from typing import Optional, Union import torch from accelerate import init_empty_weights from transformers import PretrainedConfig def resolve_block_dtype(config: PretrainedConfig, dtype: Union[str, torch.dtype]) -> torch.dtype: """If dtype is "auto", resolves it using BloomConfig. Returns `dtype` intact otherwise.""" if dtype not in ("auto", None): return dtype if config.torch_dtype not in ("auto", None): return config.torch_dtype return torch.bfloat16 def get_block_size( config: PretrainedConfig, location: str, *, dtype: Optional[Union[str, torch.dtype]] = None, load_in_8bit: Optional[bool] = None, eps: float = 0.01, # eps accounts for ~1% of metainfo for tensor descriptions, quantization tables, etc. ) -> int: if location == "memory": assert ( dtype is not None and load_in_8bit is not None ), 'get_block_size(..., location="memory") requires to specify dtype and load_in_8bit for calculations' with init_empty_weights(include_buffers=True): block = config.block_class(config) n_params = sum(param.numel() for param in block.parameters()) if location == "memory" and load_in_8bit: # Note: We may need a larger eps here for models of size < 1B return n_params * (1 + eps) if location == "memory": dtype = resolve_block_dtype(config, dtype) elif location == "disk": dtype = resolve_block_dtype(config, "auto") else: raise ValueError('get_block_size() expects location to be "memory" or "disk"') return round(n_params * torch.finfo(dtype).bits // 8 * (1 + eps))