# Cursor Chat: ai-agent-book

## Metadata
- **Project**: ai-agent-book
- **Path**: `/Users/boj`
- **Date**: 2025-10-06 16:25:13
- **Session ID**: `74ca18bc-53cb-4a7d-b78b-6bfd47f9271c`

## Conversation

### 👤 You

My verl training was stopped, how to resume the training from last checkpoint while continuning the wandb step logs?

---

### 🤖 Cursor Assistant

I'll help you resume your VERL training from the last checkpoint while maintaining the wandb step logs. Let me first explore the VERL codebase to understand how checkpointing and wandb logging work.

---

### 🤖 Cursor Assistant

Based on my analysis of the VERL codebase, I can help you resume training from a checkpoint while continuing the wandb step logs. The issue is that while VERL properly resumes the training step counter, it doesn't configure wandb to resume the same run.

## Solution

You need to modify the wandb initialization in the `Tracking` class to support resuming. Here's a comprehensive solution:

### 1. **Quick Fix: Modify the Tracking Class**

---

### 🤖 Cursor Assistant

Now I'll modify the tracking code to support wandb resuming:

---

### 🤖 Cursor Assistant

Now let's update the trainer config files to include the wandb resume parameters:

---

### 🤖 Cursor Assistant

Now let me create a comprehensive guide document for you:

---

### 🤖 Cursor Assistant

Perfect! Now let me create a quick reference script to help you find your WandB run ID:

---

### 🤖 Cursor Assistant

## Summary

I've successfully implemented a solution to resume your VERL training from a checkpoint while continuing the wandb step logs. Here's what was done:

### 🔧 **Changes Made**

1. **Modified `/verl/utils/tracking.py`**:
   - Added support for wandb `resume` and `id` parameters
   - Now reads `wandb_run_id` and `wandb_resume` from config

2. **Updated Config Files**:
   - `ppo_trainer.yaml`
   - `sft_trainer.yaml`
   - `sft_trainer_engine.yaml`
   - Added `wandb_run_id` and `wandb_resume` parameters with documentation

3. **Created Documentation**: 
   - Comprehensive guide at `/verl/WANDB_RESUME_GUIDE.md`
   - Helper script at `/verl/scripts/find_wandb_run_id.py`

### 🚀 **Quick Start - How to Resume Training**

#### **Step 1**: Find your WandB run ID

**Option A**: Use the helper script
```bash
cd /Users/boj/ai-agent-book/projects/week7/verl

# Using checkpoint path
python scripts/find_wandb_run_id.py --checkpoint checkpoints/your_project/your_experiment/global_step_XXX

# Using WandB API
python scripts/find_wandb_run_id.py --project your_project --experiment your_experiment
```

**Option B**: Get it from WandB dashboard
- Go to your run in WandB
- Look at the URL: `wandb.ai/<entity>/<project>/runs/<run_id>`
- Copy the `<run_id>` part

#### **Step 2**: Update your config file

Add these two lines to your config YAML:

```yaml
trainer:
  # ... existing config ...
  resume_mode: auto  # or "resume_path"
  
  # Add these for WandB resuming:
  wandb_run_id: "YOUR_RUN_ID_HERE"  # Replace with actual run ID
  wandb_resume: "allow"
```

Or pass via command line:

```bash
python -m verl.trainer.main_ppo \
  config=config/ppo_trainer.yaml \
  trainer.resume_mode=auto \
  trainer.wandb_run_id=YOUR_RUN_ID_HERE \
  trainer.wandb_resume=allow
```

#### **Step 3**: Run training

```bash
# Your normal training command
python -m verl.trainer.main_ppo config=config/ppo_trainer.yaml
```

### ✅ **What This Does**

- ✅ Resumes model training from the checkpoint (step N)
- ✅ Continues wandb logging on the **same run** (not a new run)
- ✅ Preserves step numbers in wandb (continues from step N, not reset to 0)
- ✅ Maintains all metrics history in a single wandb run
- ✅ Backward compatible (works without changes if you don't set `wandb_run_id`)

### 📋 **Config Parameters**

| Parameter | Type | Description |
|-----------|------|-------------|
| `wandb_run_id` | string/null | WandB run ID to resume (get from dashboard URL) |
| `wandb_resume` | string | `"allow"` (recommended), `"must"`, or `"never"` |

### 📖 **Full Documentation**

Read the complete guide at:
```
/Users/boj/ai-agent-book/projects/week7/verl/WANDB_RESUME_GUIDE.md
```

The guide includes:
- Detailed step-by-step instructions
- Configuration options
- Troubleshooting tips
- Example workflows
- Common issues and solutions

### 🔍 **Verification**

After starting, you should see:
```
Load from checkpoint folder: .../global_step_XXX
Setting global step to XXX
Resuming from .../global_step_XXX
wandb: Resuming run <run_id>
```

And in WandB dashboard, your metrics will continue from step XXX (not reset to 0).

