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v0.9.5
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16
README.md
16
README.md
@@ -15,8 +15,6 @@
|
||||
|
||||
[](https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing)
|
||||
[](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory)
|
||||
[](https://www.lab4ai.cn/course/detail?id=7c13e60f6137474eb40f6fd3983c0f46&utm_source=LLaMA-Factory)
|
||||
[](https://www.llamafactory.com.cn/?utm_source=LLaMA-Factory)
|
||||
[](https://huggingface.co/spaces/hiyouga/LLaMA-Board)
|
||||
[](https://modelscope.cn/studios/hiyouga/LLaMA-Board)
|
||||
[](https://novita.ai/templates-library/105981?sharer=88115474-394e-4bda-968e-b88e123d0c47)
|
||||
@@ -38,7 +36,7 @@
|
||||
|
||||
</div>
|
||||
|
||||
👋 Join our [WeChat](https://github.com/hiyouga/llamafactory-community/blob/main/wechat/main.jpg), [NPU](https://github.com/hiyouga/llamafactory-community/blob/main/wechat/npu.jpg), [Lab4AI](https://github.com/hiyouga/llamafactory-community/blob/main/wechat/lab4ai.jpg), [LLaMA Factory Online](https://github.com/hiyouga/llamafactory-community/blob/main/wechat/online.jpg) user group.
|
||||
👋 Join our [WeChat](https://github.com/hiyouga/llamafactory-community/blob/main/wechat/main.jpg) and [NPU](https://github.com/hiyouga/llamafactory-community/blob/main/wechat/npu.jpg) user groups.
|
||||
|
||||
\[ English | [中文](README_zh.md) \]
|
||||
|
||||
@@ -52,14 +50,11 @@ Start local training:
|
||||
Start cloud training:
|
||||
- **Colab (free)**: https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing
|
||||
- **PAI-DSW (free trial)**: https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory
|
||||
- **LLaMA Factory Online**: https://www.llamafactory.com.cn/?utm_source=LLaMA-Factory
|
||||
- **Alaya NeW (cloud GPU deal)**: https://docs.alayanew.com/docs/documents/useGuide/LLaMAFactory/mutiple/?utm_source=LLaMA-Factory
|
||||
|
||||
Read technical notes:
|
||||
- **Documentation (WIP)**: https://llamafactory.readthedocs.io/en/latest/
|
||||
- **Documentation (AMD GPU)**: https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/notebooks/fine_tune/llama_factory_llama3.html
|
||||
- **Official Blog**: https://blog.llamafactory.net/en/
|
||||
- **Official Course**: https://www.lab4ai.cn/course/detail?id=7c13e60f6137474eb40f6fd3983c0f46&utm_source=LLaMA-Factory
|
||||
|
||||
> [!NOTE]
|
||||
> Except for the above links, all other websites are unauthorized third-party websites. Please carefully use them.
|
||||
@@ -78,7 +73,6 @@ Read technical notes:
|
||||
- [Data Preparation](#data-preparation)
|
||||
- [Quickstart](#quickstart)
|
||||
- [Fine-Tuning with LLaMA Board GUI](#fine-tuning-with-llama-board-gui-powered-by-gradio)
|
||||
- [LLaMA Factory Online](#llama-factory-online)
|
||||
- [Build Docker](#build-docker)
|
||||
- [Deploy with OpenAI-style API and vLLM](#deploy-with-openai-style-api-and-vllm)
|
||||
- [Download from ModelScope Hub](#download-from-modelscope-hub)
|
||||
@@ -117,15 +111,11 @@ Read technical notes:
|
||||
|
||||
- 💡 [KTransformers Fine-Tuning × LLaMA Factory: Fine-tuning 1000 Billion models with 2 4090-GPU + CPU](https://blog.llamafactory.net/en/posts/ktransformers/) (English)
|
||||
- 💡 [Easy Dataset × LLaMA Factory: Enabling LLMs to Efficiently Learn Domain Knowledge](https://buaa-act.feishu.cn/wiki/GVzlwYcRFiR8OLkHbL6cQpYin7g) (English)
|
||||
- [Fine-tune a mental health LLM using LLaMA-Factory](https://www.lab4ai.cn/project/detail?id=25cce32ec131497b9e06a93336a0817f&type=project&utm_source=LLaMA-Factory) (Chinese)
|
||||
- [Fine-tune GPT-OSS for Role-Playing using LLaMA-Factory](https://docs.llamafactory.com.cn/docs/documents/best-practice/gptroleplay/?utm_source=LLaMA-Factory) (Chinese)
|
||||
- [A One-Stop Code-Free Model Reinforcement Learning and Deployment Platform based on LLaMA-Factory and EasyR1](https://aws.amazon.com/cn/blogs/china/building-llm-model-hub-based-on-llamafactory-and-easyr1/) (Chinese)
|
||||
- [How Apoidea Group enhances visual information extraction from banking documents with multimodal models using LLaMA-Factory on Amazon SageMaker HyperPod](https://aws.amazon.com/cn/blogs/machine-learning/how-apoidea-group-enhances-visual-information-extraction-from-banking-documents-with-multimodal-models-using-llama-factory-on-amazon-sagemaker-hyperpod/) (English)
|
||||
|
||||
<details><summary>All Blogs</summary>
|
||||
|
||||
- [Fine-tune Llama3.1-70B for Medical Diagnosis using LLaMA-Factory](https://docs.alayanew.com/docs/documents/bestPractice/bigModel/llama70B/?utm_source=LLaMA-Factory) (Chinese)
|
||||
- [Fine-tune Qwen2.5-VL for Autonomous Driving using LLaMA-Factory](https://docs.alayanew.com/docs/documents/useGuide/LLaMAFactory/mutiple/?utm_source=LLaMA-Factory) (Chinese)
|
||||
- [LLaMA Factory: Fine-tuning the DeepSeek-R1-Distill-Qwen-7B Model for News Classifier](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_deepseek_r1_distill_7b) (Chinese)
|
||||
- [A One-Stop Code-Free Model Fine-Tuning \& Deployment Platform based on SageMaker and LLaMA-Factory](https://aws.amazon.com/cn/blogs/china/a-one-stop-code-free-model-fine-tuning-deployment-platform-based-on-sagemaker-and-llama-factory/) (Chinese)
|
||||
- [LLaMA Factory Multi-Modal Fine-Tuning Practice: Fine-Tuning Qwen2-VL for Personal Tourist Guide](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_qwen2vl) (Chinese)
|
||||
@@ -661,10 +651,6 @@ See [examples/README.md](examples/README.md) for advanced usage (including distr
|
||||
llamafactory-cli webui
|
||||
```
|
||||
|
||||
### LLaMA Factory Online
|
||||
|
||||
Read our [documentation](https://docs.llamafactory.com.cn/docs/documents/quickstart/getstarted/?utm_source=LLaMA-Factory).
|
||||
|
||||
### Build Docker
|
||||
|
||||
For CUDA users:
|
||||
|
||||
16
README_zh.md
16
README_zh.md
@@ -15,8 +15,6 @@
|
||||
|
||||
[](https://colab.research.google.com/drive/1d5KQtbemerlSDSxZIfAaWXhKr30QypiK?usp=sharing)
|
||||
[](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory)
|
||||
[](https://www.lab4ai.cn/course/detail?id=7c13e60f6137474eb40f6fd3983c0f46&utm_source=LLaMA-Factory)
|
||||
[](https://www.llamafactory.com.cn/?utm_source=LLaMA-Factory)
|
||||
[](https://huggingface.co/spaces/hiyouga/LLaMA-Board)
|
||||
[](https://modelscope.cn/studios/hiyouga/LLaMA-Board)
|
||||
[](https://novita.ai/templates-library/105981?sharer=88115474-394e-4bda-968e-b88e123d0c47)
|
||||
@@ -38,7 +36,7 @@
|
||||
|
||||
</div>
|
||||
|
||||
👋 加入我们的[微信群](https://github.com/hiyouga/llamafactory-community/blob/main/wechat/main.jpg)、[NPU 用户群](https://github.com/hiyouga/llamafactory-community/blob/main/wechat/npu.jpg)、[大模型实验室群](https://github.com/hiyouga/llamafactory-community/blob/main/wechat/lab4ai.jpg) 或 [LLaMA Factory Online 用户群](https://github.com/hiyouga/llamafactory-community/blob/main/wechat/online.png)。
|
||||
👋 加入我们的[微信群](https://github.com/hiyouga/llamafactory-community/blob/main/wechat/main.jpg)和 [NPU 用户群](https://github.com/hiyouga/llamafactory-community/blob/main/wechat/npu.jpg)。
|
||||
|
||||
\[ [English](README.md) | 中文 \]
|
||||
|
||||
@@ -52,8 +50,6 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
|
||||
开始云端训练：
|
||||
- **Colab（免费）**：https://colab.research.google.com/drive/1d5KQtbemerlSDSxZIfAaWXhKr30QypiK?usp=sharing
|
||||
- **PAI-DSW（免费试用）**：https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory
|
||||
- **LLaMA Factory Online（在线微调）**：https://www.llamafactory.com.cn/?utm_source=LLaMA-Factory
|
||||
