From 67fa3b45a8cf5a64e8447e7d053513045d91856b Mon Sep 17 00:00:00 2001
From: Shangdi Yu <shangdiy@meta.com>
Date: Thu, 3 Oct 2024 16:54:12 -0700
Subject: [PATCH] training ir torchao migration (#1006)

Summary:

Migrate capture_pre_autograd_graph to export_for_training.

We still need to keep capture_pre_autograd_graph call because torch/ao's CI tests uses earlier version of pytorch that does not have export_for_training.

Differential Revision: D63859678
---
 test/dtypes/test_uint4.py            | 21 ++++++++++++++++-----
 test/integration/test_integration.py | 12 ++++++++++--
 torchao/utils.py                     |  2 +-
 3 files changed, 27 insertions(+), 8 deletions(-)

diff --git a/test/dtypes/test_uint4.py b/test/dtypes/test_uint4.py
index aa9415e51b..f16e843c4b 100644
--- a/test/dtypes/test_uint4.py
+++ b/test/dtypes/test_uint4.py
@@ -7,7 +7,6 @@
 from torch.ao.quantization.quantize_pt2e import prepare_pt2e, convert_pt2e
 from torch.ao.quantization.quantizer import QuantizationSpec, Quantizer
 
-from torch._export import capture_pre_autograd_graph
 from torch.testing._internal.common_quantization import (
     NodeSpec as ns,
     QuantizationTestCase,
@@ -26,6 +25,12 @@
 )
 import copy
 
+has_export_for_training = False
+try:
+    from torch.export import export_for_training
+    has_export_for_training = True
+except ImportError:
+    from torch._export import capture_pre_autograd_graph
 
 def _apply_weight_only_uint4_quant(model):
     def fn(mod):
@@ -203,10 +208,16 @@ def forward(self, x):
 
         # program capture
         m = copy.deepcopy(m_eager)
-        m = capture_pre_autograd_graph(
-            m,
-            example_inputs,
-        )
+        if has_export_for_training:
+            m = export_for_training(
+                m,
+                example_inputs,
+            ).module()
+        else:
+            m = capture_pre_autograd_graph(
+                    m,
+                    example_inputs,
+                ).module()
 
         m = prepare_pt2e(m, quantizer)
         # Calibrate
diff --git a/test/integration/test_integration.py b/test/integration/test_integration.py
index 5f81858ba0..ea310d1a1b 100644
--- a/test/integration/test_integration.py
+++ b/test/integration/test_integration.py
@@ -1484,11 +1484,19 @@ def forward(self, x):
 
         # make sure it compiles
         example_inputs = (x,)
-        from torch._export import capture_pre_autograd_graph
         # TODO: export changes numerics right now, this is because of functionalization according to Zhengxu
         # we can re-enable this after non-functional IR is enabled in export
         # model = torch.export.export(model, example_inputs).module()
-        model = capture_pre_autograd_graph(model, example_inputs)
+        has_export_for_training = False
+        try:
+            from torch.export import export_for_training
+            has_export_for_training = True
+        except ImportError:
+            from torch._export import capture_pre_autograd_graph
+        if has_export_for_training:
+            model = export_for_training(model, example_inputs).module()
+        else:
+            model = capture_pre_autograd_graph(model, example_inputs)
         after_export = model(x)
         self.assertTrue(torch.equal(after_export, ref))
         if api is _int8da_int8w_api:
diff --git a/torchao/utils.py b/torchao/utils.py
index 4b5409e657..a0302cabe6 100644
--- a/torchao/utils.py
+++ b/torchao/utils.py
@@ -180,7 +180,7 @@ def _the_op_that_needs_to_be_preserved(...)
 
         # after this, `_the_op_that_needs_to_be_preserved` will be preserved as
         # torch.ops.my_namespace.the_op_that_needs_to_be_preserved operator after
-        # torch.export.export / torch._export.capture_pre_autograd_graph
+        # torch.export.export / torch._export.export_for_training
 
     """
     from torch._inductor.decomposition import register_decomposition