{
  "path": "/home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass/reference.py"
}

{
  "raw": "{\"path\": \"/home/infatos"
}

{
  "raw": "{\"path\": \"/home/infatos"
}

{
  "raw": "{\"path\": \"/home/infatos"
}

{
  "raw": "{\"path\": \"/home/infatoshi/cuda/Kernel"
}

{
  "command": "nvidia-smi --query-gpu=name,compute_cap,memory.total --format=csv,noheader"
}

{
  "raw": "{\"command\":"
}

{
  "command": "which nvcc && nvcc --version | head -3"
}

{
  "raw": "{\"command\":"
}

{
  "command": "find /home/infatoshi -path \"*/fla/ops/kda*\" -type f 2>/dev/null | head -20"
}

{
  "command": "pip show flash-linear-attention 2>/dev/null | head -5 || echo \"FLA not installed\""
}

{
  "raw": "{\"command\": \"python -c \\\"import cutlass; print(cutlass.__version__)\\\" "
}

{
  "raw": "{\"command\": \""
}

{
  "path": "/home/infatoshi/.cache/uv/archive-v0/rnS-5wxCI5ghhBOYKv7Tc/fla/ops/kda/chunk_fwd.py"
}

{
  "raw": "{\"path\": \"/home/infatoshi/."
}

{
  "raw": "{\"path\": \"/home/infatoshi/."
}

{
  "command": "<239 chars \u2014 see below>"
}

python -c "
import sys
sys.path.insert(0, '/home/infatoshi/cuda/KernelBench-Hard')
from src.hardware import get as get_hw
hw = get_hw('RTX_PRO_6000')
print('Peak TFLOPS:', hw.peak_tflops_dense)
print('Peak GB/s:', hw.peak_bandwidth_gb_s)
"

{
  "path": "/home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass/sota.py"
}

{
  "command": "<139 chars \u2014 see below>"
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && python -c "
import sota
print('SOTA available:', sota.is_available())
"

{
  "path": "/home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass/solution.py",
  "content": "<3005 chars \u2014 see diff>"
}

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && python check.py",
  "timeout": 120
}

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && python benchmark.py",
  "timeout": 120
}

{
  "command": "<788 chars \u2014 see below>",
  "timeout": 120
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && python -c "
import torch
import reference
import solution
import shapes

shape = shapes.SHAPES[1]  # B=2, T=2048
for k, v in shape.items():
    setattr(reference, k, v)

init_args = reference.get_init_inputs()
ref_model = reference.Model(*init_args).cuda().eval()
sol_model = solution.Model(*init_args).cuda().eval()

torch.manual_seed(2026)
inputs = [t.cuda() if hasattr(t, 'cuda') else t for t in reference.get_inputs()]

# Warmup
with torch.no_grad():
    _ = sol_model(*inputs)

torch.cuda.synchronize()
import time
start = time.time()
with torch.no_grad():
    for _ in range(10):
        _ = sol_model(*inputs)
torch.cuda.synchronize()
elapsed = (time.time() - start) / 10 * 1000
print(f'Mean ms: {elapsed:.3f}')
"

{
  "command": "<757 chars \u2014 see below>",
  "timeout": 120
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && python -c "
import torch
import reference
import sota
import shapes

shape = shapes.SHAPES[1]  # B=2, T=2048
for k, v in shape.items():
    setattr(reference, k, v)

init_args = reference.get_init_inputs()
torch.manual_seed(2026)
inputs = [t.cuda() if hasattr(t, 'cuda') else t for t in reference.get_inputs()]
scale = float(shape['K']) ** -0.5

# Warmup
with torch.no_grad():
    _ = sota.sota_forward(*inputs, scale=scale)

torch.cuda.synchronize()
import time
start = time.time()
with torch.no_grad():
    for _ in range(50):
        _ = sota.sota_forward(*inputs, scale=scale)
torch.cuda.synchronize()
elapsed = (time.time() - start) / 50 * 1000
print(f'SOTA mean ms: {elapsed:.3f}')
"

{
  "command": "find /home/infatoshi/.cache/uv/archive-v0/rnS-5wxCI5ghhBOYKv7Tc/fla/ops -name \"chunk_delta_h.py\" -o -name \"chunk_gla*\" | head -10"
}

{
  "command": "<3210 chars \u2014 see below>",
  "timeout": 120
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && python -c "
import torch
import reference
import shapes

shape = shapes.SHAPES[1]
for k, v in shape.items():
    setattr(reference, k, v)

torch.manual_seed(2026)
inputs = [t.cuda() if hasattr(t, 'cuda') else t for t in reference.get_inputs()]
q, k, v, g, beta = inputs
scale = float(shape['K']) ** -0.5

B, T, H, K = q.shape
V = v.shape[-1]
BT = 64
NT = T // BT

qf = q.float() * scale
kf = k.float()
vf = v.float()
gf = g.float()
bf = beta.float()

# Reshape
qf = qf.view(B, NT, BT, H, K).permute(0, 3, 1, 2, 4)  # B, H, N, C, K
kf = kf.view(B, NT, BT, H, K).permute(0, 3, 1, 2, 4)
vf = vf.view(B, NT, BT, H, V).permute(0, 3, 1, 2, 4)
gf = gf.view(B, NT, BT, H, K).permute(0, 3, 1, 2, 4)
bf = bf.view(B, NT, BT, H).permute(0, 2, 1, 3)  # B, H, N, C

gf = gf.cumsum(-2)

# Build A_kk using einsum
kg = kf * gf.exp()
kgn = kf * (-gf).exp()

# B,H,N,C,K @ B,H,N,C,K -> B,H,N,C,C
A = torch.einsum('bhnik,bhnjk->bhnij', kg, kgn)

mask_upper = torch.triu(torch.ones(BT, BT, device=q.device, dtype=torch.bool), diagonal=0)
A = A * bf.unsqueeze(-1)
A = -A.masked_fill(mask_upper, 0)

# Forward substitution
for i in range(1, BT):
    A[:, :, :, i, :i] = A[:, :, :, i, :i] + (A[:, :, :, i, :, None] * A[:, :, :, :, :i]).sum(-2)

I = torch.eye(BT, device=q.device, dtype=torch.float32)
A = (A + I) * bf.unsqueeze(-2)

w = torch.matmul(A, (gf.exp() * kf))  # B,H,N,C,K
u = torch.matmul(A, vf)  # B,H,N,C,V

# Aqk
qg = qf * gf.exp()
Aqk = torch.einsum('bhnik,bhnjk->bhnij', qg, kgn)
mask_strict = torch.triu(torch.ones(BT, BT, device=q.device, dtype=torch.bool), diagonal=1)
Aqk = Aqk.masked_fill(mask_strict, 0)

# Inter-chunk
S = qf.new_zeros(B, H, K, V)
o = torch.zeros_like(vf)
for i in range(NT):
    q_i = qf[:, :, i]  # B,H,C,K
    k_i = kf[:, :, i]
    u_i = u[:, :, i]
    g_i = gf[:, :, i]
    w_i = w[:, :, i]
    
    v_i = u_i - torch.matmul(w_i, S)  # B,H,C,V
    qS = torch.matmul(q_i * g_i.exp(), S)  # B,H,C,V
    Av = torch.matmul(Aqk[:, :, i], v_i)  # B,H,C,V
    o[:, :, i] = qS + Av
    
    decay = g_i[:, :, -1].exp().unsqueeze(-1)  # B,H,K,1
    S = S * decay
    
    kg_decay = (g_i[:, :, -1:].unsqueeze(-2) - g_i).exp() * k_i  # B,H,C,K
    S = S + torch.matmul(kg_decay.transpose(-2, -1), v_i)

o = o.permute(0, 2, 3, 1, 4).contiguous().view(B, T, H, V)

print('Output shape:', o.shape)
print('Output dtype:', o.dtype)

torch.cuda.synchronize()
import time
start = time.time()
for _ in range(20):
    # just the inter-chunk loop
    S = qf.new_zeros(B, H, K, V)
    o = torch.zeros_like(vf)
    for i in range(NT):
        q_i = qf[:, :, i]
        k_i = kf[:, :, i]
        u_i = u[:, :, i]
        g_i = gf[:, :, i]
        w_i = w[:, :, i]
        
        v_i = u_i - torch.matmul(w_i, S)
        qS = torch.matmul(q_i * g_i.exp(), S)
        Av = torch.matmul(Aqk[:, :, i], v_i)
        o[:, :, i] = qS + Av
        
        decay = g_i[:, :, -1].exp().unsqueeze(-1)
        S = S * decay
        
        kg_decay = (g_i[:, :, -1:].unsqueeze(-2) - g_i).exp() * k_i
        S = S + torch.matmul(kg_decay.transpose(-2, -1), v_i)
torch.cuda.synchronize()
print(f'Inter-chunk only: {(time.time()-start)/20*1000:.3f} ms')
"

{
  "command": "<4556 chars \u2014 see below>",
  "timeout": 120
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && python -c "
import torch
import reference
import shapes

shape = shapes.SHAPES[1]
for k, v in shape.items():
    setattr(reference, k, v)

torch.manual_seed(2026)
inputs = [t.cuda() if hasattr(t, 'cuda') else t for t in reference.get_inputs()]
q, k, v, g, beta = inputs
scale = float(shape['K']) ** -0.5

B, T, H, K = q.shape
V = v.shape[-1]
BT = 64
NT = T // BT

qf = q.float() * scale
kf = k.float()
vf = v.float()
gf = g.float()
bf = beta.float()

# Reshape: B,T,H,K -> B,H,N,C,K
qf = qf.view(B, NT, BT, H, K).permute(0, 3, 1, 2, 4)
kf = kf.view(B, NT, BT, H, K).permute(0, 3, 1, 2, 4)
vf = vf.view(B, NT, BT, H, V).permute(0, 3, 1, 2, 4)
gf = gf.view(B, NT, BT, H, K).permute(0, 3, 1, 2, 4)
bf = bf.view(B, NT, BT, H).permute(0, 3, 1, 2)  # B,H,N,C

gf = gf.cumsum(-2)

# Build A_kk using einsum
kg = kf * gf.exp()
kgn = kf * (-gf).exp()

# B,H,N,C,K @ B,H,N,C,K -> B,H,N,C,C
A = torch.einsum('bhnik,bhnjk->bhnij', kg, kgn)

mask_upper = torch.triu(torch.ones(BT, BT, device=q.device, dtype=torch.bool), diagonal=0)
A = A * bf.unsqueeze(-1)  # B,H,N,C,1
A = -A.masked_fill(mask_upper, 0)

# Forward substitution
for i in range(1, BT):
    A[:, :, :, i, :i] = A[:, :, :, i, :i] + (A[:, :, :, i, :, None] * A[:, :, :, :, :i]).sum(-2)

I = torch.eye(BT, device=q.device, dtype=torch.float32)
A = (A + I) * bf.unsqueeze(-2)  # B,H,N,1,C

w = torch.matmul(A, (gf.exp() * kf))  # B,H,N,C,K
u = torch.matmul(A, vf)  # B,H,N,C,V

# Aqk
qg = qf * gf.exp()
Aqk = torch.einsum('bhnik,bhnjk->bhnij', qg, kgn)
mask_strict = torch.triu(torch.ones(BT, BT, device=q.device, dtype=torch.bool), diagonal=1)
Aqk = Aqk.masked_fill(mask_strict, 0)

# Inter-chunk
S = qf.new_zeros(B, H, K, V)
o = torch.zeros_like(vf)
for i in range(NT):
    q_i = qf[:, :, i]  # B,H,C,K
    k_i = kf[:, :, i]
    u_i = u[:, :, i]
    g_i = gf[:, :, i]
    w_i = w[:, :, i]
    
    v_i = u_i - torch.matmul(w_i, S)  # B,H,C,V
    qS = torch.matmul(q_i * g_i.exp(), S)  # B,H,C,V
    Av = torch.matmul(Aqk[:, :, i], v_i)  # B,H,C,V
    o[:, :, i] = qS + Av
    
    decay = g_i[:, :, -1].exp().unsqueeze(-1)  # B,H,K,1
    S = S * decay
    
    kg_decay = (g_i[:, :, -1:].unsqueeze(-2) - g_i).exp() * k_i  # B,H,C,K
    S = S + torch.matmul(kg_decay.transpose(-2, -1), v_i)

o = o.permute(0, 2, 3, 1, 4).contiguous().view(B, T, H, V)

print('Output shape:', o.shape)

torch.cuda.synchronize()
import time
start = time.time()
for _ in range(20):
    S = qf.new_zeros(B, H, K, V)
    out = torch.zeros_like(vf)
    for i in range(NT):
        q_i = qf[:, :, i]
        k_i = kf[:, :, i]
        u_i = u[:, :, i]
        g_i = gf[:, :, i]
        w_i = w[:, :, i]
        
        v_i = u_i - torch.matmul(w_i, S)
        qS = torch.matmul(q_i * g_i.exp(), S)
        Av = torch.matmul(Aqk[:, :, i], v_i)
        out[:, :, i] = qS + Av
        
        decay = g_i[:, :, -1].exp().unsqueeze(-1)
        S = S * decay
        
        kg_decay = (g_i[:, :, -1:].unsqueeze(-2) - g_i).exp() * k_i
        S = S + torch.matmul(kg_decay.transpose(-2, -1), v_i)
torch.cuda.synchronize()
print(f'Inter-chunk only: {(time.time()-start)/20*1000:.3f} ms')

