def test_migration_v1_to_v2(self) -> None:
"""Test Workloads List Migration from v1 to v2."""
migrator = WorkloadsListMigrator()
migrator.workloads_json = os.path.join(
os.path.dirname(__file__),
"files",
"workloads_list_v1.json",
)
self.assertEqual(migrator.require_migration, True)
migrator.migrate()
self.assertEqual(migrator.require_migration, False)
expected_json_path = os.path.join(
os.path.dirname(__file__),
"files",
"workloads_list_v2.json",
)
expected = _load_json_as_dict(expected_json_path)
self.assertEqual(type(migrator.workloads_data), dict)
self.assertDictEqual(migrator.workloads_data, expected) # type: ignore
def test_workload_migration_from_v2(self) -> None:
"""Test Workload v2 config migrator."""
workload_json_path = os.path.join(
os.path.dirname(__file__),
"files",
"workload_v2_tuned.json",
)
workload_migrator = WorkloadMigrator(
workload_json_path=workload_json_path, )
workload_migrator.migrate()
expected_json_path = os.path.join(
os.path.dirname(__file__),
"files",
"workload_v2_tuned.json",
)
expected = _load_json_as_dict(expected_json_path)
self.assertDictEqual(workload_migrator.workload_data,
expected) # type: ignore
def execute_benchmark(data: Dict[str, Any]) -> None:
"""
Execute benchmark.
Expected data:
{
"id": "configuration_id",
"workspace_path": "/path/to/workspace",
"input_model": {
"precision": "fp32",
"path": "/localdisk/fp32.pb"
},
"optimized_model": {
"precision": "int8",
"path": "/localdisk/int8.pb"
}
}
"""
from lpot.ux.utils.workload.workload import Workload
request_id = str(data.get("id", ""))
input_model = data.get("input_model", None)
input_model.update({"model_type": "input_model"})
optimized_model = data.get("optimized_model", None)
optimized_model.update({"model_type": "optimized_model"})
if not (request_id and input_model and optimized_model):
message = "Missing request id, input or optimized model data."
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_as_dict(
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,
},
)
models = [input_model, optimized_model]
benchmark_count = 0
benchmark_total = 0
for model_info in models:
benchmark_modes: List[str] = model_info.get("mode", [Benchmarks.PERF])
if (
not workload.tune and Benchmarks.ACC not in benchmark_modes
): # Accuracy information is provided only in tuning
benchmark_modes.append(Benchmarks.ACC)
model_info.update({"benchmark_modes": benchmark_modes})
benchmark_total += len(benchmark_modes)
for model_info in models:
model_precision = model_info.get("precision", None)
model_type = model_info.get("model_type", None)
model_path = model_info.get("path", None)
benchmark_modes = model_info.get("benchmark_modes", None)
if not (model_precision and model_path and model_type and benchmark_modes):
message = "Missing model precision, model path or model type."
mq.post_error(
"benchmark_finish",
{"message": message, "code": 404, "id": request_id},
)
raise ClientErrorException(message)
for benchmark_mode in benchmark_modes:
benchmark_count += 1
response_data = benchmark_model_and_respond_to_ui(
response_data=response_data,
workload=workload,
workdir=workdir,
model=model_type,
model_path=model_path,
model_precision=model_precision,
benchmark_mode=benchmark_mode,
benchmark_count=benchmark_count,
benchmark_total=benchmark_total,
)
def execute_optimization(data: Dict[str, Any]) -> dict:
"""Get configuration."""
from lpot.ux.utils.workload.workload import Workload
if not str(data.get("id", "")):
message = "Missing request id."
mq.post_error(
"optimization_finish",
{"message": message, "code": 404},
)
raise Exception(message)
request_id: str = data["id"]
workdir = Workdir(request_id=request_id, overwrite=False)
workload_path: str = workdir.workload_path
try:
workload_data = _load_json_as_dict(
os.path.join(workload_path, "workload.json"),
)
except Exception as err:
mq.post_error(
"optimization_finish",
{"message": repr(err), "code": 404, "id": request_id},
)
raise err
workload = Workload(workload_data)
optimization: Optimization = OptimizationFactory.get_optimization(
workload,
workdir.template_path,
)
send_data = {
"message": "started",
"id": request_id,
"size_input_model": get_size(optimization.input_graph),
}
workdir.clean_logs()
workdir.update_data(
request_id=request_id,
model_path=optimization.input_graph,
input_precision=optimization.input_precision,
model_output_path=optimization.output_graph,
output_precision=optimization.output_precision,
status="wip",
)
executor = Executor(
workspace_path=workload_path,
subject="optimization",
data=send_data,
log_name="output",
)
proc = executor.call(
optimization.command,
)
optimization_time = executor.process_duration
if optimization_time:
optimization_time = round(optimization_time, 2)
log.debug(f"Elapsed time: {optimization_time}")
logs = [os.path.join(workload_path, "output.txt")]
parser = OptimizationParser(logs)
if proc.is_ok:
response_data = parser.process()
if isinstance(response_data, dict):
response_data["id"] = request_id
response_data["optimization_time"] = optimization_time
response_data["size_optimized_model"] = get_size(optimization.output_graph)
response_data["model_output_path"] = optimization.output_graph
response_data["size_input_model"] = get_size(optimization.input_graph)
response_data["is_custom_dataloader"] = bool(workdir.template_path)
workdir.update_data(
request_id=request_id,
model_path=optimization.input_graph,
model_output_path=optimization.output_graph,
metric=response_data,
status="success",
execution_details={"optimization": optimization.serialize()},
input_precision=optimization.input_precision,
output_precision=optimization.output_precision,
)
response_data["execution_details"] = {"optimization": optimization.serialize()}
log.debug(f"Parsed data is {json.dumps(response_data)}")
mq.post_success("optimization_finish", response_data)
return response_data
else:
log.debug("FAIL")
workdir.update_data(
request_id=request_id,
model_path=optimization.input_graph,
input_precision=optimization.input_precision,
output_precision=optimization.output_precision,
status="error",
)
mq.post_failure("optimization_finish", {"message": "failed", "id": request_id})
raise ClientErrorException("Optimization failed during execution.")
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_as_dict(
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 = BenchmarkParser(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)