diff --git a/run.py b/run.py index 4026b2fd0..afba5ef93 100644 --- a/run.py +++ b/run.py @@ -82,13 +82,15 @@ def run_one_step_with_cudastreams(func, streamcount): def printResultSummaryTime( result_summary, model, - metrics_needed=[], + metrics_needed=None, flops_model_analyzer=None, model_flops=None, cpu_peak_mem=None, mem_device_id=None, gpu_peak_mem=None, ): + if metrics_needed is None: + metrics_needed = [] assert model is not None, "model can not be None." if args.device == "cuda": gpu_time = np.median(list(map(lambda x: x[0], result_summary))) @@ -160,11 +162,13 @@ def run_one_step( num_iter=10, export_metrics_file=None, stress=0, - metrics_needed=[], + metrics_needed=None, metrics_gpu_backend=None, model_flops=None, ): # Warm-up `nwarmup` rounds + if metrics_needed is None: + metrics_needed = [] for _i in range(nwarmup): func() diff --git a/scripts/upload_scribe.py b/scripts/upload_scribe.py index 392adca69..08cdab230 100644 --- a/scripts/upload_scribe.py +++ b/scripts/upload_scribe.py @@ -65,6 +65,7 @@ def upload(self, messages: list): ] ), }, + timeout=10.0, ) print(r.text) r.raise_for_status() diff --git a/scripts/upload_scribe_v2.py b/scripts/upload_scribe_v2.py index 441ebd548..9742cb2bf 100644 --- a/scripts/upload_scribe_v2.py +++ b/scripts/upload_scribe_v2.py @@ -98,6 +98,7 @@ def upload(self, messages: list): ] ), }, + timeout=10.0, ) print(r.text) r.raise_for_status() diff --git a/scripts/userbenchmark/upload_scribe.py b/scripts/userbenchmark/upload_scribe.py index 509768a2e..211c42365 100644 --- a/scripts/userbenchmark/upload_scribe.py +++ b/scripts/userbenchmark/upload_scribe.py @@ -64,6 +64,7 @@ def upload(self, messages: list): ] ), }, + timeout=10.0, ) print(r.text) r.raise_for_status() diff --git a/torchbenchmark/__init__.py b/torchbenchmark/__init__.py index e15acd828..f6391e039 100644 --- a/torchbenchmark/__init__.py +++ b/torchbenchmark/__init__.py @@ -385,8 +385,10 @@ def make_model_instance( test: str, device: str, batch_size: Optional[int] = None, - extra_args: List[str] = [], + extra_args: List[str] = None, ) -> None: + if extra_args is None: + extra_args = [] Model = globals()["Model"] model = Model( test=test, device=device, batch_size=batch_size, extra_args=extra_args @@ -733,7 +735,7 @@ def get_metadata_from_yaml(path): md = None if os.path.exists(metadata_path): with open(metadata_path, "r") as f: - md = yaml.load(f, Loader=yaml.FullLoader) + md = yaml.safe_load(f, Loader=yaml.FullLoader) return md diff --git a/torchbenchmark/_components/model_analyzer/TorchBenchAnalyzer.py b/torchbenchmark/_components/model_analyzer/TorchBenchAnalyzer.py index 9818798d0..ce6e7b978 100644 --- a/torchbenchmark/_components/model_analyzer/TorchBenchAnalyzer.py +++ b/torchbenchmark/_components/model_analyzer/TorchBenchAnalyzer.py @@ -26,12 +26,14 @@ class ModelAnalyzer: def __init__( self, export_metrics_file=None, - metrics_needed=[], + metrics_needed=None, metrics_gpu_backend="nvml", cpu_monitored_pid=None, ): # For debug # set_logger(logging.DEBUG) + if metrics_needed is None: + metrics_needed = [] set_logger() # delay the initialization to start_monitor self.gpu_factory = None diff --git a/torchbenchmark/_components/model_analyzer/dcgm/cpu_monitor.py b/torchbenchmark/_components/model_analyzer/dcgm/cpu_monitor.py index 8b4eeb69e..2c92f1b2f 100644 --- a/torchbenchmark/_components/model_analyzer/dcgm/cpu_monitor.py +++ b/torchbenchmark/_components/model_analyzer/dcgm/cpu_monitor.py @@ -12,7 +12,9 @@ class CPUMonitor(Monitor): A CPU monitor that uses psutil to monitor CPU usage """ - def __init__(self, frequency, metrics_needed=[], monitored_pid=None): + def __init__(self, frequency, metrics_needed=None, monitored_pid=None): + if metrics_needed is None: + metrics_needed = [] super().__init__(frequency, metrics_needed) # It is a raw record list. [timestamp, cpu_memory_usage, cpu_available_memory] self._cpu_records = [] diff --git a/torchbenchmark/canary_models/DALLE2_pytorch/__init__.py b/torchbenchmark/canary_models/DALLE2_pytorch/__init__.py index bc46969bd..0ff5cbeb9 100644 --- a/torchbenchmark/canary_models/DALLE2_pytorch/__init__.py +++ b/torchbenchmark/canary_models/DALLE2_pytorch/__init__.py @@ -21,7 +21,9 @@ class Model(BenchmarkModel): DEFAULT_EVAL_BSIZE = 1 CANNOT_SET_CUSTOM_OPTIMIZER = True - def __init__(self, test, device, batch_size=None, extra_args=[]): + def __init__(self, test, device, batch_size=None, extra_args=None): + if extra_args is None: + extra_args = [] super().__init__( test=test, device=device, batch_size=batch_size, extra_args=extra_args )