Remove redundant pt2_mode=True

backends/qnnpack/partition/support_patterns.py

@@ -48,9 +48,9 @@ def get_dynamic_quantized_graph(f, example_inputs, dynamic_shape=False):
     # Convert module
     converted_mod = _convert_to_reference_decomposed_fx(prepared_mod)
     if dynamic_shape:
-        capture_config = CaptureConfig(pt2_mode=True, enable_dynamic_shape=True)
+        capture_config = CaptureConfig(enable_dynamic_shape=True)
     else:
-        capture_config = CaptureConfig(pt2_mode=True)
+        capture_config = CaptureConfig()
     # EXIR trace
     gm = (
         exir.capture(converted_mod, example_inputs, capture_config)

backends/qnnpack/test/test_qnnpack.py

@@ -83,7 +83,7 @@ def test_qnnpack_per_channel_dynamic_mm(self):
         )
 
         # Step 2: EXIR capturing
-        capture_config = CaptureConfig(pt2_mode=True, enable_dynamic_shape=False)
+        capture_config = CaptureConfig(enable_dynamic_shape=False)
         captured_mod = exir.capture(
             converted_mod, example_inputs, config=capture_config
         ).to_edge(EDGE_COMPILE_CONFIG)
@@ -115,9 +115,7 @@ def forward(self, x):
 
         composite_model(*example_inputs)
         program = (
-            exir.capture(
-                composite_model, example_inputs, exir.CaptureConfig(pt2_mode=True)
-            )
+            exir.capture(composite_model, example_inputs, exir.CaptureConfig())
             .to_edge(EDGE_COMPILE_CONFIG)
             .to_executorch(config=EXECUTORCH_BACKEND_CONFIG)
             .program
@@ -159,7 +157,7 @@ def test_qnnpack_per_channel_dynamic_qlinear(self):
         )
 
         # Step 2: EXIR capturing
-        capture_config = CaptureConfig(pt2_mode=True, enable_dynamic_shape=False)
+        capture_config = CaptureConfig(enable_dynamic_shape=False)
         captured_mod = exir.capture(
             converted_mod, example_inputs, config=capture_config
         ).to_edge(EDGE_COMPILE_CONFIG)
@@ -202,9 +200,7 @@ def forward(self, x):
 
         composite_model(*example_inputs)
         program = (
-            exir.capture(
-                composite_model, example_inputs, exir.CaptureConfig(pt2_mode=True)
-            )
+            exir.capture(composite_model, example_inputs, exir.CaptureConfig())
             .to_edge(EDGE_COMPILE_CONFIG)
             .to_executorch(config=EXECUTORCH_BACKEND_CONFIG)
             .program
@@ -246,7 +242,7 @@ def test_qnnpack_per_tensor_dynamic_mm(self):
         )
 
         # Step 2: EXIR capturing
-        capture_config = CaptureConfig(pt2_mode=True, enable_dynamic_shape=False)
+        capture_config = CaptureConfig(enable_dynamic_shape=False)
         captured_mod = exir.capture(
             converted_mod, example_inputs, config=capture_config
         ).to_edge(EDGE_COMPILE_CONFIG)
@@ -321,7 +317,7 @@ def test_qnnpack_per_tensor_dynamic_qlinear(self):
         )
 
         # Step 2: EXIR capturing
-        capture_config = CaptureConfig(pt2_mode=True, enable_dynamic_shape=False)
+        capture_config = CaptureConfig(enable_dynamic_shape=False)
         captured_mod = exir.capture(
             converted_mod, example_inputs, config=capture_config
         ).to_edge(EDGE_COMPILE_CONFIG)
@@ -406,7 +402,7 @@ def test_qnnpack_per_channel_dynamic_mm_with_dynamic_shape(self):
         )
 
