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import os |
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import torch |
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import megatron.core.parallel_state as ps |
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class Utils: |
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world_size = torch.cuda.device_count() |
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rank = int(os.environ['LOCAL_RANK']) |
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@staticmethod |
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def initialize_distributed(): |
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print(f'Initializing torch.distributed with rank: {Utils.rank}, world_size: {Utils.world_size}') |
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torch.cuda.set_device(Utils.rank % torch.cuda.device_count()) |
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init_method = 'tcp://' |
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master_ip = os.getenv('MASTER_ADDR', 'localhost') |
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master_port = os.getenv('MASTER_PORT', '6000') |
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init_method += master_ip + ':' + master_port |
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torch.distributed.init_process_group(backend='nccl', world_size=Utils.world_size, rank=Utils.rank, init_method=init_method) |
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@staticmethod |
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def destroy_model_parallel(): |
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ps.destroy_model_parallel() |
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torch.distributed.barrier() |
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@staticmethod |
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def initialize_model_parallel(tensor_model_parallel_size = 1, pipeline_model_parallel_size = 1, virtual_pipeline_model_parallel_size = None, pipeline_model_parallel_split_rank = None): |
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ps.destroy_model_parallel() |
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if not torch.distributed.is_initialized(): |
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Utils.initialize_distributed() |
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ps.initialize_model_parallel(tensor_model_parallel_size, pipeline_model_parallel_size, virtual_pipeline_model_parallel_size, pipeline_model_parallel_split_rank) |
