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---
license: apache-2.0
language:
- th
- en
metrics:
- accuracy
base_model:
- ThaiLLM/ThaiLLM-8B
- Qwen/Qwen3-8B
pipeline_tag: text-generation
---
# OpenThaiGPT-ThaiLLM-8b-instruct-v0.7.2-research-preview
โมเดลภาษาไทย **ทดลอง** ขนาด 8 พันล้านพารามิเตอร์ พัฒนาต่อยอดจาก ThaiLLM-8B โดยทีม OpenThaiGPT ร่วมกับ ThaiLLM มุ่งเน้นการตอบคำถามเกี่ยวกับความรู้ไทย ประวัติศาสตร์ วัฒนธรรม และหน่วยงานภาครัฐ
---
## รายละเอียดโมเดล
| รายการ | ข้อมูล |
|--------|--------|
| **ชื่อโมเดล** | OpenThaiGPT-ThaiLLM-8b-instruct-v0.7.2-research-preview |
| **โมเดลพื้นฐาน** | ThaiLLM/ThaiLLM-8B และ Qwen/Qwen3-8B |
| **จำนวนพารามิเตอร์** | 8 พันล้าน (8B) |
| **ภาษาที่รองรับ** | ไทย, อังกฤษ |
| **ประเภทงาน** | Text Generation, Question Answering, Thai FAQ |
| **License** | Apache 2.0 |
---
## คำอธิบายโมเดล
โมเดลภาษาไทย **ทดลอง** ขนาด 8 พันล้านพารามิเตอร์ พัฒนาต่อยอดจาก ThaiLLM-8B โดยทีม OpenThaiGPT ร่วมกับ ThaiLLM มุ่งเน้นการตอบคำถามเกี่ยวกับความรู้ไทย ประวัติศาสตร์ วัฒนธรรม และหน่วยงานภาครัฐ ผ่านกระบวนการ Supervised Fine-tuning บนชุดข้อมูลคุณภาพสูงที่มี Chain-of-Thought Reasoning ในรูปแบบ `<think>...</think>` พร้อมเทคนิค Paraphrase Augmentation เพื่อให้โมเดลสามารถเข้าใจคำถามได้หลากหลายรูปแบบ โมเดลนี้เหมาะสำหรับงาน Thai FAQ และการตอบคำถามเกี่ยวกับบริบทไทยโดยเฉพาะ
### Model Description (English)
An experimental 8-billion parameter Thai language model, fine-tuned from Qwen3-8B by OpenThaiGPT in collaboration with ThaiLLM. Specializes in Thai knowledge Q&A, including history, culture, and government information. Trained using Supervised Fine-tuning with high-quality Chain-of-Thought reasoning data (`<think>...</think>` format) and Paraphrase Augmentation technique to ensure robust understanding across diverse question phrasings. Optimized for Thai FAQ and Thai context question-answering tasks.
---
## จุดเด่นของโมเดล
- **ความรู้ไทยเชิงลึก**: ตอบคำถามเกี่ยวกับประวัติศาสตร์ วัฒนธรรม หน่วยงานภาครัฐ และบริบทไทยได้อย่างแม่นยำ
- **Chain-of-Thought Reasoning**: รองรับการคิดวิเคราะห์แบบเป็นขั้นตอนในรูปแบบ `<think>...</think>`
- **Paraphrase Robust**: เข้าใจคำถามได้หลากหลายรูปแบบ ไม่จำกัดเฉพาะประโยคที่ตรงเป๊ะ
- **สองภาษา**: รองรับทั้งภาษาไทยและอังกฤษ
- **IFEval สูง**: ปฏิบัติตามคำสั่งได้อย่างแม่นยำ (IFEval 87.6%) และ (IFEval-TH สูงถึง 75.5%)
---
## ผลการทดสอบ (Benchmark Results)
| Benchmark | OTG-ThaiLLM v7.2 | Qwen3-8B-Instruct | หมายเหตุ |
|-----------|------|----------------|-------------------|
| AIME24 | 0.3333 | **0.6667** | คณิตศาสตร์แข่งขัน |
| AIME24-TH | 0.0667 | **0.6667** | คณิตศาสตร์แข่งขัน (ไทย) |
| Language Accuracy | **0.986** | 0.974 | ตอบถูกภาษา |
| LiveCodeBench | 0.575 | **0.87** | การเขียนโค้ด |
| LiveCodeBench-TH | 0.2125 | **0.2312** | การเขียนโค้ด (ไทย) |
| MATH500 | 0.85 | **0.926** | คณิตศาสตร์ทั่วไป |
| MATH500-TH | 0.496 | **0.63** | คณิตศาสตร์ทั่วไป (ไทย) |
| OpenThaiEval | 0.6964 | **0.7541** | ความรู้ภาษาไทย |
| HellaSwag | **0.706** | 0.6853 | Common Sense |
| HellaSwag-TH | 0.4993 | **0.5387** | Common Sense (ไทย) |
| IFEval | 0.876 | **0.9197** | การปฏิบัติตามคำสั่ง |
| IFEval-TH | 0.755 | **0.8512** | การปฏิบัติตามคำสั่ง (ไทย) |
| **AVERAGE** | 0.5877 | **0.7261** | ค่าเฉลี่ยรวม |
**หมายเหตุ**: โมเดลนี้ถูกออกแบบมาเพื่องาน Thai FAQ และความรู้ไทยโดยเฉพาะ ไม่ได้มุ่งเน้นคณิตศาสตร์แข่งขันหรือการเขียนโค้ด
---
