| """A single wide SwiGLU block at batch 8 -- the producer/consumer warp-specialisation problem. |
| |
| Three GEMVs against 805 MB of weights, with a hard dependency in the middle: `down` cannot start |
| until `silu(gate @ x) * (up @ x)` is complete for the whole 16384-wide intermediate. That makes the |
| whole thing one kernel with a grid-wide barrier in it, and inside each half the shape is the classic |
| warp-specialisation case: a stream of weight tiles that must be pulled from HBM continuously while a |
| separate set of warps consumes them against 8 resident activation rows. |
| |
| Batch 8 rather than batch 1 on purpose: each loaded weight tile is used eight times, so the consumer |
| has enough arithmetic that keeping it fed is a real scheduling problem rather than a formality, while |
| the arithmetic intensity (8 flop/byte against a ~146 flop/byte machine balance) keeps the task firmly |
| bandwidth-bound and the reward honestly a GB/s number. |
| """ |
|
|
| BODY = r''' |
| def make_weights(cfg, seed=0, device="cuda"): |
| """gate/up: (ffn, d). down: (d, ffn). 1/sqrt(fan_in) scaled, bf16.""" |
| g = torch.Generator(device=device).manual_seed(seed) |
| d, f = cfg["d"], cfg["ffn"] |
| |
| def rnd(*shape, fan_in): |
| return (torch.randn(*shape, device=device, dtype=torch.float32, generator=g) |
| / (fan_in ** 0.5)).to(torch.bfloat16) |
| |
| return {"gate": rnd(f, d, fan_in=d), "up": rnd(f, d, fan_in=d), "down": rnd(d, f, fan_in=f)} |
| |
| |
| def make_kv(cfg, batch, prefill_len, max_seq, seed=0, device="cuda"): |
| """No KV cache in this task.""" |
| return [] |
| |
| |
| def make_step_args(cfg, batch, base_pos, seed, n): |
| """(x,) per call -- a fresh (B, d) bf16 activation block.""" |
| g = torch.Generator(device="cuda").manual_seed(seed) |
| return [(torch.randn(batch, cfg["d"], device="cuda", dtype=torch.float32, |
| generator=g).to(torch.bfloat16),) for _ in range(n)] |
| |
| |
| def build_gemv(weights, kv_cache, cfg, max_seq_len): |
| """UNTIMED setup. Re-tile, interleave gate/up, allocate the intermediate, ...""" |
| return {"W": weights, "cfg": cfg} |
| |
| |
| @torch.no_grad() |
| def swiglu_gemv(handle, x): |
| """One SwiGLU block: down @ (silu(gate @ x) * (up @ x)). |
| |
| x : (B, d) bf16 |
| returns : (B, d) fp32 |
| """ |
| W = handle["W"] |
| h = F.silu(torch.matmul(x, W["gate"].T).float()) * torch.matmul(x, W["up"].T).float() |
| return torch.matmul(h.to(torch.bfloat16), W["down"].T).float() |
| ''' |
|
|
| MODEL_SRC = BODY |
|
|
