support qwen3.6 grpo & in-place add lora

cookbook/rl/short_math_grpo_moe.py

@@ -0,0 +1,271 @@
+"""GRPO training script for GSM8K dataset.
+
+Converted from the Tinker client version to Ray-based training.
+Uses short reasoning format: shorter thinking gets higher format reward.
+Answer extracted from \\boxed{} or #### format.
+"""
+import os
+import re
+from typing import List, Tuple, Dict, Any
+
+from peft import LoraConfig
+
+import twinkle
+from twinkle import DeviceMesh, DeviceGroup, get_device_placement, get_logger
+from twinkle.advantage import GRPOAdvantage
+from twinkle.checkpoint_engine import CheckpointEngineManager
+from twinkle.data_format import SamplingParams
+from twinkle.dataloader import DataLoader
+from twinkle.dataset import Dataset, DatasetMeta
+from twinkle.metric import CompletionRewardMetric
+from twinkle.model import TransformersModel
+from twinkle.processor import InputProcessor
+from twinkle.reward import GSM8KAccuracyReward
+from twinkle.reward.base import Reward
+from twinkle.sampler import vLLMSampler
+from twinkle.preprocessor.llm import GSM8KProcessor
+
+logger = get_logger()
+
+# ========== Configuration ==========
+MODEL_ID = os.environ.get('MODEL_ID', 'ms://Qwen/Qwen3.6-35B-A3B')
+USE_MEGATRON = bool(int(os.environ.get('USE_MEGATRON', '1')))
+
+MODEL_GPUS = int(os.environ.get('MODEL_GPUS', 4))
+MODEL_EP = int(os.environ.get('MODEL_EP', 2))
+MODEL_TP = int(os.environ.get('MODEL_TP', 2))
+MODEL_PP = int(os.environ.get('MODEL_PP', 2))
+
+SAMPLER_GPUS = int(os.environ.get('SAMPLER_GPUS', 4))
+SAMPLER_TP = int(os.environ.get('SAMPLER_TP', 2))
+NUM_GPUS = MODEL_GPUS + SAMPLER_GPUS
+
+NUM_GENERATIONS = int(os.environ.get('NUM_GENERATIONS', 8))
+MAX_NEW_TOKENS = int(os.environ.get('MAX_NEW_TOKENS', 4096))
+LEARNING_RATE = float(os.environ.get('LR', 1e-5))
+MAX_STEPS = int(os.environ.get('MAX_STEPS', 1000))
+BATCH_SIZE = int(os.environ.get('BATCH_SIZE', 4))
+MINI_BATCH_SIZE = int(os.environ.get('MINI_BATCH_SIZE', 4))
+MICRO_BATCH_SIZE = int(os.environ.get('MICRO_BATCH_SIZE', 1))
+GRADIENT_ACCUMULATION_STEPS = int(os.environ.get('GRADIENT_ACCUMULATION_STEPS', 1))
+ADAPTER_NAME = 'default'
+SAVE_STEPS = int(os.environ.get('SAVE_STEPS', 1000))
+LORA_RANK = int(os.environ.get('LORA_RANK', 16))
+
+SYSTEM_PROMPT = ('You are a helpful math assistant. Solve the problem with minimal but correct reasoning '
+                 'and put your final answer within \\boxed{}.')
+
+# ========== Reward Functions ==========
+class GSM8KBrevityReward(Reward):
+    """Brevity reward: rewards shorter completions that contain a valid answer.
+
+    Returns 0.0 if no valid answer format (\\boxed{} or ####).
+    Otherwise returns higher score for shorter completions (1.0 at <=200 chars).
+    """
+
+    def __call__(self, trajectories: List[Dict[str, Any]], **kwargs) -> List[float]:
+        rewards = []
+        for traj in trajectories:
+            messages = traj.get('messages', [])
+            completion = ''
+            for msg in reversed(messages):
+                if msg.get('role') == 'assistant':
+                    completion = msg.get('content', '')
+                    break
+
+            has_answer = bool(
+                re.search(r'\\boxed\{[^}]+\}', completion)
+                or re.search(r'####\s*[\-\d,\.]+', completion)
+            )
+
+            if not has_answer:
+                rewards.append(0.0)
+            else:
+                length = len(completion)
+                if length <= 200:
+                    rewards.append(1.0)
+                else:
+                    rewards.append(max(0.0, 1.0 - (length - 200) / 3000))
