Migrate from capture_pre_autograd_graph to export_for_training (#8398)

pytorch · Dec 17, 2024 · b2b890e · b2b890e
1 parent 3f915ab
commit b2b890e
Showing 1 changed file with 3 additions and 3 deletions.
diff --git a/test/stablehlo/test_pt2e_qdq.py b/test/stablehlo/test_pt2e_qdq.py
@@ -6,7 +6,7 @@
 import torch
 import torch_xla.core.xla_model as xm
 import torchvision
-from torch._export import capture_pre_autograd_graph
+from torch.export import export_for_training
 from torch.ao.quantization.quantize_pt2e import convert_pt2e, prepare_pt2e
 from torch.ao.quantization.quantizer.xnnpack_quantizer import (
     XNNPACKQuantizer, get_symmetric_quantization_config)
@@ -88,7 +88,7 @@ def test_resnet18(self):
     # Step 1: export resnet18
     args = (torch.randn(1, 3, 224, 224),)
     m = torchvision.models.resnet18().eval()
-    m = capture_pre_autograd_graph(m, args)
+    m = export_for_training(m, args).module()
 
     # Step 2: Insert observers or fake quantize modules
     quantizer = XNNPACKQuantizer().set_global(
@@ -120,7 +120,7 @@ def test_resnet18_per_channel(self):
     # Step 1: export resnet18
     args = (torch.randn(1, 3, 224, 224),)
     m = torchvision.models.resnet18().eval()
-    m = capture_pre_autograd_graph(m, args)
+    m = export_for_training(m, args).module()
 
     # Step 2: Insert observers or fake quantize modules
     quantizer = XNNPACKQuantizer().set_global(