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import argparse
import json
import math
import time
from pathlib import Path
from typing import Dict, Any
import psutil
import torch
import torch.nn as nn
from accelerate import init_empty_weights
from accelerate.utils import set_module_tensor_to_device
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
from rotorquant_weights import load_quantized_package, _unpack_3bit, _deterministic_rotor_matrix
def rss_gb() -> float:
return psutil.Process().memory_info().rss / (1024 ** 3)
def _get_parent_module(root: nn.Module, module_path: str):
if not module_path:
return None, ""
parts = module_path.split(".")
parent = root
for p in parts[:-1]:
parent = getattr(parent, p)
return parent, parts[-1]
class FusedRotorLinear(nn.Module):
def __init__(
self,
in_features: int,
out_features: int,
bias: torch.Tensor | None,
qt_raw: Dict[str, Any],
seed: int,
block_size: int,
rotor_angle_scale: float,
layer_name: str,
out_chunk_size: int = 64,
cache_weight: bool = True,
):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.block_size = block_size
self.rotor_angle_scale = rotor_angle_scale
self.layer_name = layer_name
self.out_chunk_size = out_chunk_size
self.cache_weight = cache_weight
self.row_size = int(qt_raw["row_size"])
self.row_rot_size = int(qt_raw["row_rot_size"])
self.row_padded_size = int(qt_raw["row_padded_size"])
self.n_rows = int(qt_raw["n_rows"])
packed = qt_raw["packed_indices"].to(torch.uint8)
n_values = self.n_rows * self.row_padded_size
idx = _unpack_3bit(packed, n_values=n_values, device=torch.device("cpu"))
idx = idx.reshape(self.n_rows, self.row_padded_size).to(torch.uint8)
self.register_buffer("indices", idx)
self.register_buffer("centers", qt_raw["centers"].to(torch.float16))
self.register_buffer("scales", qt_raw["scales"].to(torch.float16))
self.register_buffer("codebook", qt_raw["codebook"].to(torch.float16))
lowrank_A = qt_raw.get("lowrank_A")
lowrank_B = qt_raw.get("lowrank_B")
if lowrank_A is not None and lowrank_B is not None:
self.register_buffer("lowrank_A", lowrank_A.to(torch.float16))
self.register_buffer("lowrank_B", lowrank_B.to(torch.float16))
else:
self.lowrank_A = None
self.lowrank_B = None
R = _deterministic_rotor_matrix(
name=layer_name,
seed=seed,
device=torch.device("cpu"),
dtype=torch.float32,
angle_scale=rotor_angle_scale,
)
self.register_buffer("R", R.to(torch.float16))
if bias is not None:
self.bias = nn.Parameter(bias.to(torch.float32), requires_grad=False)
else:
self.register_parameter("bias", None)
self.register_buffer("_cached_weight", None, persistent=False)
def _decode_weight_chunk(self, s: int, e: int) -> torch.Tensor:
rows = e - s
idx = self.indices[s:e].long()
vals = self.codebook[idx]
n_blocks = self.row_padded_size // self.block_size
vals_b = vals.view(rows, n_blocks, self.block_size)
centers = self.centers[s * n_blocks : e * n_blocks].view(rows, n_blocks, 1)
scales = self.scales[s * n_blocks : e * n_blocks].view(rows, n_blocks, 1)
w_rot = (vals_b * scales + centers).view(rows, self.row_padded_size)
w_rot = w_rot[:, : self.row_rot_size]
R = self.R.to(dtype=torch.float32)
w = (w_rot.to(torch.float32).view(rows, -1, 3) @ R).view(rows, self.row_rot_size)
w = w[:, : self.row_size]
if self.lowrank_A is not None and self.lowrank_B is not None:
A = self.lowrank_A[s:e].to(torch.float32)
B = self.lowrank_B.to(torch.float32)
w = w + (A @ B)
return w[:, : self.in_features]
def forward(self, x: torch.Tensor) -> torch.Tensor:
orig_shape = x.shape[:-1]
x2 = x.reshape(-1, x.shape[-1]).to(torch.float32)
if self.cache_weight and self._cached_weight is None:
parts = []
for s in range(0, self.out_features, self.out_chunk_size):
e = min(self.out_features, s + self.out_chunk_size)
parts.append(self._decode_weight_chunk(s, e))
self._cached_weight = torch.cat(parts, dim=0).to(torch.float16)
if self._cached_weight is not None:
w = self._cached_weight.to(device=x2.device, dtype=torch.float32)
out = x2 @ w.T
else:
out = torch.empty(x2.shape[0], self.out_features, dtype=torch.float32, device=x2.device)
for s in range(0, self.out_features, self.out_chunk_size):
e = min(self.out_features, s + self.out_chunk_size)
w = self._decode_weight_chunk(s, e).to(x2.device)
out[:, s:e] = x2 @ w.T
if self.bias is not None:
out = out + self.bias
return out.reshape(*orig_shape, self.out_features)
def load_fused_model(pkg_path: str, out_chunk_size: int = 64):
t0 = time.perf_counter()
pkg = load_quantized_package(pkg_path)
model_id = pkg["model_id"]
seed = int(pkg.get("seed", 1337))
