Summary: Before we would recompile the model unnecessarily even
if the model is already in the desired mode. For training
frameworks that assume `model.train()` is idempotent and calls
this before every single training step, this led to a bunch of
tiny graphs and poor performance. This commit makes these calls
no-ops if we're already in the target train/eval mode.
Test Plan:
python test/test_quantization -k TestQuantizePT2E.test_allow_exported_model_train_eval_idempotent
[ghstack-poisoned]

…otent"
Summary: Before we would recompile the model unnecessarily even
if the model is already in the desired mode. For training
frameworks that assume `model.train()` is idempotent and calls
this before every single training step, this led to a bunch of
tiny graphs and poor performance. This commit makes these calls
no-ops if we're already in the target train/eval mode.
Test Plan:
python test/test_quantization -k TestQuantizePT2E.test_allow_exported_model_train_eval_idempotent
[ghstack-poisoned]

…otent"
Summary: Before we would recompile the model unnecessarily even
if the model is already in the desired mode. For training
frameworks that assume `model.train()` is idempotent and calls
this before every single training step, this led to a bunch of
tiny graphs and poor performance. This commit makes these calls
no-ops if we're already in the target train/eval mode.
Test Plan:
python test/test_quantization -k TestQuantizePT2E.test_allow_exported_model_train_eval_idempotent
[ghstack-poisoned]

…otent"
Summary: Before we would recompile the model unnecessarily even
if the model is already in the desired mode. For training
frameworks that assume `model.train()` is idempotent and calls
this before every single training step, this led to a bunch of
tiny graphs and poor performance. This commit makes these calls
no-ops if we're already in the target train/eval mode.
Test Plan:
python test/test_quantization -k TestQuantizePT2E.test_allow_exported_model_train_eval_idempotent
[ghstack-poisoned]