- **九章智算云（算力优惠活动）**：https://docs.alayanew.com/docs/documents/useGuide/LLaMAFactory/mutiple/?utm_source=LLaMA-Factory
|
||||
|
||||
阅读技术文档：
|
||||
- **入门教程**：https://zhuanlan.zhihu.com/p/695287607
|
||||
@@ -61,7 +57,6 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
|
||||
- **框架文档**：https://llamafactory.readthedocs.io/zh-cn/latest/
|
||||
- **框架文档（昇腾 NPU）**：https://ascend.github.io/docs/sources/llamafactory/
|
||||
- **官方博客**：https://blog.llamafactory.net/
|
||||
- **官方课程**：https://www.lab4ai.cn/course/detail?id=7c13e60f6137474eb40f6fd3983c0f46&utm_source=LLaMA-Factory
|
||||
|
||||
> [!NOTE]
|
||||
> 除上述链接以外的其他网站均为未经许可的第三方网站，请小心甄别。
|
||||
@@ -80,7 +75,6 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
|
||||
- [数据准备](#数据准备)
|
||||
- [快速开始](#快速开始)
|
||||
- [LLaMA Board 可视化微调](#llama-board-可视化微调由-gradio-驱动)
|
||||
- [LLaMA Factory Online 在线微调](#llama-factory-online-在线微调)
|
||||
- [构建 Docker](#构建-docker)
|
||||
- [利用 vLLM 部署 OpenAI API](#利用-vllm-部署-openai-api)
|
||||
- [从魔搭社区下载](#从魔搭社区下载)
|
||||
@@ -119,15 +113,11 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
|
||||
|
||||
- 💡 [KTransformers Fine-Tuning × LLaMA Factory: 用2张4090级的GPU+CPU 微调 1000B规模的超大模型](https://swcil84qspu.feishu.cn/wiki/Z1sSwb2poijybxkyPEkcDG6enVc) (中文)
|
||||
- 💡 [Easy Dataset × LLaMA Factory: 让大模型高效学习领域知识](https://buaa-act.feishu.cn/wiki/KY9xwTGs1iqHrRkjXBwcZP9WnL9)（中文）
|
||||
- [使用 LLaMA-Factory 微调心理健康大模型](https://www.lab4ai.cn/project/detail?id=25cce32ec131497b9e06a93336a0817f&type=project&utm_source=LLaMA-Factory)（中文）
|
||||
- [使用 LLaMA-Factory 构建 GPT-OSS 角色扮演模型](https://docs.llamafactory.com.cn/docs/documents/best-practice/gptroleplay/?utm_source=LLaMA-Factory)（中文）
|
||||
- [基于 LLaMA-Factory 和 EasyR1 打造一站式无代码大模型强化学习和部署平台 LLM Model Hub](https://aws.amazon.com/cn/blogs/china/building-llm-model-hub-based-on-llamafactory-and-easyr1/)（中文）
|
||||
- [通过亚马逊 SageMaker HyperPod 上的 LLaMA-Factory 增强多模态模型银行文档的视觉信息提取](https://aws.amazon.com/cn/blogs/machine-learning/how-apoidea-group-enhances-visual-information-extraction-from-banking-documents-with-multimodal-models-using-llama-factory-on-amazon-sagemaker-hyperpod/)（英文）
|
||||
|
||||
<details><summary>全部博客</summary>
|
||||
|
||||
- [使用 LLaMA-Factory 微调 Llama3.1-70B 医学诊断模型](https://docs.alayanew.com/docs/documents/bestPractice/bigModel/llama70B/?utm_source=LLaMA-Factory)（中文）
|
||||
- [使用 LLaMA-Factory 微调 Qwen2.5-VL 实现自动驾驶场景微调](https://docs.alayanew.com/docs/documents/useGuide/LLaMAFactory/mutiple/?utm_source=LLaMA-Factory)（中文）
|
||||
- [LLaMA Factory：微调 DeepSeek-R1-Distill-Qwen-7B 模型实现新闻标题分类器](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_deepseek_r1_distill_7b)（中文）
|
||||
- [基于 Amazon SageMaker 和 LLaMA-Factory 打造一站式无代码模型微调部署平台 Model Hub](https://aws.amazon.com/cn/blogs/china/a-one-stop-code-free-model-fine-tuning-deployment-platform-based-on-sagemaker-and-llama-factory/)（中文）
|
||||
- [LLaMA Factory 多模态微调实践：微调 Qwen2-VL 构建文旅大模型](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_qwen2vl)（中文）
|
||||
@@ -662,10 +652,6 @@ llamafactory-cli export examples/merge_lora/qwen3_lora_sft.yaml
|
||||
llamafactory-cli webui
|
||||
```
|
||||
|
||||
### LLaMA Factory Online 在线微调
|
||||
|
||||
详情阅读该[文档](https://docs.llamafactory.com.cn/docs/documents/quickstart/getstarted/?utm_source=LLaMA-Factory)。
|
||||
|
||||
### 构建 Docker
|
||||
|
||||
CUDA 用户：
|
||||
|
||||
@@ -0,0 +1,31 @@
|
||||
model: Qwen/Qwen3-0.6B
|
||||
model_class: llm
|
||||
|
||||
template: qwen3_nothink
|
||||
|
||||
|
||||
kernel_config:
|
||||
name: auto
|
||||
include_kernels: auto # choice: null/true/false/auto/kernel_id1,kernel_id2,kernel_id3, default is null
|
||||
|
||||
quant_config: null
|
||||
|
||||
dist_config:
|
||||
name: fsdp2
|
||||
dcp_path: null # /mnt/f/pretrain_models/Qwen3-0.6B-dcp
|
||||
|
||||
### data
|
||||
train_dataset: data/v1_sft_demo.yaml
|
||||
|
||||
### training
|
||||
output_dir: outputs/test_fsdp2
|
||||
micro_batch_size: 2
|
||||
batching_strategy: normal
|
||||
|
||||
cutoff_len: 2048
|
||||
learning_rate: 1.0e-4
|
||||
max_steps: 10
|
||||
|
||||
### sample
|
||||
sample_backend: hf
|
||||
max_new_tokens: 128
|
||||
@@ -0,0 +1,30 @@
|
||||
model: Qwen/Qwen3-0.6B
|
||||
model_class: llm
|
||||
|
||||
template: qwen3_nothink
|
||||
|
||||
kernel_config:
|
||||
name: auto
|
||||
include_kernels: auto # choice: null/true/false/auto/kernel_id1,kernel_id2,kernel_id3, default is null
|
||||
|
||||
quant_config: null
|
||||
|
||||
dist_config:
|
||||
name: fsdp2
|
||||
dcp_path: null # /mnt/f/pretrain_models/Qwen3-0.6B-dcp
|
||||
|
||||
### data
|
||||
train_dataset: data/v1_sft_demo.yaml
|
||||
|
||||
|
||||
### training
|
||||
output_dir: outputs/test_fsdp2
|
||||
micro_batch_size: 2
|
||||
batching_strategy: dynamic_batching
|
||||
cutoff_len: 2048
|
||||
learning_rate: 1.0e-4
|
||||
max_steps: 10
|
||||
|
||||
### sample
|
||||
sample_backend: hf
|
||||
max_new_tokens: 128
|
||||
@@ -0,0 +1,30 @@
|
||||
model: Qwen/Qwen3-0.6B
|
||||
model_class: llm
|
||||
|
||||
template: qwen3_nothink
|
||||
|
||||
kernel_config:
|
||||
name: auto
|
||||
include_kernels: auto # choice: null/true/false/auto/kernel_id1,kernel_id2,kernel_id3, default is null
|
||||
|
||||
quant_config: null
|
||||
|
||||
dist_config:
|
||||
name: fsdp2
|
||||
dcp_path: null # /mnt/f/pretrain_models/Qwen3-0.6B-dcp
|
||||
|
||||
### data
|
||||
train_dataset: data/v1_sft_demo.yaml
|
||||
|
||||
### training
|
||||
output_dir: outputs/test_fsdp2
|
||||
micro_batch_size: 4
|
||||
batching_strategy: dynamic_padding_free
|
||||
flash_attn: flash_attention2
|
||||
cutoff_len: 2048
|
||||
learning_rate: 1.0e-4
|
||||
max_steps: 10
|
||||
|
||||
### sample
|
||||
sample_backend: hf
|
||||
max_new_tokens: 128
|
||||
@@ -0,0 +1,30 @@
|
||||
model: Qwen/Qwen3-0.6B
|
||||
model_class: llm
|
||||
|
||||
template: qwen3_nothink
|
||||
|
||||
kernel_config:
|
||||
name: auto
|
||||
include_kernels: auto # choice: null/true/false/auto/kernel_id1,kernel_id2,kernel_id3, default is null
|
||||
|
||||
quant_config: null
|
||||
|
||||
dist_config:
|
||||
name: fsdp2
|
||||
dcp_path: null # /mnt/f/pretrain_models/Qwen3-0.6B-dcp
|
||||
|
||||
### data
|
||||
train_dataset: data/v1_sft_demo.yaml
|
||||
|
||||
### training
|
||||
output_dir: outputs/test_fsdp2
|
||||
micro_batch_size: 4
|
||||
batching_strategy: padding_free
|
||||
flash_attn: flash_attention2
|
||||
cutoff_len: 2048
|
||||
learning_rate: 1.0e-4
|
||||
max_steps: 10
|
||||
|
||||
### sample
|
||||
sample_backend: hf
|
||||
max_new_tokens: 128
|
||||
@@ -19,7 +19,7 @@
|
||||
from collections import OrderedDict
|
||||
|
||||
|
||||
VERSION = "0.9.5.dev0"
|
||||
VERSION = "0.9.5"
|
||||
|
||||
|
||||
def print_env() -> None:
|
||||
|
||||
@@ -162,8 +162,14 @@ def patch_qwen3_5_forward(model: "PreTrainedModel") -> None:
|
||||
if position_ids is not None and position_ids.ndim == 3:
|
||||
position_ids = position_ids[0]
|
||||
|
||||
# `prepare_fa_kwargs_from_position_ids` would crash on None; guard for safety.
|
||||
cu_seqlens = prepare_fa_kwargs_from_position_ids(position_ids)[0][0] if position_ids is not None else None
|
||||
# cu_seqlens for the FLA varlen path is only needed when batch_size == 1:
|
||||
# packing / neat-packing: always folded into a single sequence (bsz == 1) -> varlen
|
||||
# non-packing, bsz == 1: single segment, equivalent to a standard single sequence
|
||||
# non-packing, bsz > 1: not packed, use cu_seqlens=None and standard batched kernels
|
||||
if position_ids is not None and batch_size == 1:
|
||||
cu_seqlens = prepare_fa_kwargs_from_position_ids(position_ids)[0][0]
|
||||
else:
|
||||
cu_seqlens = None
|
||||
|
||||
# FLA varlen kernels expect [B, T, D] layout, not [B, D, T] like the
|
||||
# standard causal-conv1d path that the upstream forward uses.
|
||||
|
||||
@@ -20,7 +20,6 @@ import sys
|
||||
import time
|
||||
from collections import defaultdict
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
from dataclasses import dataclass, field
|
||||
from datetime import timedelta
|
||||
from typing import TYPE_CHECKING, Any, Optional
|
||||
|
||||
@@ -584,7 +583,7 @@ class ModuleProfilerCallback(TrainerCallback):
|
||||
if matched:
|
||||
logger.info_rank0(
|
||||
f"ModuleProfiler: registered hooks on {len(matched)} modules: {matched[:5]}"
|
||||
+ (f" ... (+{len(matched)-5} more)" if len(matched) > 5 else "")
|
||||
+ (f" ... (+{len(matched) - 5} more)" if len(matched) > 5 else "")
|
||||
)
|
||||
else:
|
||||
logger.warning_rank0(f"ModuleProfiler: no modules matched patterns {self.patterns}")
|
||||
@@ -616,7 +615,7 @@ class ModuleProfilerCallback(TrainerCallback):
|
||||
bwd = self._backward_times.get(name, [])
|
||||
fwd_mean = sum(fwd) / len(fwd) if fwd else 0.0
|
||||
bwd_mean = sum(bwd) / len(bwd) if bwd else 0.0
|
||||
lines.append(f" {name}: fwd={fwd_mean:.3f}, bwd={bwd_mean:.3f}, total={fwd_mean+bwd_mean:.3f}")
|
||||
lines.append(f" {name}: fwd={fwd_mean:.3f}, bwd={bwd_mean:.3f}, total={fwd_mean + bwd_mean:.3f}")
|
||||
|
||||
logger.info_rank0("\n".join(lines))
|
||||
self._forward_times.clear()
|
||||
|
||||
@@ -80,10 +80,10 @@ def _training_function(config: dict[str, Any]) -> None:
|
||||
if finetuning_args.early_stopping_steps is not None:
|
||||
callbacks.append(EarlyStoppingCallback(early_stopping_patience=finetuning_args.early_stopping_steps))
|
||||
|
||||
if training_args.enable_torch_profiler:
|
||||
if getattr(training_args, "enable_torch_profiler", False):
|
||||
callbacks.append(TorchProfilerCallback(training_args))
|
||||
|
||||
if training_args.profile_modules:
|
||||
if getattr(training_args, "profile_modules", None):
|
||||
callbacks.append(ModuleProfilerCallback(training_args.profile_modules))
|
||||
|
||||
callbacks.append(ReporterCallback(model_args, data_args, finetuning_args, generating_args)) # add to last
|
||||
|
||||
@@ -12,6 +12,7 @@
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
from ..utils.types import AttentionFunction
|
||||
from .arg_parser import InputArgument, get_args
|
||||
from .arg_utils import BatchingStrategy, ModelClass, SampleBackend
|
||||
from .data_args import DataArguments
|
||||
@@ -21,6 +22,7 @@ from .training_args import TrainingArguments
|
||||
|
||||
|
||||
__all__ = [
|
||||
"AttentionFunction",
|
||||
"BatchingStrategy",
|
||||
"DataArguments",
|
||||
"InputArgument",
|
||||
|
||||
@@ -57,15 +57,12 @@ def get_args(args: InputArgument = None) -> tuple[ModelArguments, DataArguments,
|
||||
print(f"Got unknown args, potentially deprecated arguments: {unknown_args}")
|
||||
raise ValueError(f"Some specified arguments are not used by the HfArgumentParser: {unknown_args}")
|
||||
|
||||
model_args, data_args, training_args, sample_args = parsed_args
|
||||
# Seed as early as possible after argument parsing so all downstream
|
||||
# components (dist init, dataloader, model init in run_* entrypoints) share the same RNG state.
|
||||
for arg in parsed_args:
|
||||
seed = getattr(arg, "seed", None)
|
||||
if seed is not None:
|
||||
set_seed(seed)
|
||||
break
|
||||
set_seed(training_args.seed, full_determinism=training_args.full_determinism)
|
||||
|
||||
return tuple(parsed_args)
|
||||
return model_args, data_args, training_args, sample_args
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
@@ -15,6 +15,7 @@
|
||||
|
||||
from dataclasses import dataclass, field
|
||||
|
||||
from ..utils.types import AttentionFunction
|
||||
from .arg_utils import ModelClass, PluginConfig, get_plugin_config
|
||||
|
||||
|
||||
@@ -32,6 +33,12 @@ class ModelArguments:
|
||||
default=False,
|
||||
metadata={"help": "Trust remote code from Hugging Face."},
|
||||
)
|
||||
flash_attn: AttentionFunction = field(
|
||||
default=AttentionFunction.SDPA,
|
||||
metadata={
|
||||
"help": "Attention implementation to use: eager, sdpa, or flash_attention_2. SDPA is the default implementation for models."
|
||||
},
|
||||
)
|
||||
model_class: ModelClass = field(
|
||||
default=ModelClass.LLM,
|
||||
metadata={"help": "Model class from Hugging Face."},
|
||||
@@ -54,6 +61,12 @@ class ModelArguments:
|
||||
)
|
||||
|
||||
def __post_init__(self) -> None:
|
||||
supported_flash_attn = [item.value for item in AttentionFunction]
|
||||
if self.flash_attn not in supported_flash_attn:
|
||||
raise ValueError(
|
||||
f"Unsupported `flash_attn`: {self.flash_attn}. Supported values are: {supported_flash_attn}."
|
||||
)
|
||||
|
||||
self.init_config = get_plugin_config(self.init_config)
|
||||
self.peft_config = get_plugin_config(self.peft_config)
|
||||
self.kernel_config = get_plugin_config(self.kernel_config)
|
||||
|
||||
@@ -85,6 +85,10 @@ class TrainingArguments:
|
||||
default=42,
|
||||
metadata={"help": "Random seed that will be set at the beginning of training."},
|
||||
)
|
||||
full_determinism: bool = field(
|
||||
default=False,
|
||||
metadata={"help": "Enable full deterministic mode for reproducible distributed training."},
|
||||
)
|
||||
resume_from_checkpoint: str | None = field(
|
||||
default=None,
|
||||
metadata={"help": "Path to a checkpoint directory to resume training from, or 'auto' to find the latest."},
|
||||
@@ -116,3 +120,9 @@ class TrainingArguments:
|
||||
self.dist_config = get_plugin_config(self.dist_config)
|
||||
self.optim_config = get_plugin_config(self.optim_config)
|
||||
self.lr_scheduler_config = get_plugin_config(self.lr_scheduler_config)
|
||||
|
||||
if str(self.batching_strategy) == str(BatchingStrategy.DYNAMIC_BATCHING):
|
||||
if self.max_steps is None or self.max_steps <= 0:
|
||||
raise ValueError("`dynamic_batching` requires `max_steps` because it is step-driven.")
|
||||
if self.save_epochs is not None:
|
||||
raise ValueError("`save_epochs` is not supported with `dynamic_batching`; use `save_steps` instead.")
|
||||
|
||||
@@ -34,7 +34,7 @@ import torch.nn.functional as F
|
||||
|
||||
from ..accelerator.helper import ReduceOp
|
||||
from ..accelerator.interface import Dim, DistributedInterface
|
||||
from ..config import TrainingArguments
|
||||
from ..config import BatchingStrategy, TrainingArguments
|
||||
from ..utils import logging
|
||||
from ..utils.callbacks import (
|
||||
CallbackHandler,
|
||||
@@ -147,13 +147,19 @@ class BaseTrainer:
|
||||
from ..plugins.model_plugins.parallelization.sequence_parallel import SequenceParallelModelPlugin
|
||||
|
||||
if model.config._attn_implementation != "flash_attention_2":
|
||||
logger.warning_rank0(
|
||||
"Sequence parallelism is optimized for flash attention only. Replace the attention implementation to flash_attention_2."
|
||||
raise ValueError(
|
||||
"Sequence parallelism requires flash attention. Please set `flash_attn: flash_attention_2`."
|
||||
)
|
||||
model.config._attn_implementation = "flash_attention_2"
|
||||
|
||||
SequenceParallelModelPlugin(self.args.dist_config.get("cp_mode", "ulysses"))(model, self.args.dist_config)
|
||||
|
||||
def _create_batch_generator(self) -> None:
|
||||
if (
|
||||
self.args.batching_strategy == BatchingStrategy.PADDING_FREE
|
||||
and getattr(self.model.config, "_attn_implementation", None) != "flash_attention_2"
|
||||
):
|
||||
raise ValueError("`padding_free` requires `flash_attn: flash_attention_2`.")
|
||||
|
||||
self.train_batch_generator = BatchGenerator(
|
||||
dataset=self.train_dataset,
|
||||
renderer=self.renderer,
|
||||
@@ -237,6 +243,7 @@ class BaseTrainer:
|
||||
self.train_batch_generator.set_epoch(epoch)
|
||||
self.callback_handler.on_epoch_begin(self.args, self.state)
|
||||
|
||||
# BatchGenerator is an iterator; each loop step calls its __next__ to produce one optimizer step.
|
||||
for micro_batches in self.train_batch_generator:
|
||||
self.global_step += 1
|
||||
|
||||
|
||||
@@ -120,6 +120,7 @@ class ModelEngine:
|
||||
init_device = DistributedInterface().current_device
|
||||
|
||||
init_kwargs = {} if self._deepspeed_zero3_enabled else {"device_map": init_device}
|
||||
logger.info_rank0(f"Using attention implementation: {self.args.flash_attn}.")
