File size: 6,978 Bytes
36a88b2 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 | """LILO — Lightweight Local Intelligence & Optimizer
Helpers for loading and running LILO-100M on CPU. Variants:
- lilo-100m (fp16) : fp16 weights (221MB on disk) — fastest, quality gate
PPL delta 0.08% vs fp32, greedy outputs 5/5 identical
- lilo-100m (int8) : INT8 group-wise weights (group=64, 119MB on disk) —
smallest/RAM-light, PPL delta 0.32%, greedy 5/5 identical
Both ship patched modeling code (freqs_cis fix + working KV cache) so single-token
decode is cheap. Generate with `generate_lilo()` (explicit KV cache).
CPU speed (measured on 2 vCPU AMD EPYC 7K62 Zen2, AVX2-only, under load):
fp16 14-22 tok/s at --threads 1 (recommended default)
int8 9-19 tok/s at --threads 1
More threads HURT on tiny models (sync overhead); scale up only on 8+ cores.
"""
import json
import os
from typing import List, Optional, Sequence
import torch
from safetensors import safe_open
from safetensors.torch import load_file
from transformers import AutoConfig, AutoTokenizer
from configuration_deepseek_v4 import DeepseekV4Config
from modeling_deepseek_v4 import (
DeepseekV4ForCausalLM,
QuantizedLinear,
QuantizedEmbedding,
)
# submodule leaf names that get INT8-quantized in the int8 variant
_LINEAR_TARGETS = (
"wq_a", "wq_b", "wkv", "wo_a", "wo_b", # attention projections
"w1", "w2", "w3", # experts (gate/up/down)
"lm_head",
)
def _swap_quantized(model, quantize_embed=True):
"""Swap nn.Linear -> QuantizedLinear and nn.Embedding -> QuantizedEmbedding.
Returns number of modules swapped."""
swapped = 0
for parent in list(dict(model.named_modules()).values()):
for name, child in list(parent.named_children()):
target = None
if name in _LINEAR_TARGETS and child.__class__.__name__ == "Linear":
target = QuantizedLinear(child.in_features, child.out_features)
elif quantize_embed and name == "embed_tokens" and child.__class__.__name__ == "Embedding":
target = QuantizedEmbedding(child.num_embeddings, child.embedding_dim)
if target is not None:
setattr(parent, name, target)
swapped += 1
return swapped
def load_lilo(path_or_repo, variant="int8", device="cpu", threads=2, hf_token=None):
"""Load a LILO model. variant: "int8" | "fp16" | "fp32"."""
path = path_or_repo
if not os.path.isdir(path):
from huggingface_hub import snapshot_download
path = snapshot_download(repo_id=path_or_repo, token=hf_token)
torch.set_num_threads(threads)
config = DeepseekV4Config.from_pretrained(path, trust_remote_code=True)
tok = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
if variant == "int8":
model = DeepseekV4ForCausalLM(config)
from safetensors.torch import load_file
sd = load_file(os.path.join(path, "model.int8.safetensors"))
with safe_open(os.path.join(path, "model.int8.safetensors"), framework="pt") as _sf:
emb_dtype = _sf.get_slice("model.embed_tokens.weight").get_dtype()
n = _swap_quantized(model, quantize_embed=(emb_dtype == "I8"))
missing, unexpected = model.load_state_dict(sd, strict=False)
bad_missing = [k for k in missing if not k.startswith("freqs_cis")]
assert not bad_missing and not unexpected, f"missing={bad_missing[:5]} unexpected={unexpected[:5]}"
model.eval()
model.model.get_freqs_cis()
print(f"[lilo] loaded INT8 variant from {path} ({n} modules swapped, "
f"{sum(t.numel() for t in sd.values())/1e6:.0f}M params)")
elif variant == "fp16":
model = DeepseekV4ForCausalLM(config)
from safetensors.torch import load_file
sd = load_file(os.path.join(path, "model.fp16.safetensors"))
missing, unexpected = model.load_state_dict(sd, strict=False)
assert not missing and not unexpected, f"missing={missing[:5]} unexpected={unexpected[:5]}"
model.eval()
model = model.to(torch.float16)
model.model.get_freqs_cis()
print(f"[lilo] loaded FP16 variant from {path}")
else:
model = DeepseekV4ForCausalLM.from_pretrained(
path, trust_remote_code=True, dtype=torch.float32, attn_implementation="eager"
)
model.eval()
model.model.get_freqs_cis()
print(f"[lilo] loaded FP32 variant from {path}")
model.to(device)
return tok, model
@torch.inference_mode()
def generate_lilo(
model,
tok,
prompt_ids: Sequence[int],
max_new_tokens: int = 200,
temperature: float = 0.7,
top_p: float = 0.9,
repetition_penalty: float = 1.0,
eos_token_id: int = 1,
stream: bool = False,
):
"""Greedy/sampling generation with an explicit KV cache.
Returns (generated_ids list). Repetition penalty is applied on the growing
sequence (standard formulation)."""
ids = torch.tensor([list(prompt_ids)], dtype=torch.long)
generated: List[int] = []
past = None
logits = None
for step in range(max_new_tokens):
if step == 0:
# chunked prefill to bound peak memory
seq = ids
chunk = 256
for s in range(0, seq.shape[1], chunk):
piece = seq[:, s : s + chunk]
out = model(input_ids=piece, past_key_values=past, use_cache=True)
past = out.past_key_values
logits = out.logits[0, -1].float()
else:
out = model(
input_ids=torch.tensor([[generated[-1]]], dtype=torch.long),
past_key_values=past,
use_cache=True,
)
past = out.past_key_values
logits = out.logits[0, -1].float()
if repetition_penalty != 1.0:
for t in set(generated):
logits[t] /= repetition_penalty if logits[t] > 0 else 1.0 / repetition_penalty
if temperature <= 0:
nxt = int(torch.argmax(logits))
else:
probs = torch.softmax(logits / max(temperature, 1e-5), dim=-1)
if top_p < 1.0:
sp, si = torch.sort(probs, descending=True)
cum = torch.cumsum(sp, dim=-1)
mask = cum - sp > top_p
sp[mask] = 0.0
probs = torch.zeros_like(probs).scatter_(0, si, sp)
probs = probs / probs.sum()
nxt = int(torch.multinomial(probs, 1))
generated.append(nxt)
if stream:
print(tok.decode([nxt]), end="", flush=True)
if nxt == eos_token_id:
break
return generated
def chat(tok, model, messages, **kw):
prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
ids = tok.encode(prompt, add_special_tokens=False)
gen = generate_lilo(model, tok, ids, **kw)
return tok.decode(gen, skip_special_tokens=True)
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