#!/usr/bin/env python3
"""Single-shot DSV4-Pro inference — Full 61-layer pipeline, 8-GPU.

Reference implementation exercising the production kernel stack end-to-end.
This file should be usable as ground truth when integrating into vLLM or SGLang.

Architecture (paper §2, verified against HuggingFace modeling_deepseek_v4.py):
  X_l → mHC.pre_block → RMSNorm → Attention → F_attn → mHC.post_block → X_mid
  X_mid → mHC.pre_block → RMSNorm → FFN(MoE) → F_ffn → mHC.post_block → X_{l+1}

Components exercised:
  - mHC (Sinkhorn-Knopp, B_l transposed, [pre,post,comb] ordering)
  - Low-rank Q: q_a_proj → q_a_norm → q_b_proj → q_b_norm
  - KV: kv_proj → kv_norm — single latent per token (MQA)
  - Compressor: CSA (ratio=4, Ca/Cb overlapping) and HCA (ratio=128)
  - Indexer: CSA top-k with its own compressor at index_head_dim
  - Partial RoPE (last 64 dims, GPT-J interleaved, YaRN factor=16) + inverse
  - Attention sinks (per-head logit bias)
  - Full attention: [compressed_kv, swa_kv] concatenated
  - Grouped output projection: wo_a (BF16 BMM) + wo_b (NVFP4)
  - MoE: 384 experts, top-6, hash (layers 0-2) + noaux_tc (3+), SwiGLU clamp
  - Shared expert (NVFP4)
  - NVFP4 two-level scale: weight_scale (E4M3) × weight_scale_2 (scalar) × input_scale (scalar)

Checkpoint key format:
  model.layers.{li}.self_attn.{kv_proj, q_a_proj, q_b_proj, o_a_proj, o_b_proj}.{weight, weight_scale, ...}
  model.layers.{li}.self_attn.compressor.{kv_proj, gate_proj}.{weight, weight_scale, ...}
  model.layers.{li}.self_attn.compressor.position_bias (BF16)
  model.layers.{li}.self_attn.compressor.kv_norm.weight (BF16)
  model.layers.{li}.self_attn.compressor.indexer.*
  model.layers.{li}.self_attn.sinks (BF16)
  model.layers.{li}.attn_hc.{fn, base, scale}
  model.layers.{li}.ffn_hc.{fn, base, scale}
  model.layers.{li}.input_layernorm.weight (BF16)
  model.layers.{li}.post_attention_layernorm.weight (BF16)
  model.layers.{li}.mlp.experts.{eid}.{gate_proj,up_proj,down_proj}.{weight, weight_scale, ...}
  model.layers.{li}.mlp.shared_experts.{gate_proj,up_proj,down_proj}.{weight, weight_scale, ...}
  model.layers.{li}.mlp.gate.{weight, e_score_correction_bias, tid2eid}
  model.embed_tokens.weight, model.norm.weight, lm_head.weight
  model.hc_head.{hc_fn, hc_base, hc_scale}
"""
import os, sys, time, json, math, argparse
import torch
import torch.nn.functional as F
from pathlib import Path

# =====================================================================
# Configuration
# =====================================================================
def parse_args():
    p = argparse.ArgumentParser()
    p.add_argument('--max-tokens', type=int, default=8192)
    p.add_argument('--prompt', type=str, default=None)
    p.add_argument('--seed', type=int, default=42)
    p.add_argument('--verbose', type=int, default=1)
    return p.parse_args()

_args = parse_args()
CHECKPOINT_DIR = "/root/nvidia-meeting/DeepSeek-V4-Pro-NVFP4"
MAX_NEW_TOKENS = _args.max_tokens
PROMPT = _args.prompt or "The capital of France is"
NUM_GPUS = 8
SEED = _args.seed
VERBOSE = _args.verbose
GROWTH_DIAG = VERBOSE >= 1

THINK_START, THINK_END = 128821, 128822
USER_TOKEN, ASSISTANT_TOKEN = 128803, 128804

# =====================================================================
# NVFP4 dequantization — two-level scale
# =====================================================================
FP4_LUT = torch.tensor([0., 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 6.0])

def dequant_nvfp4(weight, weight_scale, weight_scale_2=None, input_scale=None):
    """Dequantize NVFP4 → BF16. weight: (O,I//2) uint8, scale: (O,I//16) E4M3."""
    O, I2 = weight.shape
    I = I2 * 2
    lo = (weight & 0x0F).to(torch.int8)
    hi = (weight >> 4).to(torch.int8)
    lut = FP4_LUT.to(device=weight.device, dtype=torch.float32)
    lo_f = lut[(lo & 0x07).long()] * torch.where((lo >> 3).bool(), -1., 1.)
    hi_f = lut[(hi & 0x07).long()] * torch.where((hi >> 3).bool(), -1., 1.)
    w = torch.stack([lo_f, hi_f], -1).reshape(O, I)
    s = weight_scale.float().repeat_interleave(16, 1)
    if weight_scale_2 is not None: s = s * weight_scale_2.float()
    # NOTE: input_scale is intentionally NOT used. It's the activation
    # quantization scale (for FP8 inputs). Since we use BF16 activations,
    # the weight dequant is: lut[weight] * weight_scale * weight_scale_2.
    return (w * s).bfloat16()

def nvfp4_linear(x, weight, weight_scale, weight_scale_2=None, input_scale=None):
    return F.linear(x, dequant_nvfp4(weight, weight_scale, weight_scale_2, input_scale))

def get_nvfp4_weight(w, pfx, proj_name):
    k = f"{pfx}.{proj_name}"
    return (w.get(f"{k}.weight"), w.get(f"{k}.weight_scale"),
            w.get(f"{k}.weight_scale_2"), w.get(f"{k}.input_scale"))

def do_nvfp4_linear(x, w, pfx, proj_name):
    weight, ws, ws2, isc = get_nvfp4_weight(w, pfx, proj_name)
    if weight is None: return None
    d = x.device
    return nvfp4_linear(x, weight.to(d), ws.to(d),
                        ws2.to(d) if ws2 is not None else None,
                        isc.to(d) if isc is not None else None)

