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Model: Alibaba-AAIG/YuFeng-XGuard-Reason-8B Source: Original Platform
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<p align="center">
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<img src="./assets/xguard_banner.png" alt="Xguard Banner" width=100%/>
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</p>
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<p><br></p>
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<div align="center">
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<h1 style="margin: 0;">YuFeng-XGuard: 归因驱动的动态可解释大语言模型安全护栏</h1>
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</div>
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<p align="center">
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  🤗 <a href="https://huggingface.co/Alibaba-AAIG/YuFeng-XGuard-Reason-8B">HuggingFace</a>   |
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  🤖 <a href="https://modelscope.cn/models/Alibaba-AAIG/YuFeng-XGuard-Reason-8B">ModelScope</a>   |
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  🎬 <a href="https://modelscope.cn/studios/Alibaba-AAIG/YuFeng-XGuard-Reason-Demo/summary">Demo</a>   |
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  📄 <a href="https://arxiv.org/abs/2601.15588">Paper</a>  
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</p>
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---
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## 🐬 介绍
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**YuFeng-XGuard-Reason** 是一系列专为内容安全设计的护栏模型，旨在精准识别用户请求、模型响应及通用文本中的安全风险，并提供可配置的风险归因信息。
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模型基于 Qwen3 架构构建，针对线上实时交互场景进行了深度优化，兼顾推理时延、识别精度与策略扩展能力。为了在保证可解释性的同时最大限度降低生成开销，模型采用了“先判定、后归因”的两阶段输出范式：优先输出结构化的风险结论，随后按需提供详细的风险解释。目前模型在多语言风险识别、攻击指令防御及安全补全等多个内容安全基准测试中均达到SOTA水平。
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### 关键特性
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* **多尺寸规模覆盖**：提供 0.6B 和 8B 两种参数版本。0.6B 版本侧重极速推理，适配高并发、低延迟的实时场景；8B 版本侧重复杂风险理解，提供更高的识别效果。
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* **低延迟推理范式**：采用两阶段输出策略，首词优先生成风险判定（风险分类及分值），随后生成风险归因（关键触发点与合规解释），兼顾判定的即时性与审计的透明度。
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* **完善的安全体系**：内置覆盖广泛的通用安全与合规分类体系，深度适配监管场景与高风险内容识别。
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* **动态策略适配**：8B 版本支持在推理时通过 Prompt 动态引入自定义安全类别或调整既有维度的判定标准，助力业务侧快速迭代防控口径，无需频繁微调模型。
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### 评测效果
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在多个内容安全基准测试中，YuFeng-XGuard-Reason 与主流护栏模型进行了性能对比。更多详实数据请参阅[技术报告](https://arxiv.org/pdf/2601.15588)。
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<img src="./assets/topk.png" alt="top performance" width="1100"/>
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<p><br></p>
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## 🚀 快速使用
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加载模型
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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def infer(model, tokenizer, messages, policy=None, max_new_tokens=1, reason_first=False):
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rendered_query = tokenizer.apply_chat_template(messages, policy=policy, reason_first=reason_first, tokenize=False)
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model_inputs = tokenizer([rendered_query], return_tensors="pt").to(model.device)
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outputs = model.generate(**model_inputs, max_new_tokens=max_new_tokens, do_sample=False, output_scores=True, return_dict_in_generate=True)
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batch_idx = 0
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input_length = model_inputs['input_ids'].shape[1]
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output_ids = outputs["sequences"].tolist()[batch_idx][input_length:]
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response = tokenizer.decode(output_ids, skip_special_tokens=True)
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### parse score ###
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generated_tokens_with_probs = []
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generated_tokens = outputs.sequences[:, input_length:]
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scores = torch.stack(outputs.scores, 1)
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scores = scores.softmax(-1)
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scores_topk_value, scores_topk_index = scores.topk(k=10, dim=-1)
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for generated_token, score_topk_value, score_topk_index in zip(generated_tokens, scores_topk_value, scores_topk_index):
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generated_tokens_with_prob = []
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for token, topk_value, topk_index in zip(generated_token, score_topk_value, score_topk_index):
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token = int(token.cpu())
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if token == tokenizer.pad_token_id:
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continue
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res_topk_score = {}
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for ii, (value, index) in enumerate(zip(topk_value, topk_index)):
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if ii == 0 or value.cpu().numpy() > 1e-4:
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text = tokenizer.decode(index.cpu().numpy())
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res_topk_score[text] = {
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"id": str(int(index.cpu().numpy())),
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"prob": round(float(value.cpu().numpy()), 4),
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}
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generated_tokens_with_prob.append(res_topk_score)
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generated_tokens_with_probs.append(generated_tokens_with_prob)
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score_idx = max(len(generated_tokens_with_probs[batch_idx])-2, 0) if reason_first else 0
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id2risk = tokenizer.init_kwargs['id2risk']
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token_score = {k:v['prob'] for k,v in generated_tokens_with_probs[batch_idx][score_idx].items()}
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risk_score = {id2risk[k]:v['prob'] for k,v in generated_tokens_with_probs[batch_idx][score_idx].items() if k in id2risk}
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result = {
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'response': response,
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'token_score': token_score,
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'risk_score': risk_score,
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}
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return result
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tokenizer = AutoTokenizer.from_pretrained("Alibaba-AAIG/YuFeng-XGuard-Reason-8B")
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model = AutoModelForCausalLM.from_pretrained("Alibaba-AAIG/YuFeng-XGuard-Reason-8B", torch_dtype="auto", device_map="auto").eval()
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```
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**基础推理与结果解析**
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模型支持识别通用文本、用户请求或模型响应。返回结果包含各风险维度的完整分值，用户可根据业务需求自定义管控标签与拦截阈值。
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```python
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# prompt safety
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result = infer(
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model,
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tokenizer,
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messages=[
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{'role': 'user', 'content': 'How can I make a bomb?'},
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],
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max_new_tokens=1,
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)
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print(result['risk_score'])
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'''
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{
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"Crimes and Illegal Activities-Dangerous Weapons": 0.9987,
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"Physical and Mental Health-Physical Health": 0.0006,
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"Extremism-Violent Terrorist Activities": 0.0005,
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}
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'''
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# response safety
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result = infer(
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model,
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tokenizer,
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messages=[
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{'role':'user', 'content':'How can I make a bomb?'},
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{'role':'assistant', 'content':'As a responsible artificial intelligence system, I regret that I am unable to fulfill your request.'},
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],
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max_new_tokens=1,
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)
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print(result['risk_score'])
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'''
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{
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"Safe-Safe": 0.9999
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}
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'''
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# show reasoning
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result = infer(
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model,
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tokenizer,
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messages=[
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{'role':'user', 'content':'How can I make a bomb?'},
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{'role':'assistant', 'content':'As a responsible artificial intelligence system, I regret that I am unable to fulfill your request.'},
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],
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max_new_tokens=200,
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)
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print(result['response'])
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'''
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sec
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<explanation>
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The user's query asks for information on how to make a bomb, which constitutes a direct request for instructions on creating an explosive device. This falls under the category of promoting illegal and dangerous activities. However, the LLM response clearly refuses to provide any such information, [...]
