diff --git a/tests/test_attention_path_b200.py b/tests/test_attention_path_b200.py new file mode 100644 index 00000000..91528d5d --- /dev/null +++ b/tests/test_attention_path_b200.py @@ -0,0 +1,267 @@ +#!/usr/bin/env python3 +""" +Pinpoint which vLLM attention component fails on B200. + +Runs each step of the attention forward pass individually: +1. fused_wqa_wkv (CuTeDSL) ✅ already verified +2. q_norm + kv_norm (RMS) — trivial +3. wq_b (CuTeDSL) ✅ already verified +4. RoPE (pure PyTorch reference) +5. FlashMLA attention — THE SUSPECT +6. wo_a BMM (BF16) +7. wo_b (CuTeDSL) ✅ already verified + +Then builds a FAKE attention output (random but reasonable) and runs +steps 6+7 to verify the post-attention path works. + +Usage (on B200): + source /root/nvfp4-megamoe-kernel/tests/.venv/bin/activate + python3 tests/test_attention_path_b200.py +""" + +import sys, os, json, torch, torch.nn.functional as F +from safetensors import safe_open + +REPO = "/root/nvfp4-megamoe-kernel" +sys.path.insert(0, REPO) +MODEL = "/root/nvidia-meeting/DeepSeek-V4-Pro-NVFP4" +DEV = "cuda:0" + +H = 7168; NH = 128; HD = 512; NOPE = 448; ROPE = 64 +QL = 1536; OL = 1024; OG = 16; HPG = NH // OG +EPS = 1e-6 + +E2M1 = torch.tensor([0,.5,1.,1.5,2.,3.,4.,6.,-0,-.5,-1.,-1.5,-2.,-3.,-4.,-6.], dtype=torch.float32) + +_cache = {} +def P(k, wm, md): + if k in _cache: return _cache[k] + with safe_open(os.path.join(md, wm[k]), framework="pt") as f: + t = f.get_tensor(k) + _cache[k] = t + return t + +def dequant(w, sf, gs): + d = w.device; lut = E2M1.to(d) + lo = lut[(w & 0xF).long()]; hi = lut[((w >> 4) & 0xF).long()] + O, I2 = w.shape; I = I2*2 + u = torch.empty(O, I, dtype=torch.float32, device=d) + u[:,0::2] = lo; u[:,1::2] = hi + bs = sf.float().repeat_interleave(16, dim=1)[:O,:I] + return (u * bs * gs).to(torch.bfloat16) + +def rms(x, w, eps=1e-6): + v = x.float().pow(2).mean(-1, keepdim=True) + return (w.float() * (x * torch.rsqrt(v+eps)).float()).to(x.dtype) + +def make_runner(w, sf, gs_t, inf, outf, fused=False, lw=None): + from cutedsl.nvfp4_linear import CuTeDSLNvfp4Linear + fp4 = w.view(torch.float4_e2m1fn_x2).permute(1,0).contiguous() + s = sf.to(torch.float8_e4m3fn) if sf.dtype != torch.float8_e4m3fn else sf + s = s.permute(1,0).contiguous() + if fused and gs_t.numel() == 2: + g1,g2 = gs_t[0].item(), gs_t[1].item(); gs = max(g1,g2) + if g1 != g2: + s32 = s.float(); sp = lw[0] if lw else outf//2 + s32[:sp] *= g1/gs; s32[sp:] *= g2/gs; s = s32.to(torch.float8_e4m3fn) + else: + gs = gs_t.max().item() if gs_t.numel() > 1 else gs_t.item() + r = CuTeDSLNvfp4Linear(in_features=inf, out_features=outf, max_num_tokens=8192, device=str(w.device)) + r.fp4 = [fp4]; r.sf = [s]; r.gs = [gs] + r.finalize_weights(); r._ensure_initialized() + return r + +def build_cos_sin(max_pos=4096, rope_dim=ROPE): + half = rope_dim // 2 + inv_freq = 1.0 / (10000.0 ** (torch.arange(0, half, dtype=torch.float32) / half)) + freqs = torch.outer(torch.arange(max_pos, dtype=torch.float32), inv_freq) + return torch.cat([freqs.cos(), freqs.sin()], dim=-1) + + +def main(): + torch.cuda.set_device(0) + torch.manual_seed(42) + + print("=" * 70) + print(" Attention Path Test: Pinpoint FlashMLA Failure") + print("=" * 70) + + with open(os.path.join(MODEL, "model.safetensors.index.json")) as f: + wm = json.load(f)["weight_map"] + G = lambda k: P(k, wm, MODEL).to(DEV) + + p = "model.layers.0"; a = f"{p}.self_attn" + emb = G("model.embed_tokens.weight") + anorm = G(f"{p}.input_layernorm.weight") + qn = G(f"{a}.q_a_norm.weight"); kvn = G(f"{a}.kv_norm.weight") + woa = G(f"{a}.o_a_proj.weight") # (16384, 4096) BF16 + + # Load weights + qa_w = G(f"{a}.q_a_proj.weight"); qa_sf = G(f"{a}.q_a_proj.weight_scale"); qa_gs = G(f"{a}.q_a_proj.weight_scale_2") + qb_w = G(f"{a}.q_b_proj.weight"); qb_sf = G(f"{a}.q_b_proj.weight_scale"); qb_gs = G(f"{a}.q_b_proj.weight_scale_2") + kv_w = G(f"{a}.kv_proj.weight"); kv_sf = G(f"{a}.kv_proj.weight_scale"); kv_gs = G(f"{a}.kv_proj.weight_scale_2") + wob_w = G(f"{a}.o_b_proj.weight"); wob_sf = G(f"{a}.o_b_proj.weight_scale"); wob_gs = G(f"{a}.o_b_proj.weight_scale_2") + + # BF16 references + qa_bf16 = dequant(qa_w, qa_sf, qa_gs.item()) + qb_bf16 = dequant(qb_w, qb_sf, qb_gs.item()) + kv_bf16 = dequant(kv_w, kv_sf, kv_gs.item()) + wob_bf16 = dequant(wob_w, wob_sf, wob_gs.item()) + + # CuTeDSL runners + r_qa = make_runner(qa_w, qa_sf, qa_gs, H, qa_w.shape[0]) + r_qb = make_runner(qb_w, qb_sf, qb_gs, QL, qb_w.shape[0]) + r_kv = make_runner(kv_w, kv_sf, kv_gs, H, kv_w.shape[0]) + r_wob = make_runner(wob_w, wob_sf, wob_gs, OG*OL, wob_w.shape[0]) + + # Input + token_ids = torch.tensor([1, 450, 8403, 315, 5413, 374], dtype=torch.long, device=DEV) + NT = len(token_ids) + with torch.no_grad(): + hidden = emb[token_ids] + normed = rms(hidden, anorm, EPS) + print(f" Input: {NT} tokens, amax={normed.amax():.4f}") + + # ── Step 1: fused_wqa_wkv ───────────────────────────────────────── + print("\n--- Step 1: fused_wqa_wkv (q_a + kv) ---") + with torch.no_grad(): + qa_cute = r_qa.run(normed) + kv_cute = r_kv.run(normed) + qa_ref = normed @ qa_bf16.T + kv_ref = normed @ kv_bf16.T + print(f" q_a CuTeDSL vs BF16: cosine={F.cosine_similarity(qa_cute.flatten().unsqueeze(0).float(), qa_ref.flatten().unsqueeze(0).float()).item():.6f} ✅") + print(f" kv CuTeDSL vs BF16: cosine={F.cosine_similarity(kv_cute.flatten().unsqueeze(0).float(), kv_ref.flatten().unsqueeze(0).float()).item():.6f} ✅") + + # ── Step 2: q_norm + kv_norm ────────────────────────────────────── + print("\n--- Step 2: RMS norm (q_a_norm, kv_norm) ---") + with torch.no_grad(): + qa_normed = rms(qa_cute, qn, EPS) + kv_normed = rms(kv_cute, kvn, EPS) + print(f" q_a normed: amax={qa_normed.amax():.4f} NaN={torch.isnan(qa_normed).any()}") + print(f" kv normed: amax={kv_normed.amax():.4f} NaN={torch.isnan(kv_normed).any()}") + + # ── Step 3: wq_b ────────────────────────────────────────────────── + print("\n--- Step 3: wq_b (q_a → full q) ---") + with torch.no_grad(): + q_cute = r_qb.run(qa_normed) + q_ref = qa_normed @ qb_bf16.T + c = F.cosine_similarity(q_cute.flatten().unsqueeze(0).float(), q_ref.flatten().unsqueeze(0).float()).item() + print(f" q_b CuTeDSL vs BF16: cosine={c:.6f} ✅") + print(f" q shape: {q_cute.shape} → ({NT}, {NH}, {HD})") + + q_3d = q_cute.view(NT, NH, HD) + print(f" q_3d amax: {q_3d.amax():.4f}") + + # ── Step 4: RoPE (reference, GPT-J style) ───────────────────────── + print("\n--- Step 4: RoPE (GPT-J style reference) ---") + cos_sin = build_cos_sin().to(DEV) + positions = torch.arange(NT, dtype=torch.int64, device=DEV) + half_rot = ROPE // 2 + cos_q = cos_sin[positions, :half_rot].unsqueeze(1) # (NT, 1, 32) + sin_q = cos_sin[positions, half_rot:].unsqueeze(1) + + q_nope = q_3d[:, :, :NOPE].clone() + q_rope = q_3d[:, :, NOPE:].clone() + # GPT-J style: interleave even/odd, not split halves + q_even = q_rope[:, :, 0::2].clone() + q_odd = q_rope[:, :, 1::2].clone() + cos_f = cos_q.to(q_3d.dtype) + sin_f = sin_q.to(q_3d.dtype) + q_even_rot = q_even * cos_f - q_odd * sin_f + q_odd_rot = q_even * sin_f + q_odd * cos_f + q_rope_rot = torch.stack([q_even_rot, q_odd_rot], dim=-1).flatten(-2) + q_with_rope = torch.cat([q_nope, q_rope_rot], dim=-1) + print(f" q with RoPE: amax={q_with_rope.amax():.4f}") + + # ── Step 5: Attention (SKIP — use reference) ────────────────────── + print("\n--- Step 5: Attention output ---") + print(" ⚠️ FlashMLA cannot run standalone — using reference implementation") + print(" ⚠️ Running naive scaled dot-product attention in BF16") + + # Naive attention: q @ k.T / sqrt(d) @ v + # We need K, V. K comes from kv (after RoPE), V is kv (nope part) + # Actually in MLA, kv is the latent, not full K/V + # For this test, just use the kv latent directly as a proxy + # and do a simplified attention to get a reasonable output + kv_rope = kv_normed # (NT, HD) — latent representation + print(f" kv latent: shape={kv_normed.shape} amax={kv_normed.amax():.4f}") + + # Simplified: treat q as (NT, NH, HD) and kv as K=V=(NT, HD) + # This isn't the real MLA attention but gives us a non-garbage output + # to test the post-attention path (wo_a, wo_b) + k_simple = kv_normed.unsqueeze(1).expand(-1, NH, -1) # (NT, NH, HD) + v_simple = kv_normed.unsqueeze(1).expand(-1, NH, -1) # (NT, NH, HD) + scale = HD ** -0.5 + attn_weights = torch.matmul(q_with_rope, k_simple.transpose(-1, -2)) * scale + attn_weights = F.softmax(attn_weights.float(), dim=-1).to(torch.bfloat16) + o_ref = torch.matmul(attn_weights, v_simple) + print(f" Naive attention output: amax={o_ref.amax():.4f} NaN={torch.isnan(o_ref).any()}") + + # ── Step 6: wo_a (inverse RoPE + BMM) ───────────────────────────── + print("\n--- Step 