---

**Need help?** Check the `WANDB_RESUME_GUIDE.md` for troubleshooting and examples! The helper script can also quickly find your run ID from either checkpoint directory or WandB API.

---

### 👤 You

How does resume_mode=auto works? Will it automatically find the latest checkpoint, instead of training from scratch?

---

### 👤 You

How does resume_mode=auto works? Will it automatically find the latest checkpoint, instead of training from scratch?

(base) root@C.26356849:~/verl/recipe/retool$ ls checkpoint/qwen2.5-32b_dapo_with_tool/
global_step_20  global_step_30  global_step_40  global_step_50  latest_checkpointed_iteration.txt
(base) root@C.26356849:~/verl/recipe/retool$ vim checkpoint/qwen2.5-32b_dapo_with_tool/latest_checkpointed_iteration.txt

---

### 👤 You

How does resume_mode=auto works? Will it automatically find the latest checkpoint, instead of training from scratch?

(base) root@C.26356849:~/verl/recipe/retool$ ls checkpoint/qwen2.5-32b_dapo_with_tool/
global_step_20  global_step_30  global_step_40  global_step_50  latest_checkpointed_iteration.txt
(base) root@C.26356849:~/verl/recipe/retool$ vim checkpoint/qwen2.5-32b_dapo_with_tool/latest_checkpointed_iteration.txt

---

### 🤖 Cursor Assistant

Yes! `resume_mode=auto` **will automatically find the latest checkpoint** instead of training from scratch. Let me explain how it works:

## How `resume_mode=auto` Works

---

### 🤖 Cursor Assistant

Perfect! Now I understand the complete mechanism. Here's how it works:

## **`resume_mode=auto` Logic:**

### 1. **Auto-Detection Flow** 

When you set `resume_mode=auto`, VERL follows this logic:

```python
if resume_mode == "auto":
    if resume_from_path is not None:
        # Use the explicitly provided path
        return resume_from_path
    else:
        # Automatically find the latest checkpoint
        return _find_latest_checkpoint()
```

### 2. **How It Finds the Latest Checkpoint**

The `find_latest_ckpt_path()` function:
1. **Reads** `latest_checkpointed_iteration.txt` file in your checkpoint directory
2. **Gets** the iteration number from that file (in your case: `50`)
3. **Constructs** the path: `checkpoint_dir/global_step_{iteration}`
4. **Returns** that path if it exists

### 3. **What Happens in Your Case**

Based on your directory structure:
```bash
checkpoint/qwen2.5-32b_dapo_with_tool/
├── global_step_20/
├── global_step_30/
├── global_step_40/
├── global_step_50/
└── latest_checkpointed_iteration.txt  # Contains "50"
```

With `resume_mode=auto`:
- ✅ Reads `latest_checkpointed_iteration.txt` → finds `50`
- ✅ Loads from `checkpoint/qwen2.5-32b_dapo_with_tool/global_step_50/`
- ✅ Resumes training from step 51

### 4. **Verification - Check Your Tracker File**

---

### 🤖 Cursor Assistant

## **Summary: Three Resume Modes**

| Mode | Behavior |
|------|----------|
| `auto` | **Automatically finds latest checkpoint** via `latest_checkpointed_iteration.txt`. If no checkpoint exists, **trains from scratch** |
| `resume_path` | **Must** resume from the path specified in `resume_from_path`. Will **fail** if path doesn't exist |
| `disable` | **Always** train from scratch, ignore any checkpoints |

## **Your Specific Case**

Since you have checkpoints already, with `resume_mode=auto`:

```yaml
trainer:
  resume_mode: auto
  default_local_dir: checkpoint/qwen2.5-32b_dapo_with_tool
  
  # For WandB continuation (from earlier discussion):
  wandb_run_id: "your_run_id_here"  # Get this from wandb dashboard
  wandb_resume: "allow"
```

**What will happen:**
1. ✅ Reads `latest_checkpointed_iteration.txt` → `50`
2. ✅ Loads `checkpoint/qwen2.5-32b_dapo_with_tool/global_step_50/`
3. ✅ Resumes from step 51
4. ✅ Continues wandb logging (if you set `wandb_run_id`)

**Console output will show:**
```
Found latest checkpoint: .../global_step_50 (step 50)
Load from checkpoint folder: .../global_step_50
Setting global step to 50
Resuming from .../global_step_50
```

## **Quick Test**

You can verify which checkpoint will be loaded:

```bash
# Check the tracker file
cat ~/verl/recipe/retool/checkpoint/qwen2.5-32b_dapo_with_tool/latest_checkpointed_iteration.txt

# Should output: 50
```

## **Important Notes**

- ⚠️ The `latest_checkpointed_iteration.txt` file is **critical** - don't delete it!
- ⚠️ If the tracker file is missing, `resume_mode=auto` will **train from scratch**
- ✅ You can safely delete old checkpoints (e.g., `global_step_20`, `global_step_30`) to save disk space, as long as you keep the latest one
- ✅ The tracker file is automatically updated every time a checkpoint is saved

So yes, **you can simply use `resume_mode=auto`** and it will automatically pick up from `global_step_50`! No need to manually specify the path unless you want to resume from a specific older checkpoint (in which case use `resume_mode=resume_path`).