|
||||
|
||||
if self.args.quant_config is not None:
|
||||
from ..plugins.model_plugins.quantization import QuantizationPlugin
|
||||
@@ -164,6 +165,7 @@ class ModelEngine:
|
||||
self.args.model,
|
||||
config=self.model_config,
|
||||
dtype="auto",
|
||||
attn_implementation=self.args.flash_attn,
|
||||
trust_remote_code=self.args.trust_remote_code,
|
||||
**init_kwargs,
|
||||
)
|
||||
|
||||
@@ -42,6 +42,8 @@ from .rendering import Renderer
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
__all__ = ["BatchGenerator"]
|
||||
|
||||
|
||||
def default_collate_fn(buffer: StatefulBuffer, batch_info: BatchInfo) -> list[BatchInput] | None:
|
||||
micro_batch_size = batch_info["micro_batch_size"]
|
||||
@@ -102,19 +104,18 @@ class BatchGenerator(Iterator):
|
||||
if not self.drop_last:
|
||||
raise ValueError("Drop last must be True.")
|
||||
|
||||
self._batch_info: BatchInfo = {
|
||||
"micro_batch_size": self.micro_batch_size,
|
||||
"num_micro_batch": self.num_micro_batch,
|
||||
"cutoff_len": self.cutoff_len,
|
||||
}
|
||||
|
||||
self._init_data_provider()
|
||||
|
||||
self._is_resuming: bool = False
|
||||
self._data_iter = iter(self._data_provider)
|
||||
self._buffer = StatefulBuffer()
|
||||
|
||||
self._batch_info: BatchInfo = {
|
||||
"micro_batch_size": self.micro_batch_size,
|
||||
"num_micro_batch": self.num_micro_batch,
|
||||
"cutoff_len": self.cutoff_len,
|
||||
"data_iter": self._data_iter,
|
||||
}
|
||||
|
||||
logger.info_rank0(
|
||||
f"Init unified data loader with global batch size {self.global_batch_size}, "
|
||||
f"micro batch size {self.micro_batch_size}, "
|
||||
@@ -137,12 +138,19 @@ class BatchGenerator(Iterator):
|
||||
else:
|
||||
raise NotImplementedError("Iterable dataset is not supported yet.")
|
||||
|
||||
if self.batching_strategy == BatchingStrategy.NORMAL:
|
||||
batch_size = self.micro_batch_size * self.num_micro_batch
|
||||
else:
|
||||
from ...plugins.trainer_plugins.batching import BatchingPlugin
|
||||
|
||||
batch_size = BatchingPlugin(self.batching_strategy).get_data_provider_batch_size(self._batch_info)
|
||||
|
||||
generator_seed = torch.Generator()
|
||||
generator_seed.manual_seed(self.seed)
|
||||
|
||||
self._data_provider = StatefulDataLoader(
|
||||
self.dataset,
|
||||
batch_size=self.micro_batch_size * self.num_micro_batch,
|
||||
batch_size=batch_size,
|
||||
sampler=sampler,
|
||||
num_workers=self.batching_workers,
|
||||
collate_fn=self.renderer.process_samples,
|
||||
@@ -156,8 +164,7 @@ class BatchGenerator(Iterator):
|
||||
else:
|
||||
from ...plugins.trainer_plugins.batching import BatchingPlugin
|
||||
|
||||
self._length = BatchingPlugin(self.batching_strategy).compute_length(self._data_provider)
|
||||
raise NotImplementedError("Batching strategy other than NORMAL is not supported yet.")
|
||||
self._length = BatchingPlugin(self.batching_strategy).compute_length(self._data_provider, self._batch_info)
|
||||
|
||||
def __len__(self) -> int:
|
||||
return self._length
|
||||
@@ -190,7 +197,7 @@ class BatchGenerator(Iterator):
|
||||
else:
|
||||
from ...plugins.trainer_plugins.batching import BatchingPlugin
|
||||
|
||||
BatchingPlugin(self.batching_strategy).fill_buffer(self._buffer, self._batch_info)
|
||||
BatchingPlugin(self.batching_strategy).fill_buffer(self._buffer, self._batch_info, self._next_samples)
|
||||
|
||||
def _generate_batch(self) -> list[BatchInput] | None:
|
||||
if self.batching_strategy == BatchingStrategy.NORMAL:
|
||||
@@ -200,6 +207,20 @@ class BatchGenerator(Iterator):
|
||||
|
||||
return BatchingPlugin(self.batching_strategy).generate_batch(self._buffer, self._batch_info)
|
||||
|
||||
def _next_samples(self, restart: bool) -> list[ModelInput] | None:
|
||||
try:
|
||||
return next(self._data_iter)
|
||||
except StopIteration:
|
||||
if not restart:
|
||||
return None
|
||||
|
||||
# Dynamic batching may restart the provider to fill one token-budgeted batch.
|
||||
self._data_iter = iter(self._data_provider)
|
||||
try:
|
||||
return next(self._data_iter)
|
||||
except StopIteration:
|
||||
return None
|
||||
|
||||
def state_dict(self) -> dict[str, Any]:
|
||||
return {
|
||||
"buffer": self._buffer.state_dict(),
|
||||
|
||||
@@ -228,6 +228,30 @@ class NpuMoeFused:
|
||||
routed_out = self.experts(hidden_states, routing_weights, router_indices)
|
||||
return routed_out
|
||||
|
||||
@staticmethod
|
||||
def npu_moe_experts_v5_forward(
|
||||
self, hidden_states: torch.Tensor, top_k_index: torch.Tensor, top_k_weights: torch.Tensor
|
||||
) -> torch.Tensor:
|
||||
"""Forward pass for Transformers v5+ MoE experts using NPU fused operations.
|
||||
|
||||
Transformers v5 stores expert weights in F.linear layout:
|
||||
gate_up_proj: [num_experts, 2 * intermediate_dim, hidden_dim]
|
||||
down_proj: [num_experts, hidden_dim, intermediate_dim]
|
||||
The NPU grouped matmul path expects matmul layout, so both weights are transposed.
|
||||
"""
|
||||
hidden_states = hidden_states.reshape(-1, self.hidden_dim)
|
||||
permuted_hidden_states, row_ids_map = torch_npu.npu_moe_token_permute(
|
||||
hidden_states, top_k_index.to(torch.int32)
|
||||
)
|
||||
tokens_per_expert = torch.histc(top_k_index.float(), bins=self.num_experts, min=0, max=self.num_experts).long()
|
||||
|
||||
gate_up_proj = self.gate_up_proj.transpose(1, 2)
|
||||
down_proj = self.down_proj.transpose(1, 2)
|
||||
intermediate_hidden_states = GmmFunction.apply(permuted_hidden_states, gate_up_proj, tokens_per_expert)
|
||||
intermediate_activations = torch_npu.npu_swiglu(intermediate_hidden_states, dim=-1)
|
||||
output = GmmFunction.apply(intermediate_activations, down_proj, tokens_per_expert)
|
||||
return torch_npu.npu_moe_token_unpermute(output, row_ids_map, probs=top_k_weights)
|
||||
|
||||
|
||||
class Qwen3NpuMoeFused:
|
||||
"""Container for Qwen3 NPU fused MoE forward functions."""
|
||||
@@ -283,16 +307,30 @@ class Qwen3NpuMoeFused:
|
||||
|
||||
|
||||
# moe patch config mapping
|
||||
kernel_moe_mapping = {
|
||||
if is_transformers_version_greater_than("5.0.0"):
|
||||
kernel_moe_mapping = {
|
||||
"Qwen3MoeForCausalLM": {
|
||||
"Qwen3MoeExperts": NpuMoeFused.npu_moe_experts_v5_forward,
|
||||
},
|
||||
"Qwen3VLMoeForConditionalGeneration": {
|
||||
"Qwen3VLMoeTextExperts": NpuMoeFused.npu_moe_experts_v5_forward,
|
||||
},
|
||||
"Qwen3_5MoeForCausalLM": {
|
||||
"Qwen3_5MoeExperts": NpuMoeFused.npu_moe_experts_v5_forward,
|
||||
},
|
||||
"Qwen3_5MoeForConditionalGeneration": {
|
||||
"Qwen3_5MoeExperts": NpuMoeFused.npu_moe_experts_v5_forward,
|
||||
},
|
||||
}
|
||||
else:
|
||||
kernel_moe_mapping = {
|
||||
"Qwen3MoeForCausalLM": {
|
||||
"Qwen3MoeSparseMoeBlock": Qwen3NpuMoeFused.qwen3moe_sparse_moe_block_forward,
|
||||
},
|
||||
"Qwen3VLMoeForConditionalGeneration": {
|
||||
"Qwen3VLMoeTextExperts": NpuMoeFused.npu_moe_experts_forward,
|
||||
"Qwen3VLMoeTextSparseMoeBlock": NpuMoeFused.npu_moe_sparse_block_forward,
|
||||
}
|
||||
}
|
||||
|
||||
if not is_transformers_version_greater_than("5.0.0"):
|
||||
kernel_moe_mapping["Qwen3MoeForCausalLM"] = {
|
||||
"Qwen3MoeSparseMoeBlock": Qwen3NpuMoeFused.qwen3moe_sparse_moe_block_forward
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
|
||||
@@ -51,22 +51,17 @@ def _should_use_residual_rmsnorm(module):
|
||||
bool: ``True`` if the module uses residual parameterization, ``False`` otherwise.
|
||||
|
||||
.. note::
|
||||
This detection ensures compatibility with future model versions (e.g., Qwen3.6, Qwen4.0)
|
||||
without hardcoding version numbers. Two methods are used: weight value inspection
|
||||
(most reliable) and class name pattern matching (backward compatibility).
|
||||
This must follow the module's forward semantics. Do not infer it from trained
|
||||
weight values because standard RMSNorm weights can also be close to zero.