+        return rewards
+
+
+# ========== Dataset ==========
+def create_gsm8k_dataset():
+    dataset = Dataset(DatasetMeta('ms://modelscope/gsm8k', subset_name='main', split='train'))
+    dataset.set_template('Qwen3_5Template', model_id=MODEL_ID, max_length=4096, truncation_strategy='delete', enable_thinking=True)
+    dataset.map(GSM8KProcessor(system=SYSTEM_PROMPT))
+    dataset.encode(add_generation_prompt=True)
+    return dataset
+
+
+def compute_rewards(
+    trajectories: List[Dict[str, Any]],
+) -> Tuple[List[float], List[float], List[float]]:
+    accuracy_reward_fn = GSM8KAccuracyReward()
+    brevity_reward_fn = GSM8KBrevityReward()
+
+    accuracy_rewards = accuracy_reward_fn(trajectories)
+    brevity_rewards = brevity_reward_fn(trajectories)
+    total_rewards = [a + b for a, b in zip(accuracy_rewards, brevity_rewards)]
+    return total_rewards, brevity_rewards, accuracy_rewards
+
+
+# ========== Main ==========
+def main():
+    device_groups = [
+        DeviceGroup(name='model', ranks=list(range(MODEL_GPUS)), device_type='GPU'),
+        DeviceGroup(name='sampler', ranks=list(range(MODEL_GPUS, NUM_GPUS)), device_type='GPU', gpus_per_worker=SAMPLER_TP),
+    ]
+    dp_size = MODEL_GPUS //  (MODEL_TP * MODEL_PP)
+    model_mesh = DeviceMesh.from_sizes(world_size=MODEL_GPUS, dp_size=dp_size, tp_size=MODEL_TP, pp_size=MODEL_PP, ep_size=MODEL_EP, sequence_parallel=True)
+    sampler_dp_size = SAMPLER_GPUS //  (SAMPLER_TP)
+    sampler_mesh = DeviceMesh.from_sizes(world_size=SAMPLER_GPUS, dp_size=sampler_dp_size, tp_size=SAMPLER_TP)
+    twinkle.initialize(mode='ray', nproc_per_node=NUM_GPUS, groups=device_groups, lazy_collect=False)
+
+    lora_config = LoraConfig(
+        target_modules=['all-linear'],
+        r=LORA_RANK,
+        lora_alpha=LORA_RANK * 2,
+        lora_dropout=0.05,
+    )
+
+    if USE_MEGATRON:
+        from twinkle.model.megatron import MegatronModel
+        model = MegatronModel(
+            model_id=MODEL_ID,
+            device_mesh=model_mesh,
+            remote_group='model',
+            mixed_precision='bf16',
+        )
+    else:
+        model = TransformersModel(
+            model_id=MODEL_ID,
+            device_mesh=model_mesh,
+            remote_group='model',
+        )
+
+    model.add_adapter_to_model(ADAPTER_NAME, lora_config, gradient_accumulation_steps=1)
+    if USE_MEGATRON:
+        model.set_optimizer('default', lr=LEARNING_RATE)
+        model.set_lr_scheduler('default', lr_decay_steps=MAX_STEPS, max_lr=LEARNING_RATE)
+    else:
+        model.set_optimizer('AdamW', lr=LEARNING_RATE)
+        model.set_lr_scheduler('CosineAnnealingLR', T_max=MAX_STEPS, eta_min=0)
+
+    model.set_loss('GRPOLoss', epsilon=0.2)
+    model.set_processor(InputProcessor)
+    model.set_template('Qwen3_5Template', model_id=MODEL_ID, enable_thinking=True)
+
+    sampler = vLLMSampler(
+        model_id=MODEL_ID,
+        engine_args={
+            'tensor_parallel_size': SAMPLER_TP,
+            'gpu_memory_utilization': 0.7,
+            'max_model_len': 8192,
+            'max_lora_rank': LORA_RANK, # save as lora_config
+            # NOTE: To use enable_lora with qwen3.5, ensure vLLM includes PR https://github.com/vllm-project/vllm/pull/36976
+            # enable_lora=True used with ckpt_manager.sync_weights(merge_and_sync=False)
+            # meaning only sync lora weights, if merge_and_sync=True,
+            # lora will be merged into the base model and sync all weights to vLLM
+            'enable_lora': True,
+            'enable_tower_connector_lora': True,
+        },
+        device_mesh=sampler_mesh,
