Revert data processing of VLM

.github/workflows/pr-test.yml

@@ -63,7 +63,7 @@ jobs:
 
       - name: Install
         shell: bash
-        run: cd $GITHUB_WORKSPACE && pip install -e . --break-system-packages
+        run: cd $GITHUB_WORKSPACE && pip install -e . --no-deps --break-system-packages
 
       - name: Execute
         shell: bash
@@ -107,7 +107,7 @@ jobs:
 
       - name: Install
         shell: bash
-        run: cd $GITHUB_WORKSPACE && pip install -e . --break-system-packages
+        run: cd $GITHUB_WORKSPACE && pip install -e . --no-deps --break-system-packages
 
       - name: Execute
         shell: bash
@@ -151,7 +151,7 @@ jobs:
 
       - name: Install
         shell: bash
-        run: cd $GITHUB_WORKSPACE && pip install -e . --break-system-packages
+        run: cd $GITHUB_WORKSPACE && pip install -e . --no-deps --break-system-packages
 
       - name: Execute
         shell: bash
@@ -195,7 +195,7 @@ jobs:
 
       - name: Install
         shell: bash
-        run: cd $GITHUB_WORKSPACE && pip install -e . --break-system-packages
+        run: cd $GITHUB_WORKSPACE && pip install -e . --no-deps --break-system-packages
 
       - name: Execute
         shell: bash

.github/workflows/pr-test.yml.j2

@@ -94,7 +94,7 @@ jobs:
 
       - name: Install
         shell: bash
-        run: cd $GITHUB_WORKSPACE && pip install -e . --break-system-packages
+        run: cd $GITHUB_WORKSPACE && pip install -e . --no-deps --break-system-packages
 
       - name: Execute
         shell: bash

slime/ray/rollout.py

@@ -20,6 +20,7 @@
 from slime.utils.metric_checker import MetricChecker
 from slime.utils.metric_utils import compute_pass_rate, compute_rollout_step, compute_statistics, dict_add_prefix
 from slime.utils.misc import load_function
+from slime.utils.processing_utils import load_processor
 from slime.utils.ray_utils import Box
 from slime.utils.seqlen_balancing import get_seqlen_balanced_partitions
 from slime.utils.tracking_utils import init_tracking
@@ -77,6 +78,7 @@ def __init__(self, args, pg):
         self._metric_checker = MetricChecker.maybe_create(args)
         if self.args.use_fault_tolerance:
             self._health_monitor = RolloutHealthMonitor(self, args)
+        self.processor = None
 
     def dispose(self):
         if self._metric_checker is not None:
@@ -275,7 +277,17 @@ def _convert_samples_to_train_data(self, samples: list[Sample] | list[list[Sampl
             train_data["metadata"] = [sample.train_metadata for sample in samples]
 
         if samples[0].multimodal_inputs is not None:
-            train_data["multimodal_inputs"] = [sample.multimodal_inputs for sample in samples]
+            if self.processor is None:
+                self.processor = load_processor(self.args.hf_checkpoint, trust_remote_code=True)
+            train_data["multimodal_inputs"] = []
+            for sample in samples:
+                # Get input IDs with full prompt (text + multimodal)
+                processor_output = self.processor(text=sample.prompt, **sample.multimodal_inputs)
+
+                # Extract multimodal tokens (exclude text-related tokens)
+                train_data["multimodal_inputs"].append(
+                    {k: v for k, v in processor_output.items() if k not in ["input_ids", "attention_mask"]}
+                )
 
         if "teacher_log_probs" in samples[0].__dict__:
             train_data["teacher_log_probs"] = [sample.teacher_log_probs for sample in samples]

slime/rollout/sft_rollout.py

@@ -1,7 +1,7 @@
 import logging
 
 from slime.utils.mask_utils import MultiTurnLossMaskGenerator
-from slime.utils.processing_utils import load_processor, load_tokenizer, prepare_model_inputs
+from slime.utils.processing_utils import load_processor, load_tokenizer
 
 __all__ = ["generate_rollout"]
 
@@ -46,18 +46,7 @@ def generate_rollout(args, rollout_id, data_buffer, evaluation=False):
         messages = sample.prompt
         tools = sample.metadata.get("tools", None)
 
