bf16_cast_channel_int4_v2.py

import os
import json
from argparse import ArgumentParser
from glob import glob
from tqdm import tqdm

import torch
from safetensors.torch import load_file, save_file
from huggingface_hub import snapshot_download

import numpy as np
import matplotlib.pyplot as plt

def get_plot(matrix: torch.Tensor):
    n_rows = matrix.shape[0]
    row_labels = [f"Row_{i}" for i in range(n_rows)]

    # 为每行生成一个独立的图片并保存
    for i in range(n_rows):
        plt.figure(figsize=(8, 4))
        plt.hist(matrix[i, :], bins=20, alpha=0.7, color='green')
        plt.title(f"Distribution of {row_labels[i]}")
        plt.xlabel("Value")
        plt.ylabel("Frequency")
        plt.savefig(f"./result/row_{i}_histogram.png")  # 保存为PNG
        plt.close() 



def weight_quant(tensor: torch.Tensor):
    assert tensor.dim() == 2
    qmax = 127.0 #-127 到 127
    abs_max = torch.abs(tensor).max(dim=1, keepdim=True)[0]  # [rows, 1]
    scale = abs_max / qmax  # [rows, 1]
    assert scale.shape == (tensor.shape[0], 1)
    quantized = torch.round(tensor / scale)
    quantized = torch.clamp(quantized, -qmax, qmax)
    return quantized.to(torch.int8), scale.to(torch.float32)

def weight_quantint4(tensor: torch.Tensor):
    assert tensor.dim() == 2
    qmax = 7.0 #-7 到 7
    
    #求绝对值
    abs_value=torch.abs(tensor)
    
    #对绝对值进行排序
    sorted_matrix,_ = torch.sort(abs_value, dim=1)
    k=tensor.shape[1]
    index=int(k*0.95)
    abs_max=sorted_matrix[:,index].reshape(-1,1)
    
    # print("abs_max:",abs_max)
    # print("abs_max.shape:",abs_max.shape)
    #abs_max = torch.abs(tensor).max(dim=1, keepdim=True)[0]  # [rows, 1]
    
    scale = abs_max / qmax  # [rows, 1]
    
    assert scale.shape == (tensor.shape[0], 1)
    #量化
    quantized = torch.round(tensor / scale)
    quantized = torch.clamp(quantized, -qmax, qmax).to(torch.int8)
    
    #quantized_int8=quantized+8
    print("quantized_int8:",quantized)
    negative_mask = quantized < 0

    # 2. 对负数取绝对值并加 8
    #quantized[negative_mask] = torch.abs(quantized[negative_mask]) + 16
    quantized[negative_mask] = quantized[negative_mask] #+ 8

    print("quantized_int8_2:",quantized)

    quantized_int8=quantized.to(torch.uint8) 
    
    n, k = quantized.size()
    new_shape = (n, k // 2)
    quantized_int4= torch.empty(new_shape, dtype=torch.int8, device=tensor.device)
    
    a=quantized_int8[..., ::2]
    b = quantized_int8[..., 1::2]
        
    a_4bit = a #& 0x0F
    b_4bit = b & 0x0F
    quantized_int4 = (a_4bit << 4) |  b_4bit
    quantized_int4=quantized_int4.contiguous().to(torch.int8)   
    
    # print("quantized_int4.shape:",quantized_int4.shape)
    # quantized_int4_2=torch.repeat_interleave(quantized_int4, repeats=2, dim=-1)
    # a1= quantized_int4_2[..., ::2]
    # b1= quantized_int4_2[..., 1::2]
    
    # print("a1:",a1)
    # print("b1:",b1)
    
    # a2= a1 & 0xF0
    # b2 = (b1<<4 ) & 0xF0
    # print("a:",a2)
    # print("b:",b2)

    return quantized_int4 , scale.to(torch.float32)



def main(bf16_path, int8_path, model_name="deepseek-ai/DeepSeek-R1"):
    torch.set_default_dtype(torch.bfloat16)
    os.makedirs(int8_path, exist_ok=True)
    model_index_file = os.path.join(int8_path, "model.safetensors.index.json")
    config_file = os.path.join(int8_path, "config.json")

    if not os.path.exists(model_index_file) or not os.path.exists(config_file):
        snapshot_download(
            repo_id=model_name,
            ignore_patterns=["*.safetensors"],
            local_dir=int8_path,
            local_dir_use_symlinks=False
        )
        print(f"model index file and config file downloaded to {int8_path}")

        # modify config.json and save it
        config = json.load(open(config_file))
        # delete quantization_config
        config.pop("quantization_config", None)
        with open(config_file, "w", encoding="utf-8") as f:
            json.dump(config, f, indent=2, ensure_ascii=False, sort_keys=True)
        print(f"config.json modified and saved to {config_file}")

    with open(model_index_file, "r") as f:
        model_index = json.load(f)
    weight_map = model_index["weight_map"]
    scale_count = len([key for key in weight_map.keys() if key.endswith("_scale_inv")])
    
    safetensor_files = list(glob(os.path.join(bf16_path, "*.safetensors")))
    safetensor_files.sort()
    quant_count = 0
    new_weight_map = {}
    for safetensor_file in tqdm(safetensor_files):
        file_name = os.path.basename(safetensor_file)
        state_dict = load_file(safetensor_file, device="cuda")
        new_state_dict = {}
        for weight_name, weight in state_dict.items():
            scale_inv_name = f"{weight_name}_scale_inv"
            if scale_inv_name in weight_map:

                print("scale_inv_name:",scale_inv_name)
                
                assert weight.element_size() == 2
                quant_count += 1
                int8_weight, scale_inv = weight_quant(weight)
                new_scale_name = scale_inv_name.replace("_scale_inv", "_scale")
                
                if ".mlp.experts." in weight_name:
                    int4_weight,scale_int4 =weight_quantint4(int8_weight)
                    new_state_dict[weight_name] = int4_weight #int8_weight
                    new_state_dict[new_scale_name] = scale_inv*scale_int4/16
                else:
                    new_state_dict[weight_name] = int8_weight
                    new_state_dict[new_scale_name] = scale_inv
                
                new_weight_map[weight_name] = file_name
                new_weight_map[new_scale_name] = file_name
            else:
                print("nonono")
                new_state_dict[weight_name] = weight
                new_weight_map[weight_name] = file_name
        new_safetensor_file = os.path.join(int8_path, file_name)
        save_file(new_state_dict, new_safetensor_file)
    #assert quant_count == scale_count
    print(f"{quant_count} weights are quantized.")

    # modify model.safetensors.index.json
    with open(model_index_file, "r") as f:
        model_index = json.load(f)
    model_index["weight_map"] = new_weight_map
    with open(model_index_file, "w", encoding="utf-8") as f:
        json.dump(model_index, f, indent=2, ensure_ascii=False, sort_keys=True)
    print(f"model.safetensors.index.json modified and saved to {model_index_file}")
        

if __name__ == "__main__":
    parser = ArgumentParser()
    parser.add_argument("--input-bf16-hf-path", type=str, default="/dataset/llm-models/deepseek-r1/DeepSeek-R1-0528-bf16")
    parser.add_argument("--output-int8-hf-path", type=str, default="/FrameWork/0307/3/modeltrans/DeepSeek-R1-0528-SlimQuant-W4A8")
    parser.add_argument("--model-name", type=str, default="deepseek-ai/DeepSeek-R1")

    args = parser.parse_args()
    main(args.input_bf16_hf_path, args.output_int8_hf_path, args.model_name)
    print("done")