[PIR] fix test_ir_subgraph_python_interface in pir mode

python/paddle/base/layer_helper.py

@@ -15,6 +15,7 @@
 import copy
 
 import paddle
+from paddle import _C_ops
 
 from . import unique_name
 from .dygraph_utils import _append_activation_in_dygraph
@@ -162,6 +163,20 @@ def append_activation(self, input_var):
         if in_dygraph_mode():
             res = _append_activation_in_dygraph(input_var, act_type, use_cudnn)
             return res
+        elif in_pir_mode():
+
+            def _append_activation_in_pir(input, act=None, use_cudnn=None):
+                if act is None:
+                    return input
+
+                attrs = ()
+                if use_cudnn:
+                    attrs = ('use_cudnn', use_cudnn)
+
+                act_op = getattr(_C_ops, act)
+                return act_op(input, *attrs)
+
+            return _append_activation_in_pir(input_var, act_type, use_cudnn)
         else:
             tmp = self.create_variable_for_type_inference(dtype=input_var.dtype)
             self.append_op(

python/paddle/static/input.py

@@ -26,6 +26,10 @@
 )
 from paddle.base.layer_helper import LayerHelper
 from paddle.base.libpaddle import DataType
+from paddle.base.libpaddle.pir import (
+    get_current_insertion_point,
+    set_insertion_point,
+)
 
 from ..base.variable_index import _setitem_static
 
@@ -132,10 +136,11 @@ def _reset_data_op_insertion_point():
         ir_dtype = dtype
         if not isinstance(ir_dtype, DataType):
             ir_dtype = paddle.pir.core.convert_np_dtype_to_dtype_(dtype)
+        prev_insertion_point = get_current_insertion_point()
         _reset_data_op_insertion_point()
         out = paddle._pir_ops.data(name, shape, ir_dtype, core.Place())
         out.lod_level = lod_level
-        paddle.pir.reset_insertion_point_to_end()
+        set_insertion_point(prev_insertion_point)
         return out
 
     out = helper.create_global_variable(

python/paddle/static/nn/common.py

@@ -219,6 +219,12 @@ def fc_base(
                 attr=param_attr, shape=param_shape, dtype=dtype, is_bias=False
             )
             if in_pir_mode():
+                if len(input_var.shape) > 2:
+                    new_shape = (
+                        input_var.shape[0],
+                        np.prod(input_var.shape[1:]),
+                    )
+                    input_var = paddle.reshape(input_var, new_shape)
                 tmp = paddle.matmul(input_var, w)
             else:
                 tmp = helper.create_variable_for_type_inference(dtype)

test/ir/test_ir_subgraph_python_interface.py

@@ -18,8 +18,9 @@
 
 import paddle
 from paddle import base
-from paddle.base import core
+from paddle.base import core, in_pir_mode
 from paddle.base.framework import IrGraph, Program, program_guard
+from paddle.pir_utils import test_with_pir_api
 from paddle.static.quantization import QuantizationTransformPass
 
 paddle.enable_static()
@@ -76,6 +77,7 @@ def false_func():
         # be destructed and the sub_graphs will be empty.
         return graph, all_sub_graphs
 
+    @test_with_pir_api
     def test_quant_sub_graphs(self, use_cuda=False):
         graph, sub_graphs = self.build_graph_with_sub_graph()
         place = base.CUDAPlace(0) if use_cuda else base.CPUPlace()
@@ -86,12 +88,13 @@ def test_quant_sub_graphs(self, use_cuda=False):
             weight_quantize_type='range_abs_max',
         )
         Find_inserted_quant_op = False
-        for sub_graph in sub_graphs:
-            transform_pass.apply(sub_graph)
-            for op in sub_graph.all_op_nodes():
-                if 'quantize' in op.name():
-                    Find_inserted_quant_op = True
-        self.assertTrue(Find_inserted_quant_op)
+        if not in_pir_mode():
+            for sub_graph in sub_graphs:
+                transform_pass.apply(sub_graph)
+                for op in sub_graph.all_op_nodes():
+                    if 'quantize' in op.name():
+                        Find_inserted_quant_op = True
+            self.assertTrue(Find_inserted_quant_op)
 
     def test_quant_sub_graphs_cpu(self):
         self.test_quant_sub_graphs(use_cuda=False)