wehub-resource-sync
234 lines
8.3 KiB
Python
234 lines
8.3 KiB
Python
# Copyright (c) ModelScope Contributors. All rights reserved.
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import gradio as gr
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from typing import Type
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from ..base import BaseUI
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class Rollout(BaseUI):
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group = 'llm_grpo'
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locale_dict = {
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'num_generations': {
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'label': {
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'zh': '采样数量',
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'en': 'Number of samples'
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},
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'info': {
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'zh': '每个prompt采样的数量，即论文中的G值',
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'en': 'The number of samples for each prompt, that is, the G value in the paper'
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}
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},
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'max_completion_length': {
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'label': {
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'zh': '最大生成长度',
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'en': 'Max completion length'
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},
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'info': {
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'zh': 'GRPO算法中的最大生成长度',
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'en': 'Maximum generation length in GRPO algorithm'
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}
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},
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'async_generate': {
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'label': {
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'zh': '异步生成',
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'en': 'Async generate'
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},
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'info': {
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'zh': '异步rollout以提高训练速度',
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'en': 'Asynchronous rollout to increase training speed'
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}
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},
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'temperature': {
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'label': {
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'zh': '采样温度',
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'en': 'Temperature'
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},
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},
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'top_k': {
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'label': {
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'zh': 'Top-k',
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'en': 'Top-k'
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},
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},
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'top_p': {
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'label': {
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'zh': 'Top-p',
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'en': 'Top-p'
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},
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},
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'repetition_penalty': {
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'label': {
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'zh': '重复惩罚',
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'en': 'Repetition Penalty'
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},
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},
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'use_vllm': {
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'label': {
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'zh': '使用vLLM',
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'en': 'Using vLLM'
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},
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'info': {
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'zh': '是否使用vLLM作为GRPO生成的推理后端',
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'en': 'Whether to use vLLM as the infer_backend of generation by GRPO'
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}
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},
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'vllm_mode': {
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'label': {
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'zh': 'vLLM集成模式',
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'en': 'vLLM Integration Mode'
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},
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'info': {
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'zh':
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'Server模式使用`swift rollout`拉起的vLLM服务进行采样;Colocate模式使用程序内部署的vLLM',
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'en':
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'Server mode uses the vLLM server deployed by swift rollout for sampling,'
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' colocate mode uses vLLM deployed in the program'
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}
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},
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'vllm_gpu_memory_utilization': {
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'label': {
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'zh': 'GPU显存利用率',
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'en': 'GPU memory utilization'
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},
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'info': {
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'zh': 'vLLM透传参数',
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'en': 'vLLM transparent transmission parameters'
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}
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},
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'vllm_tensor_parallel_size': {
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'label': {
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'zh': '张量并行大小',
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'en': 'Tensor parallel size'
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},
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'info': {
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'zh': 'vLLM透传参数',
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'en': 'vLLM transparent transmission parameters'
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}
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},
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'vllm_max_model_len': {
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'label': {
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'zh': '模型支持的最大长度',
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'en': 'Max model len'
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},
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'info': {
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'zh': 'vLLM透传参数',
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'en': 'vLLM transparent transmission parameters'
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}
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},
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'sleep_level': {
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'label': {
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'zh': 'Sleep level',
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'en': 'Sleep level'
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},
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'info': {
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'zh': '训练时释放vLLM显存',
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'en': 'Release vLLM memory during training'
