【PIR/Dy2static】Fix pir test ---- PART II

python/paddle/nn/layer/norm.py

@@ -1078,7 +1078,7 @@ def forward(self, input):
                 )
             else:
                 act_op = getattr(_C_ops, self._act)
-                return act_op(input)
+                return act_op(batch_norm_out)
         else:
             # create output
             # mean and mean_out share the same memory

test/dygraph_to_static/test_bmn.py

@@ -21,7 +21,7 @@
 from dygraph_to_static_utils import (
     Dy2StTestBase,
     static_guard,
-    test_pt_only,
+    test_legacy_and_pt_and_pir,
 )
 from predictor_utils import PredictorTools
 
@@ -625,16 +625,6 @@ def val_bmn(model, args):
             float(pem_cls_loss),
         ]
 
-        print(
-            f'[VALID] iter {batch_id} '
-            + '\tLoss = {}, \ttem_loss = {}, \tpem_reg_loss = {}, \tpem_cls_loss = {}'.format(
-                '%f' % float(avg_loss),
-                '%f' % float(tem_loss),
-                '%f' % float(pem_reg_loss),
-                '%f' % float(pem_cls_loss),
-            )
-        )
-
         if batch_id == args.valid_batch_num:
             break
     return loss_data
@@ -722,17 +712,6 @@ def train_bmn(self, args, to_static):
                         float(pem_cls_loss),
                     ]
 
-                    if args.log_interval > 0 and (
-                        batch_id % args.log_interval == 0
-                    ):
-                        print(
-                            f'[TRAIN] Epoch {epoch}, iter {batch_id} '
-                            + f'\tLoss = {float(avg_loss):f}, '
-                            + f'\ttem_loss = {float(tem_loss):f}, '
-                            + f'\tpem_reg_loss = {float(pem_reg_loss):f}, '
-                            + f'\tpem_cls_loss = {float(pem_cls_loss):f}'
-                        )
-
                     # validation
                     if batch_id % args.valid_interval == 0 and batch_id > 0:
                         bmn.eval()
@@ -741,7 +720,11 @@ def train_bmn(self, args, to_static):
                         loss_data += val_loss_data
 
                     if batch_id == args.train_batch_num:
-                        if to_static:
+                        # TODO(@xiongkun): open after save / load supported in pir.
+                        if (
+                            to_static
+                            and not paddle.base.framework.use_pir_api()
+                        ):
                             paddle.jit.save(bmn, self.model_save_prefix)
                         else:
                             paddle.save(
@@ -751,7 +734,7 @@ def train_bmn(self, args, to_static):
                         break
             return np.array(loss_data)
 
-    @test_pt_only
+    @test_legacy_and_pt_and_pir
     def test_train_pir(self):
         static_res = self.train_bmn(self.args, to_static=True)
         dygraph_res = self.train_bmn(self.args, to_static=False)

test/dygraph_to_static/test_to_tensor.py

@@ -21,8 +21,6 @@
     ToStaticMode,
     disable_test_case,
     test_legacy_and_pt_and_pir,
-    test_legacy_only,
-    test_pir_only,
 )
 
 import paddle
@@ -184,6 +182,7 @@ def test_to_tensor_err_log(self):
 
 
 class TestStatic(Dy2StTestBase):
+    @test_legacy_and_pt_and_pir
     def test_static(self):
         paddle.enable_static()
         main_prog = paddle.static.Program()
@@ -194,14 +193,16 @@ def test_static(self):
             else:
                 place = paddle.CPUPlace()
 
+            paddle.set_default_dtype("float64")
             x = paddle.to_tensor(
                 paddle.randn([5, 2]),
                 dtype='float64',
                 stop_gradient=False,
                 place=place,
             )
 
-            out = paddle.static.nn.fc(x, 1)
+            fc_net = paddle.nn.Linear(2, 1)
+            out = fc_net(x)
 
             sgd = paddle.optimizer.SGD()
             sgd.minimize(paddle.mean(out))
@@ -212,29 +213,27 @@ def test_static(self):
 
 
 class TestInt16(Dy2StTestBase):
-    @test_legacy_only
+    @test_legacy_and_pt_and_pir
     def test_static(self):
         import numpy as np
 
         paddle.enable_static()
         data = np.array([1, 2], dtype="int16")
         x = paddle.to_tensor(data)
-        self.assertTrue(x.dtype == paddle.framework.core.VarDesc.VarType.INT16)
-
-        y = paddle.to_tensor([1, 2], dtype="int16")
-        self.assertTrue(y.dtype == paddle.framework.core.VarDesc.VarType.INT16)
-
-    @test_pir_only
-    def test_static_pir(self):
-        import numpy as np
-
-        paddle.enable_static()
-        data = np.array([1, 2], dtype="int16")
-        x = paddle.to_tensor(data)
-        self.assertTrue(x.dtype == paddle.base.libpaddle.DataType.INT16)
+        if paddle.base.framework.use_pir_api():
+            self.assertTrue(x.dtype == paddle.base.libpaddle.DataType.INT16)
+        else:
+            self.assertTrue(
+                x.dtype == paddle.framework.core.VarDesc.VarType.INT16
+            )
 
         y = paddle.to_tensor([1, 2], dtype="int16")
-        self.assertTrue(y.dtype == paddle.base.libpaddle.DataType.INT16)
+        if paddle.base.framework.use_pir_api():
+            self.assertTrue(y.dtype == paddle.base.libpaddle.DataType.INT16)
+        else:
+            self.assertTrue(
+                y.dtype == paddle.framework.core.VarDesc.VarType.INT16
+            )
 
 
 if __name__ == '__main__':