# =====================================================================
# RMSNorm
# =====================================================================
def rmsnorm(x, weight, eps=1e-6):
    xf = x.float()
    return (xf * xf.pow(2).mean(-1, keepdim=True).add(eps).rsqrt() * weight.float()).bfloat16()

def unweighted_rmsnorm(x, eps=1e-6):
    xf = x.float()
    return xf * xf.pow(2).mean(-1, keepdim=True).add(eps).rsqrt()

# =====================================================================
# mHC
# =====================================================================
HC_EPS = 1e-6

def sinkhorn_knopp(logits, t_max=20, eps=HC_EPS):
    M = torch.softmax(logits, -1) + eps
    M = M / (M.sum(-2, keepdim=True) + eps)
    for _ in range(t_max - 1):
        M = M / (M.sum(-1, keepdim=True) + eps)
        M = M / (M.sum(-2, keepdim=True) + eps)
    return M

class mHCBlock:
    def __init__(self, hidden_dim=7168, n_hc=4, sinkhorn_iters=20, device='cuda:0'):
        self.d, self.n_hc, self.K = hidden_dim, n_hc, n_hc * hidden_dim
        self.t_max, self.device = sinkhorn_iters, device

    def load(self, fn, base, scale):
        n = self.n_hc
        self.W_pre = fn[0:n].contiguous()
        self.W_post = fn[n:2*n].contiguous()
        self.W_comb = fn[2*n:].contiguous()
        self.S_pre = base[0:n].reshape(1, n).float()
        self.S_post = base[n:2*n].reshape(n, 1).float()
        self.S_comb = base[2*n:].reshape(n, n).float()
        self.alpha_pre, self.alpha_post, self.alpha_comb = scale[0].item(), scale[1].item(), scale[2].item()

    @staticmethod
    def init_state(emb, n_hc=4):
        return emb.unsqueeze(1).expand(-1, n_hc, -1).clone()

    def pre_block(self, X):
        T, n, d = X.shape
        Xn = unweighted_rmsnorm(X.reshape(T, self.K).bfloat16())
        W = torch.cat([self.W_pre, self.W_post, self.W_comb])
        proj = Xn @ W.T
        pre_t = self.alpha_pre * proj[:, :n] + self.S_pre.flatten().unsqueeze(0)
        post_t = self.alpha_post * proj[:, n:2*n] + self.S_post.flatten().unsqueeze(0)
        comb_t = self.alpha_comb * proj[:, 2*n:2*n+n*n] + self.S_comb.flatten().unsqueeze(0)
        A = torch.sigmoid(pre_t) + HC_EPS
        C = 2.0 * torch.sigmoid(post_t)
        B = sinkhorn_knopp(comb_t.reshape(T, n, n), t_max=self.t_max)
        x_in = torch.bmm(A.unsqueeze(1), X.float()).squeeze(1).bfloat16()
        return x_in, {'B': B, 'C': C}

    def post_block(self, X, F_out, ctx):
        BX = torch.bmm(ctx['B'].transpose(-1, -2), X.float())
        CF = ctx['C'].unsqueeze(-1) * F_out.unsqueeze(1)
        return (CF.float() + BX).bfloat16()

# =====================================================================
# HcHead
# =====================================================================
class HcHead:
    def __init__(self, hidden_dim=7168, n_hc=4, device='cuda:0'):
        self.K, self.device, self.n_hc = n_hc * hidden_dim, device, n_hc

    def load(self, fn, base, scale=None):
        self.fn = fn.to(self.device, torch.float32).contiguous()
        self.base = base.to(self.device, torch.float32).contiguous()
        self.scale = scale.to(self.device, torch.float32).item() if scale is not None else 1.0

    def forward(self, X):
        T = X.shape[0]
        Xn = unweighted_rmsnorm(X.reshape(T, self.K).bfloat16())
        mix = F.linear(Xn, self.fn[:self.n_hc]).float()
        pre = torch.sigmoid(mix * self.scale + self.base[:self.n_hc].unsqueeze(0)) + HC_EPS
        return (pre.unsqueeze(-1) * X.float()).sum(1).bfloat16()

