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[Diffusion] Disable packed QKV for FLUX & Z-Image (#16038)

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This commit is contained in:
Xiaoyu Zhang
2025-12-29 14:33:05 +08:00
committed by GitHub
parent ec8c831d5a
commit 8305dc1718
5 changed files with 57 additions and 57 deletions
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python/sglang/multimodal_gen/configs/models/dits/flux.py
+1 -18
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@@ -21,27 +21,10 @@ 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"),
]
)
stacked_params_mapping: list[tuple[str, str, str]] = field(default_factory=list)
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*)\.(.*)$": r"\1.\2",
}
)
python/sglang/multimodal_gen/configs/models/dits/zimage.py
-6
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@@ -29,9 +29,6 @@ class ZImageArchConfig(DiTArchConfig):
stacked_params_mapping: list[tuple[str, str, str]] = field(
default_factory=lambda: [
# (param_name, shard_name, shard_id)
(".to_qkv", ".to_q", "q"),
(".to_qkv", ".to_k", "k"),
(".to_qkv", ".to_v", "v"),
(".feed_forward.w13", ".feed_forward.w1", "gate"),
(".feed_forward.w13", ".feed_forward.w3", "up"),
]
@@ -39,9 +36,6 @@ class ZImageArchConfig(DiTArchConfig):
param_names_mapping: dict = field(
default_factory=lambda: {
r"(.*)\.to_q\.weight$": (r"\1.to_qkv.weight", 0, 3),
r"(.*)\.to_k\.weight$": (r"\1.to_qkv.weight", 1, 3),
r"(.*)\.to_v\.weight$": (r"\1.to_qkv.weight", 2, 3),
r"(.*)\.feed_forward\.w1\.weight$": (r"\1.feed_forward.w13.weight", 0, 2),
r"(.*)\.feed_forward\.w3\.weight$": (r"\1.feed_forward.w13.weight", 1, 2),
}
python/sglang/multimodal_gen/runtime/models/dits/flux.py
+28 -8
View File
@@ -52,13 +52,15 @@ logger = init_logger(__name__) # pylint: disable=invalid-name
def _get_qkv_projections(
attn: "FluxAttention", hidden_states, encoder_hidden_states=None
):
qkv, _ = attn.to_qkv(hidden_states)
query, key, value = qkv.chunk(3, dim=-1)
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:
added_qkv, _ = attn.to_added_qkv(encoder_hidden_states)
encoder_query, encoder_key, encoder_value = added_qkv.chunk(3, dim=-1)
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
@@ -96,8 +98,14 @@ class FluxAttention(torch.nn.Module, AttentionModuleMixin):
self.norm_q = RMSNorm(dim_head, eps=eps)
self.norm_k = RMSNorm(dim_head, eps=eps)
self.to_qkv = ColumnParallelLinear(
query_dim, self.inner_dim * 3, bias=bias, gather_output=True
self.to_q = ColumnParallelLinear(
query_dim, self.inner_dim, bias=bias, gather_output=True
)
self.to_k = ColumnParallelLinear(
query_dim, self.inner_dim, bias=bias, gather_output=True
)
self.to_v = ColumnParallelLinear(
query_dim, self.inner_dim, bias=bias, gather_output=True
)
if not self.pre_only:
@@ -113,9 +121,21 @@ 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.to_added_qkv = ColumnParallelLinear(
self.add_q_proj = ColumnParallelLinear(
added_kv_proj_dim,
self.inner_dim * 3,
self.inner_dim,
bias=added_proj_bias,
gather_output=True,
)
self.add_k_proj = ColumnParallelLinear(
added_kv_proj_dim,
self.inner_dim,
bias=added_proj_bias,
gather_output=True,
)
self.add_v_proj = ColumnParallelLinear(
added_kv_proj_dim,
self.inner_dim,
bias=added_proj_bias,
gather_output=True,
)
python/sglang/multimodal_gen/runtime/models/dits/flux_2.py
+17 -9
View File
