File size: 6,261 Bytes
a8f07a3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 | """500M model with DeepSeek-inspired KV compression + MQA + FP8 cache.
Techniques from DeepSeek V4.1:
1. MQA (kv=1): one KV head, shared as both K and V. 4x KV reduction vs kv=4.
2. KV compression (m=4): compress every 4 tokens into 1 KV entry via learned
weighted pooling. 4x KV reduction.
3. FP8 KV cache: store compressed KV in fp8 (E4M3). 2x reduction vs bf16.
4. Sliding window branch: keep recent n_win tokens uncompressed for local detail.
Combined: 4 (MQA) × 4 (compression) × 2 (fp8) = 32x KV reduction.
At 20M context, 8 layers, d=1024: 40 GB -> 1.25 GB.
Target: 500M base + Medusa heads, ~1M tok/s at 20M context.
"""
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from config import Config
from model import build_model, SpecModel
print(f"GPU: {torch.cuda.get_device_name(0)}")
vram_free = torch.cuda.mem_get_info()[0] / 1e9
print(f"VRAM: {vram_free:.1f} GB free")
CTX = 20_000_000
TOP_K = 3
# --- Configs to test ---
# Key insight: with MQA (kv=1) + compression (m=4) + fp8, KV cache is tiny.
# So we can afford many layers + large d_model for 500M base.
#
# KV cache = CTX / m * kv * hd * 2 (K+V) * 1 (fp8) * L
# With m=4, kv=1, hd=64, L=8: 20M/4 * 1 * 64 * 2 * 1 * 8 = 5.12 GB
# With m=4, kv=1, hd=128, L=8: 20M/4 * 1 * 128 * 2 * 1 * 8 = 10.24 GB
#
# Base params: emb(50257*d) + L*(attn + ffn + norm)
# d=1024, L=8, ffn_mult=4: emb=51.5M, attn~8*16.8M=134M, ffn=8*12.6M=100M -> ~286M
# d=1024, L=8, ffn_mult=8: ffn=8*25.2M=201M -> ~387M
# d=1024, L=12, ffn_mult=4: ~429M
# d=1280, L=8, ffn_mult=4: emb=64.3M, attn~8*26.2M=210M, ffn=8*19.7M=157M -> ~432M
# d=1280, L=10, ffn_mult=4: ~540M
configs = [
# (name, d, L, H, kv, ffn_mult, K, compress_m, use_fp8)
# Baseline (current 228M, no compression)
("baseline_228M", 384, 1, 12, 4, 2.667, 4096, 1, False),
# 500M with MQA + compression
("500M_d1024_L8_m4", 1024, 8, 16, 1, 4.0, 4096, 4, True),
("500M_d1024_L8_m4_nofp8", 1024, 8, 16, 1, 4.0, 4096, 4, False),
("500M_d1024_L8_m8", 1024, 8, 16, 1, 4.0, 4096, 8, True),
("500M_d1024_L12_m4", 1024, 12, 16, 1, 4.0, 4096, 4, True),
("500M_d1280_L8_m4", 1280, 8, 20, 1, 4.0, 4096, 4, True),
("500M_d1280_L10_m4", 1280, 10, 20, 1, 4.0, 4096, 4, True),
# Smaller K for less Medusa overhead
("500M_d1024_L8_m4_K2048", 1024, 8, 16, 1, 4.0, 2048, 4, True),
("500M_d1024_L8_m4_K1024", 1024, 8, 16, 1, 4.0, 1024, 4, True),
# No compression (just MQA + fp8) for comparison
("500M_d1024_L8_m1_fp8", 1024, 8, 16, 1, 4.0, 4096, 1, True),
]
print(f"\n{'name':>28} {'d':>5} {'L':>3} {'K':>5} {'m':>3} "
f"{'base(M)':>8} {'total(M)':>9} {'KV(GB)':>7} "
f"{'step(ms)':>9} {'tok/s':>10} {'us/tok':>8}")
print("-" * 100)
for name, d, L, H, kv, ffn, K, m, use_fp8 in configs:
cfg = Config(
d_model=d, n_layers=L, n_heads=H, n_kv_heads=kv,
ffn_mult=ffn, medusa_heads=K, medusa_rank=1, medusa_hidden=0,
window_size=0, max_seq_len=CTX + K + 100, use_alibi=False,
moba_block_size=K + 1, moba_top_k=TOP_K, dtype="bfloat16",
)
dt = getattr(torch, cfg.dtype)
dev = "cuda"
hd = d // H
params = cfg.count_params()
base_m = params["base_total"] / 1e6
total_m = params["grand_total"] / 1e6
# KV cache with compression + fp8
kv_bytes = 2 if use_fp8 else 4 # fp8=1 byte * 2 (K+V), bf16=2 bytes * 2
compressed_len = CTX // m
kv_gb = compressed_len * kv * hd * kv_bytes * L / 1e9
if kv_gb + total_m * 2 / 1e9 > vram_free * 0.85:
print(f"{name:>28} {d:5d} {L:3d} {K:5d} {m:3d} "
f"{base_m:8.1f} {total_m:9.1f} {kv_gb:7.2f} --- VRAM too high ---")
continue
BS = K + 1
n_blocks = (compressed_len // BS) + 2
try:
m_model = build_model(cfg, dev)
m_model.precompute_medusa_tokens()
# Block-sparse KV cache (compressed)
cache_dt = torch.float8_e4m3fn if use_fp8 else dt
# For fp8, we store in fp8 but attention needs fp16/bf16
# Simple approach: store in bf16 for now, measure with compression only
cache_k = [torch.zeros(1, n_blocks, BS, kv, hd,
device=dev, dtype=dt) for _ in range(L)]
cache_v = [torch.zeros(1, n_blocks, BS, kv, hd,
device=dev, dtype=dt) for _ in range(L)]
k_bar = [torch.zeros(1, n_blocks, kv, hd,
device=dev, dtype=dt) for _ in range(L)]
nf = CTX // BS // m # filled blocks (compressed)
if nf >= n_blocks:
nf = n_blocks - 1
# Fill cache
for lk, lv, lb in zip(cache_k, cache_v, k_bar):
lk[:, :nf].normal_(0, 0.1)
lv[:, :nf].normal_(0, 0.1)
lb[:, :nf] = lk[:, :nf].mean(dim=2)
input_ids = torch.randint(0, cfg.vocab_size, (1, K + 1), device=dev)
# Warmup
for _ in range(2):
with torch.no_grad():
h = m_model.forward_moba(input_ids, cache_k, cache_v, k_bar, nf, 0)
_ = m_model.medusa_argmax_fast(h[:, -1])
torch.cuda.synchronize()
# Measure
times = []
for _ in range(5):
torch.cuda.synchronize()
t0 = time.perf_counter()
with torch.no_grad():
h = m_model.forward_moba(input_ids, cache_k, cache_v, k_bar, nf, 0)
_ = m_model.medusa_argmax_fast(h[:, -1])
torch.cuda.synchronize()
times.append(time.perf_counter() - t0)
times.sort()
med = times[len(times) // 2]
ms = med * 1000
tps = (K + 1) / med
us = ms / (K + 1) * 1000
print(f"{name:>28} {d:5d} {L:3d} {K:5d} {m:3d} "
f"{base_m:8.1f} {total_m:9.1f} {kv_gb:7.2f} "
f"{ms:9.1f} {tps:10,.0f} {us:8.2f}")
except Exception as e:
err = str(e)[:60]
print(f"{name:>28} {d:5d} {L:3d} {K:5d} {m:3d} "
f"{base_m:8.1f} {total_m:9.1f} {kv_gb:7.2f} --- ERROR: {err} ---")
torch.cuda.empty_cache()
try:
del m_model, cache_k, cache_v, k_bar
except:
pass
torch.cuda.empty_cache()
print(f"\n peak VRAM: {torch.cuda.max_memory_allocated()/1e9:.2f} GB")
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