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[infer] fix vllm args (#7235)

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hoshi-hiyouga 2025-03-11 01:15:35 +08:00 committed by GitHub
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4 changed files with 32 additions and 26 deletions
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21
README.md
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@ -5,7 +5,7 @@
[![GitHub contributors](https://img.shields.io/github/contributors/hiyouga/LLaMA-Factory?color=orange)](https://github.com/hiyouga/LLaMA-Factory/graphs/contributors) [![GitHub contributors](https://img.shields.io/github/contributors/hiyouga/LLaMA-Factory?color=orange)](https://github.com/hiyouga/LLaMA-Factory/graphs/contributors)
[![GitHub workflow](https://github.com/hiyouga/LLaMA-Factory/actions/workflows/tests.yml/badge.svg)](https://github.com/hiyouga/LLaMA-Factory/actions/workflows/tests.yml) [![GitHub workflow](https://github.com/hiyouga/LLaMA-Factory/actions/workflows/tests.yml/badge.svg)](https://github.com/hiyouga/LLaMA-Factory/actions/workflows/tests.yml)
[![PyPI](https://img.shields.io/pypi/v/llamafactory)](https://pypi.org/project/llamafactory/) [![PyPI](https://img.shields.io/pypi/v/llamafactory)](https://pypi.org/project/llamafactory/)
[![Citation](https://img.shields.io/badge/citation-341-green)](https://scholar.google.com/scholar?cites=12620864006390196564) [![Citation](https://img.shields.io/badge/citation-349-green)](https://scholar.google.com/scholar?cites=12620864006390196564)
[![GitHub pull request](https://img.shields.io/badge/PRs-welcome-blue)](https://github.com/hiyouga/LLaMA-Factory/pulls) [![GitHub pull request](https://img.shields.io/badge/PRs-welcome-blue)](https://github.com/hiyouga/LLaMA-Factory/pulls)
[![Twitter](https://img.shields.io/twitter/follow/llamafactory_ai)](https://twitter.com/llamafactory_ai) [![Twitter](https://img.shields.io/twitter/follow/llamafactory_ai)](https://twitter.com/llamafactory_ai)
@ -412,15 +412,14 @@ huggingface-cli login
\* *estimated* \* *estimated*
| Method | Bits | 7B | 13B | 30B | 70B | 110B | 8x7B | 8x22B | | Method | Bits | 7B | 14B | 30B | 70B | `x`B |
| ------------------------ | ---- | ----- | ----- | ----- | ------ | ------ | ----- | ------ | | ------------------------------- | ---- | ----- | ----- | ----- | ------ | ------- |
| Full | 32 | 120GB | 240GB | 600GB | 1200GB | 2000GB | 900GB | 2400GB | | Full (`bf16` or `fp16`) | 32 | 120GB | 240GB | 600GB | 1200GB | `18x`GB |
| Full | 16 | 60GB | 120GB | 300GB | 600GB | 900GB | 400GB | 1200GB | | Full (`pure_bf16`) | 16 | 60GB | 120GB | 300GB | 600GB | `8x`GB |
| Freeze | 16 | 20GB | 40GB | 80GB | 200GB | 360GB | 160GB | 400GB | | Freeze/LoRA/GaLore/APOLLO/BAdam | 16 | 16GB | 32GB | 64GB | 160GB | `2x`GB |
| LoRA/GaLore/APOLLO/BAdam | 16 | 16GB | 32GB | 64GB | 160GB | 240GB | 120GB | 320GB | | QLoRA | 8 | 10GB | 20GB | 40GB | 80GB | `x`GB |
| QLoRA | 8 | 10GB | 20GB | 40GB | 80GB | 140GB | 60GB | 160GB | | QLoRA | 4 | 6GB | 12GB | 24GB | 48GB | `x/2`GB |
| QLoRA | 4 | 6GB | 12GB | 24GB | 48GB | 72GB | 30GB | 96GB | | QLoRA | 2 | 4GB | 8GB | 16GB | 24GB | `x/4`GB |
| QLoRA | 2 | 4GB | 8GB | 16GB | 24GB | 48GB | 18GB | 48GB |
## Getting Started ## Getting Started
@ -560,6 +559,8 @@ See [examples/README.md](examples/README.md) for advanced usage (including distr
> [!TIP] > [!TIP]
> Use `llamafactory-cli help` to show help information. > Use `llamafactory-cli help` to show help information.
>
> Read [FAQs](https://github.com/hiyouga/LLaMA-Factory/issues/4614) first if you encounter any problems.
