chore: import upstream snapshot with attribution

test/auto_parallel/pir/test_stack_grad_spmd_rule.py

@@ -0,0 +1,65 @@
+# Copyright (c) 2024 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 unittest
+
+import paddle
+import paddle.distributed as dist
+from paddle.distributed.auto_parallel.static.mix_to_dist_pass import (
+    apply_mix2dist_pass,
+)
+
+paddle.enable_static()
+
+
+class TestStackGradSpmdRule(unittest.TestCase):
+    def test_build_replicated_program(self):
+        main_program = paddle.base.Program()
+        with paddle.base.program_guard(main_program):
+            mesh = dist.ProcessMesh([0, 1])
+            x0 = paddle.static.data(name='x0', shape=[64, 36])
+            x1 = paddle.static.data(name='x1', shape=[64, 36])
+            x0.stop_gradient = False
+            x1.stop_gradient = False
+            y = paddle.static.data(name='y', shape=[2, 64, 36])
+            dist_x0 = dist.shard_tensor(x0, mesh, [dist.Shard(0)])
+            dist_x1 = dist.shard_tensor(x1, mesh, [dist.Shard(0)])
+            dist_out = paddle.stack([dist_x0, dist_x1], axis=0)
+            loss = paddle.mean(dist_out - y)
+
+        dist_program = main_program.clone()
+        apply_mix2dist_pass(dist_program)
+        dist_loss_value = dist_program.global_block().ops[-1].result(0)
+
+        with paddle.static.program_guard(dist_program):
+            params_grads = paddle.autograd.ir_backward.append_backward(
+                dist_loss_value
+            )
+
+        stack_grad_op = [
+            op
+            for op in dist_program.global_block().ops
+            if op.name() == "pd_op.stack_grad"
+        ]
+        stack_grad_op = stack_grad_op[0]
+        out_grad = stack_grad_op.operand_source(1)
+        x0_grad = dist_program.global_block().ops[-1].result(0)
+        x1_grad = dist_program.global_block().ops[-1].result(1)
+        self.assertEqual(out_grad.dist_attr().dims_mapping, [-1, 0, -1])
+        self.assertEqual(x0_grad.dist_attr().dims_mapping, [0, -1])
+        self.assertEqual(x1_grad.dist_attr().dims_mapping, [0, -1])
+
+
+if __name__ == "__main__":
+    unittest.main()