33 lines
1.6 KiB
Python
33 lines
1.6 KiB
Python
|
"""Ring sliding layer, concat-free (online softmax), at w4a16 and fp16."""
|
||
|
|
import json
|
||
|
|
import qai_hub as hub
|
||
|
|
HERE = "/data/zhoutaichang/embedding_infer/analysis/experiments/spark_edge"
|
||
|
|
DEV = "Samsung Galaxy S25"
|
||
|
|
OPT = {
|
||
|
|
"w4a16": "--target_runtime qnn_context_binary --quantize_full_type w4a16 --quantize_io",
|
||
|
|
"fp16": "--target_runtime qnn_context_binary --quantize_full_type float16",
|
||
|
|
}
|
||
|
|
mdl = hub.upload_model(f"{HERE}/onnx/ring2_sliding_ctx1024.onnx", name="spark25_ring2_sliding")
|
||
|
|
pend, res = {}, {}
|
||
|
|
for q, o in OPT.items():
|
||
|
|
cj = hub.submit_compile_job(model=mdl, device=hub.Device(DEV),
|
||
|
|
name=f"spark ring2 sliding {q}", options=o)
|
||
|
|
pend[q] = cj; print("compile", q, cj.job_id, flush=True)
|
||
|
|
prof = {}
|
||
|
|
for q, cj in pend.items():
|
||
|
|
st = cj.wait(); print("compile", q, st.code, (st.message or "")[:180], flush=True)
|
||
|
|
if st.success:
|
||
|
|
prof[q] = hub.submit_profile_job(model=cj.get_target_model(), device=hub.Device(DEV),
|
||
|
|
name=f"spark ring2 sliding {q}",
|
||
|
|
options="--compute_unit npu")
|
||
|
|
for q, pj in prof.items():
|
||
|
|
st = pj.wait()
|
||
|
|
if st.success:
|
||
|
|
p = pj.download_profile()
|
||
|
|
res[q] = p["execution_summary"]["estimated_inference_time"]/1e3
|
||
|
|
json.dump(p, open(f"{HERE}/profile_ring2_sliding_{q}.json","w"))
|
||
|
|
print(f" ring2 sliding {q}: {res[q]:.3f} ms", flush=True)
|
||
|
|
else:
|
||
|
|
res[q] = None; print(f" {q} FAILED:", (st.message or "")[:200], flush=True)
|
||
|
|
json.dump(res, open(f"{HERE}/aihub_ring2.json","w"), indent=1)
|
||
|
|
print("done", flush=True)
|