refactor: 训练日志组件化与 Mock 数据增强

拆分 TrainingTaskOverview 组件与 trainingLogModel 状态模型，TrainingLogView 大幅瘦身；Mock 新增按文件路由的训练日志内容与更真实的 GPU 进程占用数据，adapter 类型收敛为 AxiosAdapter，配套新增 mock 内容回归脚本。
2026-07-13 15:29:49 +08:00
parent e580ec4791
commit e212de1693
7 changed files with 1473 additions and 914 deletions
--- a/frontend/src/mock/adapter.ts
+++ b/frontend/src/mock/adapter.ts
@@ -3,7 +3,7 @@
 * 拦截所有 API 请求并返回 mock 数据
 * 通过 URL + method 路由到对应的 mock 响应
 */
-import type { AxiosInstance, AxiosRequestConfig } from 'axios'
+import type { AxiosAdapter, AxiosInstance, AxiosRequestConfig } from 'axios'
 import {
  mockLoginOk,
  mockHealth,
@@ -21,6 +21,7 @@ import {
  mockLogFiles,
  mockTrainingLogFiles,
  mockLogContent,
+  mockTrainingLogContents,
 } from './data'
 import {
  activateDatasetVersion,
@@ -164,7 +165,8 @@ async function handleMock(config: AxiosRequestConfig) {
  }
  m = url.match(/^\/dataset-manage\/([^/]+)$/)
  if (m && method === 'get') {
-    const found = mockDatasets.find((x) => String(x.id) === m[1])
+    const datasetId = m[1]
+    const found = mockDatasets.find((x) => String(x.id) === datasetId)
    return found ? ok(found) : fail('数据集不存在', 404)
  }
  if (m && (method === 'put' || method === 'delete')) {
@@ -261,7 +263,8 @@ async function handleMock(config: AxiosRequestConfig) {
  }
  m = url.match(/^\/fine-tune\/progress\/([^/]+)$/)
  if (m && method === 'get') {
-    const task = mockFineTuneList.find((t) => String(t.id) === m[1])
+    const taskId = m[1]
+    const task = mockFineTuneList.find((t) => String(t.id) === taskId)
    if (!task) return fail('任务不存在', 404)
    if (task.status === 'running') {
      return ok({
@@ -276,7 +279,8 @@ async function handleMock(config: AxiosRequestConfig) {
  }
  m = url.match(/^\/fine-tune\/([^/]+)$/)
  if (m && method === 'get') {
-    const found = mockFineTuneList.find((x) => String(x.id) === m[1])
+    const taskId = m[1]
+    const found = mockFineTuneList.find((x) => String(x.id) === taskId)
    return found ? ok(found) : fail('任务不存在', 404)
  }
  m = url.match(/^\/fine-tune\/stop\/([^/]+)$/)
@@ -296,7 +300,8 @@ async function handleMock(config: AxiosRequestConfig) {
  }
  m = url.match(/^\/model-compare\/([^/]+)$/)
  if (m && method === 'get') {
-    const found = mockCompareList.find((x) => String(x.id) === m[1])
+    const compareId = m[1]
+    const found = mockCompareList.find((x) => String(x.id) === compareId)
    return found ? ok(found) : fail('任务不存在', 404)
  }
  if (m && method === 'delete') return ok({ deleted: m[1] })
@@ -363,7 +368,14 @@ async function handleMock(config: AxiosRequestConfig) {
  if (url === '/log-files' && method === 'get') return ok(mockLogFiles)
  if (url === '/log-content' && method === 'get') return ok(mockLogContent)
  if (url === '/training-log-files' && method === 'get') return ok(mockTrainingLogFiles)
-  if (url === '/training-log-content' && method === 'get') return ok(mockLogContent)