# Full timing
torch.cuda.synchronize()
start = time.time()
for _ in range(20):
    gf = g.float().view(B, NT, BT, H, K).permute(0, 3, 1, 2, 4).cumsum(-2)
    kg = kf * gf.exp()
    kgn = kf * (-gf).exp()
    A = torch.einsum('bhnik,bhnjk->bhnij', kg, kgn)
    A = A * bf.unsqueeze(-1)
    A = -A.masked_fill(mask_upper, 0)
    for i in range(1, BT):
        A[:, :, :, i, :i] = A[:, :, :, i, :i] + (A[:, :, :, i, :, None] * A[:, :, :, :, :i]).sum(-2)
    A = (A + I) * bf.unsqueeze(-2)
    w = torch.matmul(A, (gf.exp() * kf))
    u = torch.matmul(A, vf)
    qg = qf * gf.exp()
    Aqk = torch.einsum('bhnik,bhnjk->bhnij', qg, kgn)
    Aqk = Aqk.masked_fill(mask_strict, 0)
    
    S = qf.new_zeros(B, H, K, V)
    out = torch.zeros_like(vf)
    for i in range(NT):
        q_i = qf[:, :, i]
        k_i = kf[:, :, i]
   

... (556 more chars)

{
  "command": "<3947 chars \u2014 see below>",
  "timeout": 120
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && python -c "
import torch
import reference
import shapes

shape = shapes.SHAPES[1]
for k, v in shape.items():
    setattr(reference, k, v)

torch.manual_seed(2026)
inputs = [t.cuda() if hasattr(t, 'cuda') else t for t in reference.get_inputs()]
q, k, v, g, beta = inputs
scale = float(shape['K']) ** -0.5

B, T, H, K = q.shape
V = v.shape[-1]
BT = 64
NT = T // BT

qf = q.float() * scale
kf = k.float()
vf = v.float()
gf = g.float()
bf = beta.float()

# Reshape: B,T,H,K -> B,H,N,C,K
qf = qf.view(B, NT, BT, H, K).permute(0, 3, 1, 2, 4)
kf = kf.view(B, NT, BT, H, K).permute(0, 3, 1, 2, 4)
vf = vf.view(B, NT, BT, H, V).permute(0, 3, 1, 2, 4)
gf = gf.view(B, NT, BT, H, K).permute(0, 3, 1, 2, 4)
bf = bf.view(B, NT, BT, H).permute(0, 3, 1, 2)  # B,H,N,C

print('bf shape:', bf.shape)
gf = gf.cumsum(-2)

# Build A_kk using einsum
kg = kf * gf.exp()
kgn = kf * (-gf).exp()

# B,H,N,C,K @ B,H,N,C,K -> B,H,N,C,C
A = torch.einsum('bhnik,bhnjk->bhnij', kg, kgn)
print('A shape:', A.shape)

mask_upper = torch.triu(torch.ones(BT, BT, device=q.device, dtype=torch.bool), diagonal=0)
A = A * bf[..., None]  # B,H,N,C,1
print('After beta:', A.shape)
A = -A.masked_fill(mask_upper, 0)

# Forward substitution
for i in range(1, BT):
    A[..., i, :i] = A[..., i, :i] + (A[..., i, :, None] * A[..., :, :i]).sum(-2)

I = torch.eye(BT, device=q.device, dtype=torch.float32)
A = (A + I) * bf[..., None, :]  # B,H,N,1,C
print('A final shape:', A.shape)

w = torch.matmul(A, (gf.exp() * kf))  # B,H,N,C,K
print('w shape:', w.shape)
u = torch.matmul(A, vf)  # B,H,N,C,V
print('u shape:', u.shape)

# Aqk
qg = qf * gf.exp()
Aqk = torch.einsum('bhnik,bhnjk->bhnij', qg, kgn)
mask_strict = torch.triu(torch.ones(BT, BT, device=q.device, dtype=torch.bool), diagonal=1)
Aqk = Aqk.masked_fill(mask_strict, 0)
print('Aqk shape:', Aqk.shape)

# Inter-chunk
S = qf.new_zeros(B, H, K, V)
o = torch.zeros_like(vf)
for i in range(NT):
    q_i = qf[:, :, i]  # B,H,C,K
    k_i = kf[:, :, i]
    u_i = u[:, :, i]
    g_i = gf[:, :, i]
    w_i = w[:, :, i]
    
    v_i = u_i - torch.matmul(w_i, S)  # B,H,C,V
    qS = torch.matmul(q_i * g_i.exp(), S)  # B,H,C,V
    Av = torch.matmul(Aqk[:, :, i], v_i)  # B,H,C,V
    o[:, :, i] = qS + Av
    
    decay = g_i[:, :, -1].exp().unsqueeze(-1)  # B,H,K,1
    S = S * decay
    
    kg_decay = (g_i[:, :, -1:].unsqueeze(-2) - g_i).exp() * k_i  # B,H,C,K
    S = S + torch.matmul(kg_decay.transpose(-2, -1), v_i)

o = o.permute(0, 2, 3, 1, 4).contiguous().view(B, T, H, V)
print('o shape:', o.shape)

# Timing
torch.cuda.synchronize()
import time
start = time.time()
for _ in range(50):
    gf = g.float().view(B, NT, BT, H, K).permute(0, 3, 1, 2, 4).cumsum(-2)
    kg = kf * gf.exp()
    kgn = kf * (-gf).exp()
    A = torch.einsum('bhnik,bhnjk->bhnij', kg, kgn)
    A = A * bf[..., None]
    A = -A.masked_fill(mask_upper, 0)
    for i in range(1, BT):
        A[..., i, :i] = A[..., i, :i] + (A[..., i, :, None] * A[..., :, :i]).sum(-2)
    A = (A + I) * bf[..., None, :]
    w = torch.matmul(A, (gf.exp() * kf))
    u = torch.matmul(A, vf)
    qg = qf * gf.exp()
    Aqk = torch.einsum('bhnik,bhnjk->bhnij', qg, kgn)
    Aqk = Aqk.masked_fill(mask_strict, 0)
    
    S = qf.new_zeros(B, H, K, V)
    out = torch.zeros_like(vf)
    for i in range(NT):
        q_i = qf[:, :, i]
        k_i = kf[:, :, i]
        u_i = u[:, :, i]
        g_i = gf[:, :, i]
        w_i = w[:, :, i]
        
        v_i = u_i - torch.matmul(w_i, S)
        qS = torch.matmul(q_i * g_i.exp(), S)
        Av = torch.matmul(Aqk[:, :, i], v_i)
        out[:, :, i] = qS + Av
        
        decay = g_i[:, :, -1].exp().unsqueeze(-1)
        S = S * decay
        
        kg_decay = (g_i[:, :, -1:].unsqueeze(-2) - g_i).exp() * k_i
        S = S + torch.matmul(kg_decay.transpose(-2, -1), v_i)

torch.cuda.synchronize()
print(f'Full vectorized: {(time.time()-start)/50*1000:.3f} ms')
"

{
  "command": "<2403 chars \u2014 see below>",
  "timeout": 120
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && python -c "
import torch
import reference
import shapes

shape = shapes.SHAPES[1]
for k, v in shape.items():
    setattr(reference, k, v)

torch.manual_seed(2026)
inputs = [t.cuda() if hasattr(t, 'cuda') else t for t in reference.get_inputs()]
q, k, v, g, beta = inputs
scale = float(shape['K']) ** -0.5

B, T, H, K = q.shape
V = v.shape[-1]
BT = 64
NT = T // BT

qf = q.float() * scale
kf = k.float()
vf = v.float()
gf = g.float()
bf = beta.float()

# Reshape: B,T,H,K -> B,H,N,C,K
qf = qf.view(B, NT, BT, H, K).permute(0, 3, 1, 2, 4)
kf = kf.view(B, NT, BT, H, K).permute(0, 3, 1, 2, 4)
vf = vf.view(B, NT, BT, H, V).permute(0, 3, 1, 2, 4)
gf = gf.view(B, NT, BT, H, K).permute(0, 3, 1, 2, 4)
bf = bf.view(B, NT, BT, H).permute(0, 3, 1, 2)  # B,H,N,C

gf = gf.cumsum(-2)

kg = kf * gf.exp()
kgn = kf * (-gf).exp()
A = torch.einsum('bhnik,bhnjk->bhnij', kg, kgn)
mask_upper = torch.triu(torch.ones(BT, BT, device=q.device, dtype=torch.bool), diagonal=0)
A = A * bf[..., None]
A = -A.masked_fill(mask_upper, 0)
for i in range(1, BT):
    A[..., i, :i] = A[..., i, :i] + (A[..., i, :, None] * A[..., :, :i]).sum(-2)
I = torch.eye(BT, device=q.device, dtype=torch.float32)
A = (A + I) * bf[..., None, :]

w = torch.matmul(A, (gf.exp() * kf))
u = torch.matmul(A, vf)

qg = qf * gf.exp()
Aqk = torch.einsum('bhnik,bhnjk->bhnij', qg, kgn)
mask_strict = torch.triu(torch.ones(BT, BT, device=q.device, dtype=torch.bool), diagonal=1)
Aqk = Aqk.masked_fill(mask_strict, 0)

S = qf.new_zeros(B, H, K, V)
print('S shape:', S.shape)
for i in range(NT):
    print(f'Chunk {i}')
    q_i = qf[:, :, i]
    k_i = kf[:, :, i]
    u_i = u[:, :, i]
    g_i = gf[:, :, i]
    w_i = w[:, :, i]
    print('q_i:', q_i.shape)
    print('k_i:', k_i.shape)
    print('u_i:', u_i.shape)
    print('g_i:', g_i.shape)
    print('w_i:', w_i.shape)
    
    v_i = u_i - torch.matmul(w_i, S)
    print('v_i:', v_i.shape)
    qS = torch.matmul(q_i * g_i.exp(), S)
    print('qS:', qS.shape)
    Av = torch.matmul(Aqk[:, :, i], v_i)
    print('Av:', Av.shape)
    
    decay = g_i[:, :, -1].exp().unsqueeze(-1)
    print('decay:', decay.shape)
    S = S * decay
    
    kg_decay = (g_i[:, :, -1:].unsqueeze(-2) - g_i).exp() * k_i
    print('kg_decay:', kg_decay.shape)
    S = S + torch.matmul(kg_decay.transpose(-2, -1), v_i)
    print('S:', S.shape)

print('Done')
"

{
  "command": "<3756 chars \u2014 see below>",
  "timeout": 120
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && python -c "
import torch
import reference
import shapes

shape = shapes.SHAPES[1]
for k, v in shape.items():
    setattr(reference, k, v)

torch.manual_seed(2026)
inputs = [t.cuda() if hasattr(t, 'cuda') else t for t in reference.get_inputs()]
q, k, v, g, beta = inputs
scale = float(shape['K']) ** -0.5

B, T, H, K = q.shape
V = v.shape[-1]
BT = 64
NT = T // BT

qf = q.float() * scale
kf = k.float()
vf = v.float()
gf = g.float()
bf = beta.float()

# Reshape: B,T,H,K -> B,H,N,C,K
qf = qf.view(B, NT, BT, H, K).permute(0, 3, 1, 2, 4)
kf = kf.view(B, NT, BT, H, K).permute(0, 3, 1, 2, 4)
vf = vf.view(B, NT, BT, H, V).permute(0, 3, 1, 2, 4)
gf = gf.view(B, NT, BT, H, K).permute(0, 3, 1, 2, 4)
bf = bf.view(B, NT, BT, H).permute(0, 3, 1, 2)  # B,H,N,C

gf = gf.cumsum(-2)

kg = kf * gf.exp()
kgn = kf * (-gf).exp()
A = torch.einsum('bhnik,bhnjk->bhnij', kg, kgn)
mask_upper = torch.triu(torch.ones(BT, BT, device=q.device, dtype=torch.bool), diagonal=0)
A = A * bf[..., None]
A = -A.masked_fill(mask_upper, 0)
for i in range(1, BT):
    A[..., i, :i] = A[..., i, :i] + (A[..., i, :, None] * A[..., :, :i]).sum(-2)
I = torch.eye(BT, device=q.device, dtype=torch.float32)
A = (A + I) * bf[..., None, :]

w = torch.matmul(A, (gf.exp() * kf))
u = torch.matmul(A, vf)

qg = qf * gf.exp()
Aqk = torch.einsum('bhnik,bhnjk->bhnij', qg, kgn)
mask_strict = torch.triu(torch.ones(BT, BT, device=q.device, dtype=torch.bool), diagonal=1)
Aqk = Aqk.masked_fill(mask_strict, 0)