block_size = int(pkg.get("block_size", 128))
rotor_angle_scale = float(pkg.get("rotor_angle_scale", 1.0))
config = AutoConfig.from_pretrained(model_id)
with init_empty_weights():
model = AutoModelForCausalLM.from_config(config)
model.eval()
passthrough = pkg["passthrough"]
quantized = pkg["quantized"]
consumed = set()
for w_name, qt_raw in quantized.items():
if not w_name.endswith(".weight"):
continue
mod_name = w_name[:-7]
parent, child = _get_parent_module(model, mod_name)
if parent is None or not hasattr(parent, child):
continue
old = getattr(parent, child)
if not isinstance(old, nn.Linear):
continue
if "n_rows" not in qt_raw:
continue
bias_name = f"{mod_name}.bias"
bias = passthrough.get(bias_name)
fused = FusedRotorLinear(
in_features=old.in_features,
out_features=old.out_features,
bias=bias,
qt_raw=qt_raw,
seed=seed,
block_size=block_size,
rotor_angle_scale=rotor_angle_scale,
layer_name=w_name,
out_chunk_size=out_chunk_size,
cache_weight=True,
)
setattr(parent, child, fused)
consumed.add(w_name)
if bias is not None:
consumed.add(bias_name)
for name, t in passthrough.items():
if name in consumed:
continue
set_module_tensor_to_device(model, name, "cpu", value=t)
for name, qt_raw in quantized.items():
if name in consumed:
continue
if "n_rows" not in qt_raw:
from rotorquant_weights import dequantize_to_state_dict
sd = dequantize_to_state_dict({
"bits": pkg["bits"],
"block_size": pkg["block_size"],
"seed": pkg["seed"],
"lowrank_rank": pkg.get("lowrank_rank", 0),
"rotor_angle_scale": pkg.get("rotor_angle_scale", 1.0),
"rowwise": False,
"quantized": {name: qt_raw},
"passthrough": {},
}, dtype=torch.float32, device="cpu")
set_module_tensor_to_device(model, name, "cpu", value=sd[name])
else:
from rotorquant_weights import dequantize_to_state_dict
sd = dequantize_to_state_dict({
"bits": pkg["bits"],
"block_size": pkg["block_size"],
"seed": pkg["seed"],
"lowrank_rank": pkg.get("lowrank_rank", 0),
"rotor_angle_scale": pkg.get("rotor_angle_scale", 1.0),
"rowwise": pkg.get("rowwise", True),
"quantized": {name: qt_raw},
"passthrough": {},
}, dtype=torch.float32, device="cpu")
set_module_tensor_to_device(model, name, "cpu", value=sd[name])
model = model.to(torch.float32)
load_s = time.perf_counter() - t0
return model, model_id, load_s
def run_prompt(model, tokenizer, prompt: str, max_new_tokens: int) -> str:
messages = [
{"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
{"role": "user", "content": prompt},
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inp = tokenizer([text], return_tensors="pt")
with torch.no_grad():
out = model.generate(**inp, max_new_tokens=max_new_tokens, do_sample=False)
new = out[:, inp["input_ids"].shape[1]:]
return tokenizer.batch_decode(new, skip_special_tokens=True)[0]
def parse_args():
p = argparse.ArgumentParser(description="Run fused RotorQuant runtime")
sub = p.add_subparsers(dest="cmd", required=True)
r = sub.add_parser("run")
r.add_argument("--pkg", default="artifacts/qwen2.5-0.5b-rotorq3-mlp-only.pt")
r.add_argument("--prompt", default="Explain quantization in one paragraph.")
r.add_argument("--max-new-tokens", type=int, default=64)
r.add_argument("--out-chunk-size", type=int, default=64)
b = sub.add_parser("bench")
b.add_argument("--pkg", default="artifacts/qwen2.5-0.5b-rotorq3-mlp-only.pt")
b.add_argument("--out", default="artifacts/fused_runtime_meta.json")
b.add_argument("--out-chunk-size", type=int, default=64)
return p.parse_args()
def main():
args = parse_args()
if args.cmd == "run":
model, model_id, load_s = load_fused_model(args.pkg, out_chunk_size=args.out_chunk_size)
tok = AutoTokenizer.from_pretrained(model_id)
ans = run_prompt(model, tok, args.prompt, args.max_new_tokens)
print(f"load_s={load_s:.3f}")
print("\n=== Prompt ===")
print(args.prompt)
print("\n=== Response ===")
print(ans)
elif args.cmd == "bench":
rss0 = rss_gb()
model, model_id, load_s = load_fused_model(args.pkg, out_chunk_size=args.out_chunk_size)
rss1 = rss_gb()
tok = AutoTokenizer.from_pretrained(model_id)
t0 = time.perf_counter()
_ = run_prompt(model, tok, "warmup", 32)
t1 = time.perf_counter() - t0
rss2 = rss_gb()
out = {
"pkg": args.pkg,
"load_s": load_s,
"warmup_generate_s": t1,
"rss_before_gb": rss0,
"rss_after_load_gb": rss1,
"rss_after_warmup_gb": rss2,
"out_chunk_size": args.out_chunk_size,
}
Path(args.out).write_text(json.dumps(out, indent=2), encoding="utf-8")
print(json.dumps(out, indent=2))
if __name__ == "__main__":
main()
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