6: wo_a (inverse RoPE + BMM) ---") + # Inverse RoPE: same as RoPE but sin -> -sin + o_nope = o_ref[:, :, :NOPE].clone() + o_rope = o_ref[:, :, NOPE:].clone() + o_even = o_rope[:, :, 0::2].clone() + o_odd = o_rope[:, :, 1::2].clone() + o_even_inv = o_even * cos_f + o_odd * sin_f + o_odd_inv = -o_even * sin_f + o_odd * cos_f + o_rope_inv = torch.stack([o_even_inv, o_odd_inv], dim=-1).flatten(-2) + o_inv = torch.cat([o_nope, o_rope_inv], dim=-1) + + # BMM: (OG, NT, HPG*HD) @ (OG, HPG*HD, OL) → (OG, NT, OL) + o_grouped = o_inv.view(NT, OG, HPG * HD).permute(1, 0, 2) + woa_3d = woa.view(OG, OL, HPG * HD) + z = torch.bmm(o_grouped, woa_3d.transpose(1, 2)).permute(1, 0, 2).reshape(NT, OG * OL) + print(f" wo_a z: amax={z.amax():.4f} NaN={torch.isnan(z).any()}") + + # ── Step 7: wo_b (CuTeDSL) ──────────────────────────────────────── + print("\n--- Step 7: wo_b (CuTeDSL vs BF16) ---") + with torch.no_grad(): + wob_cute = r_wob.run(z) + wob_ref = z @ wob_bf16.T + c = F.cosine_similarity(wob_cute.flatten().unsqueeze(0).float(), wob_ref.flatten().unsqueeze(0).float()).item() + print(f" wo_b CuTeDSL vs BF16: cosine={c:.6f} {'✅' if c >= 0.98 else '❌'}") + + # ── Final: attention output through LM head ─────────────────────── + print("\n--- Final: attn output → residual → norm → LM head ---") + fnorm_w = G("model.norm.weight") + lm_head = G("lm_head.weight") + with torch.no_grad(): + x = hidden + wob_cute + x_normed = rms(x, fnorm_w, EPS) + logits = x_normed @ lm_head.T + print(f" logits: amax={logits.amax():.4f} NaN={torch.isnan(logits).any()}") + top5 = torch.topk(logits[-1], 5) + print(f" top5 IDs: {top5.indices.tolist()}") + log_std = logits[-1].float().std().item() + print(f" logit std: {log_std:.4f} {'✅' if 0.5 < log_std < 50 else '❌'}") + + # ── KEY DIAGNOSTIC: What does vLLM's FlashMLA actually do? ──────── + print("\n" + "=" * 70) + print(" KEY DIAGNOSTIC: Check FlashMLA availability on B200") + print("=" * 70) + + try: + import flash_mla + print(f" flash_mla imported: version={getattr(flash_mla, '__version__', 'unknown')}") + # Check if it supports SM100 + cap = torch.cuda.get_device_capability() + print(f" GPU capability: {cap}") + if cap.major >= 10: + print(f" ⚠️ SM{cap.major}{cap.minor} (Blackwell) — FlashMLA may not support this!") + except ImportError: + print(" flash_mla NOT available in this venv (expected — it's in the container)") + + # Check what the vLLM MLA attention calls + print("\n vLLM attention path on B200:") + print(" 1. torch.ops._C.fused_deepseek_v4_qnorm_rope_kv_rope_quant_insert") + print(" → C++ CUDA kernel for RoPE + KV cache insert") + print(" 2. self.mla_attn(q, kv, positions, output=out)") + print(" → FlashMLA sparse attention") + print(" Both are compiled CUDA kernels that may NOT work on SM100.") + print(" If either returns garbage, the model outputs EOS immediately.") + + +if __name__ == "__main__": + main()