convert_scm_to_hf.py

import glob
import re
import shutil
import sys

import accelerate
import torch
from safetensors import safe_open
from configuration_bailing_shared_moe import BailingSharedMoeConfig
from modeling_bailing_moe import BailingMoeForCausalLM
from configuration_bailing_moe import BailingMoeConfig

input_model = sys.argv[1]
output_model_path = sys.argv[2]

auto_map = {
    "AutoConfig": "configuration_bailing_moe.BailingMoeConfig",
    "AutoModel": "modeling_bailing_moe.BailingMoeModel",
    "AutoModelForCausalLM": "modeling_bailing_moe.BailingMoeForCausalLM"
}
cfg_shared_moe = BailingSharedMoeConfig.from_pretrained(input_model)
cfg_standard_moe = BailingMoeConfig(
    auto_map=auto_map,
    vocab_size=cfg_shared_moe.vocab_size,
    hidden_size=cfg_shared_moe.hidden_size,
    intermediate_size=cfg_shared_moe.intermediate_size,
    num_hidden_layers=cfg_shared_moe.num_hidden_layers,
    num_attention_heads=cfg_shared_moe.num_attention_heads,
    num_key_value_heads=cfg_shared_moe.num_key_value_heads,
    hidden_act=cfg_shared_moe.hidden_act,
    max_position_embeddings=cfg_shared_moe.max_position_embeddings,
    initializer_range=cfg_shared_moe.initializer_range,
    rms_norm_eps=cfg_shared_moe.rms_norm_eps,
    use_cache=cfg_shared_moe.use_cache,
    tie_word_embeddings=cfg_shared_moe.tie_word_embeddings,
    rope_theta=cfg_shared_moe.rope_theta,
    rope_scaling=cfg_shared_moe.rope_scaling,
    use_sliding_window=cfg_shared_moe.use_sliding_window,
    sliding_window=cfg_shared_moe.sliding_window,
    max_window_layers=cfg_shared_moe.max_window_layers,
    attention_dropout=cfg_shared_moe.attention_dropout,
    moe_intermediate_size=cfg_shared_moe.moe_intermediate_size,
    num_experts_per_tok=cfg_shared_moe.num_experts_per_tok,
    num_experts=cfg_shared_moe.num_experts,
    num_shared_experts=cfg_shared_moe.num_shared_experts,
    norm_topk_prob=cfg_shared_moe.norm_topk_prob,
    output_router_logits=cfg_shared_moe.output_router_logits,
    shared_expert_intermediate_size=None,
    head_dim=cfg_shared_moe.head_dim,
    embedding_dropout=cfg_shared_moe.embedding_dropout,
    eos_token_id=cfg_shared_moe.eos_token_id,
    first_k_dense_replace=cfg_shared_moe.first_k_dense_replace,
    norm_head=cfg_shared_moe.norm_head,
    norm_softmax=cfg_shared_moe.norm_softmax,
    output_dropout=cfg_shared_moe.output_dropout,
    pad_token_id=cfg_shared_moe.pad_token_id,
    torch_dtype=cfg_shared_moe.torch_dtype,
    use_bias=cfg_shared_moe.use_bias,
    use_qkv_bias=cfg_shared_moe.use_qkv_bias
)
num_experts = cfg_standard_moe.num_experts

with accelerate.init_empty_weights():
    model_standard_moe = BailingMoeForCausalLM(cfg_shared_moe)

model_standard_moe = model_standard_moe.to(torch.bfloat16)
new_state_dict = {}
pattern = f"{input_model}/model-*-of-*.safetensors"
files = sorted(glob.glob(pattern))

if len(files) == 0:
    raise FileNotFoundError
tensors = {}

for file_path in files:
    print(f"processing {file_path}")
    with safe_open(file_path, framework="pt", device="cpu") as f:
        for key in f.keys():
            tensor = f.get_tensor(key)
            tensors[key] = tensor

for key in tensors:
    if "moe_mlp" not in key:
        new_state_dict[key] = tensors[key]
    elif "moe_mlp.output_experts" in key:
        layer_num = int(re.search(r"\d+", key).group())
        for i, tensor in enumerate(torch.unbind(tensors[key])):
            new_state_dict[
                f"model.layers.{layer_num}.mlp.experts.{i}.down_proj.weight"
            ] = tensor.contiguous()
    elif "moe_mlp.experts" in key:
        layer_num = int(re.search(r"\d+", key).group())
        for i, tensor in enumerate(torch.unbind(tensors[key])):
            (
                new_state_dict[
                    f"model.layers.{layer_num}.mlp.experts.{i}.up_proj.weight"
                ],
                new_state_dict[
                    f"model.layers.{layer_num}.mlp.experts.{i}.gate_proj.weight"
                ],
            ) = torch.chunk(tensor, 2, dim=0)

model_standard_moe.load_state_dict(new_state_dict, strict=True, assign=True)
model_standard_moe.save_pretrained(output_model_path)
cfg_standard_moe.save_pretrained(output_model_path)

shutil.copy(
    "modeling_bailing_moe.py",
    output_model_path + "/" + "modeling_bailing_moe.py",
)
shutil.copy(
    "configuration_bailing_moe.py",
    output_model_path + "/" + "configuration_bailing_moe.py",
)

for i in ["special_tokens_map.json", "tokenizer_config.json", "tokenizer.json"]:
    shutil.copy(input_model + "/" + i, output_model_path + "/" + i)