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| import time | |
| import torch | |
| import torch.nn as nn | |
| from gptq import * | |
| from modelutils import * | |
| from quant import * | |
| from transformers import AutoTokenizer | |
| DEV = torch.device('cuda:0') | |
| def get_llama(model): | |
| import torch | |
| def skip(*args, **kwargs): | |
| pass | |
| torch.nn.init.kaiming_uniform_ = skip | |
| torch.nn.init.uniform_ = skip | |
| torch.nn.init.normal_ = skip | |
| from transformers import LlamaForCausalLM | |
| model = LlamaForCausalLM.from_pretrained(model, torch_dtype='auto') | |
| model.seqlen = 2048 | |
| return model | |
| def load_quant(model, checkpoint, wbits, groupsize): | |
| from transformers import LlamaConfig, LlamaForCausalLM | |
| config = LlamaConfig.from_pretrained(model) | |
| def noop(*args, **kwargs): | |
| pass | |
| torch.nn.init.kaiming_uniform_ = noop | |
| torch.nn.init.uniform_ = noop | |
| torch.nn.init.normal_ = noop | |
| torch.set_default_dtype(torch.half) | |
| transformers.modeling_utils._init_weights = False | |
| torch.set_default_dtype(torch.half) | |
| model = LlamaForCausalLM(config) | |
| torch.set_default_dtype(torch.float) | |
| model = model.eval() | |
| layers = find_layers(model) | |
| for name in ['lm_head']: | |
| if name in layers: | |
| del layers[name] | |
| make_quant(model, layers, wbits, groupsize) | |
| print('Loading model ...') | |
| if checkpoint.endswith('.safetensors'): | |
| from safetensors.torch import load_file as safe_load | |
| model.load_state_dict(safe_load(checkpoint)) | |
| else: | |
| model.load_state_dict(torch.load(checkpoint)) | |
| model.seqlen = 2048 | |
| print('Done.') | |
| return model | |
| if __name__ == '__main__': | |
| import argparse | |
| from datautils import * | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument( | |
| 'model', type=str, | |
| help='llama model to load' | |
| ) | |
| parser.add_argument( | |
| '--wbits', type=int, default=16, choices=[2, 3, 4, 8, 16], | |
| help='#bits to use for quantization; use 16 for evaluating base model.' | |
| ) | |
| parser.add_argument( | |
| '--groupsize', type=int, default=-1, | |
| help='Groupsize to use for quantization; default uses full row.' | |
| ) | |
| parser.add_argument( | |
| '--load', type=str, default='', | |
| help='Load quantized model.' | |
| ) | |
| parser.add_argument( | |
| '--text', type=str, | |
| help='input text' | |
| ) | |
| parser.add_argument( | |
| '--min_length', type=int, default=10, | |
| help='The minimum length of the sequence to be generated.' | |
| ) | |
| parser.add_argument( | |
| '--max_length', type=int, default=50, | |
| help='The maximum length of the sequence to be generated.' | |
| ) | |
| parser.add_argument( | |
| '--top_p', type=float , default=0.95, | |
| help='If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for generation.' | |
| ) | |
| parser.add_argument( | |
| '--temperature', type=float, default=0.8, | |
| help='The value used to module the next token probabilities.' | |
| ) | |
| args = parser.parse_args() | |
| if type(args.load) is not str: | |
| args.load = args.load.as_posix() | |
| if args.load: | |
| model = load_quant(args.model, args.load, args.wbits, args.groupsize) | |
| else: | |
| model = get_llama(args.model) | |
| model.eval() | |
| model.to(DEV) | |
| tokenizer = AutoTokenizer.from_pretrained(args.model) | |
| input_ids = tokenizer.encode(args.text, return_tensors="pt").to(DEV) | |
| with torch.no_grad(): | |
| generated_ids = model.generate( | |
| input_ids, | |
| do_sample=True, | |
| min_length=args.min_length, | |
| max_length=args.max_length, | |
| top_p=args.top_p, | |
| temperature=args.temperature, | |
| ) | |
| print(tokenizer.decode([el.item() for el in generated_ids[0]])) | |