S = qf.new_zeros(B, H, K, V)
o = torch.zeros_like(vf)
for i in range(NT):
    q_i = qf[:, :, i]
    k_i = kf[:, :, i]
    u_i = u[:, :, i]
    g_i = gf[:, :, i]
    w_i = w[:, :, i]
    
    v_i = u_i - torch.matmul(w_i, S)
    qS = torch.matmul(q_i * g_i.exp(), S)
    Av = torch.matmul(Aqk[:, :, i], v_i)
    o[:, :, i] = qS + Av
    
    decay = g_i[:, :, -1].exp().unsqueeze(-1)  # B,H,K,1
    S = S * decay
    
    # g_i[:, :, -1:] is (B,H,1,128), g_i is (B,H,C,128)
    kg_decay = (g_i[:, :, -1:] - g_i).exp() * k_i  # B,H,C,K
    S = S + torch.matmul(kg_decay.transpose(-2, -1), v_i)

o = o.permute(0, 2, 3, 1, 4).contiguous().view(B, T, H, V)

# Check correctness
ref_model = reference.Model(B, T, H, K, V, BT).cuda()
with torch.no_grad():
    ref_out = ref_model(*inputs)

print('Max diff:', (ref_out.float() - o.to(ref_out.dtype).float()).abs().max().item())

# Timing
torch.cuda.synchronize()
import time
start = time.time()
for _ in range(50):
    gf = g.float().view(B, NT, BT, H, K).permute(0, 3, 1, 2, 4).cumsum(-2)
    kg = kf * gf.exp()
    kgn = kf * (-gf).exp()
    A = torch.einsum('bhnik,bhnjk->bhnij', kg, kgn)
    A = A * bf[..., None]
    A = -A.masked_fill(mask_upper, 0)
    for i in range(1, BT):
        A[..., i, :i] = A[..., i, :i] + (A[..., i, :, None] * A[..., :, :i]).sum(-2)
    A = (A + I) * bf[..., None, :]
    w = torch.matmul(A, (gf.exp() * kf))
    u = torch.matmul(A, vf)
    qg = qf * gf.exp()
    Aqk = torch.einsum('bhnik,bhnjk->bhnij', qg, kgn)
    Aqk = Aqk.masked_fill(mask_strict, 0)
    
    S = qf.new_zeros(B, H, K, V)
    out = torch.zeros_like(vf)
    for i in range(NT):
        q_i = qf[:, :, i]
        k_i = kf[:, :, i]
        u_i = u[:, :, i]
        g_i = gf[:, :, i]
        w_i = w[:, :, i]
        
        v_i = u_i - torch.matmul(w_i, S)
        qS = torch.matmul(q_i * g_i.exp(), S)
        Av = torch.matmul(Aqk[:, :, i], v_i)
        out[:, :, i] = qS + Av
        
        decay = g_i[:, :, -1].exp().unsqueeze(-1)
        S = S * decay
        
        kg_decay = (g_i[:, :, -1:] - g_i).exp() * k_i
        S = S + torch.matmul(kg_decay.transpose(-2, -1), v_i)

torch.cuda.synchronize()
print(f'Full vectorized: {(time.time()-start)/50*1000:.3f} ms')
"

{
  "path": "/home/infatoshi/.cache/uv/archive-v0/rnS-5wxCI5ghhBOYKv7Tc/fla/ops/common/chunk_delta_h.py"
}

{
  "command": "find /home/infatoshi/.cache/uv/archive-v0/rnS-5wxCI5ghhBOYKv7Tc/fla/ops/gla -name \"*.py\" | head -10"
}

{
  "command": "grep -n \"chunk_gla_fwd_o_gk\" /home/infatoshi/.cache/uv/archive-v0/rnS-5wxCI5ghhBOYKv7Tc/fla/ops/gla/chunk.py | head -10"
}

{
  "path": "/home/infatoshi/.cache/uv/archive-v0/rnS-5wxCI5ghhBOYKv7Tc/fla/ops/gla/chunk.py",
  "line_offset": 877,
  "n_lines": 200
}

{
  "command": "grep -n \"chunk_gla_fwd_kernel_o\" /home/infatoshi/.cache/uv/archive-v0/rnS-5wxCI5ghhBOYKv7Tc/fla/ops/gla/chunk.py | head -5"
}

{
  "path": "/home/infatoshi/.cache/uv/archive-v0/rnS-5wxCI5ghhBOYKv7Tc/fla/ops/gla/chunk.py",
  "line_offset": 313,
  "n_lines": 200
}

{
  "command": "<2962 chars \u2014 see below>",
  "timeout": 120
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && python -c "
import torch
import reference
import shapes

shape = shapes.SHAPES[1]
for k, v in shape.items():
    setattr(reference, k, v)

torch.manual_seed(2026)
inputs = [t.cuda() if hasattr(t, 'cuda') else t for t in reference.get_inputs()]
q, k, v, g, beta = inputs
scale = float(shape['K']) ** -0.5

B, T, H, K = q.shape
V = v.shape[-1]
BT = 64
NT = T // BT

qf = q.float() * scale
kf = k.float()
vf = v.float()
gf = g.float()
bf = beta.float()

qf = qf.view(B, NT, BT, H, K).permute(0, 3, 1, 2, 4)
kf = kf.view(B, NT, BT, H, K).permute(0, 3, 1, 2, 4)
vf = vf.view(B, NT, BT, H, V).permute(0, 3, 1, 2, 4)
gf = gf.view(B, NT, BT, H, K).permute(0, 3, 1, 2, 4)
bf = bf.view(B, NT, BT, H).permute(0, 3, 1, 2)

mask_upper = torch.triu(torch.ones(BT, BT, device=q.device, dtype=torch.bool), diagonal=0)
mask_strict = torch.triu(torch.ones(BT, BT, device=q.device, dtype=torch.bool), diagonal=1)
I = torch.eye(BT, device=q.device, dtype=torch.float32)

gf = gf.cumsum(-2)

# Time intra-chunk only
torch.cuda.synchronize()
import time
start = time.time()
for _ in range(100):
    kg = kf * gf.exp()
    kgn = kf * (-gf).exp()
    A = torch.einsum('bhnik,bhnjk->bhnij', kg, kgn)
    A = A * bf[..., None]
    A = -A.masked_fill(mask_upper, 0)
    for i in range(1, BT):
        A[..., i, :i] = A[..., i, :i] + (A[..., i, :, None] * A[..., :, :i]).sum(-2)
    A = (A + I) * bf[..., None, :]
    w = torch.matmul(A, (gf.exp() * kf))
    u = torch.matmul(A, vf)
    qg = qf * gf.exp()
    Aqk = torch.einsum('bhnik,bhnjk->bhnij', qg, kgn)
    Aqk = Aqk.masked_fill(mask_strict, 0)
torch.cuda.synchronize()
print(f'Intra-chunk: {(time.time()-start)/100*1000:.3f} ms')

# Precompute for inter-chunk timing
kg = kf * gf.exp()
kgn = kf * (-gf).exp()
A = torch.einsum('bhnik,bhnjk->bhnij', kg, kgn)
A = A * bf[..., None]
A = -A.masked_fill(mask_upper, 0)
for i in range(1, BT):
    A[..., i, :i] = A[..., i, :i] + (A[..., i, :, None] * A[..., :, :i]).sum(-2)
A = (A + I) * bf[..., None, :]
w = torch.matmul(A, (gf.exp() * kf))
u = torch.matmul(A, vf)
qg = qf * gf.exp()
Aqk = torch.einsum('bhnik,bhnjk->bhnij', qg, kgn)
Aqk = Aqk.masked_fill(mask_strict, 0)

torch.cuda.synchronize()
start = time.time()
for _ in range(100):
    S = qf.new_zeros(B, H, K, V)
    out = torch.zeros_like(vf)
    for i in range(NT):
        q_i = qf[:, :, i]
        k_i = kf[:, :, i]
        u_i = u[:, :, i]
        g_i = gf[:, :, i]
        w_i = w[:, :, i]
        
        v_i = u_i - torch.matmul(w_i, S)
        qS = torch.matmul(q_i * g_i.exp(), S)
        Av = torch.matmul(Aqk[:, :, i], v_i)
        out[:, :, i] = qS + Av
        
        decay = g_i[:, :, -1].exp().unsqueeze(-1)
        S = S * decay
        
        kg_decay = (g_i[:, :, -1:] - g_i).exp() * k_i
        S = S + torch.matmul(kg_decay.transpose(-2, -1), v_i)
torch.cuda.synchronize()
print(f'Inter-chunk: {(time.time()-start)/100*1000:.3f} ms')
"

{
  "command": "<2645 chars \u2014 see below>",
  "timeout": 120
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && python -c "
import torch
import reference
import shapes

shape = shapes.SHAPES[1]
for k, v in shape.items():
    setattr(reference, k, v)

torch.manual_seed(2026)
inputs = [t.cuda() if hasattr(t, 'cuda') else t for t in reference.get_inputs()]
q, k, v, g, beta = inputs
scale = float(shape['K']) ** -0.5

B, T, H, K = q.shape
V = v.shape[-1]
BT = 64
NT = T // BT

qf = q.float() * scale
kf = k.float()
vf = v.float()
gf = g.float()
bf = beta.float()

qf = qf.view(B, NT, BT, H, K).permute(0, 3, 1, 2, 4)
kf = kf.view(B, NT, BT, H, K).permute(0, 3, 1, 2, 4)
vf = vf.view(B, NT, BT, H, V).permute(0, 3, 1, 2, 4)
gf = gf.view(B, NT, BT, H, K).permute(0, 3, 1, 2, 4)
bf = bf.view(B, NT, BT, H).permute(0, 3, 1, 2)

mask_upper = torch.triu(torch.ones(BT, BT, device=q.device, dtype=torch.bool), diagonal=0)
mask_strict = torch.triu(torch.ones(BT, BT, device=q.device, dtype=torch.bool), diagonal=1)
I = torch.eye(BT, device=q.device, dtype=torch.float32)

gf = gf.cumsum(-2)

# Precompute masks once

# Time with bmm instead of einsum
torch.cuda.synchronize()
import time
start = time.time()
for _ in range(200):
    kg = kf * gf.exp()
    kgn = kf * (-gf).exp()
    
    # Using bmm
    BHN = B * H * NT
    kg_flat = kg.reshape(BHN, BT, K)
    kgn_flat = kgn.reshape(BHN, BT, K)
    A = torch.bmm(kg_flat, kgn_flat.transpose(-2, -1)).view(B, H, NT, BT, BT)
    
    A = A * bf[..., None]
    A = -A.masked_fill(mask_upper, 0)
    for i in range(1, BT):
        A[..., i, :i] = A[..., i, :i] + (A[..., i, :, None] * A[..., :, :i]).sum(-2)
    A = (A + I) * bf[..., None, :]
    w = torch.matmul(A, (gf.exp() * kf))
    u = torch.matmul(A, vf)
    qg = qf * gf.exp()
    qg_flat = qg.reshape(BHN, BT, K)
    Aqk = torch.bmm(qg_flat, kgn_flat.transpose(-2, -1)).view(B, H, NT, BT, BT)
    Aqk = Aqk.masked_fill(mask_strict, 0)
torch.cuda.synchronize()
print(f'With bmm: {(time.time()-start)/200*1000:.3f} ms')

# Original einsum
torch.cuda.synchronize()
start = time.time()
for _ in range(200):
    kg = kf * gf.exp()
    kgn = kf * (-gf).exp()
    A = torch.einsum('bhnik,bhnjk->bhnij', kg, kgn)
    A = A * bf[..., None]
    A = -A.masked_fill(mask_upper, 0)
    for i in range(1, BT):
        A[..., i, :i] = A[..., i, :i] + (A[..., i, :, None] * A[..., :, :i]).sum(-2)
    A = (A + I) * bf[..., None, :]
    w = torch.matmul(A, (gf.exp() * kf))
    u = torch.matmul(A, vf)
    qg = qf * gf.exp()
    Aqk = torch.einsum('bhnik,bhnjk->bhnij', qg, kgn)
    Aqk = Aqk.masked_fill(mask_strict, 0)
torch.cuda.synchronize()
print(f'With einsum: {(time.time()-start)/200*1000:.3f} ms')
"

{
  "command": "<720 chars \u2014 see below>",
  "timeout": 60
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && python -c "
import torch
import time

BHN = 512  # B*H*N for T=2048
C = 64
A = torch.randn(BHN, C, C, device='cuda')

# Time forward substitution
torch.cuda.synchronize()
start = time.time()
for _ in range(1000):
    A_copy = A.clone()
    for i in range(1, C):
        A_copy[:, i, :i] = A_copy[:, i, :i] + (A_copy[:, i, :, None] * A_copy[:, :, :i]).sum(-2)
torch.cuda.synchronize()
print(f'Forward substitution: {(time.time()-start)/1000*1000:.3f} ms')

# Compare to full matmul
torch.cuda.synchronize()
start = time.time()
for _ in range(1000):
    _ = torch.bmm(A, A)
torch.cuda.synchronize()
print(f'BMM: {(time.time()-start)/1000*1000:.3f} ms')
"