# =====================================================================
# RoPE
# =====================================================================
def build_rope_cache(max_pos, rope_dim, device, theta=10000., rope_type="default",
                     rope_factor=1., orig_max=4096, beta_fast=32, beta_slow=1):
    freqs = 1. / (theta ** (torch.arange(0, rope_dim, 2, dtype=torch.float32) / rope_dim))
    if rope_type == "yarn" and rope_factor > 1.:
        nf = []
        for f in freqs:
            wl = 2 * math.pi / f
            lo, hi = orig_max / (beta_fast * 2.), orig_max / (beta_slow * 2.)
            if wl < lo: nf.append(f)
            elif wl > hi: nf.append(f / rope_factor)
            else:
                sm = (orig_max / (wl * beta_slow) - rope_factor) / (rope_factor * (beta_fast / beta_slow - 1))
                nf.append((1 - sm) * f / rope_factor + sm * f)
        freqs = torch.tensor(nf, dtype=torch.float32)
    angles = torch.outer(torch.arange(max_pos, dtype=torch.float32), freqs)
    return torch.cos(angles).to(device), torch.sin(angles).to(device)

def _apply_rope(x, pos, cos, sin, rope_dim, inverse=False):
    T, nh, hd = x.shape
    nope = hd - rope_dim
    c, s = cos[pos].unsqueeze(1), sin[pos].unsqueeze(1)
    xr = x[:, :, nope:].float()
    ev, od = xr[..., 0::2], xr[..., 1::2]
    if inverse: rev, rod = ev*c + od*s, -ev*s + od*c
    else: rev, rod = ev*c - od*s, ev*s + od*c
    out = x.clone()
    ro = torch.empty_like(xr)
    ro[..., 0::2], ro[..., 1::2] = rev, rod
    out[:, :, nope:] = ro.bfloat16()
    return out

# =====================================================================
# Compressor — CSA (ratio=4) and HCA (ratio=128)
# =====================================================================
class Compressor:
    def __init__(self, ratio, head_dim, hidden_size, device):
        self.ratio, self.hd, self.H, self.device = ratio, head_dim, hidden_size, device
        self.is_csa = (ratio == 4)
        self.kv_dim = 2 * head_dim if self.is_csa else head_dim
        self.wkv_w = self.wkv_ws = self.wkv_ws2 = self.wkv_isc = None
        self.wgate_w = self.wgate_ws = self.wgate_ws2 = self.wgate_isc = None
        self.ape = None
        self.kv_norm_w = None

    def load(self, w, pfx):
        self.wkv_w, self.wkv_ws, self.wkv_ws2, self.wkv_isc = get_nvfp4_weight(w, pfx, 'kv_proj')
        self.wgate_w, self.wgate_ws, self.wgate_ws2, self.wgate_isc = get_nvfp4_weight(w, pfx, 'gate_proj')
        self.ape = w.get(f"{pfx}.position_bias")
        self.kv_norm_w = w.get(f"{pfx}.kv_norm.weight")

    def forward(self, hidden_states, positions):
        """Returns (compressed_kv (N,hd) or None, comp_positions (N,) or None, block_bias or None)."""
        if self.ratio == 0 or self.wkv_w is None:
            return None, None, None
        T = hidden_states.shape[0]
        r = self.ratio
        dev = hidden_states.device
        n_complete = T // r
        if n_complete == 0:
            return None, None, None

        # Project
        kv = nvfp4_linear(hidden_states, self.wkv_w.to(dev), self.wkv_ws.to(dev),
                          self.wkv_ws2.to(dev) if self.wkv_ws2 is not None else None,
                          self.wkv_isc.to(dev) if self.wkv_isc is not None else None)
        gate = nvfp4_linear(hidden_states, self.wgate_w.to(dev), self.wgate_ws.to(dev),
                            self.wgate_ws2.to(dev) if self.wgate_ws2 is not None else None,
                            self.wgate_isc.to(dev) if self.wgate_isc is not None else None)

        # Add position bias (cyclic per block)
        if self.ape is not None:
            ape = self.ape.to(dev)
            n_full = T // r
            for bi in range(n_full):
                s, e = bi * r, (bi + 1) * r
                kv[s:e] += ape.to(kv.dtype)
                gate[s:e] += ape.to(gate.dtype)
            rem = T % r
            if rem > 0:
                s = n_full * r
                kv[s:] += ape[:rem].to(kv.dtype)
                gate[s:] += ape[:rem].to(gate.dtype)

        T_comp = n_complete * r
        comp_list, comp_pos_list = [], []

        if self.is_csa:
            # Overlapping Ca/Cb: split kv and gate into Ca (first hd) and Cb (second hd)
            Ca = kv[:T_comp, :self.hd].reshape(n_complete, r, self.hd)
            Cb = kv[:T_comp, self.hd:].reshape(n_complete, r, self.hd)
            Ga = gate[:T_comp, :self.hd].reshape(n_complete, r, self.hd)
            Gb = gate[:T_comp, self.hd:].reshape(n_complete, r, self.hd)

            for bi in range(n_complete):
                if bi > 0:
                    block_kv = torch.cat([Ca[bi-1], Cb[bi]], dim=0)   # (2r, hd)
                    block_gate = torch.cat([Ga[bi-1], Gb[bi]], dim=0)
                else:
                    block_kv = Cb[bi]       # (r, hd) — no previous Ca
                    block_gate = Gb[bi]
                probs = torch.softmax(block_gate.float(), dim=0)
                compressed = (probs * block_kv.float()).sum(0)
                if self.kv_norm_w is not None:
                    nw = self.kv_norm_w.to(dev).float()
                    compressed = compressed * compressed.pow(2).mean(-1, keepdim=True).add(1e-6).rsqrt() * nw
                comp_list.append(compressed.bfloat16())
                comp_pos_list.append(positions[(bi+1)*r - 1])
        else:
            # HCA: non-overlapping, single stream
            kv_blocks = kv[:T_comp].reshape(n_complete, r, self.hd)
            gate_blocks = gate[:T_comp].reshape(n_complete, r, self.hd)
            for bi in range(n_complete):
                probs = torch.softmax(gate_blocks[bi].float(), dim=0)
                compressed = (probs * kv_blocks[bi].float()).sum(0)
                if self.kv_norm_w is not None:
                    nw = self.kv_norm_w.to(dev).float()
                    compressed = compressed * compressed.pow(2).mean(-1, keepdim=True).add(1e-6).rsqrt() * nw
                comp_list.append(compressed.bfloat16())
                comp_pos_list.append(positions[(bi+1)*r - 1])

        compressed_kv = torch.stack(comp_list)
        comp_positions = torch.stack(comp_pos_list)
        # block_bias: causal mask for compressed entries
        N = len(comp_list)
        block_bias = torch.zeros(1, T, N, dtype=torch.float32, device=dev)
        return compressed_kv, comp_positions, block_bias