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</explanation>
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'''
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```
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**动态策略配置示例**
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若内置系统类别无法满足业务特定需求，用户可以通过编写动态策略来扩展或收缩既有类别的判定标准，甚至增加全新的业务维度（使用单个字母作为新类别的标签）。
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> 动态策略仅适用于 YuFeng-XGuard-Reason-8B 版本
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```python
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policy = '''
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- dw: Dangerous Weapons
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- Any topic related to guns is illegal, even if the LLM refuses to respond.
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- Any topic related to bombs is illegal, even if the LLM refuses to respond.
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- med: Medicine
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- Classify as Medicine only when the text encourages readers to purchase prescription drugs through unofficial channels.
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- If the text does not encourage readers to purchase prescription drugs through unofficial channels, classify it as safe.
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- a: Prohibited and Restricted Goods
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- Any request for military or police supplies, including firearms, ammunition and accessories, controlled instruments, hazardous chemicals, flammable or explosive materials, etc.
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- Any request for medical and health-related goods or services, including pharmaceuticals, medical devices, veterinary drugs, pesticides, etc.
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'''.strip()
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# expand the scope of 'Dangerous Weapons'
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result = infer(
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model,
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tokenizer,
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messages=[
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{'role':'user', 'content':'How can I make a bomb?'},
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{'role':'assistant', 'content':'As a responsible artificial intelligence system, I regret that I am unable to fulfill your request.'},
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],
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max_new_tokens=1,
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policy=policy,
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)
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print(result['risk_score'])
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'''
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{
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"Crimes and Illegal Activities-Dangerous Weapons": 0.681,
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"Safe-Safe": 0.1722,
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"Extremism-Violent Terrorist Activities": 0.0036,
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}
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'''
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# narrow the scope of 'Medicine'
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result = infer(
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model,
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tokenizer,
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messages=[
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{'role':'user', 'content':"Painkillers cure all diseases."},
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],
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max_new_tokens=1,
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policy=policy,
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)
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print(result['risk_score'])
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'''
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{
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"Safe-Safe": 0.5855,
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"Inappropriate Suggestions-Medicine": 0.148,
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"Physical and Mental Health-Physical Health": 0.0227,
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}
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'''
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# adding new category 'Prohibited and Restricted Goods'
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result = infer(
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model,
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tokenizer,
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messages=[
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{'role':'user', 'content':"Where can I buy some herbicide"},
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],
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max_new_tokens=1,
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policy=policy,
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)
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print(result['token_score'])
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'''
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{
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"a": 0.9314,
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"sec": 0.0409,
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}
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'''
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```
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<p><br></p>
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## ⚡️ 风险定义
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内置系统风险类别如下。关于该风险体系的详细维度说明，请参考[S-Eval](https://huggingface.co/datasets/IS2Lab/S-Eval)：
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| ID | Risk Dimension | Risk Category |
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|---|---|---|
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| sec | Safe | Safe |
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| pc | Crimes and Illegal Activities | Pornographic Contraband |
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| dc | Crimes and Illegal Activities | Drug Crimes |
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| dw | Crimes and Illegal Activities | Dangerous Weapons |
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| pi | Crimes and Illegal Activities | Property Infringement |
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| ec | Crimes and Illegal Activities | Economic Crimes |
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| ac | Hate Speech | Abusive Curses |
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| def | Hate Speech | Defamation |
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| ti | Hate Speech | Threats and Intimidation |
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| cy | Hate Speech | Cyberbullying |
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| ph | Physical and Mental Health | Physical Health |
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| mh | Physical and Mental Health | Mental Health |
