[diffusion] refactor: refactor fuse qkv with QKVParallelLinear linear (#15090)

Co-authored-by: Mick <mickjagger19@icloud.com>

python/sglang/multimodal_gen/configs/models/dits/flux.py

@@ -21,8 +21,27 @@ class FluxArchConfig(DiTArchConfig):
     guidance_embeds: bool = False
     axes_dims_rope: Tuple[int, int, int] = (16, 56, 56)
 
+    stacked_params_mapping: list[tuple[str, str, str]] = field(
+        default_factory=lambda: [
+            # (param_name, shard_name, shard_id)
+            (".to_qkv", ".to_q", "q"),
+            (".to_qkv", ".to_k", "k"),
+            (".to_qkv", ".to_v", "v"),
+            (".to_added_qkv", ".add_q_proj", "q"),
+            (".to_added_qkv", ".add_k_proj", "k"),
+            (".to_added_qkv", ".add_v_proj", "v"),
+        ]
+    )
+
     param_names_mapping: dict = field(
         default_factory=lambda: {
+            # QKV fusion mappings
+            r"(.*)\.to_q\.(weight|bias)$": (r"\1.to_qkv.\2", 0, 3),
+            r"(.*)\.to_k\.(weight|bias)$": (r"\1.to_qkv.\2", 1, 3),
+            r"(.*)\.to_v\.(weight|bias)$": (r"\1.to_qkv.\2", 2, 3),
+            r"(.*)\.add_q_proj\.(weight|bias)$": (r"\1.to_added_qkv.\2", 0, 3),
+            r"(.*)\.add_k_proj\.(weight|bias)$": (r"\1.to_added_qkv.\2", 1, 3),
+            r"(.*)\.add_v_proj\.(weight|bias)$": (r"\1.to_added_qkv.\2", 2, 3),
             r"transformer\.(\w*)\.(.*)$": r"\1.\2",
         }
     )

python/sglang/multimodal_gen/configs/models/dits/qwenimage.py

@@ -21,8 +21,28 @@ class QwenImageArchConfig(DiTArchConfig):
     guidance_embeds: bool = False
     axes_dims_rope: Tuple[int, int, int] = (16, 56, 56)
 
+    stacked_params_mapping: list[tuple[str, str, str]] = field(
+        default_factory=lambda: [
+            # (param_name, shard_name, shard_id)
+            (".to_qkv", ".to_q", "q"),
+            (".to_qkv", ".to_k", "k"),
+            (".to_qkv", ".to_v", "v"),
+            (".to_added_qkv", ".add_q_proj", "q"),
+            (".to_added_qkv", ".add_k_proj", "k"),
+            (".to_added_qkv", ".add_v_proj", "v"),
+        ]
+    )
+
     param_names_mapping: dict = field(
         default_factory=lambda: {
+            # QKV fusion mappings
+            r"(.*)\.to_q\.(weight|bias)$": (r"\1.to_qkv.\2", 0, 3),
+            r"(.*)\.to_k\.(weight|bias)$": (r"\1.to_qkv.\2", 1, 3),
+            r"(.*)\.to_v\.(weight|bias)$": (r"\1.to_qkv.\2", 2, 3),
+            r"(.*)\.add_q_proj\.(weight|bias)$": (r"\1.to_added_qkv.\2", 0, 3),
+            r"(.*)\.add_k_proj\.(weight|bias)$": (r"\1.to_added_qkv.\2", 1, 3),
+            r"(.*)\.add_v_proj\.(weight|bias)$": (r"\1.to_added_qkv.\2", 2, 3),
+            # LoRA mappings
             r"^(transformer_blocks\.\d+\.attn\..*\.lora_[AB])\.default$": r"\1",
         }
     )

python/sglang/multimodal_gen/docs/profiling.md

@@ -47,6 +47,10 @@ By default, trace files are saved in the ./logs/ directory. The exact output fil
 ```bash
 [mm-dd hh:mm:ss] Saving profiler traces to: /sgl-workspace/sglang/logs/mocked_fake_id_for_offline_generate-5_steps-global-rank0.trace.json.gz
 ```
+{request_id}-{num_steps}_steps-global-rank{rank}.trace.json.gz
+```
+
+Example: `mocked_fake_id_for_offline_generate-5_steps-global-rank0.trace.json.gz`
 
