add cross_entropy to nn/layer and nn/functional, test=develop (#26478)

* add cross_entropy to nn/layer and nn/functional, test=develop

* use functional/cross_entropy in layer/CrossEntropy

* use functional/cross_entropy in layer/CrossEntropy, test=develop

python/paddle/nn/functional/loss.py

@@ -22,7 +22,6 @@ from ...fluid.framework import core, in_dygraph_mode
 from ...fluid.layers.nn import _elementwise_op_in_dygraph
 from ...fluid.layers import bpr_loss  #DEFINE_ALIAS
 from ...fluid.layers import center_loss  #DEFINE_ALIAS
-from ...fluid.layers import cross_entropy  #DEFINE_ALIAS
 from ...fluid.layers import dice_loss  #DEFINE_ALIAS
 from ...fluid.layers import iou_similarity  #DEFINE_ALIAS
 from ...fluid.layers import log_loss  #DEFINE_ALIAS
@@ -786,3 +785,132 @@ def mse_loss(input, label, reduction='mean', name=None):
         return paddle.sum(paddle.fluid.layers.square(
             paddle.fluid.layers.elementwise_sub(input, label)),
                           name=name)
+
+
+def cross_entropy(input,
+                  label,
+                  weight=None,
+                  ignore_index=-100,
+                  reduction='mean'):
+    """
+    This operator implements the cross entropy loss function. This OP combines ``LogSoftmax``,
+    and ``NLLLoss`` together.
+
+    It is useful when training a classification problem with ``C`` classes.
+    If provided, the optional argument ``weight`` should be a 1D Variable assigning
+    weight to each of the classes.
+
+    For predictions label, and target label, the loss is calculated as follows.
+
+    .. math::
+
+        loss_j =  -\\text{input[class]} +
+        \\log\\left(\\sum_{i=0}^{K}\\exp(\\text{input}_i)\\right), j = 1,..., K
+
+    If weight is not ``None``:
+
+    .. math::
+
+        loss_j =  \\text{weight[class]}(-\\text{input[class]} +
+        \\log\\left(\\sum_{i=0}^{K}\\exp(\\text{input}_i)\\right)), j = 1,..., K
+
+    Parameters:
+        input (Tensor): Input tensor, the data type is float32, float64. Shape is
+	    (N, C), where C is number of classes, and if shape is more than 2D, this
+	    is (N, C, D1, D2,..., Dk), k >= 1. 
+        label (Tensor): Label tensor, the data type is int64. Shape is (N), where each 
+	    value is 0 <= label[i] <= C-1, and if shape is more than 2D, this is
+	    (N, D1, D2,..., Dk), k >= 1.
+        weight (Tensor, optional): Weight tensor, a manual rescaling weight given
+            to each class and the shape is (C). It has the same dimensions as class
+	    number and the data type is float32, float64. Default is ``'None'``.
+        reduction (str, optional): Indicate how to average the loss by batch_size,
+            the candicates are ``'none'`` | ``'mean'`` | ``'sum'``.
+            If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned;
+            If :attr:`size_average` is ``'sum'``, the reduced sum loss is returned.
+            If :attr:`reduction` is ``'none'``, the unreduced loss is returned.
+            Default is ``'mean'``.
+        ignore_index (int64, optional): Specifies a target value that is ignored
+            and does not contribute to the input gradient. Default is ``-100``.
+
+    Returns:
+        The tensor variable storing the cross_entropy_loss of input and label.
+
+    Return type: Tensor.
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+            paddle.disable_static()
+            input_data = np.random.random([5, 100]).astype("float64")
+            label_data = np.random.randint(0, 100, size=(5)).astype(np.int64)
+            weight_data = np.random.random([100]).astype("float64")
+            input =  paddle.to_tensor(input_data)
+            label =  paddle.to_tensor(label_data)
+            weight = paddle.to_tensor(weight_data)
+            loss = paddle.nn.functional.cross_entropy(input=input, label=label, weight=weight)
+            print(loss.numpy())
+ 
+    """
+    if not in_dygraph_mode():
+        fluid.data_feeder.check_variable_and_dtype(
+            input, 'input', ['float32', 'float64'], 'cross_entropy_loss')
+        fluid.data_feeder.check_variable_and_dtype(label, 'label', ['int64'],
+                                                   'cross_entropy_loss')
+
+    if reduction not in ['sum', 'mean', 'none']:
+        raise ValueError(
+            "The value of 'reduction' in cross_entropy_loss should be 'sum', 'mean' or"
+            " 'none', but received %s, which is not allowed." % reduction)
+
+    #step 1. log_softmax
+    log_softmax_out = paddle.nn.functional.log_softmax(input)
+    if weight is not None and not isinstance(weight, Variable):
+        raise ValueError(
+            "The weight' is not a Variable, please convert to Variable.")
+
+    #step 2. nll_loss 
+    input = log_softmax_out
+    helper = LayerHelper('nll_loss', **locals())
+    dtype = helper.input_dtype(input)
+
+    if not in_dygraph_mode():
+        fluid.data_feeder.check_variable_and_dtype(
+            input, 'input', ['float32', 'float64'], 'nll_loss')
+        fluid.data_feeder.check_variable_and_dtype(label, 'label', ['int64'],
+                                                   'nll_loss')
+
+    x_shape = list(input.shape)
+    n = x_shape[0]
+    c = x_shape[1]
+    x_dims = len(x_shape)
+    if x_dims < 2:
+        raise ValueError('Expected 2 or more dimensions (got {})'.format(
+            x_dims))
+    if x_dims != 2 and x_dims != 4:
+        input = reshape(input, shape=[n, c, 1, -1])
+        label = reshape(label, shape=[n, 1, -1])
+        out_shape = [n] + x_shape[2:]
+
+    if not in_dygraph_mode():
+        fluid.data_feeder.check_variable_and_dtype(
+            input, 'input', ['float32', 'float64'], 'nll_loss')
+        fluid.data_feeder.check_variable_and_dtype(label, 'label', ['int64'],
+                                                   'nll_loss')
+    inputs = {'X': input, 'Label': label}
+    attrs = {'reduction': reduction, 'ignore_index': ignore_index}
+    if weight is not None:
+        if isinstance(weight, Variable):
+            inputs['Weight'] = weight
+
+    out = helper.create_variable_for_type_inference(dtype=input.dtype)
+    total_weight = helper.create_variable_for_type_inference(dtype=input.dtype)
+    outputs = {'Out': out, 'Total_weight': total_weight}
+
+    helper.append_op(
+        type='nll_loss', inputs=inputs, outputs=outputs, attrs=attrs)
+    if x_dims != 2 and x_dims != 4 and reduction == 'none':
+        out = reshape(out, shape=out_shape)
+
+    return out

