573 lines
28 KiB
Python
573 lines
28 KiB
Python
#!/usr/bin/env python3
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"""Production FMHA layer comparison test — DECODE phase.
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The key difference from test_production_fmha_layer.py:
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- That test checks FMHA cos during PREFILL (or with random Q after prefill)
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- This test checks FMHA cos during the FIRST DECODE STEP
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Why this matters:
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During decode, the KV cache has compressed entries (CSA/HCA) + SWA window.
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The CSA path uses indexer top-k to select which compressed entries to attend to.
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The HCA path gathers ALL compressed entries. The SWA-only path has no compression.
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If the per-layer cos is 0.999993 during prefill but drops during decode,
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the bug is in the decode-time KV gathering or compressed/SWA parity.
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Strategy:
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1. Run full production pipeline (single_shot_inference.py forward_layer)
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for ALL prefill tokens through layers 0-4, populating KV caches.
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2. Run the FIRST decode token through forward_layer, but capture the
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production FMHA inputs (q_heads, gathered KV) at each layer.
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3. For each layer, ALSO run reference FMHA (dequantize KV to BF16, PyTorch SDPA)
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on the SAME gathered KV that the production kernel saw.
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4. Compare raw FMHA output (before inverse RoPE, before output projection).
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Production values: HD=512, NOPE=448, ROPE=64, H=128, 61 layers, 8 GPUs.
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"""
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import os, sys, json, math, time
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import torch
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import torch.nn.functional as F
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CHECKPOINT_DIR = os.environ.get(
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"CHECKPOINT_DIR", "/root/nvidia-meeting/DeepSeek-V4-Pro-NVFP4")
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NUM_GPUS = int(os.environ.get("NUM_GPUS", "8"))
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DEVICE = "cuda:0"
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# How many layers to test (first N layers)
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TEST_LAYERS = int(os.environ.get("TEST_LAYERS", "5"))
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def cosine(a, b):
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return F.cosine_similarity(a.flatten().float(), b.flatten().float(), dim=0).item()
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def main():
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torch.manual_seed(42)
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print("=" * 70)
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print("DECODE FMHA LAYER COMPARISON TEST")
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print("Tests FMHA accuracy during DECODE (not prefill)")
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print("=" * 70)
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with open(os.path.join(CHECKPOINT_DIR, "config.json")) as f:
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cfg = json.load(f)
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n_layers = cfg["num_hidden_layers"]
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H = cfg["hidden_size"]
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hd = cfg["head_dim"]
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n_h = cfg["num_attention_heads"]
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rd = cfg.get("qk_rope_head_dim", 64)
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nope_dim = hd - rd
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cr = cfg.get("compress_ratios", [128] * n_layers)
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print(f"Model: {n_layers} layers, {n_h} heads, hd={hd}, rope_dim={rd}, nope_dim={nope_dim}")
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print(f"Compress ratios (first {TEST_LAYERS}): {cr[:TEST_LAYERS]}")
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# Import from single_shot_inference.py
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from single_shot_inference import (
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load_all_weights, make_nvfp4_linear, get_nvfp4_weight,
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rmsnorm, unweighted_rmsnorm, _apply_rope, build_rope_cache,
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KVCache, Compressor, Indexer, forward_layer, moe_forward,
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_load_moe_weights_stacked, _load_shared_expert_weights,
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_cache_layer_weights_no_experts,
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)
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from dsv4.layers.mhc import mHCLayer, mHCContext
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from dsv4.layers.router import Router
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from dsv4.layers.moe import Nvfp4MoE
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from dsv4.layers.shared_expert import Nvfp4SharedExpert
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from dsv4.layers.grouped_linear import Nvfp4GroupedLinear
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from dsv4.layers.linear import Nvfp4Linear
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from dsv4.ops.quantize import (
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rmsnorm_quantize_nvfp4, mhc_rmsnorm_quantize_nvfp4, dequantize_nvfp4,
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quantize_to_nvfp4,
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)
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print("Loading weights...")
