[Dy2St] Clean some deprecated API usage in dy2st uts (#64000)

SigureMo · web-flow · commit d9b3acc850f1 · 2024-04-30T16:39:11.000+08:00
diff --git a/python/paddle/text/datasets/wmt16.py b/python/paddle/text/datasets/wmt16.py
@@ -11,8 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-"""
-"""
+
 
 import os
 import tarfile
diff --git a/test/dygraph_to_static/bert_dygraph_model.py b/test/dygraph_to_static/bert_dygraph_model.py
@@ -405,18 +405,20 @@ def forward(
         else:
             fc_out = self.out_fc(mask_trans_feat)
 
-        mask_lm_loss = paddle.nn.functional.softmax_with_cross_entropy(
-            logits=fc_out, label=mask_label
+        mask_lm_loss = paddle.nn.functional.cross_entropy(
+            input=fc_out,
+            label=mask_label,
+            reduction="none",
         )
         mean_mask_lm_loss = paddle.mean(mask_lm_loss)
 
         next_sent_fc_out = self.next_sent_fc(next_sent_feat)
 
-        (
-            next_sent_loss,
-            next_sent_softmax,
-        ) = paddle.nn.functional.softmax_with_cross_entropy(
-            logits=next_sent_fc_out, label=labels, return_softmax=True
+        next_sent_softmax = paddle.nn.functional.softmax(next_sent_fc_out)
+        next_sent_loss = paddle.nn.functional.cross_entropy(
+            input=next_sent_fc_out,
+            label=labels,
+            reduction="none",
         )
 
         next_sent_acc = paddle.static.accuracy(
diff --git a/test/dygraph_to_static/test_ptb_lm.py b/test/dygraph_to_static/test_ptb_lm.py
@@ -213,8 +213,8 @@ def forward(self, input, label, init_hidden, init_cell):
         projection = paddle.matmul(rnn_out, self.softmax_weight)
         projection = paddle.add(projection, self.softmax_bias)
 
-        loss = paddle.nn.functional.softmax_with_cross_entropy(
-            logits=projection, label=label, soft_label=False
+        loss = paddle.nn.functional.cross_entropy(
+            input=projection, label=label, soft_label=False, reduction="none"
         )
         loss = paddle.reshape(loss, shape=[-1, self.num_steps])
         loss = paddle.mean(loss, axis=[0])
diff --git a/test/dygraph_to_static/test_transformer.py b/test/dygraph_to_static/test_transformer.py
@@ -140,7 +140,7 @@ def train_dygraph(args, batch_generator):
                             batch_id,
                             total_avg_cost,
                             total_avg_cost - loss_normalizer,
-                            np.exp([min(total_avg_cost, 100)]),
+                            np.exp([min(total_avg_cost, 100)]).item(),
                         )
                     )
                     avg_batch_time = time.time()
@@ -154,7 +154,7 @@ def train_dygraph(args, batch_generator):
                             batch_id,
                             total_avg_cost,
                             total_avg_cost - loss_normalizer,
-                            np.exp([min(total_avg_cost, 100)]),
+                            np.exp([min(total_avg_cost, 100)]).item(),
                             args.print_step / (time.time() - avg_batch_time),
                         )
                     )
diff --git a/test/dygraph_to_static/transformer_dygraph_model.py b/test/dygraph_to_static/transformer_dygraph_model.py
@@ -590,10 +590,11 @@ def __call__(self, predict, label, weights):
                 epsilon=self.label_smooth_eps,
             )
 
-        cost = paddle.nn.functional.softmax_with_cross_entropy(
-            logits=predict,
+        cost = paddle.nn.functional.cross_entropy(
+            input=predict,
             label=label_out,
             soft_label=True if self.label_smooth_eps else False,
+            reduction="none",
         )
         weighted_cost = cost * weights
         sum_cost = paddle.sum(weighted_cost)

Original file line number	Diff line number	Diff line change
`@@ -140,7 +140,7 @@ def train_dygraph(args, batch_generator):`
`140`	`140`	`batch_id,`
`141`	`141`	`total_avg_cost,`
`142`	`142`	`total_avg_cost - loss_normalizer,`
`143`		`- np.exp([min(total_avg_cost, 100)]),`
	`143`	`+ np.exp([min(total_avg_cost, 100)]).item(),`
`144`	`144`	`)`
`145`	`145`	`)`
`146`	`146`	`avg_batch_time = time.time()`
`@@ -154,7 +154,7 @@ def train_dygraph(args, batch_generator):`
`154`	`154`	`batch_id,`
`155`	`155`	`total_avg_cost,`
`156`	`156`	`total_avg_cost - loss_normalizer,`
`157`		`- np.exp([min(total_avg_cost, 100)]),`
	`157`	`+ np.exp([min(total_avg_cost, 100)]).item(),`
`158`	`158`	`args.print_step / (time.time() - avg_batch_time),`
`159`	`159`	`)`
`160`	`160`	`)`
Original file line number	Diff line number	Diff line change
`@@ -590,10 +590,11 @@ def __call__(self, predict, label, weights):`
`590`	`590`	`epsilon=self.label_smooth_eps,`
`591`	`591`	`)`
`592`	`592`
`593`		`- cost = paddle.nn.functional.softmax_with_cross_entropy(`
`594`		`- logits=predict,`
	`593`	`+ cost = paddle.nn.functional.cross_entropy(`
	`594`	`+ input=predict,`
`595`	`595`	`label=label_out,`
`596`	`596`	`soft_label=True if self.label_smooth_eps else False,`
	`597`	`+ reduction="none",`
`597`	`598`	`)`
`598`	`599`	`weighted_cost = cost * weights`
`599`	`600`	`sum_cost = paddle.sum(weighted_cost)`