{
  "command": "<1941 chars \u2014 see below>",
  "timeout": 60
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && python -c "
import torch
import time

def tri_inv_dc(A):
    '''A is (..., C, C) strictly lower triangular. Returns (I - A)^{-1}.'''
    C = A.shape[-1]
    result = torch.eye(C, device=A.device, dtype=A.dtype).expand_as(A).clone()
    
    bs = 1
    while bs < C:
        num_pairs = C // (2 * bs)
        for p in range(num_pairs):
            top_start = p * 2 * bs
            top_end = top_start + bs
            bot_start = top_end
            bot_end = bot_start + bs
            
            top = result[..., top_start:top_end, top_start:top_end]
            bot = result[..., bot_start:bot_end, bot_start:bot_end]
            off = A[..., bot_start:bot_end, top_start:top_end]
            
            result[..., bot_start:bot_end, top_start:top_end] = torch.matmul(torch.matmul(bot, off), top)
        bs *= 2
    
    return result

# Test correctness
C = 64
BHN = 512
A = torch.randn(BHN, C, C, device='cuda') * 0.1
mask = torch.tril(torch.ones(C, C, device='cuda'), diagonal=-1)
A = A * mask

# Reference: sequential forward substitution
ref = torch.eye(C, device='cuda').expand_as(A).clone()
ref[:, 0, 0] = 1
for i in range(1, C):
    ref[:, i, :i] = A[:, i, :i] + (A[:, i, :, None] * ref[:, :, :i]).sum(-2)
    ref[:, i, i] = 1

# DC approach
dc = tri_inv_dc(A)

print('Max diff:', (ref - dc).abs().max().item())

# Time
torch.cuda.synchronize()
start = time.time()
for _ in range(1000):
    _ = tri_inv_dc(A)
torch.cuda.synchronize()
print(f'DC time: {(time.time()-start)/1000*1000:.3f} ms')

# Time reference
torch.cuda.synchronize()
start = time.time()
for _ in range(1000):
    ref = torch.eye(C, device='cuda').expand_as(A).clone()
    for i in range(1, C):
        ref[:, i, :i] = A[:, i, :i] + (A[:, i, :, None] * ref[:, :, :i]).sum(-2)
        ref[:, i, i] = 1
torch.cuda.synchronize()
print(f'Loop time: {(time.time()-start)/1000*1000:.3f} ms')
"

{
  "command": "<1382 chars \u2014 see below>",
  "timeout": 60
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && python -c "
import torch

def reference_fwd_subst(A):
    C = A.shape[-1]
    for i in range(1, C):
        A[..., i, :i] = A[..., i, :i].clone() + (A[..., i, :, None].clone() * A[..., :, :i].clone()).sum(-2)
    return A

def tri_inv_dc(A):
    C = A.shape[-1]
    result = torch.eye(C, device=A.device, dtype=A.dtype).expand_as(A).clone()
    
    bs = 1
    while bs < C:
        num_pairs = C // (2 * bs)
        for p in range(num_pairs):
            top_start = p * 2 * bs
            top_end = top_start + bs
            bot_start = top_end
            bot_end = bot_start + bs
            
            top = result[..., top_start:top_end, top_start:top_end]
            bot = result[..., bot_start:bot_end, bot_start:bot_end]
            off = A[..., bot_start:bot_end, top_start:top_end]
            
            result[..., bot_start:bot_end, top_start:top_end] = torch.matmul(torch.matmul(bot, off), top)
        bs *= 2
    
    return result

# Test with small C
for C in [2, 4, 8, 16, 32, 64]:
    A = torch.randn(4, C, C, device='cuda') * 0.1
    mask = torch.tril(torch.ones(C, C, device='cuda'), diagonal=-1)
    A = A * mask
    
    ref = reference_fwd_subst(A.clone()) + torch.eye(C, device='cuda')
    dc = tri_inv_dc(A)
    
    print(f'C={C}: max_diff={(ref - dc).abs().max().item():.6f}')
"

{
  "command": "<2517 chars \u2014 see below>",
  "timeout": 60
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && python -c "
import torch
import time

def tri_inv_dc(A):
    C = A.shape[-1]
    result = torch.eye(C, device=A.device, dtype=A.dtype).expand_as(A).clone()
    
    bs = 1
    while bs < C:
        num_pairs = C // (2 * bs)
        # Batch all pairs into a single bmm
        top_list = []
        bot_list = []
        off_list = []
        for p in range(num_pairs):
            top_start = p * 2 * bs
            top_end = top_start + bs
            bot_start = top_end
            bot_end = bot_start + bs
            top_list.append(result[..., top_start:top_end, top_start:top_end])
            bot_list.append(result[..., bot_start:bot_end, bot_start:bot_end])
            off_list.append(A[..., bot_start:bot_end, top_start:top_end])
        
        if num_pairs > 0:
            # Stack and bmm
            tops = torch.stack(top_list, dim=-4)  # (num_pairs, ..., bs, bs)
            bots = torch.stack(bot_list, dim=-4)
            offs = torch.stack(off_list, dim=-4)
            
            # Reshape to merge batch dims
            batch_shape = A.shape[:-2]
            tops_flat = tops.view(num_pairs, -1, bs, bs)
            bots_flat = bots.view(num_pairs, -1, bs, bs)
            offs_flat = offs.view(num_pairs, -1, bs, bs)
            
            # bot @ off
            tmp = torch.bmm(bots_flat, offs_flat)
            # tmp @ top
            out = torch.bmm(tmp, tops_flat)
            
            # Unstack back
            out = out.view(num_pairs, *batch_shape, bs, bs)
            for p in range(num_pairs):
                top_start = p * 2 * bs
                top_end = top_start + bs
                bot_start = top_end
                bot_end = bot_start + bs
                result[..., bot_start:bot_end, top_start:top_end] = out[p]
        
        bs *= 2
    
    return result

# Performance test
C = 64
BHN = 512
A = torch.randn(BHN, C, C, device='cuda') * 0.1
mask = torch.tril(torch.ones(C, C, device='cuda'), diagonal=-1)
A = A * mask

torch.cuda.synchronize()
start = time.time()
for _ in range(1000):
    _ = tri_inv_dc(A)
torch.cuda.synchronize()
print(f'Batched DC: {(time.time()-start)/1000*1000:.3f} ms')

# Reference
torch.cuda.synchronize()
start = time.time()
for _ in range(1000):
    ref = A.clone()
    for i in range(1, C):
        ref[:, i, :i] = ref[:, i, :i] + (ref[:, i, :, None] * ref[:, :, :i]).sum(-2)
torch.cuda.synchronize()
print(f'Loop: {(time.time()-start)/1000*1000:.3f} ms')
"

{
  "command": "<2350 chars \u2014 see below>",
  "timeout": 60
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && python -c "
import torch
import time

def tri_inv_dc(A):
    C = A.shape[-1]
    result = torch.eye(C, device=A.device, dtype=A.dtype).expand_as(A).clone()
    batch_shape = A.shape[:-2]
    batch_numel = batch_shape.numel()
    
    bs = 1
    while bs < C:
        num_pairs = C // (2 * bs)
        if num_pairs > 0:
            # Stack all pairs
            tops = []
            bots = []
            offs = []
            for p in range(num_pairs):
                top_start = p * 2 * bs
                top_end = top_start + bs
                bot_start = top_end
                bot_end = bot_start + bs
                tops.append(result[..., top_start:top_end, top_start:top_end])
                bots.append(result[..., bot_start:bot_end, bot_start:bot_end])
                offs.append(A[..., bot_start:bot_end, top_start:top_end])
            
            # Shape: (num_pairs, batch_numel, bs, bs)
            tops_t = torch.stack(tops, dim=0).view(num_pairs * batch_numel, bs, bs)
            bots_t = torch.stack(bots, dim=0).view(num_pairs * batch_numel, bs, bs)
            offs_t = torch.stack(offs, dim=0).view(num_pairs * batch_numel, bs, bs)
            
            tmp = torch.bmm(bots_t, offs_t)
            out = torch.bmm(tmp, tops_t)
            out = out.view(num_pairs, *batch_shape, bs, bs)
            
            for p in range(num_pairs):
                top_start = p * 2 * bs
                top_end = top_start + bs
                bot_start = top_end
                bot_end = bot_start + bs
                result[..., bot_start:bot_end, top_start:top_end] = out[p]
        
        bs *= 2
    
    return result

# Performance test
C = 64
BHN = 512
A = torch.randn(BHN, C, C, device='cuda') * 0.1
mask = torch.tril(torch.ones(C, C, device='cuda'), diagonal=-1)
A = A * mask

torch.cuda.synchronize()
start = time.time()
for _ in range(1000):
    _ = tri_inv_dc(A)
torch.cuda.synchronize()
print(f'Batched DC: {(time.time()-start)/1000*1000:.3f} ms')

# Reference
torch.cuda.synchronize()
start = time.time()
for _ in range(1000):
    ref = A.clone()
    for i in range(1, C):
        ref[:, i, :i] = ref[:, i, :i] + (ref[:, i, :, None] * ref[:, :, :i]).sum(-2)
torch.cuda.synchronize()
print(f'Loop: {(time.time()-start)/1000*1000:.3f} ms')
"

{
  "command": "<1590 chars \u2014 see below>",
  "timeout": 120
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && python -c "
import torch
from torch.utils.cpp_extension import load_inline

cuda_source = '''
__global__ void fwd_subst_kernel(float* __restrict__ A, int C, int batch) {
    int b = blockIdx.x;
    if (b >= batch) return;
    
    float* mat = A + b * C * C;
    
    for (int i = 1; i < C; i++) {
        for (int j = 0; j < i; j++) {
            float sum = 0.0f;
            for (int k = 0; k < i; k++) {
                sum += mat[i * C + k] * mat[k * C + j];
            }
            mat[i * C + j] += sum;
        }
    }
}

torch::Tensor fwd_subst(torch::Tensor A) {
    int batch = A.size(0);
    int C = A.size(1);
    fwd_subst_kernel<<<batch, 1>>>(A.data_ptr<float>(), C, batch);
    return A;
}
'''

module = load_inline(
    name='fwd_subst',
    cpp_sources='torch::Tensor fwd_subst(torch::Tensor A);',
    cuda_sources=cuda_source,
    functions=['fwd_subst'],
    extra_cuda_cflags=['-O3'],
    verbose=False,
)

C = 64
BHN = 512
A = torch.randn(BHN, C, C, device='cuda') * 0.1
mask = torch.tril(torch.ones(C, C, device='cuda'), diagonal=-1)
A = (A * mask).contiguous()

# Correctness
ref = A.clone()
for i in range(1, C):
    ref[:, i, :i] = ref[:, i, :i] + (ref[:, i, :, None] * ref[:, :, :i]).sum(-2)

out = module.fwd_subst(A.clone())
print('Max diff:', (ref - out).abs().max().item())

# Timing
torch.cuda.synchronize()
import time
start = time.time()
for _ in range(10000):
    _ = module.fwd_subst(A.clone())
torch.cuda.synchronize()
print(f'CUDA kernel: {(time.time()-start)/10000*1000:.3f} ms')
"

{
  "command": "<2230 chars \u2014 see below>",
  "timeout": 120
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && python -c "
import torch
from torch.utils.cpp_extension import load_inline

cuda_source = '''
__global__ void fwd_subst_kernel(float* __restrict__ A, int C, int batch) {
    int b = blockIdx.x;
    if (b >= batch) return;
    
    int tid = threadIdx.x;
    
    __shared__ float s_A[64][64];
    
    float* mat = A + b * C * C;
    
    // Load matrix into shared memory
    for (int idx = tid; idx < C * C; idx += blockDim.x) {
        s_A[idx / C][idx % C] = mat[idx];
    }
    __syncthreads();
    
    // Save original row i in a shared buffer
    __shared__ float s_row[64];
    
    for (int i = 1; i < C; i++) {
        // Save original row i
        if (tid < i) {
            s_row[tid] = s_A[i][tid];
        }
        __syncthreads();
        
        // Each thread computes one j
        if (tid < i) {
            float sum = 0.0f;
            for (int k = 0; k < i; k++) {
                sum += s_row[k] * s_A[k][tid];
            }
            s_A[i][tid] += sum;
        }
        __syncthreads();
    }
    
    // Store back
    for (int idx = tid; idx < C * C; idx += blockDim.x) {
        mat[idx] = s_A[idx / C][idx % C];
    }
}

torch::Tensor fwd_subst(torch::Tensor A) {
    int batch = A.size(0);
    int C = A.size(1);
    fwd_subst_kernel<<<batch, 64>>>(A.data_ptr<float>(), C, batch);
    return A;
}
'''

module = load_inline(
    name='fwd_subst2',
    cpp_sources='torch::Tensor fwd_subst(torch::Tensor A);',
    cuda_sources=cuda_source,
    functions=['fwd_subst'],
    extra_cuda_cflags=['-O3'],
    verbose=False,
)

C = 64
BHN = 512
A = torch.randn(BHN, C, C, device='cuda') * 0.1
mask = torch.tril(torch.ones(C, C, device='cuda'), diagonal=-1)
A = (A * mask).contiguous()

# Correctness
ref = A.clone()
for i in range(1, C):
    ref[:, i, :i] = ref[:, i, :i] + (ref[:, i, :, None] * ref[:, :, :i]).sum(-2)

out = module.fwd_subst(A.clone())
print('Max diff:', (ref - out).abs().max().item())

# Timing
torch.cuda.synchronize()
import time
start = time.time()
for _ in range(10000):
    _ = module.fwd_subst(A.clone())
torch.cuda.synchronize()
print(f'CUDA kernel (64 threads): {(time.time()-start)/10000*1000:.3f} ms')
"