-        input_ids, extra_info = prepare_model_inputs(
-            messages, TOKENIZER, PROCESSOR, sample.metadata, args.apply_chat_template, args.apply_chat_template_kwargs
-        )
-
-        has_multimodal = bool(extra_info.get("images") or extra_info.get("videos"))
-        if has_multimodal:
-            sample.multimodal_inputs = extra_info["multimodal_inputs"]
-            token_ids, loss_mask = MASK_GENERATOR.get_loss_mask_with_multimodal_alignment(
-                messages, input_ids, tools=tools
-            )
-        else:
-            token_ids, loss_mask = MASK_GENERATOR.get_loss_mask(messages, tools=tools)
+        token_ids, loss_mask = MASK_GENERATOR.get_loss_mask(messages, tools=tools)
 
         response_length = MASK_GENERATOR.get_response_lengths([loss_mask])[0]
 

slime/rollout/sglang_rollout.py

@@ -20,12 +20,7 @@
 from slime.utils.http_utils import get, post
 from slime.utils.mask_utils import get_response_lengths
 from slime.utils.misc import SingletonMeta, load_function
-from slime.utils.processing_utils import (
-    encode_image_for_rollout_engine,
-    load_processor,
-    load_tokenizer,
-    prepare_model_inputs,
-)
+from slime.utils.processing_utils import encode_image_for_rollout_engine, load_processor, load_tokenizer
 from slime.utils.types import Sample
 
 from .rm_hub import async_rm, batched_async_rm
@@ -90,26 +85,21 @@ def submit_generate_tasks(self, samples: list[list[Sample]]) -> None:
 
 async def generate(args: Namespace, sample: Sample, sampling_params: dict[str, Any]) -> Sample:
     """Generate using traditional SGLang router with token-based workflow"""
+    if args.ci_test:
+        assert isinstance(sample.prompt, str)
+
     state = GenerateState(args)
     url = f"http://{args.sglang_router_ip}:{args.sglang_router_port}/generate"
 
     assert (
         sample.status == Sample.Status.PENDING or sample.status == Sample.Status.ABORTED
     ), f"Sample status is {sample.status}"
 
-    prompt_ids, extra_info = prepare_model_inputs(
-        sample.prompt,
-        state.tokenizer,
-        state.processor,
-        sample.metadata,
-        args.apply_chat_template,
-        args.apply_chat_template_kwargs,
-    )
-
-    sample.prompt = extra_info.get("formatted_prompt", sample.prompt)
-    image_data = extra_info.get("images", [])
-    video_data = extra_info.get("videos", [])
-    multimodal_inputs = extra_info.get("multimodal_inputs", None)
+    if state.processor:
+        processor_output = state.processor(text=sample.prompt, **sample.multimodal_inputs)
+        prompt_ids = processor_output["input_ids"][0]
+    else:
+        prompt_ids = state.tokenizer.encode(sample.prompt, add_special_tokens=False)
 
     if len(sample.response) > 0:
         sampling_params["max_new_tokens"] -= len(sample.tokens) - len(prompt_ids)
@@ -130,12 +120,9 @@ async def generate(args: Namespace, sample: Sample, sampling_params: dict[str, A
     if args.use_rollout_routing_replay:
         payload["return_routed_experts"] = True
 
-    if image_data:
+    if sample.multimodal_inputs and sample.multimodal_inputs["images"]:
+        image_data = sample.multimodal_inputs["images"]
         payload["image_data"] = [encode_image_for_rollout_engine(image) for image in image_data]
-        sample.multimodal_inputs = multimodal_inputs
-
-    if video_data:
-        raise NotImplementedError("Video data is not supported yet")
 
     # Use existing tokens for multi-turn or tokenize the new prompt
     if len(sample.response) > 0:

slime/utils/data.py

@@ -49,17 +49,16 @@ def _parse_generalized_path(s: str):
     return s, None
 
 
-def _should_skip_prompt(
-    prompt, tokenizer, processor, metadata, max_length, apply_chat_template, apply_chat_template_kwargs
-):
+def _should_skip_prompt(formatted_prompt: str, tokenizer, processor, max_length, multimodal_inputs=None):
     if max_length is None:
         return False
 
-    from slime.utils.processing_utils import prepare_model_inputs
+    if processor:
+        processor_output = processor(text=formatted_prompt, **multimodal_inputs)
+        input_ids = processor_output["input_ids"][0]
+    else:
+        input_ids = tokenizer.encode(formatted_prompt, add_special_tokens=False)
 
-    input_ids, _ = prepare_model_inputs(
-        prompt, tokenizer, processor, metadata, apply_chat_template, apply_chat_template_kwargs
-    )
     return len(input_ids) > max_length
 
