def benchmark_model(
response_data: dict,
workload: Workload,
workdir: Workdir,
model: str,
model_path: str,
model_precision: str,
benchmark_mode: str,
benchmark_count: int,
benchmark_total: int,
) -> dict:
"""Benchmark model and prepare response data."""
request_id = response_data.get("id")
benchmark: Benchmark = Benchmark(
workload=workload,
model_path=model_path,
precision=model_precision,
mode=benchmark_mode,
)
log_name = f"{model}_{benchmark_mode}_benchmark"
executor = Executor(
workload.workload_path,
subject="benchmark",
data={"id": request_id},
send_response=False,
log_name=log_name,
additional_log_names=["output.txt"],
)
proc = executor.call(
benchmark.command,
)
logs = [os.path.join(workload.workload_path, f"{log_name}.txt")]
if not proc.is_ok:
raise ClientErrorException("Benchmark failed during execution.")
parser = BenchmarkParserFactory.get_parser(benchmark_mode, logs)
metrics = parser.process()
metric = {}
execution_details: Dict[str, Any] = {}
if benchmark_mode == Benchmarks.PERF:
result_field = f"perf_throughput_{model}"
elif benchmark_mode == Benchmarks.ACC:
result_field = f"acc_{model}"
else:
raise InternalException(f"Benchmark mode {benchmark_mode} is not supported.")
if isinstance(metrics, dict):
metric = {result_field: metrics.get(result_field, "")}
execution_details = response_data.get("execution_details", {})
model_benchmark_details = execution_details.get(f"{model}_benchmark", {})
model_benchmark_details.update(
{
benchmark_mode: benchmark.serialize(),
},
)
response_data.update({"progress": f"{benchmark_count}/{benchmark_total}"})
response_data.update(metric)
response_data["execution_details"].update(
{f"{model}_benchmark": model_benchmark_details},
)
workdir.update_metrics(
request_id=request_id,
metric_data=metric,
)
workdir.update_execution_details(
request_id=request_id,
execution_details=execution_details,
)
log.debug(f"Parsed data is {json.dumps(response_data)}")
mq.post_success("benchmark_progress", response_data)
return response_data
def execute_benchmark(data: Dict[str, Any]) -> None:
"""
Execute benchmark.
Expected data:
{
"id": "configuration_id",
"workspace_path": "/path/to/workspace",
"models": [
{
"precision": "fp32",
"path": "/localdisk/fp32.pb"
},
{
"precision": "int8",
"path": "/localdisk/int8.pb"
}
]
}
"""
from lpot.ux.utils.workload.workload import Workload
request_id = str(data.get("id", ""))
models = data.get("models", None)
if not (request_id and models):
message = "Missing request id or model list."
mq.post_error(
"benchmark_finish",
{"message": message, "code": 404, "id": request_id},
)
raise ClientErrorException(message)
workdir = Workdir(request_id=request_id, overwrite=False)
try:
workload_path = workdir.workload_path
workload_data = load_json(
os.path.join(workload_path, "workload.json"),
)
except Exception as err:
mq.post_error(
"benchmark_finish",
{"message": repr(err), "code": 404, "id": request_id},
)
raise ClientErrorException(repr(err))
workload = Workload(workload_data)
response_data: Dict[str, Any] = {"id": request_id, "execution_details": {}}
mq.post_success(
"benchmark_start",
{
"message": "started",
"id": request_id,
},
)
for idx, model_info in enumerate(models, start=1):
model_precision = model_info.get("precision", None)
model_path = model_info.get("path", None)
benchmark_mode = model_info.get("mode", "performance")
if not (model_precision and model_path):
message = "Missing model precision or model path."
mq.post_error(
"benchmark_finish",
{"message": message, "code": 404, "id": request_id},
)
raise ClientErrorException(message)
benchmark: Benchmark = Benchmark(
workload=workload,
model_path=model_path,
datatype=model_precision,
mode=benchmark_mode,
)
log_name = f"{model_precision}_{benchmark_mode}_benchmark"
executor = Executor(
workload_path,
subject="benchmark",
data={"id": request_id},
send_response=False,
log_name=log_name,
additional_log_names=["output.txt"],
)
proc = executor.call(
benchmark.command,
)
logs = [os.path.join(workload_path, f"{log_name}.txt")]
if proc.is_ok:
parser = Parser(logs)
metrics = parser.process()
metric = {}
execution_details: Dict[str, Any] = {}
throughput_field = f"perf_throughput_{model_precision}"
if isinstance(metrics, dict):
metric = {throughput_field: metrics.get(throughput_field, "")}
execution_details = {
f"{model_precision}_benchmark": benchmark.serialize(),
}
response_data.update({"progress": f"{idx}/{len(models)}"})
response_data.update(metric)
response_data["execution_details"].update(execution_details)
workdir.update_metrics(
request_id=request_id,
metric_data=metric,
)
workdir.update_execution_details(
request_id=request_id,
execution_details=execution_details,
)
log.debug(f"Parsed data is {json.dumps(response_data)}")
mq.post_success("benchmark_progress", response_data)
else:
log.error("Benchmark failed.")
mq.post_failure("benchmark_finish", {"message": "failed", "id": request_id})
raise ClientErrorException("Benchmark failed during execution.")
mq.post_success("benchmark_finish", response_data)