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Added warnings if the model is in training mode #3676

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Added warnings if the model is in training mode #3676

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4 changes: 4 additions & 0 deletions py/torch_tensorrt/dynamo/_compiler.py
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Original file line number Diff line number Diff line change
Expand Up @@ -746,6 +746,10 @@ def compile_module(
Returns:
Compiled FX GraphModule
"""
if any(v.requires_grad for v in gm.state_dict().values()):
logger.warning(
"The model may be in training mode, which may affect the performance of the compiled model!"
)
dryrun_tracker = DryRunTracker()
if sample_kwarg_inputs is None:
sample_kwarg_inputs = {}
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12 changes: 8 additions & 4 deletions py/torch_tensorrt/dynamo/runtime/_MutableTorchTensorRTModule.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@
import warnings
from copy import deepcopy
from enum import Enum, auto
from typing import Any, Dict, Iterator, Optional, Set, Union
from typing import Any, Dict, Iterator, Optional, Set, Tuple, Union

import numpy as np
import torch
Expand Down Expand Up @@ -70,7 +70,9 @@ def __init__(
strict: bool = True,
allow_complex_guards_as_runtime_asserts: bool = False,
weight_streaming_budget: Optional[int] = None,
enabled_precisions: Optional[Set[Union[torch.dtype, dtype]]] = None,
enabled_precisions: Union[
Set[Union[torch.dtype, dtype]], Tuple[Union[torch.dtype, dtype]]
] = _defaults.ENABLED_PRECISIONS,
**kwargs: Any,
) -> None:
"""
Expand Down Expand Up @@ -128,6 +130,10 @@ def __init__(
self.refit_state = RefitState()
self.pytorch_model = _make_refit_change_trigger(pytorch_model, self.refit_state)
self.original_model = pytorch_model
if pytorch_model.training:
logger.warning(
"The model may be in training mode, which may affect the performance of the compiled model!"
)
# Process settings
self.gm: Any = None
self.exp_program: Any = None
Expand Down Expand Up @@ -163,8 +169,6 @@ def __init__(
"Weight stremaing budget is not set. Using auto weight streaming budget"
)
self.enabled_precisions = enabled_precisions
if self.enabled_precisions is None:
self.enabled_precisions = _defaults.ENABLED_PRECISIONS

cls = self.__class__
self.__class__ = type(
Expand Down
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