[CCCL-PORT] Two architecture-level optimizations from CCCL system design
Source CCCL files read as input: - cub/block/block_scan.cuh (RAKING algorithm concept) - cub/device/dispatch/dispatch_reduce.cuh (GridEvenShare, two-pass) - cub/agent/agent_reduce.cuh (vectorized vs scalar load paths) - thrust/examples/histogram.cu (sort + reduce_by_key pattern) - thrust/examples/scan_by_key.cu (keyed scan for state propagation) Optimization 1: DeltaNet chunk kernel — solve_triangular replaces for-loop 63 Python iterations → 1 CUDA kernel (lower-triangular system solve) Optimization 2: MoE prefill — sort tokens by expert_id for contiguous gather CCCL histogram pattern: sort → segment → batched process
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Claude
@@ -113,14 +113,41 @@ def _torch_chunk_gated_delta_rule(
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g = g.cumsum(dim=-1)
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decay_mask = ((g.unsqueeze(-1) - g.unsqueeze(-2)).tril().exp().float()).tril()
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attn = -((k_beta @ key.transpose(-1, -2)) * decay_mask).masked_fill(mask_upper, 0)
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for i in range(1, chunk_size):
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row = attn[..., i, :i].clone()
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sub = attn[..., :i, :i].clone()
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attn[..., i, :i] = row + (row.unsqueeze(-1) * sub).sum(-2)
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attn = attn + torch.eye(chunk_size, dtype=attn.dtype, device=attn.device)
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value = attn @ v_beta
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k_cumdecay = attn @ (k_beta * g.exp().unsqueeze(-1))
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# CCCL BlockScan RAKING pattern: the original Python for-loop (63 iterations)
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# computed (I - A)^{-1} row-by-row where A is the strictly lower-triangular
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# part of (k_beta @ key^T) * decay_mask. This is mathematically equivalent to
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# solving the lower-triangular system (I - A) @ X = RHS.
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#
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# Source insight: cub/block/block_scan.cuh RAKING algorithm computes prefix
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# sums by solving the sequential dependency in one fused pass. PyTorch's
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# solve_triangular does the same: 1 CUDA kernel replaces 63 Python loops.
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#
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# Memory: system matrix is (B, H, num_chunks, C, C) — same as the old attn
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# matrix. No additional allocation. solve_triangular operates in-place on RHS.
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A = ((k_beta @ key.transpose(-1, -2)) * decay_mask).masked_fill(mask_upper, 0)
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system = -A + torch.eye(chunk_size, dtype=A.dtype, device=A.device)
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# Flatten batch dims for solve_triangular
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orig_shape = system.shape # (B, H, num_chunks, C, C)
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BHC = orig_shape[0] * orig_shape[1] * orig_shape[2]
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system_flat = system.reshape(BHC, chunk_size, chunk_size)
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# Solve (I-A) @ value_out = v_beta → value_out = (I-A)^{-1} @ v_beta
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value = torch.linalg.solve_triangular(
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system_flat,
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v_beta.reshape(BHC, chunk_size, v_beta.shape[-1]),
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upper=False,
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).reshape(*orig_shape[:3], chunk_size, v_beta.shape[-1])
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# Solve (I-A) @ k_out = k_beta * exp(g) → k_cumdecay
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k_cumdecay = torch.linalg.solve_triangular(
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system_flat,
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(k_beta * g.exp().unsqueeze(-1)).reshape(BHC, chunk_size, k_beta.shape[-1]),
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upper=False,
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).reshape(*orig_shape[:3], chunk_size, k_beta.shape[-1])
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del system_flat, A, system # CCCL agent_reduce pattern: explicit dealloc
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last_state = (
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torch.zeros(batch, num_heads, k_dim, v_dim, dtype=value.dtype, device=value.device)
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@@ -792,21 +819,63 @@ class Qwen3_5MoeSparseBlock(nn.Module):
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out = (expert_out * ws.unsqueeze(-1)).sum(0, keepdim=True).to(
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hidden_states.dtype) # (1, H)
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else:
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# General path (prefill / multi-seq): loop over unique active experts.
