nvfp4-megamoe-kernel/tests/test_decode_vs_prefill_b200.py

#!/usr/bin/env python3
"""
DeepSeek-V4 Decode vs Prefill Consistency Test

Verifies that:
1. Decode attention (using KV cache) produces the same output as
   prefill attention (raw KV) for the same token position
2. The cosine similarity between decode and prefill outputs is > 0.98

This is the CRITICAL test: if it passes, the KV cache pipeline is correct
and the vLLM container should produce valid output.

Usage (on B200):
  cd /root/nvfp4-megamoe-kernel
  PYTHONPATH=/root/nvfp4-megamoe-kernel tests/venv/bin/python tests/test_decode_vs_prefill_b200.py
"""

import sys, os, json, torch, torch.nn.functional as F, time
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
NUM_LAYERS = 61

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 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 apply_gptj_rope(x, positions, cos_sin, nope_dim, rope_dim):
    if rope_dim == 0 or x.numel() == 0: return x
    half = rope_dim // 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_dim:].clone()
    even = x_rope[..., 0::2]; odd = x_rope[..., 1::2]
    out = x.clone()
    out[..., nope_dim:][..., 0::2] = even * cos - odd * sin
    out[..., nope_dim:][..., 1::2] = even * sin + odd * cos
    return out

def apply_inv_gptj_rope(x, positions, cos_sin, nope_dim, rope_dim):
    if rope_dim == 0 or x.numel() == 0: return x
    half = rope_dim // 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_dim:].clone()
    even = x_rope[..., 0::2]; odd = x_rope[..., 1::2]
    out = x.clone()
    out[..., nope_dim:][..., 0::2] = even * cos + odd * sin
    out[..., nope_dim:][..., 1::2] = -even * sin + odd * cos
    return out

def kv_quantize_fp8(kv_bf16):
    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):
    return (kv_fp8.to(torch.bfloat16) * inv_scale).to(torch.bfloat16)

def causal_prefill_attention(q, kv, scale):
    T, NH, HD = q.shape
    q_t = q.permute(1, 0, 2)
    kv_exp = kv.unsqueeze(0).expand(NH, -1, -1)
    out = F.scaled_dot_product_attention(q_t, kv_exp, kv_exp, is_causal=True, scale=scale)
    return out.permute(1, 0, 2)

def decode_attention(q, kv, scale):
    NH = q.shape[1]; HD = q.shape[2]
    q_t = q.permute(1, 0, 2)
    kv_exp = kv.unsqueeze(0).expand(NH, -1, -1)
    out = F.scaled_dot_product_attention(q_t, kv_exp, kv_exp, is_causal=False, scale=scale)
    return out.permute(1, 0, 2)


def test_layer_decode_vs_prefill(layer_id):
    """For a single layer, verify decode matches prefill."""
    torch.cuda.set_device(0)
    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"
    cr = 128 if layer_id == 0 else (0 if layer_id == 60 else 4)
    lt = f"C{cr}A" if cr > 1 else "SWA"
    
    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")
    
    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])
    cos_sin = build_cos_sin(max_pos=4096).to(DEV)
    
    # Paged KV cache
    block_size = 64; max_tokens = 32; num_blocks = (max_tokens + block_size - 1) // block_size
    kv_cache = torch.zeros(num_blocks, block_size, HD, dtype=torch.float8_e4m3fn, device=DEV)
    inv_scale_cache = torch.zeros(max_tokens, 1, dtype=torch.bfloat16, device=DEV)
    
    N = 8  # Prefill tokens
    token_ids = torch.tensor([1, 450, 8403, 315, 5413, 374, 2198, 643], dtype=torch.long, device=DEV)
    
    with torch.no_grad():
        # ── PREFILL: process all N tokens at once ───────────────
        positions_p = torch.arange(N, dtype=torch.int64, device=DEV)
        hidden_p = emb[token_ids]
        normed_p = rms(hidden_p, anorm, EPS)
        qa_p = r_qa.run(normed_p); kv_p = r_kv.run(normed_p)
        qa_n_p = rms(qa_p, qn, EPS); kv_n_p = rms(kv_p, kvn, EPS)
        q_p = r_qb.run(qa_n_p).view(N, NH, HD)
        q_rope_p = apply_gptj_rope(q_p, positions_p, cos_sin, NOPE, ROPE)
        kv_rope_p = apply_gptj_rope(kv_n_p.unsqueeze(1), positions_p, cos_sin, NOPE, ROPE).squeeze(1)
        
        # Write prefill KV to cache
        kv_fp8_p, inv_s_p = kv_quantize_fp8(kv_rope_p)
        slots_p = positions_p
        bi_p = slots_p // block_size; oi_p = slots_p % block_size
        kv_cache[bi_p, oi_p] = kv_fp8_p
        for t in range(N):
            inv_scale_cache[slots_p[t]] = inv_s_p[t]
        
        # Prefill attention (raw KV)
        o_prefill = causal_prefill_attention(q_rope_p, kv_rope_p, SCALE)
        o_inv_p = apply_inv_gptj_rope(o_prefill, positions_p, cos_sin, NOPE, ROPE)
        o_grp_p = o_inv_p.reshape(N, OG, HPG * HD).permute(1, 0, 2)
        woa_3d = woa.view(OG, OL, HPG * HD)
        z_p = torch.bmm(o_grp_p, woa_3d.transpose(1, 2)).permute(1, 0, 2).reshape(N, OG * OL)
        attn_prefill = r_wob.run(z_p)
        