## รายละเอียดทางเทคนิค
| พารามิเตอร์ | ค่า |
|-------------|-----|
| Base Model | Qwen3-v5IFEval-SLERP Merged |
| Training Type | Full SFT (Supervised Fine-tuning) |
| Learning Rate | 5e-7 |
| Epochs | 3 |
| Max Length | 8,192 tokens |
| Batch Size | 2 per GPU |
| Gradient Accumulation | 4 |
| Hardware | 8x NVIDIA H100 80GB |
| Framework | ms-swift |
| DeepSpeed | ZeRO Stage 3 |
## การใช้งาน
### ติดตั้ง Dependencies
```bash
pip install transformers torch accelerate
```
### ใช้งานกับ Transformers
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "openthaigpt/openthaigpt-thaillm-8b-instruct-v0.7.2-research-preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "OpenThaiGPT คืออะไร"}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=2048,
do_sample=True,
temperature=0.7,
top_p=0.9
)
response = tokenizer.batch_decode(
generated_ids[:, model_inputs.input_ids.shape[-1]:],
skip_special_tokens=True
)[0]
print(response)
```
### ใช้งานกับ vLLM
```python
from vllm import LLM, SamplingParams
llm = LLM(
model="openthaigpt/openthaigpt-thaillm-8b-instruct-v0.7.2-research-preview",
tensor_parallel_size=1,
trust_remote_code=True
)
sampling_params = SamplingParams(
temperature=0.7,
top_p=0.9,
max_tokens=2048
)
prompts = ["OpenThaiGPT คืออะไร"]
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
print(output.outputs[0].text)
```
### ใช้งานกับ Ollama
```bash
# สร้าง Modelfile
cat > Modelfile << 'EOF'
FROM openthaigpt/openthaigpt-thaillm-8b-instruct-v0.7.2-research-preview
PARAMETER temperature 0.7
PARAMETER top_p 0.9
SYSTEM "คุณคือ OpenThaiGPT ผู้ช่วย AI ภาษาไทยที่พัฒนาโดยทีม OpenThaiGPT"
EOF
# สร้างโมเดล
ollama create openthaigpt -f Modelfile
# ใช้งาน
ollama run openthaigpt "OpenThaiGPT คืออะไร"
```
---
## ตัวอย่างการใช้งาน
### คำถามเกี่ยวกับ OpenThaiGPT
**คำถาม:** OpenThaiGPT คืออะไร
**คำตอบ:**
```
<think>
ผู้ใช้ถามเกี่ยวกับ OpenThaiGPT ซึ่งเป็นโครงการที่ฉันเป็นส่วนหนึ่ง
ฉันควรอธิบายว่า OpenThaiGPT คืออะไร ใครพัฒนา และมีจุดประสงค์อะไร
</think>
OpenThaiGPT เป็นโครงการพัฒนาโมเดลภาษาไทยขนาดใหญ่ (Large Language Model)
แบบโอเพ่นซอร์ส พัฒนาโดยสมาคมปัญญาประดิษฐ์แห่งประเทศไทย (AIEAT)
ร่วมกับ iApp Technology และพันธมิตร มีเป้าหมายเพื่อสร้างโมเดล AI
ที่เข้าใจและสื่อสารภาษาไทยได้อย่างเป็นธรรมชาติ
พร้อมเผยแพร่ให้ชุมชนนักพัฒนาไทยนำไปใช้และต่อยอดได้ฟรี
```
### คำถามเกี่ยวกับหน่วยงานภาครัฐ
**คำถาม:** กระทรวงดิจิทัลเพื่อเศรษฐกิจและสังคม มีหน้าที่อะไร
**คำตอบ:**
```
<think>
ผู้ใช้ถามเกี่ยวกับกระทรวง DE ซึ่งเป็นหน่วยงานภาครัฐที่ดูแลด้านดิจิทัล
ฉันควรอธิบายภารกิจหลักและขอบเขตงานของกระทรวง
</think>
กระทรวงดิจิทัลเพื่อเศรษฐกิจและสังคม (Ministry of Digital Economy and Society)
หรือ กระทรวง DE มีภารกิจหลักในการ:
1. กำหนดนโยบายและแผนด้านดิจิทัลเพื่อเศรษฐกิจและสังคม
2. พัฒนาโครงสร้างพื้นฐานดิจิทัลของประเทศ
3. ส่งเสริมการใช้เทคโนโลยีดิจิทัลในภาคธุรกิจและประชาชน
4. กำกับดูแลกิจการโทรคมนาคมและการสื่อสาร
5. พัฒนาทักษะดิจิทัลของประชาชน
```
---
## ข้อจำกัด
1. **ไม่เหมาะกับคณิตศาสตร์ขั้นสูง**: โมเดลไม่ได้ถูกออกแบบมาสำหรับโจทย์คณิตศาสตร์แข่งขันระดับ AIME
2. **การเขียนโค้ดจำกัด**: ความสามารถด้านการเขียนโค้ดต่ำกว่าโมเดลที่เน้นด้านนี้โดยเฉพาะ
3. **ความรู้มีขอบเขต**: ความรู้ถูกจำกัดตามข้อมูลที่ใช้ฝึกสอน อาจไม่ครอบคลุมข้อมูลล่าสุด
4. **Hallucination**: อาจสร้างข้อมูลที่ไม่ถูกต้องในบางกรณี โปรดตรวจสอบข้อมูลสำคัญเสมอ