{
  "path": "/home/infatoshi/.cache/uv/archive-v0/rnS-5wxCI5ghhBOYKv7Tc/fla/ops/kda/wy_fast.py"
}

{
  "command": "<132 chars \u2014 see below>"
}

python -c "
import triton.language as tl
print('exp available:', hasattr(tl, 'exp'))
print('exp2 available:', hasattr(tl, 'exp2'))
"

{
  "command": "<3170 chars \u2014 see below>",
  "timeout": 120
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && python -c "
import torch
import reference
import shapes

shape = shapes.SHAPES[1]
for k, v in shape.items():
    setattr(reference, k, v)

torch.manual_seed(2026)
inputs = [t.cuda() if hasattr(t, 'cuda') else t for t in reference.get_inputs()]
q, k, v, g, beta = inputs
scale = float(shape['K']) ** -0.5

B, T, H, K = q.shape
V = v.shape[-1]
BT = 64
NT = T // BT

qf = q.float() * scale
kf = k.float()
vf = v.float()
gf = g.float()
bf = beta.float()

qf = qf.view(B, NT, BT, H, K).permute(0, 3, 1, 2, 4)
kf = kf.view(B, NT, BT, H, K).permute(0, 3, 1, 2, 4)
vf = vf.view(B, NT, BT, H, V).permute(0, 3, 1, 2, 4)
gf = gf.view(B, NT, BT, H, K).permute(0, 3, 1, 2, 4)
bf = bf.view(B, NT, BT, H).permute(0, 3, 1, 2)

mask_upper = torch.triu(torch.ones(BT, BT, device=q.device, dtype=torch.bool), diagonal=0)
mask_strict = torch.triu(torch.ones(BT, BT, device=q.device, dtype=torch.bool), diagonal=1)
I = torch.eye(BT, device=q.device, dtype=torch.float32)

gf = gf.cumsum(-2)

# Measure with cuda events
start_event = torch.cuda.Event(enable_timing=True)
end_event = torch.cuda.Event(enable_timing=True)

# Warmup
for _ in range(10):
    kg = kf * gf.exp()
    kgn = kf * (-gf).exp()
    A = torch.einsum('bhnik,bhnjk->bhnij', kg, kgn)
    A = A * bf[..., None]
    A = -A.masked_fill(mask_upper, 0)
    for i in range(1, BT):
        A[..., i, :i] = A[..., i, :i] + (A[..., i, :, None] * A[..., :, :i]).sum(-2)
    A = (A + I) * bf[..., None, :]
    w = torch.matmul(A, (gf.exp() * kf))
    u = torch.matmul(A, vf)
    qg = qf * gf.exp()
    Aqk = torch.einsum('bhnik,bhnjk->bhnij', qg, kgn)
    Aqk = Aqk.masked_fill(mask_strict, 0)

torch.cuda.synchronize()
start_event.record()
for _ in range(100):
    kg = kf * gf.exp()
    kgn = kf * (-gf).exp()
    A = torch.einsum('bhnik,bhnjk->bhnij', kg, kgn)
    A = A * bf[..., None]
    A = -A.masked_fill(mask_upper, 0)
    for i in range(1, BT):
        A[..., i, :i] = A[..., i, :i] + (A[..., i, :, None] * A[..., :, :i]).sum(-2)
    A = (A + I) * bf[..., None, :]
    w = torch.matmul(A, (gf.exp() * kf))
    u = torch.matmul(A, vf)
    qg = qf * gf.exp()
    Aqk = torch.einsum('bhnik,bhnjk->bhnij', qg, kgn)
    Aqk = Aqk.masked_fill(mask_strict, 0)
end_event.record()
torch.cuda.synchronize()
print(f'Intra-chunk (100 iters): {start_event.elapsed_time(end_event):.3f} ms')
print(f'Intra-chunk per iter: {start_event.elapsed_time(end_event)/100:.3f} ms')

# Measure just einsum + matmul without fwd subst
start_event.record()
for _ in range(100):
    kg = kf * gf.exp()
    kgn = kf * (-gf).exp()
    A = torch.einsum('bhnik,bhnjk->bhnij', kg, kgn)
    A = A * bf[..., None]
    A = -A.masked_fill(mask_upper, 0)
    A = (A + I) * bf[..., None, :]
    w = torch.matmul(A, (gf.exp() * kf))
    u = torch.matmul(A, vf)
    qg = qf * gf.exp()
    Aqk = torch.einsum('bhnik,bhnjk->bhnij', qg, kgn)
    Aqk = Aqk.masked_fill(mask_strict, 0)
end_event.record()
torch.cuda.synchronize()
print(f'Without fwd subst (100 iters): {start_event.elapsed_time(end_event):.3f} ms')
print(f'Without fwd subst per iter: {start_event.elapsed_time(end_event)/100:.3f} ms')
"

{
  "command": "<3009 chars \u2014 see below>",
  "timeout": 180
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && python -c "
import torch
import reference
import shapes

shape = shapes.SHAPES[1]
for k, v in shape.items():
    setattr(reference, k, v)

torch.manual_seed(2026)
inputs = [t.cuda() if hasattr(t, 'cuda') else t for t in reference.get_inputs()]
q, k, v, g, beta = inputs
scale = float(shape['K']) ** -0.5

B, T, H, K = q.shape
V = v.shape[-1]
BT = 64
NT = T // BT

qf = q.float() * scale
kf = k.float()
vf = v.float()
gf = g.float()
bf = beta.float()

qf = qf.view(B, NT, BT, H, K).permute(0, 3, 1, 2, 4)
kf = kf.view(B, NT, BT, H, K).permute(0, 3, 1, 2, 4)
vf = vf.view(B, NT, BT, H, V).permute(0, 3, 1, 2, 4)
gf = gf.view(B, NT, BT, H, K).permute(0, 3, 1, 2, 4)
bf = bf.view(B, NT, BT, H).permute(0, 3, 1, 2)

mask_upper = torch.triu(torch.ones(BT, BT, device=q.device, dtype=torch.bool), diagonal=0)
mask_strict = torch.triu(torch.ones(BT, BT, device=q.device, dtype=torch.bool), diagonal=1)
I = torch.eye(BT, device=q.device, dtype=torch.float32)

gf = gf.cumsum(-2)

kg = kf * gf.exp()
kgn = kf * (-gf).exp()
A = torch.einsum('bhnik,bhnjk->bhnij', kg, kgn)
A = A * bf[..., None]
A = -A.masked_fill(mask_upper, 0)
for i in range(1, BT):
    A[..., i, :i] = A[..., i, :i] + (A[..., i, :, None] * A[..., :, :i]).sum(-2)
A = (A + I) * bf[..., None, :]
w = torch.matmul(A, (gf.exp() * kf))
u = torch.matmul(A, vf)
qg = qf * gf.exp()
Aqk = torch.einsum('bhnik,bhnjk->bhnij', qg, kgn)
Aqk = Aqk.masked_fill(mask_strict, 0)

def inter_chunk(qf, kf, gf, w, u, Aqk):
    S = qf.new_zeros(B, H, K, V)
    o = torch.zeros_like(vf)
    for i in range(NT):
        q_i = qf[:, :, i]
        k_i = kf[:, :, i]
        u_i = u[:, :, i]
        g_i = gf[:, :, i]
        w_i = w[:, :, i]
        
        v_i = u_i - torch.matmul(w_i, S)
        qS = torch.matmul(q_i * g_i.exp(), S)
        Av = torch.matmul(Aqk[:, :, i], v_i)
        o[:, :, i] = qS + Av
        
        decay = g_i[:, :, -1].exp().unsqueeze(-1)
        S = S * decay
        
        kg_decay = (g_i[:, :, -1:] - g_i).exp() * k_i
        S = S + torch.matmul(kg_decay.transpose(-2, -1), v_i)
    return o

compiled_inter = torch.compile(inter_chunk, mode='max-autotune')

# Warmup compiled
for _ in range(5):
    _ = compiled_inter(qf, kf, gf, w, u, Aqk)

start_event = torch.cuda.Event(enable_timing=True)
end_event = torch.cuda.Event(enable_timing=True)
start_event.record()
for _ in range(100):
    _ = compiled_inter(qf, kf, gf, w, u, Aqk)
end_event.record()
torch.cuda.synchronize()
print(f'Compiled inter-chunk (100 iters): {start_event.elapsed_time(end_event):.3f} ms')
print(f'Compiled inter-chunk per iter: {start_event.elapsed_time(end_event)/100:.3f} ms')

# Baseline
start_event.record()
for _ in range(100):
    _ = inter_chunk(qf, kf, gf, w, u, Aqk)
end_event.record()
torch.cuda.synchronize()
print(f'Eager inter-chunk (100 iters): {start_event.elapsed_time(end_event):.3f} ms')
print(f'Eager inter-chunk per iter: {start_event.elapsed_time(end_event)/100:.3f} ms')
"

{
  "path": "/home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass/solution.py",
  "content": "<11019 chars \u2014 see diff>"
}

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && python check.py",
  "timeout": 120
}

{
  "command": "python -c \"import torch.utils.cpp_extension; print('available')\" 2>&1 || echo \"not available\""
}

{
  "command": "<156 chars \u2014 see below>"
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && python -c "
import torch
from torch.utils.cpp_extension import load_inline
print('OK')
"

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && python -c \"import solution; print('OK')\" 2>&1"
}

{
  "path": "/home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass/solution.py",
  "edit": {
    "old": "import torch\nimport torch.nn as nn\nimport triton\nimport triton.language as tl",
    "new": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\nimport triton\nimport triton.language as tl"
  }
}

{
  "path": "/home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass/solution.py",
  "edit": {
    "old": "_fwd_subst_mod = torch.utils.cpp_extension.load_inline(",
    "new": "_fwd_subst_mod = load_inline("
  }
}

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && python check.py",
  "timeout": 180
}

{
  "path": "/home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass/solution.py",
  "edit": {
    "old": "        grid = (triton.cdiv(V, BV), B * H)\n        kda_inter_chunk_kernel[grid](\n            q_flat,\n            k_flat,\n            u_flat,\n            w_flat,\n            g_flat,\n            Aqk_flat,\n            o,\n            B=B,\n            T=T,\n            H=H,\n            K=K,\n            V=V,\n            BT=BT,\n            BV=BV,\n            scale=self.scale,\n        )",
    "new": "        grid = (triton.cdiv(V, BV), B * H)\n        kda_inter_chunk_kernel[

... (382 more chars)

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && python check.py",
  "timeout": 180
}

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && python benchmark.py",
  "timeout": 180
}

{
  "command": "<1395 chars \u2014 see below>",
  "timeout": 120
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && python -c "
import torch
import reference
import solution
import shapes

device = torch.device('cuda:0')
for shape_idx, shape in enumerate(shapes.SHAPES):
    for k, v in shape.items():
        setattr(reference, k, v)
    
    init_args = reference.get_init_inputs()
    ref_model = reference.Model(*init_args).to(device).eval()
    sol_model = solution.Model(*init_args).to(device).eval()
    
    torch.manual_seed(2026)
    inputs = [t.to(device) if hasattr(t, 'to') else t for t in reference.get_inputs()]
    
    # Warmup
    with torch.no_grad():
        for _ in range(5):
            _ = sol_model(*inputs)
    torch.cuda.synchronize()
    
    # Time
    start = torch.cuda.Event(enable_timing=True)
    end = torch.cuda.Event(enable_timing=True)
    start.record()
    with torch.no_grad():
        for _ in range(50):
            _ = sol_model(*inputs)
    end.record()
    torch.cuda.synchronize()
    ms = start.elapsed_time(end) / 50
    
    # FLOPs
    B, T, H, K, V = shape['B'], shape['T'], shape['H'], shape['K'], shape['V']
    CHUNK_SIZE = shape['CHUNK_SIZE']
    flops = 4 * B * T * H * (K * V + CHUNK_SIZE * K + CHUNK_SIZE * V)
    tflops = flops / (ms * 1e-3) / 1e12
    peak_frac = tflops / 200.0
    
    print(f'Shape {shape_idx}: {ms:.3f} ms, {tflops:.2f} TFLOPS, peak_fraction={peak_frac:.4f}')
"

{
  "command": "<846 chars \u2014 see below>",
  "timeout": 120
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && python -c "
import torch
import reference
import solution
import shapes

device = torch.device('cuda:0')
shape = shapes.SHAPES[1]
for k, v in shape.items():
    setattr(reference, k, v)

init_args = reference.get_init_inputs()
sol_model = solution.Model(*init_args).to(device).eval()

torch.manual_seed(2026)
inputs = [t.to(device) if hasattr(t, 'to') else t for t in reference.get_inputs()]

# Profile with torch.profiler
from torch.profiler import profile, ProfilerActivity
with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA], record_shapes=True) as prof:
    with torch.no_grad():
        for _ in range(10):
            _ = sol_model(*inputs)
            torch.cuda.synchronize()

print(prof.key_averages().table(sort_by='cuda_time_total', row_limit=20))
"

