[sglang] feat: add NPU GRPO training scripts for Qwen2.5-32B (FSDP/SGLang backends) (verl-project#5062)

### What does this PR do?
add NPU GRPO training scripts for Qwen2.5-32B (FSDP/SGLang backends).
The reward curves of this scenario are also shown.

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Author: xiazhahe

examples/grpo_trainer/run_qwen2-32b_sglang_fsdp_npu.sh

@@ -0,0 +1,182 @@
+#!/bin/bash
+set -xeuo pipefail
+mkdir -p logs
+
+# Project Configuration
+project_name='GRPO-Qwen2.5-32B-BASE-SGLang'
+exp_name='GRPO-Qwen2.5-32B-BASE-FSDP-SGLang'
+
+# Necessary env
+export HCCL_CONNECT_TIMEOUT=1500
+export HCCL_HOST_SOCKET_PORT_RANGE=60000-60050
+export HCCL_NPU_SOCKET_PORT_RANGE=61000-61050
+
+export RAY_EXPERIMENTAL_NOSET_ASCEND_RT_VISIBLE_DEVICES=1
+# If the number of nodes is 16, ASCEND_RT_VISIBLE_DEVICES=0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15
+export ASCEND_RT_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+
+export DISABLE_L2_CACHE=1
+export TASK_QUEUE_ENABLE=1
+
+# Node Info
+NNODES=${NNODES:-2}
+NPUS_PER_NODE=${NPUS_PER_NODE:-8}
+
+# Model Weights Paths
+MODEL_PATH=Qwen/Qwen2.5-32B
+RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"}
+CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"}
+
+# File System Paths
+TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/datasets/deepscaler/train.parquet"}
+TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/datasets/deepscaler/test.parquet"}
+
+# Data Configuration
+max_prompt_length=$((1024 * 2))
+max_response_length=$((1024 * 8))
+
+# Training Batch Configuration
+train_prompt_bsz=32
+train_prompt_mini_bsz=32
+n_resp_per_prompt=8
+
+# Algorithm Configuration
+adv_estimator=grpo
+use_kl_in_reward=False
+kl_coef=0.0
+use_kl_loss=True
+kl_loss_coef=0.001
+
+# Performance and Memory Management Configuration
+all_offload=True
+use_dynamic_bsz=False
+
+# SGLang Configuration
+gen_tp=4
+gen_sp=1
+gen_dp=1
+gen_ep=1
+gpu_memory_utilization=0.5
+
+# Data Configuration
+DATA_CONFIG=(
+    # File Paths
+    data.train_files="${TRAIN_FILE}"
+    data.val_files="${TEST_FILE}"
+    # Data Structure
+    data.prompt_key=prompt
+    # Batch and Length Configuration
+    data.train_batch_size=${train_prompt_bsz}
+    data.max_prompt_length=${max_prompt_length}
+    data.max_response_length=${max_response_length}
+    # Preprocessing
+    data.filter_overlong_prompts=False
+    data.truncation='left'
+)
+
+# Model Configuration
+MODEL_CONFIG=(
+    # Model Path
+    actor_rollout_ref.model.path="${MODEL_PATH}"
+    # Model Processing
+    actor_rollout_ref.model.use_remove_padding=True
+    actor_rollout_ref.model.enable_gradient_checkpointing=True
+)
+
+# Reinforcement Learning Algorithm Configuration
+ALGORITHM_CONFIG=(
+    # Advantage Estimation
+    algorithm.adv_estimator=${adv_estimator}
+    # KL Divergence Control
+    algorithm.use_kl_in_reward=${use_kl_in_reward}
+)
+
+# Actor Model Configuration
+ACTOR_CONFIG=(
+    # Core Runtime Settings
+    actor_rollout_ref.actor.use_torch_compile=False
+    actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz}
+    # Loss Function Configuration
+    actor_rollout_ref.actor.use_kl_loss=${use_kl_loss}
+    actor_rollout_ref.actor.kl_loss_type=low_var_kl
+    actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef}
+    actor_rollout_ref.actor.entropy_coeff=0
+    # PPO Training Parameters
+    actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1
+    actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz}
+    # Optimizer Settings
+    actor_rollout_ref.actor.optim.lr=1e-6
+    actor_rollout_ref.actor.fsdp_config.param_offload=${all_offload}
+    actor_rollout_ref.actor.fsdp_config.optimizer_offload=${all_offload}
+    )
+
+# Reference Model Configuration
+REF_CONFIG=(
+    # Core Runtime Settings
+    actor_rollout_ref.ref.use_torch_compile=False
+    # Log Probability Inference
+    actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=1
+    actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz}
+    # Memory Optimization
+    actor_rollout_ref.ref.fsdp_config.param_offload=${all_offload}
+)
+
+# Rollout Configuration
+ROLLOUT_CONFIG=(
+    # Rollout Engine
+    actor_rollout_ref.rollout.name=sglang
+    +actor_rollout_ref.rollout.engine_kwargs.sglang.attention_backend="ascend"
+    # Generation Parameters
+    actor_rollout_ref.rollout.n=${n_resp_per_prompt}
+    actor_rollout_ref.rollout.top_p=1.0
+    actor_rollout_ref.rollout.top_k=-1
+    actor_rollout_ref.rollout.temperature=1.0
+    # Log Probability Inference
+    actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=1
+    actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz}
+    # Memory Management
+    actor_rollout_ref.rollout.gpu_memory_utilization=${gpu_memory_utilization}
+    actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp}
+    actor_rollout_ref.rollout.data_parallel_size=${gen_dp}
+    actor_rollout_ref.rollout.expert_parallel_size=${gen_ep}
+    actor_rollout_ref.rollout.enable_chunked_prefill=False
+    actor_rollout_ref.rollout.multi_stage_wake_up=True
+    # Validation Generation
+    actor_rollout_ref.rollout.val_kwargs.n=1
+    actor_rollout_ref.rollout.val_kwargs.do_sample=True
+    actor_rollout_ref.rollout.val_kwargs.top_p=1.0
+    actor_rollout_ref.rollout.val_kwargs.top_k=-1
+    actor_rollout_ref.rollout.val_kwargs.temperature=1.0
+    actor_rollout_ref.nccl_timeout=1800
+)
+
+# Trainer Configuration
+TRAINER_CONFIG=(
+    trainer.logger='["console"]'
+    trainer.project_name="${project_name}"
+    trainer.experiment_name="${exp_name}"
+    trainer.nnodes="${NNODES}"
+    trainer.n_gpus_per_node="${NPUS_PER_NODE}"
+    trainer.total_epochs=5
+    trainer.val_before_train=False
+    trainer.test_freq=-1
+    trainer.save_freq=100
+    trainer.default_local_dir="${CKPTS_DIR}"
+    trainer.critic_warmup=0
+)
+
+# Main GRPO Training Command
+# Add the reward function processing for the DeepScaler dataset here
+python3 -m verl.trainer.main_ppo \
+    --config-path=config \
+    --config-name='ppo_trainer.yaml' \
+    custom_reward_function.path=recipe/r1_ascend/deepscaler.py \
+    custom_reward_function.name=compute_score \
+    "${DATA_CONFIG[@]}" \
+    "${MODEL_CONFIG[@]}" \
+    "${ACTOR_CONFIG[@]}" \
+    "${REF_CONFIG[@]}" \
+    "${ROLLOUT_CONFIG[@]}" \
+    "${ALGORITHM_CONFIG[@]}" \
+    "${TRAINER_CONFIG[@]}" \
+    "$@" | tee logs/run_qwen2_5-32b_grpo_fsdp_sglang_npu.log