 ### View Traces
 

python/sglang/multimodal_gen/runtime/loader/component_loader.py

@@ -692,10 +692,6 @@ class TransformerLoader(ComponentLoader):
 
         model = model.eval()
 
-        if hasattr(model, "fuse_qkv_projections"):
-            logger.info("Fusing QKV projections for better performance")
-            model.fuse_qkv_projections()
-
         return model
 
 

python/sglang/multimodal_gen/runtime/models/dits/flux.py

@@ -36,17 +36,16 @@ from sglang.multimodal_gen.runtime.layers.attention import USPAttention
 
 # from sglang.multimodal_gen.runtime.layers.layernorm import LayerNorm as LayerNorm
 from sglang.multimodal_gen.runtime.layers.layernorm import RMSNorm
-from sglang.multimodal_gen.runtime.layers.linear import ReplicatedLinear
+from sglang.multimodal_gen.runtime.layers.linear import (
+    QKVParallelLinear,
+    ReplicatedLinear,
+)
 from sglang.multimodal_gen.runtime.layers.mlp import MLP
 from sglang.multimodal_gen.runtime.layers.rotary_embedding import (
     NDRotaryEmbedding,
     _apply_rotary_emb,
 )
 from sglang.multimodal_gen.runtime.models.dits.base import CachableDiT
-from sglang.multimodal_gen.runtime.models.dits.utils import (
-    delete_projection_layers,
-    fuse_linear_projections,
-)
 from sglang.multimodal_gen.runtime.platforms import (
     AttentionBackendEnum,
     current_platform,
@@ -56,45 +55,21 @@ from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
 logger = init_logger(__name__)  # pylint: disable=invalid-name
 
 
-def _get_projections(attn: "FluxAttention", hidden_states, encoder_hidden_states=None):
+def _get_qkv_projections(
-    query, _ = attn.to_q(hidden_states)
-    key, _ = attn.to_k(hidden_states)
-    value, _ = attn.to_v(hidden_states)
-
-    encoder_query = encoder_key = encoder_value = None
-    if encoder_hidden_states is not None and attn.added_kv_proj_dim is not None:
-        encoder_query, _ = attn.add_q_proj(encoder_hidden_states)
-        encoder_key, _ = attn.add_k_proj(encoder_hidden_states)
-        encoder_value, _ = attn.add_v_proj(encoder_hidden_states)
-
-    return query, key, value, encoder_query, encoder_key, encoder_value
-
-
-def _get_fused_projections(
     attn: "FluxAttention", hidden_states, encoder_hidden_states=None
 ):
     qkv, _ = attn.to_qkv(hidden_states)
     query, key, value = qkv.chunk(3, dim=-1)
 
     encoder_query = encoder_key = encoder_value = None
-    if encoder_hidden_states is not None and hasattr(attn, "to_added_qkv"):
+    if encoder_hidden_states is not None and attn.added_kv_proj_dim is not None:
         added_qkv, _ = attn.to_added_qkv(encoder_hidden_states)
         encoder_query, encoder_key, encoder_value = added_qkv.chunk(3, dim=-1)
 
     return query, key, value, encoder_query, encoder_key, encoder_value
 
 
-def _get_qkv_projections(
-    attn: "FluxAttention", hidden_states, encoder_hidden_states=None
-):
-    if attn.fused_projections:
-        return _get_fused_projections(attn, hidden_states, encoder_hidden_states)
-    return _get_projections(attn, hidden_states, encoder_hidden_states)
-
-
 class FluxAttention(torch.nn.Module, AttentionModuleMixin):
-    _supports_qkv_fusion = True
-
     def __init__(
         self,
         query_dim: int,
@@ -125,11 +100,15 @@ class FluxAttention(torch.nn.Module, AttentionModuleMixin):
         self.added_proj_bias = added_proj_bias
 
         self.norm_q = RMSNorm(dim_head, eps=eps)
-
         self.norm_k = RMSNorm(dim_head, eps=eps)
-        self.to_q = ReplicatedLinear(query_dim, self.inner_dim, bias=bias)
+
-        self.to_k = ReplicatedLinear(query_dim, self.inner_dim, bias=bias)
+        # Use QKVParallelLinear for fused QKV projections
-        self.to_v = ReplicatedLinear(query_dim, self.inner_dim, bias=bias)
+        self.to_qkv = QKVParallelLinear(
+            hidden_size=query_dim,
+            head_size=dim_head,
+            total_num_heads=num_heads,
+            bias=bias,
+        )
 
