--- datasets: - NeelNanda/pile-10k base_model: - moonshotai/Kimi-K2-Instruct --- ## Model Details This model is an FP8 model with activation per-tensor FP8 quantization of [moonshotai/Kimi-K2-Instruct](https://huggingface.co/moonshotai/Kimi-K2-Instruct) generated by [intel/auto-round](https://github.com/intel/auto-round) algorithm. Please follow the license of the original model. ## How To Use ### Sample Code ```python from transformers import AutoTokenizer, AutoModelForCausalLM import transformers from transformers.modeling_utils import no_init_weights from loguru import logger import torch from torch import nn def float8_e4m3fn_ste(x: torch.Tensor): fp8 = (x.to(torch.float8_e4m3fn).to(x.dtype) - x).detach() + x return fp8 WEIGHT_SCALE_NAME = "weight_scale" INPUT_SCALE_NAME = "act_scale" class FP8QDQLinear(torch.nn.Linear): dtype = torch.bfloat16 fp8_dtype = torch.float8_e4m3fn def __init__(self, in_features: int, out_features: int, bias: bool = True, device=None): super().__init__(in_features, out_features, bias=bias) self.in_features = in_features self.out_features = out_features self.weight = nn.Parameter( torch.empty(out_features, in_features, dtype=FP8QDQLinear.fp8_dtype), requires_grad=True ) if bias: self.bias = nn.Parameter(torch.empty(out_features)) else: self.register_parameter("bias", None) def dequant_weight_online(self): fp8_weight = self.weight # if str(self.scale_weight.device) == "meta": if not hasattr(self, WEIGHT_SCALE_NAME): print(self.name, "no scale weight") qdq_weight = fp8_weight.to(FP8QDQLinear.dtype) else: qdq_weight = fp8_weight.to(FP8QDQLinear.dtype) * self.weight_scale.to(fp8_weight.device) return qdq_weight @classmethod def create_from_linear(cls, linear: nn.Linear): qdq_linear = cls(linear.in_features, linear.out_features) qdq_linear.weight.data = linear.weight.data if linear.bias is not None: qdq_linear.bias = linear.bias return qdq_linear def forward(self, bf16_input: torch.Tensor) -> torch.Tensor: if not hasattr(self, INPUT_SCALE_NAME): print(self.name, "has no scale input") qdq_input = bf16_input else: fp8_max = torch.finfo(torch.float8_e4m3fn).max fp8_res = bf16_input / getattr(self, INPUT_SCALE_NAME).to(bf16_input.device) fp8_res = torch.clip(fp8_res, -fp8_max, fp8_max) fp8_res = float8_e4m3fn_ste(fp8_res) qdq_input = fp8_res * getattr(self, INPUT_SCALE_NAME).to(fp8_res.device) qdq_weight = self.dequant_weight_online() out = torch.nn.functional.linear(qdq_input, qdq_weight, self.bias) return out torch.nn.Linear = FP8QDQLinear def get_module(module, key): """Get module from model by key name. Args: module (torch.nn.Module): original model key (str): module name to be replaced """ name_list = key.split(".") for name in name_list: module = getattr(module, name, None) return module def qdq_eval(qmodel_path, prompt="The future of AI is"): import transformers def _patch__initialize_weights(self, module): module._is_hf_initialized = True transformers.modeling_utils.PreTrainedModel._initialize_weights = _patch__initialize_weights tokenizer = transformers.AutoTokenizer.from_pretrained(qmodel_path, trust_remote_code=True) # patch_transformers() with no_init_weights(): model = transformers.AutoModelForCausalLM.from_pretrained( qmodel_path, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, trust_remote_code=True, device_map=None ) import os from safetensors.torch import safe_open dir_ = qmodel_path for file in os.listdir(dir_): if file.endswith("safetensors"): with safe_open(os.path.join(dir_, file), framework="pt", device="cpu") as f: for weight_name in f.keys(): layer_name = ".".join(weight_name.split(".")[:-1]) module = get_module(model, layer_name) if module is None: continue module.name = layer_name if WEIGHT_SCALE_NAME in weight_name: scale = f.get_tensor(weight_name) setattr(module, WEIGHT_SCALE_NAME, scale.to(FP8QDQLinear.dtype)) if INPUT_SCALE_NAME in weight_name: scale_input = f.get_tensor(weight_name) setattr(module, INPUT_SCALE_NAME, scale_input.to(FP8QDQLinear.dtype)) for n, m in model.named_modules(): if isinstance(m, FP8QDQLinear): m.name = n encode = tokenizer.encode(prompt, return_tensors="pt") model = model.to("cpu") encode = encode.to("cpu") with torch.no_grad(): generate_kwargs = dict(do_sample=False, temperature=0.0001, top_p=0.0001) output_tokens = model.generate(encode, max_new_tokens=20, **generate_kwargs) output = tokenizer.decode(output_tokens[0], skip_special_tokens=True) logger.info(f"Output: {output}") if __name__ == "__main__": qmodel_path = "/data3/Kimi-K2-Instruct-BF16-W8AFP8/Kimi-K2-Instruct-BF16-w8afp8/" qdq_eval(qmodel_path, prompt="The future of AI is") ``` ### Generate the model pip install git+https://github.com/intel/auto-round@hengguo/export_static_fp8 ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer import transformers model_name = "Kimi-K2-Instruct-BF16" tokenizer = AutoTokenizer.from_pretrained(model_name,trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained(model_name,device_map="cpu", torch_dtype="auto",trust_remote_code=True) block = model.model.layers device_map = {} for n, m in block.named_modules(): if isinstance(m, (torch.nn.Linear, transformers.modeling_utils.Conv1D)): if "experts" in n and ("shared_experts" not in n): if int(n.split('.')[-2]) < 96: device = "cuda:1" elif int(n.split('.')[-2]) >= 96 and int(n.split('.')[-2]) < 192: device = "cuda:2" elif int(n.split('.')[-2]) >= 192 and int(n.split('.')[-2]) < 288: device = "cuda:3" elif int(n.split('.')[-2]) >= 288: device = "cuda:4" else: device = "cuda:0" n = n[2:] device_map.update({n: device}) from auto_round import AutoRound autoround = AutoRound( model=model, tokenizer=tokenizer, device_map=device_map, iters=0, lr=5e-3,nsamples=512,bits=8,act_bits=8,group_size=-1,act_group_size=0, batch_size=8, low_gpu_mem_usage=True, seqlen=2048, data_type="fp8", act_data_type="fp8",act_dynamic=False, ) autoround.quantize_and_save(format="auto_round", output_dir="tmp_autoround") ``` ## Ethical Considerations and Limitations The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs. Therefore, before deploying any applications of the model, developers should perform safety testing. ## Caveats and Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Here are a couple of useful links to learn more about Intel's AI software: - Intel Neural Compressor [link](https://github.com/intel/neural-compressor) ## Disclaimer The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes. ## Cite @article{cheng2023optimize, title={Optimize weight rounding via signed gradient descent for the quantization of llms}, author={Cheng, Wenhua and Zhang, Weiwei and Shen, Haihao and Cai, Yiyang and He, Xin and Lv, Kaokao and Liu, Yi}, journal={arXiv preprint arXiv:2309.05516}, year={2023} } [arxiv](https://arxiv.org/abs/2309.05516) [github](https://github.com/intel/auto-round)