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all_w = load_all_weights(CHECKPOINT_DIR)
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o_groups = cfg.get("o_groups", 16)
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o_rank = cfg.get("o_lora_rank", 1024)
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n_ih = cfg.get("index_n_heads", 64)
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ihd = cfg.get("index_head_dim", 128)
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itk = cfg.get("index_topk", 1024)
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rope_caches = {g: build_rope_cache(65536, rd, f"cuda:{g}", 10000., "yarn", 16., 4096, 32, 1)
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for g in range(NUM_GPUS)}
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# Build all production components
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prod_lins, attn_mhcs, ffn_mhcs = {}, {}, {}
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attn_norms, ffn_norms = {}, {}
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compressors, indexers, kv_caches = {}, {}, {}
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routers, moe_runners, se_runners = {}, {}, {}
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for li in range(TEST_LAYERS):
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gpu = li % NUM_GPUS
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dev = f"cuda:{gpu}"
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torch.cuda.set_device(gpu)
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pfx = f"model.layers.{li}.self_attn"
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mlp_pfx = f"model.layers.{li}.mlp"
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ratio = cr[li] if li < len(cr) else 128
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# Attention linears
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pl = {}
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pl['q_a'] = make_nvfp4_linear(H, 1536, dev, all_w, pfx, 'q_a_proj')
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pl['q_b'] = make_nvfp4_linear(1536, H * hd, dev, all_w, pfx, 'q_b_proj')
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pl['kv'] = make_nvfp4_linear(H, hd, dev, all_w, pfx, 'kv_proj')
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hpg = n_h // o_groups
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wo_a = Nvfp4GroupedLinear(n_local_groups=o_groups, heads_per_group=hpg,
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head_dim=hd, o_lora_rank=o_rank, max_num_tokens=8192, device=dev)
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oa_w, oa_ws, oa_ws2, oa_isc = get_nvfp4_weight(all_w, pfx, 'o_a_proj')
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if oa_w is not None and oa_ws is not None:
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wo_a.load_nvfp4_weight(oa_w.to(dev), oa_ws.to(dev),
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oa_ws2.to(dev) if oa_ws2 is not None else None,
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oa_isc.to(dev) if oa_isc is not None else None)
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else:
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oa_bf = all_w.get(f"{pfx}.o_a_proj.weight")
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if oa_bf is not None:
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wo_a.set_bf16_weight(oa_bf.bfloat16().to(dev))
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pl['o_a'] = wo_a; wo_a._use_runtime_gsa = True
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pl['o_b'] = make_nvfp4_linear(o_groups * o_rank, H, dev, all_w, pfx, 'o_b_proj')
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prod_lins[li] = pl
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# mHC
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for tag, blocks, fn_s, base_s, scale_s in [
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("attn", attn_mhcs, f"model.layers.{li}.attn_hc.fn",
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f"model.layers.{li}.attn_hc.base", f"model.layers.{li}.attn_hc.scale"),
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("ffn", ffn_mhcs, f"model.layers.{li}.ffn_hc.fn",
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f"model.layers.{li}.ffn_hc.base", f"model.layers.{li}.ffn_hc.scale"),
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]:
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fn, base, scale = all_w.get(fn_s), all_w.get(base_s), all_w.get(scale_s)
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if fn is not None and base is not None and scale is not None:
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m = mHCLayer(hidden_dim=H, n_hc=4, t_max_sinkhorn=20, device=dev)
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n = 4
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m.load_weights(
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W_pre=fn[0:n].to(dev, torch.float32), W_post=fn[n:2*n].to(dev, torch.float32),
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W_comb=fn[2*n:].to(dev, torch.float32),
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S_pre=base[0:n].reshape(1, n).to(dev, torch.float32),
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S_post=base[n:2*n].reshape(n, 1).to(dev, torch.float32),
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S_comb=base[2*n:].reshape(n, n).to(dev, torch.float32),
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alpha_pre=scale[0].item(), alpha_post=scale[1].item(), alpha_comb=scale[2].item())
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blocks[li] = m
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an_k = f"model.layers.{li}.input_layernorm.weight"
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if an_k in all_w: attn_norms[li] = all_w[an_k].to(dev, torch.float32)
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fn_k = f"model.layers.{li}.post_attention_layernorm.weight"
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if fn_k in all_w: ffn_norms[li] = all_w[fn_k].to(dev, torch.float32)
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max_comp = (8192 + ratio - 1) // ratio if ratio > 0 else 0
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kv_caches[li] = KVCache(hd, cfg.get("sliding_window", 128), max_comp=max_comp,
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device=dev, indexer_key_dim=ihd, compress_ratio=ratio, indexer_top_k=itk, rope_dim=rd)