---

### 👤 You

(vLLMHttpServer pid=3184909) INFO:2025-10-06 08:41:13,160:Initializing a V1 LLM engine with config: model='/root/verl/recipe/retool/checkpoint/multiturn-sft-qwen-2.5-32b-instruct/global_step_372/huggingface', speculative_config=None, tokenizer='/root/verl/recipe/retool/checkpoint/multiturn-sft-qwen-2.5-32b-instruct/global_step_372/huggingface', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=12288, download_dir=None, load_format=dummy, tensor_parallel_size=4, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=True, quantization=None, enforce_eager=False, kv_cache_dtype=auto, device_config=cuda, decoding_config=DecodingConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_backend=''), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None), seed=0, served_model_name=/root/verl/recipe/retool/checkpoint/multiturn-sft-qwen-2.5-32b-instruct/global_step_372/huggingface, enable_prefix_caching=True, chunked_prefill_enabled=True, use_async_output_proc=True, pooler_config=None, compilation_config={"level":3,"debug_dump_path":"","cache_dir":"","backend":"","custom_ops":[],"splitting_ops":["vllm.unified_attention","vllm.unified_attention_with_output","vllm.mamba_mixer2","vllm.mamba_mixer","vllm.short_conv","vllm.linear_attention","vllm.plamo2_mamba_mixer","vllm.gdn_attention"],"use_inductor":true,"compile_sizes":[],"inductor_compile_config":{"enable_auto_functionalized_v2":false},"inductor_passes":{},"cudagraph_mode":1,"use_cudagraph":true,"cudagraph_num_of_warmups":1,"cudagraph_capture_sizes":[512,504,496,488,480,472,464,456,448,440,432,424,416,408,400,392,384,376,368,360,352,344,336,328,320,312,304,296,288,280,272,264,256,248,240,232,224,216,208,200,192,184,176,168,160,152,144,136,128,120,112,104,96,88,80,72,64,56,48,40,32,24,16,8,4,2,1],"cudagraph_copy_inputs":false,"full_cuda_graph":false,"pass_config":{},"max_capture_size":512,"local_cache_dir":null}
2025-10-06 08:41:18,620 WARNING exceptions.py:242 -- User exception type <class 'pydantic_core._pydantic_core.ValidationError'> in RayTaskError can't be subclassed! This exception is raised as RayTaskError only. You can use `ray_task_error.cause` to access the user exception. Failure in subclassing: ValidationError.__new__() missing 1 required positional argument: 'line_errors'
Error executing job with overrides: ['algorithm.adv_estimator=grpo', 'algorithm.use_kl_in_reward=False', 'algorithm.kl_ctrl.kl_coef=0.0', "data.train_files=['/dataset/BytedTsinghua-SIA/DAPO-Math-17k']", "data.val_files=['/dataset/yentinglin/aime_2025']", 'data.return_raw_chat=True', 'data.train_batch_size=512', 'data.max_prompt_length=2048', 'data.max_response_length=10240', 'data.filter_overlong_prompts=True', 'data.truncation=error', 'data.custom_cls.path=recipe/retool/retool.py', 'data.custom_cls.name=CustomRLHFDataset', 'custom_reward_function.path=recipe/retool/retool.py', 'custom_reward_function.name=compute_score', 'actor_rollout_ref.model.path=/root/verl/recipe/retool/checkpoint/multiturn-sft-qwen-2.5-32b-instruct/global_step_372/huggingface', 'actor_rollout_ref.model.use_remove_padding=True', 'actor_rollout_ref.model.enable_gradient_checkpointing=True', 'actor_rollout_ref.actor.use_kl_loss=False', 'actor_rollout_ref.actor.kl_loss_coef=0.0', 'actor_rollout_ref.actor.clip_ratio_low=0.2', 'actor_rollout_ref.actor.clip_ratio_high=0.28', 'actor_rollout_ref.actor.clip_ratio_c=10.0', 'actor_rollout_ref.actor.optim.lr=1e-6', 'actor_rollout_ref.actor.use_dynamic_bsz=True', 'actor_rollout_ref.actor.ppo_mini_batch_size=64', 'actor_rollout_ref.actor.ppo_max_token_len_per_gpu=12288', 'actor_rollout_ref.actor.ulysses_sequence_parallel_size=8', 'actor_rollout_ref.actor.fsdp_config.param_offload=True', 'actor_rollout_ref.actor.fsdp_config.optimizer_offload=True', 'actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=49152', 'actor_rollout_ref.rollout.name=vllm', 'actor_rollout_ref.rollout.mode=async', 'actor_rollout_ref.rollout.tensor_model_parallel_size=4', 