         # Step 2: EXIR capturing
-        capture_config = CaptureConfig(pt2_mode=True, enable_dynamic_shape=True)
+        capture_config = CaptureConfig(enable_dynamic_shape=True)
         captured_mod = exir.capture(
             converted_mod, example_inputs, config=capture_config
         ).to_edge(EDGE_COMPILE_CONFIG)
@@ -438,9 +434,7 @@ def forward(self, x):
 
         composite_model(*example_inputs)
         program = (
-            exir.capture(
-                composite_model, example_inputs, exir.CaptureConfig(pt2_mode=True)
-            )
+            exir.capture(composite_model, example_inputs, exir.CaptureConfig())
             .to_edge(EDGE_COMPILE_CONFIG)
             .to_executorch(config=EXECUTORCH_BACKEND_CONFIG)
             .program
@@ -483,7 +477,7 @@ def test_qnnpack_per_channel_dynamic_qlinear_via_partitioner(self):
         )
 
         # Step 2: EXIR capturing
-        capture_config = CaptureConfig(pt2_mode=True, enable_dynamic_shape=False)
+        capture_config = CaptureConfig(enable_dynamic_shape=False)
         captured_mod = exir.capture(
             converted_mod, example_inputs, config=capture_config
         ).to_edge(EDGE_COMPILE_CONFIG)
@@ -538,7 +532,7 @@ def test_qnnpack_per_channel_dynamic_qlinear_via_partitioner(self):
         # composite_model(*example_inputs)
         # program = (
         #     exir.capture(
-        #         composite_model, example_inputs, exir.CaptureConfig(pt2_mode=True)
+        #         composite_model, example_inputs, exir.CaptureConfig()
         #     )
         #     .to_edge(EDGE_COMPILE_CONFIG)
         #     .to_executorch()

backends/qnnpack/test/test_qnnpack_partitioner.py

@@ -54,7 +54,7 @@ def get_actual_dyanmic_quantized_graph(
     converted_mod = _convert_to_reference_decomposed_fx(prepared_mod)
 
     # Step 2: EXIR capturing
-    capture_config = CaptureConfig(pt2_mode=True, enable_dynamic_shape=dynamic_shape)
+    capture_config = CaptureConfig(enable_dynamic_shape=dynamic_shape)
     dynamic_quantized_exir_graph = (
         exir.capture(converted_mod, example_inputs, config=capture_config)
         .to_edge(exir.EdgeCompileConfig(_check_ir_validity=False))

backends/vulkan/test/test_vulkan_delegate.py

@@ -61,9 +61,7 @@ def lower_module_and_test_output(
         the given sample inputs. It then runs the lowered module and compares its
         outputs with the outputs of the eager module.
         """
-        edgeir_m = exir.capture(
-            module, sample_inputs, exir.CaptureConfig(pt2_mode=True)
-        ).to_edge()
+        edgeir_m = exir.capture(module, sample_inputs, exir.CaptureConfig()).to_edge()
         lowered_module = to_backend("VulkanBackend", edgeir_m.exported_program, [])
 
         class WrappedModule(torch.nn.Module):
@@ -75,9 +73,7 @@ def forward(self, *args):
                 return self.one_module(*args)
 
         program = (
-            exir.capture(
-                WrappedModule(), sample_inputs, exir.CaptureConfig(pt2_mode=True)
-            )
+            exir.capture(WrappedModule(), sample_inputs, exir.CaptureConfig())
             .to_edge()
             .to_executorch()
             .program

backends/xnnpack/partition/support_patterns.py

@@ -41,7 +41,7 @@
         exir.capture(
             add,
             model_inputs,
-            config=CaptureConfig(pt2_mode=True, enable_dynamic_shape=True),
+            config=CaptureConfig(enable_dynamic_shape=True),
         )
         .to_edge().module
         .graph
@@ -68,7 +68,7 @@
 
 
 def _capture(module, example_inputs, pt_mode=True) -> torch.fx.GraphModule:
-    capture_config = CaptureConfig(pt2_mode=pt_mode, enable_dynamic_shape=False)
+    capture_config = CaptureConfig(enable_dynamic_shape=False)
     edge_config = exir.EdgeCompileConfig(
         _check_ir_validity=False,
         passes=[DuplicateDequantNodePass()],

backends/xnnpack/test/test_xnnpack.py

@@ -236,7 +236,7 @@ def test_xnnpack_backend_conv2d_dw(self):
         conv.eval()
         self.lower_and_test_with_partitioner(conv, example_inputs)
 
