"""
Convert Telechat weight.
Support mindformers format.
"""
import torch
import mindspore as ms
from mindformers.tools import logger
from mindformers.utils.convert_utils import ms2pt
dtype_map = {
'float32': torch.float32,
'bfloat16': torch.bfloat16,
'float16': torch.float16
}
def name_replace(name: str):
"""replace ms param name to hf."""
name = name.replace("model.tok_embeddings.embedding_weight", "transformer.word_embeddings.weight")
name = name.replace("attention_norm.weight", "input_layernorm.weight")
name = name.replace("attention.wo.weight", "self_attention.dense.weight")
name = name.replace("attention.wo.bias", "self_attention.dense.bias")
name = name.replace("attention.wq.weight", "self_attention.query.weight")
name = name.replace("attention.wk_v.weight", "self_attention.key_value.weight")
name = name.replace("feed_forward.w1.weight", "mlp.gate_proj.weight")
name = name.replace("feed_forward.w2.weight", "mlp.down_proj.weight")
name = name.replace("feed_forward.w2.bias", "mlp.down_proj.bias")
name = name.replace("feed_forward.w3.weight", "mlp.up_proj.weight")
name = name.replace("ffn_norm.weight", "post_attention_layernorm.weight")
name = name.replace("model.norm_out.weight", "transformer.ln_f.weight")
name = name.replace("lm_head.weight", "lm_head.weight")
return name
def convert_ms_to_pt(input_path, output_path, dtype=None, **kwargs):
"""convert telechat ms weight to hf."""
logger.info(f"Trying to convert mindspore checkpoint in '{input_path}'.")
model_ms = ms.load_checkpoint(input_path)
state_dict = {}
for name, value in model_ms.items():
value = ms2pt(value, dtype)
name = name_replace(name)
if name.startswith("model.layers."):
name = name.replace("model.layers.", "transformer.h.")
state_dict[name] = value
logger.info(f'\rprocessing parameter: {name} {value.shape}')
torch.save(state_dict, output_path)
logger.info(f"\rConvert telechat checkpoint finished, the huggingface checkpoint is saved in '{output_path}'.")