wehub-resource-sync
27 lines
1.1 KiB
Python
27 lines
1.1 KiB
Python
# Copyright (c) ModelScope Contributors. All rights reserved.
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import torch
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from megatron.core import mpu
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def reduce_max_stat_across_model_parallel_group(stat: float) -> float:
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"""
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Ranks without an optimizer will have no grad_norm or num_zeros_in_grad stats.
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We need to ensure the logging and writer rank has those values.
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This function reduces a stat tensor across the model parallel group.
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We use an all_reduce max since the values have already been summed across optimizer ranks where possible
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"""
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stat = torch.tensor([stat], dtype=torch.float32, device=torch.cuda.current_device())
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torch.distributed.all_reduce(stat, op=torch.distributed.ReduceOp.MAX, group=mpu.get_model_parallel_group())
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return stat.item()
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def logical_and_across_model_parallel_group(input: bool) -> bool:
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"""
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This function gathers a bool value across the model parallel group
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"""
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input = int(bool(input))
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input = torch.tensor([input], dtype=torch.int, device=torch.cuda.current_device())
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torch.distributed.all_reduce(input, op=torch.distributed.ReduceOp.MIN, group=mpu.get_model_parallel_group())
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return bool(input.item())
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