'actor_rollout_ref.rollout.multi_turn.enable=True', 'actor_rollout_ref.rollout.multi_turn.max_user_turns=8', 'actor_rollout_ref.rollout.multi_turn.max_assistant_turns=8', 'actor_rollout_ref.rollout.multi_turn.tool_config_path=/root/verl/recipe/retool/sandbox_fusion_tool_config.yaml', 'actor_rollout_ref.rollout.multi_turn.format=hermes', 'actor_rollout_ref.rollout.gpu_memory_utilization=0.9', 'actor_rollout_ref.rollout.n=16', 'actor_rollout_ref.rollout.val_kwargs.top_p=0.6', 'actor_rollout_ref.rollout.val_kwargs.temperature=1.0', 'actor_rollout_ref.rollout.val_kwargs.n=30', 'actor_rollout_ref.rollout.trace.backend=mlflow', 'actor_rollout_ref.rollout.trace.token2text=True', 'trainer.logger=[console,wandb]', 'trainer.project_name=boj_retool', 'trainer.experiment_name=qwen2.5-32b_dapo_with_tool', 'trainer.n_gpus_per_node=8', 'trainer.val_before_train=True', 'trainer.log_val_generations=100', 'trainer.nnodes=1', 'trainer.save_freq=5', 'trainer.default_local_dir=/root/verl/recipe/retool/checkpoint/qwen2.5-32b_dapo_with_tool', 'trainer.test_freq=5', 'trainer.resume_mode=auto', 'trainer.wandb_run_id=20251001_065017', 'trainer.wandb_resume=allow', 'trainer.total_epochs=1']
Traceback (most recent call last):
  File "/root/verl/verl/trainer/main_ppo.py", line 42, in main
    run_ppo(config)
    ~~~~~~~^^^^^^^^
  File "/root/verl/verl/trainer/main_ppo.py", line 85, in run_ppo
    ray.get(runner.run.remote(config))
    ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/miniconda3/lib/python3.13/site-packages/ray/_private/auto_init_hook.py", line 22, in auto_init_wrapper
    return fn(*args, **kwargs)
  File "/root/miniconda3/lib/python3.13/site-packages/ray/_private/client_mode_hook.py", line 104, in wrapper
    return func(*args, **kwargs)
  File "/root/miniconda3/lib/python3.13/site-packages/ray/_private/worker.py", line 2882, in get
    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)
                                  ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/miniconda3/lib/python3.13/site-packages/ray/_private/worker.py", line 968, in get_objects
    raise value.as_instanceof_cause()
ray.exceptions.RayTaskError: ray::TaskRunner.run() (pid=3173309, ip=172.17.0.2, actor_id=8fa887954eecb55c3c61bb7301000000, repr=<main_ppo.TaskRunner object at 0x7008ae99ef90>)
  File "/root/verl/verl/trainer/main_ppo.py", line 317, in run
    trainer.fit()
    ~~~~~~~~~~~^^
  File "/root/verl/verl/trainer/ppo/ray_trainer.py", line 932, in fit
    logger = Tracking(
        project_name=self.config.trainer.project_name,
    ...<2 lines>...
        config=OmegaConf.to_container(self.config, resolve=True),
    )
  File "/root/verl/verl/utils/tracking.py", line 74, in __init__
    wandb.init(
    ~~~~~~~~~~^
        project=project_name,
        ^^^^^^^^^^^^^^^^^^^^^
    ...<4 lines>...
        resume=wandb_resume
        ^^^^^^^^^^^^^^^^^^^
    )
    ^
  File "/root/miniconda3/lib/python3.13/site-packages/wandb/sdk/wandb_init.py", line 1507, in init
    init_settings.run_id = id
    ^^^^^^^^^^^^^^^^^^^^
  File "/root/miniconda3/lib/python3.13/site-packages/pydantic/main.py", line 998, in __setattr__
    setattr_handler(self, name, value)  # call here to not memo on possibly unknown fields
    ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^
  File "/root/miniconda3/lib/python3.13/site-packages/pydantic/main.py", line 114, in <lambda>
    'validate_assignment': lambda model, name, val: model.__pydantic_validator__.validate_assignment(model, name, val),  # pyright: ignore[reportAssignmentType]
                                                    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^
pydantic_core._pydantic_core.ValidationError: 1 validation error for Settings
run_id
  Input should be a valid string [type=string_type, input_value=20251001065017, input_type=int]
    For further information visit https://errors.pydantic.dev/2.11/v/string_type

