# Cursor Chat: ai-agent-book

## Metadata
- **Project**: ai-agent-book
- **Path**: `/Users/boj`
- **Date**: 2025-10-01 09:34:35
- **Session ID**: `70e1011c-c412-4b82-b1bb-3de996a10afc`

## Conversation

### 👤 You

~/verl/recipe/retool$ bash run_qwen2-32b_sft.sh
+ nnodes=1
+ nproc_per_node=8
+ master_addr=
+ master_port=
+ experiment_name=multiturn-sft-qwen-2.5-32b-instruct
+ HDFS_ROOT=/root/verl/recipe/retool
+ DATA_ROOT=/root/verl/recipe/retool
+ TRAIN_DATA=/root/verl/recipe/retool/dataset/wuxibin/ReTool-SFT/data/train-00000-of-00001.parquet
+ EVAL_DATA=/root/verl/recipe/retool/dataset/wuxibin/ReTool-SFT/data/train-00000-of-00001.parquet
+ MODEL_PATH=/root/verl/recipe/retool/model/Qwen2.5-32B-Instruct
+ SAVE_PATH=/root/verl/recipe/retool/checkpoint/multiturn-sft-qwen-2.5-32b-instruct
+ torchrun --nnodes=1 --nproc_per_node=8 --master-addr= --master-port= --node-rank= -m verl.trainer.fsdp_sft_trainer data.train_files=/root/verl/recipe/retool/dataset/wuxibin/ReTool-SFT/data/train-00000-of-00001.parquet data.val_files=/root/verl/recipe/retool/dataset/wuxibin/ReTool-SFT/data/train-00000-of-00001.parquet data.max_length=16384 data.train_batch_size=32 data.multiturn.enable=true data.multiturn.messages_key=messages data.multiturn.tools_key=tools data.micro_batch_size_per_gpu=4 model.partial_pretrain=/root/verl/recipe/retool/model/Qwen2.5-32B-Instruct model.strategy=fsdp trainer.default_local_dir=/root/verl/recipe/retool/checkpoint/multiturn-sft-qwen-2.5-32b-instruct trainer.project_name=wuxibin-multiturn-sft trainer.experiment_name=multiturn-sft-qwen-2.5-32b-instruct 'trainer.logger=["console","wandb"]' trainer.total_epochs=6 ulysses_sequence_parallel_size=4 use_remove_padding=true
usage: torchrun [-h] [--nnodes NNODES] [--nproc-per-node NPROC_PER_NODE] [--rdzv-backend RDZV_BACKEND] [--rdzv-endpoint RDZV_ENDPOINT] [--rdzv-id RDZV_ID] [--rdzv-conf RDZV_CONF] [--standalone]
                [--max-restarts MAX_RESTARTS] [--monitor-interval MONITOR_INTERVAL] [--start-method {spawn,fork,forkserver}] [--event-log-handler EVENT_LOG_HANDLER] [--role ROLE] [-m] [--no-python] [--run-path]
                [--log-dir LOG_DIR] [-r REDIRECTS] [-t TEE] [--local-ranks-filter LOCAL_RANKS_FILTER] [--node-rank NODE_RANK] [--master-addr MASTER_ADDR] [--master-port MASTER_PORT] [--local-addr LOCAL_ADDR]
                [--logs-specs LOGS_SPECS]
                training_script ...
torchrun: error: argument --master-port/--master_port: invalid int value: ''

--
how to specify master port because I only have one 8 GPU server

---

### 🤖 Cursor Assistant

Looking at your script, the issue is that `master_port` is set to an empty string. For a single-node, multi-GPU setup (which you have with 8 GPUs on one server), you have several options:

## Option 1: Use --standalone flag (Recommended for single node)

Modify the torchrun command to use `--standalone` instead of `--master-addr` and `--master-port`:

```bash
torchrun --nnodes=1 --nproc_per_node=8 --standalone \
    -m verl.trainer.fsdp_sft_trainer \
    data.train_files=/root/verl/recipe/retool/dataset/wuxibin/ReTool-SFT/data/train-00000-of-00001.parquet \
    ...
```

## Option 2: Set a specific port number

In your script, change:
```bash
master_port=
```

To:
```bash
master_port=29500
```

Port 29500 is commonly used for PyTorch distributed training. You can use any available port between 1024-65535.

## Option 3: Remove master-addr and master-port entirely

For single-node training, torchrun can auto-configure. Remove those arguments:

```bash
torchrun --nnodes=1 --nproc_per_node=8 \
    -m verl.trainer.fsdp_sft_trainer \
    ...
```

**I recommend Option 1 (`--standalone`)** as it explicitly tells torchrun you're running on a single node, which is cleaner and avoids any networking confusion. This flag was specifically designed for single-machine multi-GPU scenarios like yours.

To implement this, edit your `run_qwen2-32b_sft.sh` script and either add the `--standalone` flag or set `master_port=29500`.

---

### 👤 You

/root/verl/recipe/retool
(base) root@C.26352433:~/verl/recipe/retool$ bash run_qwen2-32b_sft.sh
+ nnodes=1
+ nproc_per_node=8
+ experiment_name=multiturn-sft-qwen-2.5-32b-instruct
+ HDFS_ROOT=/root/verl/recipe/retool
+ DATA_ROOT=/root/verl/recipe/retool
+ TRAIN_DATA=/root/verl/recipe/retool/dataset/wuxibin/ReTool-SFT/data/train-00000-of-00001.parquet
+ EVAL_DATA=/root/verl/recipe/retool/dataset/wuxibin/ReTool-SFT/data/train-00000-of-00001.parquet
+ MODEL_PATH=/root/verl/recipe/retool/model/Qwen2.5-32B-Instruct
+ SAVE_PATH=/root/verl/recipe/retool/checkpoint/multiturn-sft-qwen-2.5-32b-instruct
+ torchrun --nnodes=1 --standalone --nproc_per_node=8 -m verl.trainer.fsdp_sft_trainer data.train_files=/root/verl/recipe/retool/dataset/wuxibin/ReTool-SFT/data/train-00000-of-00001.parquet data.val_files=/root/verl/recipe/retool/dataset/wuxibin/ReTool-SFT/data/train-00000-of-00001.parquet data.max_length=16384 data.train_batch_size=32 data.multiturn.enable=true data.multiturn.messages_key=messages data.multiturn.tools_key=tools data.micro_batch_size_per_gpu=4 model.partial_pretrain=/root/verl/recipe/retool/model/Qwen2.5-32B-Instruct model.strategy=fsdp trainer.default_local_dir=/root/verl/recipe/retool/checkpoint/multiturn-sft-qwen-2.5-32b-instruct trainer.project_name=wuxibin-multiturn-sft trainer.experiment_name=multiturn-sft-qwen-2.5-32b-instruct 'trainer.logger=["console","wandb"]' trainer.total_epochs=6 ulysses_sequence_parallel_size=4 use_remove_padding=true
W1001 02:49:48.200000 4430 site-packages/torch/distributed/run.py:774]
W1001 02:49:48.200000 4430 site-packages/torch/distributed/run.py:774] *****************************************
W1001 02:49:48.200000 4430 site-packages/torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
W1001 02:49:48.200000 4430 site-packages/torch/distributed/run.py:774] *****************************************
/root/verl/verl/utils/dataset/rl_dataset.py:46: SyntaxWarning: invalid escape sequence '\*'
  (batch_size, \*dims) and non-tensor entries are converted to
Error executing job with overrides: ['data.train_files=/root/verl/recipe/retool/dataset/wuxibin/ReTool-SFT/data/train-00000-of-00001.parquet', 'data.val_files=/root/verl/recipe/retool/dataset/wuxibin/ReTool-SFT/data/train-00000-of-00001.parquet', 'data.max_length=16384', 'data.train_batch_size=32', 'data.multiturn.enable=true', 'data.multiturn.messages_key=messages', 'data.multiturn.tools_key=tools', 'data.micro_batch_size_per_gpu=4', 'model.partial_pretrain=/root/verl/recipe/retool/model/Qwen2.5-32B-Instruct', 'model.strategy=fsdp', 'trainer.default_local_dir=/root/verl/recipe/retool/checkpoint/multiturn-sft-qwen-2.5-32b-instruct', 'trainer.project_name=wuxibin-multiturn-sft', 'trainer.experiment_name=multiturn-sft-qwen-2.5-32b-instruct', 'trainer.logger=["console","wandb"]', 'trainer.total_epochs=6', 'ulysses_sequence_parallel_size=4', 'use_remove_padding=true']
Traceback (most recent call last):
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/utils/hub.py", line 478, in cached_files
    hf_hub_download(
    ~~~~~~~~~~~~~~~^
        path_or_repo_id,
        ^^^^^^^^^^^^^^^^
    ...<10 lines>...
        local_files_only=local_files_only,
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    )
    ^
  File "/root/miniconda3/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py", line 106, in _inner_fn
    validate_repo_id(arg_value)
    ~~~~~~~~~~~~~~~~^^^^^^^^^^^
  File "/root/miniconda3/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py", line 154, in validate_repo_id
    raise HFValidationError(
    ...<2 lines>...
    )
huggingface_hub.errors.HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/root/verl/recipe/retool/model/Qwen2.5-32B-Instruct'. Use `repo_type` argument if needed.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/root/verl/verl/trainer/fsdp_sft_trainer.py", line 820, in main
    run_sft(config)
    ~~~~~~~^^^^^^^^
  File "/root/verl/verl/trainer/fsdp_sft_trainer.py", line 800, in run_sft
    tokenizer = hf_tokenizer(local_model_path, trust_remote_code=config.model.trust_remote_code)
  File "/root/verl/verl/utils/tokenizer.py", line 60, in hf_tokenizer
    tokenizer = AutoTokenizer.from_pretrained(name_or_path, **kwargs)
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/models/auto/tokenization_auto.py", line 1058, in from_pretrained
    tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs)
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/models/auto/tokenization_auto.py", line 890, in get_tokenizer_config
    resolved_config_file = cached_file(
        pretrained_model_name_or_path,
    ...<12 lines>...
        _commit_hash=commit_hash,
    )
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/utils/hub.py", line 321, in cached_file
    file = cached_files(path_or_repo_id=path_or_repo_id, filenames=[filename], **kwargs)
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/utils/hub.py", line 531, in cached_files
    _get_cache_file_to_return(path_or_repo_id, filename, cache_dir, revision, repo_type)
    ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/utils/hub.py", line 144, in _get_cache_file_to_return
    resolved_file = try_to_load_from_cache(
        path_or_repo_id, full_filename, cache_dir=cache_dir, revision=revision, repo_type=repo_type
    )
  File "/root/miniconda3/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py", line 106, in _inner_fn
    validate_repo_id(arg_value)
    ~~~~~~~~~~~~~~~~^^^^^^^^^^^
  File "/root/miniconda3/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py", line 154, in validate_repo_id
    raise HFValidationError(
    ...<2 lines>...
    )
huggingface_hub.errors.HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/root/verl/recipe/retool/model/Qwen2.5-32B-Instruct'. Use `repo_type` argument if needed.

Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
Error executing job with overrides: ['data.train_files=/root/verl/recipe/retool/dataset/wuxibin/ReTool-SFT/data/train-00000-of-00001.parquet', 'data.val_files=/root/verl/recipe/retool/dataset/wuxibin/ReTool-SFT/data/train-00000-of-00001.parquet', 'data.max_length=16384', 'data.train_batch_size=32', 'data.multiturn.enable=true', 'data.multiturn.messages_key=messages', 'data.multiturn.tools_key=tools', 'data.micro_batch_size_per_gpu=4', 'model.partial_pretrain=/root/verl/recipe/retool/model/Qwen2.5-32B-Instruct', 'model.strategy=fsdp', 'trainer.default_local_dir=/root/verl/recipe/retool/checkpoint/multiturn-sft-qwen-2.5-32b-instruct', 'trainer.project_name=wuxibin-multiturn-sft', 'trainer.experiment_name=multiturn-sft-qwen-2.5-32b-instruct', 'trainer.logger=["console","wandb"]', 'trainer.total_epochs=6', 'ulysses_sequence_parallel_size=4', 'use_remove_padding=true']
Traceback (most recent call last):
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/utils/hub.py", line 478, in cached_files
    hf_hub_download(
    ~~~~~~~~~~~~~~~^
        path_or_repo_id,
        ^^^^^^^^^^^^^^^^
    ...<10 lines>...
        local_files_only=local_files_only,
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    )
    ^
  File "/root/miniconda3/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py", line 106, in _inner_fn
    validate_repo_id(arg_value)
    ~~~~~~~~~~~~~~~~^^^^^^^^^^^
  File "/root/miniconda3/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py", line 154, in validate_repo_id
    raise HFValidationError(
    ...<2 lines>...
    )
huggingface_hub.errors.HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/root/verl/recipe/retool/model/Qwen2.5-32B-Instruct'. Use `repo_type` argument if needed.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/root/verl/verl/trainer/fsdp_sft_trainer.py", line 820, in main
    run_sft(config)
    ~~~~~~~^^^^^^^^
  File "/root/verl/verl/trainer/fsdp_sft_trainer.py", line 800, in run_sft
    tokenizer = hf_tokenizer(local_model_path, trust_remote_code=config.model.trust_remote_code)
  File "/root/verl/verl/utils/tokenizer.py", line 60, in hf_tokenizer
    tokenizer = AutoTokenizer.from_pretrained(name_or_path, **kwargs)
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/models/auto/tokenization_auto.py", line 1058, in from_pretrained
    tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs)
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/models/auto/tokenization_auto.py", line 890, in get_tokenizer_config
    resolved_config_file = cached_file(
        pretrained_model_name_or_path,
    ...<12 lines>...
        _commit_hash=commit_hash,
    )
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/utils/hub.py", line 321, in cached_file
    file = cached_files(path_or_repo_id=path_or_repo_id, filenames=[filename], **kwargs)
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/utils/hub.py", line 531, in cached_files
    _get_cache_file_to_return(path_or_repo_id, filename, cache_dir, revision, repo_type)
    ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/utils/hub.py", line 144, in _get_cache_file_to_return
    resolved_file = try_to_load_from_cache(
        path_or_repo_id, full_filename, cache_dir=cache_dir, revision=revision, repo_type=repo_type
    )
  File "/root/miniconda3/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py", line 106, in _inner_fn
    validate_repo_id(arg_value)
    ~~~~~~~~~~~~~~~~^^^^^^^^^^^
  File "/root/miniconda3/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py", line 154, in validate_repo_id
    raise HFValidationError(
    ...<2 lines>...
    )
huggingface_hub.errors.HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/root/verl/recipe/retool/model/Qwen2.5-32B-Instruct'. Use `repo_type` argument if needed.

---

### 🤖 Cursor Assistant

Good progress! The `--standalone` flag worked. Now you have a different issue: the model files don't exist at the specified path `/root/verl/recipe/retool/model/Qwen2.5-32B-Instruct`.

The error indicates that HuggingFace's tokenizer is trying to load from a local path that doesn't contain the model files. You need to download the Qwen2.5-32B-Instruct model first.

## Solution: Download the Model

You have two options:

### Option 1: Download the model locally (Recommended for repeated training runs)

```bash
# Create the model directory
mkdir -p /root/verl/recipe/retool/model

# Download the model using huggingface-cli
huggingface-cli download Qwen/Qwen2.5-32B-Instruct \
  --local-dir /root/verl/recipe/retool/model/Qwen2.5-32B-Instruct \
  --local-dir-use-symlinks False
```

This downloads the model once and reuses it for all training runs.

### Option 2: Use the HuggingFace repo ID directly

Modify your script to use the HuggingFace repo ID instead of a local path:

```bash
MODEL_PATH=Qwen/Qwen2.5-32B-Instruct
```

This will download the model automatically on first run (it caches to `~/.cache/huggingface/`).

## Additional Notes

1. **Model size**: Qwen2.5-32B-Instruct is approximately 65GB. Ensure you have sufficient disk space.

2. **Authentication**: If the model requires authentication, login first:
   ```bash
   huggingface-cli login
   ```

3. **Check if model exists**: Before running training, verify the model files are present:
   ```bash
   ls -la /root/verl/recipe/retool/model/Qwen2.5-32B-Instruct/
   ```
   You should see files like `config.json`, `tokenizer_config.json`, and model weight files.

I recommend **Option 1** since you'll likely run multiple training experiments and don't want to re-download the model each time.

