inference/convert.py

import os
import shutil
from argparse import ArgumentParser
from glob import glob
from tqdm import tqdm, trange

import torch
from safetensors.torch import safe_open, save_file


FP4_TABLE = torch.tensor([
    0.0, 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 6.0,
    0.0, -0.5, -1.0, -1.5, -2.0, -3.0, -4.0, -6.0
], dtype=torch.float32)


def cast_e2m1fn_to_e4m3fn(x: torch.Tensor, scale: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
    """
    Casts a tensor from e2m1fn to e4m3fn losslessly.
    """
    assert x.dtype == torch.int8
    assert x.ndim == 2
    out_dim, in_dim = x.size()
    in_dim *= 2
    fp8_block_size = 128
    fp4_block_size = 32
    assert in_dim % fp8_block_size == 0 and out_dim % fp8_block_size == 0
    assert scale.size(0) == out_dim and scale.size(1) == in_dim // fp4_block_size

    x = x.view(torch.uint8)
    low  = x & 0x0F
    high = (x >> 4) & 0x0F
    x = torch.stack([FP4_TABLE[low.long()], FP4_TABLE[high.long()]], dim=-1).flatten(2)

    # max_fp4 (6.0) * MAX_OFFSET must fit in e4m3fn (max 448)
    # 6.0 * 2^6 = 384 < 448; 6.0 * 2^7 = 768 > 448; so MAX_OFFSET_BITS = 6
    MAX_OFFSET_BITS = 6

    bOut = out_dim // fp8_block_size
    bIn = in_dim // fp8_block_size
    # bOut, bIn, 128, 128
    x = x.view(bOut, fp8_block_size, bIn, fp8_block_size).transpose(1, 2)
    # bOut, bIn, 128*4
    scale = scale.float().view(bOut, fp8_block_size, bIn, -1).transpose(1, 2).flatten(2)
    ## bOut, bIn, 1
    scale_max_offset_bits = scale.amax(dim=-1, keepdim=True) / (2**MAX_OFFSET_BITS)
    # bOut, bIn, 128*4
    offset = scale / scale_max_offset_bits
    # bOut, bIn, 128, 128
    offset = offset.unflatten(-1, (fp8_block_size, -1)).repeat_interleave(fp4_block_size, dim=-1)
    x = (x * offset).transpose(1, 2).reshape(out_dim, in_dim)
    return x.to(torch.float8_e4m3fn), scale_max_offset_bits.squeeze(-1).to(torch.float8_e8m0fnu)


mapping = {
    "embed_tokens": ("embed", 0),
    "input_layernorm": ("attn_norm", None),
    "post_attention_layernorm": ("ffn_norm", None),
    "q_proj": ("wq", 0),
    "q_a_proj": ("wq_a", None),
    "q_a_layernorm": ("q_norm", None),
    "q_b_proj": ("wq_b", 0),
    "kv_a_proj_with_mqa": ("wkv_a", None),
    "kv_a_layernorm": ("kv_norm", None),
    "kv_b_proj": ("wkv_b", 0),
    "o_proj": ("wo", 1),
    "gate_proj": ("w1", 0),
    "down_proj": ("w2", 1),
    "up_proj": ("w3", 0),
    "lm_head": ("head", 0),

    "embed": ("embed", 0),
    "wq_b": ("wq_b", 0),
    "wo_a": ("wo_a", 0),
    "wo_b": ("wo_b", 1),
    "head": ("head", 0),
    "attn_sink": ("attn_sink", 0),
    "weights_proj": ("weights_proj", 0),
}


def main(hf_ckpt_path, save_path, n_experts, mp, expert_dtype):
    """
    Converts and saves model checkpoint files into a specified format.

    Args:
        hf_ckpt_path (str): Path to the directory containing the input checkpoint files.
        save_path (str): Path to the directory where the converted checkpoint files will be saved.
        n_experts (int): Total number of experts in the model.
        mp (int): Model parallelism factor.
        
    Returns:
        None
    """
    torch.set_num_threads(8)
    n_local_experts = n_experts // mp
    state_dicts = [{} for _ in range(mp)]

    for file_path in tqdm(glob(os.path.join(hf_ckpt_path, "*.safetensors"))):
        with safe_open(file_path, framework="pt", device="cpu") as f:
            for name in f.keys():
                param: torch.Tensor = f.get_tensor(name)
                if name.startswith("model."):
                    name = name[len("model."):]
                if name.startswith("mtp.") and ("emb" in name or name.endswith("head.weight")):
                    continue
                name = name.replace("self_attn", "attn")
                name = name.replace("mlp", "ffn")
                name = name.replace("weight_scale_inv", "scale")
                name = name.replace("e_score_correction_bias", "bias")
                if any(x in name for x in ["hc", "attn_sink", "tie2eid", "ape"]):    # without .weight
                    key = name.split(".")[-1]
                else:
                    key = name.split(".")[-2]
                if key in mapping:
                    new_key, dim = mapping[key]
                else:
                    new_key, dim = key, None
                name = name.replace(key, new_key)
                for i in range(mp):
                    new_param = param
                    if "experts" in name and "shared_experts" not in name:
                        idx = int(name.split(".")[-3])
                        if idx < i * n_local_experts or idx >= (i + 1) * n_local_experts:
                            continue
                    elif dim is not None:
                        assert param.size(dim) % mp == 0, f"Dimension {dim} must be divisible by {mp}"
                        shard_size = param.size(dim) // mp
                        new_param = param.narrow(dim, i * shard_size, shard_size).contiguous()
                    state_dicts[i][name] = new_param

    os.makedirs(save_path, exist_ok=True)

    for i in trange(mp):
        names = list(state_dicts[i].keys())
        for name in names:
            if name.endswith("wo_a.weight"):
                weight = state_dicts[i][name]
                scale = state_dicts[i].pop(name.replace("weight", "scale"))
                weight = weight.unflatten(0, (-1, 128)).unflatten(-1, (-1, 128)).float() * scale[:, None, :, None].float()
                state_dicts[i][name] = weight.flatten(2, 3).flatten(0, 1).bfloat16()
            elif "experts" in name and state_dicts[i][name].dtype == torch.int8:
                if expert_dtype == "fp8":
                    scale_name = name.replace("weight", "scale")
                    weight = state_dicts[i].pop(name)
                    scale = state_dicts[i].pop(scale_name)
                    state_dicts[i][name], state_dicts[i][scale_name] = cast_e2m1fn_to_e4m3fn(weight, scale)
                else:
                    state_dicts[i][name] = state_dicts[i][name].view(torch.float4_e2m1fn_x2)
        save_file(state_dicts[i], os.path.join(save_path, f"model{i}-mp{mp}.safetensors"))

    for file in ["tokenizer.json", "tokenizer_config.json"]:
        old_file_path = os.path.join(hf_ckpt_path, file)
        new_file_path = os.path.join(save_path, file)
        if os.path.exists(old_file_path):
            shutil.copyfile(old_file_path, new_file_path)


if __name__ == "__main__":
    parser = ArgumentParser()
    parser.add_argument("--hf-ckpt-path", type=str, required=True)
    parser.add_argument("--save-path", type=str, required=True)
    parser.add_argument("--n-experts", type=int, required=True)
    parser.add_argument("--model-parallel", type=int, required=True)
    parser.add_argument("--expert-dtype", type=str, choices=["fp8", "fp4"], required=False, default=None)
    args = parser.parse_args()
    assert args.n_experts % args.model_parallel == 0, "Number of experts must be divisible by model parallelism"
    main(args.hf_ckpt_path, args.save_path, args.n_experts, args.model_parallel, args.expert_dtype)