Skip to content

[KDA] Fuse scaled_dot_kkt + solve_tril + recompute_w_u for KDA #21604

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

Merged
Kangyan-Zhou merged 3 commits into from
Apr 1, 2026
Merged

[KDA] Fuse scaled_dot_kkt + solve_tril + recompute_w_u for KDA #21604

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
a4a4360
Support fuse chunk kda
Mar 28, 2026
02eba51
fix aqk setting zeros
Mar 28, 2026
e008d1c
Fix import error and benchmark
Mar 29, 2026
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
21 changes: 15 additions & 6 deletions benchmark/bench_linear_attention/bench_cutedsl_kda_decode.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -51,8 +51,11 @@ def make_inputs(
b = torch.randn(B, HV, device=device, dtype=dtype)

# prefill params for chunk_kda must keep batch dim = 1
prefill_g = torch.randn(1, B, HV, K, device=device, dtype=dtype)
prefill_beta = torch.sigmoid(torch.randn(1, B, HV, device=device, dtype=dtype))
# chunk_kda requires g, beta, v to have the same head count as k (H),
# matching the real KimiLinear model where num_heads == num_kv_heads.
prefill_v = torch.randn(1, B, H, V, device=device, dtype=dtype)
prefill_g = torch.randn(1, B, H, K, device=device, dtype=dtype)
prefill_beta = torch.sigmoid(torch.randn(1, B, H, device=device, dtype=dtype))

cu_seqlens = torch.arange(B + 1, device=device, dtype=torch.int32)

Expand All @@ -66,8 +69,11 @@ def make_inputs(
b = torch.randn(B, 1, HV, device=device, dtype=dtype)

# prefill params for chunk_kda dense path
prefill_g = torch.randn(B, 1, HV, K, device=device, dtype=dtype)
prefill_beta = torch.sigmoid(torch.randn(B, 1, HV, device=device, dtype=dtype))
# chunk_kda requires g, beta, v to have the same head count as k (H),
# matching the real KimiLinear model where num_heads == num_kv_heads.
prefill_v = torch.randn(B, 1, H, V, device=device, dtype=dtype)
prefill_g = torch.randn(B, 1, H, K, device=device, dtype=dtype)
prefill_beta = torch.sigmoid(torch.randn(B, 1, H, device=device, dtype=dtype))

cu_seqlens = torch.arange(B + 1, device=device, dtype=torch.int32)
else:
Expand All @@ -94,6 +100,7 @@ def make_inputs(
v=v,
a=a,
b=b,
prefill_v=prefill_v,
prefill_g=prefill_g,
prefill_beta=prefill_beta,
A_log=A_log,
Expand Down Expand Up @@ -147,12 +154,13 @@ def run_cutedsl(inp):

def run_prefill_then_decode_baseline(inp):
ssm_states = inp["ssm_states"].clone()
prefill_v_clone = inp["prefill_v"].clone()
v_clone = inp["v"].clone()

_ = chunk_kda(
q=inp["q"],
k=inp["k"],
v=v_clone,
v=prefill_v_clone,
g=inp["prefill_g"],
beta=inp["prefill_beta"],
initial_state=ssm_states,
Expand Down Expand Up @@ -182,12 +190,13 @@ def run_prefill_then_decode_baseline(inp):

def run_prefill_then_decode_cutedsl(inp):
ssm_states = inp["ssm_states"].clone()
prefill_v_clone = inp["prefill_v"].clone()
v_clone = inp["v"].clone()

_ = chunk_kda(
q=inp["q"],
k=inp["k"],
v=v_clone,
v=prefill_v_clone,
g=inp["prefill_g"],
beta=inp["prefill_beta"],
initial_state=ssm_states,
Expand Down
Loading
Loading