### Fine-Tuning with LLaMA Board GUI (powered by [Gradio](https://github.com/gradio-app/gradio)) ### Fine-Tuning with LLaMA Board GUI (powered by [Gradio](https://github.com/gradio-app/gradio))

21
README_zh.md
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@ -5,7 +5,7 @@
[![GitHub contributors](https://img.shields.io/github/contributors/hiyouga/LLaMA-Factory?color=orange)](https://github.com/hiyouga/LLaMA-Factory/graphs/contributors) [![GitHub contributors](https://img.shields.io/github/contributors/hiyouga/LLaMA-Factory?color=orange)](https://github.com/hiyouga/LLaMA-Factory/graphs/contributors)
[![GitHub workflow](https://github.com/hiyouga/LLaMA-Factory/actions/workflows/tests.yml/badge.svg)](https://github.com/hiyouga/LLaMA-Factory/actions/workflows/tests.yml) [![GitHub workflow](https://github.com/hiyouga/LLaMA-Factory/actions/workflows/tests.yml/badge.svg)](https://github.com/hiyouga/LLaMA-Factory/actions/workflows/tests.yml)
[![PyPI](https://img.shields.io/pypi/v/llamafactory)](https://pypi.org/project/llamafactory/) [![PyPI](https://img.shields.io/pypi/v/llamafactory)](https://pypi.org/project/llamafactory/)
[![Citation](https://img.shields.io/badge/citation-341-green)](https://scholar.google.com/scholar?cites=12620864006390196564) [![Citation](https://img.shields.io/badge/citation-349-green)](https://scholar.google.com/scholar?cites=12620864006390196564)
[![GitHub pull request](https://img.shields.io/badge/PRs-welcome-blue)](https://github.com/hiyouga/LLaMA-Factory/pulls) [![GitHub pull request](https://img.shields.io/badge/PRs-welcome-blue)](https://github.com/hiyouga/LLaMA-Factory/pulls)
[![Twitter](https://img.shields.io/twitter/follow/llamafactory_ai)](https://twitter.com/llamafactory_ai) [![Twitter](https://img.shields.io/twitter/follow/llamafactory_ai)](https://twitter.com/llamafactory_ai)
@ -414,15 +414,14 @@ huggingface-cli login
\* *估算值* \* *估算值*
| 方法 | 精度 | 7B | 13B | 30B | 70B | 110B | 8x7B | 8x22B | | 方法 | 精度 | 7B | 14B | 30B | 70B | `x`B |
| ------------------------ | ---- | ----- | ----- | ----- | ------ | ------ | ----- | ------ | | ------------------------------- | ---- | ----- | ----- | ----- | ------ | ------- |
| Full | 32 | 120GB | 240GB | 600GB | 1200GB | 2000GB | 900GB | 2400GB | | Full (`bf16` or `fp16`) | 32 | 120GB | 240GB | 600GB | 1200GB | `18x`GB |
| Full | 16 | 60GB | 120GB | 300GB | 600GB | 900GB | 400GB | 1200GB | | Full (`pure_bf16`) | 16 | 60GB | 120GB | 300GB | 600GB | `8x`GB |
| Freeze | 16 | 20GB | 40GB | 80GB | 200GB | 360GB | 160GB | 400GB | | Freeze/LoRA/GaLore/APOLLO/BAdam | 16 | 16GB | 32GB | 64GB | 160GB | `2x`GB |
| LoRA/GaLore/APOLLO/BAdam | 16 | 16GB | 32GB | 64GB | 160GB | 240GB | 120GB | 320GB | | QLoRA | 8 | 10GB | 20GB | 40GB | 80GB | `x`GB |
| QLoRA | 8 | 10GB | 20GB | 40GB | 80GB | 140GB | 60GB | 160GB | | QLoRA | 4 | 6GB | 12GB | 24GB | 48GB | `x/2`GB |
| QLoRA | 4 | 6GB | 12GB | 24GB | 48GB | 72GB | 30GB | 96GB | | QLoRA | 2 | 4GB | 8GB | 16GB | 24GB | `x/4`GB |
| QLoRA | 2 | 4GB | 8GB | 16GB | 24GB | 48GB | 18GB | 48GB |
## 如何使用 ## 如何使用
@ -563,6 +562,8 @@ llamafactory-cli export examples/merge_lora/llama3_lora_sft.yaml
> [!TIP] > [!TIP]
> 使用 `llamafactory-cli help` 显示帮助信息。 > 使用 `llamafactory-cli help` 显示帮助信息。
>
> 遇到报错请先看[常见问题](https://github.com/hiyouga/LLaMA-Factory/issues/4614)。
### LLaMA Board 可视化微调（由 [Gradio](https://github.com/gradio-app/gradio) 驱动） ### LLaMA Board 可视化微调（由 [Gradio](https://github.com/gradio-app/gradio) 驱动）

14
scripts/vllm_infer.py
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@ -38,7 +38,7 @@ def vllm_infer(
dataset_dir: str = "data", dataset_dir: str = "data",
template: str = "default", template: str = "default",
cutoff_len: int = 2048, cutoff_len: int = 2048,
max_samples: int = None, max_samples: Optional[int] = None,
