chore: import upstream snapshot with attribution

2026-07-13 12:40:42 +08:00
commit e25996e7db
15472 changed files with 3536181 additions and 0 deletions
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+# Copyright (c) 2020 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 numpy as np
+from legacy_test.test_collective_api_base import (
+    TestCollectiveAPIRunnerBase,
+    runtime_main,
+)
+
+import paddle
+from paddle import base
+from paddle.distributed import fleet
+
+paddle.enable_static()
+
+
+class TestRowParallelLinearAPI(TestCollectiveAPIRunnerBase):
+    def __init__(self):
+        self.global_ring_id = 0
+
+    def get_model(self, main_prog, startup_program, rank, dtype='float32'):
+        with base.program_guard(main_prog, startup_program):
+            fleet.init(is_collective=True)
+            np.random.seed(2020)
+            np_array = np.random.rand(1000, 16)
+
+            data = paddle.static.data(
+                name='tindata', shape=[10, 1000], dtype=dtype
+            )
+            paddle.distributed.broadcast(data, src=0)
+            data = paddle.split(data, 2, axis=1)[rank]
+            if rank == 0:
+                param_attr = paddle.base.ParamAttr(
+                    initializer=paddle.nn.initializer.Assign(
+                        np_array[0:500, :]
+                    ),
+                )
+            else:
+                param_attr = paddle.base.ParamAttr(
+                    initializer=paddle.nn.initializer.Assign(
+                        np_array[500:1000, :]
+                    ),
+                )
+
+            linear_out = paddle.distributed.split(
+                data,
+                size=(1000, 16),
+                operation='linear',
+                axis=0,
+                num_partitions=2,
+                weight_attr=param_attr,
+                bias_attr=True,
+            )
+
+            return [linear_out]
+
+
+if __name__ == "__main__":
+    runtime_main(TestRowParallelLinearAPI, "row_parallel_linear")