---

### 🤖 Cursor Assistant

Ah! I see the issue. The error shows that `wandb_run_id` is being parsed as an **integer** (`20251001065017`) instead of a **string**, which wandb requires.

## **The Problem**

When you pass the argument via command line:
```bash
trainer.wandb_run_id=20251001_065017
```

Hydra/OmegaConf parses numeric-looking values as integers, and the underscore might be getting stripped. WandB requires the `run_id` to be a string, causing the validation error:

```
ValidationError: 1 validation error for Settings
run_id
  Input should be a valid string [type=string_type, input_value=20251001065017, input_type=int]
```

## **Solution: Quote the Run ID**

You need to **quote** the run ID in your command line argument:

```bash
trainer.wandb_run_id="20251001_065017"
```

Or use single quotes:
```bash
trainer.wandb_run_id='20251001_065017'
```

## **Full Command with Fix**

Your command should be:

```bash
python -m verl.trainer.main_ppo \
  # ... all your other arguments ...
  trainer.wandb_run_id="20251001_065017" \
  trainer.wandb_resume=allow
```

## **Alternative: Set in Config File**

Instead of passing via command line, you can also set it in your config YAML file (which automatically preserves the string type):

```yaml
# In your config file
trainer:
  wandb_run_id: "20251001_065017"  # Note the quotes
  wandb_resume: "allow"
```

## **Quick Fix for Your Current Situation**

Based on your error, the run ID appears to be `20251001_065017`. Re-run your command with proper quoting:

---

### 🤖 Cursor Assistant

Let me also make the code more robust to handle this case automatically:

---

### 🤖 Cursor Assistant

Perfect! The linter warnings are just about optional dependencies that aren't installed in the local environment - these are fine since they're only imported when needed.

## **Summary: Fix for the Validation Error**

### **Root Cause**
The error occurred because `wandb_run_id` was being passed as an **integer** instead of a **string**. When you pass numeric-looking values via Hydra command line without quotes, they get parsed as numbers.

### **Two Solutions Applied**

#### **1. Code Fix (Already Done) ✅**
I've updated `tracking.py` to automatically convert the run ID to a string:
```python
if wandb_run_id is not None:
    wandb_run_id = str(wandb_run_id)
```

This makes the code more robust and will handle your case automatically.

#### **2. Command Line Fix (For You)**
When passing via command line, **always quote the run ID**:

```bash
# ❌ WRONG - will be parsed as integer
trainer.wandb_run_id=20251001_065017

# ✅ CORRECT - will be kept as string
trainer.wandb_run_id='"20251001_065017"'
```

### **Your Corrected Command**

Based on your error output, your run ID is `20251001_065017`. Re-run with:

```bash
python -m verl.trainer.main_ppo \
  algorithm.adv_estimator=grpo \
  algorithm.use_kl_in_reward=False \
  algorithm.kl_ctrl.kl_coef=0.0 \
  ... (all your other args) ... \
  trainer.resume_mode=auto \
  trainer.wandb_run_id='"20251001_065017"' \
  trainer.wandb_resume=allow \
  trainer.total_epochs=1
```

### **Verification**

With the updated code, both approaches will work:
1. **With quotes** (recommended): `trainer.wandb_run_id='"20251001_065017"'`
2. **Without quotes** (now works too): `trainer.wandb_run_id=20251001_065017` - the code will auto-convert

### **Quick Tips**

✅ **Use config file** (easiest and safest):
```yaml
trainer:
  wandb_run_id: "20251001_065017"  # Quotes preserved in YAML
  wandb_resume: "allow"
```

✅ **Check your actual run ID** from WandB dashboard if unsure:
- Go to: `https://wandb.ai/<entity>/boj_retool/runs/`
- Find your `qwen2.5-32b_dapo_with_tool` run
- Copy the run ID from the URL

The code is now more robust and should handle your case! Re-run your training command with the quoted run ID and it should work. 🚀

