer, paddle.metric.Accuracy(), strategy=dist_strategy
+    )
+
+    # train
+    train_dataset = MyDataset(batch_num * batch_size)
+    engine.fit(train_dataset, epochs=2, batch_size=batch_size)
+
+    train_dataset1 = MyDataset1(batch_size * batch_num)
+    engine.fit(train_dataset1, epochs=2, batch_size=None)
+
+    # eval
+    eval_dataset = MyDataset(batch_size)
+    engine.evaluate(eval_dataset, batch_size=batch_size)
+
+    # predict
+    test_dataset = MyDataset(batch_size)
+    engine.predict(test_dataset, batch_size=batch_size)
+
+    # save
+    temp_dir = tempfile.TemporaryDirectory()
+    model_filename = os.path.join(temp_dir.name, 'mlp_inf')
+    engine.save(model_filename, training=False)
+    temp_dir.cleanup()
+
+
+if __name__ == "__main__":
+    train(fetch=True)