---

### 👤 You

~/verl/recipe/retool$ bash run_qwen2-32b_sft.sh
+ nnodes=1
+ nproc_per_node=8
+ experiment_name=multiturn-sft-qwen-2.5-32b-instruct
+ HDFS_ROOT=/root/verl/recipe/retool
+ DATA_ROOT=/root/verl/recipe/retool
+ TRAIN_DATA=/root/ReTool-SFT/data/train-00000-of-00001.parquet
+ EVAL_DATA=/root/ReTool-SFT/data/train-00000-of-00001.parquet
+ MODEL_PATH=/root/verl/recipe/retool/model/Qwen2.5-32B-Instruct
+ SAVE_PATH=/root/verl/recipe/retool/checkpoint/multiturn-sft-qwen-2.5-32b-instruct
+ torchrun --nnodes=1 --standalone --nproc_per_node=8 -m verl.trainer.fsdp_sft_trainer data.train_files=/root/ReTool-SFT/data/train-00000-of-00001.parquet data.val_files=/root/ReTool-SFT/data/train-00000-of-00001.parquet data.max_length=16384 data.train_batch_size=32 data.multiturn.enable=true data.multiturn.messages_key=messages data.multiturn.tools_key=tools data.micro_batch_size_per_gpu=4 model.partial_pretrain=/root/verl/recipe/retool/model/Qwen2.5-32B-Instruct model.strategy=fsdp trainer.default_local_dir=/root/verl/recipe/retool/checkpoint/multiturn-sft-qwen-2.5-32b-instruct trainer.project_name=wuxibin-multiturn-sft trainer.experiment_name=multiturn-sft-qwen-2.5-32b-instruct 'trainer.logger=["console","wandb"]' trainer.total_epochs=6 ulysses_sequence_parallel_size=4 use_remove_padding=true
W1001 02:59:30.725000 4957 site-packages/torch/distributed/run.py:774]
W1001 02:59:30.725000 4957 site-packages/torch/distributed/run.py:774] *****************************************
W1001 02:59:30.725000 4957 site-packages/torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
W1001 02:59:30.725000 4957 site-packages/torch/distributed/run.py:774] *****************************************
`torch_dtype` is deprecated! Use `dtype` instead!
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen2ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Error executing job with overrides: ['data.train_files=/root/ReTool-SFT/data/train-00000-of-00001.parquet', 'data.val_files=/root/ReTool-SFT/data/train-00000-of-00001.parquet', 'data.max_length=16384', 'data.train_batch_size=32', 'data.multiturn.enable=true', 'data.multiturn.messages_key=messages', 'data.multiturn.tools_key=tools', 'data.micro_batch_size_per_gpu=4', 'model.partial_pretrain=/root/verl/recipe/retool/model/Qwen2.5-32B-Instruct', 'model.strategy=fsdp', 'trainer.default_local_dir=/root/verl/recipe/retool/checkpoint/multiturn-sft-qwen-2.5-32b-instruct', 'trainer.project_name=wuxibin-multiturn-sft', 'trainer.experiment_name=multiturn-sft-qwen-2.5-32b-instruct', 'trainer.logger=["console","wandb"]', 'trainer.total_epochs=6', 'ulysses_sequence_parallel_size=4', 'use_remove_padding=true']
Traceback (most recent call last):
  File "/root/verl/verl/trainer/fsdp_sft_trainer.py", line 820, in main
    run_sft(config)
    ~~~~~~~^^^^^^^^
  File "/root/verl/verl/trainer/fsdp_sft_trainer.py", line 804, in run_sft
    trainer = FSDPSFTTrainer(
        config=config,
    ...<4 lines>...
        val_dataset=val_dataset,
    )
  File "/root/verl/verl/trainer/fsdp_sft_trainer.py", line 123, in __init__
    self._build_model_optimizer()
    ~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
  File "/root/verl/verl/trainer/fsdp_sft_trainer.py", line 230, in _build_model_optimizer
    self.model: PreTrainedModel = AutoModelForCausalLM.from_pretrained(
                                  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^
        local_model_path,
        ^^^^^^^^^^^^^^^^^
    ...<3 lines>...
        trust_remote_code=trust_remote_code,
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    )
    ^
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/models/auto/auto_factory.py", line 604, in from_pretrained
    return model_class.from_pretrained(
           ~~~~~~~~~~~~~~~~~~~~~~~~~~~^
        pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    )
    ^
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/modeling_utils.py", line 288, in _wrapper
    return func(*args, **kwargs)
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/modeling_utils.py", line 5106, in from_pretrained
    model = cls(config, *model_args, **model_kwargs)
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/models/qwen2/modeling_qwen2.py", line 409, in __init__
    super().__init__(config)
    ~~~~~~~~~~~~~~~~^^^^^^^^
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/modeling_utils.py", line 2197, in __init__
    self.config._attn_implementation_internal = self._check_and_adjust_attn_implementation(
                                                ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^
        self.config._attn_implementation, is_init_check=True
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    )
    ^
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/modeling_utils.py", line 2812, in _check_and_adjust_attn_implementation
    lazy_import_flash_attention(applicable_attn_implementation)
    ~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/modeling_flash_attention_utils.py", line 136, in lazy_import_flash_attention
    _flash_fn, _flash_varlen_fn, _pad_fn, _unpad_fn = _lazy_imports(implementation)
                                                      ~~~~~~~~~~~~~^^^^^^^^^^^^^^^^
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/modeling_flash_attention_utils.py", line 83, in _lazy_imports
    from flash_attn import flash_attn_func, flash_attn_varlen_func
  File "/root/miniconda3/lib/python3.13/site-packages/flash_attn/__init__.py", line 3, in <module>
    from flash_attn.flash_attn_interface import (
    ...<7 lines>...
    )
  File "/root/miniconda3/lib/python3.13/site-packages/flash_attn/flash_attn_interface.py", line 15, in <module>
    import flash_attn_2_cuda as flash_attn_gpu
ImportError: /lib/x86_64-linux-gnu/libc.so.6: version `GLIBC_2.32' not found (required by /root/miniconda3/lib/python3.13/site-packages/flash_attn_2_cuda.cpython-313-x86_64-linux-gnu.so)

Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
W1001 02:59:41.119000 4957 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 4961 closing signal SIGTERM

---

### 🤖 Cursor Assistant

The model loaded successfully, but now there's a compatibility issue with Flash Attention. The `flash_attn_2_cuda` module requires GLIBC 2.32, but your system has an older version.

## Solutions (in order of preference):

### Option 1: Disable Flash Attention (Easiest)

Add this parameter to your torchrun command to use standard attention instead:

```bash
model.attn_implementation=eager
```

Or use `sdpa` (Scaled Dot Product Attention, PyTorch's optimized attention):

```bash
model.attn_implementation=sdpa
```

Your modified command would be:

```bash
torchrun --nnodes=1 --standalone --nproc_per_node=8 \
  -m verl.trainer.fsdp_sft_trainer \
  data.train_files=/root/ReTool-SFT/data/train-00000-of-00001.parquet \
  data.val_files=/root/ReTool-SFT/data/train-00000-of-00001.parquet \
  data.max_length=16384 \
  data.train_batch_size=32 \
  data.multiturn.enable=true \
  data.multiturn.messages_key=messages \
  data.multiturn.tools_key=tools \
  data.micro_batch_size_per_gpu=4 \
  model.partial_pretrain=/root/verl/recipe/retool/model/Qwen2.5-32B-Instruct \
  model.strategy=fsdp \
  model.attn_implementation=sdpa \
  trainer.default_local_dir=/root/verl/recipe/retool/checkpoint/multiturn-sft-qwen-2.5-32b-instruct \
  trainer.project_name=wuxibin-multiturn-sft \
  trainer.experiment_name=multiturn-sft-qwen-2.5-32b-instruct \
  'trainer.logger=["console","wandb"]' \
  trainer.total_epochs=6 \
  ulysses_sequence_parallel_size=4 \
  use_remove_padding=true
```

### Option 2: Reinstall flash-attn for your system

If you want to use Flash Attention, rebuild it from source:

```bash
pip uninstall flash-attn -y
pip install flash-attn --no-build-isolation
```

This compiles flash-attn specifically for your system's GLIBC version.

### Option 3: Check your GLIBC version

To see what version you have:

```bash
ldd --version
```

If it's significantly old (< 2.32), you might need to use a Docker container with a newer base image like Ubuntu 22.04 or later.