+  if (url === '/training-log-content' && method === 'get') {
+    const file = String(params.file || '')
+    return ok(mockTrainingLogContents[file] || {
+      file,
+      size: '0 KB',
+      content: `[Mock] 未找到训练日志内容：${file}`,
+    })
+  }

  // 未匹配的请求 → 兜底返回空成功（避免阻断 UI）
  console.warn('[Mock] 未匹配路由:', method.toUpperCase(), url, params)
@@ -380,12 +392,13 @@ function safeJSON(str: string) {

 /** 给 axios instance 安装 mock adapter */
 export function installMockAdapter(instance: AxiosInstance) {
-  instance.defaults.adapter = async (config: AxiosRequestConfig) => {
+  const adapter = async (config: AxiosRequestConfig) => {
    try {
      const response = await handleMock(config)
      return response
-    } catch (e: any) {
-      return fail(e.message || 'Mock 错误', 500, config)
+    } catch (error: unknown) {
+      return fail(error instanceof Error ? error.message : 'Mock 错误', 500, config)
    }
  }
+  instance.defaults.adapter = adapter as AxiosAdapter
 }
--- a/frontend/src/mock/data.ts
+++ b/frontend/src/mock/data.ts
@@ -58,37 +58,40 @@ export const mockSystemInfo: SystemInfo = {
      id: 0,
      uuid: 'GPU-MOCK-A800-00',
      name: 'NVIDIA A800',
-      status: 'idle',
-      gpu_percent: 0,
-      memory_used_gb: 0,
+      status: 'busy',
+      gpu_percent: 74,
+      memory_used_gb: 41.8,
      memory_total_gb: 80,
-      memory_percent: 0,
-      temperature: 32,
-      power_w: 38,
+      memory_percent: 52.3,
+      temperature: 63,
+      power_w: 286,
      power_limit_w: 400,
-      fan_speed: 0,
+      fan_speed: 51,
      clock_mhz: 1410,
      driver_version: '535.86.10',
-      processes: [],
+      processes: [
+        { pid: 28741, name: 'python', task_name: 'medical-cpt-001 / rank 0', user: 'trainer', memory_used_gb: 39.6 },
+        { pid: 28768, name: 'python', task_name: '训练指标采集', user: 'trainer', memory_used_gb: 2.2 },
+      ],
    },
    {
      id: 1,
      uuid: 'GPU-MOCK-A800-01',
      name: 'NVIDIA A800',
      status: 'busy',
-      gpu_percent: 28,
-      memory_used_gb: 22.5,
+      gpu_percent: 71,
+      memory_used_gb: 42.1,
      memory_total_gb: 80,
-      memory_percent: 28.1,
-      temperature: 52,
-      power_w: 165,
+      memory_percent: 52.6,
+      temperature: 62,
+      power_w: 279,
      power_limit_w: 400,
-      fan_speed: 32,
+      fan_speed: 49,
      clock_mhz: 1410,
      driver_version: '535.86.10',
      processes: [
-        { pid: 18421, name: 'python', task_name: '指令微调任务', user: 'trainer', memory_used_gb: 18.6 },
-        { pid: 18503, name: 'python', task_name: '训练指标采集', user: 'trainer', memory_used_gb: 3.9 },
+        { pid: 28742, name: 'python', task_name: 'medical-cpt-001 / rank 1', user: 'trainer', memory_used_gb: 39.9 },
+        { pid: 28769, name: 'python', task_name: '训练指标采集', user: 'trainer', memory_used_gb: 2.2 },
      ],
    },
    {
@@ -249,7 +252,7 @@ export const mockLocalModels = {
 }

 // ============ 数据集 ============
