[VLM] Support PP for Qwen2.5-VL (#13075)

Co-authored-by: luoyuan.luo <luoyuan.luo@antgroup.com>
Co-authored-by: Tianyu Guo <guoty9@mail2.sysu.edu.cn>

python/sglang/srt/models/qwen2_5_vl.py

@@ -40,6 +40,7 @@ from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import (
     Qwen2_5_VisionRotaryEmbedding,
 )
 
+from sglang.srt.distributed.parallel_state import get_pp_group
 from sglang.srt.layers.attention.vision import VisionAttention
 from sglang.srt.layers.layernorm import RMSNorm
 from sglang.srt.layers.linear import (
@@ -50,13 +51,14 @@ from sglang.srt.layers.linear import (
 from sglang.srt.layers.logits_processor import LogitsProcessor
 from sglang.srt.layers.pooler import Pooler, PoolingType
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.layers.utils import PPMissingLayer, get_layer_id
 from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead
 from sglang.srt.managers.mm_utils import (
     MultiModalityDataPaddingPatternMultimodalTokens,
     general_mm_embed_routine,
 )
 from sglang.srt.managers.schedule_batch import MultimodalDataItem, MultimodalInputs
-from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
 from sglang.srt.model_loader.weight_utils import default_weight_loader
 from sglang.srt.models.qwen2 import Qwen2Model
 from sglang.srt.models.utils import permute_inv
@@ -482,6 +484,7 @@ class Qwen2_5_VLForConditionalGeneration(nn.Module):
     ) -> None:
         super().__init__()
 
+        self.pp_group = get_pp_group()
         self.config = config
         self.visual = Qwen2_5_VisionTransformer(
             config.vision_config,
@@ -498,15 +501,20 @@ class Qwen2_5_VLForConditionalGeneration(nn.Module):
             prefix=add_prefix("model", prefix),
         )
 
-        if config.tie_word_embeddings:
-            self.lm_head = self.model.embed_tokens
+        if self.pp_group.is_last_rank:
+            if self.pp_group.world_size == 1 and self.config.tie_word_embeddings:
+                self.lm_head = self.model.embed_tokens
+            else:
+                self.lm_head = ParallelLMHead(
+                    self.config.vocab_size,
+                    self.config.hidden_size,
+                    quant_config=quant_config,
+                    prefix=add_prefix("lm_head", prefix),
+                )
         else:
-            self.lm_head = ParallelLMHead(
-                config.vocab_size,
-                config.hidden_size,
-                quant_config=quant_config,
-                prefix=add_prefix("lm_head", prefix),
-            )
+            # ranks other than the last rank will have a placeholder layer
+            self.lm_head = PPMissingLayer()
+
         self.is_mrope_enabled = "mrope_section" in self.config.rope_scaling
 
         self.logits_processor = LogitsProcessor(config)
@@ -551,6 +559,7 @@ class Qwen2_5_VLForConditionalGeneration(nn.Module):
         positions: torch.Tensor,
         forward_batch: ForwardBatch,
         get_embedding: bool = False,
+        pp_proxy_tensors: Optional[PPProxyTensors] = None,
     ):
         """Run forward pass for Qwen2_5-VL.
 
@@ -583,18 +592,25 @@ class Qwen2_5_VLForConditionalGeneration(nn.Module):
             language_model=self.model,
             multimodal_model=self,
             positions=positions,
+            pp_proxy_tensors=pp_proxy_tensors,
         )
 
         aux_hidden_states = None
         if self.capture_aux_hidden_states:
             hidden_states, aux_hidden_states = hidden_states
 
-        if not get_embedding:
-            return self.logits_processor(
-                input_ids, hidden_states, self.lm_head, forward_batch, aux_hidden_states
-            )
+        if self.pp_group.is_last_rank:
+            if not get_embedding:
+                return self.logits_processor(
+                    input_ids,
+                    hidden_states,
+                    self.lm_head,
+                    forward_batch,
+                )
+            else:
+                return self.pooler(hidden_states, forward_batch)
         else:
-            return self.pooler(hidden_states, forward_batch)
+            return hidden_states
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
         stacked_params_mapping = [
@@ -620,6 +636,16 @@ class Qwen2_5_VLForConditionalGeneration(nn.Module):
                 ):
                     continue
                 name = name.replace(weight_name, param_name)
+                layer_id = get_layer_id(name)
+                if (
+                    layer_id is not None
+                    and hasattr(self.model, "start_layer")
+                    and (
+                        layer_id < self.model.start_layer
+                        or layer_id >= self.model.end_layer
+                    )
+                ):
+                    continue
 
                 # Skip loading extra bias for GPTQ models.
                 if name.endswith(".bias") and name not in params_dict:
@@ -637,7 +663,10 @@ class Qwen2_5_VLForConditionalGeneration(nn.Module):
                     # Skip loading extra bias for GPTQ models.
                     if name.endswith(".bias") and name not in params_dict:
                         continue
-                    param = params_dict[name]
+                    if name in params_dict.keys():
+                        param = params_dict[name]
+                    else:
+                        continue
                 except KeyError:
                     print(params_dict.keys())
                     raise