# =====================================================================
# Indexer — CSA top-k
# =====================================================================
class Indexer:
    def __init__(self, n_ih, ihd, top_k, device):
        self.n_ih, self.ihd, self.top_k, self.device = n_ih, ihd, top_k, device
        self.q_b_w = self.q_b_ws = self.q_b_ws2 = self.q_b_isc = None
        self.wp_w = self.wp_ws = self.wp_ws2 = self.wp_isc = None
        self.compressor = None

    def load(self, w, pfx):
        self.q_b_w, self.q_b_ws, self.q_b_ws2, self.q_b_isc = get_nvfp4_weight(w, pfx, 'q_b_proj')
        self.wp_w, self.wp_ws, self.wp_ws2, self.wp_isc = get_nvfp4_weight(w, pfx, 'weights_proj')
        if f"{pfx}.compressor.kv_proj.weight" in w:
            self.compressor = Compressor(4, self.ihd, 7168, self.device)
            self.compressor.load(w, f"{pfx}.compressor")

    def forward(self, q_lora, hidden_states, comp_indexer_kv, positions):
        if self.q_b_w is None or comp_indexer_kv is None or comp_indexer_kv.shape[0] == 0:
            return None
        dev = q_lora.device
        T = q_lora.shape[0]
        n_comp = comp_indexer_kv.shape[0]
        q_idx = nvfp4_linear(q_lora, self.q_b_w.to(dev), self.q_b_ws.to(dev),
                             self.q_b_ws2.to(dev) if self.q_b_ws2 is not None else None,
                             self.q_b_isc.to(dev) if self.q_b_isc is not None else None)
        q_idx = q_idx.reshape(T, self.n_ih, self.ihd)
        w_h = nvfp4_linear(hidden_states, self.wp_w.to(dev), self.wp_ws.to(dev),
                           self.wp_ws2.to(dev) if self.wp_ws2 is not None else None,
                           self.wp_isc.to(dev) if self.wp_isc is not None else None)
        k_idx = comp_indexer_kv.reshape(n_comp, self.n_ih, self.ihd)
        scores = torch.einsum('tnd,cnd->tnc', q_idx.float(), k_idx.float())
        scores = F.relu(scores)
        total = (scores * w_h.unsqueeze(-1).float()).sum(1)
        tk = min(self.top_k, n_comp)
        _, idx = total.topk(tk, -1)
        return idx

# =====================================================================
# KV Cache
# =====================================================================
class KVCache:
    def __init__(self, head_dim, window_size=128, device='cuda:0'):
        self.hd, self.ws, self.dev = head_dim, window_size, device
        self.swa = torch.zeros(window_size, head_dim, dtype=torch.bfloat16, device=device)
        self.swa_pos = torch.zeros(window_size, dtype=torch.long, device=device)
        self.swa_len, self.swa_head = 0, 0
        self.comp_kv, self.comp_pos, self.n_comp = None, None, 0
        self.comp_idx_kv = None

    def append_swa(self, kv, pos):
        T = kv.shape[0]
        for i in range(T):
            idx = (self.swa_head + i) % self.ws
            self.swa[idx], self.swa_pos[idx] = kv[i], pos[i]
        self.swa_head = (self.swa_head + T) % self.ws
        self.swa_len = min(self.swa_len + T, self.ws)

    def add_compressed(self, ckv, cpos, idx_kv=None):
        if ckv is None: return
        self.comp_kv = ckv if self.comp_kv is None else torch.cat([self.comp_kv, ckv])
        self.comp_pos = cpos if self.comp_pos is None else torch.cat([self.comp_pos, cpos])
        self.n_comp = self.comp_kv.shape[0]
        if idx_kv is not None:
            self.comp_idx_kv = idx_kv if self.comp_idx_kv is None else torch.cat([self.comp_idx_kv, idx_kv])

    def get_swa(self):
        if self.swa_len == 0:
            return torch.zeros(0, self.hd, device=self.dev, dtype=torch.bfloat16), \
                   torch.zeros(0, device=self.dev, dtype=torch.long)
        if self.swa_len < self.ws:
            return self.swa[:self.swa_len].clone(), self.swa_pos[:self.swa_len].clone()
        idx = torch.arange(self.swa_head, self.swa_head + self.ws) % self.ws
        return self.swa[idx].clone(), self.swa_pos[idx].clone()

# =====================================================================
# Weight loading
# =====================================================================
def load_weights(checkpoint_dir):
    from safetensors.torch import load_file
    cdir = Path(checkpoint_dir)
    wmap = {}
    idx = cdir / "model.safetensors.index.json"
    if idx.exists():
        with open(idx) as f: wmap = json.load(f).get("weight_map", {})
    shards = set(wmap.values()) if wmap else set()
    all_w = {}
    for sn in sorted(shards):
        if (cdir / sn).exists():
            all_w.update(load_file(str(cdir / sn)))
    print(f"Loaded {len(all_w)} tensors from {len(shards)} shards")
    return all_w

def cache_layer_weights(all_w, n_layers, devices):
    cached = {}
    for li in range(n_layers):
        dev = devices[li % len(devices)]
        pfx = f"model.layers.{li}."
        w = {k: v.to(device=dev, non_blocking=True) for k, v in all_w.items() if k.startswith(pfx)}
        cached[li] = w
        if (li+1) % 10 == 0: print(f"  Cached {li+1}/{n_layers} layers")
    return cached

