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50 lines
1.8 KiB
50 lines
1.8 KiB
import inspect
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import torch.nn as nn
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from torch.optim import Optimizer
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from colossalai.utils import is_no_pp_or_last_stage
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from .naive_amp import NaiveAMPOptimizer, NaiveAMPModel
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from .grad_scaler import DynamicGradScaler, ConstantGradScaler
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def convert_to_naive_amp(model: nn.Module, optimizer: Optimizer, amp_config):
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"""A helper function to wrap training components with naive AMP modules
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:param model: your model object
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:type model: :class:`torch.nn.Module`
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:param optimizer: your optimizer object
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:type optimizer: :class:`torch.optim.Optimzer`
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:param amp_config: configuration for naive mode amp
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:type amp_config: :class:`colossalai.context.Config` or dict
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:return: (model, optimizer)
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:rtype: Tuple
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"""
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if isinstance(model, nn.ModuleList):
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# interleaved pipeline
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module_list = []
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for chunk, m in enumerate(model):
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output_to_fp32 = is_no_pp_or_last_stage() and chunk == len(model) - 1
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module_list.append(NaiveAMPModel(m, output_to_fp32=output_to_fp32))
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model = nn.ModuleList(module_list)
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else:
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output_to_fp32 = is_no_pp_or_last_stage()
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model = NaiveAMPModel(model, output_to_fp32=output_to_fp32)
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use_dynamic_grad_scaler = amp_config.pop('dynamic_grad_scale', True)
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if use_dynamic_grad_scaler:
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scaler_class = DynamicGradScaler
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else:
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scaler_class = ConstantGradScaler
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sig = inspect.signature(scaler_class.__init__)
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kwargs = dict()
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for param in sig.parameters.values():
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if param.name in amp_config:
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kwargs[param.name] = amp_config.pop(param.name)
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grad_scaler = scaler_class(**kwargs)
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optimizer = NaiveAMPOptimizer(optimizer, grad_scaler, **amp_config)
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return model, optimizer
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__all__ = ['convert_to_naive_amp', 'NaiveAMPOptimizer']
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