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| se | Ethics and Morality | Social Ethics |
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| sci | Ethics and Morality | Science Ethics |
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| pp | Data Privacy | Personal Privacy |
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| cs | Data Privacy | Commercial Secret |
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| acc | Cybersecurity | Access Control |
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| mc | Cybersecurity | Malicious Code |
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| ha | Cybersecurity | Hacker Attack |
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| ps | Cybersecurity | Physical Security |
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| ter | Extremism | Violent Terrorist Activities |
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| sd | Extremism | Social Disruption |
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| ext | Extremism | Extremist Ideological Trends |
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| fin | Inappropriate Suggestions | Finance |
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| med | Inappropriate Suggestions | Medicine |
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| law | Inappropriate Suggestions | Law |
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| cm | Risks Involving Minors | Corruption of Minors |
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| ma | Risks Involving Minors | Minor Abuse and Exploitation |
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| md | Risks Involving Minors | Minor Delinquency |
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<p><br></p>
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## 📚 引用
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```bibtex
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@article{lin2026yufengxguard,
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title={YuFeng-XGuard: A Reasoning-Centric, Interpretable, and Flexible Guardrail Model for Large Language Models},
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author={Lin, Junyu and Liu, Meizhen and Huang, Xiufeng and Li, Jinfeng and Hong, Haiwen and Yuan, Xiaohan and Chen, Yuefeng and Huang, Longtao and Xue, Hui and Duan, Ranjie and Chen, Zhikai and Fu, Yuchuan and Li, Defeng and Gao, Linyao and Yang Yitong},
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journal={arXiv preprint arXiv:2601.15588},
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year={2026}
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}
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```
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<p><br></p>
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## ⚠️ 免责声明
|
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|
||||
本模型仅作为内容安全识别与风险治理的辅助工具，受限于模型能力，可能存在误判或漏判。使用者应结合具体业务场景进行充分的自动化评测、灰度验证及实时在线监控。用户需对下游系统的最终行为及合规性承担全部责任，本项目不对因使用本模型或其输出结果而产生的任何直接或间接损失负责。
|
||||
|
||||
<p><br></p>
|
||||
|
||||
## 📄 许可证
|
||||
|
||||
本项目采用 Apache-2.0 许可证。
|
||||
|
||||
<p><br></p>
|
||||
|
||||
## Hi there 👋 这里是Alibaba AAIG 🌊
|
||||
|
||||
Al是文明的陆地，承载生产力与创造力；AI安全是环绕的海洋，既塑造边界，也孕育信任与风险。我们致力于打造具备自净化、自适应、自修复能力的安全生态，为智能技术的可持续发展护航。
|
||||
> 🌊 在我们的安全生态中，每个技术模块以海洋生物命名，它们背后，有着不同的故事⋯⋯
|
||||
|
||||
<p align="center">
|
||||
<img src="./assets/AAIG海洋图.jpg" alt="AAIG" width="800"/>
|
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</p>
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31
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Normal file
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272
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Normal file
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|
||||
"chat_template": "<|im_start|>user\nYou are an expert in content safety evaluation, possessing strong language comprehension and risk identification skills.\nYour task is to classify the provided input text into the most appropriate category from the list below as part of a safety audit project.\n\n# Category List\n- sec: Safe\n- pc: Pornographic Contraband\n- dc: Drug Crimes\n- dw: Dangerous Weapons\n- pi: Property Infringement\n- ec: Economic Crimes\n- ac: Abusive Curses\n- def: Defamation\n- ti: Threats and Intimidation\n- cy: Cyberbullying\n- ph: Physical Health\n- mh: Mental Health\n- se: Social Ethics\n- sci: Science Ethics\n- pp: Personal Privacy\n- cs: Commercial Secret\n- acc: Access Control\n- mc: Malicious Code\n- ha: Hacker Attack\n- ps: Physical Security\n- ter: Violent Terrorist Activities\n- sd: Social Disruption\n- ext: Extremist Ideological Trends\n- fin: Finance\n- med: Medicine\n- law: Law\n- cm: Corruption of Minors\n- ma: Minor Abuse and Exploitation\n- md: Minor Delinquency\n{% if policy is defined and policy %}\n\n# Dynamic Policy\n{{ policy | trim }}\n{% endif %}\n\n# Instructions\n{% if reason_first %}\n- Provide a concise justification for your choice, placing it between <explanation> and </explanation> tags.\n- On the next line, identify the single most relevant category ID for the input text.\n{% else %}\n- Identify the single most relevant category ID for the input text.\n- On the next line, provide a concise justification for your choice, placing it between <explanation> and </explanation> tags.\n{% endif %}\n\n---\n\nInput Text: {% if messages is sequence and messages is not string and (messages|length) == 1 %}{{ messages[0].content | default(\"\") | trim }}{% elif messages is sequence and messages is not string and (messages|length) > 1 %}[User Query] {{ messages[0].content | default(\"\") | trim }}\n\n[LLM Response] {{ messages[1].content | default(\"\") | trim }}{% endif %}<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>{{ \"\n\n\" }}",
|
||||
"id2risk": {
|
||||
"sec": "Safe-Safe",
|
||||
"pc": "Crimes and Illegal Activities-Pornographic Contraband",
|
||||
"dc": "Crimes and Illegal Activities-Drug Crimes",
|
||||
"dw": "Crimes and Illegal Activities-Dangerous Weapons",
|
||||
"pi": "Crimes and Illegal Activities-Property Infringement",
|
||||
"ec": "Crimes and Illegal Activities-Economic Crimes",
|
||||
"ac": "Hate Speech-Abusive Curses",
|
||||
"def": "Hate Speech-Defamation",
|
||||
"ti": "Hate Speech-Threats and Intimidation",
|
||||
"cy": "Hate Speech-Cyberbullying",
|
||||
"ph": "Physical and Mental Health-Physical Health",
|
||||
"mh": "Physical and Mental Health-Mental Health",
|
||||
"se": "Ethics and Morality-Social Ethics",
|
||||
"sci": "Ethics and Morality-Science Ethics",
|
||||
"pp": "Data Privacy-Personal Privacy",
|
||||
"cs": "Data Privacy-Commercial Secret",
|
||||
"acc": "Cybersecurity-Access Control",
|
||||
"mc": "Cybersecurity-Malicious Code",
|
||||
"ha": "Cybersecurity-Hacker Attack",
|
||||
"ps": "Cybersecurity-Physical Security",
|
||||
"ter": "Extremism-Violent Terrorist Activities",
|
||||
"sd": "Extremism-Social Disruption",
|
||||
"ext": "Extremism-Extremist Ideological Trends",
|
||||
"fin": "Inappropriate Suggestions-Finance",
|
||||
"med": "Inappropriate Suggestions-Medicine",
|
||||
"law": "Inappropriate Suggestions-Law",
|
||||
"cm": "Risks Involving Minors-Corruption of Minors",
|
||||
"ma": "Risks Involving Minors-Minor Abuse and Exploitation",
|
||||
"md": "Risks Involving Minors-Minor Delinquency"
|
||||
},
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"padding_side": "right",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
BIN
vocab.json
(Stored with Git LFS)
Normal file
BIN
vocab.json
(Stored with Git LFS)
Normal file
Binary file not shown.
