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Merge pull request #447 from nemonameless/add_largedit_uvit_mp
add dit uvit dp mp sharding parallel
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86 changes: 86 additions & 0 deletions
86
ppdiffusers/examples/class_conditional_image_generation/DiT/diffusion/dist_env.py
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| # 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. | ||
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| import random | ||
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| import numpy as np | ||
| import paddle | ||
| import paddle.distributed as dist | ||
| from paddle.distributed import fleet | ||
| from paddle.distributed.fleet.meta_parallel import get_rng_state_tracker | ||
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| def set_hyrbid_parallel_seed(basic_seed, data_world_rank, mp_rank, pp_rank=0): | ||
| device_id = paddle.device.get_device() | ||
| assert "gpu" in device_id | ||
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| random.seed(basic_seed + data_world_rank) | ||
| np.random.seed(basic_seed + data_world_rank) | ||
| paddle.seed(basic_seed + data_world_rank) | ||
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| # local_seed/ global_seed is used to control dropout in ModelParallel | ||
| local_seed = 1024 + basic_seed + mp_rank * 100 + data_world_rank | ||
| global_seed = 2048 + basic_seed + data_world_rank | ||
| tracker = get_rng_state_tracker() | ||
| tracker.add("global_seed", global_seed) | ||
| tracker.add("local_seed", local_seed) | ||
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| def setdistenv(args): | ||
| world_size = dist.get_world_size() | ||
| if world_size > 1: | ||
| args.dp_degree = max(args.dp_degree, 1) | ||
| args.sharding_parallel_degree = max(args.sharding_parallel_degree, 1) | ||
| args.tensor_parallel_degree = max(args.tensor_parallel_degree, 1) | ||
| args.sep_parallel_degree = max(args.sep_parallel_degree, 1) | ||
| args.pipeline_parallel_degree = max(args.pipeline_parallel_degree, 1) | ||
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| assert ( | ||
| world_size % (args.tensor_parallel_degree * args.pipeline_parallel_degree) == 0 | ||
| ), f"Total world_size:{world_size} shoule be devided by tensor_parallel_degree: {args.tensor_parallel_degree} and pipeline_parallel_degree: {args.pipeline_parallel_degree}." | ||
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| args.dp_degree = world_size // ( | ||
| args.tensor_parallel_degree * args.sharding_parallel_degree * args.pipeline_parallel_degree | ||
| ) | ||
| strategy = fleet.DistributedStrategy() | ||
| strategy.hybrid_configs = { | ||
| "dp_degree": args.dp_degree, | ||
| "mp_degree": args.tensor_parallel_degree, | ||
| "sharding_degree": args.sharding_parallel_degree, | ||
| "pp_degree": args.pipeline_parallel_degree, | ||
| } | ||
| # strategy.find_unused_parameters = True | ||
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| # set control in tensor parallel | ||
| strategy.tensor_parallel_configs = {"tensor_init_seed": args.seed} | ||
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| fleet.init(is_collective=True, strategy=strategy) | ||
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| args.rank = dist.get_rank() | ||
| # obtain rank message of hybrid parallel | ||
| hcg = fleet.get_hybrid_communicate_group() | ||
| args.mp_rank = hcg.get_model_parallel_rank() | ||
| args.dp_rank = hcg.get_data_parallel_rank() | ||
| args.sharding_rank = hcg.get_sharding_parallel_rank() | ||
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| args.data_world_rank = args.dp_rank * args.sharding_parallel_degree + args.sharding_rank | ||
| args.data_world_size = world_size // abs(args.tensor_parallel_degree * args.pipeline_parallel_degree) | ||
| else: | ||
| args.data_world_rank = 0 | ||
| args.data_world_size = 1 | ||
| args.mp_rank = 0 | ||
| args.rank = 0 | ||
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| # seed control in hybrid parallel | ||
| set_hyrbid_parallel_seed(args.seed, args.data_world_rank, args.mp_rank) |
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