Skip to content

Revert "[Perf] DSV3.2 Indexer Fused Weights Projection" (#38684) #38806

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Draft
vllm-agent wants to merge 1 commit into
base: main
Choose a base branch
from
Draft

Revert "[Perf] DSV3.2 Indexer Fused Weights Projection" (#38684) #38806

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
3 changes: 0 additions & 3 deletions vllm/model_executor/models/deepseek_mtp.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -241,9 +241,6 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
("gate_up_proj", "up_proj", 1),
("fused_qkv_a_proj", "q_a_proj", 0),
("fused_qkv_a_proj", "kv_a_proj_with_mqa", 1),
# Fused indexer wk + weights_proj
("wk_weights_proj", "wk", 0),
("wk_weights_proj", "weights_proj", 1),
]

expert_params_mapping = SharedFusedMoE.make_expert_params_mapping(
Expand Down
36 changes: 14 additions & 22 deletions vllm/model_executor/models/deepseek_v2.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -639,19 +639,21 @@ def __init__(
quant_config=quant_config,
prefix=f"{prefix}.wq_b",
)
# Fused wk + weights_proj: single GEMM producing [head_dim + n_head].
# weights_proj does not get quantized, so we run both with quant_config=None
# wk may be upcasted from the default quant; experiments show fusion is always
# faster unless WK proj is in FP4, which is not the case for all known quants.
self.wk_weights_proj = MergedColumnParallelLinear(
self.wk = ReplicatedLinear(
hidden_size,
[self.head_dim, self.n_head],
self.head_dim,
bias=False,
quant_config=None,
disable_tp=True,
prefix=f"{prefix}.wk_weights_proj",
quant_config=quant_config,
prefix=f"{prefix}.wk",
)
self.k_norm = LayerNorm(self.head_dim, eps=1e-6)
self.weights_proj = ReplicatedLinear(
hidden_size,
self.n_head,
bias=False,
quant_config=None,
prefix=f"{prefix}.weights_proj",
)
self.softmax_scale = self.head_dim**-0.5

self.scale_fmt = "ue8m0"
Expand Down Expand Up @@ -692,11 +694,7 @@ def forward(
q, [self.rope_dim, self.head_dim - self.rope_dim], dim=-1
)

# Fused wk + weights_proj: one GEMM, then split
kw, _ = self.wk_weights_proj(hidden_states)
k = kw[:, : self.head_dim]
weights_raw = kw[:, self.head_dim :]

k, _ = self.wk(hidden_states)
k = self.k_norm(k)
k_pe, k_nope = torch.split(
k, [self.rope_dim, self.head_dim - self.rope_dim], dim=-1
Expand Down Expand Up @@ -725,8 +723,9 @@ def forward(
q_fp8 = q_fp8.view(-1, self.n_head, self.head_dim)
q_scale = q_scale.view(-1, self.n_head, 1)

weights, _ = self.weights_proj(hidden_states)
weights = (
weights_raw.unsqueeze(-1) * q_scale * self.softmax_scale * self.n_head**-0.5
weights.unsqueeze(-1) * q_scale * self.softmax_scale * self.n_head**-0.5
)
weights = weights.squeeze(-1)

Expand Down Expand Up @@ -1439,13 +1438,6 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
("qkv_proj", "k_proj", "k"),
("qkv_proj", "v_proj", "v"),
]
# Fused indexer wk + weights_proj (shard 0 = wk, shard 1 = weights_proj)
indexer_fused_mapping = [
("wk_weights_proj", "wk", 0),
("wk_weights_proj", "weights_proj", 1),
]
stacked_params_mapping.extend(indexer_fused_mapping)

if self.use_mha:
stacked_params_mapping.extend(mha_params_mapping)
else:
Expand Down