50 lines
2.1 KiB
Python
50 lines
2.1 KiB
Python
|
"""The architecture's repeating 4-layer block (3 sliding + 1 full) on a real S25.
|
||
|
|
|
||
|
|
Profiling one layer at a time pays the graph's Input/Output boundary once per
|
||
|
|
layer. A real decode step pays it once for all 28. Measuring 1 layer and 4
|
||
|
|
layers at the same context separates the fixed per-graph cost from the true
|
||
|
|
per-layer cost, so the 28-layer step can be extrapolated honestly.
|
||
|
|
"""
|
||
|
|
import json
|
||
|
|
import qai_hub as hub
|
||
|
|
|
||
|
|
HERE = "/data/zhoutaichang/embedding_infer/analysis/experiments/spark_edge"
|
||
|
|
DEV = "Samsung Galaxy S25"
|
||
|
|
OPT = {
|
||
|
|
"fp16": "--target_runtime qnn_context_binary --quantize_full_type float16",
|
||
|
|
"w4a16": "--target_runtime qnn_context_binary --quantize_full_type w4a16 --quantize_io",
|
||
|
|
}
|
||
|
|
|
||
|
|
res, pend = {}, {}
|
||
|
|
mdl = hub.upload_model(f"{HERE}/onnx/ring_block4_ctx1024.onnx", name="spark25_block4")
|
||
|
|
print("uploaded", flush=True)
|
||
|
|
for q in ("w4a16", "fp16"):
|
||
|
|
try:
|
||
|
|
cj = hub.submit_compile_job(model=mdl, device=hub.Device(DEV),
|
||
|
|
name=f"spark block4 {q}", options=OPT[q])
|
||
|
|
pend[q] = cj
|
||
|
|
print("compile", q, cj.job_id, flush=True)
|
||
|
|
except Exception as e:
|
||
|
|
print("submit failed", q, str(e)[:200], flush=True)
|
||
|
|
|
||
|
|
prof = {}
|
||
|
|
for q, cj in pend.items():
|
||
|
|
st = cj.wait()
|
||
|
|
print("compile", q, st.code, (st.message or "")[:200], flush=True)
|
||
|
|
if st.success:
|
||
|
|
prof[q] = hub.submit_profile_job(model=cj.get_target_model(),
|
||
|
|
device=hub.Device(DEV),
|
||
|
|
name=f"spark block4 {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_block4_{q}.json", "w"))
|
||
|
|
print(f" block4 {q}: {res[q]:.3f} ms", flush=True)
|
||
|
|
else:
|
||
|
|
res[q] = None
|
||
|
|
print(f" block4 {q} FAILED: {(st.message or '')[:250]}", flush=True)
|
||
|
|
json.dump(res, open(f"{HERE}/aihub_block4.json", "w"), indent=1)
|
||
|
|
print("done", flush=True)
|