-export const mockDatasets: DatasetItem[] = [
+export const mockDatasets: DatasetItem[] = ([
  {
    id: 1,
    name: '金融问答-训练集',
@@ -273,7 +276,7 @@ export const mockDatasets: DatasetItem[] = [
  { id: 8, name: '通用指令构造集', type: 'train', storage_type: 'local', source: 'task', task_id: 492015, size: '148 MB', count: 12600, description: '由指令微调数据构造任务生成', create_time: '2026-07-09T01:42:00Z' },
  { id: 9, name: '用户反馈脱敏集', type: 'test', storage_type: 'minio', source: 'task', task_id: 731948, size: '72 MB', count: 9340, description: '由敏感信息脱敏任务生成', create_time: '2026-07-09T09:18:00Z' },
  { id: 10, name: '多轮对话增强集', type: 'eval', storage_type: 'local', source: 'task', task_id: 582012, size: '41 MB', count: 2780, description: '由多轮对话拼接任务生成', create_time: '2026-07-10T02:06:00Z' },
-].map((dataset) => ({
+] satisfies DatasetItem[]).map((dataset) => ({
  ...dataset,
  files: dataset.files?.length
    ? dataset.files
@@ -339,12 +342,181 @@ export const mockDatasetPreviews: Record<string, string> = {

 // ============ 训练任务 ============
 export const mockFineTuneList: FineTuneTask[] = [
-  { id: 103942, name: 'finance-sft-001', description: '金融领域 SFT 训练', status: 'completed', train_type: 'SFT', train_method: 'lora', template: 'qwen', base_model: 1, train_dataset_id: 1, gpus: [0], progress: 100, train_duration: '2小时18分钟', create_time: '2026-01-15T08:00:00Z' },
-  { id: 349102, name: 'legal-sft-002', description: '法律文书 SFT', status: 'completed', train_type: 'SFT', train_method: 'lora', template: 'qwen', base_model: 1, train_dataset_id: 2, gpus: [1], progress: 100, train_duration: '1小时46分钟', create_time: '2026-01-18T10:00:00Z' },
-  { id: 849301, name: 'medical-cpt-001', description: '医疗领域继续预训练', status: 'running', train_type: 'CPT', train_method: 'lora', template: 'qwen2_5', base_model: 2, train_dataset_id: 6, gpus: [0, 1], progress: 64, train_duration: '36分钟', create_time: '2026-02-05T09:00:00Z' },
-  { id: 593021, name: 'service-dpo-001', description: '客服对话偏好训练', status: 'pending', train_type: 'DPO', train_method: 'lora', template: 'qwen', base_model: 1, train_dataset_id: 3, gpus: [2], progress: 0, train_duration: '-', create_time: '2026-02-08T14:00:00Z' },
-  { id: 201948, name: 'finance-sft-002', description: '金融领域二轮微调', status: 'failed', train_type: 'SFT', train_method: 'lora', template: 'qwen', base_model: 1, train_dataset_id: 1, gpus: [3], progress: 32, train_duration: '18分钟', create_time: '2026-02-10T11:00:00Z' },
-  { id: 940212, name: 'general-sft-001', description: '通用能力微调', status: 'completed', train_type: 'SFT', train_method: 'full', template: 'llama3', base_model: 3, train_dataset_id: 3, gpus: [0, 2], progress: 100, train_duration: '3小时05分钟', create_time: '2026-02-12T13:00:00Z' },
+  {
+    id: 103942,
+    name: 'finance-sft-001',
+    description: '金融领域 SFT 训练',
+    status: 'completed',
+    train_type: 'SFT',
+    train_method: 'lora',
+    template: 'qwen',
+    base_model: 1,
+    train_dataset_id: 1,
+    output_model_name: 'qwen2.5-7b-finance-sft-v1',
+    auto_merge: true,
+    gpus: [0],
+    batch_size: 8,
+    learning_rate: 0.00002,
+    n_epochs: 3,
+    save_steps: 100,
+    lr_scheduler_type: 'cosine',
+    max_length: 2048,
+    warmup_ratio: 0.05,
+    weight_decay: 0.01,
+    lora_rank: 16,
+    lora_alpha: 32,
+    lora_dropout: 0.05,
+    quantization_bit: 0,
+    process_id: 12345,
+    progress: 100,
+    train_duration: '2小时18分钟',
+    create_time: '2026-01-15T08:00:00Z',
+  },
+  {
+    id: 349102,
+    name: 'legal-sft-002',
+    description: '法律文书 SFT',
+    status: 'completed',
+    train_type: 'SFT',
+    train_method: 'lora',
+    template: 'qwen',
+    base_model: 1,
+    train_dataset_id: 2,
+    output_model_name: 'qwen2.5-7b-legal-sft-v2',
+    auto_merge: true,
+    gpus: [1],
+    batch_size: 4,
+    learning_rate: 0.000015,
+    n_epochs: 4,
+    save_steps: 120,
+    lr_scheduler_type: 'linear',
+    max_length: 4096,
+    warmup_ratio: 0.03,
+    weight_decay: 0.01,
+    lora_rank: 32,
+    lora_alpha: 64,
+    lora_dropout: 0.05,
+    quantization_bit: 0,
+    process_id: 12350,
+    progress: 100,
+    train_duration: '1小时46分钟',
+    create_time: '2026-01-18T10:00:00Z',
+  },
+  {
+    id: 849301,
+    name: 'medical-cpt-001',
+    description: '医疗领域继续预训练',
+    status: 'running',
+    train_type: 'CPT',
+    train_method: 'lora',
+    template: 'qwen2_5',
+    base_model: 2,
+    train_dataset_id: 6,
+    output_model_name: 'qwen2.5-14b-medical-cpt-v1',
+    auto_merge: true,
+    gpus: [0, 1],
+    batch_size: 2,
+    learning_rate: 0.0001,
+    n_epochs: 3,
+    save_steps: 200,
+    lr_scheduler_type: 'cosine',
+    max_length: 4096,
+    warmup_ratio: 0.03,
+    weight_decay: 0.01,
+    lora_rank: 16,
+    lora_alpha: 32,
+    lora_dropout: 0.05,
+    quantization_bit: 0,
+    process_id: 28741,
+    progress: 64,
+    train_duration: '36分钟',
+    create_time: '2026-07-13T06:40:00Z',
+  },
+  {
+    id: 593021,
+    name: 'service-dpo-001',
+    description: '客服对话偏好训练',
+    status: 'pending',
+    train_type: 'DPO',
+    train_method: 'lora',
+    template: 'qwen',
+    base_model: 1,
+    train_dataset_id: 3,
+    output_model_name: 'qwen2.5-7b-service-dpo-v1',
+    auto_merge: true,
+    gpus: [2],
+    batch_size: 4,
+    learning_rate: 0.000005,
+    n_epochs: 2,
+    save_steps: 100,
+    lr_scheduler_type: 'cosine',
+    max_length: 2048,
+    warmup_ratio: 0.1,
+    weight_decay: 0,
+    lora_rank: 16,
+    lora_alpha: 32,
+    lora_dropout: 0.1,
+    quantization_bit: 0,
+    progress: 0,
+    train_duration: '等待调度',
+    create_time: '2026-02-08T14:00:00Z',
+  },
+  {
+    id: 201948,
+    name: 'finance-sft-002',
+    description: '金融领域二轮微调',
+    status: 'failed',
+    train_type: 'SFT',
+    train_method: 'lora',
+    template: 'qwen',
+    base_model: 1,
+    train_dataset_id: 1,
+    output_model_name: 'qwen2.5-7b-finance-sft-v2',
