[VLM] optimize VLM processing

.github/workflows/pr-test.yml

@@ -48,7 +48,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        info: [{"num_gpus": 8, "test_file": "test_quick_start_glm4_9B.py"}, {"num_gpus": 8, "test_file": "test_qwen3_30B_A3B.py"}, {"num_gpus": 8, "test_file": "test_qwen3_4B_ppo.py"}, {"num_gpus": 8, "test_file": "test_moonlight_16B_A3B.py"}, {"num_gpus": 2, "test_file": "test_qwen3_4B_fsdp_true_on_policy.py"}, {"num_gpus": 8, "test_file": "test_mimo_7B_mtp_only_grad.py"}]
+        info: [{"num_gpus": 8, "test_file": "test_quick_start_glm4_9B.py"}, {"num_gpus": 8, "test_file": "test_qwen3_30B_A3B.py"}, {"num_gpus": 8, "test_file": "test_qwen3_4B_ppo.py"}, {"num_gpus": 8, "test_file": "test_moonlight_16B_A3B.py"}, {"num_gpus": 2, "test_file": "test_qwen3_4B_fsdp_true_on_policy.py"}, {"num_gpus": 8, "test_file": "test_mimo_7B_mtp_only_grad.py"}, {"num_gpus": 8, "test_file": "test_qwen3_vl_4B_fsdp.py"}]
     defaults:
       run:
         working-directory: ${{ github.workspace }}

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

@@ -8,6 +8,7 @@
         {'test_file': 'test_moonlight_16B_A3B.py', 'num_gpus': 8},
         {'test_file': 'test_qwen3_4B_fsdp_true_on_policy.py', 'num_gpus': 2},
         {'test_file': 'test_mimo_7B_mtp_only_grad.py', 'num_gpus': 8},
+        {'test_file': 'test_qwen3_vl_4B_fsdp.py', 'num_gpus': 8},
       ],
     },
     'e2e-test-long': {

docs/en/get_started/customization.md

@@ -270,7 +270,7 @@ dict: {
     "rollout_log_probs": list,            # Log probs (for off-policy correction)
     "rollout_routed_experts": list,       # Routed experts (for MoE)
     "metadata": list,                     # Train metadata
-    "multimodal_inputs": list,            # Multimodal inputs (for VLM)
+    "multimodal_train_inputs": list,      # Multimodal tensors (for VLM)
     "teacher_log_probs": list,            # Teacher log probs (for distillation)
 }
 ```

docs/zh/get_started/customization.md

@@ -270,7 +270,7 @@ dict: {
     "rollout_log_probs": list,            # log 概率（用于离策略校正）
     "rollout_routed_experts": list,       # 路由专家（用于 MoE）
     "metadata": list,                     # 训练元数据
-    "multimodal_inputs": list,            # 多模态输入（用于 VLM）
+    "multimodal_train_inputs": list,      # 多模态张量（用于 VLM）
     "teacher_log_probs": list,            # 教师 log 概率（用于蒸馏）
 }
 ```

examples/geo3k_vlm/run_geo3k_vlm.sh

@@ -80,8 +80,6 @@ fi
 # Common args
 CKPT_ARGS=(
    --hf-checkpoint /root/models/${MODEL_NAME}
-   # vl model has rotary base 5000000
-   --rotary-base 5000000
 )
 
 ROLLOUT_ARGS=(
@@ -154,41 +152,43 @@ MISC_ARGS=(
 # Backend-specific args
 if [ "$TRAIN_BACKEND" = "fsdp" ]; then
    BACKEND_ARGS=(
-   --train-backend fsdp
-   --gradient-checkpointing
-   --sglang-attention-backend fa3
-   --attn-implementation flash_attention_3
-   --update-weight-buffer-size 536870912
+      --train-backend fsdp
+      --gradient-checkpointing
+      --sglang-attention-backend fa3
+      --attn-implementation flash_attention_3
+      --update-weight-buffer-size 536870912
    )
    MODEL_ARGS=()
 else
    # megatron backend (default)
    BACKEND_ARGS=(
-   --train-backend megatron
-   --load /root/models/${MODEL_NAME}
-   --tensor-model-parallel-size 4
-   --sequence-parallel
-   --pipeline-model-parallel-size 1
-   --context-parallel-size 1
-   --expert-model-parallel-size 1
-   --expert-tensor-parallel-size 1
-   --recompute-granularity full
-   --recompute-method uniform
-   --recompute-num-layers 1
-   --use-dynamic-batch-size
-   --max-tokens-per-gpu 4096
-   --attention-dropout 0.0
-   --hidden-dropout 0.0
-   --accumulate-allreduce-grads-in-fp32
-   --attention-softmax-in-fp32
-   --attention-backend flash
-   --megatron-to-hf-mode bridge
+      --train-backend megatron
+      --load /root/models/${MODEL_NAME}
+      --tensor-model-parallel-size 4
+      --sequence-parallel
+      --pipeline-model-parallel-size 1
+      --context-parallel-size 1
+      --expert-model-parallel-size 1
+      --expert-tensor-parallel-size 1
+      --recompute-granularity full
+      --recompute-method uniform
+      --recompute-num-layers 1
+      --use-dynamic-batch-size
+      --max-tokens-per-gpu 4096
+      --attention-dropout 0.0
+      --hidden-dropout 0.0
+      --accumulate-allreduce-grads-in-fp32
+      --attention-softmax-in-fp32
+      --attention-backend flash
+      --megatron-to-hf-mode bridge
    )
 
    # get MODEL_ARGS from scripts/models for megatron backend
    SLIME_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")/../.." &>/dev/null && pwd)"
    MODEL_ARGS_FILE=$(echo "$MODEL_NAME" | sed 's/-Instruct//g; s/-Thinking//g; s/Qwen3-VL-/qwen3-/g; s/-2B/-1.7B/g')
-   source "${SLIME_DIR}/scripts/models/${MODEL_ARGS_FILE}.sh"
+   # VL models require rotary-base 5000000
+   MODEL_ARGS_ROTARY_BASE=5000000 source "${SLIME_DIR}/scripts/models/${MODEL_ARGS_FILE}.sh"
+
 fi
 
 # Start Ray if not using external Ray

examples/geo3k_vlm/run_geo3k_vlm_sft.sh

@@ -72,7 +72,6 @@ fi
 CKPT_ARGS=(
    --hf-checkpoint /root/models/${MODEL_NAME}
    --load /root/models/${MODEL_NAME}
-   --rotary-base 5000000
 )
 
 SFT_ARGS=(
@@ -152,7 +151,8 @@ else
    # get MODEL_ARGS from scripts/models for megatron backend
    SLIME_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")/../.." &>/dev/null && pwd)"
    MODEL_ARGS_FILE=$(echo "$MODEL_NAME" | sed 's/-Instruct//g; s/-Thinking//g; s/Qwen3-VL-/qwen3-/g; s/-2B/-1.7B/g')
-   source "${SLIME_DIR}/scripts/models/${MODEL_ARGS_FILE}.sh"
+   # VL models require rotary-base 5000000
+   MODEL_ARGS_ROTARY_BASE=5000000 source "${SLIME_DIR}/scripts/models/${MODEL_ARGS_FILE}.sh"
 fi
 
 # Start Ray if not using external Ray

scripts/models/qwen3-1.7B.sh

@@ -10,7 +10,7 @@ MODEL_ARGS=(
    --disable-bias-linear
    --normalization "RMSNorm"
    --norm-epsilon 1e-6
-   --rotary-base 1000000
+   --rotary-base "${MODEL_ARGS_ROTARY_BASE:-1000000}"
    --vocab-size 151936
    --kv-channels 128
    --qk-layernorm

scripts/models/qwen3-235B-A22B.sh

@@ -32,7 +32,7 @@ MODEL_ARGS=(
    --untie-embeddings-and-output-weights
    --vocab-size 151936
 
-   --rotary-base 1000000
+   --rotary-base "${MODEL_ARGS_ROTARY_BASE:-1000000}"
 
    # moe
    --moe-ffn-hidden-size 1536

scripts/models/qwen3-30B-A3B.sh

@@ -32,7 +32,7 @@ MODEL_ARGS=(
    --untie-embeddings-and-output-weights
    --vocab-size 151936
 
-   --rotary-base 1000000
+   --rotary-base "${MODEL_ARGS_ROTARY_BASE:-1000000}"
 
    # moe
    --moe-ffn-hidden-size 768

scripts/models/qwen3-8B.sh

@@ -10,7 +10,7 @@ MODEL_ARGS=(
    --disable-bias-linear
    --normalization "RMSNorm"
    --norm-epsilon 1e-6
-   --rotary-base 1000000
+   --rotary-base "${MODEL_ARGS_ROTARY_BASE:-1000000}"
    --vocab-size 151936
    --kv-channels 128
    --qk-layernorm

slime/backends/fsdp_utils/actor.py

@@ -463,8 +463,10 @@ def _packed_data(
                     rollout_log_probs=(
                         rollout_data["rollout_log_probs"][start:end] if "rollout_log_probs" in rollout_data else None
                     ),
-                    multimodal_inputs=(
-                        rollout_data["multimodal_inputs"][start:end] if "multimodal_inputs" in rollout_data else None
+                    multimodal_train_inputs=(
+                        rollout_data["multimodal_train_inputs"][start:end]
+                        if "multimodal_train_inputs" in rollout_data
+                        else None
                     ),
                     num_packs=mbs_size,
                 )
@@ -890,8 +892,8 @@ def _get_model_inputs_args(self, packed_sequence: dict) -> dict:
             "position_ids": position_ids,
             "attention_mask": None,
         }
-        if packed_sequence.get("multimodal_inputs"):
-            model_args.update(packed_sequence["multimodal_inputs"])
+        if packed_sequence.get("multimodal_train_inputs"):
+            model_args.update(packed_sequence["multimodal_train_inputs"])
         return model_args
 
 

slime/backends/fsdp_utils/data_packing.py

@@ -17,7 +17,7 @@ def pack_sequences(
     advantages: list[float],
     returns: list[float],
     rollout_log_probs: list[list[float]] | None = None,
-    multimodal_inputs: list[dict] | None = None,
+    multimodal_train_inputs: list[dict] | None = None,
     max_tokens_per_gpu: int | None = None,
     num_packs: int | None = None,
 ) -> list[dict]:
@@ -33,7 +33,7 @@ def pack_sequences(
         advantages: List of advantages per sequence
         returns: List of returns per sequence
         rollout_log_probs: List of rollout log probabilities per sequence
-        multimodal_inputs: List of dict of multimodal tokens per sequence
+        multimodal_train_inputs: List of dict of multimodal tensors for training per sequence
         max_tokens_per_gpu: Maximum tokens per GPU pack
         num_packs: Explicit number of packs to create
 
@@ -100,19 +100,19 @@ def pack_sequences(
             ),
         }
 
-        # Collect and add multimodal inputs for this partition
-        if multimodal_inputs:
+        # Collect and add multimodal training tensors for this partition
+        if multimodal_train_inputs:
             multimodal_data = {}  # key -> concatenated tensor
             multimodal_num_items = {}  # key -> list of item counts per sequence
             for i in indices:
-                for key, mm_tensor in multimodal_inputs[i].items():
+                for key, mm_tensor in multimodal_train_inputs[i].items():
                     if key not in multimodal_data:
                         multimodal_data[key] = mm_tensor
                         multimodal_num_items[key] = [mm_tensor.size(0)]
                     else:
                         multimodal_data[key] = torch.cat([multimodal_data[key], mm_tensor], dim=0)
                         multimodal_num_items[key].append(mm_tensor.size(0))
-            packed_batch["multimodal_inputs"] = multimodal_data
+            packed_batch["multimodal_train_inputs"] = multimodal_data
             packed_batch["multimodal_num_items"] = multimodal_num_items
 
         result.append(packed_batch)
@@ -157,8 +157,8 @@ def unpack_sequences(packed_batch: dict) -> list[dict]:
                 # Skip multimodal_num_items - it's metadata
                 if key == "multimodal_num_items":
                     continue
-                # Handle multimodal_inputs dict: split each tensor using multimodal_num_items
-                elif key == "multimodal_inputs" and isinstance(value, dict):
+                # Handle multimodal_train_inputs dict: split each tensor using multimodal_num_items
+                elif key == "multimodal_train_inputs" and isinstance(value, dict):
                     instance[key] = {}
                     for mm_key, mm_tensor in value.items():
                         if mm_key in multimodal_num_items:

slime/backends/megatron_utils/actor.py

@@ -193,15 +193,15 @@ def _get_rollout_data(self, rollout_data_ref: Box) -> RolloutBatch:
         rollout_data["loss_masks"] = [
             torch.tensor(t, dtype=torch.int, device=torch.cuda.current_device()) for t in rollout_data["loss_masks"]
         ]
-        if "multimodal_inputs" in rollout_data:
-            # Move multimodal inputs to GPU in advance
-            rollout_data["multimodal_inputs"] = [
+        if "multimodal_train_inputs" in rollout_data:
+            # Move multimodal training tensors to GPU in advance
+            rollout_data["multimodal_train_inputs"] = [
                 (
                     {key: tensor.to(device=torch.cuda.current_device()) for key, tensor in mm_dict.items()}
                     if mm_dict is not None
                     else None
                 )
-                for mm_dict in rollout_data["multimodal_inputs"]
+                for mm_dict in rollout_data["multimodal_train_inputs"]
             ]
         if "rollout_log_probs" in rollout_data:
             rollout_data["rollout_log_probs"] = [

slime/backends/megatron_utils/data.py

@@ -107,12 +107,12 @@ def get_batch(
     assert loss_masks.shape == tokens.shape, f"loss_masks.shape: {loss_masks.shape}, tokens.shape: {tokens.shape}"
     batch["full_loss_masks"] = loss_masks
 
-    # Process multimodal inputs if present
-    multimodal_inputs = batch.get("multimodal_inputs", None)
-    if multimodal_inputs is not None:
+    # Process multimodal training tensors if present
+    multimodal_train_inputs = batch.get("multimodal_train_inputs", None)
+    if multimodal_train_inputs is not None:
         multimodal_data = {}  # key -> concatenated tensor
         multimodal_num_items = {}  # key -> list of item counts per sequence
-        for mm_input_dict in multimodal_inputs:
+        for mm_input_dict in multimodal_train_inputs:
             if mm_input_dict is not None:
                 for key, mm_tensor in mm_input_dict.items():
                     if key not in multimodal_data:
@@ -121,7 +121,7 @@ def get_batch(
                     else:
                         multimodal_data[key] = torch.cat([multimodal_data[key], mm_tensor], dim=0)
                         multimodal_num_items[key].append(mm_tensor.size(0))
-        batch["multimodal_inputs"] = multimodal_data
+        batch["multimodal_train_inputs"] = multimodal_data
         batch["multimodal_num_items"] = multimodal_num_items
 
     return batch
@@ -349,7 +349,7 @@ def log_rollout_data(rollout_id: int, args: Namespace, rollout_data: RolloutBatc
         for key, val in rollout_data.items():
             if key in [
                 "tokens",
-                "multimodal_inputs",
+                "multimodal_train_inputs",
                 "loss_masks",
                 "sample_indices",
                 "rollout_routed_experts",

slime/backends/megatron_utils/model.py

@@ -207,7 +207,7 @@ def forward_step(
             [
                 "tokens",
                 "loss_masks",
-                "multimodal_inputs",
+                "multimodal_train_inputs",
                 "total_lengths",
                 "response_lengths",
             ],
@@ -225,7 +225,7 @@ def forward_step(
             labels=None,
             packed_seq_params=packed_seq_params,
             loss_mask=batch["full_loss_masks"],
-            **(batch["multimodal_inputs"] if batch["multimodal_inputs"] is not None else {}),
+            **(batch["multimodal_train_inputs"] if batch["multimodal_train_inputs"] is not None else {}),
         )
 
         return output_tensor, partial(
@@ -354,7 +354,7 @@ def forward_step(data_iterator: DataIterator, model: GPTModel, return_schedule_p
             data_iterator,
             [
                 "tokens",
-                "multimodal_inputs",
+                "multimodal_train_inputs",
                 "packed_seq_params",
                 "total_lengths",
                 "response_lengths",
@@ -392,7 +392,7 @@ def forward_step(data_iterator: DataIterator, model: GPTModel, return_schedule_p
                 packed_seq_params=batch["packed_seq_params"],
                 loss_mask=batch["full_loss_masks"],
                 mtp_kwargs={"mtp_labels": batch["tokens"]} if args.enable_mtp_training else {},
-                **(batch["multimodal_inputs"] if batch["multimodal_inputs"] is not None else {}),
+                **(batch["multimodal_train_inputs"] if batch["multimodal_train_inputs"] is not None else {}),
             )
 
         if os.environ.get("ENABLE_ROUTING_REPLAY", "0") == "1":

slime/ray/rollout.py

@@ -20,7 +20,6 @@
 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
@@ -78,7 +77,6 @@ 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:
@@ -276,18 +274,8 @@ def _convert_samples_to_train_data(self, samples: list[Sample] | list[list[Sampl
         if samples[0].train_metadata is not None:
             train_data["metadata"] = [sample.train_metadata for sample in samples]
 
-        if samples[0].multimodal_inputs is not None:
-            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 samples[0].multimodal_train_inputs is not None:
+            train_data["multimodal_train_inputs"] = [sample.multimodal_train_inputs for sample in samples]
 
         if "teacher_log_probs" in samples[0].__dict__:
             train_data["teacher_log_probs"] = [sample.teacher_log_probs for sample in samples]
@@ -320,7 +308,7 @@ def _split_train_data_by_dp(self, data, dp_size):
             rollout_data["partition"] = partition
             for key in [
                 "tokens",
-                "multimodal_inputs",
+                "multimodal_train_inputs",
                 "response_lengths",
                 "rewards",
                 "truncated",

slime/rollout/sglang_rollout.py

@@ -98,6 +98,9 @@ async def generate(args: Namespace, sample: Sample, sampling_params: dict[str, A
     if state.processor:
         processor_output = state.processor(text=sample.prompt, **sample.multimodal_inputs)
         prompt_ids = processor_output["input_ids"][0]
+        sample.multimodal_train_inputs = {
+            k: v for k, v in processor_output.items() if k not in ["input_ids", "attention_mask"]
+        } or None
     else:
         prompt_ids = state.tokenizer.encode(sample.prompt, add_special_tokens=False)