Remove jit from the framework code (#1787)

Summary:
Jit(torchscript) is now in archive mode and there is no need that we add it to the explicit options.

Jit(torchscript) is still supported with the extra args option `--backend torchsript`.

Fixes #1777

Pull Request resolved: #1787

Reviewed By: msaroufim

Differential Revision: D47774649

Pulled By: xuzhao9

fbshipit-source-id: 7eb25e54d016da9a49b93c514cc302f6d8d4573c

conftest.py

@@ -11,9 +11,6 @@ def pytest_addoption(parser):
  help="Disable checks/assertions for machine configuration for stable benchmarks")
  parser.addoption("--disable_nograd", action='store_true',
  help="Disable no_grad for eval() runs")
- parser.addoption("--check_opt_vs_noopt_jit",
- action='store_true',
- help="The best attempt to check results for inference runs. Not all models support this!")
  parser.addoption("--cpu_only", action='store_true',
  help="Run benchmarks on cpu only and ignore machine configuration checks")
  parser.addoption("--cuda_only", action='store_true',

gen_summary_metadata.py

@@ -52,7 +52,7 @@ def _extract_detail(path: str) -> Dict[str, Any]:
  # Separate train and eval to isolated processes.
  task_t = ModelTask(path, timeout=TIMEOUT)
  try:
- task_t.make_model_instance(device=device, jit=False)
+ task_t.make_model_instance(device=device)
  task_t.set_train()
  task_t.train()
  task_t.extract_details_train()
@@ -64,7 +64,7 @@ def _extract_detail(path: str) -> Dict[str, Any]:
 
  task_e = ModelTask(path, timeout=TIMEOUT)
  try:
- task_e.make_model_instance(device=device, jit=False)
+ task_e.make_model_instance(device=device)
  task_e.set_eval()
  task_e.eval()
  task_e.extract_details_eval()

run.py

@@ -293,13 +293,6 @@ def _validate_profile_options(profile_options: str):
  default="cpu",
  help="Which device to use.",
  )
- parser.add_argument(
- "-m",
- "--mode",
- choices=["eager", "jit"],
- default="eager",
- help="Which mode to run.",
- )
  parser.add_argument(
  "-t",
  "--test",
@@ -407,7 +400,6 @@ def _validate_profile_options(profile_options: str):
  m = Model(
  device=args.device,
  test=args.test,
- jit=(args.mode == "jit"),
  batch_size=args.bs,
  extra_args=extra_args,
  )