+    auto_merge: false,
+    gpus: [3],
+    batch_size: 8,
+    learning_rate: 0.00002,
+    n_epochs: 3,
+    save_steps: 100,
+    lr_scheduler_type: 'cosine',
+    max_length: 2048,
+    warmup_ratio: 0.05,
+    weight_decay: 0.01,
+    lora_rank: 16,
+    lora_alpha: 32,
+    lora_dropout: 0.05,
+    quantization_bit: 0,
+    process_id: 27654,
+    progress: 32,
+    train_duration: '18分钟',
+    create_time: '2026-02-10T11:00:00Z',
+  },
+  {
+    id: 940212,
+    name: 'general-sft-001',
+    description: '通用能力全参数微调',
+    status: 'completed',
+    train_type: 'SFT',
+    train_method: 'full',
+    template: 'llama3',
+    base_model: 3,
+    train_dataset_id: 3,
+    output_model_name: 'llama3-8b-general-sft-v1',
+    auto_merge: false,
+    gpus: [0, 2],
+    batch_size: 2,
+    learning_rate: 0.00001,
+    n_epochs: 2,
+    save_steps: 250,
+    lr_scheduler_type: 'cosine',
+    max_length: 4096,
+    warmup_ratio: 0.03,
+    weight_decay: 0.1,
+    process_id: 26318,
+    progress: 100,
+    train_duration: '3小时05分钟',
+    create_time: '2026-02-12T13:00:00Z',
+  },
 ]

 // ============ 模型推理/对比 ============
@@ -549,11 +721,48 @@ export const mockLogFiles: LogFile[] = [
 ]

 export const mockTrainingLogFiles: TrainingLogFile[] = [
+  { file: 'medical-cpt-001_pid28741.log', name: 'medical-cpt-001', size: '6.8 MB', pid: 28741, date: '2026-07-13' },
  { file: 'qwen-ft-finance-001_pid12345.log', name: 'finance-sft-001', size: '4.5 MB', pid: 12345, date: '2026-02-15' },
  { file: 'llama3-ft-customer-service_pid12346.log', name: 'service-dpo-001', size: '2.1 MB', pid: 12346, date: '2026-02-18' },
  { file: 'qwen-ft-legal-002_pid12350.log', name: 'legal-sft-002', size: '5.8 MB', pid: 12350, date: '2026-02-20' },
 ]

+const medicalTrainingLogLines = [
+  '[2026-07-13 14:40:01] INFO: Launching distributed training with torchrun --nproc_per_node=2',
+  '[2026-07-13 14:40:02] INFO: Process rank: 0, world size: 2, device: cuda:0, distributed training: True',
+  '[2026-07-13 14:40:04] INFO: Loading tokenizer from /data/models/qwen2.5-14b-instruct',
+  '[2026-07-13 14:40:16] INFO: Loading checkpoint shards: 100% | 8/8 | 00:12',
+  '[2026-07-13 14:40:18] INFO: Loading dataset 医疗问答-训练集 (9,800 samples)',
+  '[2026-07-13 14:40:27] INFO: Tokenizing dataset: 100% | 9,800/9,800 | 00:09',
+  '[2026-07-13 14:40:28] INFO: LoRA config: rank=16, alpha=32, dropout=0.05, target_modules=q_proj,k_proj,v_proj,o_proj',
+  '[2026-07-13 14:40:29] INFO: Trainable params: 83,886,080 / 14,787,584,000 (0.5673%)',
+  '[2026-07-13 14:40:30] INFO: ***** Running training *****',
+  '[2026-07-13 14:40:30] INFO:   Num examples = 9,800',
+  '[2026-07-13 14:40:30] INFO:   Num Epochs = 3',
+  '[2026-07-13 14:40:30] INFO:   Instantaneous batch size per device = 2',
+  '[2026-07-13 14:40:30] INFO:   Total train batch size = 32',
+  '[2026-07-13 14:40:30] INFO:   Gradient Accumulation steps = 8',
+  '[2026-07-13 14:40:30] INFO:   Total optimization steps = 921',