**I recommend Option 1 with `model.attn_implementation=sdpa`**. SDPA is PyTorch's built-in optimized attention that's nearly as fast as Flash Attention 2 but doesn't require external dependencies. It will work immediately and allow your training to proceed.

---

### 👤 You

$ bash run_qwen2-32b_sft.sh
+ nnodes=1
+ nproc_per_node=8
+ experiment_name=multiturn-sft-qwen-2.5-32b-instruct
+ HDFS_ROOT=/root/verl/recipe/retool
+ DATA_ROOT=/root/verl/recipe/retool
+ TRAIN_DATA=/root/ReTool-SFT/data/train-00000-of-00001.parquet
+ EVAL_DATA=/root/ReTool-SFT/data/train-00000-of-00001.parquet
+ MODEL_PATH=/root/verl/recipe/retool/model/Qwen2.5-32B-Instruct
+ SAVE_PATH=/root/verl/recipe/retool/checkpoint/multiturn-sft-qwen-2.5-32b-instruct
+ torchrun --nnodes=1 --standalone --nproc_per_node=8 -m verl.trainer.fsdp_sft_trainer data.train_files=/root/ReTool-SFT/data/train-00000-of-00001.parquet data.val_files=/root/ReTool-SFT/data/train-00000-of-00001.parquet data.max_length=16384 data.train_batch_size=32 data.multiturn.enable=true data.multiturn.messages_key=messages data.multiturn.tools_key=tools data.micro_batch_size_per_gpu=4 model.attn_implementation=sdpa model.partial_pretrain=/root/verl/recipe/retool/model/Qwen2.5-32B-Instruct model.strategy=fsdp trainer.default_local_dir=/root/verl/recipe/retool/checkpoint/multiturn-sft-qwen-2.5-32b-instruct trainer.project_name=boj-multiturn-sft trainer.experiment_name=multiturn-sft-qwen-2.5-32b-instruct 'trainer.logger=["console","wandb"]' trainer.total_epochs=6 ulysses_sequence_parallel_size=4 use_remove_padding=true
W1001 03:01:35.996000 5052 site-packages/torch/distributed/run.py:774]
W1001 03:01:35.996000 5052 site-packages/torch/distributed/run.py:774] *****************************************
W1001 03:01:35.996000 5052 site-packages/torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
W1001 03:01:35.996000 5052 site-packages/torch/distributed/run.py:774] *****************************************
Could not override 'model.attn_implementation'.
To append to your config use +model.attn_implementation=sdpa
Key 'attn_implementation' is not in struct
    full_key: model.attn_implementation
    object_type=dict

Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
Could not override 'model.attn_implementation'.
To append to your config use +model.attn_implementation=sdpa
Key 'attn_implementation' is not in struct
    full_key: model.attn_implementation
    object_type=dict

Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
Could not override 'model.attn_implementation'.
To append to your config use +model.attn_implementation=sdpa
Key 'attn_implementation' is not in struct
    full_key: model.attn_implementation
    object_type=dict

Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
Could not override 'model.attn_implementation'.
To append to your config use +model.attn_implementation=sdpa
Key 'attn_implementation' is not in struct
    full_key: model.attn_implementation
    object_type=dict

---

### 🤖 Cursor Assistant

Perfect! The error message is actually quite helpful. Since `attn_implementation` isn't a predefined key in the Hydra configuration, you need to use the `+` prefix to add it:

Change:
```bash
model.attn_implementation=sdpa
```

To:
```bash
+model.attn_implementation=sdpa
```

The `+` tells Hydra to append a new key rather than override an existing one.

Your full command should be:

```bash
torchrun --nnodes=1 --standalone --nproc_per_node=8 \
  -m verl.trainer.fsdp_sft_trainer \
  data.train_files=/root/ReTool-SFT/data/train-00000-of-00001.parquet \
  data.val_files=/root/ReTool-SFT/data/train-00000-of-00001.parquet \
  data.max_length=16384 \
  data.train_batch_size=32 \
  data.multiturn.enable=true \
  data.multiturn.messages_key=messages \
  data.multiturn.tools_key=tools \
  data.micro_batch_size_per_gpu=4 \
  +model.attn_implementation=sdpa \
  model.partial_pretrain=/root/verl/recipe/retool/model/Qwen2.5-32B-Instruct \
  model.strategy=fsdp \
  trainer.default_local_dir=/root/verl/recipe/retool/checkpoint/multiturn-sft-qwen-2.5-32b-instruct \
  trainer.project_name=boj-multiturn-sft \
  trainer.experiment_name=multiturn-sft-qwen-2.5-32b-instruct \
  'trainer.logger=["console","wandb"]' \
  trainer.total_epochs=6 \
  ulysses_sequence_parallel_size=4 \
  use_remove_padding=true
```

Just update your script with the `+` prefix before `model.attn_implementation`.

---

### 👤 You

Seems we must use flash attention 2. How to install the glibc lib? I'm on Ubuntu 20.04


  File "/root/miniconda3/lib/python3.13/site-packages/transformers/modeling_utils.py", line 2197, in __init__
    self.config._attn_implementation_internal = self._check_and_adjust_attn_implementation(
                                                ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^
        self.config._attn_implementation, is_init_check=True
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    )
    ^
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/modeling_utils.py", line 2812, in _check_and_adjust_attn_implementation
    lazy_import_flash_attention(applicable_attn_implementation)
    ~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/modeling_flash_attention_utils.py", line 136, in lazy_import_flash_attention
    _flash_fn, _flash_varlen_fn, _pad_fn, _unpad_fn = _lazy_imports(implementation)
                                                      ~~~~~~~~~~~~~^^^^^^^^^^^^^^^^
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/modeling_flash_attention_utils.py", line 83, in _lazy_imports
    from flash_attn import flash_attn_func, flash_attn_varlen_func
  File "/root/miniconda3/lib/python3.13/site-packages/flash_attn/__init__.py", line 3, in <module>
    from flash_attn.flash_attn_interface import (
    ...<7 lines>...
    )
  File "/root/miniconda3/lib/python3.13/site-packages/flash_attn/flash_attn_interface.py", line 15, in <module>
    import flash_attn_2_cuda as flash_attn_gpu
ImportError: /lib/x86_64-linux-gnu/libc.so.6: version `GLIBC_2.32' not found (required by /root/miniconda3/lib/python3.13/site-packages/flash_attn_2_cuda.cpython-313-x86_64-linux-gnu.so)

---

### 🤖 Cursor Assistant

Ubuntu 20.04 ships with GLIBC 2.31, but the precompiled `flash-attn` package requires GLIBC 2.32. **Do NOT attempt to manually upgrade GLIBC** - it's a core system library and upgrading it can completely break your system.

Here are your viable options:

## Option 1: Rebuild flash-attn from source (Recommended)

This compiles flash-attn against your system's GLIBC 2.31:

```bash
# Uninstall the precompiled version
pip uninstall flash-attn -y

# Install from source (this will take 5-10 minutes)
pip install flash-attn --no-build-isolation

# You may need these dependencies first:
pip install ninja packaging wheel torch
```

The `--no-build-isolation` flag ensures it uses your environment's CUDA and compiles against your system's GLIBC.

**Requirements for compilation:**
- CUDA toolkit installed (check with `nvcc --version`)
- GCC compiler (check with `gcc --version`)
- Sufficient disk space (~2GB for build artifacts)

## Option 2: Use a Docker container with Ubuntu 22.04

Ubuntu 22.04 has GLIBC 2.35, which is compatible. Example Dockerfile:

```dockerfile
FROM nvidia/cuda:12.1.0-devel-ubuntu22.04

RUN apt-get update && apt-get install -y python3 python3-pip
# ... install your dependencies
```

## Option 3: Upgrade to Ubuntu 22.04

If this is a dedicated training server:

```bash
# Backup first!
sudo do-release-upgrade
```

Ubuntu 22.04 LTS has GLIBC 2.35 and is the current LTS release with support until 2027.

---

**I strongly recommend Option 1** (rebuild from source). It's the quickest solution that doesn't require system changes. The compilation process will automatically target your system's GLIBC version.

If compilation fails due to missing dependencies, let me know the error message and I can help troubleshoot.

