import torch import subprocess import os subprocess.call('pip install einops', shell=True) subprocess.call('pip install flash-attn==1.0.3.post0', shell=True) print('Installing einops and flash-attn...') print('Done.') print('Loading MPT-7B Storywriter 4bit 128G... Please wait...') def _cast_if_autocast_enabled(tensor): if torch.is_autocast_enabled(): if tensor.device.type == 'cuda': dtype = torch.get_autocast_gpu_dtype() elif tensor.device.type == 'cpu': dtype = torch.get_autocast_cpu_dtype() else: raise NotImplementedError() return tensor.to(dtype=dtype) return tensor class LPLayerNorm(torch.nn.LayerNorm): def __init__(self, normalized_shape, eps=1e-05, elementwise_affine=True, device=None, dtype=None): super().__init__(normalized_shape=normalized_shape, eps=eps, elementwise_affine=elementwise_affine, device=device, dtype=dtype) def forward(self, x): module_device = x.device downcast_x = _cast_if_autocast_enabled(x) downcast_weight = _cast_if_autocast_enabled(self.weight) if self.weight is not None else self.weight downcast_bias = _cast_if_autocast_enabled(self.bias) if self.bias is not None else self.bias with torch.autocast(enabled=False, device_type=module_device.type): return torch.nn.functional.layer_norm(downcast_x, self.normalized_shape, downcast_weight, downcast_bias, self.eps) def rms_norm(x, weight=None, eps=1e-05): output = x / torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + eps) if weight is not None: return output * weight return output class RMSNorm(torch.nn.Module): def __init__(self, normalized_shape, eps=1e-05, weight=True, dtype=None, device=None): super().__init__() self.eps = eps if weight: self.weight = torch.nn.Parameter(torch.ones(normalized_shape, dtype=dtype, device=device)) else: self.register_parameter('weight', None) def forward(self, x): return rms_norm(x.float(), self.weight, self.eps).to(dtype=x.dtype) class LPRMSNorm(RMSNorm): def __init__(self, normalized_shape, eps=1e-05, weight=True, dtype=None, device=None): super().__init__(normalized_shape=normalized_shape, eps=eps, weight=weight, dtype=dtype, device=device) def forward(self, x): downcast_x = _cast_if_autocast_enabled(x) downcast_weight = _cast_if_autocast_enabled(self.weight) if self.weight is not None else self.weight with torch.autocast(enabled=False, device_type=x.device.type): return rms_norm(downcast_x, downcast_weight, self.eps).to(dtype=x.dtype) NORM_CLASS_REGISTRY = {'layernorm': torch.nn.LayerNorm, 'low_precision_layernorm': LPLayerNorm, 'rmsnorm': RMSNorm, 'low_precision_rmsnorm': LPRMSNorm}