+  '[2026-07-13 14:42:18] INFO: step=40 {\'loss\': 2.684, \'grad_norm\': 1.184, \'learning_rate\': 9.82e-05, \'epoch\': 0.13}',
+  '[2026-07-13 14:44:26] INFO: step=80 {\'loss\': 2.312, \'grad_norm\': 1.092, \'learning_rate\': 9.68e-05, \'epoch\': 0.26}',
+  '[2026-07-13 14:46:34] INFO: step=120 {\'loss\': 2.084, \'grad_norm\': 1.037, \'learning_rate\': 9.43e-05, \'epoch\': 0.39}',
+  '[2026-07-13 14:48:42] INFO: step=160 {\'loss\': 1.932, \'grad_norm\': 0.986, \'learning_rate\': 9.08e-05, \'epoch\': 0.52}',
+  '[2026-07-13 14:50:49] INFO: step=200 {\'loss\': 1.801, \'grad_norm\': 0.944, \'learning_rate\': 8.64e-05, \'epoch\': 0.65}',
+  '[2026-07-13 14:50:54] INFO: Saving checkpoint to /data/checkpoints/medical-cpt-001/checkpoint-200',
+  '[2026-07-13 14:52:58] INFO: step=240 {\'loss\': 1.696, \'grad_norm\': 0.913, \'learning_rate\': 8.15e-05, \'epoch\': 0.78}',
+  '[2026-07-13 14:55:06] INFO: step=280 {\'loss\': 1.611, \'grad_norm\': 0.887, \'learning_rate\': 7.61e-05, \'epoch\': 0.91}',
+  '[2026-07-13 14:57:13] INFO: step=320 {\'loss\': 1.532, \'grad_norm\': 0.852, \'learning_rate\': 7.06e-05, \'epoch\': 1.04}',
+  '[2026-07-13 14:59:21] INFO: step=360 {\'loss\': 1.461, \'grad_norm\': 0.829, \'learning_rate\': 6.49e-05, \'epoch\': 1.17}',
+  '[2026-07-13 15:01:29] INFO: step=400 {\'loss\': 1.396, \'grad_norm\': 0.811, \'learning_rate\': 5.91e-05, \'epoch\': 1.30}',
+  '[2026-07-13 15:01:34] INFO: Saving checkpoint to /data/checkpoints/medical-cpt-001/checkpoint-400',
+  '[2026-07-13 15:03:37] INFO: step=440 {\'loss\': 1.337, \'grad_norm\': 0.795, \'learning_rate\': 5.35e-05, \'epoch\': 1.43}',
+  '[2026-07-13 15:05:45] INFO: step=480 {\'loss\': 1.286, \'grad_norm\': 0.776, \'learning_rate\': 4.80e-05, \'epoch\': 1.56}',
+  '[2026-07-13 15:07:53] INFO: step=520 {\'loss\': 1.232, \'grad_norm\': 0.758, \'learning_rate\': 4.28e-05, \'epoch\': 1.69}',
+  '[2026-07-13 15:11:59] INFO: step=560 {\'loss\': 1.184, \'grad_norm\': 0.741, \'learning_rate\': 3.79e-05, \'epoch\': 1.82}',
+  '[2026-07-13 15:16:05] INFO: step=590 {\'loss\': 1.146, \'grad_norm\': 0.728, \'learning_rate\': 3.44e-05, \'epoch\': 1.92}',
+  '[2026-07-13 15:16:06] INFO: Training is running normally, estimated remaining time: 00:20:15',
+]
+
 const fakeLogLines = [
  "[2026-02-15 08:30:12] INFO: Loading model from /data/models/qwen2.5-7b",
  "[2026-02-15 08:30:13] INFO: Loading dataset finance-train-001 (8560 samples)",
@@ -591,3 +800,16 @@ export const mockLogContent: LogContent = {
  size: '2.3 MB',
  content: fakeLogLines.join('\n'),
 }
+
+export const mockTrainingLogContents: Record<string, LogContent> = {
+  'medical-cpt-001_pid28741.log': {
+    file: 'medical-cpt-001_pid28741.log',
+    size: '6.8 MB',
+    content: medicalTrainingLogLines.join('\n'),
+  },
+  'qwen-ft-finance-001_pid12345.log': {
+    file: 'qwen-ft-finance-001_pid12345.log',
+    size: '4.5 MB',
+    content: fakeLogLines.join('\n'),
+  },
+}