---
## ประวัติเวอร์ชัน
| เวอร์ชัน | วันที่ | การเปลี่ยนแปลงหลัก |
|----------|--------|-------------------|
| v1 | 13 ธ.ค. 2567 | LoRA SFT ครั้งแรก (Thai instruction only) |
| v2 | 14 ธ.ค. 2567 | Full SFT + Math/Code datasets |
| v3 | 15 ธ.ค. 2567 | Thinking-only datasets |
| v4 | 16 ธ.ค. 2567 | Balanced Thai:English (2:1) |
| v5 | 17 ธ.ค. 2567 | GRPO Math + Coding + IFEval |
| v6 | 18 ธ.ค. 2567 | IFEval-focused + DPO Final |
| v7 | 20 ธ.ค. 2567 | Thai Knowledge SFT on SLERP Merge |
| v7.1 | 21 ธ.ค. 2567 | 100x Identity Repetition |
| **v7.2** | **22 ธ.ค. 2567** | **Paraphrase Augmentation (Current)** |
---
## ผู้พัฒนา
- **OpenThaiGPT** - สมาคมปัญญาประดิษฐ์แห่งประเทศไทย (AIEAT)
- **ThaiLLM** - โครงการพัฒนาโมเดลภาษาไทย
- **Siam AI Corperation** - ผู้สนับสนุนทรัพยากรและโครงสร้างพื้นฐาน
- **iApp Technology Co., Ltd.** - ผู้ร่วมพัฒนา
---
## ลิงก์ที่เกี่ยวข้อง
| รายการ | ลิงก์ |
|--------|-------|
| **OpenThaiGPT Project** | https://openthaigpt.aieat.or.th/ |
| **ThaiLLM HuggingFace** | https://huggingface.co/ThaiLLM |
| **Base Model (ThaiLLM-8B)** | https://huggingface.co/ThaiLLM/ThaiLLM-8B |
| **AIEAT** | https://aieat.or.th/ |
| **iApp Technology** | https://iapp.co.th/ |
---
## การอ้างอิง
หากนำโมเดลนี้ไปใช้ในงานวิจัยหรือโครงการ กรุณาอ้างอิงดังนี้:
```bibtex
@misc{openthaigpt-thaillm-8b-v7p2-research-preview,
author = {OpenThaiGPT and ThaiLLM Team},
title = {OpenThaiGPT-ThaiLLM-8B-v7.2-Research-Preview: A Thai Knowledge-focused Language Model},
year = {2025},
publisher = {HuggingFace},
howpublished = {\url{https://huggingface.co/openthaigpt/openthaigpt-thaillm-8b-instruct-v0.7.2-research-preview}}
}
```
---
## License
โมเดลนี้เผยแพร่ภายใต้ [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0)
---
*อัปเดตล่าสุด: 23 ธันวาคม 2567*

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{
"</think>": 151668,
"</tool_call>": 151658,
"</tool_response>": 151666,
"<think>": 151667,
"<tool_call>": 151657,
"<tool_response>": 151665,
"<|box_end|>": 151649,
"<|box_start|>": 151648,
"<|endoftext|>": 151643,
"<|file_sep|>": 151664,
"<|fim_middle|>": 151660,
"<|fim_pad|>": 151662,
"<|fim_prefix|>": 151659,
"<|fim_suffix|>": 151661,
"<|im_end|>": 151645,
"<|im_start|>": 151644,
"<|image_pad|>": 151655,
"<|object_ref_end|>": 151647,
"<|object_ref_start|>": 151646,
"<|quad_end|>": 151651,
"<|quad_start|>": 151650,
"<|repo_name|>": 151663,
"<|video_pad|>": 151656,
"<|vision_end|>": 151653,
"<|vision_pad|>": 151654,
"<|vision_start|>": 151652
}

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0].role == 'system' %}
{{- messages[0].content + '\n\n' }}
{%- endif %}
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0].role == 'system' %}
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
{%- for message in messages[::-1] %}
{%- set index = (messages|length - 1) - loop.index0 %}
{%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
{%- set ns.multi_step_tool = false %}
{%- set ns.last_query_index = index %}
{%- endif %}
{%- endfor %}
{%- for message in messages %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{%- set content = message.content %}
{%- set reasoning_content = '' %}
{%- if message.reasoning_content is defined and message.reasoning_content is not none %}