+        remote_group='sampler',
+    )
+    sampler.set_template('Qwen3_5Template', model_id=MODEL_ID, enable_thinking=True)
+
+    ckpt_manager = CheckpointEngineManager(model=model, sampler=sampler)
+
+    GLOBAL_BATCH_SIZE = BATCH_SIZE * GRADIENT_ACCUMULATION_STEPS
+    dataloader = DataLoader(
+        dataset=create_gsm8k_dataset,
+        batch_size=GLOBAL_BATCH_SIZE,
+        min_batch_size=GLOBAL_BATCH_SIZE,
+        device_mesh=model_mesh,
+        remote_group='model',
+    )
+
+    advantage_fn = GRPOAdvantage()
+    metrics = CompletionRewardMetric()
+    sampling_params = SamplingParams(max_tokens=MAX_NEW_TOKENS, num_samples=1, logprobs=1, temperature=1.0, top_p=0.95)
+
+    optim_step = 0
+    logger.info('Starting GSM8K GRPO training (short reasoning)')
+    logger.info(get_device_placement())
+
+    for batch in dataloader:
+        if optim_step >= MAX_STEPS:
+            break
+
+        metrics.reset()
+        expand_prompts = []
+        for prompt in batch:
+            expand_prompts.extend([prompt] * NUM_GENERATIONS)
+
+        # enable_lora=True used with ckpt_manager.sync_weights(merge_and_sync=False)
+        # meaning only sync lora weights, if merge_and_sync=True,
+        # lora will be merged into the base model and sync all weights to vLLM
+        ckpt_manager.sync_weights(merge_and_sync=False)
+        sampler.reset_prefix_cache()
+
+        sample_responses = sampler.sample(
+            expand_prompts,
+            sampling_params,
+        )
+
+        all_input_data: List[Dict[str, Any]] = []
+        all_old_logps: List[List[float]] = []
+        all_completion_lengths: List[int] = []
+
+        for sample_response in sample_responses:
+            for sequence in sample_response.sequences:
+                all_input_data.append(sequence.new_input_feature)
+                all_old_logps.append([logprob[0][1] for logprob in sequence.logprobs])
+                all_completion_lengths.append(len(sequence.tokens))
+
+        total_rewards, brevity_rewards, accuracy_rewards = compute_rewards(all_input_data)
+
+        metrics.accumulate(
+            completion_lengths=all_completion_lengths,
+            rewards={
+                'total': total_rewards,
+                'brevity': brevity_rewards,
+                'accuracy': accuracy_rewards,
+            },
+        )
+
+        advantages = advantage_fn(total_rewards, num_generations=NUM_GENERATIONS, scale='group').tolist()
+
+        total_completions = len(all_input_data)
+        for mb_start in range(0, total_completions, MINI_BATCH_SIZE):
+            mb_end = min(mb_start + MINI_BATCH_SIZE, total_completions)
+            mb_inputs = all_input_data[mb_start:mb_end]
+            mb_old_logps = all_old_logps[mb_start:mb_end]
+            mb_advantages = advantages[mb_start:mb_end]
+
+            model.forward_backward(
+                inputs=mb_inputs,
+                old_logps=mb_old_logps,
+                advantages=mb_advantages,
+                micro_batch_size=MICRO_BATCH_SIZE,
+            )
+            model.clip_grad_and_step()
+            optim_step += 1
+
+            if optim_step >= MAX_STEPS:
+                break
+            if optim_step % SAVE_STEPS == 0:
+                model.save(f'math-grpo-checkpoint-{optim_step}')
+
+        log_dict = metrics.calculate()
+        log_dict.update(model.calculate_metric(is_training=True))
+        metrics.reset()
+        logger.info(f'[Step {optim_step}/{MAX_STEPS}] {log_dict}')
+
+    logger.info(f'Training completed. optim_steps={optim_step}')
+    model.save('math-grpo-final')
+
+
+if __name__ == '__main__':
+    main()