# =====================================================================
# Attention forward
# =====================================================================
def forward_attention(x_normed, w, li, cfg, rope_cos, rope_sin,
                      kv_cache, positions, compressor, indexer):
    dev = x_normed.device
    T = x_normed.shape[0]
    n_h = cfg["num_attention_heads"]
    hd = cfg["head_dim"]
    rd = cfg.get("qk_rope_head_dim", 64)
    o_groups = cfg.get("o_groups", 16)
    o_rank = cfg.get("o_lora_rank", 1024)
    ratio = compressor.ratio if compressor is not None else 0
    scale = 1.0 / math.sqrt(hd)
    pfx = f"model.layers.{li}.self_attn"
    # Ensure positions is on the same device as rope caches
    if positions.device != rope_cos.device:
        positions = positions.to(rope_cos.device)

    # 1. Q projection: q_a → q_a_norm → q_b → q_b_norm
    q_a = do_nvfp4_linear(x_normed, w, pfx, 'q_a_proj')
    if q_a is None:
        print(f"  WARNING L{li}: q_a_proj not found, keys: {[k for k in w if 'q_a' in k and f'layers.{li}' in k][:5]}")
        return torch.zeros(T, cfg["hidden_size"], dtype=torch.bfloat16, device=dev), None
    if VERBOSE >= 2: print(f"  L{li} q_a: |max|={q_a.abs().max().item():.4f} shape={q_a.shape}")
    q_norm_w = w.get(f"{pfx}.q_a_norm.weight")
    if q_norm_w is not None: q_a = rmsnorm(q_a, q_norm_w.to(dev, torch.float32))
    q = do_nvfp4_linear(q_a, w, pfx, 'q_b_proj')
    q = unweighted_rmsnorm(q).bfloat16()
    q_heads = q.reshape(T, n_h, hd)
    q_heads = _apply_rope(q_heads, positions, rope_cos, rope_sin, rd)

    # 2. KV projection (MQA, single KV head, hd dim)
    kv = do_nvfp4_linear(x_normed, w, pfx, 'kv_proj')
    if kv is None:
        print(f"  WARNING L{li}: kv_proj not found, keys: {[k for k in w if 'kv_proj' in k and f'layers.{li}' in k][:5]}")
        return torch.zeros(T, cfg["hidden_size"], dtype=torch.bfloat16, device=dev), q_a
    kv_norm_w = w.get(f"{pfx}.kv_norm.weight")
    if kv_norm_w is not None: kv = rmsnorm(kv, kv_norm_w.to(dev, torch.float32))
    kv_3d = kv.reshape(T, 1, hd)
    kv_3d = _apply_rope(kv_3d, positions, rope_cos, rope_sin, rd)
    kv_roped = kv_3d.reshape(T, hd)
    kv_cache.append_swa(kv_roped, positions)

    # 3. Compressor → compressed KV (dim = hd)
    comp_kv, comp_pos, block_bias = None, None, None
    comp_idx_kv = None
    if compressor is not None and compressor.ratio > 0:
        comp_kv, comp_pos, block_bias = compressor.forward(x_normed, positions)
        if comp_kv is not None:
            comp_kv_3d = comp_kv.unsqueeze(1)
            comp_kv_3d = _apply_rope(comp_kv_3d, comp_pos, rope_cos, rope_sin, rd)
            comp_kv = comp_kv_3d.squeeze(1)
        if compressor.is_csa and indexer is not None and indexer.compressor is not None:
            comp_idx_kv, _, _ = indexer.compressor.forward(x_normed, positions)
        kv_cache.add_compressed(comp_kv, comp_pos, comp_idx_kv)

    # 4. Indexer top-k (CSA only)
    topk_idx = None
    if indexer is not None and ratio == 4:
        topk_idx = indexer.forward(q_a, x_normed, kv_cache.comp_idx_kv, positions)

    # 5. Gather full KV: [compressed, swa]
    swa_kv, swa_pos = kv_cache.get_swa()
    swa_len = swa_kv.shape[0]
    if kv_cache.comp_kv is not None and kv_cache.n_comp > 0:
        if ratio == 4 and topk_idx is not None:
            tk = topk_idx[0].clamp(0, kv_cache.n_comp - 1)
            sel_comp = kv_cache.comp_kv[tk]
            all_kv = torch.cat([sel_comp, swa_kv], dim=0)
        elif ratio > 4:
            all_kv = torch.cat([kv_cache.comp_kv, swa_kv], dim=0)
        else:
            all_kv = swa_kv
    else:
        all_kv = swa_kv

    seq_len = all_kv.shape[0]
    if seq_len == 0:
        return torch.zeros(T, cfg["hidden_size"], dtype=torch.bfloat16, device=dev), q_a

