[model] feat: add qwen3 grpo 8b/32b script on ASCEND NPU (#3310)

### What does this PR do?

add examples/grpo_trainer/run_qwen3_32b_npu.sh
<img width="1014" height="1111" alt="image"
src="https://github.com/user-attachments/assets/8cd59fc2-5f6a-419e-87ac-bf35a71856fb"
/>

add examples/grpo_trainer/run_qwen3_8b_npu.sh
<img width="844" height="930" alt="image"
src="https://github.com/user-attachments/assets/5c23c7a4-8729-4007-8828-027a8cda4779"
/>



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---------

Signed-off-by: ZLiao <a627465478@gmail.com>
Co-authored-by: ZLiao <a627465478@gmail.com>

docs/ascend_tutorial/ascend_quick_start.rst

@@ -179,6 +179,10 @@ vllm & vllm-ascend
 +-----------+-------------------------+-------------+-------------------+-------------------+-------------------+--------------------------+
 |   GRPO    | Qwen2.5-VL-32B-instruct |    0.79%    |        0.568      |        FSDP       |    vllm-ascend    |    Atlas 200T A2 Box16   |
 +-----------+-------------------------+-------------+-------------------+-------------------+-------------------+--------------------------+
+|   GRPO    | Qwen3-8B                |    1.55%    |        1.012      |        FSDP       |    vllm-ascend    |    Atlas 200T A2 Box16   |
++-----------+-------------------------+-------------+-------------------+-------------------+-------------------+--------------------------+
+|   GRPO    | Qwen3-32B               |    0.64%    |        0.696      |        FSDP       |    vllm-ascend    |    Atlas 200T A2 Box16   |
++-----------+-------------------------+-------------+-------------------+-------------------+-------------------+--------------------------+
 |   DAPO    | Qwen2.5-7B-instruct     |    3.83%    |        pending    |        FSDP       |    vllm-ascend    |    Atlas 200T A2 Box16   |
 +-----------+-------------------------+-------------+-------------------+-------------------+-------------------+--------------------------+
 |   DAPO    | Qwen2.5-32B             |    3.42%    |        pending    |        FSDP       |    vllm-ascend    |    Atlas 200T A2 Box16   |

examples/grpo_trainer/run_qwen3-32b_npu.sh

@@ -0,0 +1,59 @@
+set -x
+
+project_name='GRPO-Qwen3'
+exp_name='GRPO-Qwen3-32b-npu'
+gen_tp=4
+RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"}
+MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen3-32B"}
+TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/gsm8k/train.parquet"}
+TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/gsm8k/test.parquet"}
+
+python3 -m verl.trainer.main_ppo \
+    algorithm.adv_estimator=grpo \
+    data.train_files="${TRAIN_FILE}" \
+    data.val_files="${TEST_FILE}" \
+    data.train_batch_size=1024 \
+    data.max_prompt_length=2048 \
+    data.max_response_length=2048 \
+    data.filter_overlong_prompts=True \
+    data.truncation='error' \
+    data.shuffle=False \
+    actor_rollout_ref.model.path=${MODEL_PATH} \
+    actor_rollout_ref.actor.optim.lr=1e-6 \
+    actor_rollout_ref.model.use_remove_padding=True \
+    actor_rollout_ref.actor.ulysses_sequence_parallel_size=4 \
+    +actor_rollout_ref.actor.fsdp_config.mixed_precision.param_dtype=bf16 \
+    +actor_rollout_ref.actor.fsdp_config.mixed_precision.reduce_dtype=bf16 \
+    +actor_rollout_ref.actor.fsdp_config.mixed_precision.buffer_dtype=fp32 \
+    actor_rollout_ref.actor.ppo_mini_batch_size=64 \
+    actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=8 \
+    actor_rollout_ref.actor.use_kl_loss=True \
+    actor_rollout_ref.actor.entropy_coeff=0 \
+    actor_rollout_ref.actor.kl_loss_coef=0.001 \
+    actor_rollout_ref.actor.kl_loss_type=low_var_kl \
+    actor_rollout_ref.model.enable_gradient_checkpointing=True \
+    actor_rollout_ref.actor.fsdp_config.param_offload=True \
+    actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \
+    actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=8 \
+    actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \
+    actor_rollout_ref.rollout.name=vllm \
+    actor_rollout_ref.rollout.gpu_memory_utilization=0.7 \
+    actor_rollout_ref.rollout.n=4 \
+    actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=8 \
+    actor_rollout_ref.ref.fsdp_config.param_offload=True \
+    actor_rollout_ref.actor.use_torch_compile=False \
+    actor_rollout_ref.ref.use_torch_compile=False \
+    actor_rollout_ref.rollout.enable_chunked_prefill=True \
+    actor_rollout_ref.rollout.max_num_batched_tokens=32768 \
+    algorithm.use_kl_in_reward=False \
+    trainer.critic_warmup=0 \
+    trainer.logger=['console','tensorboard'] \
+    trainer.project_name="${project_name}" \
+    trainer.experiment_name="${exp_name}" \
+    trainer.n_gpus_per_node=8 \
+    trainer.nnodes=4 \
+    trainer.resume_from_path=checkpoints/ \
+    trainer.save_freq=500 \
+    trainer.test_freq=50 \
+    trainer.total_epochs=50 \
+    trainer.device=npu $@

