[pt2e][quant] Make move_exported_model_to_train/eval idempotent #142239
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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]
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/142239
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…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]
jerryzh168
approved these changes
Dec 6, 2024
jerryzh168
reviewed
Dec 6, 2024
…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]
andrewor14
added a commit
that referenced
this pull request
Dec 6, 2024
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-source-id: f0e6c78 Pull Request resolved: #142239
…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]
andrewor14
added a commit
that referenced
this pull request
Dec 6, 2024
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-source-id: 6848f32 Pull Request resolved: #142239
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Stack from ghstack (oldest at bottom):
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 callsthis 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