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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 | #!/usr/bin/env python3
"""
Pure unary model loader - ALL matmuls are AND+popcount
(c) 2026 OpenTransformers Ltd / Scott Bisset
"""
import ctypes, numpy as np, os, sys, json, time
def load_and_run(model_dir, prompt, max_tokens=128, temperature=0.0, top_p=0.9, a_planes=4):
config = json.load(open(os.path.join(model_dir, "config.json")))
manifest = json.load(open(os.path.join(model_dir, "manifest.json")))
w_planes = manifest["n_planes"]
n_layers = config["num_hidden_layers"]
hidden = config["hidden_size"]
inter = config["intermediate_size"]
n_heads = config["num_attention_heads"]
n_kv_heads = config["num_key_value_heads"]
head_dim = config.get("head_dim", hidden // n_heads)
vocab = config["vocab_size"]
rope_theta = config.get("rope_theta", 10000.0)
tie_embeddings = 1 if config.get("tie_word_embeddings", False) else 0
print(f"Config: {n_layers}L, hidden={hidden}, inter={inter}, heads={n_heads}/{n_kv_heads}")
print(f"Weight planes: {w_planes}, Activation planes: {a_planes}")
print(f"Plane pairs per element: {w_planes * a_planes}")
print(f"Tied embeddings: {'yes' if tie_embeddings else 'no'}")
engine_path = os.path.join(os.path.dirname(os.path.abspath(model_dir)), "pure_unary_engine.so")
lib = ctypes.CDLL(engine_path)
lib.model_alloc.restype = ctypes.c_void_p
lib.model_alloc.argtypes = [
ctypes.c_int, ctypes.c_int, # w_planes, a_planes
ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_int,
ctypes.c_int, ctypes.c_int, ctypes.c_int,
ctypes.c_float, ctypes.c_int,
]
lib.forward_token.restype = ctypes.POINTER(ctypes.c_float)
lib.forward_token.argtypes = [ctypes.c_void_p, ctypes.c_int, ctypes.c_int]
lib.generate.restype = ctypes.c_int
lib.generate.argtypes = [
ctypes.c_void_p,
ctypes.POINTER(ctypes.c_int), ctypes.c_int,
ctypes.POINTER(ctypes.c_int), ctypes.c_int,
ctypes.c_float, ctypes.c_float, ctypes.c_int
]
u16p = ctypes.POINTER(ctypes.c_uint16)
f32p = ctypes.POINTER(ctypes.c_float)
u64p = ctypes.POINTER(ctypes.c_uint64)
lib.model_set_embed.argtypes = [ctypes.c_void_p, u16p]
lib.model_set_final_norm.argtypes = [ctypes.c_void_p, f32p]
lib.layer_set_norms.argtypes = [ctypes.c_void_p, ctypes.c_int, f32p, f32p]
lib.layer_set_qk_norm.argtypes = [ctypes.c_void_p, ctypes.c_int, f32p, f32p]
lib.layer_set_linears.argtypes = [
ctypes.c_void_p, ctypes.c_int,
u64p, u64p, f32p, ctypes.c_int, ctypes.c_int,
u64p, u64p, f32p, ctypes.c_int, ctypes.c_int,
u64p, u64p, f32p, ctypes.c_int, ctypes.c_int,
u64p, u64p, f32p, ctypes.c_int, ctypes.c_int,
u64p, u64p, f32p, ctypes.c_int, ctypes.c_int,
u64p, u64p, f32p, ctypes.c_int, ctypes.c_int,
u64p, u64p, f32p, ctypes.c_int, ctypes.c_int,
ctypes.c_int,
]
lib.model_reset_cache.argtypes = [ctypes.c_void_p]
print("Allocating model...")
model = lib.model_alloc(
w_planes, a_planes,
hidden, inter, n_heads, n_kv_heads,
head_dim, n_layers, vocab, rope_theta, tie_embeddings
)
_refs = []
def load_fp16(name):
fname = name.replace(".", "_") + ".fp16"
data = np.fromfile(os.path.join(model_dir, fname), dtype=np.uint16)
_refs.append(data)
return data.ctypes.data_as(u16p)
def load_f32(name):
fname = name.replace(".", "_") + ".fp16"
data = np.fromfile(os.path.join(model_dir, fname), dtype=np.uint16)
f32 = data.view(np.float16).astype(np.float32)
_refs.append(f32)
return f32.ctypes.data_as(f32p)
def load_unary(name):
fname = name.replace(".", "_")
sign = np.fromfile(os.path.join(model_dir, f"{fname}.sign"), dtype=np.uint64)
planes = np.fromfile(os.path.join(model_dir, f"{fname}.planes"), dtype=np.uint64)
scales = np.fromfile(os.path.join(model_dir, f"{fname}.scales"), dtype=np.float32)
_refs.extend([sign, planes, scales])
return (sign.ctypes.data_as(u64p), planes.ctypes.data_as(u64p),
scales.ctypes.data_as(f32p))
print("Loading embeddings...")
