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bbd7f1f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | """TransformerEngine DotProductAttention (cuDNN fused, thd, head_dim 256, bf16) fwd/bwd at the
training sequence lengths vs an fp32 SDPA reference. DPA returns [T, H*D]."""
import torch
print("torch", torch.__version__, "| device", torch.cuda.get_device_name(0), flush=True)
import transformer_engine.pytorch as te
from transformer_engine.pytorch.attention import DotProductAttention
print("TE", getattr(te, "__version__", "?"), flush=True)
dev = "cuda"
torch.manual_seed(0)
def bad(t):
return bool(torch.isnan(t).any() or torch.isinf(t).any())
def rel(a, b):
return ((a.float() - b.float()).abs().max() / b.float().abs().max().clamp_min(1e-6)).item()
H, Hkv, D = 6, 1, 256 # per TP rank of 4: 24/4 heads, 4/4 kv heads
for Tl in ([400, 300], [4000, 3500], [8700, 9100, 400], [12288], [9000, 3200, 80, 8]):
try:
T = sum(Tl)
cu = torch.tensor([0] + list(torch.cumsum(torch.tensor(Tl), 0)), dtype=torch.int32, device=dev)
dpa = DotProductAttention(num_attention_heads=H, kv_channels=D, num_gqa_groups=Hkv, attention_dropout=0.0,
qkv_format="thd", attn_mask_type="padding_causal").to(dev)
q = torch.randn(T, H, D, device=dev, dtype=torch.bfloat16, requires_grad=True)
k = torch.randn(T, Hkv, D, device=dev, dtype=torch.bfloat16, requires_grad=True)
v = torch.randn(T, Hkv, D, device=dev, dtype=torch.bfloat16, requires_grad=True)
out = dpa(q, k, v, cu_seqlens_q=cu, cu_seqlens_kv=cu, max_seqlen_q=max(Tl), max_seqlen_kv=max(Tl))
out = out.view(T, H, D)
do = torch.randn_like(out)
(out * do).sum().backward()
fused = {n: t.grad.detach().clone() for n, t in (("q", q), ("k", k), ("v", v))}
print(f"TE fused thd hd256 T={T} seqs={Tl}: out nan/inf {bad(out)} | grad nan/inf", {n: bad(t) for n, t in fused.items()}, flush=True)
for t in (q, k, v):
t.grad = None
ref_out = []
for i in range(len(Tl)):
s, e = int(cu[i]), int(cu[i + 1])
qi = q[s:e].float().transpose(0, 1) # [H, L, D]
ki = k[s:e].float().transpose(0, 1).expand(H, -1, -1)
vi = v[s:e].float().transpose(0, 1).expand(H, -1, -1)
oi = torch.nn.functional.scaled_dot_product_attention(qi, ki, vi, is_causal=True)
ref_out.append(oi.transpose(0, 1))
ref = torch.cat(ref_out, dim=0)
(ref * do.float()).sum().backward()
print(f" fwd rel err {rel(out, ref):.3e} | grad rel err " + ", ".join(f"{n}={rel(fused[n], t.grad):.3e}" for n, t in (("q", q), ("k", k), ("v", v))), flush=True)
del q, k, v, out, do, fused, ref, dpa
torch.cuda.empty_cache()
except Exception as exc: # noqa: BLE001
print(f" case {Tl} failed: {type(exc).__name__}: {str(exc)[:300]}", flush=True)
print("DONE", flush=True)
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