 
@@ -140,6 +139,7 @@ def __init__(
             prompt = _build_messages(data, prompt_key, as_conversation, multimodal_keys)
 
             metadata = data.get(metadata_key) or {}
+            tools = None
             if tool_key is not None and tool_key in data:
                 tools = data[tool_key]
                 if isinstance(tools, str):
@@ -149,17 +149,37 @@ def __init__(
                 assert isinstance(tools, list), f"tools must be a list, got {type(tools)} instead"
                 metadata["tools"] = tools
 
+            if apply_chat_template:
+                formatted_prompt = tokenizer.apply_chat_template(
+                    prompt,
+                    tools=tools,
+                    tokenize=False,
+                    add_generation_prompt=True,
+                    **(apply_chat_template_kwargs or {}),
+                )
+            else:
+                formatted_prompt = prompt
+
+            if processor:
+                # temporary solution, will write image utils for slime later
+                from qwen_vl_utils import process_vision_info
+
+                assert isinstance(prompt, list)
+                images, videos = process_vision_info(prompt)
+                multimodal_inputs = {"images": images, "videos": videos}
+            else:
+                multimodal_inputs = None
+
             # TODO: this is slow.
-            if _should_skip_prompt(
-                prompt, tokenizer, processor, metadata, max_length, apply_chat_template, apply_chat_template_kwargs
-            ):
+            if _should_skip_prompt(formatted_prompt, tokenizer, processor, max_length, multimodal_inputs):
                 continue
 
             self.origin_samples.append(
                 Sample(
-                    prompt=prompt,
+                    prompt=formatted_prompt,
                     label=data[label_key] if label_key is not None else None,
                     metadata=metadata,
+                    multimodal_inputs=multimodal_inputs,
                 )
             )
 

slime/utils/processing_utils.py

@@ -2,7 +2,6 @@
 import io
 import logging
 
-import numpy as np
 from transformers import AutoProcessor, AutoTokenizer, PreTrainedTokenizerBase, ProcessorMixin
 
 logger = logging.getLogger(__name__)
@@ -26,62 +25,6 @@ def load_processor(name_or_path: str, **kwargs):
     return proc
 
 
-def prepare_model_inputs(
-    prompt, tokenizer, processor=None, metadata=None, apply_chat_template=False, apply_chat_template_kwargs=None
-):
-    """Prepare all inputs for model inference.
-
-    Returns:
-        tuple: (input_ids, extra_info)
-            - input_ids: Token IDs for the prompt
-            - extra_info: Dict with 'images', 'videos', 'multimodal_inputs' (or empty dict)
-    """
-    tools = metadata.get("tools") if metadata else None
-    if isinstance(prompt, (list, np.ndarray)):
-        assert (
-            apply_chat_template
-        ), f"apply_chat_template must be True when prompt is a list or numpy array, current prompt is {prompt}"
-        formatted_prompt = tokenizer.apply_chat_template(
-            prompt,
-            tools=tools,
-            tokenize=False,
-            add_generation_prompt=True,
-            **(apply_chat_template_kwargs or {}),
-        )
-    elif isinstance(prompt, str):
-        assert (
-            not apply_chat_template
-        ), f"apply_chat_template must be False when prompt is a string, current prompt is {prompt}"
-        formatted_prompt = prompt
-    else:
-        raise ValueError(f"Invalid prompt type: {type(prompt)}, current prompt is {prompt}")
-
-    if not processor:
-        input_ids = tokenizer.encode(formatted_prompt, add_special_tokens=False)
-        return input_ids, {"formatted_prompt": formatted_prompt}
-    else:
-        # temporary solution, will write image utils for slime later
-        from qwen_vl_utils import process_vision_info
-
-        images, videos = process_vision_info(prompt)
-
-        # Get input IDs with full prompt (text + multimodal)
-        processor_output = processor(text=formatted_prompt, images=images, videos=videos)
-        input_ids = processor_output["input_ids"][0]
-
-        # Extract multimodal tokens (exclude text-related tokens)
-        multimodal_inputs = {k: v for k, v in processor_output.items() if k not in ["input_ids", "attention_mask"]}
-
-        extra_info = {
-            "formatted_prompt": formatted_prompt,
-            "images": images,
-            "videos": videos,
-            "multimodal_inputs": multimodal_inputs,
-        }
-
-        return input_ids, extra_info
-
-
 def encode_image_for_rollout_engine(image) -> str:
     """Load an image from path, ensure RGB, encode as JPEG base64 string."""
     buffer = io.BytesIO()