|
||||
"""
|
||||
if hasattr(module, "weight") and module.weight is not None:
|
||||
weight_mean = module.weight.data.mean().item()
|
||||
if abs(weight_mean) < 0.3:
|
||||
return True
|
||||
residual_rmsnorm_classes = {
|
||||
"Qwen3_5RMSNorm",
|
||||
"Qwen3_5MoeRMSNorm",
|
||||
"Qwen3NextRMSNorm",
|
||||
}
|
||||
|
||||
class_name = module.__class__.__name__
|
||||
residual_patterns = ["Qwen3_5", "Qwen3_6", "Qwen4"]
|
||||
for pattern in residual_patterns:
|
||||
if pattern in class_name:
|
||||
return True
|
||||
|
||||
return False
|
||||
return class_name in residual_rmsnorm_classes
|
||||
|
||||
|
||||
def npu_rms_norm_forward(self, hidden_states):
|
||||
@@ -82,7 +77,7 @@ def npu_rms_norm_forward(self, hidden_states):
|
||||
_eps = getattr(self, "variance_epsilon", None) or getattr(self, "eps", 1e-6)
|
||||
|
||||
if hasattr(self, "weight") and self.weight is not None:
|
||||
if _should_use_residual_rmsnorm(self):
|
||||
if getattr(self, "_npu_use_residual_rmsnorm", False):
|
||||
effective_weight = 1.0 + self.weight.float()
|
||||
else:
|
||||
effective_weight = self.weight.float()
|
||||
@@ -162,6 +157,7 @@ class NpuRMSNormKernel(BaseKernel):
|
||||
if "Gated" in module.__class__.__name__:
|
||||
module.forward = types.MethodType(npu_gated_rms_norm_forward, module)
|
||||
else:
|
||||
module._npu_use_residual_rmsnorm = _should_use_residual_rmsnorm(module)
|
||||
module.forward = types.MethodType(npu_rms_norm_forward, module)
|
||||
|
||||
return model
|
||||
|
||||
@@ -114,7 +114,6 @@ class UlyssesAttention(torch.nn.Module):
|
||||
# TODO (Reza): change the api on the megatron-deepspeed side so that we only receive all data (q,k, and v) together!
|
||||
# in shape : e.g., [s/p:h:]
|
||||
# (bs, seq_len/N, head_cnt, head_size) -> (bs, seq_len, head_cnt/N, head_size)
|
||||
|
||||
# scatter 2, gather 1
|
||||
q = SeqAllToAll4D.apply(self.spg, query, self.scatter_idx, self.gather_idx)
|
||||
k = SeqAllToAll4D.apply(self.spg, key, self.scatter_idx, self.gather_idx)
|
||||
@@ -123,10 +122,15 @@ class UlyssesAttention(torch.nn.Module):
|
||||
if softmax_scale is None:
|
||||
softmax_scale = q.shape[-1] ** -0.5
|
||||
|
||||
if attention_mask is None:
|
||||
if position_ids is not None:
|
||||
attention_mask = torch.ones_like(position_ids).to(torch.int64)
|
||||
global_position_ids = [
|
||||
torch.empty_like(position_ids) for _ in range(get_ulysses_sequence_parallel_world_size(self.spg))
|
||||
]
|
||||
dist.all_gather(global_position_ids, position_ids, group=self.spg)
|
||||
position_ids = torch.cat(global_position_ids, dim=-1).contiguous()
|
||||
attention_mask = None
|
||||
else:
|
||||
if attention_mask is None:
|
||||
attention_mask = torch.ones(q.shape[0], q.shape[1], dtype=torch.int64, device=q.device)
|
||||
else:
|
||||
attention_mask = attention_mask.to(torch.int64)
|
||||
|
||||
@@ -12,23 +12,272 @@
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
from collections.abc import Callable
|
||||
from math import ceil
|
||||
from typing import Any
|
||||
|
||||
import torch
|
||||
from torch.utils.data import default_collate
|
||||
|
||||
from ...utils.constants import IGNORE_INDEX
|
||||
from ...utils.helper import pad_and_truncate
|
||||
from ...utils.objects import StatefulBuffer
|
||||
from ...utils.plugin import BasePlugin
|
||||
from ...utils.types import BatchInfo, BatchInput, DataLoader
|
||||
from ...utils.types import BatchInfo, BatchInput, DataLoader, ModelInput
|
||||
|
||||
|
||||
class BatchingPlugin(BasePlugin):
|
||||
def compute_length(self, data_provider: DataLoader) -> int:
|
||||
def get_data_provider_batch_size(self, batch_info: BatchInfo) -> int:
|
||||
"""Return the raw data provider batch size for this batching strategy."""
|
||||
return self["get_data_provider_batch_size"](batch_info)
|
||||
|
||||
def compute_length(self, data_provider: DataLoader, batch_info: BatchInfo) -> int:
|
||||
"""Compute the length of the batch generator.
|
||||
|
||||
The approximate length is used to calculate the lr schedule.
|
||||
"""
|
||||
raise NotImplementedError()
|
||||
return self["compute_length"](data_provider, batch_info)
|
||||
|
||||
def fill_buffer(self, buffer: StatefulBuffer, batch_info: BatchInfo) -> None:
|
||||
def fill_buffer(
|
||||
self,
|
||||
buffer: StatefulBuffer,
|
||||
batch_info: BatchInfo,
|
||||
next_samples: Callable[[bool], list[ModelInput] | None],
|
||||
) -> None:
|
||||
"""Fill the buffer with data."""
|
||||
raise NotImplementedError()
|
||||
return self["fill_buffer"](buffer, batch_info, next_samples)
|
||||
|
||||
def generate_batch(self, buffer: StatefulBuffer, batch_info: BatchInfo) -> list[BatchInput] | None:
|
||||
"""Generate a batch from the buffer."""
|
||||
raise NotImplementedError()
|
||||
return self["generate_batch"](buffer, batch_info)
|
||||
|
||||
|
||||
def _get_dynamic_micro_batch_sizes(samples: list[ModelInput], batch_info: BatchInfo) -> list[int]:
|
||||
"""Return sample counts for micro batches formed by one padded-token budget."""
|
||||
budget = batch_info["cutoff_len"] * batch_info["micro_batch_size"]
|
||||
cutoff_len = batch_info["cutoff_len"]
|
||||
sizes = []
|
||||
index = 0
|
||||
while index < len(samples) and len(sizes) < batch_info["num_micro_batch"]:
|
||||
max_sample_len = 0
|
||||
used = 0
|
||||
is_complete = False
|
||||
while index + used < len(samples):
|
||||
sample_len = min(len(samples[index + used]["input_ids"]), cutoff_len)
|
||||
padded_tokens = max(max_sample_len, sample_len) * (used + 1)
|
||||
if used > 0 and padded_tokens > budget:
|
||||
is_complete = True
|
||||
break
|
||||
|
||||
max_sample_len = max(max_sample_len, sample_len)
|
||||
used += 1
|
||||
if max_sample_len * used >= budget:
|
||||
is_complete = True
|
||||
break
|
||||
|
||||
if used == 0 or not is_complete:
|
||||
break
|
||||
|
||||
sizes.append(used)
|
||||
index += used
|
||||
|
||||
return sizes
|
||||
|
||||
|
||||
def _get_dynamic_padding_free_micro_batch_sizes(samples: list[ModelInput], batch_info: BatchInfo) -> list[int]:
|
||||
budget = batch_info["cutoff_len"] * batch_info["micro_batch_size"]
|
||||
cutoff_len = batch_info["cutoff_len"]
|
||||
sizes = []
|
||||
index = 0
|
||||
|
||||
while index < len(samples) and len(sizes) < batch_info["num_micro_batch"]:
|
||||
current_tokens = 0
|
||||
used = 0
|
||||
is_complete = False
|
||||
|
||||
while index + used < len(samples):
|
||||
sample = samples[index + used]
|
||||
sample_len = min(len(sample["input_ids"]), cutoff_len)
|
||||
|
||||
if current_tokens + sample_len > budget:
|
||||
is_complete = True
|
||||
break
|
||||
|
||||
current_tokens += sample_len
|
||||
used += 1
|
||||
|
||||
if used <= 0 or not is_complete:
|
||||
break
|
||||
|
||||
sizes.append(used)
|
||||
index += used
|
||||
|
||||
return sizes
|
||||
|
||||
|
||||
def _pack_padding_free_samples(samples: list[ModelInput], cutoff_len: int) -> BatchInput | None:
|
||||
"""Pack fixed samples into one padding-free sequence without a token budget."""
|
||||
packed: dict[str, list[Any]] = {}
|
||||
position_ids: list[int] = []
|
||||
|
||||
for sample_index, sample in enumerate(samples):
