【Fix PIR Unittest No.498,505,275】Fix some test case in PIR (#65617)

* pir fix max_pool * refine
PaddlePaddle · Jul 2, 2024 · ea399ed · ea399ed
1 parent 99bbfe6
commit ea399ed
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Showing 3 changed files with 39 additions and 32 deletions.
diff --git a/python/paddle/nn/functional/pooling.py b/python/paddle/nn/functional/pooling.py
@@ -1844,7 +1844,7 @@ def adaptive_max_pool1d(
     pool_size = [1] + convert_to_list(output_size, 1, 'pool_size')
 
     x = unsqueeze(x, [2])
-    if in_dygraph_mode():
+    if in_dynamic_or_pir_mode():
         pool_out = _C_ops.max_pool2d_with_index(
             x, pool_size, [1, 1], [0, 0], False, True, False
         )
@@ -1944,7 +1944,7 @@ def adaptive_max_pool2d(
             output_size[0] = in_h
         if output_size[1] is None:
             output_size[1] = in_w
-    if in_dygraph_mode():
+    if in_dynamic_or_pir_mode():
         pool_out = _C_ops.max_pool2d_with_index(
             x, output_size, [1, 1], [0, 0], False, True, False
         )
@@ -2041,7 +2041,7 @@ def adaptive_max_pool3d(
         if output_size[2] is None:
             output_size[2] = in_w
 
-    if in_dygraph_mode():
+    if in_dynamic_or_pir_mode():
         # By default, strides is [1,1,1] and paddings is [0, 0, 0]
         pool_out = _C_ops.max_pool3d_with_index(
             x, output_size, [1, 1, 1], [0, 0, 0], False, True, False

diff --git a/...d/legacy_test/test_adaptive_max_pool1d.py → test/legacy_test/test_adaptive_max_pool1d.py b/...d/legacy_test/test_adaptive_max_pool1d.py → test/legacy_test/test_adaptive_max_pool1d.py
diff --git a/...d/legacy_test/test_adaptive_max_pool2d.py → test/legacy_test/test_adaptive_max_pool2d.py b/...d/legacy_test/test_adaptive_max_pool2d.py → test/legacy_test/test_adaptive_max_pool2d.py
@@ -279,46 +279,53 @@ def test_static_graph(self):
         for use_cuda in (
             [False, True] if core.is_compiled_with_cuda() else [False]
         ):
-            place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
-            paddle.enable_static()
-            x = paddle.static.data(
-                name="x", shape=[2, 3, 7, 7], dtype="float32"
-            )
+            with paddle.static.program_guard(
+                paddle.static.Program(), paddle.static.Program()
+            ):
+                place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
+                paddle.enable_static()
+                x = paddle.static.data(
+                    name="x", shape=[2, 3, 7, 7], dtype="float32"
+                )
 
-            adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D(output_size=[3, 3])
-            out_1 = adaptive_max_pool(x=x)
+                adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D(
+                    output_size=[3, 3]
+                )
+                out_1 = adaptive_max_pool(x=x)
 
-            adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D(output_size=5)
-            out_2 = adaptive_max_pool(x=x)
+                adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D(output_size=5)
+                out_2 = adaptive_max_pool(x=x)
 
-            adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D(output_size=[2, 5])
-            out_3 = adaptive_max_pool(x=x)
+                adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D(
+                    output_size=[2, 5]
+                )
+                out_3 = adaptive_max_pool(x=x)
 
-            #    adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D(
-            #        output_size=[3, 3], data_format="NHWC")
-            #    out_4 = adaptive_max_pool(x=x)
+                #    adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D(
+                #        output_size=[3, 3], data_format="NHWC")
+                #    out_4 = adaptive_max_pool(x=x)
 
-            adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D(
-                output_size=[None, 3]
-            )
-            out_5 = adaptive_max_pool(x=x)
+                adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D(
+                    output_size=[None, 3]
+                )
+                out_5 = adaptive_max_pool(x=x)
 
-            exe = paddle.static.Executor(place=place)
-            [res_1, res_2, res_3, res_5] = exe.run(
-                base.default_main_program(),
-                feed={"x": self.x_np},
-                fetch_list=[out_1, out_2, out_3, out_5],
-            )
+                exe = paddle.static.Executor(place=place)
+                [res_1, res_2, res_3, res_5] = exe.run(
+                    base.default_main_program(),
+                    feed={"x": self.x_np},
+                    fetch_list=[out_1, out_2, out_3, out_5],
+                )
 
-            np.testing.assert_allclose(res_1, self.res_1_np)
+                np.testing.assert_allclose(res_1, self.res_1_np)
 
-            np.testing.assert_allclose(res_2, self.res_2_np)
+                np.testing.assert_allclose(res_2, self.res_2_np)
 
-            np.testing.assert_allclose(res_3, self.res_3_np)
+                np.testing.assert_allclose(res_3, self.res_3_np)
 
-            # np.testing.assert_allclose(res_4, self.res_4_np)
+                # np.testing.assert_allclose(res_4, self.res_4_np)
 
-            np.testing.assert_allclose(res_5, self.res_5_np)
+                np.testing.assert_allclose(res_5, self.res_5_np)
 
     def test_dynamic_graph(self):
         for use_cuda in (