pt2e quantization tutorial related updates

prototype_source/pt2e_quant_ptq.rst

@@ -51,7 +51,6 @@ The PyTorch 2 export quantization API looks like this:
 .. code:: python
 
   import torch
-  from torch._export import capture_pre_autograd_graph
   class M(torch.nn.Module):
      def __init__(self):
         super().__init__()
@@ -65,9 +64,9 @@ The PyTorch 2 export quantization API looks like this:
   m = M().eval()
 
   # 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
-  m = capture_pre_autograd_graph(m, *example_inputs)
+  # This is available for pytorch 2.5+, for more details on lower pytorch versions
+  # please check `Export the model with torch.export` section
+  m = torch.export.export_for_training(m, example_inputs).module()
   # we get a model with aten ops
 
 
@@ -77,7 +76,7 @@ The PyTorch 2 export quantization API looks like this:
     convert_pt2e,
   )
 
-  from torch.ao.quantization.quantizer import (
+  from torch.ao.quantization.quantizer.xnnpack_quantizer import (
     XNNPACKQuantizer,
     get_symmetric_quantization_config,
   )
@@ -280,10 +279,7 @@ and rename it to ``data/resnet18_pretrained_float.pth``.
         return model
 
     def print_size_of_model(model):
-        if isinstance(model, torch.jit.RecursiveScriptModule):
-            torch.jit.save(model, "temp.p")
-        else:
-            torch.jit.save(torch.jit.script(model), "temp.p")
+        torch.save(model.state_dict(), "temp.p")
         print("Size (MB):", os.path.getsize("temp.p")/1e6)
         os.remove("temp.p")
 
@@ -351,18 +347,28 @@ Here is how you can use ``torch.export`` to export the model:
 
 .. code-block:: python
 
-    from torch._export import capture_pre_autograd_graph
-
     example_inputs = (torch.rand(2, 3, 224, 224),)
-    exported_model = capture_pre_autograd_graph(model_to_quantize, example_inputs)
+    # for pytorch 2.5+
+    exported_model = torch.export.export_for_training(model_to_quantize, example_inputs).module()
+
+    # for pytorch 2.4 and before
+    # from torch._export import capture_pre_autograd_graph
+    # exported_model = capture_pre_autograd_graph(model_to_quantize, example_inputs)
+
     # or capture with dynamic dimensions
+    # for pytorch 2.5+
+    dynamic_shapes = tuple(
+      {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()
+
+    # for pytorch 2.4 and before
+    # dynamic_shape API may vary as well
     # from torch._export import dynamic_dim
     # exported_model = capture_pre_autograd_graph(model_to_quantize, example_inputs, constraints=[dynamic_dim(example_inputs[0], 0)])
 
 
-``capture_pre_autograd_graph`` is a short term API, it will be updated to use the offical ``torch.export`` API when that is ready.
-
-
 Import the Backend Specific Quantizer and Configure how to Quantize the Model
 -----------------------------------------------------------------------------
 
@@ -454,7 +460,7 @@ we offer in the long term might change based on feedback from PyTorch users.
        out_fp32, out_scale, out_zero_point, out_quant_min, out_quant_max, torch.int8)
        return out_i8
 
-* Reference Quantized Model Representation (available in the nightly build)
+* Reference Quantized Model Representation
 
   We will have a special representation for selected ops, for example, quantized linear. Other ops are represented as ``dq -> float32_op -> q`` and ``q/dq`` are decomposed into more primitive operators.
   You can get this representation by using ``convert_pt2e(..., use_reference_representation=True)``.
@@ -485,8 +491,6 @@ Now we can compare the size and model accuracy with baseline model.
 .. code-block:: python
 
     # Baseline model size and accuracy
-    scripted_float_model_file = "resnet18_scripted.pth"
-
     print("Size of baseline model")
     print_size_of_model(float_model)
 
@@ -495,6 +499,8 @@ Now we can compare the size and model accuracy with baseline model.
 