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if ratio > 0: compressors[li] = Compressor(ratio, hd, H, dev)
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if ratio == 4: indexers[li] = Indexer(n_ih, ihd, itk, dev)
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# Router
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is_hash = (li < cfg.get("num_hash_layers", 3)) and (f"{mlp_pfx}.gate.tid2eid" in all_w)
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router = Router(hidden_size=H, num_experts=cfg["n_routed_experts"],
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top_k=cfg.get("num_experts_per_tok", 6),
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routed_scaling_factor=cfg.get("routed_scaling_factor", 2.5),
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mode="hash" if is_hash else "dense",
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vocab_size=cfg.get("vocab_size", 128000) if is_hash else None, device=dev)
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if is_hash:
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router.load_weights(hash_lut=all_w[f"{mlp_pfx}.gate.tid2eid"].to(dev, torch.int32))
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else:
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eb = all_w.get(f"{mlp_pfx}.gate.e_score_correction_bias")
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gate_w, gate_ws, gate_ws2, gate_isc = get_nvfp4_weight(all_w, mlp_pfx, 'gate')
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E = cfg["n_routed_experts"]
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if gate_w is not None and gate_ws is not None:
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gate_lin = Nvfp4Linear(in_features=H, out_features=E, device=dev)
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gate_lin.fp4 = [gate_w.to(dev).view(torch.float4_e2m1fn_x2) if gate_w.dtype == torch.uint8 else gate_w.to(dev)]
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gate_lin.sf = [gate_ws.to(dev)]
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ws2_v = gate_ws2.float().item() if gate_ws2 is not None else 1.0
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isc_v = gate_isc.float().item() if gate_isc is not None else 1.0/(6.0*448.0)
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gate_lin.gs = [1.0]
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gate_lin.ws2 = [torch.tensor([ws2_v], device=dev, dtype=torch.float32)]
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gate_lin._activation_global_scale = isc_v
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gate_lin._use_runtime_gsa = True
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gate_lin.finalize_weights()
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router.load_nvfp4_gate(gate_lin)
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router.load_weights(e_bias=eb.to(dev, torch.float32))
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else:
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gw = all_w.get(f"{mlp_pfx}.gate.weight")
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if gw is not None:
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g_bf16 = gw if gw.shape == (E, H) else gw.T.contiguous()
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g_bf16 = g_bf16.bfloat16().to(dev)
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g_fp4, g_sf, g_gs = quantize_to_nvfp4(g_bf16)
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gate_lin = Nvfp4Linear(in_features=H, out_features=E, device=dev)
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gate_lin.fp4 = [g_fp4]
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gate_lin.sf = [g_sf]
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gate_lin.gs = [g_gs]
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gate_lin.ws2 = [torch.tensor([g_gs], device=dev, dtype=torch.float32)]
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gate_lin._activation_global_scale = 1.0 / (6.0 * 448.0)
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gate_lin._use_runtime_gsa = True
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gate_lin.finalize_weights()
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router.load_nvfp4_gate(gate_lin)
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router.load_weights(e_bias=eb.to(dev, torch.float32))
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router.finalize_weights(); routers[li] = router
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moe = Nvfp4MoE(num_experts=cfg["n_routed_experts"], hidden_size=H,
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intermediate_size=cfg.get("moe_intermediate_size", 3072),
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top_k=cfg.get("num_experts_per_tok", 6), device=dev)
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moe.set_swiglu_limit(cfg.get("swiglu_limit", 10.0)); moe.set_fused_swiglu(True)
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_load_moe_weights_stacked(all_w, li, mlp_pfx, dev, moe, cfg)
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moe._ensure_stacked(); moe._use_runtime_gsa = True; moe_runners[li] = moe
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se = Nvfp4SharedExpert(hidden_size=H, intermediate_size=cfg.get("moe_intermediate_size", 3072),
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device=dev, swiglu_limit=cfg.get("swiglu_limit", 10.0))
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se.set_fused_swiglu(True)
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_load_shared_expert_weights(all_w, li, mlp_pfx, dev, se, cfg)
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se._ensure_initialized(); se._use_runtime_gsa = True; se_runners[li] = se
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torch.cuda.empty_cache()
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for li in range(TEST_LAYERS):
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pfx = f"model.layers.{li}.self_attn.compressor"
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dev = f"cuda:{li % NUM_GPUS}"
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if li in compressors: compressors[li].load(all_w, pfx, dev=dev)
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if li in indexers: indexers[li].load(all_w, f"{pfx}.indexer", dev=dev)
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print("Components built")
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# Embedding + tokenizer
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(CHECKPOINT_DIR)
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bos = tokenizer.bos_token_id or 0
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USER_TOKEN, ASSISTANT_TOKEN, THINK_START = 128803, 128804, 128821
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input_ids = [bos, USER_TOKEN]
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input_ids += tokenizer.encode('\n\nThe capital of France is', add_special_tokens=False)
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input_ids.append(ASSISTANT_TOKEN)
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input_ids.append(THINK_START)
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print(f"Input: {len(input_ids)} tokens: {input_ids}")
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torch.cuda.set_device(0)
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embed_w = all_w.get("model.embed_tokens.weight")
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embed = torch.nn.Embedding.from_pretrained(embed_w.bfloat16().to(DEVICE))
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devs_list = [f"cuda:{g}" for g in range(NUM_GPUS)]
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layer_w = _cache_layer_weights_no_experts(all_w, TEST_LAYERS, devs_list)
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del all_w; import gc; gc.collect()
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for g in range(NUM_GPUS): torch.cuda.set_device(g); torch.cuda.empty_cache()
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torch.cuda.set_device(0)
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# ================================================================
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# PHASE 1: Run full production pipeline to populate KV caches
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# ================================================================
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print(f"\n{'='*70}")
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print("PHASE 1: Populating KV caches (prefill)")
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print(f"{'='*70}")
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for pi, tid_val in enumerate(input_ids):
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t1 = time.time()
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tid = torch.tensor([tid_val], dtype=torch.long, device=DEVICE)
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pos = torch.tensor([pi], dtype=torch.long, device=DEVICE)
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tid32 = torch.tensor([tid_val], dtype=torch.int32, device=DEVICE)
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X = mHCLayer.init_state(embed(tid))
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for li in range(TEST_LAYERS):
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gpu = li % NUM_GPUS
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if X.device != torch.device(f"cuda:{gpu}"): X = X.to(f"cuda:{gpu}")
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torch.cuda.set_device(gpu)
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X = forward_layer(X, layer_w[li], li, cfg, *rope_caches[gpu],
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attn_mhcs.get(li), ffn_mhcs.get(li), attn_norms.get(li), ffn_norms.get(li),
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kv_caches[li], pos, tid32, compressors.get(li), indexers.get(li),
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moe_runners.get(li), se_runners.get(li), routers.get(li),
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prod_lin=prod_lins.get(li), _use_fused_rmsnorm_quantize=True)
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if pi % 5 == 0:
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print(f" Token {pi}/{len(input_ids)}: {time.time()-t1:.2f}s", flush=True)
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# Print KV cache state after prefill
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print(f"\nKV cache state after prefill ({len(input_ids)} tokens):")
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for li in range(TEST_LAYERS):
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kc = kv_caches[li]
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ratio = cr[li] if li < len(cr) else 128
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print(f" L{li} (ratio={ratio}): n_comp={kc.n_comp} swa_len={kc.swa_len} "
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f"total_KV={kc.n_comp + kc.swa_len}")
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# ================================================================
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# PHASE 2: Run ONE decode step, capturing FMHA inputs/outputs
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# ================================================================
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print(f"\n{'='*70}")
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print("PHASE 2: Decode FMHA comparison per layer")
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print(f"{'='*70}")
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# Use a real next token — the model's own greedy output would require
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# a full forward pass to get logits. Instead, use a reasonable continuation
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# token. For "The capital of France is" → the space token or a letter.
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# Actually, we need to run the FULL decode forward pass (all layers) to get
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# the actual Q at each layer. So we'll intercept inside forward_attention.
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#
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# Approach: duplicate the forward_attention logic, capturing FMHA inputs
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# at each layer, then compare against reference SDPA.
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# First, we need the hidden state X at the decode position.
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# We'll re-run the decode step manually, layer by layer, capturing
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# the production FMHA inputs and comparing against reference.