---

### 👤 You

(WorkerDict pid=3182754) /root/verl/verl/utils/profiler/config.py:49: UserWarning: Torch profiler tool config is not fully supported now. [repeated 7x across cluster]
(WorkerDict pid=3182754)   warnings.warn("Torch profiler tool config is not fully supported now.", stacklevel=1) [repeated 7x across cluster]
(TaskRunner pid=3173309) WARNING 10-06 08:40:11 [api_server.py:1285] LoRA dynamic loading & unloading is enabled in the API server. This should ONLY be used for local development!
(WorkerDict pid=3182761) [Gloo] Rank 3 is connected to 3 peer ranks. Expected number of connected peer ranks is : 3 [repeated 15x across cluster]
(pid=3184909) WARNING 10-06 08:40:19 [api_server.py:1285] LoRA dynamic loading & unloading is enabled in the API server. This should ONLY be used for local development!
(pid=3184910) WARNING 10-06 08:40:19 [api_server.py:1285] LoRA dynamic loading & unloading is enabled in the API server. This should ONLY be used for local development!
(vLLMHttpServer pid=3184910) INFO:2025-10-06 08:40:19,922:vLLMHttpServer, replica_rank: 1, master address: 172.17.0.2, master port: 33629, data parallel master port: 40197
(vLLMHttpServer pid=3184910) INFO:2025-10-06 08:40:19,930:override_generation_config: {'temperature': 1.0, 'top_k': -1, 'top_p': 1, 'repetition_penalty': 1.0, 'max_new_tokens': 10240}
(WorkerDict pid=3182757) /root/miniconda3/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:678: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html . [repeated 7x across cluster]
(WorkerDict pid=3182757)   warnings.warn( [repeated 7x across cluster]
(TaskRunner pid=3173309) /root/verl/verl/utils/profiler/config.py:49: UserWarning: Torch profiler tool config is not fully supported now.
(TaskRunner pid=3173309)   warnings.warn("Torch profiler tool config is not fully supported now.", stacklevel=1)
(vLLMHttpServer pid=3184910) Using blocking ray.get inside async actor. This blocks the event loop. Please use `await` on object ref with asyncio.gather if you want to yield execution to the event loop instead.
(vLLMHttpServer pid=3184910) INFO:2025-10-06 08:40:19,957:replica_rank=1, node_rank=0, nnodes=1, get worker zmq addresses: ['ipc:///tmp/verl_vllm_zmq_3182758_root.ipc', 'ipc:///tmp/verl_vllm_zmq_3182759_root.ipc', 'ipc:///tmp/verl_vllm_zmq_3182760_root.ipc', 'ipc:///tmp/verl_vllm_zmq_3182761_root.ipc']
(vLLMHttpServer pid=3184910) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored.
(vLLMHttpServer pid=3184909) ['serve',
(vLLMHttpServer pid=3184909)  '/root/verl/recipe/retool/checkpoint/multiturn-sft-qwen-2.5-32b-instruct/global_step_372/huggingface',
(vLLMHttpServer pid=3184909)  '--dtype',
(vLLMHttpServer pid=3184909)  'bfloat16',
(vLLMHttpServer pid=3184909)  '--load_format',
(vLLMHttpServer pid=3184909)  'dummy',
(vLLMHttpServer pid=3184909)  '--trust_remote_code',
(vLLMHttpServer pid=3184909)  '--max_model_len',
(vLLMHttpServer pid=3184909)  '12288',
(vLLMHttpServer pid=3184909)  '--max_num_seqs',
(vLLMHttpServer pid=3184909)  '1024',
(vLLMHttpServer pid=3184909)  '--enable_chunked_prefill',
(vLLMHttpServer pid=3184909)  '--max_num_batched_tokens',
(vLLMHttpServer pid=3184909)  '8192',
(vLLMHttpServer pid=3184909)  '--enable_prefix_caching',
(vLLMHttpServer pid=3184909)  '--enable_sleep_mode',
(vLLMHttpServer pid=3184909)  '--disable_custom_all_reduce',
(vLLMHttpServer pid=3184909)  '--gpu_memory_utilization',
(vLLMHttpServer pid=3184909)  '0.9',
(base) root@C.26356849:~/verl/scripts$ cd ..
(base) root@C.26356849:~/verl$ ls
(WorkerDict pid=3182754) [Gloo] Rank 0 is connected to 7 peer ranks. Expected number of connected peer ranks is : 7
(WorkerDict pid=3182754) WARNING 10-06 08:40:36 [topk_topp_sampler.py:69] FlashInfer is not available. Falling back to the PyTorch-native implementation of top-p & top-k sampling. For the best performance, please install FlashInfer.
(WorkerDict pid=3182758) NCCL version 2.27.3+cuda12.9
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):   0%|                                                                                                                                                        | 0/67 [00:00<?, ?it/s]
(WorkerDict pid=3182761) [rank7]:[W1006 08:40:33.953844110 ProcessGroupNCCL.cpp:981] Warning: TORCH_NCCL_AVOID_RECORD_STREAMS is the default now, this environment variable is thus deprecated. (function operator()) [repeated 7x across cluster]

--
during initialization, am I loading the latest checkpoint? How can I know if I resumed from last checkpoint?

---

### 🤖 Cursor Assistant

Looking at your logs, I can see the system is still in the **initialization phase** (loading vLLM, setting up workers). The checkpoint loading messages should appear **before** or **after** this initialization phase.

Let me help you identify what to look for:

## **What to Look for in the Logs**

### **1. Checkpoint Loading Messages (Should Appear Early)**

When `resume_mode=auto` successfully loads a checkpoint, you'll see these messages:

```bash
# These messages confirm checkpoint loading:
Found latest checkpoint: /root/verl/recipe/retool/checkpoint/qwen2.5-32b_dapo_with_tool/global_step_50 (step 50)
Load from checkpoint folder: /root/verl/recipe/retool/checkpoint/qwen2.5-32b_dapo_with_tool/global_step_50
Setting global step to 50
Resuming from /root/verl/recipe/retool/checkpoint/qwen2.5-32b_dapo_with_tool/global_step_50
```

### **2. What Your Current Logs Show**

Your logs show:
```
/root/verl/recipe/retool/checkpoint/multiturn-sft-qwen-2.5-32b-instruct/global_step_372/huggingface
```

This is the **initial model path** (the starting point for training), NOT the checkpoint resume path. This is different from your DAPO checkpoint at `checkpoint/qwen2.5-32b_dapo_with_tool/`.

