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from typing import Any, List |
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from swift.llm import MODEL_MAPPING, TEMPLATE_MAPPING, ModelType, 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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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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text = text[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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break |
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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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fpaths = ['docs/source/Instruction/支持的模型和数据集.md', 'docs/source_en/Instruction/Supported-models-and-datasets.md'] |
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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 | ' |
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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 = 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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template = model_meta.template |
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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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if is_megatron_available(): |
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from swift.megatron import model |
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support_megatron = getattr(model_meta, 'support_megatron', False) |
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for word in ['gptq', 'awq', 'bnb', 'aqlm', 'int', 'nf4', 'fp8']: |
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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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mg_count += 1 |
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r = f'|{ms_model_id}|{model_type}|{template}|{requires}|{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)}, Megatron支持模型: {mg_count}') |
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text = ['', ''] |
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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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