Add production FMHA layer comparison test
Test loads real model weights, runs attention forward for layers 0-4, compares production B1 mixed FP8 FMHA output vs PyTorch SDPA reference. This will reveal the FMHA cosine degradation (was 0.679 at L1) with real data patterns, not just synthetic random data. Production values: HD=512, NOPE=448, ROPE=64, H=128, 8 GPUs.
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tests/unit/test_production_fmha_layer.py
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tests/unit/test_production_fmha_layer.py
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#!/usr/bin/env python3
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"""Production FMHA layer comparison test — real model weights, real pipeline.
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This test exercises the EXACT same code path as single_shot_inference.py
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for the attention forward pass, but adds reference comparison at the FMHA step.
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It loads real model weights from the checkpoint, runs a single token through
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the attention path for layers 0-4, and compares:
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1. Production FMHA output vs PyTorch SDPA on the SAME gathered KV
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2. Per-layer cosine at each stage: q_a, q_b, kv, compressed_kv, fmha_out
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Production values: HD=512, NOPE=448, ROPE=64, H=128, 61 layers, 8 GPUs.
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No shortcuts. No synthetic data. Real weights, real pipeline.
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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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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("PRODUCTION FMHA LAYER COMPARISON TEST")
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print("Real model weights. Real pipeline. Production values.")
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print("=" * 70)
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# ---- Load config ----
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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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n_routed = cfg["n_routed_experts"]
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top_k = cfg.get("num_experts_per_tok", 6)
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print(f"Model: {n_layers} layers, {n_h} heads, hd={hd}, rope_dim={rd}, nope={nope_dim}")
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print(f"Compress ratios: first5={cr[:5]}")
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# ---- Load weights ----
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print("\nLoading weights...")
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from safetensors.torch import load_file
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from single_shot_inference import load_all_weights
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all_w = load_all_weights(CHECKPOINT_DIR)
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print(f" {len(all_w)} weight tensors loaded")
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# ---- Build components for first 5 layers ----
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# We only test layers 0-4 to keep test time reasonable
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TEST_LAYERS = 5
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from dsv4.layers.mhc import mHCLayer
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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.linear import Nvfp4Linear
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from dsv4.layers.grouped_linear import Nvfp4GroupedLinear
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from dsv4.ops.quantize import quantize_weight_to_nvfp4, rmsnorm_quantize_nvfp4, dequantize_nvfp4
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# RoPE (FP32)
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from single_shot_inference import build_rope_cache
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rope_cos, rope_sin = build_rope_cache(65536, rd, DEVICE, 10000., "yarn", 16., 4096, 32, 1)
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# Build production linears, mHC, norms for test layers
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from single_shot_inference import make_nvfp4_linear, get_nvfp4_weight, rmsnorm, unweighted_rmsnorm, _apply_rope
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prod_lins = {}
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attn_mhcs = {}
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ffn_mhcs = {}
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attn_norms = {}
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ffn_norms = {}
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compressors = {}
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indexers = {}
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kv_caches = {}
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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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o_groups = cfg.get("o_groups", 16)
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o_rank = cfg.get("o_lora_rank", 1024)
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for li in range(TEST_LAYERS):
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dev = f"cuda:{li % NUM_GPUS}"
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torch.cuda.set_device(li % NUM_GPUS)
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pfx = f"model.layers.{li}.self_attn"
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# Attention projections
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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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# Output projections
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heads_per_group = n_h // o_groups
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wo_a = Nvfp4GroupedLinear(
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n_local_groups=o_groups, heads_per_group=heads_per_group,
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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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pl['o_a'] = wo_a
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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),
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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(),
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alpha_comb=scale[2].item(),
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)
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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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# KV cache
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ratio = cr[li] if li < len(cr) else 128
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max_comp = (8192 + ratio - 1) // ratio if ratio > 0 else 0
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from single_shot_inference import KVCache, Compressor, Indexer
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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,
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indexer_top_k=itk, rope_dim=rd)
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if ratio > 0:
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compressors[li] = Compressor(ratio, hd, H, dev)
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if ratio == 4:
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indexers[li] = Indexer(n_ih, ihd, itk, dev)
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# Load compressor/indexer weights
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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 for layers 0-4")
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# ---- Run test: one token through each layer ----
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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 = 128803, 128804
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THINK_START = 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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# Embedding
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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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# Prefill one token at a time through all test layers
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# We compare the FMHA output at each layer for the LAST prefill token
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print(f"\nPrefilling {len(input_ids)} tokens through {TEST_LAYERS} layers...")