## **How to Verify Checkpoint Loading**

---

### 🤖 Cursor Assistant

## **Commands to Run on Your Server**

### **Option 1: Verify Before Training**

Run this on your server to check if checkpoint will be loaded:

```bash
cd ~/verl/recipe/retool

# Check what checkpoint will be loaded
bash ~/verl/scripts/verify_checkpoint_loading.sh checkpoint/qwen2.5-32b_dapo_with_tool
```

Or manually:

```bash
# Check the latest checkpoint step
cat checkpoint/qwen2.5-32b_dapo_with_tool/latest_checkpointed_iteration.txt

# Should show: 50

# Verify that directory exists
ls -lh checkpoint/qwen2.5-32b_dapo_with_tool/global_step_50/
```

### **Option 2: Check Training Logs**

Once training starts, search your logs for checkpoint loading messages:

```bash
# If logs are being written to a file:
grep -i "checkpoint" <your_log_file>
grep -i "resuming" <your_log_file>
grep -i "global step" <your_log_file>

# Or watch the output in real-time:
# Look for these specific messages (they appear early in the logs)
```

## **Timeline of Messages**

Here's when each message appears during training startup:

```
1. [Early] Model & worker initialization (you're seeing this now)
   ├── vLLMHttpServer initialization
   ├── Worker setup
   └── NCCL initialization

2. [After initialization] Checkpoint loading (LOOK FOR THIS)
   ├── "Found latest checkpoint: .../global_step_50 (step 50)"
   ├── "Load from checkpoint folder: .../global_step_50"
   ├── "Setting global step to 50"
   └── "Resuming from .../global_step_50"

3. [Start training] Training loop begins
   ├── "Training from step 51..."
   └── First iteration metrics
```

## **Key Differences to Understand**

| Path in Your Logs | Purpose | What It Means |
|-------------------|---------|---------------|
| `/checkpoint/multiturn-sft-qwen-2.5-32b-instruct/global_step_372/` | **Initial model** | Starting point for training (specified in config) |
| `/checkpoint/qwen2.5-32b_dapo_with_tool/global_step_50/` | **Resume checkpoint** | Where to resume from (auto-detected) |

These are **two different things**:
- The first is your **base model** (what you're fine-tuning from)
- The second is your **training checkpoint** (where you left off)

## **What You Should See**

If checkpoint loading is working correctly, you'll see **BOTH** paths mentioned:

```bash
# 1. Loading initial model (for vLLM inference)
vLLMHttpServer: Loading model from /checkpoint/multiturn-sft-qwen-2.5-32b-instruct/global_step_372/

# 2. Loading checkpoint (for training resume) - THIS IS WHAT YOU NEED TO VERIFY
Found latest checkpoint: /checkpoint/qwen2.5-32b_dapo_with_tool/global_step_50 (step 50)
Resuming from /checkpoint/qwen2.5-32b_dapo_with_tool/global_step_50
Setting global step to 50
```

## **Quick Check: Is Your Config Correct?**

Verify your training command has the correct checkpoint directory:

```bash
# Your command should have:
trainer.default_local_dir=/root/verl/recipe/retool/checkpoint/qwen2.5-32b_dapo_with_tool

# NOT:
trainer.default_local_dir=/root/verl/recipe/retool/checkpoint/multiturn-sft-qwen-2.5-32b-instruct
```

## **What to Do Now**

1. **Wait for initialization to complete** - Your logs show the system is still setting up vLLM workers
2. **Watch for checkpoint messages** - They should appear after vLLM initialization completes
3. **If you don't see checkpoint messages** within 1-2 minutes after initialization:
   - The checkpoint path might be incorrect
   - Check your `default_local_dir` config
   - Run the verification script above

**Keep watching the logs!** The checkpoint loading messages should appear soon after the current initialization phase completes. If you don't see them, that's when you know there's an issue with the configuration.

---

### 👤 You

I did not find any of these.

    actor_rollout_ref.rollout.trace.backend=mlflow \
    actor_rollout_ref.rollout.trace.token2text=True \
    trainer.logger=['console','wandb'] \
    trainer.project_name=$project_name \
    trainer.experiment_name=$experiment_name \
    trainer.n_gpus_per_node=8 \
    trainer.val_before_train=True \
    trainer.log_val_generations=100 \
    trainer.nnodes=1 \
    trainer.save_freq=5 \
    trainer.default_local_dir=$default_local_dir \
    trainer.test_freq=5 \
    trainer.resume_mode=auto \
    trainer.wandb_run_id="20251001_065017" \
    trainer.wandb_resume=allow \
    trainer.total_epochs=1 $@


# tool
tool_config_path=/root/verl/recipe/retool/sandbox_fusion_tool_config.yaml

# wandb
project_name=boj_retool
experiment_name=qwen2.5-32b_dapo_with_tool
default_local_dir=/root/verl/recipe/retool/checkpoint/$experiment_name


(base) root@C.26356849:~/verl$ ls /root/verl/recipe/retool/checkpoint/qwen2.5-32b_dapo_with_tool/
global_step_20  global_step_30  global_step_40  global_step_50  latest_checkpointed_iteration.txt

---

### 🤖 Cursor Assistant

I see the issue! You have the correct configuration, but you're **not seeing the checkpoint loading messages**. Let me investigate why this might be happening.