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}
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},
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'vllm_server_host': {
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'label': {
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'zh': 'vLLM服务主机',
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'en': 'vLLM server host'
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},
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},
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'vllm_server_port': {
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'label': {
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'zh': 'vLLM服务端口',
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'en': 'vLLM server port'
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},
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},
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'vllm_server_timeout': {
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'label': {
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'zh': '服务超时时间',
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'en': 'Server timeout'
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},
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'info': {
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'zh': '连接vLLM服务的超时时间',
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'en': 'Timeout for connecting to vLLM server'
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}
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},
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'offload_model': {
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'label': {
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'zh': '卸载模型',
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'en': 'Offload model'
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},
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'info': {
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'zh': '是否在vLLM推理时卸载模型',
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'en': 'Whether to offload the model during vLLM inference'
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}
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},
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'offload_optimizer': {
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'label': {
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'zh': '卸载优化器',
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'en': 'Offload optimizer'
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},
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'info': {
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'zh': '是否在vLLM推理时卸载优化器参数',
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'en': 'Whether to offload optimizer parameters during vLLM inference'
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}
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},
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'colocate_param': {
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'label': {
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'zh': 'Colocate模式参数',
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'en': 'Colocate mode parameters'
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}
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},
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'server_param': {
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'label': {
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'zh': 'Server模式参数',
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'en': 'Server mode parameters'
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}
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},
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'rollout_param': {
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'label': {
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'zh': 'Rollout设置(更多参数->GRPO高级参数设置)',
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'en': 'Rollout settings(more params->GRPO advanced settings)'
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}
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}
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}
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tabs_to_filter = {
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'colocate': [
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'vllm_enable_prefix_caching', 'vllm_gpu_memory_utilization', 'vllm_tensor_parallel_size',
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'vllm_max_model_len', 'sleep_level', 'offload_model', 'offload_optimizer'
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],
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'server': ['async_generate', 'vllm_server_host', 'vllm_server_port', 'vllm_server_timeout'],
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'llm_rollout':
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['tensor_parallel_size', 'data_parallel_size', 'max_model_len', 'gpu_memory_utilization', 'port']
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}
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@classmethod
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def do_build_ui(cls, base_tab: Type['BaseUI']):
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with gr.Accordion(elem_id='rollout_param', open=False):
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with gr.Row():
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gr.Slider(elem_id='temperature', minimum=0.0, maximum=10, step=0.1, value=1.0)
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gr.Slider(elem_id='top_k', minimum=1, maximum=100, step=5, value=80)
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gr.Slider(elem_id='top_p', minimum=0.0, maximum=1.0, step=0.05, value=1.0)
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gr.Slider(elem_id='repetition_penalty', minimum=0.0, maximum=10, step=0.05, value=1.05)
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with gr.Row():
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gr.Checkbox(elem_id='use_vllm', value=True, scale=4)
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gr.Dropdown(elem_id='vllm_mode', choices=['colocate', 'server'], scale=4)
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gr.Slider(elem_id='num_generations', minimum=1, maximum=64, step=1, scale=4)
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gr.Textbox(elem_id='max_completion_length', lines=1, value='512', scale=4)
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with gr.Accordion(elem_id='colocate_param', open=True):
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with gr.Row():
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gr.Textbox(elem_id='vllm_gpu_memory_utilization', lines=1, value='0.5', scale=4)
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gr.Textbox(elem_id='vllm_tensor_parallel_size', lines=1, value='1', scale=4)
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gr.Textbox(elem_id='vllm_max_model_len', lines=1, value='', scale=4)
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gr.Dropdown(elem_id='sleep_level', choices=['0', '1'], value='0', scale=4, allow_custom_value=True)
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gr.Checkbox(elem_id='offload_model', value=True, scale=4)
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gr.Checkbox(elem_id='offload_optimizer', value=True, scale=4)
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with gr.Accordion(elem_id='server_param', open=True):
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with gr.Row():
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gr.Checkbox(elem_id='async_generate', scale=4)
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gr.Textbox(elem_id='vllm_server_host', value='127.0.0.1', scale=4)
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gr.Textbox(elem_id='vllm_server_port', lines=1, scale=4)
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gr.Textbox(elem_id='vllm_server_timeout', lines=1, scale=4, value=120)
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@staticmethod
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def update_num_gen(per_device_batch_size, steps_per_generation, num_processes):
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return int(per_device_batch_size) * int(steps_per_generation) * int(num_processes)
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