         if not self.pre_only:
             self.to_out = torch.nn.ModuleList([])
@@ -142,14 +121,12 @@ class FluxAttention(torch.nn.Module, AttentionModuleMixin):
         if added_kv_proj_dim is not None:
             self.norm_added_q = RMSNorm(dim_head, eps=eps)
             self.norm_added_k = RMSNorm(dim_head, eps=eps)
-            self.add_q_proj = ReplicatedLinear(
+            # Use QKVParallelLinear for added (encoder) QKV projections
-                added_kv_proj_dim, self.inner_dim, bias=added_proj_bias
+            self.to_added_qkv = QKVParallelLinear(
-            )
+                hidden_size=added_kv_proj_dim,
-            self.add_k_proj = ReplicatedLinear(
+                head_size=dim_head,
-                added_kv_proj_dim, self.inner_dim, bias=added_proj_bias
+                total_num_heads=num_heads,
-            )
+                bias=added_proj_bias,
-            self.add_v_proj = ReplicatedLinear(
-                added_kv_proj_dim, self.inner_dim, bias=added_proj_bias
             )
             self.to_add_out = ReplicatedLinear(self.inner_dim, query_dim, bias=out_bias)
 
@@ -166,30 +143,6 @@ class FluxAttention(torch.nn.Module, AttentionModuleMixin):
             },
         )
 
-        self.fused_projections = False
-
-    @torch.no_grad()
-    def fuse_projections(self):
-        if self.fused_projections:
-            return
-
-        self.to_qkv = fuse_linear_projections(
-            self.to_q, self.to_k, self.to_v, self.use_bias, ReplicatedLinear
-        )
-        delete_projection_layers(self, ["to_q", "to_k", "to_v"])
-
-        if self.added_kv_proj_dim is not None:
-            self.to_added_qkv = fuse_linear_projections(
-                self.add_q_proj,
-                self.add_k_proj,
-                self.add_v_proj,
-                self.added_proj_bias,
-                ReplicatedLinear,
-            )
-            delete_projection_layers(self, ["add_q_proj", "add_k_proj", "add_v_proj"])
-
-        self.fused_projections = True
-
     def forward(
         self,
         x: torch.Tensor,
@@ -445,6 +398,8 @@ class FluxTransformer2DModel(CachableDiT):
     Reference: https://blackforestlabs.ai/announcing-black-forest-labs/
     """
 
+    param_names_mapping = FluxConfig().arch_config.param_names_mapping
+
     def __init__(self, config: FluxConfig, hf_config: dict[str, Any]) -> None:
         super().__init__(config=config, hf_config=hf_config)
         self.config = config.arch_config
@@ -503,15 +458,6 @@ class FluxTransformer2DModel(CachableDiT):
             bias=True,
         )
 
-    def fuse_qkv_projections(self):
-        for block in list(self.transformer_blocks) + list(
-            self.single_transformer_blocks
-        ):
-            if hasattr(block.attn, "fuse_projections") and getattr(
-                block.attn, "_supports_qkv_fusion", True
-            ):
-                block.attn.fuse_projections()
-
     def forward(
         self,
         hidden_states: torch.Tensor,

python/sglang/multimodal_gen/runtime/models/dits/flux_2.py

@@ -23,15 +23,12 @@ from diffusers.models.normalization import AdaLayerNormContinuous
 from sglang.multimodal_gen.configs.models.dits.flux import FluxConfig
 from sglang.multimodal_gen.runtime.layers.attention import USPAttention
 from sglang.multimodal_gen.runtime.layers.layernorm import RMSNorm
+from sglang.multimodal_gen.runtime.layers.linear import QKVParallelLinear
 from sglang.multimodal_gen.runtime.layers.rotary_embedding import (
     NDRotaryEmbedding,
     _apply_rotary_emb,
 )
 from sglang.multimodal_gen.runtime.models.dits.base import CachableDiT
-from sglang.multimodal_gen.runtime.models.dits.utils import (
-    delete_projection_layers,
-    fuse_linear_projections,
-)
 from sglang.multimodal_gen.runtime.platforms import (
     AttentionBackendEnum,
     current_platform,
@@ -41,40 +38,18 @@ from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
 logger = init_logger(__name__)  # pylint: disable=invalid-name
 
