File size: 5,912 Bytes
19ed98b | 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 | #!/usr/bin/env python3
"""Packed unary loader. Loads weights, passes pointers to C engine."""
import ctypes, os, sys, time, json
import numpy as np
from ctypes import c_int, c_float, c_void_p, POINTER, c_uint8, c_uint64
class PackedEngine:
def __init__(self, model_dir, engine_path="./packed_engine.so"):
self.lib = ctypes.CDLL(engine_path)
self.lib.model_alloc.restype = c_void_p
self.lib.forward_token.restype = POINTER(c_float)
self.model_dir = model_dir
with open(os.path.join(model_dir, "manifest.json")) as f:
self.manifest = json.load(f)
with open(os.path.join(model_dir, "config.json")) as f:
self.config = json.load(f)
self.arrays = [] # prevent GC
self.model = self.lib.model_alloc()
self._load_weights()
def _keep(self, arr):
self.arrays.append(arr)
return arr.ctypes.data
def _load_file(self, key, ext, dtype):
path = os.path.join(self.model_dir, key.replace(".", "_") + ext)
return np.fromfile(path, dtype=dtype)
def _load_weights(self):
t0 = time.time()
fp16_keys = self.manifest["fp16"]
packed_keys = self.manifest["packed"]
# Embeddings
emb = self._load_file("model.embed_tokens.weight", ".fp16", np.uint16)
self.lib.model_set_embed(self.model, self._keep(emb))
print(f" Embeddings: {emb.nbytes/1e6:.1f} MB")
# LM head
lm = self._load_file("lm_head.weight", ".fp16", np.uint16)
od, id_ = fp16_keys["lm_head.weight"]
self.lib.model_set_lm_head(self.model, self._keep(lm), od, id_)
print(f" LM head: {lm.nbytes/1e6:.1f} MB")
# Final norm
fn = self._load_file("model.norm.weight", ".fp16", np.uint16).astype(np.float32)
# fp16 stored, convert
fn_f16 = self._load_file("model.norm.weight", ".fp16", np.float16)
fn = fn_f16.astype(np.float32)
self.lib.model_set_final_norm(self.model, self._keep(fn))
n_layers = self.config["num_hidden_layers"]
for l in range(n_layers):
pfx = f"model.layers.{l}"
# Norms
in_f16 = self._load_file(f"{pfx}.input_layernorm.weight", ".fp16", np.float16)
pn_f16 = self._load_file(f"{pfx}.post_attention_layernorm.weight", ".fp16", np.float16)
in_f = in_f16.astype(np.float32)
pn_f = pn_f16.astype(np.float32)
self.lib.layer_set_norms(self.model, l, self._keep(in_f), self._keep(pn_f))
# Biases (Q/K/V)
qb = kb = vb = None
qb_key = f"{pfx}.self_attn.q_proj.bias"
if qb_key in fp16_keys:
qb_f16 = self._load_file(qb_key, ".fp16", np.float16)
qb = qb_f16.astype(np.float32)
kb_f16 = self._load_file(f"{pfx}.self_attn.k_proj.bias", ".fp16", np.float16)
kb = kb_f16.astype(np.float32)
vb_f16 = self._load_file(f"{pfx}.self_attn.v_proj.bias", ".fp16", np.float16)
vb = vb_f16.astype(np.float32)
self.lib.layer_set_bias(self.model, l,
self._keep(qb), self._keep(kb), self._keep(vb))
else:
self.lib.layer_set_bias(self.model, l, None, None, None)
# 7 linear layers: q,k,v,o,gate,up,down
args = []
for name in ['self_attn.q_proj','self_attn.k_proj','self_attn.v_proj',
'self_attn.o_proj','mlp.gate_proj','mlp.up_proj','mlp.down_proj']:
key = f"{pfx}.{name}.weight"
shape = packed_keys[key]
od, id_ = shape
mags = self._load_file(key, ".mags", np.uint8)
signs = self._load_file(key, ".signs", np.uint64)
scales = self._load_file(key, ".scales", np.float32)
rmm = self._load_file(key, ".rmm", np.uint8)
args.extend([self._keep(mags), self._keep(signs),
self._keep(scales), self._keep(rmm), od, id_])
self.lib.layer_set_linears(self.model, l, *args)
if (l+1) % 7 == 0 or l == n_layers-1:
print(f" Loaded {l+1}/{n_layers} layers")
dt = time.time() - t0
total = sum(a.nbytes for a in self.arrays)
print(f"\nModel loaded in {dt:.1f}s, {total/1e6:.0f} MB in Python arrays")
def generate(self, token_ids, max_new_tokens=100, temperature=0.6, top_p=0.9, eos_id=151643):
prompt = (c_int * len(token_ids))(*token_ids)
output = (c_int * max_new_tokens)()
self.lib.model_reset_cache(self.model)
t0 = time.time()
n = self.lib.generate(self.model, prompt, len(token_ids),
output, max_new_tokens, c_float(temperature),
c_float(top_p), eos_id)
dt = time.time() - t0
tokens = [output[i] for i in range(n)]
return tokens, n, dt
if __name__ == "__main__":
from transformers import AutoTokenizer
model_dir = sys.argv[1] if len(sys.argv) > 1 else "deepseek-r1-1.5b-packed"
tok_dir = sys.argv[2] if len(sys.argv) > 2 else "deepseek-r1-1.5b-hf"
print("Loading tokenizer...")
tok = AutoTokenizer.from_pretrained(tok_dir, trust_remote_code=True)
print("Loading packed unary engine...")
engine = PackedEngine(model_dir, "./packed_engine.so")
prompts = ["What is 2+2?", "Explain gravity in one sentence.", "Write a haiku about snow."]
for prompt in prompts:
msgs = [{"role": "user", "content": prompt}]
ids = tok.apply_chat_template(msgs, add_generation_prompt=True)
tokens, n, dt = engine.generate(ids, max_new_tokens=100, temperature=0.6)
text = tok.decode(tokens, skip_special_tokens=False)
print(f"\n[{prompt}] ({n} tok, {dt:.1f}s, {n/dt:.1f} tok/s)")
print(text[:300])
print("---")
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