python/paddle/nn/layer/loss.py

@@ -21,7 +21,6 @@ from .. import functional as F
 from paddle.fluid.framework import core, in_dygraph_mode, _varbase_creator
 
 __all__ = [
-    #       'NCELoss',
     'CrossEntropyLoss',
     'MSELoss',
     'L1Loss',
@@ -119,7 +118,7 @@ class CrossEntropyLoss(fluid.dygraph.Layer):
                 print(output.numpy())
     """
 
-    def __init__(self, weight=None, reduction='mean', ignore_index=-100):
+    def __init__(self, weight=None, ignore_index=-100, reduction='mean'):
         super(CrossEntropyLoss, self).__init__()
         self.weight = weight
         self.reduction = reduction
@@ -137,18 +136,12 @@ class CrossEntropyLoss(fluid.dygraph.Layer):
                 " 'none', but received %s, which is not allowed." %
                 self.reduction)
 
-        log_softmax = paddle.nn.LogSoftmax()
-        log_softmax_out = log_softmax(input)
-        if self.weight is not None and not isinstance(self.weight,
-                                                      fluid.framework.Variable):
-            raise ValueError(
-                "The weight' is not a Variable, please convert to Variable.")
-        nll_loss = paddle.nn.loss.NLLLoss(
+        return paddle.nn.functional.cross_entropy(
+            input,
+            label,
             weight=self.weight,
-            reduction=self.reduction,
-            ignore_index=self.ignore_index)
-
-        return nll_loss(log_softmax_out, label)
+            ignore_index=self.ignore_index,
+            reduction=self.reduction)
 
 
 class MSELoss(fluid.dygraph.layers.Layer):