|
||||
# Padding-free still truncates each sample by cutoff_len before packing
|
||||
# all samples into one contiguous sequence.
|
||||
sample_len = min(len(sample["input_ids"]), cutoff_len)
|
||||
if sample_len <= 0:
|
||||
continue
|
||||
|
||||
for key, value in sample.items():
|
||||
if key in ("attention_mask", "position_ids") or isinstance(value, str):
|
||||
continue
|
||||
|
||||
if key not in packed:
|
||||
packed[key] = []
|
||||
|
||||
sliced_value = list(value[:sample_len])
|
||||
if sample_index > 0 and sliced_value:
|
||||
if key == "labels":
|
||||
sliced_value[0] = IGNORE_INDEX
|
||||
elif key == "loss_weights":
|
||||
sliced_value[0] = 0.0
|
||||
|
||||
packed[key].extend(sliced_value)
|
||||
|
||||
position_ids.extend(range(sample_len))
|
||||
|
||||
if not position_ids:
|
||||
return None
|
||||
|
||||
packed["position_ids"] = position_ids
|
||||
packed["attention_mask"] = [1] * len(position_ids)
|
||||
return {key: torch.tensor(value).unsqueeze(0) for key, value in packed.items()}
|
||||
|
||||
|
||||
@BatchingPlugin("padding_free").register("get_data_provider_batch_size")
|
||||
def get_padding_free_data_provider_batch_size(batch_info: BatchInfo) -> int:
|
||||
return batch_info["micro_batch_size"] * batch_info["num_micro_batch"]
|
||||
|
||||
|
||||
@BatchingPlugin("padding_free").register("compute_length")
|
||||
def compute_padding_free_length(data_provider: DataLoader, batch_info: BatchInfo) -> int:
|
||||
return len(data_provider)
|
||||
|
||||
|
||||
@BatchingPlugin("padding_free").register("fill_buffer")
|
||||
def fill_padding_free_buffer(
|
||||
buffer: StatefulBuffer,
|
||||
batch_info: BatchInfo,
|
||||
next_samples: Callable[[bool], list[ModelInput] | None],
|
||||
) -> None:
|
||||
while len(buffer) < batch_info["micro_batch_size"] * batch_info["num_micro_batch"]:
|
||||
samples = next_samples(False)
|
||||
if samples is None:
|
||||
break
|
||||
|
||||
buffer.put(samples)
|
||||
|
||||
|
||||
@BatchingPlugin("padding_free").register("generate_batch")
|
||||
def generate_padding_free_batch(buffer: StatefulBuffer, batch_info: BatchInfo) -> list[BatchInput] | None:
|
||||
micro_batch_size = batch_info["micro_batch_size"]
|
||||
num_micro_batch = batch_info["num_micro_batch"]
|
||||
cutoff_len = batch_info["cutoff_len"]
|
||||
batch_size = micro_batch_size * num_micro_batch
|
||||
if len(buffer) < batch_size:
|
||||
return None
|
||||
|
||||
samples = buffer.get(batch_size)
|
||||
batch = []
|
||||
for i in range(num_micro_batch):
|
||||
micro_batch = samples[i * micro_batch_size : (i + 1) * micro_batch_size]
|
||||
packed_micro_batch = _pack_padding_free_samples(micro_batch, cutoff_len)
|
||||
if packed_micro_batch is None:
|
||||
return None
|
||||
|
||||
batch.append(packed_micro_batch)
|
||||
|
||||
return batch
|
||||
|
||||
|
||||
@BatchingPlugin("dynamic_batching").register("get_data_provider_batch_size")
|
||||
def get_dynamic_batching_data_provider_batch_size(batch_info: BatchInfo) -> int:
|
||||
return 1
|
||||
|
||||
|
||||
@BatchingPlugin("dynamic_batching").register("compute_length")
|
||||
def compute_dynamic_batching_length(data_provider: DataLoader, batch_info: BatchInfo) -> int:
|
||||
batch_size = batch_info["micro_batch_size"] * batch_info["num_micro_batch"]
|
||||
return ceil(len(data_provider) / batch_size)
|
||||
|
||||
|
||||
@BatchingPlugin("dynamic_batching").register("fill_buffer")
|
||||
def fill_dynamic_batching_buffer(
|
||||
buffer: StatefulBuffer,
|
||||
batch_info: BatchInfo,
|
||||
next_samples: Callable[[bool], list[ModelInput] | None],
|
||||
) -> None:
|
||||
while len(_get_dynamic_micro_batch_sizes(buffer.samples, batch_info)) < batch_info["num_micro_batch"]:
|
||||
samples = next_samples(True)
|
||||
if samples is None:
|
||||
break
|
||||
|
||||
buffer.put(samples)
|
||||
|
||||
|
||||
@BatchingPlugin("dynamic_batching").register("generate_batch")
|
||||
def generate_dynamic_batching_batch(buffer: StatefulBuffer, batch_info: BatchInfo) -> list[BatchInput] | None:
|
||||
micro_batch_sample_counts = _get_dynamic_micro_batch_sizes(buffer.samples, batch_info)
|
||||
if len(micro_batch_sample_counts) < batch_info["num_micro_batch"]:
|
||||
return None
|
||||
|
||||
batch = []
|
||||
cutoff_len = batch_info["cutoff_len"]
|
||||
for num_samples in micro_batch_sample_counts:
|
||||
samples = buffer.get(num_samples)
|
||||
batch.append(default_collate(pad_and_truncate(samples, cutoff_len)))
|
||||
|
||||
return batch
|
||||
|
||||
|
||||
@BatchingPlugin("dynamic_padding_free").register("get_data_provider_batch_size")
|
||||
def get_dynamic_padding_free_data_provider_batch_size(batch_info: BatchInfo) -> int:
|
||||
return 1
|
||||
|
||||
|
||||
@BatchingPlugin("dynamic_padding_free").register("compute_length")
|
||||
def compute_dynamic_padding_free_length(data_provider: DataLoader, batch_info: BatchInfo) -> int:
|
||||
batch_size = batch_info["micro_batch_size"] * batch_info["num_micro_batch"]
|
||||
return ceil(len(data_provider) / batch_size)
|
||||
|
||||
|
||||
@BatchingPlugin("dynamic_padding_free").register("fill_buffer")
|
||||
def fill_dynamic_padding_free_buffer(
|
||||
buffer: StatefulBuffer,
|
||||
batch_info: BatchInfo,
|
||||
next_samples: Callable[[bool], list[ModelInput] | None],
|
||||
) -> None:
|
||||
while len(_get_dynamic_padding_free_micro_batch_sizes(buffer.samples, batch_info)) < batch_info["num_micro_batch"]:
|
||||
samples = next_samples(True)
|
||||
if samples is None:
|
||||
break
|
||||
buffer.put(samples)
|
||||
|
||||
|
||||
@BatchingPlugin("dynamic_padding_free").register("generate_batch")
|
||||
def generate_dynamic_padding_free_batch(buffer: StatefulBuffer, batch_info: BatchInfo) -> list[BatchInput] | None:
|
||||
micro_batch_sample_counts = _get_dynamic_padding_free_micro_batch_sizes(buffer.samples, batch_info)
|
||||
if len(micro_batch_sample_counts) < batch_info["num_micro_batch"]:
|
||||
return None
|
||||
|
||||
batch = []
|
||||
cutoff_len = batch_info["cutoff_len"]
|
||||
|
||||
for num_samples in micro_batch_sample_counts:
|
||||
samples = buffer.get(num_samples)
|
||||
packed_batch = _pack_padding_free_samples(samples, cutoff_len)
|
||||
if packed_batch is None:
|
||||
return None
|
||||
|
||||
batch.append(packed_batch)
|
||||
|
||||
return batch
|
||||
|
||||
@@ -61,6 +61,9 @@ def load_checkpoint_fsdp2(model: HFModel, optimizer: torch.optim.Optimizer, ckpt
|
||||
|
||||
@DistributedPlugin("deepspeed").register()
|
||||
def shard_model_deepspeed(model: HFModel, dist_config: PluginConfig, **kwargs) -> HFModel:
|
||||
if dist_config.get("cp_size", 1) > 1:
|
||||
raise ValueError("CP currently requires `dist_config.name: fsdp2`.")
|
||||
|
||||
from .deepspeed import DeepSpeedEngine
|
||||
|
||||
return DeepSpeedEngine(
|
||||
|
||||
@@ -13,21 +13,45 @@
|
||||
# limitations under the License.
|
||||
|
||||
|
||||
import random
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
from transformers import PreTrainedTokenizer
|
||||
from transformers import set_seed as hf_set_seed
|
||||
|
||||
from ..accelerator.helper import is_torch_npu_available
|
||||
from ..accelerator.interface import DistributedInterface
|
||||
from .constants import IGNORE_INDEX
|
||||
from .types import BatchInput, ModelInput, Processor, Tensor
|
||||
|
||||
|
||||
def set_seed(seed: int) -> None:
|
||||
def enable_full_determinism(seed: int) -> None:
|
||||
"""Enable full deterministic mode for reproducible distributed training."""
|
||||
random.seed(seed)
|
||||
np.random.seed(seed)
|
||||
torch.manual_seed(seed)
|
||||
torch.cuda.manual_seed(seed)
|
||||
torch.cuda.manual_seed_all(seed)
|
||||
torch.use_deterministic_algorithms(True, warn_only=True)
|
||||
torch.backends.cudnn.deterministic = True
|
||||
torch.backends.cudnn.benchmark = False
|
||||
torch.backends.cudnn.enabled = False
|
||||
if is_torch_npu_available():
|
||||
torch.npu.manual_seed(seed)
|
||||
torch.npu.manual_seed_all(seed)
|
||||
|
||||
|
||||
def set_seed(seed: int, full_determinism: bool = False) -> None:
|
||||
"""Set seed for reproducibility.
|
||||
|
||||
Args:
|
||||
seed: Random seed.
|
||||
full_determinism: Whether to enable full deterministic mode.
|
||||
"""
|
||||
if full_determinism:
|
||||
enable_full_determinism(seed)
|
||||
else:
|
||||
hf_set_seed(seed)
|
||||
|
||||
|
||||
|
||||
@@ -33,6 +33,10 @@ class StatefulBuffer:
|
||||
def size(self) -> int:
|
||||
return self._buffer_size
|
||||
|
||||
@property
|
||||
def samples(self) -> list[ModelInput]:
|
||||
return self._buffer
|
||||
|
||||
def put(self, samples: list[ModelInput]) -> None:
|
||||
"""Add samples to the buffer."""
|
||||
num_tokens = sum(len(sample["input_ids"]) for sample in samples)
|
||||
|
||||
@@ -12,7 +12,7 @@
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
from collections.abc import Iterator
|
||||
from enum import StrEnum, unique
|
||||
from typing import TYPE_CHECKING, Any, Literal, NamedTuple, NotRequired, TypedDict, Union
|
||||
|
||||
|
||||
@@ -54,6 +54,13 @@ else:
|
||||
ProcessGroup = None
|
||||
|
||||
|
||||
@unique
|
||||
class AttentionFunction(StrEnum):
|
||||
EAGER = "eager"
|
||||
SDPA = "sdpa"
|
||||
FLASH_ATTENTION_2 = "flash_attention_2"
|
||||
|
||||
|
||||
class DatasetInfo(TypedDict, total=False):
|
||||
path: str
|
||||
"""Local file path."""
|
||||
@@ -171,8 +178,6 @@ class BatchInfo(TypedDict):
|
||||
"""Number of micro batches."""
|
||||
cutoff_len: int
|
||||
"""Cutoff length."""
|
||||
data_iter: Iterator[list[ModelInput]]
|
||||
"""Data iterator."""