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# Decode token: use the actual next position
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decode_pos = len(input_ids)
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# Use a common token — the " " (space) token
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decode_tid = tokenizer.encode(" the", add_special_tokens=False)
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if len(decode_tid) > 0:
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decode_tid = decode_tid[0]
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else:
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decode_tid = tokenizer.convert_tokens_to_ids(" ")
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print(f"Decode token: id={decode_tid} pos={decode_pos}")
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# Get initial hidden state from embedding
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dec_tid = torch.tensor([decode_tid], dtype=torch.long, device=DEVICE)
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dec_tid32 = torch.tensor([decode_tid], dtype=torch.int32, device=DEVICE)
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dec_pos = torch.tensor([decode_pos], dtype=torch.long, device=DEVICE)
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X = mHCLayer.init_state(embed(dec_tid))
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print(f"Initial X: shape={tuple(X.shape)} |X|={X.abs().max().item():.4f}")
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results = {}
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for li in range(TEST_LAYERS):
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gpu = li % NUM_GPUS
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dev = f"cuda:{gpu}"
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torch.cuda.set_device(gpu)
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if X.device != torch.device(f"cuda:{gpu}"): X = X.to(dev)
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ratio = cr[li] if li < len(cr) else 128
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kc = kv_caches[li]
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pfx = f"model.layers.{li}.self_attn"
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scale = 1.0 / math.sqrt(hd)
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# ---- mHC pre_block + rmsnorm (same as forward_layer) ----
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attn_mhc = attn_mhcs.get(li)
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ffn_mhc = ffn_mhcs.get(li)
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attn_norm_w = attn_norms.get(li)
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ffn_norm_w = ffn_norms.get(li)
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A_l_a, B_l_a, C_l_a = attn_mhc._dynamic_params(X)
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ctx_a = mHCContext(B_l=B_l_a, C_l=C_l_a)
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# Fused mHC + rmsnorm + NVFP4 quantize (production path)
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x_quant_attn = mhc_rmsnorm_quantize_nvfp4(
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X, A_l_a, attn_norm_w.to(dev, torch.float32))
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x_normed = dequantize_nvfp4(x_quant_attn.x_fp4, x_quant_attn.x_sf, x_quant_attn.gsa)
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# ---- Manually replicate forward_attention to capture FMHA inputs ----
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T = x_normed.shape[0]
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pl = prod_lins[li]
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# 1. Q projection
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q_a = pl['q_a'].run_from_quantized(x_quant_attn)
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q_norm_w = layer_w[li].get(f"{pfx}.q_a_norm.weight")
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if q_norm_w is not None:
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q_a_quant = rmsnorm_quantize_nvfp4(q_a, q_norm_w.to(dev, torch.float32))
|
|
