clear fluid api: sigmoid_cross_entropy_with_logits (#48146)

* clear fluid api: sigmoid_cross_entropy_with_logits

* fix loss.py

* change paddle.nn.functional.sigmoid_cross_entropy_with_logits

* delete sigmoid_cross_entropy_with_logits

* fix binary_cross_entropy_with_logits

* fix ci bug

* fix ci buf

python/paddle/fluid/layers/loss.py

@@ -38,7 +38,6 @@
     'cross_entropy',
     'square_error_cost',
     'softmax_with_cross_entropy',
-    'sigmoid_cross_entropy_with_logits',
 ]
 
 kIgnoreIndex = -100
@@ -292,66 +291,3 @@ def softmax_with_cross_entropy(
         return_softmax,
         axis,
     )
-
-
-@templatedoc()
-def sigmoid_cross_entropy_with_logits(
-    x, label, ignore_index=kIgnoreIndex, name=None, normalize=False
-):
-    """
-
-    ${comment}
-
-    Args:
-        x(Tensor): a 2-D tensor with shape N x D, where N is the batch size and
-                D is the number of classes. This input is a tensor of logits computed
-                by the previous operator. Logits are unscaled log probabilities given
-                as log(p/(1-p)) The data type should be float32 or float64.
-        label (Tensor): a 2-D tensor of the same type and shape as X.
-                This input is a tensor of probabalistic labels for each logit.
-        ignore_index(int): Specifies a target value that is ignored and
-                does not contribute to the input gradient.
-        name(str|None): The default value is None.  Normally there is
-            no need for user to set this property.  For more information,
-            please refer to :ref:`api_guide_Name`
-        normalize(bool): If true, divide the output by the number of
-            targets != ignore_index.
-
-    Returns:
-        out(Tensor): ${out_comment}
-
-    Examples:
-        .. code-block:: python
-
-
-            import paddle
-
-            input = paddle.rand(shape=[10], dtype='float32')
-            label = paddle.rand(shape=[10], dtype='float32')
-            loss = paddle.fluid.layers.sigmoid_cross_entropy_with_logits(input, label,
-                                                            ignore_index=-1, normalize=True)
-            print(loss)
-    """
-
-    if in_dygraph_mode():
-        return _C_ops.sigmoid_cross_entropy_with_logits(
-            x, label, normalize, int(ignore_index)
-        )
-    check_variable_and_dtype(
-        x,
-        'input',
-        ['float16', 'float32', 'float64'],
-        'sigmoid_cross_entropy_with_logits',
-    )
-
-    helper = LayerHelper("sigmoid_cross_entropy_with_logits", **locals())
-
-    out = helper.create_variable_for_type_inference(dtype=x.dtype)
-
-    helper.append_op(
-        type="sigmoid_cross_entropy_with_logits",
-        inputs={"X": x, "Label": label},
-        attrs={"ignore_index": ignore_index, 'normalize': normalize},
-        outputs={"Out": out},
-    )
-    return out

python/paddle/fluid/tests/unittests/dygraph_to_static/test_word2vec.py

@@ -14,6 +14,7 @@
 
 import math
 import random
+import paddle
 import numpy as np
 import paddle
 import paddle.fluid as fluid
@@ -262,7 +263,9 @@ def forward(self, center_words, target_words, label):
 
         pred = paddle.nn.functional.sigmoid(word_sim)
 
-        loss = fluid.layers.sigmoid_cross_entropy_with_logits(word_sim, label)
+        loss = paddle.nn.functional.binary_cross_entropy_with_logits(
+            word_sim, label
+        )
         loss = fluid.layers.reduce_mean(loss)
 
         return pred, loss

python/paddle/fluid/tests/unittests/ipu/test_dy2static_ipu.py

@@ -271,7 +271,7 @@ def create_model(self, use_ipu=False):
 
 class TestWithoutIdentityLoss5(TestBase):
     def set_op_attrs(self):
-        self.loss_op = paddle.fluid.layers.sigmoid_cross_entropy_with_logits
+        self.loss_op = paddle.nn.functional.binary_cross_entropy_with_logits
 
     def set_data_feed(self):
         self.data = paddle.uniform((8, 3, 10, 10), dtype='float32')

python/paddle/fluid/tests/unittests/test_dist_transpiler.py

@@ -427,10 +427,10 @@ def net_conf(self):
             true_logits, shape=[-1, neg_num], value=0.0, dtype='float32'
         )
 
-        true_xent = fluid.layers.sigmoid_cross_entropy_with_logits(
+        true_xent = paddle.nn.functional.binary_cross_entropy_with_logits(
             true_logits, label_ones
         )
-        neg_xent = fluid.layers.sigmoid_cross_entropy_with_logits(
+        neg_xent = paddle.nn.functional.binary_cross_entropy_with_logits(
             neg_logits, label_zeros
         )
         cost = fluid.layers.elementwise_add(

python/paddle/fluid/tests/unittests/test_imperative_gan.py

@@ -80,8 +80,8 @@ def func_test_gan_float32(self):
 
             d_real = discriminator(img)
             d_loss_real = fluid.layers.reduce_mean(
-                fluid.layers.sigmoid_cross_entropy_with_logits(
-                    x=d_real,
+                paddle.nn.functional.binary_cross_entropy_with_logits(
+                    logit=d_real,
                     label=fluid.layers.fill_constant(
                         shape=[2, 1], dtype='float32', value=1.0
                     ),
@@ -90,8 +90,8 @@ def func_test_gan_float32(self):
 