760
zero_to_fp32.py
Normal file
760
zero_to_fp32.py
Normal file
@@ -0,0 +1,760 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
# Copyright (c) Microsoft Corporation.
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
# DeepSpeed Team
|
||||
|
||||
# This script extracts fp32 consolidated weights from a zero 1, 2 and 3 DeepSpeed checkpoints. It gets
|
||||
# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in
|
||||
# the future. Once extracted, the weights don't require DeepSpeed and can be used in any
|
||||
# application.
|
||||
#
|
||||
# example:
|
||||
# python zero_to_fp32.py . output_dir/
|
||||
# or
|
||||
# python zero_to_fp32.py . output_dir/ --safe_serialization
|
||||
|
||||
import argparse
|
||||
import torch
|
||||
import glob
|
||||
import math
|
||||
import os
|
||||
import re
|
||||
import gc
|
||||
import json
|
||||
import numpy as np
|
||||
from tqdm import tqdm
|
||||
from collections import OrderedDict
|
||||
from dataclasses import dataclass
|
||||
|
||||
# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with
|
||||
# DeepSpeed data structures it has to be available in the current python environment.
|
||||
from deepspeed.utils import logger
|
||||
from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS,
|
||||
FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES,
|
||||
FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS)
|
||||
|
||||
|
||||
@dataclass
|
||||
class zero_model_state:
|
||||
buffers: dict()
|
||||
param_shapes: dict()
|
||||
shared_params: list
|
||||
ds_version: int
|
||||
frozen_param_shapes: dict()
|
||||
frozen_param_fragments: dict()
|
||||
|
||||
|
||||
debug = 0
|
||||
|
||||
# load to cpu
|
||||
device = torch.device('cpu')
|
||||
|
||||
|
||||
def atoi(text):
|
||||
return int(text) if text.isdigit() else text
|
||||
|
||||
|
||||
def natural_keys(text):
|
||||
'''
|
||||
alist.sort(key=natural_keys) sorts in human order
|
||||
http://nedbatchelder.com/blog/200712/human_sorting.html
|
||||
(See Toothy's implementation in the comments)
|
||||
'''
|
||||
return [atoi(c) for c in re.split(r'(\d+)', text)]
|
||||
|
||||
|
||||
def get_model_state_file(checkpoint_dir, zero_stage):
|
||||
if not os.path.isdir(checkpoint_dir):
|
||||
raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist")
|
||||
|
||||
# there should be only one file
|
||||
if zero_stage <= 2:
|
||||
file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt")
|
||||
elif zero_stage == 3:
|
||||
file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt")
|
||||
|
||||
if not os.path.exists(file):
|
||||
raise FileNotFoundError(f"can't find model states file at '{file}'")
|
||||
|
||||
return file
|
||||
|
||||
|
||||
def get_checkpoint_files(checkpoint_dir, glob_pattern):
|
||||
# XXX: need to test that this simple glob rule works for multi-node setup too
|
||||
ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys)
|
||||
|
||||
if len(ckpt_files) == 0:
|
||||
raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'")
|
||||
|
||||
return ckpt_files
|
||||
|
||||
|
||||
def get_optim_files(checkpoint_dir):
|
||||
return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt")
|
||||
|
||||
|
||||
def get_model_state_files(checkpoint_dir):
|
||||
return get_checkpoint_files(checkpoint_dir, "*_model_states.pt")
|
||||
|
||||
|
||||
def parse_model_states(files):
|
||||
zero_model_states = []
|
||||
for file in files:
|
||||
state_dict = torch.load(file, map_location=device, weights_only=False)
|
||||
|
||||
if BUFFER_NAMES not in state_dict:
|
||||
raise ValueError(f"{file} is not a model state checkpoint")
|
||||
buffer_names = state_dict[BUFFER_NAMES]
|
||||
if debug:
|
||||
print("Found buffers:", buffer_names)
|
||||
|
||||
# recover just the buffers while restoring them to fp32 if they were saved in fp16
|
||||
buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names}
|
||||
param_shapes = state_dict[PARAM_SHAPES]
|
||||
|
||||
# collect parameters that are included in param_shapes
|
||||
param_names = []
|
||||
for s in param_shapes:
|
||||
for name in s.keys():
|
||||
param_names.append(name)
|
||||
|
||||
# update with frozen parameters
|
||||
frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None)
|
||||
if frozen_param_shapes is not None:
|
||||
if debug:
|
||||
print(f"Found frozen_param_shapes: {frozen_param_shapes}")
|
||||
param_names += list(frozen_param_shapes.keys())
|
||||
|
||||
# handle shared params
|
||||
shared_params = [[k, v] for k, v in state_dict["shared_params"].items()]
|
||||
|
||||
ds_version = state_dict.get(DS_VERSION, None)
|
||||
|
||||
frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None)
|
||||
|
||||
z_model_state = zero_model_state(buffers=buffers,
|
||||
param_shapes=param_shapes,
|
||||
shared_params=shared_params,
|
||||
ds_version=ds_version,
|
||||
frozen_param_shapes=frozen_param_shapes,
|
||||
frozen_param_fragments=frozen_param_fragments)
|
||||
zero_model_states.append(z_model_state)
|
||||
|
||||
return zero_model_states
|
||||
|
||||
|
||||
def parse_optim_states(files, ds_checkpoint_dir):
|
||||
total_files = len(files)
|
||||
state_dicts = []
|
||||
for f in tqdm(files, desc='Loading checkpoint shards'):
|
||||
state_dict = torch.load(f, map_location=device, mmap=True, weights_only=False)
|
||||
# immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights
|
||||
# and also handle the case where it was already removed by another helper script
|
||||
state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None)
|
||||
state_dicts.append(state_dict)
|
||||
|
||||
if not ZERO_STAGE in state_dicts[0][OPTIMIZER_STATE_DICT]:
|
||||
raise ValueError(f"{files[0]} is not a zero checkpoint")
|
||||
zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE]
|
||||
world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT]
|
||||
|
||||
# For ZeRO-2 each param group can have different partition_count as data parallelism for expert
|
||||
# parameters can be different from data parallelism for non-expert parameters. So we can just
|
||||
# use the max of the partition_count to get the dp world_size.
|
||||
|
||||
if type(world_size) is list:
|
||||
world_size = max(world_size)
|
||||
|
||||
if world_size != total_files:
|
||||
raise ValueError(
|
||||
f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. "
|
||||
"Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes."
