[fix] Improve the params template for generation (#351)

fix the issue[#331](#331)

.github/workflows/vllm.yml

@@ -51,3 +51,8 @@ jobs:
           pip3 install --upgrade vllm
           cd tests/rollout
           torchrun --standalone --nnodes=1 --nproc_per_node=4 $(which pytest) -s test_vllm_spmd.py
+      - name: Run QWen 0.5B generation test
+        run: |
+          cd tests/generation
+          bash ./run_gen_qwen05.sh 4 $HOME/data/gen/qwen_05_gen_test.parquet
+          rm -rf $HOME/data/gen/qwen_05_gen_test.parquet

tests/generation/run_gen_qwen05.sh

@@ -0,0 +1,30 @@
+# Tested with 1 & 4 GPUs
+set -x
+
+if [ "$#" -lt 2 ]; then
+    echo "Usage: run_gen_qwen05.sh <nproc_per_node> <save_path> [other_configs...]"
+    exit 1
+fi
+
+nproc_per_node=$1
+save_path=$2
+
+# Shift the arguments so $@ refers to the rest
+shift 2
+
+python3 -m verl.trainer.main_generation \
+    trainer.nnodes=1 \
+    trainer.n_gpus_per_node=$nproc_per_node \
+    data.path=$HOME/data/gsm8k/test.parquet \
+    data.prompt_key=prompt \
+    data.n_samples=1 \
+    data.output_path=$save_path \
+    model.path=Qwen/Qwen2.5-0.5B-Instruct \
+    +model.trust_remote_code=True \
+    rollout.temperature=1.0 \
+    rollout.top_k=50 \
+    rollout.top_p=0.7 \
+    rollout.prompt_length=2048 \
+    rollout.response_length=1024 \
+    rollout.tensor_model_parallel_size=2 \
+    rollout.gpu_memory_utilization=0.8

verl/trainer/config/generation.yaml

@@ -32,4 +32,35 @@ rollout:
   log_prob_micro_batch_size: null # will be deprecated, use log_prob_micro_batch_size_per_gpu
   log_prob_micro_batch_size_per_gpu: 8
   # for hf rollout
-  do_sample: True
+  do_sample: True
+  disable_log_stats: True
+  enable_chunked_prefill: True
+  n: 1
+actor:
+  strategy: fsdp  # This is for backward-compatibility
+  ppo_mini_batch_size: 256
+  ppo_micro_batch_size: null # will be deprecated, use ppo_micro_batch_size_per_gpu
+  ppo_micro_batch_size_per_gpu: null
+  use_dynamic_bsz: False
+  ppo_max_token_len_per_gpu: 16384 # n * ${data.max_prompt_length} + ${data.max_response_length}
+  grad_clip: 1.0
+  clip_ratio: 0.2
+  entropy_coeff: 0.001
+  use_kl_loss: False # True for GRPO
+  kl_loss_coef: 0.001 # for grpo
+  kl_loss_type: low_var_kl # for grpo
+  ppo_epochs: 1
+  shuffle: False
+  ulysses_sequence_parallel_size: 1 # sp size
+  optim:
+    lr: 1e-6
+    lr_warmup_steps_ratio: 0.  # the total steps will be injected during runtime
+    min_lr_ratio: null   # only useful for warmup with cosine
+    warmup_style: constant  # select from constant/cosine
+    total_training_steps: -1  # must be override by program
+  fsdp_config:
+    wrap_policy:
+      min_num_params: 0
+    param_offload: False
+    optimizer_offload: False
+    fsdp_size: -1

verl/trainer/main_generation.py

@@ -59,7 +59,7 @@ def main(config):
     if tokenizer.pad_token is None:
         tokenizer.pad_token = tokenizer.eos_token
 
-    ray_cls_with_init = RayClassWithInitArgs(cls=ray.remote(ActorRolloutRefWorker), config=config, role='rollout')
+    ray_cls_with_init = RayClassWithInitArgs(cls=ray.remote(ActorRolloutRefWorker), config=config, role='actor_rollout')
     resource_pool = RayResourcePool(process_on_nodes=[config.trainer.n_gpus_per_node] * config.trainer.nnodes)
     wg = RayWorkerGroup(resource_pool=resource_pool, ray_cls_with_init=ray_cls_with_init)
     wg.init_model()