lib.model_set_embed(model, load_fp16("model.embed_tokens.weight"))
print("Loading final norm...")
lib.model_set_final_norm(model, load_f32("model.norm.weight"))
print(f"Loading {n_layers} layers...")
for l in range(n_layers):
p = f"model.layers.{l}"
lib.layer_set_norms(model, l,
load_f32(f"{p}.input_layernorm.weight"),
load_f32(f"{p}.post_attention_layernorm.weight"))
# QK-Norm (Qwen3)
qn_path = os.path.join(model_dir, f"{p.replace('.','_')}_self_attn_q_norm_weight.fp16")
if os.path.exists(qn_path):
lib.layer_set_qk_norm(model, l,
load_f32(f"{p}.self_attn.q_norm.weight"),
load_f32(f"{p}.self_attn.k_norm.weight"))
q_s, q_p, q_sc = load_unary(f"{p}.self_attn.q_proj.weight")
k_s, k_p, k_sc = load_unary(f"{p}.self_attn.k_proj.weight")
v_s, v_p, v_sc = load_unary(f"{p}.self_attn.v_proj.weight")
o_s, o_p, o_sc = load_unary(f"{p}.self_attn.o_proj.weight")
g_s, g_p, g_sc = load_unary(f"{p}.mlp.gate_proj.weight")
u_s, u_p, u_sc = load_unary(f"{p}.mlp.up_proj.weight")
d_s, d_p, d_sc = load_unary(f"{p}.mlp.down_proj.weight")
um = manifest["unary"]
lib.layer_set_linears(model, l,
q_s, q_p, q_sc, um[f"{p}.self_attn.q_proj.weight"][0], um[f"{p}.self_attn.q_proj.weight"][1],
k_s, k_p, k_sc, um[f"{p}.self_attn.k_proj.weight"][0], um[f"{p}.self_attn.k_proj.weight"][1],
v_s, v_p, v_sc, um[f"{p}.self_attn.v_proj.weight"][0], um[f"{p}.self_attn.v_proj.weight"][1],
o_s, o_p, o_sc, um[f"{p}.self_attn.o_proj.weight"][0], um[f"{p}.self_attn.o_proj.weight"][1],
g_s, g_p, g_sc, um[f"{p}.mlp.gate_proj.weight"][0], um[f"{p}.mlp.gate_proj.weight"][1],
u_s, u_p, u_sc, um[f"{p}.mlp.up_proj.weight"][0], um[f"{p}.mlp.up_proj.weight"][1],
d_s, d_p, d_sc, um[f"{p}.mlp.down_proj.weight"][0], um[f"{p}.mlp.down_proj.weight"][1],
w_planes)
if (l + 1) % 6 == 0 or l == n_layers - 1:
print(f" Loaded layer {l+1}/{n_layers}")
# Tokenize
print("Tokenizing...")
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
input_ids = tokenizer.encode(prompt)
print(f"Prompt: {len(input_ids)} tokens -> {repr(prompt[:60])}")
eos_token = config.get("eos_token_id", 151645)
prompt_arr = (ctypes.c_int * len(input_ids))(*input_ids)
out_arr = (ctypes.c_int * max_tokens)()
print(f"\nGenerating (temp={temperature}, top_p={top_p}, a_planes={a_planes})...")
t0 = time.time()
n_gen = lib.generate(
model, prompt_arr, len(input_ids),
out_arr, max_tokens,
ctypes.c_float(temperature), ctypes.c_float(top_p), eos_token
)
dt = time.time() - t0
out_ids = [out_arr[i] for i in range(n_gen)]
text = tokenizer.decode(out_ids, skip_special_tokens=True)
print(f"\n=== PURE UNARY Output ({n_gen} tokens in {dt:.1f}s = {n_gen/dt:.2f} tok/s) ===")
print(text)
print(f"\nDecode speed: {n_gen/dt:.2f} tok/s")
return text
if __name__ == "__main__":
model_dir = sys.argv[1] if len(sys.argv) > 1 else "qwen3-4b-thinking-unary"
prompt = sys.argv[2] if len(sys.argv) > 2 else "What is 2+2? Think step by step."
max_tokens = int(sys.argv[3]) if len(sys.argv) > 3 else 32
a_planes = int(sys.argv[4]) if len(sys.argv) > 4 else 4
load_and_run(model_dir, prompt, max_tokens=max_tokens, a_planes=a_planes)
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