vllm_config: str = "{}", vllm_config: str = "{}",
save_name: str = "generated_predictions.jsonl", save_name: str = "generated_predictions.jsonl",
temperature: float = 0.95, temperature: float = 0.95,
@ -46,6 +46,7 @@ def vllm_infer(
top_k: int = 50, top_k: int = 50,
max_new_tokens: int = 1024, max_new_tokens: int = 1024,
repetition_penalty: float = 1.0, repetition_penalty: float = 1.0,
skip_special_tokens: bool = True,
seed: Optional[int] = None, seed: Optional[int] = None,
pipeline_parallel_size: int = 1, pipeline_parallel_size: int = 1,
image_max_pixels: int = 768 * 768, image_max_pixels: int = 768 * 768,
@ -97,19 +98,21 @@ def vllm_infer(
multi_modal_data = None multi_modal_data = None
inputs.append({"prompt_token_ids": sample["input_ids"], "multi_modal_data": multi_modal_data}) inputs.append({"prompt_token_ids": sample["input_ids"], "multi_modal_data": multi_modal_data})
prompts.append(tokenizer.decode(sample["input_ids"], skip_special_tokens=False)) prompts.append(tokenizer.decode(sample["input_ids"], skip_special_tokens=skip_special_tokens))
labels.append( labels.append(
tokenizer.decode(list(filter(lambda x: x != IGNORE_INDEX, sample["labels"])), skip_special_tokens=False) tokenizer.decode(
list(filter(lambda x: x != IGNORE_INDEX, sample["labels"])), skip_special_tokens=skip_special_tokens
)
) )
sampling_params = SamplingParams( sampling_params = SamplingParams(
repetition_penalty=generating_args.repetition_penalty or 1.0, # repetition_penalty must > 0 repetition_penalty=generating_args.repetition_penalty or 1.0, # repetition_penalty must > 0
temperature=generating_args.temperature, temperature=generating_args.temperature,
top_p=generating_args.top_p or 1.0, # top_p must > 0 top_p=generating_args.top_p or 1.0, # top_p must > 0
top_k=generating_args.top_k, top_k=generating_args.top_k or -1, # top_k must > 0
stop_token_ids=template_obj.get_stop_token_ids(tokenizer), stop_token_ids=template_obj.get_stop_token_ids(tokenizer),
max_tokens=generating_args.max_new_tokens, max_tokens=generating_args.max_new_tokens,
skip_special_tokens=False, skip_special_tokens=skip_special_tokens,
seed=seed, seed=seed,
) )
if model_args.adapter_name_or_path is not None: if model_args.adapter_name_or_path is not None:
@ -121,6 +124,7 @@ def vllm_infer(
"model": model_args.model_name_or_path, "model": model_args.model_name_or_path,
"trust_remote_code": True, "trust_remote_code": True,
"dtype": model_args.infer_dtype, "dtype": model_args.infer_dtype,
"max_model_len": cutoff_len + max_new_tokens,
"tensor_parallel_size": (get_device_count() // pipeline_parallel_size) or 1, "tensor_parallel_size": (get_device_count() // pipeline_parallel_size) or 1,
"pipeline_parallel_size": pipeline_parallel_size, "pipeline_parallel_size": pipeline_parallel_size,
"disable_log_stats": True, "disable_log_stats": True,

2
src/llamafactory/chat/vllm_engine.py
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@ -170,7 +170,7 @@ class VllmEngine(BaseEngine):
or 1.0, # repetition_penalty must > 0 or 1.0, # repetition_penalty must > 0
temperature=temperature if temperature is not None else self.generating_args["temperature"], temperature=temperature if temperature is not None else self.generating_args["temperature"],
top_p=(top_p if top_p is not None else self.generating_args["top_p"]) or 1.0, # top_p must > 0 top_p=(top_p if top_p is not None else self.generating_args["top_p"]) or 1.0, # top_p must > 0
top_k=top_k if top_k is not None else self.generating_args["top_k"], top_k=(top_k if top_k is not None else self.generating_args["top_k"]) or -1, # top_k must > 0
stop=stop, stop=stop,
stop_token_ids=self.template.get_stop_token_ids(self.tokenizer), stop_token_ids=self.template.get_stop_token_ids(self.tokenizer),
max_tokens=max_tokens, max_tokens=max_tokens,