examples/grpo_trainer/run_qwen3-8b_npu.sh

@@ -0,0 +1,59 @@
+set -x
+
+project_name='GRPO-Qwen3'
+exp_name='GRPO-Qwen3-8B-npu'
+gen_tp=2
+RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"}
+MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen3-8B"}
+CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"}
+TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/dapo-math-17k.parquet"}
+TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/aime-2024.parquet"}
+
+python3 -m verl.trainer.main_ppo \
+    algorithm.adv_estimator=grpo \
+    data.train_files="${TRAIN_FILE}" \
+    data.val_files="${TEST_FILE}" \
+    data.train_batch_size=256 \
+    data.max_prompt_length=512 \
+    data.max_response_length=1024 \
+    data.filter_overlong_prompts=True \
+    data.truncation='error' \
+    actor_rollout_ref.model.path=${MODEL_PATH} \
+    actor_rollout_ref.actor.optim.lr=1e-6 \
+    actor_rollout_ref.model.use_remove_padding=True \
+    actor_rollout_ref.actor.ppo_mini_batch_size=64 \
+    actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=10 \
+    actor_rollout_ref.actor.use_kl_loss=True \
+    actor_rollout_ref.actor.kl_loss_coef=0.001 \
+    actor_rollout_ref.actor.kl_loss_type=low_var_kl \
+    actor_rollout_ref.actor.entropy_coeff=0 \
+    actor_rollout_ref.actor.use_torch_compile=False \
+    actor_rollout_ref.ref.use_torch_compile=False \
+    actor_rollout_ref.model.enable_gradient_checkpointing=True \
+    actor_rollout_ref.actor.fsdp_config.param_offload=False \
+    actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \
+    actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=32 \
+    actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \
+    actor_rollout_ref.rollout.name=vllm \
+    actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \
+    actor_rollout_ref.rollout.n=5 \
+    actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=32 \
+    actor_rollout_ref.ref.fsdp_config.param_offload=True \
+    algorithm.use_kl_in_reward=False \
+    trainer.critic_warmup=0 \
+    trainer.logger='["console","wandb"]' \
+    trainer.project_name="${project_name}" \
+    trainer.experiment_name="${exp_name}" \
+    trainer.n_gpus_per_node=8 \
+    trainer.nnodes=1 \
+    trainer.default_local_dir=${CKPTS_DIR} \
+    trainer.device=npu \
+    trainer.resume_mode=auto \
+    actor_rollout_ref.actor.fsdp_config.forward_prefetch=True \
+    actor_rollout_ref.ref.fsdp_config.forward_prefetch=True \
+    ++actor_rollout_ref.actor.entropy_from_logits_with_chunking=True \
+    ++actor_rollout_ref.ref.entropy_from_logits_with_chunking=True \
+    trainer.val_before_train=True \
+    trainer.save_freq=5 \
+    trainer.test_freq=5 \
+    trainer.total_epochs=15