     # Quantized model size and accuracy
     print("Size of model after quantization")
+    # export again to remove unused weights
+    quantized_model = torch.export.export_for_training(quantized_model, example_inputs).module()
     print_size_of_model(quantized_model)
 
     top1, top5 = evaluate(quantized_model, criterion, data_loader_test)

prototype_source/pt2e_quant_ptq_x86_inductor.rst

@@ -0,0 +1,10 @@
+Quantization in PyTorch 2.0 Export Tutorial
+===========================================
+
+This tutorial has been moved.
+
+Redirecting in 3 seconds...
+
+.. raw:: html
+
+   <meta http-equiv="Refresh" content="3; url='https://pytorch.org/tutorials/prototype/pt2e_quant_x86_inductor.html'" />

prototype_source/pt2e_quant_qat.rst

@@ -18,7 +18,7 @@ to the post training quantization (PTQ) flow for the most part:
     prepare_qat_pt2e,
     convert_pt2e,
   )
-  from torch.ao.quantization.quantizer import (
+  from torch.ao.quantization.quantizer.xnnpack_quantizer import (
     XNNPACKQuantizer,
     get_symmetric_quantization_config,
   )
@@ -36,9 +36,9 @@ to the post training quantization (PTQ) flow for the most part:
   m = M()
 
   # 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
-  m = capture_pre_autograd_graph(m, *example_inputs)
+  # This is available for pytorch 2.5+, for more details on lower pytorch versions
+  # please check `Export the model with torch.export` section
+  m = torch.export.export_for_training(m, example_inputs).module()
   # we get a model with aten ops
 
   # Step 2. quantization-aware training
@@ -272,24 +272,35 @@ Here is how you can use ``torch.export`` to export the model:
     from torch._export import capture_pre_autograd_graph
 
     example_inputs = (torch.rand(2, 3, 224, 224),)
-    exported_model = capture_pre_autograd_graph(float_model, example_inputs)
+    # for pytorch 2.5+
+    exported_model = torch.export.export_for_training(float_model, example_inputs).module()
+    # for pytorch 2.4 and before
+    # from torch._export import capture_pre_autograd_graph
+    # exported_model = capture_pre_autograd_graph(model_to_quantize, example_inputs)
 
 
 .. code:: python
 
     # or, to capture with dynamic dimensions:
-    from torch._export import dynamic_dim
 
-    example_inputs = (torch.rand(2, 3, 224, 224),)
-    exported_model = capture_pre_autograd_graph(
-        float_model,
-        example_inputs,
-        constraints=[dynamic_dim(example_inputs[0], 0)],
+    # for pytorch 2.5+
+    dynamic_shapes = tuple(
+      {0: torch.export.Dim("dim")} if i == 0 else None
+      for i in range(len(example_inputs))
     )
-.. note::
-
-   ``capture_pre_autograd_graph`` is a short term API, it will be updated to use the offical ``torch.export`` API when that is ready.
-
+    exported_model = torch.export.export_for_training(float_model, example_inputs, dynamic_shapes=dynamic_shapes).module()
+
+    # for pytorch 2.4 and before
+    # dynamic_shape API may vary as well
+    # from torch._export import dynamic_dim
+
+    # example_inputs = (torch.rand(2, 3, 224, 224),)
+    # exported_model = capture_pre_autograd_graph(
+    #     float_model,
+    #     example_inputs,
+    #     constraints=[dynamic_dim(example_inputs[0], 0)],
+    # )
+
 
 Import the Backend Specific Quantizer and Configure how to Quantize the Model
 -----------------------------------------------------------------------------

prototype_source/quantization_in_pytorch_2_0_export_tutorial.rst

@@ -0,0 +1,10 @@
+Quantization in PyTorch 2.0 Export Tutorial
+===========================================
+
+This tutorial has been moved.
+
+Redirecting in 3 seconds...
+
+.. raw:: html
+
+   <meta http-equiv="Refresh" content="3; url='https://pytorch.org/tutorials/prototype/pt2e_quant_ptq.html'" />