wehub-resource-sync
129 lines
5.3 KiB
Python
129 lines
5.3 KiB
Python
from itertools import chain
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from typing import Any, List
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from swift.model import MODEL_MAPPING, ModelType
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from swift.template import TEMPLATE_MAPPING, TemplateType
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from swift.utils import is_megatron_available
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def get_url_suffix(model_id):
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if ':' in model_id:
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return model_id.split(':')[0]
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return model_id
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supported_mcore_model_types = None
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def get_cache_mapping(fpath):
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with open(fpath, 'r', encoding='utf-8') as f:
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text = f.read()
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idx = text.find('| Model ID |')
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end_idx = text.find('| Dataset ID |')
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text = text[idx:end_idx]
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text_list = text.split('\n')[2:]
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cache_mapping = {}
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for text in text_list:
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if not text:
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continue
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items = text.split('|')
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if len(items) < 6:
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continue
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cache_mapping[items[1]] = items[5]
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return cache_mapping
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def get_model_info_table():
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global supported_mcore_model_types
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fpaths = [
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'docs/source/Instruction/Supported-models-and-datasets.md',
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'docs/source_en/Instruction/Supported-models-and-datasets.md'
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]
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cache_mapping = get_cache_mapping(fpaths[0])
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end_words = [['### 多模态大模型', '## 数据集'], ['### Multimodal large models', '## Datasets']]
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result = [
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'| Model ID | Model Type | Default Template | Default Agent Template | '
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'Requires | Support Megatron | Tags | HF Model ID |\n'
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'| -------- | -----------| ---------------- | ---------------------- | '
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'-------- | ---------------- | ---- | ----------- |\n'
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] * 2
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res_llm: List[Any] = []
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res_mllm: List[Any] = []
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mg_count_llm = 0
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mg_count_mllm = 0
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for template in TemplateType.get_template_name_list():
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assert template in TEMPLATE_MAPPING
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for model_type in ModelType.get_model_name_list():
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model_meta = MODEL_MAPPING[model_type]
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for group in model_meta.model_groups:
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for model in group.models:
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ms_model_id = model.ms_model_id
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hf_model_id = model.hf_model_id
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if ms_model_id:
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ms_model_id = f'[{ms_model_id}](https://modelscope.cn/models/{get_url_suffix(ms_model_id)})'
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else:
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ms_model_id = '-'
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if hf_model_id:
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hf_model_id = f'[{hf_model_id}](https://huggingface.co/{get_url_suffix(hf_model_id)})'
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else:
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hf_model_id = '-'
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tags = ', '.join(group.tags or model_meta.tags) or '-'
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requires = ', '.join(group.requires or model_meta.requires) or '-'
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template = group.template or model_meta.template
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template_meta = TEMPLATE_MAPPING.get(template)
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agent_template = template_meta.agent_template if template_meta else ''
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agent_template = agent_template or ''
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if is_megatron_available():
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from mcore_bridge.model import MODEL_MAPPING as MCORE_MODEL_MAPPING
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if supported_mcore_model_types is None:
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supported_mcore_model_types = set(
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list(chain.from_iterable([v.model_types for k, v in MCORE_MODEL_MAPPING.items()])))
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if model_meta.mcore_model_type is not None:
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support_megatron = True
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elif model_meta.model_type in supported_mcore_model_types:
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support_megatron = True
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else:
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support_megatron = False
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for word in ['gptq', 'awq', 'bnb', 'aqlm', 'int4', 'int8', 'nf4']:
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if word in ms_model_id.lower():
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support_megatron = False
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break
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support_megatron = '✔' if support_megatron else '✘'
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else:
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support_megatron = cache_mapping.get(ms_model_id, '✘')
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if support_megatron == '✔':
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if model_meta.is_multimodal:
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mg_count_mllm += 1
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else:
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mg_count_llm += 1
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r = (f'|{ms_model_id}|{model_type}|{template}|{agent_template}|{requires}|'
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f'{support_megatron}|{tags}|{hf_model_id}|\n')
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if model_meta.is_multimodal:
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res_mllm.append(r)
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else:
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res_llm.append(r)
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print(f'LLM总数: {len(res_llm)}, MLLM总数: {len(res_mllm)}')
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print(f'[Megatron] LLM总数: {mg_count_llm}, MLLM总数: {mg_count_mllm}')
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text = ['', ''] # llm, mllm
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for i, res in enumerate([res_llm, res_mllm]):
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for r in res:
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text[i] += r
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result[i] += text[i]
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for i, fpath in enumerate(fpaths):
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with open(fpath, 'r', encoding='utf-8') as f:
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text = f.read()
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llm_start_idx = text.find('| Model ID |')
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mllm_start_idx = text[llm_start_idx + 1:].find('| Model ID |') + llm_start_idx + 1
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llm_end_idx = text.find(end_words[i][0])
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mllm_end_idx = text.find(end_words[i][1])
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output = text[:llm_start_idx] + result[0] + '\n\n' + text[llm_end_idx:mllm_start_idx] + result[
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1] + '\n\n' + text[mllm_end_idx:]
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with open(fpath, 'w', encoding='utf-8') as f:
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f.write(output)
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if __name__ == '__main__':
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get_model_info_table()
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