#!/usr/bin/env python3 """ DeepSeek-V4 Decode Attention Pipeline Test REPRODUCES THE BUG: The vLLM Blackwell path uses raw KV for attention, which means decode (generating token N+1 when tokens 0..N are in the KV cache) produces garbage because the cache is never written to. This test simulates the actual decode scenario: 1. Prefill: compute KV for N tokens, write to paged cache 2. Decode: compute KV for 1 new token, write to cache, then attend to ALL cached KV The key insight: during decode, you can't use raw KV — you need the KV cache because previous tokens' KV was computed in a prior forward pass. Architecture: - KV latent is (T, 512) — single head, shared across all 128 Q heads - After kv_norm + RoPE, KV is quantized to fp8 and stored in paged cache - Attention: Q (128 heads) × K^T → softmax → × V - For CSA/HCA: attention attends to compressed positions (every 4th or 128th) - For SWA: attention attends to last WINDOW tokens Usage (on B200): cd /root/nvfp4-megamoe-kernel PYTHONPATH=/root/nvfp4-megamoe-kernel tests/venv/bin/python tests/test_decode_attention_b200.py """ import sys, os, json, torch, torch.nn.functional as F, math 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; WINDOW = 128; SCALE = HD ** -0.5 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 dsv4.layers.linear import Nvfp4Linear 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 = Nvfp4Linear(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 apply_gptj_rope(x, positions, cos_sin, nope, rope): if rope == 0 or x.numel() == 0: return x half = rope // 2 cos = cos_sin[positions, :half].to(x.dtype) sin = cos_sin[positions, half:2*half].to(x.dtype) if x.dim() == 3: cos = cos.unsqueeze(1); sin = sin.unsqueeze(1) x_rope = x[..., nope:].clone() even = x_rope[..., 0::2]; odd = x_rope[..., 1::2] out = x.clone() out[..., nope:][..., 0::2] = even * cos - odd * sin out[..., nope:][..., 1::2] = even * sin + odd * cos return out def apply_inv_gptj_rope(x, positions, cos_sin, nope, rope): if rope == 0 or x.numel() == 0: return x half = rope // 2 cos = cos_sin[positions, :half].to(x.dtype) sin = cos_sin[positions, half:2*half].to(x.dtype) if x.dim() == 3: cos = cos.unsqueeze(1); sin = sin.unsqueeze(1) x_rope = x[..., nope:].clone() even = x_rope[..., 0::2]; odd = x_rope[..., 1::2] out = x.clone() out[..., nope:][..., 0::2] = even * cos + odd * sin out[..., nope:][..., 1::2] = -even * sin + odd * cos return out # ── KV Cache Kernels ──────────────────────────────────────────────── def kv_quantize_fp8(kv_bf16): """BF16 KV → fp8_e4m3 with per-token scale.""" amax = kv_bf16.float().abs().amax(dim=-1, keepdim=True).clamp(min=1e-12) fp8_max = torch.tensor(448.0, dtype=torch.float32, device=kv_bf16.device) scale = fp8_max / amax kv_fp8 = (kv_bf16.float() * scale).to(torch.float8_e4m3fn) inv_scale = (amax / fp8_max).to(torch.bfloat16) return kv_fp8, inv_scale def kv_dequantize_fp8(kv_fp8, inv_scale): """fp8 KV → BF16.""" return (kv_fp8.to(torch.bfloat16) * inv_scale).to(torch.bfloat16) def paged_kv_write(kv_data, slot_mapping, cache, block_size): """Write data into paged cache. Works for fp8 or bf16. kv_data: (T, D) tensor to write slot_mapping: (T,) slot indices cache: (num_blocks, block_size, D) cache tensor """ for t in range(kv_data.shape[0]): slot = slot_mapping[t].item() block_idx = slot // block_size offset = slot % block_size if block_idx < cache.shape[0] and offset < cache.shape[1]: cache[block_idx, offset] = kv_data[t] def paged_kv_read(slot_mapping, cache, block_size, num_tokens, head_dim): """Read KV from paged cache.""" device = cache.device kv = torch.zeros(num_tokens, head_dim, dtype=cache.dtype, device=device) for t in range(num_tokens): slot = slot_mapping[t].item() block_idx = slot // block_size offset = slot % block_size if block_idx < cache.shape[0] and offset < cache.shape[1]: kv[t] = cache[block_idx, offset] return kv # ── Attention ──────────────────────────────────────────────────────── def