Add num_workers argument in model benchmark (#511)

Change num_workers to configurable in model benchmark data loader.

superbench/benchmarks/model_benchmarks/model_base.py

@@ -78,6 +78,13 @@ class ModelBenchmark(Benchmark):
             required=False,
             help='The number of batch size.',
         )
+        self._parser.add_argument(
+            '--num_workers',
+            type=int,
+            default=8,
+            required=False,
+            help='Number of subprocesses to use for data loading.',
+        )
         self._parser.add_argument(
             '--precision',
             type=Precision,

superbench/benchmarks/model_benchmarks/pytorch_base.py

@@ -181,7 +181,7 @@ class PytorchBase(ModelBenchmark):
             dataset=self._dataset,
             batch_size=self._args.batch_size,
             shuffle=False,
-            num_workers=8,
+            num_workers=self._args.num_workers,
             sampler=train_sampler,
             drop_last=True,
             pin_memory=self._args.pin_memory

tests/benchmarks/model_benchmarks/test_model_base.py

@@ -167,6 +167,7 @@ def test_arguments_related_interfaces():
   --no_gpu              Disable GPU training.
   --num_steps int       The number of test step.
   --num_warmup int      The number of warmup step.
+  --num_workers int     Number of subprocesses to use for data loading.
   --pin_memory          Enable option to pin memory in data loader.
   --precision Precision [Precision ...]
                         Model precision. E.g. fp8_hybrid fp8_e4m3 fp8_e5m2
@@ -206,6 +207,7 @@ def test_preprocess():
   --no_gpu              Disable GPU training.
   --num_steps int       The number of test step.
   --num_warmup int      The number of warmup step.
+  --num_workers int     Number of subprocesses to use for data loading.
   --pin_memory          Enable option to pin memory in data loader.
   --precision Precision [Precision ...]
                         Model precision. E.g. fp8_hybrid fp8_e4m3 fp8_e5m2