feat: Support DP Attention for step3_vl

python/sglang/srt/layers/attention/vision.py

@@ -11,6 +11,7 @@
 import torch.nn.functional as F
 from einops import rearrange
 
+from sglang.srt.layers.dp_attention import get_attention_tp_rank, get_attention_tp_size
 from sglang.srt.utils import is_cuda, print_info_once
 
 _is_cuda = is_cuda()
@@ -365,19 +366,20 @@ def __init__(
         **kwargs,
     ):
         super().__init__()
-        world_size = parallel_state.get_tensor_model_parallel_world_size()
-        self.tp_size = world_size
-        self.tp_rank = parallel_state.get_tensor_model_parallel_rank()
+        attn_tp_rank = get_attention_tp_rank()
+        attn_tp_size = get_attention_tp_size()
+        self.tp_size = attn_tp_size
+        self.tp_rank = attn_tp_rank
         self.dropout = dropout
         self.head_size = embed_dim // num_heads
         self.hidden_size_per_attention_head = dist_utils.divide(
             projection_size, num_heads
         )
         self.num_attention_heads_per_partition = dist_utils.divide(
-            num_dummy_heads + num_heads, world_size
+            num_dummy_heads + num_heads, self.tp_size
         )
         self.num_attention_kv_heads_per_partition = dist_utils.divide(
-            num_dummy_heads + num_heads, world_size
+            num_dummy_heads + num_heads, self.tp_size
         )
 
         self.q_size = self.num_attention_heads_per_partition * self.head_size
@@ -427,6 +429,8 @@ def __init__(
                 total_num_kv_heads=num_dummy_heads + num_heads,
                 bias=qkv_bias,
                 quant_config=quant_config,
+                tp_rank=self.tp_rank,
+                tp_size=self.tp_size,
                 prefix=add_prefix("qkv_proj", prefix),
             )
         else:
@@ -435,13 +439,17 @@ def __init__(
                 output_size=3 * self.dummy_dim,
                 bias=qkv_bias,
                 quant_config=quant_config,
+                tp_rank=self.tp_rank,
+                tp_size=self.tp_size,
                 prefix=add_prefix("qkv_proj", prefix),
             )
         self.proj = RowParallelLinear(
             input_size=self.dummy_dim,
             output_size=embed_dim,
             bias=proj_bias,
             quant_config=quant_config,
+            tp_rank=self.tp_rank,
+            tp_size=self.tp_size,
             prefix=add_prefix("proj", prefix),
         )
 

python/sglang/srt/models/step3_vl.py

@@ -531,11 +531,18 @@ def __init__(
         prefix: str = "",
     ) -> None:
         super().__init__()
+        # Since this is a dense model,
+        # the MLP component likewise adopts a DP-MLP approach modeled after DP Attention.
+        # This choice may not represent the optimal solution and remains open to further deliberation.
+        attn_tp_rank = get_attention_tp_rank()
+        attn_tp_size = get_attention_tp_size()
         self.fc1 = ColumnParallelLinear(
             dim,
             intermediate_size,
             bias=bias,
             quant_config=quant_config,
+            tp_rank=attn_tp_rank,
+            tp_size=attn_tp_size,
             prefix=add_prefix("gate_proj", prefix),
         )
         self.act = ACT2FN[hidden_act]  # quick_gelu
@@ -544,6 +551,8 @@ def __init__(
             dim,
             bias=bias,
             quant_config=quant_config,
+            tp_rank=attn_tp_rank,
+            tp_size=attn_tp_size,
             prefix=add_prefix("down_proj", prefix),
         )
 

python/sglang/srt/multimodal/processors/step3_vl.py

@@ -8,7 +8,7 @@
 from PIL import Image
 from torchvision import transforms
 from torchvision.transforms import InterpolationMode
-from transformers import BatchFeature, TensorType
+from transformers import BatchFeature, ProcessorMixin, TensorType
 
 from sglang.srt.models.step3_vl import Step3VLForConditionalGeneration
 from sglang.srt.multimodal.processors.base_processor import (
@@ -276,6 +276,8 @@ def __init__(
         super().__init__()
 
         self.config = config
+        if isinstance(tokenizer, ProcessorMixin):
+            tokenizer = tokenizer.tokenizer
         self.tokenizer = tokenizer
 
         self.image_size = 728