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
@@ -0,0 +1,95 @@
+# Copyright (c) 2025 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 paddle
+import paddle.distributed as dist
+
+_enable_auto_dp_mode = False
+
+
+def _fake_replicate_grad_to_partial(grad, partial_axis):
+    new_placements = grad.placements
+    assert new_placements[partial_axis] == dist.Replicate(), (
+        "when reshard fake replicated grad to partial, the partial axis of grad should be Replicate"
+    )
+
+    new_placements[partial_axis] = dist.Partial(dist.ReduceType.kRedSum)
+
+    grad_mesh = grad.process_mesh
+    grad = dist.auto_parallel.api.dtensor_to_local(
+        grad, grad_mesh, grad.placements
+    )
+    grad = dist.auto_parallel.api.dtensor_from_local(
+        grad, grad_mesh, new_placements
+    )
+    return grad
+
+
+def _convert_fake_replicate_grad_to_partial(params_grads):
+    # skip non-parallel cases
+    world_size = paddle.distributed.get_world_size()
+    if world_size == 1:
+        return
+
+    if isinstance(params_grads, list):
+        for idx in range(len(params_grads)):
+            param, grad = params_grads[idx][0], params_grads[idx][1]
+            if grad.is_dist():
+                grad_placements = grad.placements
+                if not isinstance(grad_placements[0], dist.Partial):
+                    grad = _fake_replicate_grad_to_partial(grad, 0)
+            else:
+                default_grad_placements = [
+                    dist.Partial(dist.ReduceType.kRedSum)
+                ]
+                default_grad_mesh = dist.ProcessMesh(
+                    list(range(0, world_size)), dim_names=["dp"]
+                )
+                grad = dist.auto_parallel.api.dtensor_from_local(
+                    grad, default_grad_mesh, default_grad_placements
+                )
+            params_grads[idx] = (param, grad)
+    else:
+        for idx in range(len(params_grads['params'])):
+            grad = params_grads['params'][idx][1]
+            if grad.is_dist():
+                grad_placements = grad.placements
+                if not isinstance(grad_placements[0], dist.Partial):
+                    grad = _fake_replicate_grad_to_partial(grad, 0)
+            else:
+                default_grad_placements = [
+                    dist.Partial(dist.ReduceType.kRedSum)
+                ]
+                default_grad_mesh = dist.ProcessMesh(
+                    list(range(0, world_size)), dim_names=["dp"]
+                )
+                grad = dist.auto_parallel.api.dtensor_from_local(
+                    grad, default_grad_mesh, default_grad_placements
+                )
+            params_grads['params'][idx] = (params_grads['params'][idx][0], grad)
+
+
+def in_auto_dp_mode():
+    world_size = paddle.distributed.get_world_size()
+    if world_size <= 1:
+        return False
+
+    global _enable_auto_dp_mode
+    return _enable_auto_dp_mode
+
+
+def _enable_auto_dp():
+    global _enable_auto_dp_mode
+    _enable_auto_dp_mode = True