src/twinkle/patch/vllm_moe_loader.py

@@ -79,6 +79,10 @@ def __call__(self, model, **kwargs):
         # (False, 'model.layers.0.mlp.experts.w2_weight')          use mlp.experts.weight_loader
 
         # Early return if no MOE models are supported
+        # expected_lora_modules : up_proj -> experts.0.up_proj
+        from vllm.model_executor.models.qwen3_5 import Qwen3_5MoeForConditionalGeneration
+        Qwen3_5MoeForConditionalGeneration.is_3d_moe_weight = False
+
         if not SUPPORTED_MOE_MODELS:
             return
 

src/twinkle/sampler/vllm_sampler/vllm_engine.py

@@ -1,6 +1,7 @@
 # Copyright (c) ModelScope Contributors. All rights reserved.
 import inspect
 import os
+import re
 import torch
 import uuid
 from typing import Any, Dict, List, Optional, Union
@@ -512,7 +513,14 @@ async def _sync_iter():
         sync_id = uuid.uuid4().hex
         zmq_handle = f'ipc:///tmp/twinkle-ipc-{device_uuid}-{os.getpid()}-{sync_id}.sock'
 
+        env_bucket_mb = os.environ.get('TWINKLE_VLLM_BUCKET_SIZE_MB')
+        if env_bucket_mb is not None:
+            bucket_size_mb = int(env_bucket_mb)
+        if bucket_size_mb <= 0:
+            raise ValueError(f'bucket_size_mb must be > 0, got {bucket_size_mb}')
+
         bucket_size = bucket_size_mb << 20
+        lora_mode = bool(base_sync_done and peft_config)
 
         # Create transfer buffer
         buffer = None
@@ -575,9 +583,14 @@ async def _chain_first():
         offset = 0
         bucket_meta: list[dict] = []
         n_weights = 0
+        current_expert_layer: Optional[str] = None
+
+        def _get_expert_layer_prefix(weight_name: str) -> Optional[str]:
+            m = re.match(r'^(.*\.mlp\.experts)\.\d+\.', weight_name)
+            return m.group(1) if m else None
 
         async def _flush_bucket(is_last: bool) -> None:
-            nonlocal offset, bucket_meta
+            nonlocal offset, bucket_meta, current_expert_layer
             if not bucket_meta and not is_last:
                 return
             if buffer.device.type != 'cpu':
@@ -593,6 +606,7 @@ async def _flush_bucket(is_last: bool) -> None:
             )
             offset = 0
             bucket_meta = []
+            current_expert_layer = None
 
         async for name, weight in _chain_first():
             if use_shm and weight.device.type != 'cpu':
@@ -602,6 +616,15 @@ async def _flush_bucket(is_last: bool) -> None:
 
             weight_u8 = weight.view(-1).view(torch.uint8)
             total_nbytes = int(weight_u8.numel())
+            expert_layer_prefix = _get_expert_layer_prefix(name) if lora_mode else None
+            if lora_mode and offset > 0:
+                # Keep each expert layer in an isolated bucket to avoid sending
+                # partial expert-layer weights.
+                if current_expert_layer != expert_layer_prefix:
+                    await _flush_bucket(is_last=False)
+            if lora_mode:
+                current_expert_layer = expert_layer_prefix
+
             chunk_offset = 0
             while chunk_offset < total_nbytes:
                 if offset >= bucket_size: