support transformers multi-modal grpo

cookbook/rl/mm_grpo.py

@@ -0,0 +1,235 @@
+"""Multimodal GRPO training demo with Qwen3.5 VL model on CLEVR dataset.
+
+This script demonstrates on-policy GRPO (Group Relative Policy Optimization)
+for visual question answering using:
+  - Model: Qwen3.5-2B (vision-language model)
+  - Dataset: AI-ModelScope/clevr_cogen_a_train (CLEVR visual reasoning)
+  - Rewards: accuracy (answer correctness) + format (<think>/<answer> tags)
+  - Template: Qwen3_5Template (handles vision token embedding merge)
+
+Architecture:
+  - Separate GPU groups for training model and vLLM sampler (Ray mode)
+  - LoRA fine-tuning with NCCL weight sync between model and sampler
+  - GRPO loss with PPO-style clipping (epsilon=0.2)
+
+Usage:
+    python mm_grpo.py
+
+Environment variables:
+    MODEL_ID       : Model path (default: ms://Qwen/Qwen3.5-2B)
+    MODEL_GPUS     : GPUs for training model (default: 2)
+    SAMPLER_GPUS   : GPUs for vLLM sampler (default: 1)
+    NUM_GENERATIONS: Completions per prompt for GRPO grouping (default: 4)
+    MAX_NEW_TOKENS : Max generation length (default: 4096)
+    LR             : Learning rate (default: 5e-5)
+    MAX_STEPS      : Total optimization steps (default: 200)
+    BATCH_SIZE     : Global prompt-level batch size (default: 1)
+    MINI_BATCH_SIZE: Global completion-level mini-batch size (default: 4)
+    MICRO_BATCH_SIZE: Per-device micro-batch size (default: 1)
+    DATA_SLICE     : Number of dataset samples to use (default: 2000)
+    SAVE_STEPS     : Checkpoint save interval (default: 50)
+"""
+import os
+from typing import Any, Dict, List, Tuple
+
+from peft import LoraConfig
+
+import twinkle
+from twinkle import DeviceGroup, DeviceMesh, 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 DatasetMeta, LazyDataset
+from twinkle.metric import CompletionRewardMetric
+from twinkle.model import TransformersModel
+from twinkle.preprocessor.mm import CLEVRProcessor
+from twinkle.processor import InputProcessor
+from twinkle.reward import FormatReward, MultiModalAccuracyReward
+from twinkle.sampler import vLLMSampler
+from twinkle.template import Qwen3_5Template
+
+logger = get_logger()
+
+MODEL_ID = os.environ.get('MODEL_ID', 'ms://Qwen/Qwen3.5-2B')
+
+MODEL_GPUS = int(os.environ.get('MODEL_GPUS', 2))
+SAMPLER_GPUS = int(os.environ.get('SAMPLER_GPUS', 1))
+NUM_GPUS = MODEL_GPUS + SAMPLER_GPUS
+
+NUM_GENERATIONS = int(os.environ.get('NUM_GENERATIONS', 4))
+MAX_NEW_TOKENS = int(os.environ.get('MAX_NEW_TOKENS', 4096))
+LEARNING_RATE = float(os.environ.get('LR', 5e-5))
+MAX_STEPS = int(os.environ.get('MAX_STEPS', 200))
+BATCH_SIZE = int(os.environ.get('BATCH_SIZE', 1))
+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))
+DATA_SLICE = int(os.environ.get('DATA_SLICE', 2000))
+ADAPTER_NAME = 'default'
+SAVE_STEPS = int(os.environ.get('SAVE_STEPS', 50))
+
+
+def create_clevr_dataset():
+    dataset = LazyDataset(
+        DatasetMeta('ms://AI-ModelScope/clevr_cogen_a_train', split='train',
+                    data_slice=range(DATA_SLICE)),
+    )
+    dataset.cast_column('image', decode=False)
+    dataset.set_template('Qwen3_5Template', model_id=MODEL_ID, max_length=4096)
+    dataset.map(CLEVRProcessor(), remove_columns=['image', 'problem', 'solution'])
+    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 = MultiModalAccuracyReward()
+    format_reward_fn = FormatReward()
+    accuracy_rewards = accuracy_reward_fn(trajectories)
+    format_rewards = format_reward_fn(trajectories, trajectories)
+    total_rewards = [a + f for a, f in zip(accuracy_rewards, format_rewards)]
+    return total_rewards, format_rewards, accuracy_rewards
+
+
+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_GPUS,
+        ),
+    ]
+    model_mesh = DeviceMesh.from_sizes(world_size=MODEL_GPUS, dp_size=MODEL_GPUS)
+    sampler_mesh = DeviceMesh.from_sizes(world_size=1, dp_size=1)
+    twinkle.initialize(mode='ray', nproc_per_node=NUM_GPUS, groups=device_groups, lazy_collect=False)
+
+    lora_config = LoraConfig(
+        target_modules=[
+            'q_proj', 'k_proj', 'v_proj', 'o_proj',
+            'gate_proj', 'up_proj', 'down_proj',
+            'in_proj_qkv', 'in_proj_z', 'in_proj_a', 'in_proj_b', 'out_proj',
+        ],
+    )
+
+    from modelscope import Qwen3_5ForConditionalGeneration
+    model = TransformersModel(
+        model_id=MODEL_ID,
+        model_cls=Qwen3_5ForConditionalGeneration,
+        device_mesh=model_mesh,
+        remote_group='model',
+    )
+
+    model.add_adapter_to_model(ADAPTER_NAME, lora_config, gradient_accumulation_steps=GRADIENT_ACCUMULATION_STEPS)
+    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)
+
+    sampler = vLLMSampler(
+        model_id=MODEL_ID,
+        engine_args={
+            'gpu_memory_utilization': 0.8,
+            'max_model_len': 8192,
+            'max_lora_rank': 8,
+            'enable_lora': True,
+            'limit_mm_per_prompt': {'image': 1, 'video': 0},
+            'mm_processor_cache_gb': 0,
+        },
+        device_mesh=sampler_mesh,
+        remote_group='sampler',
+    )
+    sampler.set_template(Qwen3_5Template, model_id=MODEL_ID)
+
+    ckpt_manager = CheckpointEngineManager(model=model, sampler=sampler)
+
+    GLOBAL_BATCH_SIZE = BATCH_SIZE * GRADIENT_ACCUMULATION_STEPS
+    dataloader = DataLoader(
+        dataset=create_clevr_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,
+    )
+
+    optim_step = 0
+    logger.info(get_device_placement())
+
+    for batch in dataloader:
+        if optim_step >= MAX_STEPS:
+            break
+        metrics.reset()
+        global_prompts = batch if isinstance(batch, list) else [batch]
+
+        ckpt_manager.sync_weights(merge_and_sync=False)
+        sampler.reset_prefix_cache()
+        sample_responses = sampler.sample(
+            global_prompts * NUM_GENERATIONS,
+            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, format_rewards, accuracy_rewards = compute_rewards(all_input_data)
+        metrics.accumulate(
+            completion_lengths=all_completion_lengths,
+            rewards={
+                'total': total_rewards,
+                'format': format_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'mm-grpo-clevr-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('mm-grpo-clevr-checkpoint')
+
+
+if __name__ == '__main__':
+    main()