full_causal_attention(q, kv, scale): """Full causal self-attention. q: (T_q, NH, HD), kv: (T_kv, HD). Works for prefill (T_q == T_kv) and decode (T_q == 1, T_kv > 1). Uses SDPA for efficiency. """ T_q, NH, HD = q.shape T_kv = kv.shape[0] # q: (NH, T_q, HD), k/v: (NH, T_kv, HD) — shared KV across heads q_t = q.permute(1, 0, 2) # (NH, T_q, HD) kv_exp = kv.unsqueeze(0).expand(NH, -1, -1) # (NH, T_kv, HD) v_exp = kv_exp.clone() # Causal mask: query at position i can attend to positions <= i # For decode (T_q=1), all T_kv positions are valid (position T_kv-1 attends to 0..T_kv-1) if T_q == T_kv: # Prefill: standard causal attn_mask = torch.tril(torch.ones(T_q, T_kv, device=q.device, dtype=torch.bool)).unsqueeze(0).expand(NH, -1, -1) out = F.scaled_dot_product_attention(q_t, kv_exp, v_exp, attn_mask=attn_mask, scale=scale) else: # Decode or mixed: no masking needed (all positions are in the past) out = F.scaled_dot_product_attention(q_t, kv_exp, v_exp, is_causal=False, scale=scale) return out.permute(1, 0, 2) # (T_q, NH, HD) def swa_decode_attention(q_new, kv_cache_bf16, positions_new, scale, window_size=WINDOW): """Decode-time sliding window attention. q_new: (1, NH, HD) — single new query token with RoPE kv_cache_bf16: (total_len, HD) — ALL cached KV (already with RoPE) positions_new: (1,) — position of the new token """ total_len = kv_cache_bf16.shape[0] pos = positions_new[0].item() window_start = max(0, pos - window_size + 1) window_len = pos - window_start + 1 # Get the KV window kv_window = kv_cache_bf16[window_start:pos+1] # (window_len, HD) NH = q_new.shape[1] HD = q_new.shape[2] # Multi-head attention q_2d = q_new.reshape(NH, HD) # (NH, HD) k_exp = kv_window.unsqueeze(0).expand(NH, -1, -1) # (NH, window_len, HD) v_exp = k_exp.clone() # scores: (NH, 1, window_len) scores = torch.matmul(q_2d.unsqueeze(1), k_exp.transpose(-1, -2)) * scale weights = F.softmax(scores.float(), dim=-1).to(q_new.dtype) out = torch.matmul(weights, v_exp).squeeze(1) # (NH, HD) return out.unsqueeze(0) # (1, NH, HD) def test_prefill_decode(layer_id, compress_ratio): """Test the full prefill + decode attention pipeline. Simulates what vLLM actually does: 1. PREFILL: Process N tokens, write their KV to the paged cache 2. DECODE: Process 1 new token, write its KV to the cache, attend to all cached KV Compares decode output against a full BF16 reference (which processes all tokens at once). """ torch.cuda.set_device(0) torch.manual_seed(42) torch.cuda.empty_cache() 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 = f"model.layers.{layer_id}"; a = f"{p}.self_attn" layer_type = "SWA" if compress_ratio <= 1 else f"CSA(c={compress_ratio})" print(f"\n{'='*70}") print(f" Layer {layer_id} — {layer_type} — Prefill+Decode Test") print(f"{'='*70}") # Load weights 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") 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") # 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]) # Setup N_PREFILL = 8 # Number of prefill tokens N_DECODE = 1 # Single decode token N_TOTAL = N_PREFILL + N_DECODE token_ids = torch.tensor([1, 450, 8403, 315, 5413, 374, 2198, 643, 991], dtype=torch.long, device=DEV) assert len(token_ids) >= N_TOTAL cos_sin = build_cos_sin(max_pos=4096).to(DEV) # Paged KV cache block_size = 256 num_blocks = 64 # Cache stores fp8 KV (with per-token inv_scale stored separately) kv_cache_fp8 = torch.zeros(num_blocks, block_size, HD, dtype=torch.float8_e4m3fn, device=DEV) # Per-token inv scales (indexed by slot) inv_scale_cache = torch.zeros(num_blocks * block_size, 1, dtype=torch.bfloat16, device=DEV) # RoPE'd BF16 KV cache (for reference — in production, RoPE is applied after dequant) kv_cache_bf16 = torch.zeros(N_TOTAL, HD, dtype=torch.bfloat16, device=DEV) with torch.no_grad(): # ════════════════════════════════════════════════════════════════ # STEP 1: PREFILL — process tokens 0..N_PREFILL-1 # ════════════════════════════════════════════════════════════════ prefill_ids = token_ids[:N_PREFILL] prefill_pos = torch.arange(N_PREFILL, dtype=torch.int64, device=DEV) prefill_slots = prefill_pos # slot = position (simplified) hidden_prefill = emb[prefill_ids] normed_prefill = rms(hidden_prefill, anorm, EPS) # Project KV kv_prefill = r_kv.run(normed_prefill) kv_normed_prefill = rms(kv_prefill, kvn, EPS) # Apply RoPE to KV BEFORE caching kv_rope_prefill = apply_gptj_rope(kv_normed_prefill.unsqueeze(1), prefill_pos, cos_sin, NOPE, ROPE).squeeze(1) # Quantize to fp8 kv_fp8_prefill, inv_scale_prefill = kv_quantize_fp8(kv_rope_prefill) # Write to paged cache paged_kv_write(kv_fp8_prefill, prefill_slots, kv_cache_fp8, block_size) # Write inv_scale to flat cache for t in range(N_PREFILL): slot = prefill_slots[t].item() inv_scale_cache[slot] = inv_scale_prefill[t] # Also store BF16 reference (for verification) kv_cache_bf16[:N_PREFILL] = kv_rope_prefill print(f" Prefill: {N_PREFILL} tokens written to KV cache") print(f" KV cache fp8 amax: {kv_fp8_prefill.float().abs().max():.4f}") print(f" KV BF16 amax: {kv_rope_prefill.amax():.4f}") # Verify roundtrip: read back and compare kv_read = paged_kv_read(prefill_slots, kv_cache_fp8, block_size, N_PREFILL, HD) inv_read = inv_scale_cache[prefill_slots] kv_dequant = kv_dequantize_fp8(kv_read, inv_read) c = F.cosine_similarity(kv_rope_prefill.flatten().unsqueeze(0).float(), kv_dequant.flatten().unsqueeze(0).float()).item() print(f" KV cache roundtrip cosine: {c:.6f} {'✅' if c>=0.99 else '❌'}") # ════════════════════════════════════════════════════════════════ # STEP 2: DECODE — process token N_PREFILL # ════════════════════════════════════════════════════════════════ decode_id = token_ids[N_PREFILL:N_PREFILL + N_DECODE] decode_pos = torch.tensor([N_PREFILL], dtype=torch.int64, device=DEV) decode_slot = decode_pos hidden_decode = emb[decode_id] normed_decode = rms(hidden_decode, anorm, EPS) # Project Q and KV qa_decode = r_qa.run(normed_decode) kv_decode = r_kv.run(normed_decode) qa_n_decode = rms(qa_decode, qn, EPS) kv_n_decode = rms(kv_decode, kvn, EPS) q_decode = r_qb.run(qa_n_decode).view(N_DECODE, NH, HD) q_rope_decode = apply_gptj_rope(q_decode, decode_pos, cos_sin, NOPE, ROPE) # Apply RoPE to KV kv_rope_decode = apply_gptj_rope(kv_n_decode.unsqueeze(1), decode_pos, cos_sin, NOPE, ROPE).squeeze(1) # Write decode KV to cache kv_fp8_decode, inv_scale_decode = kv_quantize_fp8(kv_rope_decode) paged_kv_write(kv_fp8_decode, decode_slot, kv_cache_fp8, block_size) for t in range(N_DECODE): slot = decode_slot[t].item() inv_scale_cache[slot] = inv_scale_decode[t] kv_cache_bf16[N_PREFILL:N_PREFILL + N_DECODE] = kv_rope_decode print(f"\n Decode: token {N_PREFILL} written to KV cache") # ════════════════════════════════════════════════════════════════ # STEP 3: DECODE ATTENTION using KV cache # ════════════════════════════════════════════════════════════════ # Read ALL KV from cache (tokens 0..N_PREFILL) all_slots = torch.arange(N_TOTAL, dtype=torch.int64, device=DEV) kv_all_fp8 = paged_kv_read(all_slots, kv_cache_fp8, block_size, N_TOTAL, HD) inv_scale_all = inv_scale_cache[all_slots] kv_all_dequant = kv_dequantize_fp8(kv_all_fp8, inv_scale_all) # SWA: attend to last WINDOW tokens (or all if total < WINDOW) if N_TOTAL <= WINDOW: # Full attention within window o_from_cache = full_causal_attention( q_rope_decode, # (1, NH, HD) — only the decode token kv_all_dequant, # (N_TOTAL, HD) — all cached KV SCALE, ) else: o_from_cache = swa_decode_attention( q_rope_decode, kv_all_dequant, decode_pos, SCALE, WINDOW, ) # ════════════════════════════════════════════════════════════════ # STEP 4: BF16 REFERENCE — process ALL tokens at once # ════════════════════════════════════════════════════════════════ all_ids = token_ids[:N_TOTAL] all_pos = torch.arange(N_TOTAL, dtype=torch.int64, device=DEV) hidden_all = emb[all_ids] normed_all = rms(hidden_all, anorm, EPS) qa_all = r_qa.run(normed_all) kv_all = r_kv.run(normed_all) qa_n_all = rms(qa_all, qn, EPS) kv_n_all = rms(kv_all, kvn, EPS) q_all = r_qb.run(qa_n_all).view(N_TOTAL, NH, HD) q_rope_all = apply_gptj_rope(q_all, all_pos, cos_sin, NOPE, ROPE) kv_rope_all = apply_gptj_rope(kv_n_all.unsqueeze(1), all_pos, cos_sin, NOPE, ROPE).squeeze(1) # Full BF16 attention on all tokens o_ref_all = full_causal_attention(q_rope_all, kv_rope_all, SCALE) o_ref_decode = o_ref_all[N_PREFILL:] # Only the decode token's output # ════════════════════════════════════════════════════════════════ # COMPARE: cached KV decode vs BF16 reference decode # ════════════════════════════════════════════════════════════════ c = F.cosine_similarity(o_from_cache.flatten().unsqueeze(0).float(), o_ref_decode.flatten().unsqueeze(0).float()).item() print(f"\n Decode attention (cached KV) vs BF16 reference cosine: {c:.6f} {'✅' if c>=0.98 else '❌'}") print(f" Cached output amax: {o_from_cache.amax():.4f} BF16 ref amax: {o_ref_decode.amax():.4f}") print(f" Cached output NaN: {torch.isnan(o_from_cache).any()} BF16 NaN: {torch.isnan(o_ref_decode).any()}") # ════════════════════════════════════════════════════════════════ # STEP 5: Full output pipeline — inverse RoPE + o_a BMM + o_b # ════════════════════════════════════════════════════════════════ # Using cached attention output o_inv = apply_inv_gptj_rope(o_from_cache, decode_pos, cos_sin, NOPE, ROPE) o_grouped = o_inv.view(N_DECODE, OG, HPG * HD).permute(1, 0, 2) woa_3d = woa.view(OG, OL, HPG * HD) z_cached = torch.bmm(o_grouped, woa_3d.transpose(1, 2)).permute(1, 0, 2).reshape(N_DECODE, OG * OL) attn_out_cached = r_wob.run(z_cached) # Using BF16 reference o_inv_ref = apply_inv_gptj_rope(o_ref_decode, decode_pos, cos_sin, NOPE, ROPE) o_grouped_ref = o_inv_ref.view(N_DECODE, OG, HPG * HD).permute(1, 0, 2) z_ref = torch.bmm(o_grouped_ref, woa_3d.transpose(1, 2)).permute(1, 0, 2).reshape(N_DECODE, OG * OL) attn_out_ref = r_wob.run(z_ref) c_full = F.cosine_similarity(attn_out_cached.flatten().unsqueeze(0).float(), attn_out_ref.flatten().unsqueeze(0).float()).item() print(f" Full output (cached) vs BF16 reference cosine: {c_full:.6f} {'✅' if c_full>=0.98 else '❌'}") # ════════════════════════════════════════════════════════════════ # BUG REPRODUCTION: What vLLM currently does (uses raw kv, not cache) # ════════════════════════════════════════════════════════════════ print(f"\n --- BUG REPRODUCTION: vLLM Blackwell path ---") # vLLM's _attention_impl_blackwell calls full_sdpa_attention(q, kv, scale) # where kv is the RAW projection output (not from cache) # For decode, this only has 1 token of KV — missing all the prior tokens! o_buggy = full_causal_attention(q_rope_decode, kv_n_decode, SCALE) c_bug = F.cosine_similarity(o_buggy.flatten().unsqueeze(0).float(), o_ref_decode.flatten().unsqueeze(0).float()).item() print(f" Buggy (raw kv, no cache) cosine: {c_bug:.6f} ❌ (should be low — missing context)") print(f" This is why vLLM produces garbage: decode only has 1 KV vector,") print(f" but needs to attend to ALL prior tokens' KV from the cache.") # Cleanup del r_qa, r_qb, r_kv, r_wob torch.cuda.empty_cache() return c, c_full def main(): print("=" * 70) print(" DeepSeek-V4 Decode Attention Pipeline Test") print(" Reproduces the vLLM Blackwell bug: KV cache not used for decode") print("=" * 70) # Test SWA layer (layer 60, compress_ratio=0) c_swa, c_swa_full = test_prefill_decode(60, 0) # Test C128A layer (layer 0, compress_ratio=128) — for this test, # we just do full attention (not compressed) since compression # requires the compressor/indexer which is a separate concern # c_c128, c_c128_full = test_prefill_decode(0, 128) print(f"\n{'='*70}") print(f" SUMMARY") print(f" Layer 60 (SWA): decode attention cosine = {c_swa:.6f}, full output = {c_swa_full:.6f}") print(f"{'='*70}") print(f"\n KEY TAKEAWAY: The KV cache write/read + attention pipeline") print(f" must work for decode. Once verified, we can build the vLLM") print(f" attention backend that uses this pipeline.") if __name__ == "__main__": main()