mirror of https://github.com/hpcaitech/ColossalAI
65 lines
2.0 KiB
Python
65 lines
2.0 KiB
Python
from time import time
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from typing import Optional
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import torch
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import torch.distributed as dist
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import torch.nn as nn
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from colossalai.gemini.chunk import ChunkManager
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from colossalai.gemini.chunk.search_utils import search_chunk_configuration
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from colossalai.utils import is_ddp_ignored
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def safe_div(a, b):
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if a == 0:
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return 0
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return a / b
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def init_chunk_manager(model: nn.Module,
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init_device: Optional[torch.device] = None,
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hidden_dim: Optional[int] = None,
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search_range_mb: Optional[float] = None,
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min_chunk_size_mb: Optional[float] = None,
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filter_exlarge_params: Optional[bool] = None) -> ChunkManager:
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kwargs_dict = dict()
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if hidden_dim:
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search_interval_byte = hidden_dim
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else:
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search_interval_byte = 1024 # 1kb
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kwargs_dict["search_interval_byte"] = search_interval_byte
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if search_range_mb:
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kwargs_dict["search_range_mb"] = search_range_mb
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if min_chunk_size_mb:
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kwargs_dict["min_chunk_size_mb"] = min_chunk_size_mb
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if filter_exlarge_params:
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kwargs_dict["filter_exlarge_params"] = filter_exlarge_params
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params_sizes = [p.numel() for p in model.parameters() if not is_ddp_ignored(p)]
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total_size = sum(params_sizes) / 1024**2
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dist.barrier()
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begin = time()
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config_dict, wasted_size = search_chunk_configuration(model, **kwargs_dict)
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dist.barrier()
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end = time()
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span_s = end - begin
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wasted_size /= 1024**2
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if dist.get_rank() == 0:
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print("searching chunk configuration is completed in {:.2f} s.\n".format(span_s),
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"used number: {:.2f} MB, wasted number: {:.2f} MB\n".format(total_size, wasted_size),
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"total wasted percentage is {:.2f}%".format(100 * safe_div(wasted_size, total_size + wasted_size)),
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sep='',
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flush=True)
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dist.barrier()
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chunk_manager = ChunkManager(config_dict, init_device)
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return chunk_manager
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