fix(GDN): remove pre-cumsum clamp — match xllm reference, fix 99.98% NaN

ROOT CAUSE: g.clamp(-5,2) before cumsum corrupted gate values.
The GDN algorithm computes decay_mask = exp(g_i - g_j) which is
numerically stable via subtraction cancelling cumsum growth.
Pre-clamping g distorts these differences → wrong decay rates → NaN.

xllm reference: qwen3_gated_delta_net_base.cpp lines 170-238
- cumsum first (no pre-clamp)
- difference form: (g_i_last - g[:, i]).exp() for state update
- k_cumdecay uses g.exp() directly (not clamped)

Removed: g.clamp(-5,2), g.clamp(-20,20), g_exp_cache, g_clamped
Added: xllm-style g_i_last/g_exp_term/k_g_exp state update
This commit is contained in:
project6
2026-08-10 07:44:03 +00:00
parent a0d76bc06e
commit c17c490e06

View File

@@ -264,13 +264,12 @@ def _torch_chunk_gated_delta_rule(
torch.ones(chunk_size, chunk_size, dtype=torch.bool, device=query.device), torch.ones(chunk_size, chunk_size, dtype=torch.bool, device=query.device),
diagonal=0) diagonal=0)
# CCCL accumulator_t pattern: clamp BEFORE cumsum to prevent overflow # Match xllm qwen3_gated_delta_net_base.cpp line 170-175:
# at the source. Without this, individual g values of ±10 accumulate # cumsum first, then difference form (g_i - g_j) which is numerically
# over 64 positions to ±640 — far beyond float32 exp() safe range (~88). # stable — the subtraction cancels cumsum growth so exp() stays bounded.
g = g.clamp(-5.0, 2.0) # Do NOT clamp g before cumsum — that corrupts gate values and causes NaN.
g = g.cumsum(dim=-1) g = g.cumsum(dim=-1)
g = g.clamp(-20.0, 20.0) decay_mask = (g.unsqueeze(-1) - g.unsqueeze(-2)).tril().exp().to(torch.float32).tril()
decay_mask = ((g.unsqueeze(-1) - g.unsqueeze(-2)).tril().exp().float()).tril()
attn = -((_ix_matmul(k_beta, key.transpose(-1, -2))) * decay_mask).masked_fill(mask_upper, 0) attn = -((_ix_matmul(k_beta, key.transpose(-1, -2))) * decay_mask).masked_fill(mask_upper, 0)
for i in range(1, chunk_size): for i in range(1, chunk_size):
row = attn[..., i, :i].clone() row = attn[..., i, :i].clone()
@@ -278,7 +277,7 @@ def _torch_chunk_gated_delta_rule(
attn[..., i, :i] = row + (row.unsqueeze(-1) * sub).sum(-2) attn[..., i, :i] = row + (row.unsqueeze(-1) * sub).sum(-2)
attn = attn + torch.eye(chunk_size, dtype=attn.dtype, device=attn.device) attn = attn + torch.eye(chunk_size, dtype=attn.dtype, device=attn.device)
value = _ix_matmul(attn, v_beta) value = _ix_matmul(attn, v_beta)
k_cumdecay = _ix_matmul(attn, k_beta * g.clamp(-20, 20).exp().unsqueeze(-1)) k_cumdecay = _ix_matmul(attn, k_beta * g.exp().unsqueeze(-1))
last_state = ( last_state = (
torch.zeros(batch, num_heads, k_dim, v_dim, dtype=value.dtype, device=value.device) torch.zeros(batch, num_heads, k_dim, v_dim, dtype=value.dtype, device=value.device)
@@ -304,22 +303,22 @@ def _torch_chunk_gated_delta_rule(
* decay_mask[:, :, i] * decay_mask[:, :, i]
).masked_fill_(mask_upper2, 0) ).masked_fill_(mask_upper2, 0)
# dispatch_scan.cuh Phase 2: sequential state propagation (scan kernel). # State propagation — match xllm qwen3_gated_delta_net_base.cpp line 218-238
# Only state-dependent ops remain in this loop.
g_exp_cache = g.clamp(-20, 20).exp() # pre-compute once
g_clamped = g.clamp(-20, 20) # keep raw clamped g for difference computation
for i in range(num_chunks): for i in range(num_chunks):
q_i = query[:, :, i]
k_i = key[:, :, i]
v_i = value[:, :, i]
v_prime = _ix_matmul(k_cumdecay[:, :, i], last_state) v_prime = _ix_matmul(k_cumdecay[:, :, i], last_state)
v_new = value[:, :, i] - v_prime v_new = v_i - v_prime
attn_inter = _ix_matmul(query[:, :, i] * g_exp_cache[:, :, i, :, None], last_state) # attn_inter: q * exp(g) @ state — xllm line 228
attn_inter = _ix_matmul(q_i * g[:, :, i].unsqueeze(-1).exp(), last_state)
core_out[:, :, i] = attn_inter + _ix_matmul(attn_i_all[:, :, i], v_new) core_out[:, :, i] = attn_inter + _ix_matmul(attn_i_all[:, :, i], v_new)
# State update uses difference form: exp(g[-1] - g[:]) to avoid division # State update — xllm line 230-237: difference form for numerical stability
last_state = ( g_i_last = g[:, :, i, -1].unsqueeze(-1) # (B, H, 1)
last_state * g_exp_cache[:, :, i, -1, None, None] g_exp_term = (g_i_last - g[:, :, i]).exp().unsqueeze(-1) # (B, H, C, 1)
+ _ix_matmul( k_g_exp = (k_i * g_exp_term).transpose(-1, -2).contiguous()
(key[:, :, i] * (g_clamped[:, :, i, -1, None] - g_clamped[:, :, i]).exp()[..., None]) last_state = (last_state * g_i_last.unsqueeze(-1).exp()
.transpose(-1, -2), v_new) + _ix_matmul(k_g_exp, v_new))
)
if not output_final_state: if not output_final_state:
last_state = None last_state = None