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Fix NaN in all 3 gated deltanet Helion references and submission #131

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Merged
S1ro1 merged 2 commits into from
Mar 14, 2026
Merged

Fix NaN in all 3 gated deltanet Helion references and submission #131

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24b5bce
Fix NaN in gated_deltanet_chunk_fwd_o reference and submission
yf225 Mar 14, 2026
a611712
Fix NaN in _chunk_scaled_dot_kkt_fwd_eager across all 3 gated deltane…
yf225 Mar 14, 2026
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3 changes: 2 additions & 1 deletion problems/helion/gated_deltanet_chunk_fwd_h_py/reference.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,8 +19,9 @@ def _chunk_scaled_dot_kkt_fwd_eager(k, g_cumsum, beta, chunk_size):
g_c = g_cumsum.float().reshape(B, NT, C, H).permute(0, 1, 3, 2)
beta_c = beta.float().reshape(B, NT, C, H).permute(0, 1, 3, 2)
kkt = k_c @ k_c.transpose(-1, -2)
g_diff = g_c.unsqueeze(-1) - g_c.unsqueeze(-2)
strict_lower = torch.tril(torch.ones(C, C, device=k.device), diagonal=-1)
g_diff = g_c.unsqueeze(-1) - g_c.unsqueeze(-2)
g_diff = g_diff * strict_lower
A = kkt * beta_c.unsqueeze(-1) * torch.exp(g_diff) * strict_lower
return A.permute(0, 1, 3, 2, 4).reshape(B, T, H, C).to(torch.float32)

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11 changes: 7 additions & 4 deletions problems/helion/gated_deltanet_chunk_fwd_o_py/reference.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,8 +19,9 @@ def _chunk_scaled_dot_kkt_fwd_eager(k, g_cumsum, beta, chunk_size):
g_c = g_cumsum.float().reshape(B, NT, C, H).permute(0, 1, 3, 2)
beta_c = beta.float().reshape(B, NT, C, H).permute(0, 1, 3, 2)
kkt = k_c @ k_c.transpose(-1, -2)
g_diff = g_c.unsqueeze(-1) - g_c.unsqueeze(-2)
strict_lower = torch.tril(torch.ones(C, C, device=k.device), diagonal=-1)
g_diff = g_c.unsqueeze(-1) - g_c.unsqueeze(-2)
g_diff = g_diff * strict_lower
A = kkt * beta_c.unsqueeze(-1) * torch.exp(g_diff) * strict_lower
return A.permute(0, 1, 3, 2, 4).reshape(B, T, H, C).to(torch.float32)

Expand Down Expand Up @@ -103,9 +104,11 @@ def ref_kernel(data: input_t) -> output_t:
v_c = v_new.float().reshape(B, NT, C, H, V).permute(0, 1, 3, 2, 4)
g_c = g.float().reshape(B, NT, C, H).permute(0, 1, 3, 2)
o_inter = (q_c @ h.float()) * torch.exp(g_c).unsqueeze(-1)
qk = q_c @ k_c.transpose(-1, -2) * torch.exp(g_c.unsqueeze(-1) - g_c.unsqueeze(-2))
causal = torch.tril(torch.ones(C, C, device=q.device))
o = (o_inter + (qk * causal) @ v_c) * scale
causal = torch.tril(torch.ones(C, C, dtype=torch.bool, device=q.device))
g_diff = g_c.unsqueeze(-1) - g_c.unsqueeze(-2)
g_diff = torch.where(causal, g_diff, torch.zeros_like(g_diff))
qk = q_c @ k_c.transpose(-1, -2) * torch.exp(g_diff) * causal
o = (o_inter + qk @ v_c) * scale
return o.permute(0, 1, 3, 2, 4).reshape(B, T, H, V).to(q.dtype)


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17 changes: 11 additions & 6 deletions problems/helion/gated_deltanet_chunk_fwd_o_py/submission.py
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Expand Up @@ -54,15 +54,20 @@ def kernel(
c_idx = tile_t.begin // C

g_vals = g[b_idx, tile_t, h_idx]
q_s = q[b_idx, tile_t, h_idx, :] * torch.exp(g_vals)[:, None]
k_s = k[b_idx, tile_t, h_idx, :] * torch.exp(-g_vals)[:, None]
q_tile = q[b_idx, tile_t, h_idx, :]
k_tile = k[b_idx, tile_t, h_idx, :]
v_tile = v[b_idx, tile_t, h_idx, :]

sim = hl.dot(q_s, k_s.T)
# intra-chunk: q @ k^T * exp(g_i - g_j), with causal mask
qk = hl.dot(q_tile, k_tile.T)
idx = hl.arange(tile_t.block_size)
mask = idx[:, None] >= idx[None, :]
sim = torch.where(mask, sim, 0.0)
local_out = hl.dot(sim.to(v.dtype), v[b_idx, tile_t, h_idx, :])
g_diff = g_vals[:, None] - g_vals[None, :]
causal_mask = idx[:, None] >= idx[None, :]
sim = torch.where(causal_mask, qk * torch.exp(g_diff), 0.0)
local_out = hl.dot(sim.to(v.dtype), v_tile)

# inter-chunk: (q @ h) * exp(g)
q_s = q_tile * torch.exp(g_vals)[:, None]
global_out = hl.dot(q_s, h[b_idx, c_idx, h_idx, :, :])

out[b_idx, tile_t, h_idx, :] = ((global_out + local_out) * scale).to(out.dtype)
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3 changes: 2 additions & 1 deletion problems/helion/gated_deltanet_recompute_w_u_py/reference.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,8 +19,9 @@ def _chunk_scaled_dot_kkt_fwd_eager(k, g_cumsum, beta, chunk_size):
g_c = g_cumsum.float().reshape(B, NT, C, H).permute(0, 1, 3, 2)
beta_c = beta.float().reshape(B, NT, C, H).permute(0, 1, 3, 2)
kkt = k_c @ k_c.transpose(-1, -2)
g_diff = g_c.unsqueeze(-1) - g_c.unsqueeze(-2)
strict_lower = torch.tril(torch.ones(C, C, device=k.device), diagonal=-1)
g_diff = g_c.unsqueeze(-1) - g_c.unsqueeze(-2)
g_diff = g_diff * strict_lower
A = kkt * beta_c.unsqueeze(-1) * torch.exp(g_diff) * strict_lower
return A.permute(0, 1, 3, 2, 4).reshape(B, T, H, C).to(torch.float32)

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