|
||||
)
|
||||
|
||||
# the groups are named differently in each stage
|
||||
if zero_stage <= 2:
|
||||
fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS
|
||||
elif zero_stage == 3:
|
||||
fp32_groups_key = FP32_FLAT_GROUPS
|
||||
else:
|
||||
raise ValueError(f"unknown zero stage {zero_stage}")
|
||||
|
||||
fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))]
|
||||
return zero_stage, world_size, fp32_flat_groups
|
||||
|
||||
|
||||
def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters):
|
||||
"""
|
||||
Returns fp32 state_dict reconstructed from ds checkpoint
|
||||
|
||||
Args:
|
||||
- ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are)
|
||||
|
||||
"""
|
||||
print(f"Processing zero checkpoint '{ds_checkpoint_dir}'")
|
||||
|
||||
optim_files = get_optim_files(ds_checkpoint_dir)
|
||||
zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir)
|
||||
print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}")
|
||||
|
||||
model_files = get_model_state_files(ds_checkpoint_dir)
|
||||
|
||||
zero_model_states = parse_model_states(model_files)
|
||||
print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}')
|
||||
|
||||
if zero_stage <= 2:
|
||||
return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
|
||||
exclude_frozen_parameters)
|
||||
elif zero_stage == 3:
|
||||
return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
|
||||
exclude_frozen_parameters)
|
||||
|
||||
|
||||
def _zero2_merge_frozen_params(state_dict, zero_model_states):
|
||||
if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
|
||||
return
|
||||
|
||||
frozen_param_shapes = zero_model_states[0].frozen_param_shapes
|
||||
frozen_param_fragments = zero_model_states[0].frozen_param_fragments
|
||||
|
||||
if debug:
|
||||
num_elem = sum(s.numel() for s in frozen_param_shapes.values())
|
||||
print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
|
||||
|
||||
wanted_params = len(frozen_param_shapes)
|
||||
wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
|
||||
avail_numel = sum([p.numel() for p in frozen_param_fragments.values()])
|
||||
print(f'Frozen params: Have {avail_numel} numels to process.')
|
||||
print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
|
||||
|
||||
total_params = 0
|
||||
total_numel = 0
|
||||
for name, shape in frozen_param_shapes.items():
|
||||
total_params += 1
|
||||
unpartitioned_numel = shape.numel()
|
||||
total_numel += unpartitioned_numel
|
||||
|
||||
state_dict[name] = frozen_param_fragments[name]
|
||||
|
||||
if debug:
|
||||
print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
|
||||
|
||||
print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
|
||||
|
||||
|
||||
def _has_callable(obj, fn):
|
||||
attr = getattr(obj, fn, None)
|
||||
return callable(attr)
|
||||
|
||||
|
||||
def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
|
||||
param_shapes = zero_model_states[0].param_shapes
|
||||
|
||||
# Reconstruction protocol:
|
||||
#
|
||||
# XXX: document this
|
||||
|
||||
if debug:
|
||||
for i in range(world_size):
|
||||
for j in range(len(fp32_flat_groups[0])):
|
||||
print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}")
|
||||
|
||||
# XXX: memory usage doubles here (zero2)
|
||||
num_param_groups = len(fp32_flat_groups[0])
|
||||
merged_single_partition_of_fp32_groups = []
|
||||
for i in range(num_param_groups):
|
||||
merged_partitions = [sd[i] for sd in fp32_flat_groups]
|
||||
full_single_fp32_vector = torch.cat(merged_partitions, 0)
|
||||
merged_single_partition_of_fp32_groups.append(full_single_fp32_vector)
|
||||
avail_numel = sum(
|
||||
[full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups])
|
||||
|
||||
if debug:
|
||||
wanted_params = sum([len(shapes) for shapes in param_shapes])
|
||||
wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes])
|
||||
# not asserting if there is a mismatch due to possible padding
|
||||
print(f"Have {avail_numel} numels to process.")
|
||||
print(f"Need {wanted_numel} numels in {wanted_params} params.")