|
||||
|
||||
|
||||
class ModelOutput(NamedTuple):
|
||||
|
||||
@@ -58,10 +58,3 @@ def test_multi_device():
|
||||
master_port = find_available_port()
|
||||
world_size = 2
|
||||
mp.spawn(_all_reduce_tests, args=(world_size, master_port), nprocs=world_size)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
"""
|
||||
python tests_v1/accelerator/test_interface.py
|
||||
"""
|
||||
test_all_device()
|
||||
|
||||
@@ -70,13 +70,3 @@ def test_get_args_from_yaml(tmp_path: Path):
|
||||
assert training_args.bf16 is False
|
||||
assert training_args.dist_config is None
|
||||
assert sample_args.sample_backend == "hf"
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
"""
|
||||
python -m tests_v1.config.test_args_parser
|
||||
"""
|
||||
import tempfile
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmp_dir:
|
||||
test_get_args_from_yaml(tmp_path=Path(tmp_dir))
|
||||
|
||||
@@ -30,10 +30,3 @@ def test_map_dataset(num_samples: int):
|
||||
for index in indexes:
|
||||
print(data_engine[index])
|
||||
assert data_engine[index] == {"_dataset_name": "default", **original_data[index]}
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
"""
|
||||
python -m tests_v1.core.test_data_engine
|
||||
"""
|
||||
test_map_dataset(1)
|
||||
|
||||
@@ -41,11 +41,3 @@ def test_tiny_qwen_with_kernel_plugin():
|
||||
assert model_engine.model.model.layers[0].input_layernorm.forward.__code__ != npu_rms_norm_forward.__code__
|
||||
|
||||
assert "Qwen3ForCausalLM" in model_engine.model.__class__.__name__
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
"""
|
||||
python -m tests_v1.core.test_model_loader
|
||||
"""
|
||||
test_tiny_qwen()
|
||||
test_tiny_qwen_with_kernel_plugin()
|
||||
|
||||
@@ -16,6 +16,164 @@ from llamafactory.v1.config import DataArguments, ModelArguments, TrainingArgume
|
||||
from llamafactory.v1.core.data_engine import DataEngine
|
||||
from llamafactory.v1.core.model_engine import ModelEngine
|
||||
from llamafactory.v1.core.utils.batching import BatchGenerator
|
||||
from llamafactory.v1.plugins.trainer_plugins.batching import (
|
||||
BatchingPlugin,
|
||||
_get_dynamic_micro_batch_sizes,
|
||||
_get_dynamic_padding_free_micro_batch_sizes,
|
||||
)
|
||||
from llamafactory.v1.utils.constants import IGNORE_INDEX
|
||||
from llamafactory.v1.utils.objects import StatefulBuffer
|
||||
|
||||
|
||||
def _make_model_input(length: int, start: int = 0):
|
||||
input_ids = list(range(start, start + length))
|
||||
return {
|
||||
"input_ids": input_ids,
|
||||
"attention_mask": [1] * length,
|
||||
"labels": input_ids.copy(),
|
||||
"loss_weights": [1.0] * length,
|
||||
}
|
||||
|
||||
|
||||
class _RestartableDataProvider:
|
||||
def __init__(self, batches):
|
||||
self.batches = batches
|
||||
self.num_iters = 0
|
||||
|
||||
def __iter__(self):
|
||||
self.num_iters += 1
|
||||
return iter(self.batches)
|
||||
|
||||
|
||||
def test_padding_free():
|
||||
buffer = StatefulBuffer()
|
||||
# Input samples:
|
||||
# sample 0 input_ids: [0, 1]
|
||||
# sample 1 input_ids: [10, 11, 12, 13]
|
||||
buffer.put([_make_model_input(2, 0), _make_model_input(4, 10)])
|
||||
batch_info = {"micro_batch_size": 2, "num_micro_batch": 1, "cutoff_len": 3}
|
||||
|
||||
batch = BatchingPlugin("padding_free").generate_batch(buffer, batch_info)
|
||||
|
||||
# Output batch:
|
||||
# sample 1 is truncated to [10, 11, 12]
|
||||
# both samples are packed into one sequence: [[0, 1, 10, 11, 12]]
|
||||
assert batch is not None
|
||||
assert len(batch) == 1
|
||||
assert batch[0]["input_ids"].shape == (1, 5)
|
||||
assert batch[0]["input_ids"].tolist() == [[0, 1, 10, 11, 12]]
|
||||
assert batch[0]["attention_mask"].tolist() == [[1, 1, 1, 1, 1]]
|
||||
assert batch[0]["position_ids"].tolist() == [[0, 1, 0, 1, 2]]
|
||||
assert batch[0]["labels"].tolist() == [[0, 1, IGNORE_INDEX, 11, 12]]
|
||||
assert batch[0]["loss_weights"].tolist() == [[1.0, 1.0, 0.0, 1.0, 1.0]]
|
||||
assert len(buffer) == 0
|
||||
|
||||
|
||||
def test_batching_plugin_data_provider_batch_sizes():
|
||||
batch_info = {
|
||||
"micro_batch_size": 2,
|
||||
"num_micro_batch": 3,
|
||||
"cutoff_len": 10,
|
||||
}
|
||||
|
||||
assert BatchingPlugin("padding_free").get_data_provider_batch_size(batch_info) == 6
|
||||
assert BatchingPlugin("dynamic_batching").get_data_provider_batch_size(batch_info) == 1
|
||||
assert BatchingPlugin("dynamic_padding_free").get_data_provider_batch_size(batch_info) == 1
|
||||
|
||||
|
||||
def test_dynamic_batching():
|
||||
# Input samples:
|
||||
# sample lengths: [3, 4, 6, 2, 8, 9]
|
||||
# input_ids:
|
||||
# [0, 1, 2]
|
||||
# [10, 11, 12, 13]
|
||||
# [20, 21, 22, 23, 24, 25]
|
||||
# [30, 31]
|
||||
# [40, 41, 42, 43, 44, 45, 46, 47]
|
||||
# [50, 51, 52, 53, 54, 55, 56, 57, 58]
|
||||
samples = [
|
||||
_make_model_input(3, 0),
|
||||
_make_model_input(4, 10),
|
||||
_make_model_input(6, 20),
|
||||
_make_model_input(2, 30),
|
||||
_make_model_input(8, 40),
|
||||
_make_model_input(9, 50),
|
||||
]
|
||||
batch_info = {"micro_batch_size": 2, "num_micro_batch": 1, "cutoff_len": 10}
|
||||
|
||||
# Dynamic batching output plan:
|
||||
# dynamic batching reads one sample at a time and uses cutoff_len * micro_batch_size
|
||||
# as the padded-token budget for one training micro batch.
|
||||
# [3, 4, 6] fits within budget 20 as shape [3, 6]; adding [2] would exceed it.
|
||||
assert _get_dynamic_micro_batch_sizes(samples, batch_info) == [3]
|
||||
|
||||
buffer = StatefulBuffer()
|
||||
buffer.put(samples)
|
||||
batch = BatchingPlugin("dynamic_batching").generate_batch(buffer, batch_info)
|
||||
|
||||
assert batch is not None
|
||||
assert len(batch) == 1
|
||||
assert batch[0]["input_ids"].shape == (3, 6)
|
||||
assert batch[0]["input_ids"].tolist()[0] == [0, 1, 2, 0, 0, 0]
|
||||
assert len(buffer) == 3
|
||||
|
||||
|
||||
def test_dynamic_batching_returns_none_when_token_budget_is_incomplete():
|
||||
buffer = StatefulBuffer()
|
||||
# Input buffer:
|
||||
# only one sample with length [6].
|
||||
# cutoff_len * micro_batch_size gives a padded-token budget of 20.
|
||||
# this buffer has not filled the budget and has no next sample to prove overflow,
|
||||
# so dynamic batching cannot produce a batch yet.
|
||||
buffer.put([_make_model_input(6, 0)])
|
||||
batch_info = {"micro_batch_size": 2, "num_micro_batch": 1, "cutoff_len": 10}
|
||||
|
||||
assert _get_dynamic_micro_batch_sizes(buffer.samples, batch_info) == []
|
||||
assert BatchingPlugin("dynamic_batching").generate_batch(buffer, batch_info) is None
|
||||
# Batch generation does not read from the data iterator. It only returns None and keeps
|
||||
# existing samples in the buffer; BatchGenerator._fill_buffer handles refilling.
|
||||
assert len(buffer) == 1
|
||||
|
||||
|
||||
def test_dynamic_batching_fill_buffer_restarts_until_micro_batch_is_complete():
|
||||
# Input data provider:
|
||||
# each iterator pass yields one sample with length [6].
|
||||
# each yielded item is a list[ModelInput], matching BatchGenerator._next_samples.
|
||||
# _fill_buffer keeps restarting the iterator until the next appended sample
|
||||
# proves that the previous dynamic micro batch has reached its budget boundary.
|
||||
samples = [_make_model_input(6, 0)]
|
||||
data_provider = _RestartableDataProvider([[sample] for sample in samples])
|
||||
|
||||
batch_generator = BatchGenerator.__new__(BatchGenerator)
|
||||
batch_generator.batching_strategy = "dynamic_batching"
|
||||
batch_generator.micro_batch_size = 2
|
||||
batch_generator.num_micro_batch = 1
|
||||
batch_generator._buffer = StatefulBuffer()
|
||||
batch_generator._data_provider = data_provider
|
||||
batch_generator._data_iter = iter(data_provider)
|
||||
batch_generator._batch_info = {
|
||||
"micro_batch_size": 2,
|
||||
"num_micro_batch": 1,
|
||||
"cutoff_len": 10,
|
||||
}
|
||||
|
||||
batch_generator._fill_buffer()
|
||||
|
||||
# Filled buffer after restart:
|
||||
# existing buffer [6, 6, 6] is kept; the fourth [6] remains for the next batch
|
||||
# because adding it to the first dynamic micro batch would exceed the budget.
|
||||
assert data_provider.num_iters == 4
|
||||
assert _get_dynamic_micro_batch_sizes(batch_generator._buffer.samples, batch_generator._batch_info) == [3]
|
||||
|
||||
batch = batch_generator._generate_batch()
|
||||
|
||||
# Output batch:
|
||||
# dynamic batching returns [micro_batch_0]
|
||||
# micro_batch_0 consumes [6, 6, 6] => 3 samples, padded to shape [3, 6].
|
||||
assert batch is not None
|
||||
assert len(batch) == 1
|
||||
assert batch[0]["input_ids"].shape == (3, 6)
|
||||
assert len(batch_generator._buffer) == 1
|
||||
|
||||
|
||||
def test_normal_batching():
|
||||
@@ -45,8 +203,166 @@ def test_normal_batching():
|
||||
assert batch[0]["input_ids"].shape == (4, 10)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
def test_dynamic_padding_free():
|
||||
"""Test core logic of dynamic padding free strategy: pack samples by total token budget without padding."""