q_a = dequantize_nvfp4(q_a_quant.x_fp4, q_a_quant.x_sf, q_a_quant.gsa)
|
|
q = pl['q_b'].run_from_quantized(q_a_quant)
|
|
else:
|
|
q = pl['q_b'](q_a)
|
|
q = unweighted_rmsnorm(q).bfloat16()
|
|
q_heads = q.reshape(T, n_h, hd)
|
|
q_heads = _apply_rope(q_heads, dec_pos.to(dev), *rope_caches[gpu][:2], rd)
|
|
|
|
# 2. KV projection + cache
|
|
kv = pl['kv'].run_from_quantized(x_quant_attn)
|
|
kv_norm_w = layer_w[li].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, dec_pos.to(dev), *rope_caches[gpu][:2], rd)
|
|
kv_roped = kv_3d.reshape(T, hd)
|
|
kc.append_swa(kv_roped, dec_pos.to(dev))
|
|
|
|
# 3. Compressor → compressed KV
|
|
compressor = compressors.get(li)
|
|
indexer = indexers.get(li)
|
|
comp_pos, block_bias = None, None
|
|
if compressor is not None and compressor.ratio > 0:
|
|
comp_kv_fp32, comp_pos, block_bias = compressor.forward(x_normed, dec_pos.to(dev))
|
|
if comp_kv_fp32 is not None:
|
|
from dsv4.kernels.cuda.loader import get_cuda_module
|
|
kv_mod = get_cuda_module("kv_quantize", ["kv_quantize.cu"])
|
|
nope_fp32 = comp_kv_fp32[:, :nope_dim].contiguous()
|
|
rope_bf16 = comp_kv_fp32[:, nope_dim:].bfloat16().contiguous()
|
|
rope_3d = rope_bf16.unsqueeze(1)
|
|
rope_3d = _apply_rope(rope_3d, comp_pos, *rope_caches[gpu][:2], rd)
|
|
rope_bf16 = rope_3d.squeeze(1)
|
|
nope_fp8, nope_scale = kv_mod.quantize_fp8_e4m3_from_fp32(nope_fp32)
|
|
kc.set_compressed_mixed(nope_fp8, nope_scale, rope_bf16, comp_pos)
|
|
if compressor.is_csa and indexer is not None and indexer.compressor is not None:
|
|
comp_idx_kv, _, _ = indexer.compressor.forward(x_normed, dec_pos.to(dev))
|
|
kc.set_indexer_keys_fp8(comp_idx_kv)
|
|
|
|
# 4. Indexer top-k (CSA layers)
|
|
topk_idx = None
|
|
if indexer is not None and ratio == 4:
|
|
topk_idx = indexer.forward(q_a, x_normed, kc, dec_pos.to(dev), layer_idx=li)
|
|
if topk_idx is not None:
|
|
print(f" L{li} CSA: indexer topk shape={tuple(topk_idx.shape)} "
|
|
f"range=[{topk_idx.min().item()}, {topk_idx.max().item()}] "
|
|
f"n_comp={kc.n_comp}", flush=True)
|
|
|
|
# 5. Gather KV — same logic as forward_attention
|
|
swa_kv, _swa_pos = kc.get_swa()
|
|
swa_len = swa_kv.shape[0]
|
|
|
|
if kc.n_comp > 0:
|
|
if ratio == 4:
|
|
# CSA: gather top-k compressed rows
|
|
assert topk_idx is not None, f"CSA layer {li}: indexer returned no top-k"
|
|
tk = topk_idx[0].clamp(0, kc.n_comp - 1).int()
|
|
kv_nope_fp8, kv_nope_scale, kv_rope_bf16 = kc.gather_mixed_selective(tk)
|
|
gather_mode = f"CSA top-k ({tk.numel()} comp + {swa_len} SWA)"
|
|
elif ratio > 4:
|
|
kv_nope_fp8, kv_nope_scale, kv_rope_bf16 = kc.gather_mixed_all()
|
|
gather_mode = f"HCA all ({kc.n_comp} comp + {swa_len} SWA)"
|
|
else:
|
|
kv_nope_fp8, kv_nope_scale, kv_rope_bf16 = kc.gather_mixed_swa_only()
|
|
gather_mode = f"SWA-only ({swa_len} SWA)"
|
|
else:
|
|
kv_nope_fp8, kv_nope_scale, kv_rope_bf16 = kc.gather_mixed_swa_only()
|
|
gather_mode = f"SWA-only ({swa_len} SWA)"
|
|
|
|
seq_len = kv_nope_scale.shape[0]
|
|
if seq_len == 0:
|
|
print(f" L{li}: SKIPPED (seq_len=0)")
|
|
continue
|
|
|
|
print(f" L{li}: {gather_mode} → seq_len={seq_len}", flush=True)
|
|
|
|
# 6. Run production mixed FP8 FMHA
|
|
from dsv4.kernels.attention.fmha_mixed_fp8_op import fmha_mixed_fp8_decode_raw
|
|
q_4d = q_heads.permute(1, 0, 2).unsqueeze(0).contiguous() # (1, n_h, T, hd)
|
|
sinks = layer_w[li].get(f"{pfx}.sinks")
|
|
sink_bias = None
|
|
if sinks is not None:
|
|
sink_bias = sinks.to(device=dev).float().reshape(n_h)
|
|
|
|
try:
|
|
o_prod_4d, lse_prod = fmha_mixed_fp8_decode_raw(
|
|
q_4d, kv_nope_fp8, kv_nope_scale, kv_rope_bf16,
|
|
scale, attn_sink=sink_bias, rope_dim=rd)
|
|
except Exception as e:
|
|
print(f" L{li}: PROD FMHA FAILED: {e}")
|
|
results[li] = {'cos': -1.0, 'error': str(e)}
|
|
continue
|
|
o_prod = o_prod_4d.squeeze(0) # (n_h, T, hd)
|
|
|
|
# 7. Reference: dequantize mixed KV to BF16, run reference with sink bias
|
|
nope_dequant = kv_nope_fp8.view(torch.float8_e4m3fn).float() * kv_nope_scale.unsqueeze(-1).float()
|
|
kv_full = torch.cat([nope_dequant.bfloat16(), kv_rope_bf16], dim=-1) # (N, hd)
|
|
k_4d = kv_full.unsqueeze(0).unsqueeze(0).expand(1, 1, -1, -1) # (1, 1, N, hd)
|
|
v_4d = k_4d.clone()
|
|
if sink_bias is not None:
|
|
# DSV4 sink is denominator-only: O = sum(P*V) / (sum(P) + exp(sb))
|
|
# where P = softmax(QK*scale). The sink has NO V contribution.
|
|
# Reference: compute O_no_sink, then scale by correction factor.
|
|
q_ref = q_4d.float() # (1, H, T, hd)
|
|
k_ref = k_4d.float().expand(1, n_h, -1, -1) # (1, H, N, hd)
|
|
v_ref = v_4d.float().expand(1, n_h, -1, -1) # (1, H, N, hd)
|
|
scores = torch.matmul(q_ref, k_ref.transpose(-2, -1)) * scale # (1, H, T, N)
|
|
# O_no_sink = softmax(scores) @ V
|
|
O_no_sink = F.softmax(scores, dim=-1) @ v_ref # (1, H, T, hd)
|
|
# Correction: O_with_sink = O_no_sink * Z / (Z + exp(sb))
|
|
# Z = sum(exp(scores - max)) per head, but more conveniently:
|
|
# Z / (Z + exp(sb)) = 1 / (1 + exp(sb) / Z) = 1 / (1 + exp(sb - log(Z)))
|
|
# log(Z) = logsumexp(scores)
|
|
lse = torch.logsumexp(scores, dim=-1, keepdim=True) # (1, H, T, 1)
|
|
# sb shape: (n_h,) → (1, n_h, 1, 1)
|
|
sb_4d = sink_bias.reshape(1, n_h, 1, 1)
|
|
correction = 1.0 / (1.0 + torch.exp(sb_4d - lse))
|
|
o_ref_4d = (O_no_sink * correction).bfloat16() # (1, H, T, hd)
|
|
else:
|
|
o_ref_4d = F.scaled_dot_product_attention(q_4d, k_4d, v_4d, scale=scale) # (1, H, T, hd)
|
|
o_ref = o_ref_4d.squeeze(0) # (n_h, T, hd)
|
|
|
|
# 8. Compare
|
|
cos_val = cosine(o_prod, o_ref)
|
|
mag_prod = o_prod.float().abs().max().item()
|
|
mag_ref = o_ref.float().abs().max().item()
|
|
|
|
# Per-head cosine AND magnitude ratio
|
|
o_prod_h = o_prod.float().squeeze(1) # (n_h, hd)
|
|
o_ref_h = o_ref.float().squeeze(1)
|
|
per_head_cos = F.cosine_similarity(o_prod_h, o_ref_h, dim=-1)
|
|
per_head_mag_prod = o_prod_h.abs().max(dim=-1).values # (n_h,)
|
|
per_head_mag_ref = o_ref_h.abs().max(dim=-1).values # (n_h,)
|
|
per_head_mag_ratio = (per_head_mag_prod / (per_head_mag_ref + 1e-8)) # (n_h,)
|
|
min_head = per_head_cos.min().item()
|
|
mean_head = per_head_cos.mean().item()
|
|
worst_heads = per_head_cos.argsort()[:5]
|
|
# Find heads with worst magnitude ratio
|
|
worst_mag = per_head_mag_ratio.sub(1.0).abs().argsort(descending=True)[:5]
|
|
|
|
results[li] = {
|
|
'cos': cos_val, 'mag_prod': mag_prod, 'mag_ref': mag_ref,
|
|
'seq_len': seq_len, 'ratio': ratio, 'gather_mode': gather_mode,
|
|
'n_comp': kc.n_comp, 'swa_len': swa_len,
|
|
'min_head_cos': min_head, 'mean_head_cos': mean_head,
|
|
}
|
|
|
|
status = "PASS" if cos_val >= 0.999 else "FAIL"
|
|
print(f" L{li}: {status} cos={cos_val:.6f} min_head={min_head:.6f} mean_head={mean_head:.6f} "
|
|
f"|prod|={mag_prod:.4f} |ref|={mag_ref:.4f} seq={seq_len} {gather_mode}", flush=True)
|
|
if cos_val < 0.999:
|
|
cos_list = [f'{c:.4f}' for c in per_head_cos[worst_heads].tolist()]
|
|
mag_list = [f'{r:.4f}' for r in per_head_mag_ratio[worst_mag].tolist()]
|
|
print(f" Worst heads (cos): {worst_heads.tolist()} cos={cos_list}")
|
|
print(f" Worst heads (mag): {worst_mag.tolist()} ratio={mag_list}")