             d_fake = discriminator(generator(noise))
             d_loss_fake = fluid.layers.reduce_mean(
-                fluid.layers.sigmoid_cross_entropy_with_logits(
-                    x=d_fake,
+                paddle.nn.functional.binary_cross_entropy_with_logits(
+                    logit=d_fake,
                     label=fluid.layers.fill_constant(
                         shape=[2, 1], dtype='float32', value=0.0
                     ),
@@ -113,8 +113,8 @@ def func_test_gan_float32(self):
 
             d_fake = discriminator(generator(noise))
             g_loss = fluid.layers.reduce_mean(
-                fluid.layers.sigmoid_cross_entropy_with_logits(
-                    x=d_fake,
+                paddle.nn.functional.binary_cross_entropy_with_logits(
+                    logit=d_fake,
                     label=fluid.layers.fill_constant(
                         shape=[2, 1], dtype='float32', value=1.0
                     ),
@@ -165,17 +165,18 @@ def func_test_gan_float32(self):
 
             d_real = discriminator(to_variable(np.ones([2, 1], np.float32)))
             d_loss_real = fluid.layers.reduce_mean(
-                fluid.layers.sigmoid_cross_entropy_with_logits(
-                    x=d_real, label=to_variable(np.ones([2, 1], np.float32))
+                paddle.nn.functional.binary_cross_entropy_with_logits(
+                    logit=d_real, label=to_variable(np.ones([2, 1], np.float32))
                 )
             )
 
             d_fake = discriminator(
                 generator(to_variable(np.ones([2, 2], np.float32)))
             )
             d_loss_fake = fluid.layers.reduce_mean(
-                fluid.layers.sigmoid_cross_entropy_with_logits(
-                    x=d_fake, label=to_variable(np.zeros([2, 1], np.float32))
+                paddle.nn.functional.binary_cross_entropy_with_logits(
+                    logit=d_fake,
+                    label=to_variable(np.zeros([2, 1], np.float32)),
                 )
             )
 
@@ -189,8 +190,8 @@ def func_test_gan_float32(self):
                 generator(to_variable(np.ones([2, 2], np.float32)))
             )
             g_loss = fluid.layers.reduce_mean(
-                fluid.layers.sigmoid_cross_entropy_with_logits(
-                    x=d_fake, label=to_variable(np.ones([2, 1], np.float32))
+                paddle.nn.functional.binary_cross_entropy_with_logits(
+                    logit=d_fake, label=to_variable(np.ones([2, 1], np.float32))
                 )
             )
             g_loss.backward()
@@ -219,17 +220,19 @@ def func_test_gan_float32(self):
 
             d_real2 = discriminator2(to_variable(np.ones([2, 1], np.float32)))
             d_loss_real2 = fluid.layers.reduce_mean(
-                fluid.layers.sigmoid_cross_entropy_with_logits(
-                    x=d_real2, label=to_variable(np.ones([2, 1], np.float32))
+                paddle.nn.functional.binary_cross_entropy_with_logits(
+                    logit=d_real2,
+                    label=to_variable(np.ones([2, 1], np.float32)),
                 )
             )
 
             d_fake2 = discriminator2(
                 generator2(to_variable(np.ones([2, 2], np.float32)))
             )
             d_loss_fake2 = fluid.layers.reduce_mean(
-                fluid.layers.sigmoid_cross_entropy_with_logits(
-                    x=d_fake2, label=to_variable(np.zeros([2, 1], np.float32))
+                paddle.nn.functional.binary_cross_entropy_with_logits(
+                    logit=d_fake2,
+                    label=to_variable(np.zeros([2, 1], np.float32)),
                 )
             )
 
@@ -243,8 +246,9 @@ def func_test_gan_float32(self):
                 generator2(to_variable(np.ones([2, 2], np.float32)))
             )
             g_loss2 = fluid.layers.reduce_mean(
-                fluid.layers.sigmoid_cross_entropy_with_logits(
-                    x=d_fake2, label=to_variable(np.ones([2, 1], np.float32))
+                paddle.nn.functional.binary_cross_entropy_with_logits(
+                    logit=d_fake2,
+                    label=to_variable(np.ones([2, 1], np.float32)),
                 )
             )
             g_loss2.backward()

python/paddle/fluid/tests/unittests/test_imperative_star_gan_with_gradient_penalty.py

@@ -381,7 +381,9 @@ def loss_cls(cls, label, cfg):
     cls_shape = cls.shape
     cls = paddle.reshape(cls, [-1, cls_shape[1] * cls_shape[2] * cls_shape[3]])
     return (
-        paddle.sum(fluid.layers.sigmoid_cross_entropy_with_logits(cls, label))
+        paddle.sum(
+            paddle.nn.functional.binary_cross_entropy_with_logits(cls, label)
+        )
         / cfg.batch_size
     )
 

python/paddle/fluid/tests/unittests/test_layers.py

@@ -3152,17 +3152,6 @@ def make_word_embedding(self):
             avg_cost = paddle.mean(cost)
             return avg_cost
 
-    def make_sigmoid_cross_entropy(self):
-        with program_guard(
-            fluid.default_main_program(), fluid.default_startup_program()
-        ):
-            dat = self._get_data(name='data', shape=[10], dtype='float32')
-            lbl = self._get_data(name='label', shape=[10], dtype='float32')
-            ignore_index = -1
-            return layers.sigmoid_cross_entropy_with_logits(
-                x=dat, label=lbl, ignore_index=ignore_index
-            )
-
     def make_pool2d(self):
         with program_guard(
             fluid.default_main_program(), fluid.default_startup_program()