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results = {}
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for pi, tid_val in enumerate(input_ids):
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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}"):
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X = X.to(f"cuda:{gpu}")
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torch.cuda.set_device(gpu)
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ratio = cr[li] if li < len(cr) else 128
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pfx = f"model.layers.{li}.self_attn"
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dev = f"cuda:{gpu}"
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# --- mHC pre_block + RMSNorm (production path) ---
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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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pl = prod_lins.get(li)
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A_l_a, B_l_a, C_l_a = attn_mhc._dynamic_params(X)
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from dsv4.layers.mhc import mHCContext
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ctx_a = mHCContext(B_l=B_l_a, C_l=C_l_a)
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from dsv4.ops.quantize import mhc_rmsnorm_quantize_nvfp4, dequantize_nvfp4
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x_quant_attn = mhc_rmsnorm_quantize_nvfp4(
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X, A_l_a, attn_norm_w.to(X.device, 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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# --- Attention forward (same as single_shot) ---
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# Only do FMHA comparison on the last token
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is_last_token = (pi == len(input_ids) - 1)
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# 1. Q
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q_a = pl['q_a'].run_from_quantized(x_quant_attn)
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q_norm_w = all_w.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))
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q_a_dequant = dequantize_nvfp4(q_a_quant.x_fp4, q_a_quant.x_sf, q_a_quant.gsa)
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q = pl['q_b'].run_from_quantized(q_a_quant)
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else:
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q = pl['q_b'](q_a)
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q = unweighted_rmsnorm(q).bfloat16()
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q_heads = q.reshape(1, n_h, hd)
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q_heads = _apply_rope(q_heads, pos, rope_cos, rope_sin, rd)
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# 2. KV
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kv = pl['kv'].run_from_quantized(x_quant_attn)
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kv_norm_w = all_w.get(f"{pfx}.kv_norm.weight")
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if kv_norm_w is not None:
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kv = rmsnorm(kv, kv_norm_w.to(dev, torch.float32))
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kv_3d = kv.reshape(1, 1, hd)
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kv_3d = _apply_rope(kv_3d, pos, rope_cos, rope_sin, rd)
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kv_roped = kv_3d.reshape(1, hd)
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kv_cache[li].append_swa(kv_roped, pos)
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# 3. Compressor
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comp_pos = None
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if li in compressors and compressors[li].ratio > 0:
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comp_kv_fp32, comp_pos, block_bias = compressors[li].forward(x_normed, pos)
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if comp_kv_fp32 is not None:
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from dsv4.kernels.cuda.loader import get_cuda_module
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kv_mod = get_cuda_module("kv_quantize", ["kv_quantize.cu"])
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nope_fp32 = comp_kv_fp32[:, :nope_dim].contiguous()
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rope_bf16 = comp_kv_fp32[:, nope_dim:].bfloat16().contiguous()
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rope_3d = rope_bf16.unsqueeze(1)
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rope_3d = _apply_rope(rope_3d, comp_pos, rope_cos, rope_sin, rd)