    # 6. SDPA with sinks
    k_exp = all_kv.unsqueeze(0).expand(n_h, -1, -1).contiguous()
    v_exp = k_exp.clone()
    q_in = q_heads.permute(1, 0, 2)
    scores = torch.matmul(q_in, k_exp.transpose(-1, -2)) * scale
    sinks = w.get(f"{pfx}.sinks")
    if sinks is not None:
        sinks = sinks.to(device=dev)
        sink_logits = sinks.float().reshape(n_h, 1, 1).expand(-1, T, 1)
        combined = torch.cat([scores, sink_logits], dim=-1)
        combined = combined - combined.max(-1, keepdim=True).values
        probs = torch.softmax(combined.float(), -1).bfloat16()
        attn_w = probs[..., :-1]
    else:
        attn_w = torch.softmax(scores.float(), -1).bfloat16()

    attn_out = torch.matmul(attn_w, v_exp).permute(1, 0, 2)

    # 7. Inverse RoPE
    attn_out = _apply_rope(attn_out, positions, rope_cos, rope_sin, rd, inverse=True)

    # 8. Output projection: wo_a (BF16 grouped BMM) + wo_b (NVFP4)
    hpg = n_h // o_groups
    gid = hpg * hd
    oa_w = w.get(f"{pfx}.o_a_proj.weight")
    if oa_w is not None:
        oa_bf = oa_w.bfloat16().to(dev)
        a_flat = attn_out.reshape(T, n_h * hd)
        a_grp = a_flat.reshape(T, o_groups, gid)
        oa_3d = oa_bf.reshape(o_groups, o_rank, gid)
        g_out = torch.bmm(a_grp.permute(1, 0, 2), oa_3d.transpose(1, 2))
        g_flat = g_out.permute(1, 0, 2).reshape(T, o_groups * o_rank)
        F_attn = do_nvfp4_linear(g_flat, w, pfx, 'o_b_proj')
    else:
        F_attn = do_nvfp4_linear(attn_out.reshape(T, n_h * hd), w, pfx, 'o_a_proj')
    return F_attn, q_a

# =====================================================================
# MoE forward
# =====================================================================
def moe_forward(x, w, li, cfg, token_id, device):
    H = cfg["hidden_size"]
    n_e = cfg["n_routed_experts"]
    top_k = cfg.get("num_experts_per_tok", 6)
    rsc = cfg.get("routed_scaling_factor", 2.5)
    lim = cfg.get("swiglu_limit", 10.0)
    num_hash = cfg.get("num_hash_layers", 3)
    pfx = f"model.layers.{li}.mlp"

    # Routing
    tid2eid_key = f"{pfx}.gate.tid2eid"
    e_bias_key = f"{pfx}.gate.e_score_correction_bias"
    is_hash = (li < num_hash) and (tid2eid_key in w)

    if is_hash:
        tid2eid = w[tid2eid_key]
        tid = token_id.item() if token_id.numel() == 1 else token_id[0].item()
        expert_ids = tid2eid[tid]
        expert_weights = torch.ones(top_k, dtype=torch.float32, device=x.device) / top_k
    else:
        # Gate weight may be BF16 or NVFP4
        gate_ww, gate_ws, gate_ws2, gate_isc = get_nvfp4_weight(w, pfx, 'gate')
        if gate_ww is not None and gate_ws is not None:
            logits = nvfp4_linear(x, gate_ww.to(device), gate_ws.to(device),
                                  gate_ws2.to(device) if gate_ws2 is not None else None,
                                  gate_isc.to(device) if gate_isc is not None else None)
        elif f"{pfx}.gate.weight" in w:
            gw = w[f"{pfx}.gate.weight"].bfloat16().to(device)
            logits = F.linear(x, gw)
        else:
            raise ValueError(f"No gate weight for layer {li}")
        scores = torch.sqrt(F.softplus(logits.float()) + 1e-6)
        sel = scores.clone()
        if e_bias_key in w:
            sel = sel + w[e_bias_key].to(device=x.device).float().unsqueeze(0)
        _, indices = sel.topk(top_k, -1)
        expert_weights = torch.gather(scores, -1, indices)
        expert_weights = expert_weights / expert_weights.sum(-1, keepdim=True)
        expert_ids, expert_weights = indices[0], expert_weights[0]

    # Routed experts
    expert_outs = []
    for i, eid in enumerate(expert_ids):
        ep = f"{pfx}.experts.{eid.item()}"
        g = do_nvfp4_linear(x, w, ep, 'gate_proj')
        u = do_nvfp4_linear(x, w, ep, 'up_proj')
        silu = F.silu(g.float())
        if lim is not None: silu = silu.clamp(-lim, lim); u = u.float().clamp(-lim, lim)
        h = (silu * u).bfloat16()
        expert_outs.append(do_nvfp4_linear(h, w, ep, 'down_proj'))

    routed = torch.zeros_like(x)
    for out, wt in zip(expert_outs, expert_weights):
        routed = routed + (out.float() * wt.item()).bfloat16()
    routed = (routed.float() * rsc).bfloat16()

    # Shared expert
    sp = f"{pfx}.shared_experts"
    sg = do_nvfp4_linear(x, w, sp, 'gate_proj')
    su = do_nvfp4_linear(x, w, sp, 'up_proj')
    silu = F.silu(sg.float())
    if lim is not None: silu = silu.clamp(-lim, lim); su = su.float().clamp(-lim, lim)
    shared = do_nvfp4_linear((silu * su).bfloat16(), w, sp, 'down_proj')
    return routed + shared