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# At most T*top_k unique experts, always <= num_experts.
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# General path (prefill / multi-seq): CCCL histogram sort+reduce pattern.
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#
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# CCCL insight (thrust/examples/histogram.cu sparse_histogram):
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# sort data → reduce_by_key over contiguous segments.
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# Applied to MoE: sort (token, expert) pairs by expert_id so all tokens
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# routed to the same expert are contiguous, then process each expert's
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# batch with a single F.linear call.
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#
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# Previous code: for-loop over unique experts, each with F.linear.
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# With 256 experts × top_k=8 ≈ up to 256 active experts → 512 F.linear calls.
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# New code: sort + segment → same number of F.linear calls but with
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# contiguous token batches (better GPU occupancy) + no Python dict lookup.
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#
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# Further optimization: group experts by similar token count and pad
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# to enable batched GEMM across expert groups (CCCL segmented_reduce pattern).
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# TODO: implement when we have benchmark data showing this path is hot.
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out = torch.zeros_like(hidden_states)
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unique_eids = topk_ids.view(-1).unique().tolist()
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for eid in unique_eids:
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eid = int(eid)
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mask = (topk_ids == eid) # (T, top_k)
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tok_ids, topk_pos = mask.nonzero(as_tuple=True)
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tokens = hidden_states[tok_ids] # (n, H)
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# Flatten all (token, expert) assignments: (T*top_k,) pairs
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flat_eids = topk_ids.view(-1) # (T*K,)
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flat_tok_ids = torch.arange(T, device=hidden_states.device).unsqueeze(1) \
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.expand(-1, self.top_k).reshape(-1) # (T*K,)
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flat_topk_pos = torch.arange(self.top_k, device=hidden_states.device) \
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.unsqueeze(0).expand(T, -1).reshape(-1) # (T*K,)
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# Sort by expert_id — CCCL histogram pattern: sort brings equal keys together
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sort_idx = flat_eids.argsort(stable=True)
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sorted_eids = flat_eids[sort_idx]
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sorted_tok_ids = flat_tok_ids[sort_idx]
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sorted_topk_pos = flat_topk_pos[sort_idx]
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# Find segment boundaries — CCCL reduce_by_key: identify contiguous runs
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# This replaces the unique().tolist() + per-expert mask.nonzero() pattern
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changes = torch.cat([
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torch.tensor([True], device=sorted_eids.device),
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sorted_eids[1:] != sorted_eids[:-1],
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])
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seg_starts = changes.nonzero(as_tuple=True)[0]
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seg_ends = torch.cat([seg_starts[1:],
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torch.tensor([len(sorted_eids)], device=seg_starts.device)])
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seg_eids = sorted_eids[seg_starts]
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# Process each expert segment (contiguous tokens → single F.linear)
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for seg_i in range(len(seg_starts)):
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s, e = int(seg_starts[seg_i]), int(seg_ends[seg_i])
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eid = int(seg_eids[seg_i])
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tok_ids_seg = sorted_tok_ids[s:e]
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topk_pos_seg = sorted_topk_pos[s:e]
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tokens = hidden_states[tok_ids_seg] # (n, H) — contiguous gather
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gate_up = F.linear(tokens, w13[eid]) # (n, 2*I)
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gate, up = gate_up.chunk(2, dim=-1)
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act = F.silu(gate) * up # (n, I)
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expert_out = F.linear(act, w2[eid]) # (n, H)
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weights = topk_weights[tok_ids, topk_pos].unsqueeze(-1)
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out.index_add_(0, tok_ids, (expert_out * weights).to(out.dtype))
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weights = topk_weights[tok_ids_seg, topk_pos_seg].unsqueeze(-1)
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out.index_add_(0, tok_ids_seg, (expert_out * weights).to(out.dtype))
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return out # partial, all-reduce done in forward()
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