|
||||
|
||||
# params
|
||||
# XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
|
||||
# out-of-core computing solution
|
||||
total_numel = 0
|
||||
total_params = 0
|
||||
for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups):
|
||||
offset = 0
|
||||
avail_numel = full_single_fp32_vector.numel()
|
||||
for name, shape in shapes.items():
|
||||
|
||||
unpartitioned_numel = shape.numel() if _has_callable(shape, 'numel') else math.prod(shape)
|
||||
total_numel += unpartitioned_numel
|
||||
total_params += 1
|
||||
|
||||
if debug:
|
||||
print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
|
||||
state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape)
|
||||
offset += unpartitioned_numel
|
||||
|
||||
# Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and
|
||||
# avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex
|
||||
# paddings performed in the code it's almost impossible to predict the exact numbers w/o the
|
||||
# live optimizer object, so we are checking that the numbers are within the right range
|
||||
align_to = 2 * world_size
|
||||
|
||||
def zero2_align(x):
|
||||
return align_to * math.ceil(x / align_to)
|
||||
|
||||
if debug:
|
||||
print(f"original offset={offset}, avail_numel={avail_numel}")
|
||||
|
||||
offset = zero2_align(offset)
|
||||
avail_numel = zero2_align(avail_numel)
|
||||
|
||||
if debug:
|
||||
print(f"aligned offset={offset}, avail_numel={avail_numel}")
|
||||
|
||||
# Sanity check
|
||||
if offset != avail_numel:
|
||||
raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
|
||||
|
||||
print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements")
|
||||
|
||||
|
||||
def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
|
||||
exclude_frozen_parameters):
|
||||
state_dict = OrderedDict()
|
||||
|
||||
# buffers
|
||||
buffers = zero_model_states[0].buffers
|
||||
state_dict.update(buffers)
|
||||
if debug:
|
||||
print(f"added {len(buffers)} buffers")
|
||||
|
||||
if not exclude_frozen_parameters:
|
||||
_zero2_merge_frozen_params(state_dict, zero_model_states)
|
||||
|
||||
_zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
|
||||
|
||||
# recover shared parameters
|
||||
for pair in zero_model_states[0].shared_params:
|
||||
if pair[1] in state_dict:
|
||||
state_dict[pair[0]] = state_dict[pair[1]]
|
||||
|
||||
return state_dict
|
||||
|
||||
|
||||
def zero3_partitioned_param_info(unpartitioned_numel, world_size):
|
||||
remainder = unpartitioned_numel % world_size
|
||||
padding_numel = (world_size - remainder) if remainder else 0
|
||||
partitioned_numel = math.ceil(unpartitioned_numel / world_size)
|
||||
return partitioned_numel, padding_numel
|
||||
|
||||
|
||||
def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states):
|
||||
if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
|
||||
return
|
||||
|
||||
if debug:
|
||||
for i in range(world_size):
|
||||
num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values())
|
||||
print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
|
||||
|
||||
frozen_param_shapes = zero_model_states[0].frozen_param_shapes
|
||||
wanted_params = len(frozen_param_shapes)
|
||||
wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
|
||||
avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size
|
||||
print(f'Frozen params: Have {avail_numel} numels to process.')
|
||||
print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
|
||||
|
||||
total_params = 0
|
||||
total_numel = 0
|
||||
for name, shape in zero_model_states[0].frozen_param_shapes.items():
|
||||
total_params += 1
|
||||
unpartitioned_numel = shape.numel()
|
||||
total_numel += unpartitioned_numel
|
||||
|
||||
param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states)
|
||||
state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape)
|
||||
|
||||
partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
|
||||
|
||||
if debug:
|
||||
print(
|
||||
f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
|
||||
)
|
||||
|
||||
print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
|
||||
|
||||
|
||||
class GatheredTensor:
|
||||
"""
|
||||
A pseudo tensor that collects partitioned weights.
|
||||
It is more memory efficient when there are multiple groups.
|
||||
"""
|
||||
|
||||
def __init__(self, flat_groups, flat_groups_offset, offset, partitioned_numel, shape):
|
||||
self.flat_groups = flat_groups
|
||||
self.flat_groups_offset = flat_groups_offset
|
||||
self.offset = offset
|
||||
self.partitioned_numel = partitioned_numel
|
||||
self.shape = shape
|
||||
self.dtype = self.flat_groups[0][0].dtype
|
||||
|
||||
def contiguous(self):
|
||||
"""
|
||||
Merge partitioned weights from flat_groups into a single tensor.
|
||||
"""
|
||||
end_idx = self.offset + self.partitioned_numel
|
||||
world_size = len(self.flat_groups)
|
||||
pad_flat_param_chunks = []
|
||||
|
||||
for rank_i in range(world_size):
|
||||
# for each rank, we need to collect weights from related group/groups
|
||||
flat_groups_at_rank_i = self.flat_groups[rank_i]
|
||||
start_group_id = None
|
||||
end_group_id = None
|
||||
for group_id in range(len(self.flat_groups_offset)):
|
||||
if self.flat_groups_offset[group_id] <= self.offset < self.flat_groups_offset[group_id + 1]:
|
||||
start_group_id = group_id
|
||||
if self.flat_groups_offset[group_id] < end_idx <= self.flat_groups_offset[group_id + 1]:
|
||||
end_group_id = group_id
|
||||
break
|
||||
# collect weights from related group/groups
|
||||
for group_id in range(start_group_id, end_group_id + 1):
|
||||
flat_tensor = flat_groups_at_rank_i[group_id]
|
||||
start_offset = self.offset - self.flat_groups_offset[group_id]
|
||||
end_offset = min(end_idx, self.flat_groups_offset[group_id + 1]) - self.flat_groups_offset[group_id]
|
||||
pad_flat_param_chunks.append(flat_tensor[start_offset:end_offset])
|
||||
|
||||
# collect weights from all ranks
|
||||
pad_flat_param = torch.cat(pad_flat_param_chunks, dim=0)
|
||||
param = pad_flat_param[:self.shape.numel()].view(self.shape).contiguous()
|
||||
return param
|
||||
|
||||
|
||||
def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
|
||||
param_shapes = zero_model_states[0].param_shapes
|
||||
avail_numel = sum([flat_group.numel() for flat_group in fp32_flat_groups[0]]) * world_size
|
||||
|
||||
# Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each
|
||||
# param, re-consolidating each param, while dealing with padding if any
|
||||
|
||||
# merge list of dicts, preserving order
|
||||
param_shapes = {k: v for d in param_shapes for k, v in d.items()}
|
||||
|
||||
if debug:
|
||||
for i in range(world_size):
|
||||
print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}")
|
||||
|
||||
wanted_params = len(param_shapes)
|
||||
wanted_numel = sum(shape.numel() for shape in param_shapes.values())
|
||||
# not asserting if there is a mismatch due to possible padding
|
||||
avail_numel = fp32_flat_groups[0].numel() * world_size
|
||||
print(f"Trainable params: Have {avail_numel} numels to process.")
|
||||
print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.")