 
-def _get_projections(attn: "Flux2Attention", hidden_states, encoder_hidden_states=None):
-    query = attn.to_q(hidden_states)
-    key = attn.to_k(hidden_states)
-    value = attn.to_v(hidden_states)
-
-    encoder_query = encoder_key = encoder_value = None
-    if encoder_hidden_states is not None and attn.added_kv_proj_dim is not None:
-        encoder_query = attn.add_q_proj(encoder_hidden_states)
-        encoder_key = attn.add_k_proj(encoder_hidden_states)
-        encoder_value = attn.add_v_proj(encoder_hidden_states)
-
-    return query, key, value, encoder_query, encoder_key, encoder_value
-
-
-def _get_fused_projections(
-    attn: "Flux2Attention", hidden_states, encoder_hidden_states=None
-):
-    qkv = attn.to_qkv(hidden_states)
-    query, key, value = qkv.chunk(3, dim=-1)
-
-    encoder_query = encoder_key = encoder_value = None
-    if encoder_hidden_states is not None and hasattr(attn, "to_added_qkv"):
-        added_qkv = attn.to_added_qkv(encoder_hidden_states)
-        encoder_query, encoder_key, encoder_value = added_qkv.chunk(3, dim=-1)
-
-    return query, key, value, encoder_query, encoder_key, encoder_value
-
-
 def _get_qkv_projections(
     attn: "Flux2Attention", hidden_states, encoder_hidden_states=None
 ):
-    if attn.fused_projections:
+    qkv, _ = attn.to_qkv(hidden_states)
-        return _get_fused_projections(attn, hidden_states, encoder_hidden_states)
+    query, key, value = qkv.chunk(3, dim=-1)
-    return _get_projections(attn, hidden_states, encoder_hidden_states)
+
+    encoder_query = encoder_key = encoder_value = None
+    if encoder_hidden_states is not None and attn.added_kv_proj_dim is not None:
+        added_qkv, _ = attn.to_added_qkv(encoder_hidden_states)
+        encoder_query, encoder_key, encoder_value = added_qkv.chunk(3, dim=-1)
+
+    return query, key, value, encoder_query, encoder_key, encoder_value
 
 
 class Flux2SwiGLU(nn.Module):
@@ -120,8 +95,6 @@ class Flux2FeedForward(nn.Module):
 
 
 class Flux2Attention(torch.nn.Module, AttentionModuleMixin):
-    _supports_qkv_fusion = True
-
     def __init__(
         self,
         query_dim: int,
@@ -150,9 +123,13 @@ class Flux2Attention(torch.nn.Module, AttentionModuleMixin):
         self.added_kv_proj_dim = added_kv_proj_dim
         self.added_proj_bias = added_proj_bias
 
-        self.to_q = torch.nn.Linear(query_dim, self.inner_dim, bias=bias)
+        # Use QKVParallelLinear for fused QKV projections
-        self.to_k = torch.nn.Linear(query_dim, self.inner_dim, bias=bias)
+        self.to_qkv = QKVParallelLinear(
-        self.to_v = torch.nn.Linear(query_dim, self.inner_dim, bias=bias)
+            hidden_size=query_dim,
+            head_size=dim_head,
+            total_num_heads=num_heads,
+            bias=bias,
+        )
 
         # QK Norm
         self.norm_q = RMSNorm(dim_head, eps=eps)
@@ -165,14 +142,12 @@ class Flux2Attention(torch.nn.Module, AttentionModuleMixin):
         if added_kv_proj_dim is not None:
             self.norm_added_q = RMSNorm(dim_head, eps=eps)
             self.norm_added_k = RMSNorm(dim_head, eps=eps)
-            self.add_q_proj = torch.nn.Linear(
+            # Use QKVParallelLinear for added (encoder) QKV projections
-                added_kv_proj_dim, self.inner_dim, bias=added_proj_bias
+            self.to_added_qkv = QKVParallelLinear(
-            )
+                hidden_size=added_kv_proj_dim,
-            self.add_k_proj = torch.nn.Linear(
+                head_size=dim_head,
-                added_kv_proj_dim, self.inner_dim, bias=added_proj_bias
+                total_num_heads=num_heads,
-            )
+                bias=added_proj_bias,
-            self.add_v_proj = torch.nn.Linear(
-                added_kv_proj_dim, self.inner_dim, bias=added_proj_bias
             )
             self.to_add_out = torch.nn.Linear(self.inner_dim, query_dim, bias=out_bias)
 
