mirror of
https://git.datalinker.icu/comfyanonymous/ComfyUI
synced 2026-11-12 12:13:29 +08:00
87 lines
2.8 KiB
Python
87 lines
2.8 KiB
Python
import torch
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from torch.autograd import Variable
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from torch.nn.parameter import Parameter
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_FLOAT_TYPES = (torch.FloatTensor, torch.cuda.FloatTensor)
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_HALF_TYPES = (torch.HalfTensor, torch.cuda.HalfTensor)
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def conversion_helper(val, conversion):
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"""Apply conversion to val. Recursively apply conversion if `val`
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#is a nested tuple/list structure."""
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if isinstance(val, dict):
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res_dict = {}
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for k, v in val.items():
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if k!= 'cos_cis_img' and k != 'sin_cis_img':
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res_dict[k] = conversion_helper(v, conversion)
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else:
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res_dict[k] = v
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return res_dict
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if not isinstance(val, (tuple, list)):
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return conversion(val)
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rtn = [conversion_helper(v, conversion) for v in val]
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if isinstance(val, tuple):
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rtn = tuple(rtn)
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return rtn
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def fp32_to_float16(val, float16_convertor):
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"""Convert fp32 `val` to fp16/bf16"""
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def half_conversion(val):
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val_typecheck = val
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if isinstance(val_typecheck, (Parameter, Variable)):
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val_typecheck = val.data
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if isinstance(val_typecheck, _FLOAT_TYPES):
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val = float16_convertor(val)
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return val
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return conversion_helper(val, half_conversion)
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def float16_to_fp32(val):
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"""Convert fp16/bf16 `val` to fp32"""
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def float_conversion(val):
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val_typecheck = val
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if isinstance(val_typecheck, (Parameter, Variable)):
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val_typecheck = val.data
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if isinstance(val_typecheck, (_HALF_TYPES,)):
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val = val.float()
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return val
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return conversion_helper(val, float_conversion)
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class Float16Module(torch.nn.Module):
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def __init__(self, module, args):
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super(Float16Module, self).__init__()
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self.add_module('module', module.half())
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def float16_convertor(val):
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return val.half()
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self.float16_convertor = float16_convertor
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self.config = self.module.config
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self.dtype = torch.float16
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def forward(self, *inputs, **kwargs):
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inputs = fp32_to_float16(inputs, self.float16_convertor)
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kwargs = fp32_to_float16(kwargs, self.float16_convertor)
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outputs = self.module(*inputs, **kwargs)
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outputs = float16_to_fp32(outputs)
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return outputs
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def state_dict(self, destination=None, prefix='', keep_vars=False):
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return self.module.state_dict(destination, prefix, keep_vars)
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def state_dict_for_save_checkpoint(self, destination=None, prefix='',
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keep_vars=False):
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return self.module.state_dict_for_save_checkpoint(destination, prefix,
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keep_vars)
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def load_state_dict(self, state_dict, strict=True):
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self.module.load_state_dict(state_dict, strict=strict)
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