Fuse gate_proj and up_proj in Qwen 2.5 VL's vision MLP (#9661)

Signed-off-by: Xinyuan Tong <xinyuantong.cs@gmail.com>
Co-authored-by: Xiang (Kevin) Li <lik@nvidia.com>
Co-authored-by: Xinyuan Tong <115166877+JustinTong0323@users.noreply.github.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Co-authored-by: Xinyuan Tong <xinyuantong.cs@gmail.com>

python/sglang/srt/models/qwen2_5_vl.py

@@ -43,7 +43,11 @@ from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import (
 from sglang.srt.hf_transformers_utils import get_processor
 from sglang.srt.layers.attention.vision import VisionAttention
 from sglang.srt.layers.layernorm import RMSNorm
-from sglang.srt.layers.linear import ColumnParallelLinear, RowParallelLinear
+from sglang.srt.layers.linear import (
+    ColumnParallelLinear,
+    MergedColumnParallelLinear,
+    RowParallelLinear,
+)
 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
@@ -62,7 +66,6 @@ logger = logging.getLogger(__name__)
 
 
 class Qwen2_5_VLMLP(nn.Module):
-
     def __init__(
         self,
         in_features: int,
@@ -73,19 +76,12 @@ class Qwen2_5_VLMLP(nn.Module):
         prefix: str = "",
     ):
         super().__init__()
-        self.gate_proj = ColumnParallelLinear(
-            in_features,
-            hidden_features,
+        self.gate_up_proj = MergedColumnParallelLinear(
+            input_size=in_features,
+            output_sizes=[hidden_features] * 2,  # [gate_proj, up_proj]
             bias=bias,
             quant_config=quant_config,
-            prefix=add_prefix("gate_proj", prefix),
-        )
-        self.up_proj = ColumnParallelLinear(
-            in_features,
-            hidden_features,
-            bias=bias,
-            quant_config=quant_config,
-            prefix=add_prefix("up_proj", prefix),
+            prefix=add_prefix("gate_up_proj", prefix),
         )
         self.down_proj = RowParallelLinear(
             hidden_features,
@@ -97,12 +93,11 @@ class Qwen2_5_VLMLP(nn.Module):
         self.act = ACT2FN[hidden_act]
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x_parallel_gate, _ = self.gate_proj(x)
-        x_parallel_gate = self.act(x_parallel_gate)
-        x_parallel_up, _ = self.up_proj(x)
-        x_parallel = x_parallel_gate * x_parallel_up
-        x, _ = self.down_proj(x_parallel)
-        return x
+        gate_up, _ = self.gate_up_proj(x)
+        gate, up = gate_up.chunk(2, dim=-1)
+        x = self.act(gate) * up
+        x_down, _ = self.down_proj(x)
+        return x_down
 
 
 class Qwen2_5_VisionBlock(nn.Module):
@@ -353,7 +348,7 @@ class Qwen2_5_VisionTransformer(nn.Module):
 
     @property
     def device(self) -> torch.device:
-        return self.blocks[0].mlp.gate_proj.weight.device
+        return self.patch_embed.proj.weight.device
 
     def rot_pos_emb(self, grid_thw: torch.Tensor) -> torch.Tensor:
         pos_ids = []
@@ -468,9 +463,8 @@ cached_get_processor = lru_cache(get_processor)
 class Qwen2_5_VLForConditionalGeneration(nn.Module):
     # BitandBytes specific attributes
     default_bitsandbytes_target_modules = [
-        ".gate_proj.",
+        ".gate_up_proj.",
         ".down_proj.",
-        ".up_proj.",
         ".q_proj.",
         ".k_proj.",
         ".v_proj.",
@@ -617,7 +611,11 @@ class Qwen2_5_VLForConditionalGeneration(nn.Module):
             for param_name, weight_name, shard_id in stacked_params_mapping:
                 if weight_name not in name:
                     continue
-                if "visual" in name:
+                if (
+                    "visual" in name
+                    and "up_proj" not in name
+                    and "gate_proj" not in name
+                ):
                     continue
                 name = name.replace(weight_name, param_name)