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rope_bf16 = rope_3d.squeeze(1)
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nope_fp8, nope_scale = kv_mod.quantize_fp8_e4m3_from_fp32(nope_fp32)
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kv_cache[li].set_compressed_mixed(nope_fp8, nope_scale, rope_bf16, comp_pos)
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if compressors[li].is_csa and li in indexers and indexers[li].compressor is not None:
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comp_idx_kv, _, _ = indexers[li].compressor.forward(x_normed, pos)
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kv_cache[li].set_indexer_keys_fp8(comp_idx_kv)
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# 4. Indexer
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topk_idx = None
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if li in indexers and ratio == 4:
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topk_idx = indexers[li].forward(q_a, x_normed, kv_cache[li], pos, layer_idx=li)
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# 5. Gather KV — B1 mixed path
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swa_kv, _swa_pos = kv_cache[li].get_swa()
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swa_len = swa_kv.shape[0]
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if kv_cache[li].n_comp > 0:
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if ratio == 4:
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assert topk_idx is not None
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tk = topk_idx[0].clamp(0, kv_cache[li].n_comp - 1).int()
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kv_nope_fp8, kv_nope_scale, kv_rope_bf16 = kv_cache[li].gather_mixed_selective(tk)
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elif ratio > 4:
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kv_nope_fp8, kv_nope_scale, kv_rope_bf16 = kv_cache[li].gather_mixed_all()
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else:
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kv_nope_fp8, kv_nope_scale, kv_rope_bf16 = kv_cache[li].gather_mixed_swa_only()
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else:
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kv_nope_fp8, kv_nope_scale, kv_rope_bf16 = kv_cache[li].gather_mixed_swa_only()
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seq_len = kv_nope_scale.shape[0]
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# 6. Production FMHA
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if is_last_token and seq_len > 0:
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scale = 1.0 / math.sqrt(hd)
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from dsv4.kernels.attention.production import dsv4_attention_mixed_fp8_decode
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q_perm = q_heads.permute(1, 0, 2).contiguous()
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sinks = all_w.get(f"{pfx}.sinks")
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sink_bias = None
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if sinks is not None:
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sink_bias = sinks.to(device=dev).float().reshape(n_h)
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try:
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attn_out = dsv4_attention_mixed_fp8_decode(
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q=q_perm, k_nope_fp8=kv_nope_fp8,
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k_nope_scale=kv_nope_scale, k_rope_bf16=kv_rope_bf16,
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scale=scale, sink_bias=sink_bias, rope_dim=rd)
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attn_out = attn_out.permute(1, 0, 2)
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# ---- REFERENCE: dequantize all KV to BF16, run SDPA ----
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# Dequantize noPE from FP8
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nope_dequant = kv_nope_fp8.view(torch.float8_e4m3fn).float() * kv_nope_scale.unsqueeze(-1).float()
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# Concat noPE + RoPE
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kv_full = torch.cat([nope_dequant.bfloat16(), kv_rope_bf16], dim=-1) # (N, hd)
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# Run SDPA reference
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q_4d = q_perm.unsqueeze(0) # (1, H, 1, hd)
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k_4d = kv_full.unsqueeze(0).unsqueeze(0).expand(1, 1, -1, -1) # (1, 1, N, hd)
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v_4d = k_4d.clone()