|
|
print(f" Mag ratio range: [{per_head_mag_ratio.min().item():.4f}, {per_head_mag_ratio.max().item():.4f}]")
|
|
|
|
# ---- Continue through the rest of the layer (so subsequent layers get correct X) ----
|
|
# Apply inverse RoPE to production output
|
|
attn_out = o_prod.permute(1, 0, 2) # (T, n_h, hd)
|
|
attn_out = _apply_rope(attn_out, dec_pos.to(dev), *rope_caches[gpu][:2], rd, inverse=True)
|
|
|
|
# Output projection
|
|
wo_a_lin = pl.get('o_a')
|
|
if wo_a_lin is not None:
|
|
g_3d = wo_a_lin.run(attn_out)
|
|
g_flat = g_3d.reshape(T, -1)
|
|
F_attn = pl['o_b'](g_flat)
|
|
else:
|
|
hpg_fb = n_h // o_groups; gid_fb = hpg_fb * hd
|
|
oa_full = layer_w[li].get(f"{pfx}.o_a_proj.weight")
|
|
if oa_full is not None:
|
|
oa_bf = oa_full.bfloat16().to(dev); a_flat = attn_out.reshape(T, n_h * hd)
|
|
a_grp = a_flat.reshape(T, o_groups, gid_fb); oa_3d = oa_bf.reshape(o_groups, o_rank, gid_fb)
|
|
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 = pl['o_b'](g_flat)
|
|
else:
|
|
F_attn = torch.zeros(T, H, dtype=torch.bfloat16, device=dev)
|
|
|
|
# mHC post_block
|
|
X_mid = attn_mhc.post_block(X, F_attn, ctx_a)
|
|
|
|
# FFN mHC + MoE
|
|
A_l_f, B_l_f, C_l_f = ffn_mhc._dynamic_params(X_mid)
|
|
ctx_f = mHCContext(B_l=B_l_f, C_l=C_l_f)
|
|
x_quant_ffn = mhc_rmsnorm_quantize_nvfp4(
|
|
X_mid, A_l_f, ffn_norm_w.to(dev, torch.float32))
|
|
x_ffn = dequantize_nvfp4(x_quant_ffn.x_fp4, x_quant_ffn.x_sf, x_quant_ffn.gsa)
|
|
F_ffn = moe_forward(x_ffn, li, moe_runners.get(li), se_runners.get(li),
|
|
routers.get(li), dec_tid32.to(dev))
|
|
X = ffn_mhc.post_block(X_mid, F_ffn, ctx_f)
|
|
|
|
# ================================================================
|
|
# Summary
|
|
# ================================================================
|
|
print(f"\n{'='*70}")
|
|
print("DECODE FMHA COMPARISON SUMMARY")
|
|
print(f"{'='*70}")
|
|
all_pass = True
|
|
for li in sorted(results.keys()):
|
|
r = results[li]
|
|
c = r.get('cos', -1.0)
|
|
status = "PASS" if c >= 0.999 else "FAIL"
|
|
if c < 0.999: all_pass = False
|
|
print(f" L{li}: {status} cos={c:.6f} seq={r.get('seq_len','?')} "
|
|
f"mode={r.get('gather_mode','?')} "
|
|
f"n_comp={r.get('n_comp','?')} swa={r.get('swa_len','?')}")
|
|
|
|
print()
|
|
if all_pass:
|
|
print("ALL DECODE LAYERS PASSED (cos >= 0.999)")
|
|
else:
|
|
print("SOME DECODE LAYERS FAILED — investigate KV gathering or compressed/SWA parity")
|
|
print()
|
|
print("If prefill cos was 0.999993 but decode cos < 0.999:")
|
|
print(" → Bug is in decode-time KV gathering or compressed/SWA parity")
|
|
print(" → Check: gather_mixed_selective (CSA), gather_mixed_all (HCA)")
|
|
print(" → Check: SWA positions vs compressed positions (causality)")
|
|
print(" → Check: indexer top-k indices validity")
|
|
return 0 if all_pass else 1
|
|
|
|
|
|
if __name__ == "__main__":
|
|
sys.exit(main())
|