{
  "command": "<4892 chars \u2014 see below>",
  "timeout": 120
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && python -c "
import torch
import reference
import solution
import shapes

device = torch.device('cuda:0')
shape = shapes.SHAPES[1]
for k, v in shape.items():
    setattr(reference, k, v)

init_args = reference.get_init_inputs()
sol_model = solution.Model(*init_args).to(device).eval()

torch.manual_seed(2026)
inputs = [t.to(device) if hasattr(t, 'to') else t for t in reference.get_inputs()]

# Warmup
with torch.no_grad():
    for _ in range(10):
        _ = sol_model(*inputs)

torch.cuda.synchronize()

# Time with different num_warps by modifying the kernel launch
# Actually, let's just test num_warps=8 directly
from solution import kda_inter_chunk_kernel

B, T, H, K, V = shape['B'], shape['T'], shape['H'], shape['K'], shape['V']
BT = shape['CHUNK_SIZE']
BV = 64

# Get the intermediate tensors
with torch.no_grad():
    # Run forward once to get tensors
    qf = inputs[0].float() * (float(K) ** -0.5)
    kf = inputs[1].float()
    vf = inputs[2].float()
    gf = inputs[3].float()
    bf = inputs[4].float()
    
    B_, T_, H_, K_ = qf.shape
    V_ = vf.shape[-1]
    NT_ = T_ // BT
    
    qf = qf.view(B_, NT_, BT, H_, K_).permute(0, 3, 1, 2, 4)
    kf = kf.view(B_, NT_, BT, H_, K_).permute(0, 3, 1, 2, 4)
    vf = vf.view(B_, NT_, BT, H_, V_).permute(0, 3, 1, 2, 4)
    gf = gf.view(B_, NT_, BT, H_, K_).permute(0, 3, 1, 2, 4)
    bf = bf.view(B_, NT_, BT, H_).permute(0, 3, 1, 2)
    
    gf = gf.cumsum(-2)
    
    kg = kf * gf.exp()
    kgn = kf * (-gf).exp()
    BHN = B_ * H_ * NT_
    kg_flat = kg.reshape(BHN, BT, K_)
    kgn_flat = kgn.reshape(BHN, BT, K_)
    A = torch.bmm(kg_flat, kgn_flat.transpose(-2, -1)).view(B_, H_, NT_, BT, BT)
    mask_upper = torch.triu(torch.ones(BT, BT, device=device, dtype=torch.bool), diagonal=0)
    A = A * bf[..., None]
    A = -A.masked_fill(mask_upper, 0)
    A_flat = A.view(BHN, BT, BT).contiguous()
    A_flat = solution._fwd_subst_mod.fwd_subst(A_flat)
    A = A_flat.view(B_, H_, NT_, BT, BT)
    I = torch.eye(BT, device=device, dtype=torch.float32)
    A = (A + I) * bf[..., None, :]
    w = torch.matmul(A, (gf.exp() * kf))
    u = torch.matmul(A, vf)
    qg = qf * gf.exp()
    qg_flat = qg.reshape(BHN, BT, K_)
    Aqk = torch.bmm(qg_flat, kgn_flat.transpose(-2, -1)).view(B_, H_, NT_, BT, BT)
    mask_strict = torch.triu(torch.ones(BT, BT, device=device, dtype=torch.bool), diagonal=1)
    Aqk = Aqk.masked_fill(mask_strict, 0)
    
    q_flat = qf.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, K_)
    k_flat = kf.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, K_)
    u_flat = u.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, V_)
    w_flat = w.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, K_)
    g_flat = gf.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, K_)
    Aqk_flat = Aqk.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, BT)
    o = torch.empty_like(inputs[2])
    
    scale = float(K_) ** -0.5
    
    # Test with num_warps=8
    grid = (triton.cdiv(V_, BV), B_ * H_)
    kda_inter_chunk_kernel[grid](
        q_flat, k_flat, u_flat, w_flat, g_flat, Aqk_flat, o,
        B=B_, T=T_, H=H_, K=K_, V=V_, BT=BT, BV=BV, scale=scale,
        num_warps=8, num_stages=1,
    )
    torch.cuda.synchronize()
    
    start = torch.cuda.Event(enable_timing=True)
    end = torch.cuda.Event(enable_timing=True)
    start.record()
    for _ in range(100):
        kda_inter_chunk_kernel[grid](
            q_flat, k_flat, u_flat, w_flat, g_flat, Aqk_flat, o,
            B=B_, T=T_, H=H_, K=K_, V=V_, BT=BT, BV=BV, scale=scale,
            num_warps=8, num_stages=1,
        )
    end.record()
    torch.cuda.synchronize()
    print(f'num_warps=8: {start.elapsed_time(end)/100:.4f} ms')
    
    # Test with num_warps=4, BV=32
    BV2 = 32
    o2 = torch.empty_like(inputs[2])
    grid2 = (triton.cdiv(V_, BV2), B_ * H_)
    kda_inter_chunk_kernel[grid2](
        q_flat, k_flat, u_flat, w_flat, g_flat, Aqk_flat, o2,
        B=B_, T=T_, H=H_, K=K_, V=V_, BT=BT, BV=BV2, scale=s

... (892 more chars)

{
  "command": "<4708 chars \u2014 see below>",
  "timeout": 120
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && python -c "
import torch
import triton
import reference
import solution
import shapes

device = torch.device('cuda:0')
shape = shapes.SHAPES[1]
for k, v in shape.items():
    setattr(reference, k, v)

init_args = reference.get_init_inputs()
sol_model = solution.Model(*init_args).to(device).eval()

torch.manual_seed(2026)
inputs = [t.to(device) if hasattr(t, 'to') else t for t in reference.get_inputs()]

# Warmup
with torch.no_grad():
    for _ in range(10):
        _ = sol_model(*inputs)

torch.cuda.synchronize()

from solution import kda_inter_chunk_kernel

B, T, H, K, V = shape['B'], shape['T'], shape['H'], shape['K'], shape['V']
BT = shape['CHUNK_SIZE']
BV = 64

# Get the intermediate tensors
with torch.no_grad():
    qf = inputs[0].float() * (float(K) ** -0.5)
    kf = inputs[1].float()
    vf = inputs[2].float()
    gf = inputs[3].float()
    bf = inputs[4].float()
    
    B_, T_, H_, K_ = qf.shape
    V_ = vf.shape[-1]
    NT_ = T_ // BT
    
    qf = qf.view(B_, NT_, BT, H_, K_).permute(0, 3, 1, 2, 4)
    kf = kf.view(B_, NT_, BT, H_, K_).permute(0, 3, 1, 2, 4)
    vf = vf.view(B_, NT_, BT, H_, V_).permute(0, 3, 1, 2, 4)
    gf = gf.view(B_, NT_, BT, H_, K_).permute(0, 3, 1, 2, 4)
    bf = bf.view(B_, NT_, BT, H_).permute(0, 3, 1, 2)
    
    gf = gf.cumsum(-2)
    
    kg = kf * gf.exp()
    kgn = kf * (-gf).exp()
    BHN = B_ * H_ * NT_
    kg_flat = kg.reshape(BHN, BT, K_)
    kgn_flat = kgn.reshape(BHN, BT, K_)
    A = torch.bmm(kg_flat, kgn_flat.transpose(-2, -1)).view(B_, H_, NT_, BT, BT)
    mask_upper = torch.triu(torch.ones(BT, BT, device=device, dtype=torch.bool), diagonal=0)
    A = A * bf[..., None]
    A = -A.masked_fill(mask_upper, 0)
    A_flat = A.view(BHN, BT, BT).contiguous()
    A_flat = solution._fwd_subst_mod.fwd_subst(A_flat)
    A = A_flat.view(B_, H_, NT_, BT, BT)
    I = torch.eye(BT, device=device, dtype=torch.float32)
    A = (A + I) * bf[..., None, :]
    w = torch.matmul(A, (gf.exp() * kf))
    u = torch.matmul(A, vf)
    qg = qf * gf.exp()
    qg_flat = qg.reshape(BHN, BT, K_)
    Aqk = torch.bmm(qg_flat, kgn_flat.transpose(-2, -1)).view(B_, H_, NT_, BT, BT)
    mask_strict = torch.triu(torch.ones(BT, BT, device=device, dtype=torch.bool), diagonal=1)
    Aqk = Aqk.masked_fill(mask_strict, 0)
    
    q_flat = qf.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, K_)
    k_flat = kf.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, K_)
    u_flat = u.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, V_)
    w_flat = w.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, K_)
    g_flat = gf.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, K_)
    Aqk_flat = Aqk.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, BT)
    o = torch.empty_like(inputs[2])
    
    scale = float(K_) ** -0.5
    
    # Test with num_warps=8
    grid = (triton.cdiv(V_, BV), B_ * H_)
    kda_inter_chunk_kernel[grid](
        q_flat, k_flat, u_flat, w_flat, g_flat, Aqk_flat, o,
        B=B_, T=T_, H=H_, K=K_, V=V_, BT=BT, BV=BV, scale=scale,
        num_warps=8, num_stages=1,
    )
    torch.cuda.synchronize()
    
    start = torch.cuda.Event(enable_timing=True)
    end = torch.cuda.Event(enable_timing=True)
    start.record()
    for _ in range(100):
        kda_inter_chunk_kernel[grid](
            q_flat, k_flat, u_flat, w_flat, g_flat, Aqk_flat, o,
            B=B_, T=T_, H=H_, K=K_, V=V_, BT=BT, BV=BV, scale=scale,
            num_warps=8, num_stages=1,
        )
    end.record()
    torch.cuda.synchronize()
    print(f'num_warps=8: {start.elapsed_time(end)/100:.4f} ms')
    
    # Test with BV=32
    BV2 = 32
    o2 = torch.empty_like(inputs[2])
    grid2 = (triton.cdiv(V_, BV2), B_ * H_)
    kda_inter_chunk_kernel[grid2](
        q_flat, k_flat, u_flat, w_flat, g_flat, Aqk_flat, o2,
        B=B_, T=T_, H=H_, K=K_, V=V_, BT=BT, BV=BV2, scale=scale,
        num_warps=4, num_stages=1,
    )
    torch.cuda.synchronize()
    
    start.record()
    for _ in range(100):
        kda_inter_chunk_

... (708 more chars)

{
  "path": "/home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass/solution.py",
  "edit": {
    "old": "        BV = 64\n        grid = (triton.cdiv(V, BV), B * H)\n        kda_inter_chunk_kernel[grid](\n            q_flat,\n            k_flat,\n            u_flat,\n            w_flat,\n            g_flat,\n            Aqk_flat,\n            o,\n            B=B,\n            T=T,\n            H=H,\n            K=K,\n            V=V,\n            BT=BT,\n            BV=BV,\n            scale=self.scale,\n            num_warps=4,\n            num_stages=1,\n        )",
    "new": "     

... (469 more chars)

{
  "command": "<1334 chars \u2014 see below>",
  "timeout": 120
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && python -c "
import torch
import reference
import solution
import shapes

device = torch.device('cuda:0')
for shape_idx, shape in enumerate(shapes.SHAPES):
    for k, v in shape.items():
        setattr(reference, k, v)
    
    init_args = reference.get_init_inputs()
    sol_model = solution.Model(*init_args).to(device).eval()
    
    torch.manual_seed(2026)
    inputs = [t.to(device) if hasattr(t, 'to') else t for t in reference.get_inputs()]
    
    # Warmup
    with torch.no_grad():
        for _ in range(10):
            _ = sol_model(*inputs)
    torch.cuda.synchronize()
    
    # Time
    start = torch.cuda.Event(enable_timing=True)
    end = torch.cuda.Event(enable_timing=True)
    start.record()
    with torch.no_grad():
        for _ in range(50):
            _ = sol_model(*inputs)
    end.record()
    torch.cuda.synchronize()
    ms = start.elapsed_time(end) / 50
    
    # FLOPs
    B, T, H, K, V = shape['B'], shape['T'], shape['H'], shape['K'], shape['V']
    CHUNK_SIZE = shape['CHUNK_SIZE']
    flops = 4 * B * T * H * (K * V + CHUNK_SIZE * K + CHUNK_SIZE * V)
    tflops = flops / (ms * 1e-3) / 1e12
    peak_frac = tflops / 200.0
    
    print(f'Shape {shape_idx}: {ms:.3f} ms, {tflops:.2f} TFLOPS, peak_fraction={peak_frac:.4f}')
"

{
  "command": "<816 chars \u2014 see below>",
  "timeout": 120
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && python -c "
import torch
import reference
import solution
import shapes

device = torch.device('cuda:0')
shape = shapes.SHAPES[1]
for k, v in shape.items():
    setattr(reference, k, v)

init_args = reference.get_init_inputs()
sol_model = solution.Model(*init_args).to(device).eval()

torch.manual_seed(2026)
inputs = [t.to(device) if hasattr(t, 'to') else t for t in reference.get_inputs()]

from torch.profiler import profile, ProfilerActivity
with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA], record_shapes=True) as prof:
    with torch.no_grad():
        for _ in range(10):
            _ = sol_model(*inputs)
            torch.cuda.synchronize()

print(prof.key_averages().table(sort_by='cuda_time_total', row_limit=25))
"

