【PIR API adaptor No.287】 Migrate npair_loss to pir

python/paddle/nn/functional/loss.py

@@ -350,10 +350,11 @@ def npair_loss(anchor, positive, labels, l2_reg=0.002):
                     2.94269347)
 
     """
-    if anchor.size == 0:
-        raise ValueError("The dims of anchor should be greater than 0.")
-    if positive.size == 0:
-        raise ValueError("The dims of positive should be greater than 0.")
+    if in_dynamic_mode():
+        if anchor.size == 0:
+            raise ValueError("The dims of anchor should be greater than 0.")
+        if positive.size == 0:
+            raise ValueError("The dims of positive should be greater than 0.")
     check_variable_and_dtype(
         anchor, 'anchor', ['float32', 'float64'], 'npair_loss'
     )

test/legacy_test/test_npair_loss_op.py

@@ -18,7 +18,8 @@
 
 import paddle
 from paddle import base
-from paddle.base import Program, core, program_guard
+from paddle.base import core
+from paddle.pir_utils import test_with_pir_api
 
 
 def npairloss(anchor, positive, labels, l2_reg=0.002):
@@ -58,74 +59,81 @@ def __assert_close(self, tensor, np_array, msg, atol=1e-4):
             np.array(tensor), np_array, rtol=1e-05, atol=atol, err_msg=msg
         )
 
+    @test_with_pir_api
     def test_npair_loss(self):
-        reg_lambda = 0.002
-        num_data, feat_dim, num_classes = 18, 6, 3
-
-        place = core.CPUPlace()
-        exe = base.Executor(place)
-        exe.run(base.default_startup_program())
-
-        embeddings_anchor = np.random.rand(num_data, feat_dim).astype(
-            np.float32
-        )
-        embeddings_positive = np.random.rand(num_data, feat_dim).astype(
-            np.float32
-        )
-        row_labels = np.random.randint(0, num_classes, size=(num_data)).astype(
-            np.float32
-        )
-        out_loss = npairloss(
-            embeddings_anchor,
-            embeddings_positive,
-            row_labels,
-            l2_reg=reg_lambda,
-        )
+        main = paddle.static.Program()
+        startup = paddle.static.Program()
+        with paddle.static.program_guard(main, startup):
+            reg_lambda = 0.002
+            num_data, feat_dim, num_classes = 18, 6, 3
+
+            place = core.CPUPlace()
+            exe = base.Executor(place)
+            exe.run(startup)
+
+            embeddings_anchor = np.random.rand(num_data, feat_dim).astype(
+                np.float32
+            )
+            embeddings_positive = np.random.rand(num_data, feat_dim).astype(
+                np.float32
+            )
+            row_labels = np.random.randint(
+                0, num_classes, size=(num_data)
+            ).astype(np.float32)
+            out_loss = npairloss(
+                embeddings_anchor,
+                embeddings_positive,
+                row_labels,
+                l2_reg=reg_lambda,
+            )
 
-        anc = paddle.static.data(
-            dtype='float32',
-            name='anc',
-            shape=embeddings_anchor.shape,
-        )
-        pos = paddle.static.data(
-            dtype='float32',
-            name='pos',
-            shape=embeddings_positive.shape,
-        )
-        lab = paddle.static.data(
-            dtype='float32',
-            name='lab',
-            shape=row_labels.shape,
-        )
+            anc = paddle.static.data(
+                dtype='float32',
+                name='anc',
+                shape=embeddings_anchor.shape,
+            )
+            pos = paddle.static.data(
+                dtype='float32',
+                name='pos',
+                shape=embeddings_positive.shape,
+            )
+            lab = paddle.static.data(
+                dtype='float32',
+                name='lab',
+                shape=row_labels.shape,
+            )
 
-        npair_loss_op = paddle.nn.functional.npair_loss(
-            anchor=anc, positive=pos, labels=lab, l2_reg=reg_lambda
-        )
-        out_tensor = exe.run(
-            feed={
-                'anc': embeddings_anchor,
-                'pos': embeddings_positive,
-                'lab': row_labels,
-            },
-            fetch_list=[npair_loss_op.name],
-        )
+            npair_loss_op = paddle.nn.functional.npair_loss(
+                anchor=anc, positive=pos, labels=lab, l2_reg=reg_lambda
+            )
+            out_tensor = exe.run(
+                feed={
+                    'anc': embeddings_anchor,
+                    'pos': embeddings_positive,
+                    'lab': row_labels,
+                },
+                fetch_list=[npair_loss_op],
+            )
 
-        self.__assert_close(
-            out_tensor,
-            out_loss,
-            "inference output are different at "
-            + str(place)
-            + ", "
-            + str(np.dtype('float32'))
-            + str(np.array(out_tensor))
-            + str(out_loss),
-            atol=1e-3,
-        )
+            self.__assert_close(
+                out_tensor,
+                out_loss,
+                "inference output are different at "
+                + str(place)
+                + ", "
+                + str(np.dtype('float32'))
+                + str(np.array(out_tensor))
+                + str(out_loss),
+                atol=1e-3,
+            )
 
 
 class TestNpairLossOpError(unittest.TestCase):
+    @test_with_pir_api
     def test_errors(self):
-        with program_guard(Program(), Program()):
+        with paddle.static.program_guard(
+            paddle.static.Program(), paddle.static.Program()
+        ):
             anchor_np = np.random.random((2, 4)).astype("float32")
             positive_np = np.random.random((2, 4)).astype("float32")
             labels_np = np.random.random(2).astype("float32")