NXP backend: Disable training mode for converted models.

Author: MartinPavella

backends/nxp/tests/executorch_pipeline.py

@@ -4,6 +4,8 @@
 # LICENSE file in the root directory of this source tree.
 
 import torch
+from torch import nn
+from torchao.quantization.pt2e.quantize_pt2e import convert_pt2e, prepare_pt2e
 
 from executorch import exir
 from executorch.backends.nxp.backend.custom_delegation_options import (
@@ -25,8 +27,6 @@
     ExecutorchProgramManager,
 )
 from executorch.extension.export_util.utils import export_to_edge
-from torch import nn
-from torchao.quantization.pt2e.quantize_pt2e import convert_pt2e, prepare_pt2e
 
 
 def _quantize_model(model, calibration_inputs: list[tuple[torch.Tensor]]):
@@ -70,6 +70,9 @@ def to_quantized_edge_program(
         else tuple(torch.ones(input_shape) for input_shape in input_shapes)
     )
 
+    # Make sure the model is in the evaluation mode.
+    model.eval()
+
     exir_program_aten = torch.export.export_for_training(
         model, example_input, strict=True
     )
@@ -126,5 +129,9 @@ def to_edge_program(
         if type(input_shapes) is tuple
         else tuple(torch.ones(input_shape) for input_shape in input_shapes)
     )
+
+    # Make sure the model is in the evaluation mode.
+    model.eval()
+
     exir_program = torch.export.export(model, example_input)
     return exir.to_edge(exir_program)