src/twinkle/dataset/base.py

@@ -168,6 +168,19 @@ def _load_dataset(dataset_meta: DatasetMeta, **kwargs):
             dataset = dataset.select(iter_list)
         return dataset
 
+    @remote_function()
+    def cast_column(self, column: str, decode: bool = True) -> None:
+        """Cast an image/audio column's decode mode.
+
+        Useful for setting ``decode=False`` before ``.map()`` to keep media
+        as raw bytes and avoid expensive PIL encode/decode round-trips.
+        """
+        from datasets import Image as ImageFeature
+        for key in list(self.datasets.keys()):
+            self.datasets[key] = self.datasets[key].cast_column(column, ImageFeature(decode=decode))
+        if len(self.datasets) == 1:
+            self.dataset = self.datasets[next(iter(self.datasets.keys()))]
+
     @remote_function()
     def map(self,
             preprocess_func: Union[Preprocessor, Callable, str, Type[Preprocessor]],

src/twinkle/dataset/lazy_dataset.py

@@ -22,6 +22,7 @@ def encode(self, **kwargs):
         assert self.template.truncation_strategy != 'split', ('Lazy tokenize does not support '
                                                               'truncation_strategy==`split`')
         self.do_encode = True
+        self.encode_kwargs = kwargs
 
     @remote_function()
     def check(self, **kwargs):
@@ -33,7 +34,11 @@ def __getitem__(self, idx):
         item = self.dataset[idx]
         # may raise errors
         if self.do_encode:
-            item = self.template.batch_encode([item])[0]
+            encoded = self.template.batch_encode([item], **self.encode_kwargs)[0]
+            for key in item:
+                if key not in encoded:
+                    encoded[key] = item[key]
+            item = encoded
         elif self.do_check:
             item = self.template.check(item)
         return item

src/twinkle/preprocessor/__init__.py

@@ -3,3 +3,4 @@
 from .dpo import EmojiDPOProcessor
 from .llm import (AlpacaProcessor, CompetitionMathGRPOProcessor, CompetitionMathProcessor, CountdownProcessor,
                   GSM8KProcessor, SelfCognitionProcessor)
+from .mm import CLEVRProcessor, VisionQAProcessor

src/twinkle/preprocessor/mm.py

@@ -0,0 +1,67 @@
+# Copyright (c) ModelScope Contributors. All rights reserved.
+import re
+from typing import Any, Dict, List, Optional
+
+from twinkle.data_format import Message, Trajectory
+from .base import Preprocessor
+
+
+class CLEVRProcessor(Preprocessor):
+    """Preprocessor for CLEVR-CoGenT visual reasoning dataset (prompt-only, for GRPO).
+
+    Dataset fields: image (PIL.Image or dict), problem (str), solution (str with <answer> tags)
+    Produces prompt-only trajectories with image in the user message and
+    ground truth stored in user_data for reward computation.
+
+    For fast ``.map()`` performance, call ``dataset.cast_column('image', decode=False)``
+    before mapping so that images stay as Arrow-native bytes dicts.
+    """
+
+    DEFAULT_SYSTEM = ('A conversation between User and Assistant. The user asks a question, '
+                      'and the Assistant solves it. The assistant first thinks about the reasoning '
+                      'process in the mind and then provides the user with the answer. The reasoning '
+                      'process and answer are enclosed within <think> </think> and <answer> </answer> '
+                      'tags, respectively, i.e., <think> reasoning process here </think>'
+                      '<answer> answer here </answer>')
+
+    def __init__(self, system: Optional[str] = None):
+        self.system = system if system is not None else self.DEFAULT_SYSTEM
+
+    @staticmethod
+    def extract_ground_truth(solution: str) -> str:
+        """Extract answer text from <answer>...</answer> tags."""
+        match = re.search(r'<answer>\s*(.*?)\s*</answer>', solution, re.DOTALL)
+        return match.group(1).strip() if match else solution.strip()
+
+    def __call__(self, rows: Dict[str, List[Any]]) -> Dict[str, List[Any]]:
+        rows = self.map_col_to_row(rows)
+        rows = [self.preprocess(row) for row in rows]
+        rows = self.map_row_to_col(rows)
+        return rows
+
+    def preprocess(self, row) -> Trajectory:
+        image = row['image']
+        problem = row['problem']
+        solution = row.get('solution', '')
+        ground_truth = self.extract_ground_truth(solution)
+
+        messages = [
+            Message(role='system', content=[{
+                'type': 'text',
+                'text': self.system
+            }]),
+            Message(role='user', content=[
+                {
+                    'type': 'image',
+                    'image': image
+                },
+                {
+                    'type': 'text',
+                    'text': problem
+                },
+            ]),
+        ]
+        return Trajectory(
+            messages=messages,
+            user_data=[('ground_truth', ground_truth), ('solution', solution)],
+        )

src/twinkle/reward/__init__.py

@@ -3,3 +3,4 @@
 from .format_reward import FormatReward
 from .gsm8k import GSM8KAccuracyReward, GSM8KFormatReward
 from .math_reward import MathReward
+from .mm_reward import MultiModalAccuracyReward