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