Set strict export explicitly for API change

Author: CuiYifeng

docs/source/quick_start.rst

@@ -156,7 +156,7 @@ API Example::
               model(image)
 
   # Step 1. program capture
-  m = export(m, *example_inputs).module()
+  m = export(m, *example_inputs, strict=True).module()
   # we get a model with aten ops
 
   # Step 2. quantization

docs/source/tutorials_source/pt2e_quant_openvino.rst

@@ -107,7 +107,7 @@ We will start by performing the necessary imports, capturing the FX Graph from t
 
     # Capture the FX Graph to be quantized
     with torch.no_grad(), nncf.torch.disable_patching():
-        exported_model = torch.export.export(model, example_inputs).module()
+        exported_model = torch.export.export(model, example_inputs, strict=True).module()
 
 
 

docs/source/tutorials_source/pt2e_quant_ptq.rst

@@ -66,7 +66,7 @@ The PyTorch 2 export quantization API looks like this:
   # Step 1. program capture
   # This is available for pytorch 2.6+, for more details on lower pytorch versions
   # please check `Export the model with torch.export` section
-  m = torch.export.export(m, example_inputs).module()
+  m = torch.export.export(m, example_inputs, strict=True).module()
   # we get a model with aten ops
 
 
@@ -350,7 +350,7 @@ Here is how you can use ``torch.export`` to export the model:
 
     example_inputs = (torch.rand(2, 3, 224, 224),)
     # for pytorch 2.6+
-    exported_model = torch.export.export(model_to_quantize, example_inputs).module()
+    exported_model = torch.export.export(model_to_quantize, example_inputs, strict=True).module()
 
     # for pytorch 2.5 and before
     # from torch._export import capture_pre_autograd_graph
@@ -362,7 +362,7 @@ Here is how you can use ``torch.export`` to export the model:
       {0: torch.export.Dim("dim")} if i == 0 else None
       for i in range(len(example_inputs))
     )
-    exported_model = torch.export.export_for_training(model_to_quantize, example_inputs, dynamic_shapes=dynamic_shapes).module()
+    exported_model = torch.export.export_for_training(model_to_quantize, example_inputs, dynamic_shapes=dynamic_shapes, strict=True).module()
 
     # for pytorch 2.5 and before
     # dynamic_shape API may vary as well

docs/source/tutorials_source/pt2e_quant_qat.rst

@@ -38,7 +38,7 @@ to the post training quantization (PTQ) flow for the most part:
   # Step 1. program capture
   # This is available for pytorch 2.6+, for more details on lower pytorch versions
   # please check `Export the model with torch.export` section
-  m = torch.export.export(m, example_inputs).module()
+  m = torch.export.export(m, example_inputs, strict=True).module()
   # we get a model with aten ops
 
   # Step 2. quantization-aware training
@@ -273,7 +273,7 @@ Here is how you can use ``torch.export`` to export the model:
 
     example_inputs = (torch.rand(2, 3, 224, 224),)
     # for pytorch 2.6+
-    exported_model = torch.export.export(float_model, example_inputs).module()
+    exported_model = torch.export.export(float_model, example_inputs, strict=True).module()
     # for pytorch 2.5 and before
     # from torch._export import capture_pre_autograd_graph
     # exported_model = capture_pre_autograd_graph(model_to_quantize, example_inputs)
@@ -288,7 +288,7 @@ Here is how you can use ``torch.export`` to export the model:
       {0: torch.export.Dim("dim")} if i == 0 else None
       for i in range(len(example_inputs))
     )
-    exported_model = torch.export.export(float_model, example_inputs, dynamic_shapes=dynamic_shapes).module()
+    exported_model = torch.export.export(float_model, example_inputs, dynamic_shapes=dynamic_shapes, strict=True).module()
 
     # for pytorch 2.5 and before
     # dynamic_shape API may vary as well

docs/source/tutorials_source/pt2e_quant_x86_inductor.rst

@@ -104,7 +104,8 @@ We will start by performing the necessary imports, capturing the FX Graph from t
         # Note: requires torch >= 2.6
         exported_model = export(
             model,
-            example_inputs
+            example_inputs,
+            strict=True
         )
 
 
@@ -266,7 +267,7 @@ The PyTorch 2 Export QAT flow is largely similar to the PTQ flow:
   # Step 1. program capture
   # NOTE: this API will be updated to torch.export API in the future, but the captured
   # result shoud mostly stay the same
-  exported_model = export(m, example_inputs)
+  exported_model = export(m, example_inputs, strict=True)
   # we get a model with aten ops
 
   # Step 2. quantization-aware training

docs/source/tutorials_source/pt2e_quant_xpu_inductor.rst

@@ -85,6 +85,7 @@ We will start by performing the necessary imports, capturing the FX Graph from t
         exported_model = export(
             model,
             example_inputs,
+            strict=True
         ).module()