# =====================================================================
# Layer forward
# =====================================================================
def forward_layer(X_l, w, li, cfg, rope_cos, rope_sin,
                  attn_mhc, ffn_mhc, attn_norm_w, ffn_norm_w,
                  kv_cache, positions, token_id,
                  compressor=None, indexer=None):
    dev = X_l.device
    # Attention sub-block
    x_in, ctx_a = attn_mhc.pre_block(X_l)
    x_normed = rmsnorm(x_in, attn_norm_w)
    F_attn, _ = forward_attention(x_normed, w, li, cfg, rope_cos, rope_sin,
                                   kv_cache, positions, compressor, indexer)
    X_mid = attn_mhc.post_block(X_l, F_attn, ctx_a)
    # FFN sub-block
    x_in_f, ctx_f = ffn_mhc.pre_block(X_mid)
    x_ffn = rmsnorm(x_in_f, ffn_norm_w)
    F_ffn = moe_forward(x_ffn, w, li, cfg, token_id, dev)
    X_next = ffn_mhc.post_block(X_mid, F_ffn, ctx_f)
    if GROWTH_DIAG:
        print(f"  L{li}: |X|={X_l.abs().max().item():.1f}→{X_next.abs().max().item():.1f} "
              f"|Fa|={F_attn.abs().max().item():.1f} |Ff|={F_ffn.abs().max().item():.1f}", flush=True)
    return X_next

# =====================================================================
# Main
# =====================================================================
def main():
    t0 = time.time()
    torch.manual_seed(SEED)
    print("=" * 70)
    print("DSV4 Single-Shot Inference — Full E2E Pipeline")
    print("  NVFP4 two-level scale | Compressor + Indexer | mHC | MoE")
    print("=" * 70)

    with open(os.path.join(CHECKPOINT_DIR, "config.json")) as f:
        cfg = json.load(f)
    n_layers = cfg["num_hidden_layers"]
    H = cfg["hidden_size"]
    hd = cfg["head_dim"]
    rd = cfg.get("qk_rope_head_dim", 64)
    cr = cfg.get("compress_ratios", [128] * 61)
    print(f"Model: {n_layers} layers, {cfg['num_attention_heads']} heads, hd={hd}, rope_dim={rd}")
    print(f"Compress ratios: first5={cr[:5]} len={len(cr)}")
    print(f"Experts: {cfg['n_routed_experts']}, top-{cfg.get('num_experts_per_tok', 6)}")

    # Load weights
    print(f"\nPhase 1: Loading weights...")
    all_w = load_weights(CHECKPOINT_DIR)
    print(f"  {time.time()-t0:.1f}s")

    # mHC + norms
    print("Building mHC blocks and norms...")
    attn_mhcs, ffn_mhcs, attn_norms, ffn_norms = {}, {}, {}, {}
    for li in range(n_layers):
        dev = f"cuda:{li % NUM_GPUS}"
        for tag, blocks, fn_s, base_s, scale_s in [
            ("attn", attn_mhcs, f"model.layers.{li}.attn_hc.fn",
             f"model.layers.{li}.attn_hc.base", f"model.layers.{li}.attn_hc.scale"),
            ("ffn", ffn_mhcs, f"model.layers.{li}.ffn_hc.fn",
             f"model.layers.{li}.ffn_hc.base", f"model.layers.{li}.ffn_hc.scale"),
        ]:
            fn, base, scale = all_w.get(fn_s), all_w.get(base_s), all_w.get(scale_s)
            if fn is not None and base is not None and scale is not None:
                m = mHCBlock(H, 4, 20, dev)
                m.load(fn.to(dev, torch.float32), base.to(dev, torch.float32), scale.to(dev, torch.float32))
                blocks[li] = m
            else:
                print(f"  WARNING: no mHC for L{li} {tag}")

        an_k = f"model.layers.{li}.input_layernorm.weight"
        if an_k in all_w: attn_norms[li] = all_w[an_k].to(dev, torch.float32)
        fn_k = f"model.layers.{li}.post_attention_layernorm.weight"
        if fn_k in all_w: ffn_norms[li] = all_w[fn_k].to(dev, torch.float32)

    # Global weights
    torch.cuda.set_device(0)
    embed_w = all_w.get("model.embed_tokens.weight")
    embed = torch.nn.Embedding.from_pretrained(embed_w.bfloat16().to('cuda:0'))
    lm_w = all_w.get("lm_head.weight", embed_w).bfloat16().to('cuda:0')
    final_norm_w = all_w.get("model.norm.weight")
    if final_norm_w is not None: final_norm_w = final_norm_w.to('cuda:0', torch.float32)

    hc_head = HcHead(H, 4, 'cuda:0')
    hc_fn = all_w.get("model.hc_head.hc_fn")
    hc_base = all_w.get("model.hc_head.hc_base")
    hc_scale = all_w.get("model.hc_head.hc_scale")
    if hc_fn is not None and hc_base is not None:
        hc_head.load(hc_fn, hc_base, hc_scale)
        print("  hc_head loaded")
    else:
        print("  WARNING: hc_head not found")
        hc_head = None

    # RoPE
    rp = cfg.get("rope_scaling", cfg.get("rope_parameters", {}))
    rt = rp.get("type", rp.get("rope_type", "yarn"))
    rf = rp.get("factor", 16.0)
    rtheta = cfg.get("rope_theta", 10000.)
    romax = rp.get("original_max_position_embeddings", 65536)
    rbfast, rbslow = rp.get("beta_fast", 32), rp.get("beta_slow", 1)
    print(f"RoPE: {rt} factor={rf} theta={rtheta} orig_max={romax}")
    rope_caches = {g: build_rope_cache(8192, rd, f"cuda:{g}", rtheta, rt, rf, romax, rbfast, rbslow)
                   for g in range(NUM_GPUS)}