-    @torch.inference_mode()  # TODO Use pt2_mode=True for capturing.
+    @torch.inference_mode()  # TODO Use  for capturing.
     def test_xnnpack_backend_mm(self):
         in_sizes = [1, 4, 4]
         input_sizes = [4, 37, 17]
@@ -329,7 +329,7 @@ def forward(self, x, y):
         )
         self.lower_and_test_with_partitioner(module, model_inputs)
 
-    @torch.inference_mode()  # TODO Use pt2_mode=True for capturing.
+    @torch.inference_mode()  # TODO Use  for capturing.
     def test_xnnpack_backend_linear(self):
         in_size = 2
         input_size = 3

backends/xnnpack/utils/configs.py

@@ -34,8 +34,6 @@ def get_xnnpack_executorch_backend_config(
 
 def get_xnnpack_capture_config(dynamic_shape=False, enable_aot: Optional[bool] = None):
     if enable_aot is None:
-        return CaptureConfig(pt2_mode=True, enable_dynamic_shape=dynamic_shape)
+        return CaptureConfig(enable_dynamic_shape=dynamic_shape)
     else:
-        return CaptureConfig(
-            pt2_mode=True, enable_dynamic_shape=dynamic_shape, enable_aot=enable_aot
-        )
+        return CaptureConfig(enable_dynamic_shape=dynamic_shape, enable_aot=enable_aot)

bundled_program/tests/common.py

@@ -229,7 +229,7 @@ def get_common_program() -> Tuple[Program, BundledConfig]:
         DEFAULT_INT_INPUT,
     )
     program = (
-        exir.capture(eager_model, capture_input, CaptureConfig(pt2_mode=True))
+        exir.capture(eager_model, capture_input, CaptureConfig())
         .to_edge()
         .to_executorch()
         .program

exir/backend/test/demos/rpc/test_rpc.py

@@ -103,7 +103,7 @@ def test_delegate_whole_program(self):
         simple_net = self.get_a_simple_net()
         simple_net_input = simple_net.get_example_inputs()
         exported_program = exir.capture(
-            simple_net, simple_net_input, exir.CaptureConfig(pt2_mode=True)
+            simple_net, simple_net_input, exir.CaptureConfig()
         ).to_edge(
             exir.EdgeCompileConfig(
                 _check_ir_validity=False,
@@ -125,9 +125,7 @@ def forward(self, *args):
         composite_model = CompositeModule()
 
         exec_prog = (
-            exir.capture(
-                composite_model, simple_net_input, exir.CaptureConfig(pt2_mode=True)
-            )
+            exir.capture(composite_model, simple_net_input, exir.CaptureConfig())
             .to_edge()
             .to_executorch()
         )
@@ -165,9 +163,7 @@ def forward(self, a, x, b):
         model = Model()
         inputs = (torch.ones(2, 2), torch.ones(2, 2), torch.ones(2, 2))
 
-        exported_program = exir.capture(
-            model, inputs, exir.CaptureConfig(pt2_mode=True)
-        ).to_edge()
+        exported_program = exir.capture(model, inputs, exir.CaptureConfig()).to_edge()
 