|
||||
# Construct test samples (lengths: 3, 4, 6, 2, 8, 9)
|
||||
# input_ids breakdown:
|
||||
# sample 0: [0,1,2] (length=3)
|
||||
# sample 1: [10,11,12,13] (length=4)
|
||||
# sample 2: [20,21,22,23,24,25] (length=6)
|
||||
# sample 3: [30,31] (length=2)
|
||||
# sample 4: [40-47] (length=8)
|
||||
# sample 5: [50-58] (length=9)
|
||||
samples = [
|
||||
_make_model_input(3, 0),
|
||||
_make_model_input(4, 10),
|
||||
_make_model_input(6, 20),
|
||||
_make_model_input(2, 30),
|
||||
_make_model_input(8, 40),
|
||||
_make_model_input(9, 50),
|
||||
]
|
||||
# Batch config: micro_batch_size=2 → token budget = cutoff_len * micro_batch_size = 10*2=20
|
||||
batch_info = {"micro_batch_size": 2, "num_micro_batch": 1, "cutoff_len": 10}
|
||||
|
||||
# Budget=20: 3+4+6+2=15 ≤20 (adding 8 would exceed) → first 4 samples are selected
|
||||
assert _get_dynamic_padding_free_micro_batch_sizes(samples, batch_info) == [4]
|
||||
|
||||
buffer = StatefulBuffer()
|
||||
buffer.put(samples)
|
||||
batch = BatchingPlugin("dynamic_padding_free").generate_batch(buffer, batch_info)
|
||||
|
||||
assert batch is not None
|
||||
assert len(batch) == 1 # num_micro_batch=1
|
||||
packed_batch = batch[0]
|
||||
|
||||
# Total packed length: 3+4+6+2=15 → input_ids shape = (1,15) (no padding)
|
||||
assert packed_batch["input_ids"].shape == (1, 15)
|
||||
|
||||
# Verify input_ids concatenation (first label of non-initial samples set to IGNORE_INDEX)
|
||||
assert packed_batch["input_ids"].tolist() == [
|
||||
[
|
||||
0,
|
||||
1,
|
||||
2, # Sample 0
|
||||
10,
|
||||
11,
|
||||
12,
|
||||
13, # Sample 1
|
||||
20,
|
||||
21,
|
||||
22,
|
||||
23,
|
||||
24,
|
||||
25, # Sample 2
|
||||
30,
|
||||
31,
|
||||
] # Sample 3
|
||||
]
|
||||
|
||||
# Verify labels (first token of non-initial samples is IGNORE_INDEX)
|
||||
assert packed_batch["labels"].tolist() == [
|
||||
[
|
||||
0,
|
||||
1,
|
||||
2, # Sample 0
|
||||
IGNORE_INDEX,
|
||||
11,
|
||||
12,
|
||||
13, # Sample 1
|
||||
IGNORE_INDEX,
|
||||
21,
|
||||
22,
|
||||
23,
|
||||
24,
|
||||
25, # Sample 2
|
||||
IGNORE_INDEX,
|
||||
31,
|
||||
] # Sample 3
|
||||
]
|
||||
|
||||
# Verify attention_mask
|
||||
assert packed_batch["attention_mask"].tolist() == [[1] * 15]
|
||||
|
||||
# Verify position_ids
|
||||
assert packed_batch["position_ids"].tolist() == [
|
||||
[
|
||||
0,
|
||||
1,
|
||||
2, # Sample 0
|
||||
0,
|
||||
1,
|
||||
2,
|
||||
3, # Sample 1
|
||||
0,
|
||||
1,
|
||||
2,
|
||||
3,
|
||||
4,
|
||||
5, # Sample 2
|
||||
0,
|
||||
1,
|
||||
] # Sample 3
|
||||
]
|
||||
|
||||
# Verify remaining samples in buffer: 6-4=2 samples (length 8,9)
|
||||
assert len(buffer) == 2
|
||||
|
||||
|
||||
def test_dynamic_padding_free_returns_none_when_token_budget_is_incomplete():
|
||||
buffer = StatefulBuffer()
|
||||
buffer.put([_make_model_input(6, 0)])
|
||||
batch_info = {"micro_batch_size": 2, "num_micro_batch": 1, "cutoff_len": 10}
|
||||
|
||||
assert _get_dynamic_micro_batch_sizes(buffer.samples, batch_info) == []
|
||||
assert BatchingPlugin("dynamic_padding_free").generate_batch(buffer, batch_info) is None
|
||||
# Batch generation does not read from the data iterator. It only returns None and keeps
|
||||
# existing samples in the buffer; BatchGenerator._fill_buffer handles refilling.
|
||||
assert len(buffer) == 1
|
||||
|
||||
|
||||
def test_dynamic_padding_free_fill_buffer_restarts_until_micro_batch_is_complete():
|
||||
"""Test fill_buffer logic for dynamic_padding_free: restart data iterator until token budget is full.
|
||||
|
||||
Data provider yields one sample of length 6 per iteration.
|
||||
_fill_buffer keeps restarting iterator until next sample exceeds budget.
|
||||
Budget = 2 * 10 = 20 tokens.
|
||||
3 samples (6*3=18) fit; 4th sample (24) exceeds budget.
|
||||
So buffer will have 4 samples after fill_buffer.
|
||||
"""
|
||||
python -m tests_v1.core.utils.test_batching
|
||||
"""
|
||||
test_normal_batching()
|
||||
samples = [_make_model_input(6, 0)]
|
||||
data_provider = _RestartableDataProvider([[sample] for sample in samples])
|
||||
|
||||
batch_generator = BatchGenerator.__new__(BatchGenerator)
|
||||
batch_generator.batching_strategy = "dynamic_padding_free"
|
||||
batch_generator.micro_batch_size = 2
|
||||
batch_generator.num_micro_batch = 1
|
||||
batch_generator._buffer = StatefulBuffer()
|
||||
batch_generator._data_provider = data_provider
|
||||
batch_generator._data_iter = iter(data_provider)
|
||||
batch_generator._batch_info = {
|
||||
"micro_batch_size": 2,
|
||||
"num_micro_batch": 1,
|
||||
"cutoff_len": 10,
|
||||
}
|
||||
|
||||
# Execute fill buffer (will restart iterator multiple times to collect enough samples)
|
||||
batch_generator._fill_buffer()
|
||||
|
||||
# Buffer after restarts:
|
||||
# 3 samples can fit (18 tokens)
|
||||
# 4th sample is kept in buffer for next batch
|
||||
# => num_iters = 4
|
||||
assert data_provider.num_iters == 4
|
||||
assert _get_dynamic_padding_free_micro_batch_sizes(
|
||||
batch_generator._buffer.samples, batch_generator._batch_info
|
||||
) == [3]
|
||||
|
||||
batch = batch_generator._generate_batch()
|
||||
|
||||
# Output batch:
|
||||
# dynamic_padding_free returns [micro_batch_0]
|
||||
# 3 samples packed into shape [1, 18]
|
||||
assert batch is not None
|
||||
assert len(batch) == 1
|
||||
assert batch[0]["input_ids"].shape == (1, 18)
|
||||
assert len(batch_generator._buffer) == 1
|
||||
|
||||
@@ -227,17 +227,3 @@ def test_process_dpo_samples():
|
||||
assert model_inputs[0]["token_type_ids"] == [1] * len(hf_inputs) + [2] * len(hf_inputs)
|
||||
assert model_inputs[0]["extra_info"] == "test"
|
||||
assert model_inputs[0]["_dataset_name"] == "default"
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
"""
|
||||
python -m tests_v1.core.utils.test_rendering
|
||||
"""
|
||||
test_chatml_rendering()
|
||||
test_chatml_parse()
|
||||
test_chatml_rendering_remote(16)
|
||||
test_qwen3_nothink_rendering()
|
||||
test_qwen3_nothink_parse()
|
||||
test_qwen3_nothink_rendering_remote(16)
|
||||
test_process_sft_samples()
|
||||
test_process_dpo_samples()
|
||||
|
||||
@@ -117,12 +117,3 @@ def test_pair_converter(num_samples: int):
|
||||
],
|
||||
}
|
||||
assert data_engine[index] == {"_dataset_name": "tiny_dataset", **expected_data}
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
"""
|
||||
python -m tests_v1.plugins.data_plugins.test_converter
|
||||
"""
|
||||
test_alpaca_converter(1)
|
||||
test_sharegpt_converter()
|
||||
test_pair_converter(1)
|
||||
|
||||
@@ -52,12 +52,3 @@ def test_init_on_default():
|
||||
)
|
||||
model_engine = ModelEngine(model_args=model_args)
|
||||
assert model_engine.model.device == DistributedInterface().current_device
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
"""
|
||||
python tests_v1/plugins/model_plugins/test_init_plugin.py
|
||||
"""
|
||||
test_init_on_meta()
|
||||
test_init_on_rank0()
|
||||
test_init_on_default()
|
||||
|
||||
@@ -35,10 +35,3 @@ def test_sync_sampler():
|
||||
"role": "assistant",
|
||||
"content": [{"type": "text", "value": "This is a test."}],
|
||||
}
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
"""
|
||||
python tests_v1/sampler/test_cli_sampler.py
|
||||
"""
|
||||
test_sync_sampler()
|
||||
|
||||
Reference in New Issue
Block a user