{
  "command": "which ncu || which nv-nsight-cu-cli || echo \"ncu not found\""
}

{
  "command": "<3242 chars \u2014 see below>",
  "timeout": 120
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && python -c "
import torch
import triton
import reference
import solution
import shapes

device = torch.device('cuda:0')
shape = shapes.SHAPES[1]
for k, v in shape.items():
    setattr(reference, k, v)

init_args = reference.get_init_inputs()
sol_model = solution.Model(*init_args).to(device).eval()

torch.manual_seed(2026)
inputs = [t.to(device) if hasattr(t, 'to') else t for t in reference.get_inputs()]

# Warmup
with torch.no_grad():
    for _ in range(5):
        _ = sol_model(*inputs)

# Now run just the inter-chunk kernel with ncu
from solution import kda_inter_chunk_kernel

B, T, H, K, V = shape['B'], shape['T'], shape['H'], shape['K'], shape['V']
BT = shape['CHUNK_SIZE']
BV = 32

with torch.no_grad():
    qf = inputs[0].float() * (float(K) ** -0.5)
    kf = inputs[1].float()
    vf = inputs[2].float()
    gf = inputs[3].float()
    bf = inputs[4].float()
    
    B_, T_, H_, K_ = qf.shape
    V_ = vf.shape[-1]
    NT_ = T_ // BT
    
    qf = qf.view(B_, NT_, BT, H_, K_).permute(0, 3, 1, 2, 4).contiguous()
    kf = kf.view(B_, NT_, BT, H_, K_).permute(0, 3, 1, 2, 4).contiguous()
    vf = vf.view(B_, NT_, BT, H_, V_).permute(0, 3, 1, 2, 4).contiguous()
    gf = gf.view(B_, NT_, BT, H_, K_).permute(0, 3, 1, 2, 4).contiguous()
    bf = bf.view(B_, NT_, BT, H_).permute(0, 3, 1, 2).contiguous()
    
    gf = gf.cumsum(-2)
    
    kg = kf * gf.exp()
    kgn = kf * (-gf).exp()
    BHN = B_ * H_ * NT_
    kg_flat = kg.reshape(BHN, BT, K_)
    kgn_flat = kgn.reshape(BHN, BT, K_)
    A = torch.bmm(kg_flat, kgn_flat.transpose(-2, -1)).view(B_, H_, NT_, BT, BT)
    mask_upper = torch.triu(torch.ones(BT, BT, device=device, dtype=torch.bool), diagonal=0)
    A = A * bf[..., None]
    A = -A.masked_fill(mask_upper, 0)
    A_flat = A.view(BHN, BT, BT).contiguous()
    A_flat = solution._fwd_subst_mod.fwd_subst(A_flat)
    A = A_flat.view(B_, H_, NT_, BT, BT)
    I = torch.eye(BT, device=device, dtype=torch.float32)
    A = (A + I) * bf[..., None, :]
    w = torch.matmul(A, (gf.exp() * kf))
    u = torch.matmul(A, vf)
    qg = qf * gf.exp()
    qg_flat = qg.reshape(BHN, BT, K_)
    Aqk = torch.bmm(qg_flat, kgn_flat.transpose(-2, -1)).view(B_, H_, NT_, BT, BT)
    mask_strict = torch.triu(torch.ones(BT, BT, device=device, dtype=torch.bool), diagonal=1)
    Aqk = Aqk.masked_fill(mask_strict, 0)
    
    q_flat = qf.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, K_)
    k_flat = kf.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, K_)
    u_flat = u.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, V_)
    w_flat = w.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, K_)
    g_flat = gf.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, K_)
    Aqk_flat = Aqk.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, BT)
    o = torch.empty_like(inputs[2])
    
    scale = float(K_) ** -0.5
    
    grid = (triton.cdiv(V_, BV), B_ * H_)
    
    # Run many times for profiling
    for _ in range(100):
        kda_inter_chunk_kernel[grid](
            q_flat, k_flat, u_flat, w_flat, g_flat, Aqk_flat, o,
            B=B_, T=T_, H=H_, K=K_, V=V_, BT=BT, BV=BV, scale=scale,
            num_warps=4, num_stages=1,
        )
    torch.cuda.synchronize()
    print('Done')
" 2>&1 | tail -5

{
  "command": "<3246 chars \u2014 see below>",
  "timeout": 120
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && ncu --kernel-name kda_inter_chunk_kernel --metrics sm__warps_active.avg.pct_of_peak_sustained_elapsed,gpu__time_duration.avg,launch__block_size python -c "
import torch
import triton
import reference
import solution
import shapes

device = torch.device('cuda:0')
shape = shapes.SHAPES[1]
for k, v in shape.items():
    setattr(reference, k, v)

init_args = reference.get_init_inputs()
sol_model = solution.Model(*init_args).to(device).eval()

torch.manual_seed(2026)
inputs = [t.to(device) if hasattr(t, 'to') else t for t in reference.get_inputs()]

from solution import kda_inter_chunk_kernel

B, T, H, K, V = shape['B'], shape['T'], shape['H'], shape['K'], shape['V']
BT = shape['CHUNK_SIZE']
BV = 32

with torch.no_grad():
    qf = inputs[0].float() * (float(K) ** -0.5)
    kf = inputs[1].float()
    vf = inputs[2].float()
    gf = inputs[3].float()
    bf = inputs[4].float()
    
    B_, T_, H_, K_ = qf.shape
    V_ = vf.shape[-1]
    NT_ = T_ // BT
    
    qf = qf.view(B_, NT_, BT, H_, K_).permute(0, 3, 1, 2, 4).contiguous()
    kf = kf.view(B_, NT_, BT, H_, K_).permute(0, 3, 1, 2, 4).contiguous()
    vf = vf.view(B_, NT_, BT, H_, V_).permute(0, 3, 1, 2, 4).contiguous()
    gf = gf.view(B_, NT_, BT, H_, K_).permute(0, 3, 1, 2, 4).contiguous()
    bf = bf.view(B_, NT_, BT, H_).permute(0, 3, 1, 2).contiguous()
    
    gf = gf.cumsum(-2)
    
    kg = kf * gf.exp()
    kgn = kf * (-gf).exp()
    BHN = B_ * H_ * NT_
    kg_flat = kg.reshape(BHN, BT, K_)
    kgn_flat = kgn.reshape(BHN, BT, K_)
    A = torch.bmm(kg_flat, kgn_flat.transpose(-2, -1)).view(B_, H_, NT_, BT, BT)
    mask_upper = torch.triu(torch.ones(BT, BT, device=device, dtype=torch.bool), diagonal=0)
    A = A * bf[..., None]
    A = -A.masked_fill(mask_upper, 0)
    A_flat = A.view(BHN, BT, BT).contiguous()
    A_flat = solution._fwd_subst_mod.fwd_subst(A_flat)
    A = A_flat.view(B_, H_, NT_, BT, BT)
    I = torch.eye(BT, device=device, dtype=torch.float32)
    A = (A + I) * bf[..., None, :]
    w = torch.matmul(A, (gf.exp() * kf))
    u = torch.matmul(A, vf)
    qg = qf * gf.exp()
    qg_flat = qg.reshape(BHN, BT, K_)
    Aqk = torch.bmm(qg_flat, kgn_flat.transpose(-2, -1)).view(B_, H_, NT_, BT, BT)
    mask_strict = torch.triu(torch.ones(BT, BT, device=device, dtype=torch.bool), diagonal=1)
    Aqk = Aqk.masked_fill(mask_strict, 0)
    
    q_flat = qf.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, K_)
    k_flat = kf.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, K_)
    u_flat = u.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, V_)
    w_flat = w.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, K_)
    g_flat = gf.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, K_)
    Aqk_flat = Aqk.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, BT)
    o = torch.empty_like(inputs[2])
    
    scale = float(K_) ** -0.5
    
    grid = (triton.cdiv(V_, BV), B_ * H_)
    
    for _ in range(10):
        kda_inter_chunk_kernel[grid](
            q_flat, k_flat, u_flat, w_flat, g_flat, Aqk_flat, o,
            B=B_, T=T_, H=H_, K=K_, V=V_, BT=BT, BV=BV, scale=scale,
            num_warps=4, num_stages=1,
        )
    torch.cuda.synchronize()
" 2>&1 | grep -E "(Duration|Warp|block_size|Kernel)" | head -20

{
  "command": "<3126 chars \u2014 see below>",
  "timeout": 120
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && ncu --kernel-name-regex "kda_inter" --metrics gpu__time_duration.avg python -c "
import torch
import triton
import reference
import solution
import shapes

device = torch.device('cuda:0')
shape = shapes.SHAPES[1]
for k, v in shape.items():
    setattr(reference, k, v)

init_args = reference.get_init_inputs()
sol_model = solution.Model(*init_args).to(device).eval()

torch.manual_seed(2026)
inputs = [t.to(device) if hasattr(t, 'to') else t for t in reference.get_inputs()]

from solution import kda_inter_chunk_kernel

B, T, H, K, V = shape['B'], shape['T'], shape['H'], shape['K'], shape['V']
BT = shape['CHUNK_SIZE']
BV = 32

with torch.no_grad():
    qf = inputs[0].float() * (float(K) ** -0.5)
    kf = inputs[1].float()
    vf = inputs[2].float()
    gf = inputs[3].float()
    bf = inputs[4].float()
    
    B_, T_, H_, K_ = qf.shape
    V_ = vf.shape[-1]
    NT_ = T_ // BT
    
    qf = qf.view(B_, NT_, BT, H_, K_).permute(0, 3, 1, 2, 4).contiguous()
    kf = kf.view(B_, NT_, BT, H_, K_).permute(0, 3, 1, 2, 4).contiguous()
    vf = vf.view(B_, NT_, BT, H_, V_).permute(0, 3, 1, 2, 4).contiguous()
    gf = gf.view(B_, NT_, BT, H_, K_).permute(0, 3, 1, 2, 4).contiguous()
    bf = bf.view(B_, NT_, BT, H_).permute(0, 3, 1, 2).contiguous()
    
    gf = gf.cumsum(-2)
    
    kg = kf * gf.exp()
    kgn = kf * (-gf).exp()
    BHN = B_ * H_ * NT_
    kg_flat = kg.reshape(BHN, BT, K_)
    kgn_flat = kgn.reshape(BHN, BT, K_)
    A = torch.bmm(kg_flat, kgn_flat.transpose(-2, -1)).view(B_, H_, NT_, BT, BT)
    mask_upper = torch.triu(torch.ones(BT, BT, device=device, dtype=torch.bool), diagonal=0)
    A = A * bf[..., None]
    A = -A.masked_fill(mask_upper, 0)
    A_flat = A.view(BHN, BT, BT).contiguous()
    A_flat = solution._fwd_subst_mod.fwd_subst(A_flat)
    A = A_flat.view(B_, H_, NT_, BT, BT)
    I = torch.eye(BT, device=device, dtype=torch.float32)
    A = (A + I) * bf[..., None, :]
    w = torch.matmul(A, (gf.exp() * kf))
    u = torch.matmul(A, vf)
    qg = qf * gf.exp()
    qg_flat = qg.reshape(BHN, BT, K_)
    Aqk = torch.bmm(qg_flat, kgn_flat.transpose(-2, -1)).view(B_, H_, NT_, BT, BT)
    mask_strict = torch.triu(torch.ones(BT, BT, device=device, dtype=torch.bool), diagonal=1)
    Aqk = Aqk.masked_fill(mask_strict, 0)
    
    q_flat = qf.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, K_)
    k_flat = kf.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, K_)
    u_flat = u.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, V_)
    w_flat = w.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, K_)
    g_flat = gf.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, K_)
    Aqk_flat = Aqk.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, BT)
    o = torch.empty_like(inputs[2])
    
    scale = float(K_) ** -0.5
    
    grid = (triton.cdiv(V_, BV), B_ * H_)
    
    for _ in range(100):
        kda_inter_chunk_kernel[grid](
            q_flat, k_flat, u_flat, w_flat, g_flat, Aqk_flat, o,
            B=B_, T=T_, H=H_, K=K_, V=V_, BT=BT, BV=BV, scale=scale,
            num_warps=4, num_stages=1,
        )
    torch.cuda.synchronize()
" 2>&1 | tail -30

{
  "command": "<3109 chars \u2014 see below>",
  "timeout": 120
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && ncu -k "kda_inter" --metrics gpu__time_duration.avg python -c "
import torch
import triton
import reference
import solution
import shapes

device = torch.device('cuda:0')
shape = shapes.SHAPES[1]
for k, v in shape.items():
    setattr(reference, k, v)

init_args = reference.get_init_inputs()
sol_model = solution.Model(*init_args).to(device).eval()

torch.manual_seed(2026)
inputs = [t.to(device) if hasattr(t, 'to') else t for t in reference.get_inputs()]

from solution import kda_inter_chunk_kernel

B, T, H, K, V = shape['B'], shape['T'], shape['H'], shape['K'], shape['V']
BT = shape['CHUNK_SIZE']
BV = 32

with torch.no_grad():
    qf = inputs[0].float() * (float(K) ** -0.5)
    kf = inputs[1].float()
    vf = inputs[2].float()
    gf = inputs[3].float()
    bf = inputs[4].float()
    
