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InternLM
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fix small bugs of convert2hf;remove low_cpu_usage

pull/23/head
x54-729 2023-07-10 17:06:09 +08:00
parent 987602d4df
commit 2f56b6a00b
3 changed files with 4 additions and 4 deletions
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4
tools/transformers/README.md
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@ -4,10 +4,10 @@
## 权重转换 ## 权重转换
`convert2hf.py` 可以将训练保存的权重一键转换为 transformers 格式。 `convert2hf.py` 可以将训练保存的权重一键转换为 transformers 格式。在根目录下执行：
```bash ```bash
python convert2hf.py --src_folder origin_ckpt/ --tgt_folder hf_ckpt/ --tokenizer tokenizes/tokenizer.model python tools/transformers/convert2hf.py --src_folder origin_ckpt/ --tgt_folder hf_ckpt/ --tokenizer tokenizes/tokenizer.model
``` ```
然后可以使用 `from_pretrained` 接口加载： 然后可以使用 `from_pretrained` 接口加载：

2
tools/transformers/convert2hf.py
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@ -85,7 +85,7 @@ def convert2hf(model_config, states_tp_pps):
config.save_pretrained(folder) config.save_pretrained(folder)
torch.save(current_states, os.path.join(folder, "pytorch_model.bin")) torch.save(current_states, os.path.join(folder, "pytorch_model.bin"))
model = InternLMForCausalLM.from_pretrained(folder, torch_dtype=torch.float16, low_cpu_mem_usage=True) model = InternLMForCausalLM.from_pretrained(folder, torch_dtype=torch.float16)
del model.config._name_or_path del model.config._name_or_path
return config, model return config, model

2
tools/transformers/modeling_internlm.py
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@ -31,7 +31,7 @@ from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutpu
from transformers.modeling_utils import PreTrainedModel from transformers.modeling_utils import PreTrainedModel
from transformers.generation.streamers import BaseStreamer from transformers.generation.streamers import BaseStreamer
from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
from .configuration_internlm import InternLMConfig from configuration_internlm import InternLMConfig
logger = logging.get_logger(__name__) logger = logging.get_logger(__name__)