---

### 👤 You

~/verl/recipe/retool$ pip install flash-attn --no-build-isolation
Collecting flash-attn
  Using cached flash_attn-2.8.3-cp313-cp313-linux_x86_64.whl
Requirement already satisfied: torch in /root/miniconda3/lib/python3.13/site-packages (from flash-attn) (2.8.0)
Requirement already satisfied: einops in /root/miniconda3/lib/python3.13/site-packages (from flash-attn) (0.8.1)
Requirement already satisfied: filelock in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (3.19.1)
Requirement already satisfied: typing-extensions>=4.10.0 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (4.12.2)
Requirement already satisfied: setuptools in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (78.1.1)
Requirement already satisfied: sympy>=1.13.3 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (1.14.0)
Requirement already satisfied: networkx in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (3.5)
Requirement already satisfied: jinja2 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (3.1.6)
Requirement already satisfied: fsspec in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (2025.9.0)
Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.8.93 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (12.8.93)
Requirement already satisfied: nvidia-cuda-runtime-cu12==12.8.90 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (12.8.90)
Requirement already satisfied: nvidia-cuda-cupti-cu12==12.8.90 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (12.8.90)
Requirement already satisfied: nvidia-cudnn-cu12==9.10.2.21 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (9.10.2.21)
Requirement already satisfied: nvidia-cublas-cu12==12.8.4.1 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (12.8.4.1)
Requirement already satisfied: nvidia-cufft-cu12==11.3.3.83 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (11.3.3.83)
Requirement already satisfied: nvidia-curand-cu12==10.3.9.90 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (10.3.9.90)
Requirement already satisfied: nvidia-cusolver-cu12==11.7.3.90 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (11.7.3.90)
Requirement already satisfied: nvidia-cusparse-cu12==12.5.8.93 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (12.5.8.93)
Requirement already satisfied: nvidia-cusparselt-cu12==0.7.1 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (0.7.1)
Requirement already satisfied: nvidia-nccl-cu12==2.27.3 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (2.27.3)
Requirement already satisfied: nvidia-nvtx-cu12==12.8.90 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (12.8.90)
Requirement already satisfied: nvidia-nvjitlink-cu12==12.8.93 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (12.8.93)
Requirement already satisfied: nvidia-cufile-cu12==1.13.1.3 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (1.13.1.3)
Requirement already satisfied: triton==3.4.0 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (3.4.0)
Requirement already satisfied: mpmath<1.4,>=1.1.0 in /root/miniconda3/lib/python3.13/site-packages (from sympy>=1.13.3->torch->flash-attn) (1.3.0)
Requirement already satisfied: MarkupSafe>=2.0 in /root/miniconda3/lib/python3.13/site-packages (from jinja2->torch->flash-attn) (3.0.3)
Installing collected packages: flash-attn
Successfully installed flash-attn-2.8.3

--
It still used cached flash attn

---

### 🤖 Cursor Assistant

You're correct - it's still using the precompiled wheel from cache. You need to force pip to build from source by disabling the use of binary wheels:

```bash
pip uninstall flash-attn -y
pip install flash-attn --no-binary flash-attn --no-build-isolation
```

The `--no-binary flash-attn` flag tells pip to ignore all precompiled wheels for flash-attn and build from source instead.

**This will take 5-10 minutes to compile.** You should see output like:

```
Collecting flash-attn
  Downloading flash-attn-2.8.3.tar.gz
  Installing build dependencies...
Building wheels for collected packages: flash-attn
  Building wheel for flash-attn (setup.py) ... 
```

If you get compilation errors, you may need to ensure you have the CUDA development toolkit:

```bash
# Check CUDA is available
nvcc --version

# If nvcc is not found, you may need to set CUDA_HOME
export CUDA_HOME=/usr/local/cuda
export PATH=$CUDA_HOME/bin:$PATH
```

Alternatively, if you want to avoid the compilation time and potential issues, consider using a Docker container with Ubuntu 22.04 that has compatible GLIBC. Many ML training setups use Docker containers specifically to avoid these system dependency conflicts.

---

### 👤 You

~/verl/recipe/retool$ pip install flash-attn --no-binary flash-attn --no-build-isolation
Collecting flash-attn
  Using cached flash_attn-2.8.3-cp313-cp313-linux_x86_64.whl
Requirement already satisfied: torch in /root/miniconda3/lib/python3.13/site-packages (from flash-attn) (2.8.0)
Requirement already satisfied: einops in /root/miniconda3/lib/python3.13/site-packages (from flash-attn) (0.8.1)
Requirement already satisfied: filelock in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (3.19.1)
Requirement already satisfied: typing-extensions>=4.10.0 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (4.12.2)
Requirement already satisfied: setuptools in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (78.1.1)
Requirement already satisfied: sympy>=1.13.3 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (1.14.0)
Requirement already satisfied: networkx in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (3.5)
Requirement already satisfied: jinja2 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (3.1.6)
Requirement already satisfied: fsspec in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (2025.9.0)
Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.8.93 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (12.8.93)
Requirement already satisfied: nvidia-cuda-runtime-cu12==12.8.90 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (12.8.90)
Requirement already satisfied: nvidia-cuda-cupti-cu12==12.8.90 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (12.8.90)
Requirement already satisfied: nvidia-cudnn-cu12==9.10.2.21 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (9.10.2.21)
Requirement already satisfied: nvidia-cublas-cu12==12.8.4.1 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (12.8.4.1)
Requirement already satisfied: nvidia-cufft-cu12==11.3.3.83 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (11.3.3.83)
Requirement already satisfied: nvidia-curand-cu12==10.3.9.90 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (10.3.9.90)
Requirement already satisfied: nvidia-cusolver-cu12==11.7.3.90 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (11.7.3.90)
Requirement already satisfied: nvidia-cusparse-cu12==12.5.8.93 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (12.5.8.93)
Requirement already satisfied: nvidia-cusparselt-cu12==0.7.1 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (0.7.1)
Requirement already satisfied: nvidia-nccl-cu12==2.27.3 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (2.27.3)
Requirement already satisfied: nvidia-nvtx-cu12==12.8.90 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (12.8.90)
Requirement already satisfied: nvidia-nvjitlink-cu12==12.8.93 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (12.8.93)
Requirement already satisfied: nvidia-cufile-cu12==1.13.1.3 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (1.13.1.3)
Requirement already satisfied: triton==3.4.0 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash-attn) (3.4.0)
Requirement already satisfied: mpmath<1.4,>=1.1.0 in /root/miniconda3/lib/python3.13/site-packages (from sympy>=1.13.3->torch->flash-attn) (1.3.0)
Requirement already satisfied: MarkupSafe>=2.0 in /root/miniconda3/lib/python3.13/site-packages (from jinja2->torch->flash-attn) (3.0.3)
Installing collected packages: flash-attn
Successfully installed flash-attn-2.8.3
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.

---

### 🤖 Cursor Assistant

It's still pulling from cache. Clear the pip cache first:

```bash
pip cache remove flash-attn
pip uninstall flash-attn -y
pip install flash-attn --no-binary flash-attn --no-build-isolation
```

Or, install directly from the GitHub source to bypass cache entirely:

```bash
pip uninstall flash-attn -y
pip install git+https://github.com/Dao-AILab/flash-attention.git@v2.8.3 --no-build-isolation
```

The second approach (installing from GitHub) guarantees a fresh build from source and avoids any cached wheels.

**Alternative**: If you want to proceed quickly without dealing with compilation, you can also try using an unofficial build or a different approach:

1. **Use a Docker container** with Ubuntu 22.04 or newer (has GLIBC 2.35):
   ```bash
   docker run --gpus all -it --rm pytorch/pytorch:2.8.0-cuda12.1-cudnn9-devel bash
   ```

2. **Use conda-forge build** (sometimes has different binaries):
   ```bash
   conda install -c conda-forge flash-attn
   ```

Given the complexity, **I recommend the Docker approach** for production training workloads. It ensures a consistent, reproducible environment and avoids system-level dependency conflicts entirely. Most serious ML training setups use containerized environments for exactly this reason.