         # First lower to demo backend
         demo_backend_lowered = exported_program

exir/backend/test/demos/test_delegate_aten_mode.py

@@ -37,7 +37,7 @@ def forward(self, a, x, b):
         add_mul_module = AddMulModule()
         model_inputs = (torch.ones(2, 2), 2 * torch.ones(2, 2), 3 * torch.ones(2, 2))
         edge_graph_module = exir.capture(
-            add_mul_module, model_inputs, exir.CaptureConfig(pt2_mode=True)
+            add_mul_module, model_inputs, exir.CaptureConfig()
         ).to_edge()
         max_value = model_inputs[0].shape[0]
         compile_specs = [CompileSpec("max_value", bytes([max_value]))]
@@ -60,9 +60,7 @@ def forward(self, a, x, b):
         composite_model(*model_inputs)
 
         exec_prog = (
-            exir.capture(
-                composite_model, model_inputs, exir.CaptureConfig(pt2_mode=True)
-            )
+            exir.capture(composite_model, model_inputs, exir.CaptureConfig())
             .to_edge()
             .to_executorch()
         )

exir/backend/test/demos/test_xnnpack_qnnpack.py

@@ -81,7 +81,7 @@ def forward(self, x, y):
         )
 
         # Step 2: EXIR capturing
-        capture_config = CaptureConfig(pt2_mode=True, enable_dynamic_shape=False)
+        capture_config = CaptureConfig(enable_dynamic_shape=False)
         captured_mod = exir.capture(
             converted_mod, example_inputs, config=capture_config
         ).to_edge(

exir/backend/test/hta_partitioner_demo.py

@@ -62,7 +62,7 @@ def forward(self, x_raw, h, c):
             exir.capture(
                 LSTMConvPattern(),
                 (input_x, input_h, input_c),
-                exir.CaptureConfig(pt2_mode=True, enable_aot=True),
+                exir.CaptureConfig(enable_aot=True),
             )
             .to_edge(
                 # torch._export.verifier.SpecViolationError: Operator torch._ops.aten.mkldnn_rnn_layer.default is not Aten Canonical.
@@ -74,7 +74,7 @@ def forward(self, x_raw, h, c):
             exir.capture(
                 LSTMConvPattern(),
                 (input_x, input_h, input_c),
-                exir.CaptureConfig(pt2_mode=True),
+                exir.CaptureConfig(),
             )
             .to_edge(
                 # torch._export.verifier.SpecViolationError: Operator torch._ops.aten.mkldnn_rnn_layer.default is not Aten Canonical.
@@ -90,7 +90,7 @@ def sub(x, y):
             exir.capture(
                 sub,
                 (input_x, input_h),
-                exir.CaptureConfig(pt2_mode=True, enable_aot=True, _unlift=False),
+                exir.CaptureConfig(enable_aot=True, _unlift=False),
             )
             .to_edge(exir.EdgeCompileConfig(_use_edge_ops=True))
             .exported_program.graph_module
@@ -99,7 +99,7 @@ def sub(x, y):
             exir.capture(
                 sub,
                 (input_x, input_h),
-                exir.CaptureConfig(pt2_mode=True),
+                exir.CaptureConfig(),
             )
             .to_edge()
             .exported_program.graph_module
@@ -236,7 +236,7 @@ def forward(self, x_raw, h, c):
             exir.capture(
                 LSTMConvPattern(),
                 (input_x, input_h, input_c),
-                exir.CaptureConfig(pt2_mode=True, enable_aot=True),
+                exir.CaptureConfig(enable_aot=True),
             )
             .to_edge(
                 # torch._export.verifier.SpecViolationError: Operator torch._ops.aten.mkldnn_rnn_layer.default is not Aten Canonical.
@@ -248,7 +248,7 @@ def forward(self, x_raw, h, c):
             exir.capture(
                 LSTMConvPattern(),
                 (input_x, input_h, input_c),
-                exir.CaptureConfig(pt2_mode=True),
+                exir.CaptureConfig(),
             )
             .to_edge(
                 # torch._export.verifier.SpecViolationError: Operator torch._ops.aten.mkldnn_rnn_layer.default is not Aten Canonical.