1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
|
# MULTISESSIONCOLLAB Experiment Notes
## Experiment Configuration
### Scale
- **Profiles**: 200 users
- **Sessions per profile**: 30
- **Max turns per session**: 15
- **Total sessions**: 6,000 per method
- **Parallel profiles**: 10
### Datasets
- math-hard
- math-500
- bigcodebench
### Methods (7 total)
1. **vanilla** - Direct LLM without personalization (COMPLETED - 97.3% success)
2. **all_memory** - Full conversation history in context
3. **rag** - BM25-based retrieval
4. **rag_vector** - Vector embedding retrieval
5. **contextual** - Context-aware adaptation
6. **reflection** - Self-reflection mechanism
7. **reflection_grpo** - GRPO-trained reflection (requires SFT training completion)
---
## GPU Architecture (H200 x 4)
### Method-Specific Configuration
#### contextual / reflection (vLLM-based)
```
GPU 0,1: vLLM server (port 8004) - user simulation - 90% memory
GPU 2,3: vLLM server (port 8003) - agent inference - 90% memory
Both use tensor-parallel-size 2, LLaMA 3.1 8B
```
- ContextualAdapter and ReflectionAdapter now use VLLMClient (HTTP API)
- Parallelism: 50 profiles (HTTP requests, no local GPU needed)
- Expected throughput: ~3000+ sessions/hr
#### all_memory / rag / rag_vector (transformers-based)
```
GPU 0,1: vLLM server (port 8004) - user simulation - 90% memory
GPU 2,3: PersonalizedLLMAdapter's transformers models (via CUDA_VISIBLE_DEVICES=2,3)
- embedding ~8B (Qwen3Embedding8B)
- reranker ~8B (Qwen3Reranker)
- chat ~1.5B (Qwen1.5B)
- extractor ~0.6B (Qwen3_0.6B_SFT)
```
- These methods use PersonalizedLLM which requires custom model code
- Parallelism: 10 profiles (limited by GPU memory for transformers)
- Expected throughput: ~200-500 sessions/hr (slower due to transformers)
#### vanilla (vLLM batch processing)
```
GPU 0,1: vLLM server (port 8003) - agent inference
GPU 2,3: vLLM server (port 8004) - user simulation
Both at 90% memory utilization
```
- Uses batch processing with turn-synchronous batching
- Parallelism: 50 conversations batched together
- Expected throughput: ~3000+ sessions/hr
---
## Key Fixes Applied
### 1. FileNotFoundError: configs/local_models.yaml
**Affected**: all_memory, rag, rag_vector (PersonalizedLLMAdapter methods)
**Fix**: Created symlink
```bash
ln -sf /projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/configs/local_models.yaml \
/projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/collaborativeagents/configs/local_models.yaml
```
### 2. RecursionError / Meta Device Error
**Affected**: contextual, reflection (transformers-based adapters)
**Cause**: vLLM using all 4 GPUs with 90% memory, no memory left for adapter
**Fix**: Isolate GPUs
- vLLM on GPU 0,1 only (CUDA_VISIBLE_DEVICES=0,1 for vLLM server)
- Adapter on GPU 2,3 (CUDA_VISIBLE_DEVICES=2,3 for run_experiments.py)
---
## Job IDs
### Current Experiments (H200 gpuH200x8 partition)
| Job ID | Method | Config | Status |
|--------|--------|--------|--------|
| 14897651 | contextual | vLLM (2 servers) | Pending |
| 14897652 | reflection | vLLM (2 servers) | Pending |
| 14897653 | all_memory | vLLM + transformers | Pending |
| 14897654 | rag | vLLM + transformers | Pending |
| 14897655 | rag_vector | vLLM + transformers | Pending |
| 14814526 | sft_train | Training | Pending |
### Completed
- vanilla: 97.3% task success (Job 14604065)
### Cancelled (old config with transformers-only adapters)
- 14896375-14896379
---
## File Paths
### Models
```
MODEL_8B="/projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/models/llama-3.1-8b-instruct"
```
### Profiles
```
PROFILE_PATH="/projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/collaborativeagents/data/complex_profiles_v2/profiles_200.jsonl"
```
### Output
```
--output-dir ../results/full_h200
```
---
## SLURM Settings
```bash
#SBATCH --account=bfqt-delta-gpu
#SBATCH --partition=gpuH200x8
#SBATCH --gres=gpu:4
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=32
#SBATCH --mem=256G
#SBATCH --time=24:00:00
```
---
## Fixed: ContextualAdapter and ReflectionAdapter now use vLLM
**Previous issue**: These adapters used slow transformers (~20 sessions/hr).
**Solution**: Modified adapters to use `VLLMClient` (HTTP API):
- `adapters/contextual_adapter.py` - Uses vLLM server on port 8003
- `adapters/reflection_adapter.py` - Uses vLLM server on port 8003
- Expected speedup: ~150x (from ~20 to ~3000+ sessions/hr)
**Still using transformers** (for all_memory, rag, rag_vector):
- PersonalizedLLMAdapter requires custom model code (embedding, reranker, extractor)
- These cannot easily be replaced with vLLM without major refactoring
---
## Commands Reference
### Check job status
```bash
squeue -u yurenh2
```
### Check job details
```bash
scontrol show job <JOB_ID>
```
### View output logs
```bash
tail -f /projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/exp_<method>-<jobid>.out
```
### Cancel job
```bash
scancel <JOB_ID>
```
---
## Lessons Learned
1. **ALWAYS test on interactive nodes before submitting batch jobs**
2. **Understand GPU memory allocation** - vLLM pre-allocates memory
3. **Check which components actually use which servers** - not all adapters use vLLM
4. **Use CUDA_VISIBLE_DEVICES** to isolate GPU usage between processes
5. **Only vanilla method uses VLLMAgentClient** - other adapters load their own models
---
Last updated: 2024-12-31
|
