File size: 5,372 Bytes
d6bb310 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 | from itertools import chain
from typing import Any, List
from swift.model import MODEL_MAPPING, ModelType
from swift.template import TEMPLATE_MAPPING, TemplateType
from swift.utils import is_megatron_available
def get_url_suffix(model_id):
if ':' in model_id:
return model_id.split(':')[0]
return model_id
supported_mcore_model_types = None
def get_cache_mapping(fpath):
with open(fpath, 'r', encoding='utf-8') as f:
text = f.read()
idx = text.find('| Model ID |')
end_idx = text.find('| Dataset ID |')
text = text[idx:end_idx]
text_list = text.split('\n')[2:]
cache_mapping = {}
for text in text_list:
if not text:
continue
items = text.split('|')
if len(items) < 6:
continue
cache_mapping[items[1]] = items[5]
return cache_mapping
def get_model_info_table():
global supported_mcore_model_types
fpaths = [
'docs/source/Instruction/Supported-models-and-datasets.md',
'docs/source_en/Instruction/Supported-models-and-datasets.md'
]
cache_mapping = get_cache_mapping(fpaths[0])
end_words = [['### 多模态大模型', '## 数据集'], ['### Multimodal large models', '## Datasets']]
result = [
'| Model ID | Model Type | Default Template | '
'Requires | Support Megatron | Tags | HF Model ID |\n'
'| -------- | -----------| ---------------- | '
'-------- | ---------------- | ---- | ----------- |\n'
] * 2
res_llm: List[Any] = []
res_mllm: List[Any] = []
mg_count_llm = 0
mg_count_mllm = 0
for template in TemplateType.get_template_name_list():
assert template in TEMPLATE_MAPPING
for model_type in ModelType.get_model_name_list():
model_meta = MODEL_MAPPING[model_type]
for group in model_meta.model_groups:
for model in group.models:
ms_model_id = model.ms_model_id
hf_model_id = model.hf_model_id
if ms_model_id:
ms_model_id = f'[{ms_model_id}](https://modelscope.cn/models/{get_url_suffix(ms_model_id)})'
else:
ms_model_id = '-'
if hf_model_id:
hf_model_id = f'[{hf_model_id}](https://huggingface.co/{get_url_suffix(hf_model_id)})'
else:
hf_model_id = '-'
tags = ', '.join(group.tags or model_meta.tags) or '-'
requires = ', '.join(group.requires or model_meta.requires) or '-'
template = group.template or model_meta.template
if is_megatron_available():
from mcore_bridge.model import MODEL_MAPPING as MCORE_MODEL_MAPPING
if supported_mcore_model_types is None:
supported_mcore_model_types = set(
list(chain.from_iterable([v.model_types for k, v in MCORE_MODEL_MAPPING.items()])))
if model_meta.mcore_model_type is not None:
support_megatron = True
elif model_meta.model_type in supported_mcore_model_types:
support_megatron = True
else:
support_megatron = False
for word in ['gptq', 'awq', 'bnb', 'aqlm', 'int4', 'int8', 'nf4']:
if word in ms_model_id.lower():
support_megatron = False
break
if support_megatron and 'fp8' in ms_model_id.lower() and not any(
word in ms_model_id.lower() for word in ['qwen', 'longcat', 'intern']):
support_megatron = False
support_megatron = '✔' if support_megatron else '✘'
else:
support_megatron = cache_mapping.get(ms_model_id, '✘')
if support_megatron == '✔':
if model_meta.is_multimodal:
mg_count_mllm += 1
else:
mg_count_llm += 1
r = f'|{ms_model_id}|{model_type}|{template}|{requires}|{support_megatron}|{tags}|{hf_model_id}|\n'
if model_meta.is_multimodal:
res_mllm.append(r)
else:
res_llm.append(r)
print(f'LLM总数: {len(res_llm)}, MLLM总数: {len(res_mllm)}')
print(f'[Megatron] LLM总数: {mg_count_llm}, MLLM总数: {mg_count_mllm}')
text = ['', ''] # llm, mllm
for i, res in enumerate([res_llm, res_mllm]):
for r in res:
text[i] += r
result[i] += text[i]
for i, fpath in enumerate(fpaths):
with open(fpath, 'r', encoding='utf-8') as f:
text = f.read()
llm_start_idx = text.find('| Model ID |')
mllm_start_idx = text[llm_start_idx + 1:].find('| Model ID |') + llm_start_idx + 1
llm_end_idx = text.find(end_words[i][0])
mllm_end_idx = text.find(end_words[i][1])
output = text[:llm_start_idx] + result[0] + '\n\n' + text[llm_end_idx:mllm_start_idx] + result[
1] + '\n\n' + text[mllm_end_idx:]
with open(fpath, 'w', encoding='utf-8') as f:
f.write(output)
if __name__ == '__main__':
get_model_info_table()
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