@@ -189,30 +164,6 @@ class Flux2Attention(torch.nn.Module, AttentionModuleMixin):
             },
         )
 
-        self.fused_projections = False
-
-    @torch.no_grad()
-    def fuse_projections(self):
-        if self.fused_projections:
-            return
-
-        self.to_qkv = fuse_linear_projections(
-            self.to_q, self.to_k, self.to_v, self.use_bias, torch.nn.Linear
-        )
-        delete_projection_layers(self, ["to_q", "to_k", "to_v"])
-
-        if self.added_kv_proj_dim is not None:
-            self.to_added_qkv = fuse_linear_projections(
-                self.add_q_proj,
-                self.add_k_proj,
-                self.add_v_proj,
-                self.added_proj_bias,
-                torch.nn.Linear,
-            )
-            delete_projection_layers(self, ["add_q_proj", "add_k_proj", "add_v_proj"])
-
-        self.fused_projections = True
-
     def forward(
         self,
         hidden_states: torch.Tensor,
@@ -655,6 +606,8 @@ class Flux2Transformer2DModel(CachableDiT):
 
     """
 
+    param_names_mapping = FluxConfig().arch_config.param_names_mapping
+
     def __init__(self, config: FluxConfig, hf_config: dict[str, Any]):
         super().__init__(config=config, hf_config=hf_config)
         patch_size: int = config.patch_size
@@ -746,15 +699,6 @@ class Flux2Transformer2DModel(CachableDiT):
 
         self.gradient_checkpointing = False
 
-    def fuse_qkv_projections(self):
-        for block in list(self.transformer_blocks) + list(
-            self.single_transformer_blocks
-        ):
-            if hasattr(block.attn, "fuse_projections") and getattr(
-                block.attn, "_supports_qkv_fusion", True
-            ):
-                block.attn.fuse_projections()
-
     def forward(
         self,
         hidden_states: torch.Tensor,

python/sglang/multimodal_gen/runtime/models/dits/qwen_image.py

@@ -16,60 +16,35 @@ from diffusers.models.normalization import AdaLayerNormContinuous
 from sglang.multimodal_gen.configs.models.dits.qwenimage import QwenImageDitConfig
 from sglang.multimodal_gen.runtime.layers.attention import USPAttention
 from sglang.multimodal_gen.runtime.layers.layernorm import LayerNorm, RMSNorm
-from sglang.multimodal_gen.runtime.layers.linear import ReplicatedLinear
+from sglang.multimodal_gen.runtime.layers.linear import (
+    QKVParallelLinear,
+    ReplicatedLinear,
+)
 from sglang.multimodal_gen.runtime.layers.triton_ops import (
     apply_rotary_embedding,
     fuse_scale_shift_kernel,
 )
 from sglang.multimodal_gen.runtime.models.dits.base import CachableDiT
-from sglang.multimodal_gen.runtime.models.dits.utils import (
-    delete_projection_layers,
-    fuse_linear_projections,
-)
 from sglang.multimodal_gen.runtime.platforms import AttentionBackendEnum
 from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
 
 logger = init_logger(__name__)  # pylint: disable=invalid-name
 
 
-def _get_projections(
+def _get_qkv_projections(
-    attn: "QwenImageCrossAttention", hidden_states, encoder_hidden_states=None
-):
-    img_query, _ = attn.to_q(hidden_states)
-    img_key, _ = attn.to_k(hidden_states)
-    img_value, _ = attn.to_v(hidden_states)
-
-    txt_query = txt_key = txt_value = None
-    if encoder_hidden_states is not None and hasattr(attn, "add_q_proj"):
-        txt_query, _ = attn.add_q_proj(encoder_hidden_states)
-        txt_key, _ = attn.add_k_proj(encoder_hidden_states)
-        txt_value, _ = attn.add_v_proj(encoder_hidden_states)
-
-    return img_query, img_key, img_value, txt_query, txt_key, txt_value
-
-
-def _get_fused_projections(
     attn: "QwenImageCrossAttention", hidden_states, encoder_hidden_states=None
 ):
     img_qkv, _ = attn.to_qkv(hidden_states)
     img_query, img_key, img_value = img_qkv.chunk(3, dim=-1)
 