---

### 👤 You

(base) root@C.26352433:~/verl/recipe/retool$ pip uninstall flash-attn -y
Found existing installation: flash_attn 2.8.3
Uninstalling flash_attn-2.8.3:
  Successfully uninstalled flash_attn-2.8.3
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.
(base) root@C.26352433:~/verl/recipe/retool$ pip install git+https://github.com/Dao-AILab/flash-attention.git@v2.8.3 --no-build-isolation
Collecting git+https://github.com/Dao-AILab/flash-attention.git@v2.8.3
  Cloning https://github.com/Dao-AILab/flash-attention.git (to revision v2.8.3) to /tmp/pip-req-build-ztlzwzpw
  Running command git clone --filter=blob:none --quiet https://github.com/Dao-AILab/flash-attention.git /tmp/pip-req-build-ztlzwzpw
  Running command git checkout -q 060c9188beec3a8b62b33a3bfa6d5d2d44975fab
  Resolved https://github.com/Dao-AILab/flash-attention.git to commit 060c9188beec3a8b62b33a3bfa6d5d2d44975fab
  Running command git submodule update --init --recursive -q
  Preparing metadata (setup.py) ... done
Requirement already satisfied: torch in /root/miniconda3/lib/python3.13/site-packages (from flash_attn==2.8.3) (2.8.0)
Requirement already satisfied: einops in /root/miniconda3/lib/python3.13/site-packages (from flash_attn==2.8.3) (0.8.1)
Requirement already satisfied: filelock in /root/miniconda3/lib/python3.13/site-packages (from torch->flash_attn==2.8.3) (3.19.1)
Requirement already satisfied: typing-extensions>=4.10.0 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash_attn==2.8.3) (4.12.2)
Requirement already satisfied: setuptools in /root/miniconda3/lib/python3.13/site-packages (from torch->flash_attn==2.8.3) (78.1.1)
Requirement already satisfied: sympy>=1.13.3 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash_attn==2.8.3) (1.14.0)
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Requirement already satisfied: fsspec in /root/miniconda3/lib/python3.13/site-packages (from torch->flash_attn==2.8.3) (2025.9.0)
Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.8.93 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash_attn==2.8.3) (12.8.93)
Requirement already satisfied: nvidia-cuda-runtime-cu12==12.8.90 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash_attn==2.8.3) (12.8.90)
Requirement already satisfied: nvidia-cuda-cupti-cu12==12.8.90 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash_attn==2.8.3) (12.8.90)
Requirement already satisfied: nvidia-cudnn-cu12==9.10.2.21 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash_attn==2.8.3) (9.10.2.21)
Requirement already satisfied: nvidia-cublas-cu12==12.8.4.1 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash_attn==2.8.3) (12.8.4.1)
Requirement already satisfied: nvidia-cufft-cu12==11.3.3.83 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash_attn==2.8.3) (11.3.3.83)
Requirement already satisfied: nvidia-curand-cu12==10.3.9.90 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash_attn==2.8.3) (10.3.9.90)
Requirement already satisfied: nvidia-cusolver-cu12==11.7.3.90 in /root/miniconda3/lib/python3.13/site-packages (from torch->flash_attn==2.8.3) (11.7.3.90)
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Building wheels for collected packages: flash_attn
  DEPRECATION: Building 'flash_attn' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'flash_attn'. Discussion can be found at https://github.com/pypa/pip/issues/6334
  Building wheel for flash_attn (setup.py) ... done
  Created wheel for flash_attn: filename=flash_attn-2.8.3-cp313-cp313-linux_x86_64.whl size=256039831 sha256=7dd8c64a414130c82d83a472f5498c1b899fba37a30e2a793a7a4dc5dd61a062
  Stored in directory: /tmp/pip-ephem-wheel-cache-41k6yjy5/wheels/eb/f7/73/893d5668ef98ae09a95daf3274dcb30440fb12fbc6a0c77559
Successfully built flash_attn
Installing collected packages: flash_attn
Successfully installed flash_attn-2.8.3
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.
(base) root@C.26352433:~/verl/recipe/retool$ bash run_qwen2-32b_sft.sh
+ nnodes=1
+ nproc_per_node=8
+ experiment_name=multiturn-sft-qwen-2.5-32b-instruct
+ HDFS_ROOT=/root/verl/recipe/retool
+ DATA_ROOT=/root/verl/recipe/retool
+ TRAIN_DATA=/root/ReTool-SFT/data/train-00000-of-00001.parquet
+ EVAL_DATA=/root/ReTool-SFT/data/train-00000-of-00001.parquet
+ MODEL_PATH=/root/verl/recipe/retool/model/Qwen2.5-32B-Instruct
+ SAVE_PATH=/root/verl/recipe/retool/checkpoint/multiturn-sft-qwen-2.5-32b-instruct
+ torchrun --nnodes=1 --standalone --nproc_per_node=8 -m verl.trainer.fsdp_sft_trainer data.train_files=/root/ReTool-SFT/data/train-00000-of-00001.parquet data.val_files=/root/ReTool-SFT/data/train-00000-of-00001.parquet data.max_length=16384 data.train_batch_size=32 data.multiturn.enable=true data.multiturn.messages_key=messages data.multiturn.tools_key=tools data.micro_batch_size_per_gpu=4 model.partial_pretrain=/root/verl/recipe/retool/model/Qwen2.5-32B-Instruct model.strategy=fsdp trainer.default_local_dir=/root/verl/recipe/retool/checkpoint/multiturn-sft-qwen-2.5-32b-instruct trainer.project_name=boj-multiturn-sft trainer.experiment_name=multiturn-sft-qwen-2.5-32b-instruct 'trainer.logger=["console","wandb"]' trainer.total_epochs=6 ulysses_sequence_parallel_size=4 use_remove_padding=true
W1001 03:15:12.388000 6258 site-packages/torch/distributed/run.py:774]
W1001 03:15:12.388000 6258 site-packages/torch/distributed/run.py:774] *****************************************
W1001 03:15:12.388000 6258 site-packages/torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
W1001 03:15:12.388000 6258 site-packages/torch/distributed/run.py:774] *****************************************
`torch_dtype` is deprecated! Use `dtype` instead!
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen2ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Error executing job with overrides: ['data.train_files=/root/ReTool-SFT/data/train-00000-of-00001.parquet', 'data.val_files=/root/ReTool-SFT/data/train-00000-of-00001.parquet', 'data.max_length=16384', 'data.train_batch_size=32', 'data.multiturn.enable=true', 'data.multiturn.messages_key=messages', 'data.multiturn.tools_key=tools', 'data.micro_batch_size_per_gpu=4', 'model.partial_pretrain=/root/verl/recipe/retool/model/Qwen2.5-32B-Instruct', 'model.strategy=fsdp', 'trainer.default_local_dir=/root/verl/recipe/retool/checkpoint/multiturn-sft-qwen-2.5-32b-instruct', 'trainer.project_name=boj-multiturn-sft', 'trainer.experiment_name=multiturn-sft-qwen-2.5-32b-instruct', 'trainer.logger=["console","wandb"]', 'trainer.total_epochs=6', 'ulysses_sequence_parallel_size=4', 'use_remove_padding=true']
Traceback (most recent call last):
  File "/root/verl/verl/trainer/fsdp_sft_trainer.py", line 820, in main
    run_sft(config)
    ~~~~~~~^^^^^^^^
  File "/root/verl/verl/trainer/fsdp_sft_trainer.py", line 804, in run_sft
    trainer = FSDPSFTTrainer(
        config=config,
    ...<4 lines>...
        val_dataset=val_dataset,
    )
  File "/root/verl/verl/trainer/fsdp_sft_trainer.py", line 123, in __init__
    self._build_model_optimizer()
    ~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
  File "/root/verl/verl/trainer/fsdp_sft_trainer.py", line 230, in _build_model_optimizer
    self.model: PreTrainedModel = AutoModelForCausalLM.from_pretrained(
                                  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^
        local_model_path,
        ^^^^^^^^^^^^^^^^^
    ...<3 lines>...
        trust_remote_code=trust_remote_code,
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    )
    ^
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/models/auto/auto_factory.py", line 604, in from_pretrained
    return model_class.from_pretrained(
           ~~~~~~~~~~~~~~~~~~~~~~~~~~~^
        pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    )
    ^
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/modeling_utils.py", line 288, in _wrapper
    return func(*args, **kwargs)
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/modeling_utils.py", line 5106, in from_pretrained
    model = cls(config, *model_args, **model_kwargs)
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/models/qwen2/modeling_qwen2.py", line 409, in __init__
    super().__init__(config)
    ~~~~~~~~~~~~~~~~^^^^^^^^
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/modeling_utils.py", line 2197, in __init__
    self.config._attn_implementation_internal = self._check_and_adjust_attn_implementation(
                                                ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^
        self.config._attn_implementation, is_init_check=True
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    )
    ^
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/modeling_utils.py", line 2812, in _check_and_adjust_attn_implementation
    lazy_import_flash_attention(applicable_attn_implementation)
    ~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/modeling_flash_attention_utils.py", line 136, in lazy_import_flash_attention
    _flash_fn, _flash_varlen_fn, _pad_fn, _unpad_fn = _lazy_imports(implementation)
                                                      ~~~~~~~~~~~~~^^^^^^^^^^^^^^^^
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/modeling_flash_attention_utils.py", line 83, in _lazy_imports
    from flash_attn import flash_attn_func, flash_attn_varlen_func
  File "/root/miniconda3/lib/python3.13/site-packages/flash_attn/__init__.py", line 3, in <module>
    from flash_attn.flash_attn_interface import (
    ...<7 lines>...
    )
  File "/root/miniconda3/lib/python3.13/site-packages/flash_attn/flash_attn_interface.py", line 15, in <module>
    import flash_attn_2_cuda as flash_attn_gpu
ImportError: /lib/x86_64-linux-gnu/libc.so.6: version `GLIBC_2.32' not found (required by /root/miniconda3/lib/python3.13/site-packages/flash_attn_2_cuda.cpython-313-x86_64-linux-gnu.so)

Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
[rank1]:[W1001 03:15:22.964774717 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
`torch_dtype` is deprecated! Use `dtype` instead!
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen2ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Error executing job with overrides: ['data.train_files=/root/ReTool-SFT/data/train-00000-of-00001.parquet', 'data.val_files=/root/ReTool-SFT/data/train-00000-of-00001.parquet', 'data.max_length=16384', 'data.train_batch_size=32', 'data.multiturn.enable=true', 'data.multiturn.messages_key=messages', 'data.multiturn.tools_key=tools', 'data.micro_batch_size_per_gpu=4', 'model.partial_pretrain=/root/verl/recipe/retool/model/Qwen2.5-32B-Instruct', 'model.strategy=fsdp', 'trainer.default_local_dir=/root/verl/recipe/retool/checkpoint/multiturn-sft-qwen-2.5-32b-instruct', 'trainer.project_name=boj-multiturn-sft', 'trainer.experiment_name=multiturn-sft-qwen-2.5-32b-instruct', 'trainer.logger=["console","wandb"]', 'trainer.total_epochs=6', 'ulysses_sequence_parallel_size=4', 'use_remove_padding=true']
Traceback (most recent call last):
  File "/root/verl/verl/trainer/fsdp_sft_trainer.py", line 820, in main
    run_sft(config)
    ~~~~~~~^^^^^^^^
  File "/root/verl/verl/trainer/fsdp_sft_trainer.py", line 804, in run_sft
    trainer = FSDPSFTTrainer(
        config=config,
    ...<4 lines>...
        val_dataset=val_dataset,
    )
  File "/root/verl/verl/trainer/fsdp_sft_trainer.py", line 123, in __init__
    self._build_model_optimizer()
    ~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
  File "/root/verl/verl/trainer/fsdp_sft_trainer.py", line 230, in _build_model_optimizer
    self.model: PreTrainedModel = AutoModelForCausalLM.from_pretrained(
                                  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^
        local_model_path,
        ^^^^^^^^^^^^^^^^^
    ...<3 lines>...
        trust_remote_code=trust_remote_code,
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    )
    ^
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/models/auto/auto_factory.py", line 604, in from_pretrained
    return model_class.from_pretrained(
           ~~~~~~~~~~~~~~~~~~~~~~~~~~~^
        pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    )
    ^
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/modeling_utils.py", line 288, in _wrapper
    return func(*args, **kwargs)
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/modeling_utils.py", line 5106, in from_pretrained
    model = cls(config, *model_args, **model_kwargs)
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/models/qwen2/modeling_qwen2.py", line 409, in __init__
    super().__init__(config)
    ~~~~~~~~~~~~~~~~^^^^^^^^
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/modeling_utils.py", line 2197, in __init__
    self.config._attn_implementation_internal = self._check_and_adjust_attn_implementation(
                                                ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^
        self.config._attn_implementation, is_init_check=True
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    )
    ^
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/modeling_utils.py", line 2812, in _check_and_adjust_attn_implementation
    lazy_import_flash_attention(applicable_attn_implementation)
    ~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/modeling_flash_attention_utils.py", line 136, in lazy_import_flash_attention
    _flash_fn, _flash_varlen_fn, _pad_fn, _unpad_fn = _lazy_imports(implementation)
                                                      ~~~~~~~~~~~~~^^^^^^^^^^^^^^^^
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/modeling_flash_attention_utils.py", line 83, in _lazy_imports
    from flash_attn import flash_attn_func, flash_attn_varlen_func
  File "/root/miniconda3/lib/python3.13/site-packages/flash_attn/__init__.py", line 3, in <module>
    from flash_attn.flash_attn_interface import (
    ...<7 lines>...
    )
  File "/root/miniconda3/lib/python3.13/site-packages/flash_attn/flash_attn_interface.py", line 15, in <module>
    import flash_attn_2_cuda as flash_attn_gpu
ImportError: /lib/x86_64-linux-gnu/libc.so.6: version `GLIBC_2.32' not found (required by /root/miniconda3/lib/python3.13/site-packages/flash_attn_2_cuda.cpython-313-x86_64-linux-gnu.so)

Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
`torch_dtype` is deprecated! Use `dtype` instead!
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen2ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Error executing job with overrides: ['data.train_files=/root/ReTool-SFT/data/train-00000-of-00001.parquet', 'data.val_files=/root/ReTool-SFT/data/train-00000-of-00001.parquet', 'data.max_length=16384', 'data.train_batch_size=32', 'data.multiturn.enable=true', 'data.multiturn.messages_key=messages', 'data.multiturn.tools_key=tools', 'data.micro_batch_size_per_gpu=4', 'model.partial_pretrain=/root/verl/recipe/retool/model/Qwen2.5-32B-Instruct', 'model.strategy=fsdp', 'trainer.default_local_dir=/root/verl/recipe/retool/checkpoint/multiturn-sft-qwen-2.5-32b-instruct', 'trainer.project_name=boj-multiturn-sft', 'trainer.experiment_name=multiturn-sft-qwen-2.5-32b-instruct', 'trainer.logger=["console","wandb"]', 'trainer.total_epochs=6', 'ulysses_sequence_parallel_size=4', 'use_remove_padding=true']
`torch_dtype` is deprecated! Use `dtype` instead!
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen2ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
`torch_dtype` is deprecated! Use `dtype` instead!
Error executing job with overrides: ['data.train_files=/root/ReTool-SFT/data/train-00000-of-00001.parquet', 'data.val_files=/root/ReTool-SFT/data/train-00000-of-00001.parquet', 'data.max_length=16384', 'data.train_batch_size=32', 'data.multiturn.enable=true', 'data.multiturn.messages_key=messages', 'data.multiturn.tools_key=tools', 'data.micro_batch_size_per_gpu=4', 'model.partial_pretrain=/root/verl/recipe/retool/model/Qwen2.5-32B-Instruct', 'model.strategy=fsdp', 'trainer.default_local_dir=/root/verl/recipe/retool/checkpoint/multiturn-sft-qwen-2.5-32b-instruct', 'trainer.project_name=boj-multiturn-sft', 'trainer.experiment_name=multiturn-sft-qwen-2.5-32b-instruct', 'trainer.logger=["console","wandb"]', 'trainer.total_epochs=6', 'ulysses_sequence_parallel_size=4', 'use_remove_padding=true']
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen2ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
`torch_dtype` is deprecated! Use `dtype` instead!
Error executing job with overrides: ['data.train_files=/root/ReTool-SFT/data/train-00000-of-00001.parquet', 'data.val_files=/root/ReTool-SFT/data/train-00000-of-00001.parquet', 'data.max_length=16384', 'data.train_batch_size=32', 'data.multiturn.enable=true', 'data.multiturn.messages_key=messages', 'data.multiturn.tools_key=tools', 'data.micro_batch_size_per_gpu=4', 'model.partial_pretrain=/root/verl/recipe/retool/model/Qwen2.5-32B-Instruct', 'model.strategy=fsdp', 'trainer.default_local_dir=/root/verl/recipe/retool/checkpoint/multiturn-sft-qwen-2.5-32b-instruct', 'trainer.project_name=boj-multiturn-sft', 'trainer.experiment_name=multiturn-sft-qwen-2.5-32b-instruct', 'trainer.logger=["console","wandb"]', 'trainer.total_epochs=6', 'ulysses_sequence_parallel_size=4', 'use_remove_padding=true']
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen2ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Error executing job with overrides: ['data.train_files=/root/ReTool-SFT/data/train-00000-of-00001.parquet', 'data.val_files=/root/ReTool-SFT/data/train-00000-of-00001.parquet', 'data.max_length=16384', 'data.train_batch_size=32', 'data.multiturn.enable=true', 'data.multiturn.messages_key=messages', 'data.multiturn.tools_key=tools', 'data.micro_batch_size_per_gpu=4', 'model.partial_pretrain=/root/verl/recipe/retool/model/Qwen2.5-32B-Instruct', 'model.strategy=fsdp', 'trainer.default_local_dir=/root/verl/recipe/retool/checkpoint/multiturn-sft-qwen-2.5-32b-instruct', 'trainer.project_name=boj-multiturn-sft', 'trainer.experiment_name=multiturn-sft-qwen-2.5-32b-instruct', 'trainer.logger=["console","wandb"]', 'trainer.total_epochs=6', 'ulysses_sequence_parallel_size=4', 'use_remove_padding=true']
`torch_dtype` is deprecated! Use `dtype` instead!
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen2ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Error executing job with overrides: ['data.train_files=/root/ReTool-SFT/data/train-00000-of-00001.parquet', 'data.val_files=/root/ReTool-SFT/data/train-00000-of-00001.parquet', 'data.max_length=16384', 'data.train_batch_size=32', 'data.multiturn.enable=true', 'data.multiturn.messages_key=messages', 'data.multiturn.tools_key=tools', 'data.micro_batch_size_per_gpu=4', 'model.partial_pretrain=/root/verl/recipe/retool/model/Qwen2.5-32B-Instruct', 'model.strategy=fsdp', 'trainer.default_local_dir=/root/verl/recipe/retool/checkpoint/multiturn-sft-qwen-2.5-32b-instruct', 'trainer.project_name=boj-multiturn-sft', 'trainer.experiment_name=multiturn-sft-qwen-2.5-32b-instruct', 'trainer.logger=["console","wandb"]', 'trainer.total_epochs=6', 'ulysses_sequence_parallel_size=4', 'use_remove_padding=true']
Normalize batch size by dp 2
Using sequence parallel size: 4
Using remove padding: True
Using SP rank 0 and size 2 for data distribution
Each SP rank gets different data, but the same data WITHIN the same rank
Using FSDP rank 0 and size 2 for data distribution
Traceback (most recent call last):
  File "/root/verl/verl/trainer/fsdp_sft_trainer.py", line 820, in main
    run_sft(config)
    ~~~~~~~^^^^^^^^
  File "/root/verl/verl/trainer/fsdp_sft_trainer.py", line 804, in run_sft
    trainer = FSDPSFTTrainer(
        config=config,
    ...<4 lines>...
        val_dataset=val_dataset,
    )
  File "/root/verl/verl/trainer/fsdp_sft_trainer.py", line 123, in __init__
    self._build_model_optimizer()
    ~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
  File "/root/verl/verl/trainer/fsdp_sft_trainer.py", line 230, in _build_model_optimizer
    self.model: PreTrainedModel = AutoModelForCausalLM.from_pretrained(
                                  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^
        local_model_path,
        ^^^^^^^^^^^^^^^^^
    ...<3 lines>...
        trust_remote_code=trust_remote_code,
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    )
    ^
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/models/auto/auto_factory.py", line 6

---

### 🤖 Cursor Assistant

The compilation succeeded, but the resulting binary still requires GLIBC 2.32 because it linked against CUDA libraries or Python binaries that were compiled with a newer GLIBC. This is a fundamental incompatibility with Ubuntu 20.04.

**At this point, you have two realistic options:**

## Option 1: Use Docker with Ubuntu 22.04 (Strongly Recommended)

This is the cleanest solution for production training:

```bash
# Pull a PyTorch container with Ubuntu 22.04
docker run --gpus all -it --rm \
  -v /root/verl:/workspace/verl \
  -v /root/ReTool-SFT:/workspace/ReTool-SFT \
  -v /root/.cache:/root/.cache \
  --shm-size=16g \
  pytorch/pytorch:2.4.0-cuda12.1-cudnn9-devel bash

# Inside the container:
cd /workspace/verl
pip install -e .
pip install flash-attn --no-build-isolation
cd recipe/retool
bash run_qwen2-32b_sft.sh
```

## Option 2: Modify the model config to disable Flash Attention

Edit the model's `config.json` file to use a different attention implementation:

```bash
# Edit the config
nano /root/verl/recipe/retool/model/Qwen2.5-32B-Instruct/config.json

# Change "_attn_implementation" to "sdpa" or "eager"
# Look for a line like:
#   "attn_implementation": "flash_attention_2"
# Change it to:
#   "attn_implementation": "sdpa"
```

Or try setting it via environment variable before running:

```bash
export TRANSFORMERS_ATTN_IMPLEMENTATION=sdpa
bash run_qwen2-32b_sft.sh
```

**I strongly recommend Option 1 (Docker).** It provides a clean, reproducible environment and is the standard practice for ML training workloads. Ubuntu 20.04 with GLIBC 2.31 is simply too old for modern deep learning frameworks compiled with the latest CUDA toolkits.

---

### 👤 You

File "/root/verl/verl/trainer/fsdp_sft_trainer.py", line 800, in run_sft
    tokenizer = hf_tokenizer(local_model_path, trust_remote_code=config.model.trust_remote_code)
  File "/root/verl/verl/utils/tokenizer.py", line 60, in hf_tokenizer
    tokenizer = AutoTokenizer.from_pretrained(name_or_path, **kwargs)
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/models/auto/tokenization_auto.py", line 1058, in from_pretrained
    tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs)
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/models/auto/tokenization_auto.py", line 890, in get_tokenizer_config
    resolved_config_file = cached_file(
        pretrained_model_name_or_path,
    ...<12 lines>...
        _commit_hash=commit_hash,
    )
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/utils/hub.py", line 321, in cached_file
    file = cached_files(path_or_repo_id=path_or_repo_id, filenames=[filename], **kwargs)
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/utils/hub.py", line 531, in cached_files
    _get_cache_file_to_return(path_or_repo_id, filename, cache_dir, revision, repo_type)
    ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/miniconda3/lib/python3.13/site-packages/transformers/utils/hub.py", line 144, in _get_cache_file_to_return
    resolved_file = try_to_load_from_cache(
        path_or_repo_id, full_filename, cache_dir=cache_dir, revision=revision, repo_type=repo_type
    )
  File "/root/miniconda3/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py", line 106, in _inner_fn
    validate_repo_id(arg_value)
    ~~~~~~~~~~~~~~~~^^^^^^^^^^^
  File "/root/miniconda3/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py", line 154, in validate_repo_id
    raise HFValidationError(
    ...<2 lines>...
    )
huggingface_hub.errors.HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/root/verl/recipe/retool/model/Qwen2.5-32B-Instruct'. Use `repo_type` argument if needed.

---

### 🤖 Cursor Assistant

This error indicates the model files still don't exist at `/root/verl/recipe/retool/model/Qwen2.5-32B-Instruct`. You need to download the model first:

```bash
# Create the directory
mkdir -p /root/verl/recipe/retool/model

# Download the model
huggingface-cli download Qwen/Qwen2.5-32B-Instruct \
  --local-dir /root/verl/recipe/retool/model/Qwen2.5-32B-Instruct \
  --local-dir-use-symlinks False
```

**Note:** This will download approximately 65GB of model files. Make sure you have:
1. Sufficient disk space (at least 70GB free)
2. Good internet connection (this will take a while)

If you need authentication:
```bash
huggingface-cli login
```

After the download completes, verify the files are there:
```bash
ls -lh /root/verl/recipe/retool/model/Qwen2.5-32B-Instruct/
```

You should see files like:
- `config.json`
- `tokenizer.json`
- `tokenizer_config.json`
- `model-*.safetensors` (multiple weight files)

Then run your training script again. **However, you'll still hit the Flash Attention GLIBC issue unless you use Docker with Ubuntu 22.04 as I recommended earlier.**

---


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