    B_, T_, H_, K_ = qf.shape
    V_ = vf.shape[-1]
    NT_ = T_ // BT
    
    qf = qf.view(B_, NT_, BT, H_, K_).permute(0, 3, 1, 2, 4).contiguous()
    kf = kf.view(B_, NT_, BT, H_, K_).permute(0, 3, 1, 2, 4).contiguous()
    vf = vf.view(B_, NT_, BT, H_, V_).permute(0, 3, 1, 2, 4).contiguous()
    gf = gf.view(B_, NT_, BT, H_, K_).permute(0, 3, 1, 2, 4).contiguous()
    bf = bf.view(B_, NT_, BT, H_).permute(0, 3, 1, 2).contiguous()
    
    gf = gf.cumsum(-2)
    
    kg = kf * gf.exp()
    kgn = kf * (-gf).exp()
    BHN = B_ * H_ * NT_
    kg_flat = kg.reshape(BHN, BT, K_)
    kgn_flat = kgn.reshape(BHN, BT, K_)
    A = torch.bmm(kg_flat, kgn_flat.transpose(-2, -1)).view(B_, H_, NT_, BT, BT)
    mask_upper = torch.triu(torch.ones(BT, BT, device=device, dtype=torch.bool), diagonal=0)
    A = A * bf[..., None]
    A = -A.masked_fill(mask_upper, 0)
    A_flat = A.view(BHN, BT, BT).contiguous()
    A_flat = solution._fwd_subst_mod.fwd_subst(A_flat)
    A = A_flat.view(B_, H_, NT_, BT, BT)
    I = torch.eye(BT, device=device, dtype=torch.float32)
    A = (A + I) * bf[..., None, :]
    w = torch.matmul(A, (gf.exp() * kf))
    u = torch.matmul(A, vf)
    qg = qf * gf.exp()
    qg_flat = qg.reshape(BHN, BT, K_)
    Aqk = torch.bmm(qg_flat, kgn_flat.transpose(-2, -1)).view(B_, H_, NT_, BT, BT)
    mask_strict = torch.triu(torch.ones(BT, BT, device=device, dtype=torch.bool), diagonal=1)
    Aqk = Aqk.masked_fill(mask_strict, 0)
    
    q_flat = qf.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, K_)
    k_flat = kf.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, K_)
    u_flat = u.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, V_)
    w_flat = w.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, K_)
    g_flat = gf.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, K_)
    Aqk_flat = Aqk.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, BT)
    o = torch.empty_like(inputs[2])
    
    scale = float(K_) ** -0.5
    
    grid = (triton.cdiv(V_, BV), B_ * H_)
    
    for _ in range(100):
        kda_inter_chunk_kernel[grid](
            q_flat, k_flat, u_flat, w_flat, g_flat, Aqk_flat, o,
            B=B_, T=T_, H=H_, K=K_, V=V_, BT=BT, BV=BV, scale=scale,
            num_warps=4, num_stages=1,
        )
    torch.cuda.synchronize()
" 2>&1 | tail -30

{
  "command": "<3218 chars \u2014 see below>",
  "timeout": 120
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && ncu -k "kda_inter_chunk_kernel" --metrics gpu__time_duration.avg,sm__warps_active.avg.pct_of_peak_sustained_elapsed python -c "
import torch
import triton
import reference
import solution
import shapes

device = torch.device('cuda:0')
shape = shapes.SHAPES[1]
for k, v in shape.items():
    setattr(reference, k, v)

init_args = reference.get_init_inputs()
sol_model = solution.Model(*init_args).to(device).eval()

torch.manual_seed(2026)
inputs = [t.to(device) if hasattr(t, 'to') else t for t in reference.get_inputs()]

from solution import kda_inter_chunk_kernel

B, T, H, K, V = shape['B'], shape['T'], shape['H'], shape['K'], shape['V']
BT = shape['CHUNK_SIZE']
BV = 32

with torch.no_grad():
    qf = inputs[0].float() * (float(K) ** -0.5)
    kf = inputs[1].float()
    vf = inputs[2].float()
    gf = inputs[3].float()
    bf = inputs[4].float()
    
    B_, T_, H_, K_ = qf.shape
    V_ = vf.shape[-1]
    NT_ = T_ // BT
    
    qf = qf.view(B_, NT_, BT, H_, K_).permute(0, 3, 1, 2, 4).contiguous()
    kf = kf.view(B_, NT_, BT, H_, K_).permute(0, 3, 1, 2, 4).contiguous()
    vf = vf.view(B_, NT_, BT, H_, V_).permute(0, 3, 1, 2, 4).contiguous()
    gf = gf.view(B_, NT_, BT, H_, K_).permute(0, 3, 1, 2, 4).contiguous()
    bf = bf.view(B_, NT_, BT, H_).permute(0, 3, 1, 2).contiguous()
    
    gf = gf.cumsum(-2)
    
    kg = kf * gf.exp()
    kgn = kf * (-gf).exp()
    BHN = B_ * H_ * NT_
    kg_flat = kg.reshape(BHN, BT, K_)
    kgn_flat = kgn.reshape(BHN, BT, K_)
    A = torch.bmm(kg_flat, kgn_flat.transpose(-2, -1)).view(B_, H_, NT_, BT, BT)
    mask_upper = torch.triu(torch.ones(BT, BT, device=device, dtype=torch.bool), diagonal=0)
    A = A * bf[..., None]
    A = -A.masked_fill(mask_upper, 0)
    A_flat = A.view(BHN, BT, BT).contiguous()
    A_flat = solution._fwd_subst_mod.fwd_subst(A_flat)
    A = A_flat.view(B_, H_, NT_, BT, BT)
    I = torch.eye(BT, device=device, dtype=torch.float32)
    A = (A + I) * bf[..., None, :]
    w = torch.matmul(A, (gf.exp() * kf))
    u = torch.matmul(A, vf)
    qg = qf * gf.exp()
    qg_flat = qg.reshape(BHN, BT, K_)
    Aqk = torch.bmm(qg_flat, kgn_flat.transpose(-2, -1)).view(B_, H_, NT_, BT, BT)
    mask_strict = torch.triu(torch.ones(BT, BT, device=device, dtype=torch.bool), diagonal=1)
    Aqk = Aqk.masked_fill(mask_strict, 0)
    
    q_flat = qf.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, K_)
    k_flat = kf.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, K_)
    u_flat = u.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, V_)
    w_flat = w.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, K_)
    g_flat = gf.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, K_)
    Aqk_flat = Aqk.permute(0, 2, 3, 4, 1).reshape(B_, T_, H_, BT)
    o = torch.empty_like(inputs[2])
    
    scale = float(K_) ** -0.5
    
    grid = (triton.cdiv(V_, BV), B_ * H_)
    
    for _ in range(100):
        kda_inter_chunk_kernel[grid](
            q_flat, k_flat, u_flat, w_flat, g_flat, Aqk_flat, o,
            B=B_, T=T_, H=H_, K=K_, V=V_, BT=BT, BV=BV, scale=scale,
            num_warps=4, num_stages=1,
        )
    torch.cuda.synchronize()
" 2>&1 | grep -E "(Duration|Warp|Kernel|kda_inter)" | head -20

{
  "path": "/home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass/solution.py",
  "content": "<10407 chars \u2014 see diff>"
}

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && python check.py",
  "timeout": 120
}

{
  "command": "<1334 chars \u2014 see below>",
  "timeout": 120
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && python -c "
import torch
import reference
import solution
import shapes

device = torch.device('cuda:0')
for shape_idx, shape in enumerate(shapes.SHAPES):
    for k, v in shape.items():
        setattr(reference, k, v)
    
    init_args = reference.get_init_inputs()
    sol_model = solution.Model(*init_args).to(device).eval()
    
    torch.manual_seed(2026)
    inputs = [t.to(device) if hasattr(t, 'to') else t for t in reference.get_inputs()]
    
    # Warmup
    with torch.no_grad():
        for _ in range(10):
            _ = sol_model(*inputs)
    torch.cuda.synchronize()
    
    # Time
    start = torch.cuda.Event(enable_timing=True)
    end = torch.cuda.Event(enable_timing=True)
    start.record()
    with torch.no_grad():
        for _ in range(50):
            _ = sol_model(*inputs)
    end.record()
    torch.cuda.synchronize()
    ms = start.elapsed_time(end) / 50
    
    # FLOPs
    B, T, H, K, V = shape['B'], shape['T'], shape['H'], shape['K'], shape['V']
    CHUNK_SIZE = shape['CHUNK_SIZE']
    flops = 4 * B * T * H * (K * V + CHUNK_SIZE * K + CHUNK_SIZE * V)
    tflops = flops / (ms * 1e-3) / 1e12
    peak_frac = tflops / 200.0
    
    print(f'Shape {shape_idx}: {ms:.3f} ms, {tflops:.2f} TFLOPS, peak_fraction={peak_frac:.4f}')
"

{
  "command": "<835 chars \u2014 see below>",
  "timeout": 60
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && python -c "
import torch
import time

C = 64
BHN = 512
A = torch.randn(BHN, C, C, device='cuda') * 0.1
mask = torch.tril(torch.ones(C, C, device='cuda'), diagonal=-1)
A = A * mask
I = torch.eye(C, device='cuda').expand_as(A)

# Test solve_triangular
start = time.time()
for _ in range(100):
    L = I - A
    X = torch.linalg.solve_triangular(L, I, upper=False)
torch.cuda.synchronize()
print(f'solve_triangular: {(time.time()-start)/100*1000:.3f} ms')

# Reference forward substitution
start = time.time()
for _ in range(100):
    ref = A.clone()
    for i in range(1, C):
        ref[:, i, :i] = ref[:, i, :i] + (ref[:, i, :, None] * ref[:, :, :i]).sum(-2)
    ref = ref + I
torch.cuda.synchronize()
print(f'fwd subst loop: {(time.time()-start)/100*1000:.3f} ms')
"

{
  "command": "<1588 chars \u2014 see below>",
  "timeout": 300
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && python -c "
import torch
import reference
import solution
import shapes

device = torch.device('cuda:0')
shape = shapes.SHAPES[1]
for k, v in shape.items():
    setattr(reference, k, v)

init_args = reference.get_init_inputs()
sol_model = solution.Model(*init_args).to(device).eval()

# Compile the forward method
compiled_model = torch.compile(sol_model, mode='max-autotune')

torch.manual_seed(2026)
inputs = [t.to(device) if hasattr(t, 'to') else t for t in reference.get_inputs()]

# Warmup
with torch.no_grad():
    for _ in range(10):
        _ = compiled_model(*inputs)

torch.cuda.synchronize()

# Time
start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
start.record()
with torch.no_grad():
    for _ in range(50):
        _ = compiled_model(*inputs)
end.record()
torch.cuda.synchronize()
ms = start.elapsed_time(end) / 50

B, T, H, K, V = shape['B'], shape['T'], shape['H'], shape['K'], shape['V']
CHUNK_SIZE = shape['CHUNK_SIZE']
flops = 4 * B * T * H * (K * V + CHUNK_SIZE * K + CHUNK_SIZE * V)
tflops = flops / (ms * 1e-3) / 1e12
peak_frac = tflops / 200.0

print(f'Compiled: {ms:.3f} ms, {tflops:.2f} TFLOPS, peak_fraction={peak_frac:.4f}')

# Baseline
start.record()
with torch.no_grad():
    for _ in range(50):
        _ = sol_model(*inputs)
end.record()
torch.cuda.synchronize()
ms2 = start.elapsed_time(end) / 50
tflops2 = flops / (ms2 * 1e-3) / 1e12
peak_frac2 = tflops2 / 200.0
print(f'Eager: {ms2:.3f} ms, {tflops2:.2f} TFLOPS, peak_fraction={peak_frac2:.4f}')
"

{
  "command": "<1421 chars \u2014 see below>",
  "timeout": 300
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/02_kda_cutlass && python -c "
import torch
import reference
import solution
import shapes

device = torch.device('cuda:0')
for shape_idx, shape in enumerate(shapes.SHAPES):
    for k, v in shape.items():
        setattr(reference, k, v)
    
    init_args = reference.get_init_inputs()
    sol_model = solution.Model(*init_args).to(device).eval()
    compiled_model = torch.compile(sol_model, mode='max-autotune')
    
    torch.manual_seed(2026)
    inputs = [t.to(device) if hasattr(t, 'to') else t for t in reference.get_inputs()]
    
    # Extensive warmup
    with torch.no_grad():
        for _ in range(20):
            _ = compiled_model(*inputs)
    torch.cuda.synchronize()
    
    # Time
    start = torch.cuda.Event(enable_timing=True)
    end = torch.cuda.Event(enable_timing=True)
    start.record()
    with torch.no_grad():
        for _ in range(50):
            _ = compiled_model(*inputs)
    end.record()
    torch.cuda.synchronize()
    ms = start.elapsed_time(end) / 50
    
    # FLOPs
    B, T, H, K, V = shape['B'], shape['T'], shape['H'], shape['K'], shape['V']
    CHUNK_SIZE = shape['CHUNK_SIZE']
    flops = 4 * B * T * H * (K * V + CHUNK_SIZE * K + CHUNK_SIZE * V)
    tflops = flops / (ms * 1e-3) / 1e12
    peak_frac = tflops / 200.0
    
    print(f'Shape {shape_idx}: {ms:.3f} ms, {tflops:.2f} TFLOPS, peak_fraction={peak_frac:.4f}')
"