        # ── DECODE: process token N (one at a time) ────────────
        decode_id = torch.tensor([991], dtype=torch.long, device=DEV)
        pos_d = torch.tensor([N], dtype=torch.int64, device=DEV)
        hidden_d = emb[decode_id]
        normed_d = rms(hidden_d, anorm, EPS)
        qa_d = r_qa.run(normed_d); kv_d = r_kv.run(normed_d)
        qa_n_d = rms(qa_d, qn, EPS); kv_n_d = rms(kv_d, kvn, EPS)
        q_d = r_qb.run(qa_n_d).view(1, NH, HD)
        q_rope_d = apply_gptj_rope(q_d, pos_d, cos_sin, NOPE, ROPE)
        kv_rope_d = apply_gptj_rope(kv_n_d.unsqueeze(1), pos_d, cos_sin, NOPE, ROPE).squeeze(1)
        
        # Write decode KV to cache
        kv_fp8_d, inv_s_d = kv_quantize_fp8(kv_rope_d)
        slot_d = pos_d[0].item()
        bi_d = slot_d // block_size; oi_d = slot_d % block_size
        kv_cache[bi_d, oi_d] = kv_fp8_d[0]
        inv_scale_cache[slot_d] = inv_s_d[0]
        
        # Decode attention: read from cache
        all_slots = torch.arange(N + 1, dtype=torch.int64, device=DEV)
        all_bi = all_slots // block_size; all_oi = all_slots % block_size
        kv_cached_fp8 = kv_cache[all_bi, all_oi]
        kv_cached_inv = inv_scale_cache[all_slots]
        kv_cached = kv_dequantize_fp8(kv_cached_fp8, kv_cached_inv)
        
        # SWA window
        ws = max(0, N - WINDOW + 1)
        kv_window = kv_cached[ws:]
        o_decode = decode_attention(q_rope_d, kv_window, SCALE)
        
        # Full output pipeline for decode
        o_inv_d = apply_inv_gptj_rope(o_decode, pos_d, cos_sin, NOPE, ROPE)
        o_grp_d = o_inv_d.reshape(1, OG, HPG * HD).permute(1, 0, 2)
        z_d = torch.bmm(o_grp_d, woa_3d.transpose(1, 2)).permute(1, 0, 2).reshape(1, OG * OL)
        attn_decode = r_wob.run(z_d)
        
        # ── REFERENCE: prefill all N+1 tokens, take the last ────
        all_ids = torch.cat([token_ids, decode_id])
        all_pos = torch.arange(N + 1, dtype=torch.int64, device=DEV)
        hidden_ref = emb[all_ids]
        normed_ref = rms(hidden_ref, anorm, EPS)
        qa_ref = r_qa.run(normed_ref); kv_ref = r_kv.run(normed_ref)
        qa_n_ref = rms(qa_ref, qn, EPS); kv_n_ref = rms(kv_ref, kvn, EPS)
        q_ref = r_qb.run(qa_n_ref).view(N + 1, NH, HD)
        q_rope_ref = apply_gptj_rope(q_ref, all_pos, cos_sin, NOPE, ROPE)
        kv_rope_ref = apply_gptj_rope(kv_n_ref.unsqueeze(1), all_pos, cos_sin, NOPE, ROPE).squeeze(1)
        o_ref = causal_prefill_attention(q_rope_ref, kv_rope_ref, SCALE)
        o_inv_ref = apply_inv_gptj_rope(o_ref[-1:], pos_d, cos_sin, NOPE, ROPE)
        o_grp_ref = o_inv_ref.reshape(1, OG, HPG * HD).permute(1, 0, 2)
        z_ref = torch.bmm(o_grp_ref, woa_3d.transpose(1, 2)).permute(1, 0, 2).reshape(1, OG * OL)
        attn_ref = r_wob.run(z_ref)
        
        # ── COMPARE ─────────────────────────────────────────────
        # Decode attention output vs reference
        c_attn = F.cosine_similarity(o_decode.flatten().unsqueeze(0).float(), o_ref[-1:].flatten().unsqueeze(0).float()).item()
        # Full output vs reference
        c_full = F.cosine_similarity(attn_decode.flatten().unsqueeze(0).float(), attn_ref.flatten().unsqueeze(0).float()).item()
        
    del r_qa, r_qb, r_kv, r_wob
    torch.cuda.empty_cache()
    _cache.clear()
    
    return c_attn, c_full


def main():
    print("=" * 70)
    print("  DeepSeek-V4 Decode vs Prefill Consistency Test")
    print("  Verifies KV cache produces same output as full prefill")
    print("=" * 70)
    
    test_layers = [
        (0, "C128A"),
        (1, "C4A"),
        (2, "C4A"),
        (30, "C4A"),
        (60, "SWA"),
    ]
    
    for layer_id, lt in test_layers:
        c_attn, c_full = test_layer_decode_vs_prefill(layer_id)
        status = "✅" if c_full >= 0.98 else "❌"
        print(f"  Layer {layer_id} ({lt}): attn={c_attn:.4f} full={c_full:.4f} {status}")
    
    print(f"\n{'='*70}")
    print(f"  If all layers pass (≥0.98), the KV cache pipeline is correct.")
    print(f"  The vLLM container should produce valid output.")
    print(f"{'='*70}")


if __name__ == "__main__":
    main()