@@ -38,13 +38,15 @@ logger = init_logger(__name__) # pylint: disable=invalid-name
def _get_qkv_projections(
attn: "Flux2Attention", hidden_states, encoder_hidden_states=None
):
qkv, _ = attn.to_qkv(hidden_states)
query, key, value = qkv.chunk(3, dim=-1)
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:
added_qkv, _ = attn.to_added_qkv(encoder_hidden_states)
encoder_query, encoder_key, encoder_value = added_qkv.chunk(3, dim=-1)
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
@@ -120,8 +122,9 @@ class Flux2Attention(torch.nn.Module, AttentionModuleMixin):
self.added_kv_proj_dim = added_kv_proj_dim
self.added_proj_bias = added_proj_bias
# Use ReplicatedLinear for fused QKV projections
self.to_qkv = ReplicatedLinear(query_dim, self.inner_dim * 3, bias=bias)
self.to_q = ReplicatedLinear(query_dim, self.inner_dim, bias=bias)
self.to_k = ReplicatedLinear(query_dim, self.inner_dim, bias=bias)
self.to_v = ReplicatedLinear(query_dim, self.inner_dim, bias=bias)
# QK Norm
self.norm_q = RMSNorm(dim_head, eps=eps)
@@ -134,9 +137,14 @@ 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)
# Use ReplicatedLinear for added (encoder) QKV projections
self.to_added_qkv = ReplicatedLinear(
added_kv_proj_dim, self.inner_dim * 3, bias=added_proj_bias
self.add_q_proj = ReplicatedLinear(
added_kv_proj_dim, self.inner_dim, bias=added_proj_bias
)
self.add_k_proj = ReplicatedLinear(
added_kv_proj_dim, self.inner_dim, bias=added_proj_bias
)
self.add_v_proj = ReplicatedLinear(
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)
python/sglang/multimodal_gen/runtime/models/dits/zimage.py
+11 -16
View File
@@ -11,7 +11,6 @@ from sglang.multimodal_gen.runtime.layers.layernorm import RMSNorm
from sglang.multimodal_gen.runtime.layers.linear import (
ColumnParallelLinear,
MergedColumnParallelLinear,
QKVParallelLinear,
ReplicatedLinear,
RowParallelLinear,
)
@@ -110,16 +109,13 @@ class ZImageAttention(nn.Module):
super().__init__()
self.dim = dim
self.head_dim = dim // num_heads
self.num_heads = num_heads
self.num_kv_heads = num_kv_heads
self.qk_norm = qk_norm
# Use QKVParallelLinear for QKV projection (fused)
self.to_qkv = QKVParallelLinear(
hidden_size=dim,
head_size=self.head_dim,
total_num_heads=num_heads,
total_num_kv_heads=num_kv_heads,
bias=False,
)
self.to_q = ReplicatedLinear(dim, dim, bias=False)
self.to_k = ReplicatedLinear(dim, self.head_dim * num_kv_heads, bias=False)
self.to_v = ReplicatedLinear(dim, self.head_dim * num_kv_heads, bias=False)
if self.qk_norm:
self.norm_q = RMSNorm(self.head_dim, eps=eps)
@@ -146,14 +142,13 @@ class ZImageAttention(nn.Module):
hidden_states: torch.Tensor,
freqs_cis: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
):
qkv, _ = self.to_qkv(hidden_states)
q_dim = self.to_qkv.num_heads * self.head_dim
kv_dim = self.to_qkv.num_kv_heads * self.head_dim
q, k, v = torch.split(qkv, [q_dim, kv_dim, kv_dim], dim=-1)
q, _ = self.to_q(hidden_states)
k, _ = self.to_k(hidden_states)
v, _ = self.to_v(hidden_states)
q = q.view(*q.shape[:-1], self.to_qkv.num_heads, self.head_dim)
k = k.view(*k.shape[:-1], self.to_qkv.num_kv_heads, self.head_dim)
v = v.view(*v.shape[:-1], self.to_qkv.num_kv_heads, self.head_dim)
q = q.view(*q.shape[:-1], self.num_heads, self.head_dim)
k = k.view(*k.shape[:-1], self.num_kv_heads, self.head_dim)
v = v.view(*v.shape[:-1], self.num_kv_heads, self.head_dim)
if self.norm_q is not None:
q = self.norm_q(q)