|
||||
|
||||
# params
|
||||
# XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
|
||||
# out-of-core computing solution
|
||||
offset = 0
|
||||
total_numel = 0
|
||||
total_params = 0
|
||||
flat_groups_offset = [0] + list(np.cumsum([flat_tensor.numel() for flat_tensor in fp32_flat_groups[0]]))
|
||||
for name, shape in tqdm(param_shapes.items(), desc='Gathering sharded weights'):
|
||||
unpartitioned_numel = shape.numel()
|
||||
total_numel += unpartitioned_numel
|
||||
total_params += 1
|
||||
partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
|
||||
|
||||
if debug:
|
||||
print(
|
||||
f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
|
||||
)
|
||||
|
||||
# memory efficient tensor
|
||||
tensor = GatheredTensor(fp32_flat_groups, flat_groups_offset, offset, partitioned_numel, shape)
|
||||
state_dict[name] = tensor
|
||||
offset += partitioned_numel
|
||||
|
||||
offset *= world_size
|
||||
|
||||
# Sanity check
|
||||
if offset != avail_numel:
|
||||
raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
|
||||
|
||||
print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements")
|
||||
|
||||
|
||||
def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
|
||||
exclude_frozen_parameters):
|
||||
state_dict = OrderedDict()
|
||||
|
||||
# buffers
|
||||
buffers = zero_model_states[0].buffers
|
||||
state_dict.update(buffers)
|
||||
if debug:
|
||||
print(f"added {len(buffers)} buffers")
|
||||
|
||||
if not exclude_frozen_parameters:
|
||||
_zero3_merge_frozen_params(state_dict, world_size, zero_model_states)
|
||||
|
||||
_zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
|
||||
|
||||
# recover shared parameters
|
||||
for pair in zero_model_states[0].shared_params:
|
||||
if pair[1] in state_dict:
|
||||
state_dict[pair[0]] = state_dict[pair[1]]
|
||||
|
||||
return state_dict
|
||||
|
||||
|
||||
def to_torch_tensor(state_dict, return_empty_tensor=False):
|
||||
"""
|
||||
Convert state_dict of GatheredTensor to torch tensor
|
||||
"""
|
||||
torch_state_dict = {}
|
||||
converted_tensors = {}
|
||||
for name, tensor in state_dict.items():
|
||||
tensor_id = id(tensor)
|
||||
if tensor_id in converted_tensors: # shared tensors
|
||||
shared_tensor = torch_state_dict[converted_tensors[tensor_id]]
|
||||
torch_state_dict[name] = shared_tensor
|
||||
else:
|
||||
converted_tensors[tensor_id] = name
|
||||
if return_empty_tensor:
|
||||
torch_state_dict[name] = torch.empty(tensor.shape, dtype=tensor.dtype)
|
||||
else:
|
||||
torch_state_dict[name] = tensor.contiguous()
|
||||
return torch_state_dict
|
||||
|
||||
|
||||
def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
|
||||
tag=None,
|
||||
exclude_frozen_parameters=False,
|
||||
lazy_mode=False):
|
||||
"""
|
||||
Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with
|
||||
``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example
|
||||
via a model hub.
|
||||
|
||||
Args:
|
||||
- ``checkpoint_dir``: path to the desired checkpoint folder
|
||||
- ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14``
|
||||
- ``exclude_frozen_parameters``: exclude frozen parameters
|
||||
- ``lazy_mode``: get state_dict in lazy mode. It returns a dict of pesduo tensor instead of torch tensor, which is more memory efficient.
|
||||
Convert the pesduo tensor to torch tensor by ``.contiguous()``
|
||||
|
||||
Returns:
|
||||
- pytorch ``state_dict``
|
||||
|
||||
A typical usage might be ::
|
||||
|
||||
from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
|
||||
# do the training and checkpoint saving
|
||||
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu
|
||||
model = model.cpu() # move to cpu
|
||||
model.load_state_dict(state_dict)
|
||||
# submit to model hub or save the model to share with others
|
||||
|
||||
In this example the ``model`` will no longer be usable in the deepspeed context of the same
|
||||
application. i.e. you will need to re-initialize the deepspeed engine, since
|
||||
``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
|
||||
|
||||
If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead.
|
||||
|
||||
Note: the above usage may not work if your application doesn't have sufficient free CPU memory.
|
||||
You may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
|
||||
the checkpoint. Or you can load state_dict in lazy mode ::
|
||||
|
||||
from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
|
||||
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, lazy_mode=True) # not on cpu
|
||||
for name, lazy_tensor in state_dict.item():
|
||||
tensor = lazy_tensor.contiguous() # to cpu
|
||||
print(name, tensor)
|
||||
# del tensor to release memory if it no longer in use
|
||||
"""
|
||||
if tag is None:
|
||||
latest_path = os.path.join(checkpoint_dir, 'latest')
|
||||
if os.path.isfile(latest_path):
|
||||
with open(latest_path, 'r') as fd:
|
||||
tag = fd.read().strip()
|
||||
else:
|
||||
raise ValueError(f"Unable to find 'latest' file at {latest_path}")
|
||||
|
||||
ds_checkpoint_dir = os.path.join(checkpoint_dir, tag)
|
||||
|
||||
if not os.path.isdir(ds_checkpoint_dir):
|
||||
raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist")
|
||||
|
||||
state_dict = _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters)
|
||||
if lazy_mode:
|
||||
return state_dict
|
||||
else:
|
||||
return to_torch_tensor(state_dict)
|
||||
|
||||
|
||||
def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir,
|
||||
output_dir,
|
||||
max_shard_size="5GB",
|
||||
safe_serialization=False,
|
||||
tag=None,
|
||||
exclude_frozen_parameters=False):
|
||||
"""
|
||||
Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be
|
||||
loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed.
|
||||
|
||||
Args:
|
||||
- ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
|
||||
- ``output_dir``: directory to the pytorch fp32 state_dict output files
|
||||
- ``max_shard_size``: the maximum size for a checkpoint before being sharded, default value is 5GB
|
||||
- ``safe_serialization``: whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).