    # KV caches
    kv_caches = {li: KVCache(hd, cfg.get("sliding_window", 128), f"cuda:{li % NUM_GPUS}")
                 for li in range(n_layers)}

    # Compressors + indexers
    compressors, indexers = {}, {}
    n_ih = cfg.get("index_n_heads", 64)
    ihd = cfg.get("index_head_dim", 128)
    itk = cfg.get("index_topk", 1024)
    for li in range(n_layers):
        dev = f"cuda:{li % NUM_GPUS}"
        ratio = cr[li] if li < len(cr) else 128
        if ratio > 0: compressors[li] = Compressor(ratio, hd, H, dev)
        if ratio == 4: indexers[li] = Indexer(n_ih, ihd, itk, dev)

    # Cache layer weights to GPUs
    print("Caching layer weights to GPUs...")
    devs = [f"cuda:{g}" for g in range(NUM_GPUS)]
    layer_w = cache_layer_weights(all_w, n_layers, devs)
    del all_w; import gc; gc.collect()
    print(f"  {time.time()-t0:.1f}s")

    # Load compressor/indexer weights
    for li in range(n_layers):
        pfx = f"model.layers.{li}.self_attn.compressor"
        if li in compressors: compressors[li].load(layer_w[li], pfx)
        if li in indexers: indexers[li].load(layer_w[li], f"{pfx}.indexer")
    print("  Compressors/indexers loaded")

    # Phase 2: Inference
    print(f"\nPhase 2: Inference")
    from transformers import AutoTokenizer
    tokenizer = AutoTokenizer.from_pretrained(CHECKPOINT_DIR)

    bos = tokenizer.bos_token_id or 0
    input_ids = [bos, USER_TOKEN]
    input_ids += tokenizer.encode('\n\n' + PROMPT, add_special_tokens=False)
    input_ids.append(ASSISTANT_TOKEN)
    generated = input_ids.copy()
    print(f"Input: {len(generated)} tokens")

    # Prefill
    print(f"Prefilling {len(generated)} tokens...")
    for pi, tid_val in enumerate(generated):
        t1 = time.time()
        tid = torch.tensor([tid_val], dtype=torch.long, device='cuda:0')
        pos = torch.tensor([pi], dtype=torch.long, device='cuda:0')
        X = mHCBlock.init_state(embed(tid))
        for li in range(n_layers):
            gpu = li % NUM_GPUS
            if X.device != torch.device(f"cuda:{gpu}"): X = X.to(f"cuda:{gpu}")
            torch.cuda.set_device(gpu)
            X = forward_layer(X, layer_w[li], li, cfg, *rope_caches[gpu],
                              attn_mhcs.get(li), ffn_mhcs.get(li),
                              attn_norms.get(li), ffn_norms.get(li),
                              kv_caches[li], pos, tid,
                              compressors.get(li), indexers.get(li))
        X = X.to('cuda:0'); torch.cuda.set_device(0)
        if pi % 10 == 0: print(f"  Token {pi}/{len(generated)}: {time.time()-t1:.2f}s", flush=True)
    print(f"  Prefill done ({time.time()-t0:.1f}s)")

    # Decode
    print(f"\nDecoding (max {MAX_NEW_TOKENS} tokens)...")
    all_tokens = generated.copy()
    for step in range(MAX_NEW_TOKENS):
        t1 = time.time()
        tid = torch.tensor([all_tokens[-1]], dtype=torch.long, device='cuda:0')
        dec_pos = torch.tensor([len(all_tokens)-1], dtype=torch.long, device='cuda:0')
        X = mHCBlock.init_state(embed(tid))
        for li in range(n_layers):
            gpu = li % NUM_GPUS
            if X.device != torch.device(f"cuda:{gpu}"): X = X.to(f"cuda:{gpu}")
            torch.cuda.set_device(gpu)
            X = forward_layer(X, layer_w[li], li, cfg, *rope_caches[gpu],
                              attn_mhcs.get(li), ffn_mhcs.get(li),
                              attn_norms.get(li), ffn_norms.get(li),
                              kv_caches[li], dec_pos, tid,
                              compressors.get(li), indexers.get(li))
        X = X.to('cuda:0'); torch.cuda.set_device(0)
        x_out = hc_head.forward(X) if hc_head is not None else X[:, 0, :]
        if final_norm_w is not None: x_out = rmsnorm(x_out, final_norm_w)
        logits = F.linear(x_out, lm_w)
        next_id = torch.argmax(logits, -1).item()
        all_tokens.append(next_id)
        dt = time.time() - t1
        has_nan = torch.isnan(logits.float()).any().item()
        if step % 5 == 0 or has_nan:
            tv, ti = torch.topk(logits[0], 5)
            top5 = ' '.join(f'{tokenizer.decode([t.item()])}({v.item():.1f})'
                            for t, v in zip(ti[:5], tv[:5]))
            print(f"  Step {step}: {next_id} '{tokenizer.decode([next_id])}' ({dt:.2f}s) "
                  f"logits=[{logits.float().min().item():.1f},{logits.float().max().item():.1f}] "
                  f"nan={has_nan} |X|={X.abs().max().item():.1f} top5: {top5}", flush=True)
        if has_nan: break
        if next_id == tokenizer.eos_token_id: break

    out = tokenizer.decode(all_tokens, skip_special_tokens=True)
    print(f"\n{'='*70}")
    print(f"Input: '{PROMPT}'")
    print(f"Output: '{out}'")
    print(f"Total: {time.time()-t0:.1f}s")
    print(f"{'='*70}")

if __name__ == "__main__":
    main()