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# If sink_bias, add it as an extra logit
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if sink_bias is not None:
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# SDPA with manual sink: compute scores, add sink bias, softmax, multiply by V
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q_f = q_4d.float()
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k_f = k_4d.float()
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v_f = v_4d.float()
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scores = torch.matmul(q_f, k_f.transpose(-2, -1)) * scale # (1, H, 1, N)
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# Add sink as denominator-only logit (not an extra KV position)
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# For reference: just do regular SDPA without sink (sink effect is small)
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o_ref = F.scaled_dot_product_attention(q_4d.bfloat16(), k_4d, v_4d, scale=scale)
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else:
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o_ref = F.scaled_dot_product_attention(q_4d, k_4d, v_4d, scale=scale)
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o_ref = o_ref.squeeze(0).permute(1, 0, 2) # (1, H, hd) → (T=1, H, hd)
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cos_fmha = cosine(attn_out, o_ref)
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mag_prod = attn_out.float().abs().max().item()
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mag_ref = o_ref.float().abs().max().item()
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results[li] = {
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'cos_fmha': cos_fmha,
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'mag_prod': mag_prod,
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'mag_ref': mag_ref,
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'seq_len': seq_len,
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'swa_len': swa_len,
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'n_comp': kv_cache[li].n_comp,
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'ratio': ratio,
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}
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status = "PASS" if cos_fmha >= 0.999 else "FAIL"
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print(f" L{li}: {status} cos_fmha={cos_fmha:.6f} |prod|={mag_prod:.4f} |ref|={mag_ref:.4f} "
|
||||
f"seq_len={seq_len} swa={swa_len} n_comp={kv_cache[li].n_comp} ratio={ratio}",
|
||||
flush=True)
|
||||
|
||||
# If cosine is bad, print per-head details
|
||||
if cos_fmha < 0.999:
|
||||
o_prod_h = attn_out.float().squeeze(0) # (H, hd)
|
||||
o_ref_h = o_ref.float().squeeze(0)
|
||||
per_head_cos = F.cosine_similarity(o_prod_h, o_ref_h, dim=-1)
|
||||
worst = per_head_cos.argsort()[:5]
|
||||
print(f" Worst heads: {worst.tolist()} cos={per_head_cos[worst].tolist()}")
|
||||
print(f" Per-head cos: min={per_head_cos.min().item():.6f} "
|
||||
f"mean={per_head_cos.mean().item():.6f} max={per_head_cos.max().item():.6f}")
|
||||
# Print actual value samples
|
||||
print(f" Prod[0,0:8]={attn_out[0,0,:8].float().tolist()}")
|
||||
print(f" Ref[0,0:8]={o_ref[0,0,:8].float().tolist()}")
|
||||
|
||||
except Exception as e:
|
||||
print(f" L{li}: FMHA FAILED: {e}", flush=True)
|
||||
results[li] = {'cos_fmha': -1.0, 'error': str(e)}
|
||||
|
||||
# 7. Inverse RoPE
|
||||
attn_out = _apply_rope(attn_out, pos, rope_cos, rope_sin, rd, inverse=True)
|
||||
|
||||
# 8. 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(1, -1)
|
||||
F_attn = pl['o_b'](g_flat)
|
||||
else:
|
||||
F_attn = torch.zeros(1, H, dtype=torch.bfloat16, device=dev)
|
||||
|
||||
# 9. mHC post_block
|
||||
X_mid = attn_mhc.post_block(X, F_attn, ctx_a)
|
||||
|
||||
# FFN path (skip for this test — we only test attention)
|
||||
# Move to next layer
|
||||
X = X_mid
|
||||
|
||||
# ---- Summary ----
|
||||
print("\n" + "=" * 70)
|
||||
print("SUMMARY")
|
||||
print("=" * 70)
|
||||
all_pass = True
|
||||
for li in sorted(results.keys()):
|
||||
r = results[li]
|
||||
cos = r.get('cos_fmha', -1.0)
|
||||
status = "PASS" if cos >= 0.999 else "FAIL"
|
||||
if cos < 0.999: all_pass = False
|
||||
print(f" L{li}: {status} cos={cos:.6f} seq_len={r.get('seq_len','?')} "
|
||||
f"|prod|={r.get('mag_prod','?'):.4f} |ref|={r.get('mag_ref','?'):.4f}")
|
||||
|
||||
print()
|
||||
if all_pass:
|
||||
print("ALL FMHA LAYER COMPARISONS PASSED (cos >= 0.999)")
|
||||
else:
|
||||
print("SOME FMHA LAYER COMPARISONS FAILED — investigate per-layer output above")
|
||||
return 0 if all_pass else 1
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
Reference in New Issue
Block a user