{%- set reasoning_content = message.reasoning_content %}
{%- else %}
{%- if '</think>' in message.content %}
{%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
{%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
{%- endif %}
{%- endif %}
{%- if loop.index0 > ns.last_query_index %}
{%- if loop.last or (not loop.last and reasoning_content) %}
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- if message.tool_calls %}
{%- for tool_call in message.tool_calls %}
{%- if (loop.first and content) or (not loop.first) %}
{{- '\n' }}
{%- endif %}
{%- if tool_call.function %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
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"<|video_pad|>"
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#!/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 json
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)
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 files:
state_dict = torch.load(f, map_location=device)
# 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}")
if zero_stage <= 2:
fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))]
elif zero_stage == 3:
# if there is more than one param group, there will be multiple flattened tensors - one
# flattened tensor per group - for simplicity merge them into a single tensor
#
# XXX: could make the script more memory efficient for when there are multiple groups - it
# will require matching the sub-lists of param_shapes for each param group flattened tensor
fp32_flat_groups = [
torch.cat(state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key], 0) 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")
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 = fp32_flat_groups[0].numel() * 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
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}"
)
# XXX: memory usage doubles here
state_dict[name] = torch.cat(
tuple(fp32_flat_groups[i].narrow(0, offset, partitioned_numel) for i in range(world_size)),
0).narrow(0, 0, unpartitioned_numel).view(shape)
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 get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag=None, exclude_frozen_parameters=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
Returns:
- pytorch ``state_dict``
Note: this approach may not work if your application doesn't have sufficient free CPU memory and
you may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
the checkpoint.
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.
"""
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")
return _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters)
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)
# 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")
state_dict_split = split_torch_state_dict_into_shards(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
filename_to_tensors = state_dict_split.filename_to_tensors.items()
for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"):
shard = {tensor: state_dict[tensor].contiguous() for tensor in tensors}
output_path = os.path.join(output_dir, shard_file)
if safe_serialization:
save_file(shard, output_path, metadata={"format": "pt"})
else:
torch.save(shard, output_path)
# 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)