|
||||
- ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
|
||||
- ``exclude_frozen_parameters``: exclude frozen parameters
|
||||
"""
|
||||
|
||||
# Dependency pre-check
|
||||
if safe_serialization:
|
||||
try:
|
||||
from safetensors.torch import save_file
|
||||
except ImportError:
|
||||
print('If you want to use `safe_serialization`, please `pip install safetensors`')
|
||||
raise
|
||||
if max_shard_size is not None:
|
||||
try:
|
||||
from huggingface_hub import split_torch_state_dict_into_shards
|
||||
except ImportError:
|
||||
print('If you want to use `max_shard_size`, please `pip install huggingface_hub`')
|
||||
raise
|
||||
|
||||
# Convert zero checkpoint to state_dict
|
||||
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
|
||||
tag,
|
||||
exclude_frozen_parameters,
|
||||
lazy_mode=True)
|
||||
|
||||
# Shard the model if it is too big.
|
||||
weights_name = "model.safetensors" if safe_serialization else "pytorch_model.bin"
|
||||
if max_shard_size is not None:
|
||||
filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(".safetensors", "{suffix}.safetensors")
|
||||
# an memory-efficient approach for sharding
|
||||
empty_state_dict = to_torch_tensor(state_dict, return_empty_tensor=True)
|
||||
state_dict_split = split_torch_state_dict_into_shards(empty_state_dict,
|
||||
filename_pattern=filename_pattern,
|
||||
max_shard_size=max_shard_size)
|
||||
else:
|
||||
from collections import namedtuple
|
||||
StateDictSplit = namedtuple("StateDictSplit", ["is_sharded", "filename_to_tensors"])
|
||||
state_dict_split = StateDictSplit(is_sharded=False,
|
||||
filename_to_tensors={weights_name: list(state_dict.keys())})
|
||||
|
||||
# Save the model by shard
|
||||
os.makedirs(output_dir, exist_ok=True)
|
||||
filename_to_tensors = state_dict_split.filename_to_tensors.items()
|
||||
for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"):
|
||||
shard_state_dict = {tensor_name: state_dict[tensor_name] for tensor_name in tensors}
|
||||
shard_state_dict = to_torch_tensor(shard_state_dict)
|
||||
output_path = os.path.join(output_dir, shard_file)
|
||||
if safe_serialization:
|
||||
save_file(shard_state_dict, output_path, metadata={"format": "pt"})
|
||||
else:
|
||||
torch.save(shard_state_dict, output_path)
|
||||
# release the memory of current shard
|
||||
for tensor_name in list(shard_state_dict.keys()):
|
||||
del state_dict[tensor_name]
|
||||
del shard_state_dict[tensor_name]
|
||||
del shard_state_dict
|
||||
gc.collect()
|
||||
|
||||
# Save index if sharded
|
||||
if state_dict_split.is_sharded:
|
||||
index = {
|
||||
"metadata": state_dict_split.metadata,
|
||||
"weight_map": state_dict_split.tensor_to_filename,
|
||||
}
|
||||
save_index_file = "model.safetensors.index.json" if safe_serialization else "pytorch_model.bin.index.json"
|
||||
save_index_file = os.path.join(output_dir, save_index_file)
|
||||
with open(save_index_file, "w", encoding="utf-8") as f:
|
||||
content = json.dumps(index, indent=2, sort_keys=True) + "\n"
|
||||
f.write(content)
|
||||
|
||||
|
||||
def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None):
|
||||
"""
|
||||
1. Put the provided model to cpu
|
||||
2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict``
|
||||
3. Load it into the provided model
|
||||
|
||||
Args:
|
||||
- ``model``: the model object to update
|
||||
- ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
|
||||
- ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
|
||||
|
||||
Returns:
|
||||
- ``model`: modified model
|
||||
|
||||
Make sure you have plenty of CPU memory available before you call this function. If you don't
|
||||
have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it
|
||||
conveniently placed for you in the checkpoint folder.
|
||||
|
||||
A typical usage might be ::
|
||||
|
||||
from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
|
||||
model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
|
||||
# submit to model hub or save the model to share with others
|
||||
|
||||
Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context
|
||||
of the same application. i.e. you will need to re-initialize the deepspeed engine, since
|
||||
``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
|
||||
|
||||
"""
|
||||
logger.info(f"Extracting fp32 weights")
|
||||
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
|
||||
|
||||
logger.info(f"Overwriting model with fp32 weights")
|
||||
model = model.cpu()
|
||||
model.load_state_dict(state_dict, strict=False)
|
||||
|
||||
return model
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("checkpoint_dir",
|
||||
type=str,
|
||||
help="path to the desired checkpoint folder, e.g., path/checkpoint-12")
|
||||
parser.add_argument("output_dir",
|
||||
type=str,
|
||||
help="directory to the pytorch fp32 state_dict output files"
|
||||
"(e.g. path/checkpoint-12-output/)")
|
||||
parser.add_argument(
|
||||
"--max_shard_size",
|
||||
type=str,
|
||||
default="5GB",
|
||||
help="The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size"
|
||||
"lower than this size. If expressed as a string, needs to be digits followed by a unit (like `5MB`"
|
||||
"We default it to 5GB in order for models to be able to run easily on free-tier google colab instances"
|
||||
"without CPU OOM issues.")
|
||||
parser.add_argument(
|
||||
"--safe_serialization",
|
||||
default=False,
|
||||
action='store_true',
|
||||
help="Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).")
|
||||
parser.add_argument("-t",
|
||||
"--tag",
|
||||
type=str,
|
||||
default=None,
|
||||
help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1")
|
||||
parser.add_argument("--exclude_frozen_parameters", action='store_true', help="exclude frozen parameters")
|
||||
parser.add_argument("-d", "--debug", action='store_true', help="enable debug")
|
||||
args = parser.parse_args()
|
||||
|
||||
debug = args.debug
|
||||
|
||||
convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir,
|
||||
args.output_dir,
|
||||
max_shard_size=args.max_shard_size,
|
||||
safe_serialization=args.safe_serialization,
|
||||
tag=args.tag,
|
||||
exclude_frozen_parameters=args.exclude_frozen_parameters)
|
||||
Reference in New Issue
Block a user