     txt_query = txt_key = txt_value = None
-    if encoder_hidden_states is not None and hasattr(attn, "to_added_qkv"):
+    if encoder_hidden_states is not None and attn.added_kv_proj_dim is not None:
         txt_qkv, _ = attn.to_added_qkv(encoder_hidden_states)
         txt_query, txt_key, txt_value = txt_qkv.chunk(3, dim=-1)
 
     return img_query, img_key, img_value, txt_query, txt_key, txt_value
 
 
-def _get_qkv_projections(
-    attn: "QwenImageCrossAttention", hidden_states, encoder_hidden_states=None
-):
-    if attn.fused_projections:
-        return _get_fused_projections(attn, hidden_states, encoder_hidden_states)
-    return _get_projections(attn, hidden_states, encoder_hidden_states)
-
-
 class QwenTimestepProjEmbeddings(nn.Module):
     def __init__(self, embedding_dim):
         super().__init__()
@@ -260,8 +235,6 @@ class QwenEmbedRope(nn.Module):
 
 
 class QwenImageCrossAttention(nn.Module):
-    _supports_qkv_fusion = True
-
     def __init__(
         self,
         dim: int,  # query_dim
@@ -286,26 +259,30 @@ class QwenImageCrossAttention(nn.Module):
         self.qk_norm = qk_norm
         self.eps = eps
         self.parallel_attention = parallel_attention
+        self.added_kv_proj_dim = added_kv_proj_dim
+
+        # Use QKVParallelLinear for fused QKV projections
+        self.to_qkv = QKVParallelLinear(
+            hidden_size=dim,
+            head_size=head_dim,
+            total_num_heads=num_heads,
+            bias=True,
+        )
 
-        # layers
-        self.to_q = ReplicatedLinear(dim, dim)
-        self.to_k = ReplicatedLinear(dim, dim)
-        self.to_v = ReplicatedLinear(dim, dim)
         if self.qk_norm:
             self.norm_q = RMSNorm(head_dim, eps=eps) if qk_norm else nn.Identity()
             self.norm_k = RMSNorm(head_dim, eps=eps) if qk_norm else nn.Identity()
+
         self.inner_dim = out_dim if out_dim is not None else head_dim * num_heads
         self.inner_kv_dim = self.inner_dim
+
         if added_kv_proj_dim is not None:
-            self.add_k_proj = ReplicatedLinear(
+            # Use QKVParallelLinear for added (encoder) QKV projections
-                added_kv_proj_dim, self.inner_kv_dim, bias=True
+            self.to_added_qkv = QKVParallelLinear(
-            )
+                hidden_size=added_kv_proj_dim,
-            self.add_v_proj = ReplicatedLinear(
+                head_size=head_dim,
-                added_kv_proj_dim, self.inner_kv_dim, bias=True
+                total_num_heads=num_heads,
-            )
+                bias=True,
-            if context_pre_only is not None:
-                self.add_q_proj = ReplicatedLinear(
-                    added_kv_proj_dim, self.inner_dim, bias=True
             )
 
         if context_pre_only is not None and not context_pre_only:
@@ -338,31 +315,6 @@ class QwenImageCrossAttention(nn.Module):
             },
         )
 
