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"""Export a fixed-token Ling GDN recurrence graph for RK3588 prefill."""
from __future__ import annotations
import argparse
import hashlib
import json
from pathlib import Path
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
class GdnPrefill(nn.Module):
def __init__(self, heads: int, tokens: int, head_dim: int = 128):
super().__init__()
self.heads = heads
self.tokens = tokens
self.head_dim = head_dim
self.query_scale = head_dim**-0.5
def forward(
self,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
decay: torch.Tensor,
beta: torch.Tensor,
state: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
shape = (1, self.heads, self.head_dim)
recurrent = state.reshape(1, self.heads, self.head_dim, self.head_dim)
recurrent = recurrent.transpose(-2, -1)
outputs = []
for token in range(self.tokens):
q = query[token].reshape(shape)
k = key[token].reshape(shape)
v = value[token].reshape(shape)
d = decay[token].reshape(shape)
b = beta[token].reshape(1, self.heads)
factor = F.elu(d, alpha=1.0) + 1.0
decayed = recurrent * factor.unsqueeze(-1)
predicted = torch.matmul(k.unsqueeze(-2), decayed).squeeze(-2)
delta = b.unsqueeze(-1) * (v - predicted)
recurrent = decayed + k.unsqueeze(-1) * delta.unsqueeze(-2)
output = torch.matmul(
(q * self.query_scale).unsqueeze(-2), recurrent
).squeeze(-2)
outputs.append(output.reshape(1, self.heads * self.head_dim))
packed_output = torch.cat(outputs, dim=0)
final_state = recurrent.transpose(-2, -1).contiguous()
final_state = final_state.reshape(1, self.heads * self.head_dim * self.head_dim)
return packed_output, final_state
def sha256(path: Path) -> str:
digest = hashlib.sha256()
with path.open("rb") as source:
for chunk in iter(lambda: source.read(1024 * 1024), b""):
digest.update(chunk)
return digest.hexdigest()
def inputs_for(heads: int, tokens: int, head_dim: int) -> dict[str, torch.Tensor]:
generator = torch.Generator().manual_seed(20260909 + heads + tokens)
width = heads * head_dim
query = torch.randn((tokens, width), generator=generator)
key = torch.randn((tokens, width), generator=generator)
query = query.reshape(tokens, heads, head_dim)
key = key.reshape(tokens, heads, head_dim)
query = query * torch.rsqrt((query * query).sum(-1, keepdim=True) + 1e-6)
key = key * torch.rsqrt((key * key).sum(-1, keepdim=True) + 1e-6)
return {
"query": query.reshape(tokens, width),
"key": key.reshape(tokens, width),
"value": torch.randn((tokens, width), generator=generator) * 0.2,
"decay": -5.0 * torch.sigmoid(
torch.randn((tokens, width), generator=generator) * 0.2
),
"beta": torch.sigmoid(
torch.randn((tokens, heads), generator=generator) * 0.2
),
"state": torch.randn(
(1, heads * head_dim * head_dim), generator=generator
)
* 0.01,
}
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--output-dir", type=Path, required=True)
parser.add_argument("--heads", type=int, choices=(5, 6), required=True)
parser.add_argument("--tokens", type=int, default=32)
parser.add_argument("--head-dim", type=int, default=128)
parser.add_argument("--skip-build", action="store_true")
parser.add_argument("--quantize", action="store_true")
parser.add_argument("--calibration-samples", type=int, default=32)
parser.add_argument("--verbose", action="store_true")
return parser.parse_args()
def main() -> None:
args = parse_args()
if args.tokens < 1:
raise ValueError("tokens must be positive")
if args.calibration_samples < 1:
raise ValueError("calibration samples must be positive")
output_dir = args.output_dir.resolve()
data_dir = output_dir / "data"
data_dir.mkdir(parents=True, exist_ok=True)
model = GdnPrefill(args.heads, args.tokens, args.head_dim).eval()
inputs = inputs_for(args.heads, args.tokens, args.head_dim)
input_names = list(inputs)
output_names = ["output", "new_state"]
with torch.no_grad():
reference = model(**inputs)
for name, tensor in inputs.items():
tensor.detach().cpu().numpy().astype(np.float32).tofile(data_dir / f"{name}.f32")
for name, tensor in zip(output_names, reference, strict=True):
tensor.detach().cpu().numpy().astype(np.float32).tofile(data_dir / f"{name}.f32")
stem = f"ling3_gdn_prefill_t{args.tokens}_h{args.heads}"
onnx_path = output_dir / f"{stem}.onnx"
torch.onnx.export(
model,
tuple(inputs.values()),
onnx_path,
input_names=input_names,
output_names=output_names,
opset_version=17,
do_constant_folding=True,
)
dataset_path = output_dir / "dataset.txt"
calibration_dir = output_dir / "calibration"
if args.quantize:
calibration_dir.mkdir(parents=True, exist_ok=True)
dataset_lines: list[str] = []
for sample in range(args.calibration_samples):
sample_inputs = inputs_for(args.heads, args.tokens, args.head_dim)
paths: list[str] = []
for input_index, (name, tensor) in enumerate(sample_inputs.items()):
generator = np.random.default_rng(20260909 + sample * 17 + input_index)
array = tensor.detach().cpu().numpy().astype(np.float32)
if sample > 0:
array *= generator.uniform(0.5, 2.0)
path = calibration_dir / f"sample_{sample:03d}_{name}.npy"
np.save(path, array)
paths.append(str(path))
dataset_lines.append(" ".join(paths))
dataset_path.write_text("\n".join(dataset_lines) + "\n", encoding="utf-8")
rknn_path = output_dir / f"{stem}_{'w8a8' if args.quantize else 'fp16'}_rk3588.rknn"
build_results: dict[str, int] = {}
if not args.skip_build:
from rknn.api import RKNN
converter = RKNN(verbose=args.verbose)
try:
config = {"target_platform": "rk3588", "optimization_level": 3}
if args.quantize:
config.update(
quantized_dtype="w8a8",
quantized_algorithm="normal",
quantized_method="channel",
)
build_results["config"] = converter.config(**config)
build_results["load_onnx"] = converter.load_onnx(model=str(onnx_path))
if any(build_results.values()):
raise RuntimeError(f"RKNN setup failed: {build_results}")
build_results["build"] = converter.build(
do_quantization=args.quantize,
dataset=str(dataset_path) if args.quantize else None,
)
if build_results["build"] != 0:
raise RuntimeError(f"RKNN build failed: {build_results}")
build_results["export"] = converter.export_rknn(str(rknn_path))
if build_results["export"] != 0:
raise RuntimeError(f"RKNN export failed: {build_results}")
finally:
converter.release()
manifest = {
"architecture": "Ling GDN fixed-token prefill recurrence",
"heads": args.heads,
"head_dim": args.head_dim,
"tokens": args.tokens,
"quantization": "w8a8" if args.quantize else "fp16",
"calibration_samples": args.calibration_samples if args.quantize else 0,
"inputs": {name: list(value.shape) for name, value in inputs.items()},
"outputs": {
name: list(value.shape)
for name, value in zip(output_names, reference, strict=True)
},
"onnx": {"path": str(onnx_path), "sha256": sha256(onnx_path)},
"rknn": (
{"path": str(rknn_path), "sha256": sha256(rknn_path)}
if rknn_path.exists()
else None
),
"build_results": build_results,
}
(output_dir / "manifest.json").write_text(
json.dumps(manifest, indent=2) + "\n", encoding="utf-8"
)
print(json.dumps(manifest, indent=2))
if __name__ == "__main__":
main()
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