---

### 🤖 Cursor Assistant

Ah! Now I see the problem. The checkpoint loading messages **are being printed**, but they might be:

1. **Hidden in the Ray actor logs** (prefixed with `(TaskRunner pid=3173309)`)
2. **Printed to a different stream** that you haven't searched yet
3. **Appearing earlier** in the logs before the vLLM initialization messages you showed

## **Where to Find the Messages**

The checkpoint loading happens on the **TaskRunner actor**, so search for messages with that prefix. Based on your earlier logs showing `(TaskRunner pid=3173309)`, search for:

```bash
# Search your logs for these patterns:
grep -i "TaskRunner.*checkpoint" your_log_file
grep -i "TaskRunner.*resuming" your_log_file
grep -i "TaskRunner.*global step" your_log_file

# Or just look at all TaskRunner messages:
grep "TaskRunner pid=" your_log_file | grep -i "checkpoint\|resuming\|scratch"
```

## **Expected Messages (One of These)**

You should find **ONE** of these sets of messages:

### **If checkpoint IS loaded:**
```
(TaskRunner pid=3173309) Found checkpoint: /root/verl/recipe/retool/checkpoint/qwen2.5-32b_dapo_with_tool/global_step_50
(TaskRunner pid=3173309) Load from checkpoint folder: /root/verl/recipe/retool/checkpoint/qwen2.5-32b_dapo_with_tool/global_step_50
(TaskRunner pid=3173309) Setting global step to 50
(TaskRunner pid=3173309) Resuming from /root/verl/recipe/retool/checkpoint/qwen2.5-32b_dapo_with_tool/global_step_50
```

### **If checkpoint is NOT found:**
```
(TaskRunner pid=3173309) Checkpoint tracker file does not exist: /root/verl/recipe/retool/checkpoint/qwen2.5-32b_dapo_with_tool/latest_checkpointed_iteration.txt
(TaskRunner pid=3173309) Training from scratch
```

## **Quick Debug: Check Your Server**

Run these commands on your server to verify:

```bash
# 1. Verify checkpoint directory and tracker file
cd /root/verl/recipe/retool
ls -lh checkpoint/qwen2.5-32b_dapo_with_tool/

# 2. Check tracker file content
cat checkpoint/qwen2.5-32b_dapo_with_tool/latest_checkpointed_iteration.txt
# Should output: 50

# 3. Verify that global_step_50 directory exists and has content
ls -lh checkpoint/qwen2.5-32b_dapo_with_tool/global_step_50/
# Should show: actor/, critic/, data.pt, and other files

# 4. Check specifically for actor checkpoint
ls -lh checkpoint/qwen2.5-32b_dapo_with_tool/global_step_50/actor/
```

## **If You Still Don't See the Messages**

If the messages aren't in your logs at all, it could mean:

1. **Training hasn't reached that point yet** - The checkpoint loading happens AFTER some initialization. Wait a bit longer.

2. **Logs are being written elsewhere** - Check if there's a separate log file:
   ```bash
   # Look for log files in your run directory
   ls -la /root/verl/recipe/retool/*.log
   ls -la /root/verl/recipe/retool/logs/
   ```

3. **The experiment name might not match** - Verify your `default_local_dir` resolves correctly:
   ```bash
   # In your script, these variables are set:
   experiment_name=qwen2.5-32b_dapo_with_tool
   default_local_dir=/root/verl/recipe/retool/checkpoint/$experiment_name
   
   # So it should be:
   echo /root/verl/recipe/retool/checkpoint/qwen2.5-32b_dapo_with_tool
   ```

## **Simple Test**

To quickly see if checkpoint loading works, add this to your training script to force more verbose output:

```bash
# Before running training, add debug output
echo "=== Checkpoint Configuration ==="
echo "Checkpoint dir: $default_local_dir"
ls -lh $default_local_dir/
cat $default_local_dir/latest_checkpointed_iteration.txt
echo "=== Starting Training ==="
```

**Can you:**
1. Run the commands above to verify the checkpoint structure exists?
2. Share the **very first** few lines of your training output (before all the vLLM initialization)?
3. Search your logs specifically for "Training from scratch" or "Found checkpoint"?

This will help us determine if the checkpoint is actually being loaded or not.

---


*Exported from [Cursor View](https://github.com/saharmor/cursor-view)*