-        self.fused_projections = False
-        self.added_kv_proj_dim_val = added_kv_proj_dim
-
-    @torch.no_grad()
-    def fuse_projections(self):
-        if self.fused_projections:
-            return
-
-        self.to_qkv = fuse_linear_projections(
-            self.to_q, self.to_k, self.to_v, use_bias=False, linear_cls=ReplicatedLinear
-        )
-        delete_projection_layers(self, ["to_q", "to_k", "to_v"])
-
-        if self.added_kv_proj_dim_val is not None and hasattr(self, "add_q_proj"):
-            self.to_added_qkv = fuse_linear_projections(
-                self.add_q_proj,
-                self.add_k_proj,
-                self.add_v_proj,
-                use_bias=True,
-                linear_cls=ReplicatedLinear,
-            )
-            delete_projection_layers(self, ["add_q_proj", "add_k_proj", "add_v_proj"])
-
-        self.fused_projections = True
-
     def forward(
         self,
         hidden_states: torch.Tensor,
@@ -579,6 +531,8 @@ class QwenImageTransformer2DModel(CachableDiT):
     _skip_layerwise_casting_patterns = ["pos_embed", "norm"]
     _repeated_blocks = ["QwenImageTransformerBlock"]
 
+    param_names_mapping = QwenImageDitConfig().arch_config.param_names_mapping
+
     def __init__(
         self,
         config: QwenImageDitConfig,
@@ -625,13 +579,6 @@ class QwenImageTransformer2DModel(CachableDiT):
             self.inner_dim, patch_size * patch_size * self.out_channels, bias=True
         )
 
-    def fuse_qkv_projections(self):
-        for block in self.transformer_blocks:
-            if hasattr(block.attn, "fuse_projections") and getattr(
-                block.attn, "_supports_qkv_fusion", True
-            ):
-                block.attn.fuse_projections()
-
     def forward(
         self,
         hidden_states: torch.Tensor,

python/sglang/multimodal_gen/runtime/models/dits/utils.py

@@ -1,43 +0,0 @@
-from typing import Union
-
-import torch
-import torch.nn as nn
-
-from sglang.multimodal_gen.runtime.layers.linear import ReplicatedLinear
-
-
-def fuse_linear_projections(
-    q_proj: Union[nn.Linear, ReplicatedLinear],
-    k_proj: Union[nn.Linear, ReplicatedLinear],
-    v_proj: Union[nn.Linear, ReplicatedLinear],
-    use_bias: bool,
-    linear_cls: type = None,
-) -> Union[nn.Linear, ReplicatedLinear]:
-    device = q_proj.weight.data.device
-    dtype = q_proj.weight.data.dtype
-
-    concatenated_weights = torch.cat(
-        [q_proj.weight.data, k_proj.weight.data, v_proj.weight.data]
-    )
-    in_features = concatenated_weights.shape[1]
-    out_features = concatenated_weights.shape[0]
-
-    if linear_cls is None:
-        linear_cls = type(q_proj)
-
-    fused_layer = linear_cls(in_features, out_features, bias=use_bias)
-    fused_layer.weight.data = concatenated_weights.to(device=device, dtype=dtype)
-
-    if use_bias:
-        concatenated_bias = torch.cat(
-            [q_proj.bias.data, k_proj.bias.data, v_proj.bias.data]
-        )
-        fused_layer.bias.data = concatenated_bias.to(device=device, dtype=dtype)
-
-    return fused_layer
-
-
-def delete_projection_layers(module: nn.Module, layer_names: list[str]) -> None:
-    for name in layer_names:
-        if hasattr(module, name):
-            delattr(module, name)

python/sglang/multimodal_gen/test/server/perf_baselines.json

@@ -584,7 +584,7 @@
                 "48": 247.48,
                 "49": 247.54
             },
-            "expected_e2e_ms": 16563.83,
+            "expected_e2e_ms": 18382.19,
             "expected_avg_denoise_ms": 260.76,
             "expected_median_denoise_ms": 247.84
         },
@@ -706,7 +706,7 @@
                 "TimestepPreparationStage": 2.9,
                 "LatentPreparationStage": 1.25,
                 "ImageVAEEncodingStage": 1655.89,
-                "DenoisingStage": 100544.98,
+                "DenoisingStage": 106972.82,
                 "DecodingStage": 1355.52,
                 "per_frame_generation": null
             },
@@ -753,7 +753,7 @@
                 "39": 1599.78
             },
             "expected_e2e_ms": 123182.9887,
-            "expected_avg_denoise_ms": 2513.52,
+            "expected_avg_denoise_ms": 2831.00,
             "expected_median_denoise_ms": 1600.09
         },
         "wan2_1_i2v_14b_480P_2gpu": {