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0d4541f397
### What does this PR do?
This PR tries to fix #3491
### Checklist Before Starting
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- `{modules}` include `fsdp`, `megatron`, `sglang`, `vllm`, `rollout`,
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### Test
Tested with [latest
transformers](6e50a8afb2)
<img width="2448" height="540" alt="image"
src="https://github.com/user-attachments/assets/06d40f40-572c-4454-8e08-115857f61f21"
/>
<img width="2796" height="1394" alt="image"
src="https://github.com/user-attachments/assets/17489b9c-e376-46e3-80d8-71106d304077"
/>
<img width="2098" height="744" alt="image"
src="https://github.com/user-attachments/assets/8c7f736d-bf09-4ba9-9cf4-0d56e367c526"
/>
### API and Usage Example
> Demonstrate how the API changes if any, and provide usage example(s)
if possible.
```python
# Add code snippet or script demonstrating how to use this
```
### Design & Code Changes
#### ⚠️ Breaking
We adopt a new format for Qwen2VL's position ids: (4, batch size, seq
len)
Assuming a vision position ids (mrope) has a shape of (3, batch size,
seq len) and a text position ids (normal rope) has a shape of (1, batch
size, seq len), we concatenate both to obtain the final position ids.
This aligns with the implementation in the Transformers >= 4.54.0 🤗
https://github.com/huggingface/transformers/blob/v4.54.0/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py#L1469
#### 🎤 New
We have refactored the Qwen2VL and Qwen2.5VL patches, supporting
no-image input for FSDP by introducing fake ViT inputs. We have also
removed some redundant code for better maintainability.
#### 🚨 Changes
We move the ulysses logic into the attention function. So the position
ids will be scattered before the language model part.
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48 lines
2.1 KiB
Bash
48 lines
2.1 KiB
Bash
set -x
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ENGINE=${1:-vllm}
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python3 -m verl.trainer.main_ppo \
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algorithm.adv_estimator=grpo \
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data.train_files=$HOME/data/geo3k/train.parquet \
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data.val_files=$HOME/data/geo3k/test.parquet \
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data.train_batch_size=512 \
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data.max_prompt_length=1024 \
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data.max_response_length=2048 \
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data.filter_overlong_prompts=True \
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data.truncation='error' \
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data.image_key=images \
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actor_rollout_ref.model.path=Qwen/Qwen2.5-VL-7B-Instruct \
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actor_rollout_ref.actor.optim.lr=1e-6 \
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actor_rollout_ref.model.use_remove_padding=True \
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actor_rollout_ref.model.use_fused_kernels=True \
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actor_rollout_ref.actor.ppo_mini_batch_size=128 \
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actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=10 \
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actor_rollout_ref.actor.use_kl_loss=True \
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actor_rollout_ref.actor.kl_loss_coef=0.01 \
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actor_rollout_ref.actor.kl_loss_type=low_var_kl \
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actor_rollout_ref.actor.entropy_coeff=0 \
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actor_rollout_ref.model.enable_gradient_checkpointing=True \
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actor_rollout_ref.actor.fsdp_config.param_offload=False \
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actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \
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actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=20 \
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actor_rollout_ref.rollout.tensor_model_parallel_size=2 \
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actor_rollout_ref.rollout.name=$ENGINE \
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+actor_rollout_ref.rollout.engine_kwargs.vllm.disable_mm_preprocessor_cache=True \
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actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \
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actor_rollout_ref.rollout.enable_chunked_prefill=False \
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actor_rollout_ref.rollout.enforce_eager=False \
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actor_rollout_ref.rollout.free_cache_engine=True \
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actor_rollout_ref.rollout.n=5 \
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actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=20 \
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actor_rollout_ref.ref.fsdp_config.param_offload=True \
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algorithm.use_kl_in_reward=False \
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trainer.critic_warmup=0 \
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trainer.logger='["console","wandb"]' \
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trainer.project_name='verl_grpo_example_geo3k' \
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trainer.experiment_name='qwen2_5_vl_7b_function_rm' \
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trainer.n_gpus_per_node=8 \
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trainer.nnodes=1 \
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trainer.save_freq=20 \
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trainer.test_freq=5 \
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trainer.total_epochs=15 $@
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