【Paddle Hackathon No.11】 (#45595)

* 2022-08-30_update nn.layer.loss nn.functional.loss, test_file

* 2022-08-30_update nn.layer.loss nn.functional.loss, test_file

* fix: test_file

* fix: test_file, docs, multi_margin_loss

* fix: doc weight function

* fix: test_multi_margin_loss

* fix: weight np.testing.assert_allclose

* fix: test_file

* fix: en_doc

* 2022-10-10

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

+# -*- coding: utf-8 -*
+#   Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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 paddle
+import numpy as np
+import unittest
+
+
+def call_MultiMarginLoss_layer(
+    input,
+    label,
+    p=1,
+    margin=1.0,
+    weight=None,
+    reduction='mean',
+):
+    triplet_margin_loss = paddle.nn.MultiMarginLoss(p=p,
+                                                    margin=margin,
+                                                    weight=weight,
+                                                    reduction=reduction)
+    res = triplet_margin_loss(
+        input=input,
+        label=label,
+    )
+    return res
+
+
+def call_MultiMarginLoss_functional(
+    input,
+    label,
+    p=1,
+    margin=1.0,
+    weight=None,
+    reduction='mean',
+):
+    res = paddle.nn.functional.multi_margin_loss(input=input,
+                                                 label=label,
+                                                 p=p,
+                                                 margin=margin,
+                                                 weight=weight,
+                                                 reduction=reduction)
+    return res
+
+
+def test_static(place,
+                input_np,
+                label_np,
+                p=1,
+                margin=1.0,
+                weight_np=None,
+                reduction='mean',
+                functional=False):
+    prog = paddle.static.Program()
+    startup_prog = paddle.static.Program()
+    with paddle.static.program_guard(prog, startup_prog):
+        input = paddle.static.data(name='input',
+                                   shape=input_np.shape,
+                                   dtype=input_np.dtype)
+        label = paddle.static.data(name='label',
+                                   shape=label_np.shape,
+                                   dtype=label_np.dtype)
+        feed_dict = {
+            "input": input_np,
+            "label": label_np,
+        }
+        weight = None
+        if weight_np is not None:
+            weight = paddle.static.data(name='weight',
+                                        shape=weight_np.shape,
+                                        dtype=weight_np.dtype)
+            feed_dict['weight'] = weight_np
+        if functional:
+            res = call_MultiMarginLoss_functional(input=input,
+                                                  label=label,
+                                                  p=p,
+                                                  margin=margin,
+                                                  weight=weight,
+                                                  reduction=reduction)
+        else:
+            res = call_MultiMarginLoss_layer(input=input,
+                                             label=label,
+                                             p=p,
+                                             margin=margin,
+                                             weight=weight,
+                                             reduction=reduction)
+
+        exe = paddle.static.Executor(place)
+        static_result = exe.run(prog, feed=feed_dict, fetch_list=[res])
+    return static_result[0]
+
+
+def test_static_data_shape(place,
+                           input_np,
+                           label_np,
+                           wrong_label_shape=None,
+                           weight_np=None,
+                           wrong_weight_shape=None,
+                           functional=False):
+    prog = paddle.static.Program()
+    startup_prog = paddle.static.Program()
+    with paddle.static.program_guard(prog, startup_prog):
+        input = paddle.static.data(name='input',
+                                   shape=input_np.shape,
+                                   dtype=input_np.dtype)
+        if wrong_label_shape is None:
+            label_shape = label_np.shape
+        else:
+            label_shape = wrong_label_shape
+        label = paddle.static.data(name='label',
+                                   shape=label_shape,
+                                   dtype=label_np.dtype)
+        feed_dict = {
+            "input": input_np,
+            "label": label_np,
+        }
+        weight = None
+        if weight_np is not None:
+            if wrong_weight_shape is None:
+                weight_shape = weight_np.shape
+            else:
+                weight_shape = wrong_weight_shape
+            weight = paddle.static.data(name='weight',
+                                        shape=weight_shape,
+                                        dtype=weight_np.dtype)
+            feed_dict['weight'] = weight_np
+        if functional:
+            res = call_MultiMarginLoss_functional(
+                input=input,
+                label=label,
+                weight=weight,
+            )
+        else:
+            res = call_MultiMarginLoss_layer(
+                input=input,
+                label=label,
+                weight=weight,
+            )
+
+        exe = paddle.static.Executor(place)
+        static_result = exe.run(prog, feed=feed_dict, fetch_list=[res])
+    return static_result
+
+
+def test_dygraph(place,
+                 input,
+                 label,
+                 p=1,
+                 margin=1.0,
+                 weight=None,
+                 reduction='mean',
+                 functional=False):
+    paddle.disable_static()
+    input = paddle.to_tensor(input)
+    label = paddle.to_tensor(label)
+
+    if weight is not None:
+        weight = paddle.to_tensor(weight)
+    if functional:
+        dy_res = call_MultiMarginLoss_functional(input=input,
+                                                 label=label,
+                                                 p=p,
+                                                 margin=margin,
+                                                 weight=weight,
+                                                 reduction=reduction)
+    else:
+        dy_res = call_MultiMarginLoss_layer(input=input,
+                                            label=label,
+                                            p=p,
+                                            margin=margin,
+                                            weight=weight,
+                                            reduction=reduction)
+    dy_result = dy_res.numpy()
+    paddle.enable_static()
+    return dy_result
+
+
+def calc_multi_margin_loss(
+    input,
+    label,
+    p=1,
+    margin=1.0,
+    weight=None,
+    reduction='mean',
+):
+    index_sample = np.array([input[i, label[i]]
+                             for i in range(label.size)]).reshape(-1, 1)
+    if weight is None:
+        expected = np.mean(np.maximum(margin + input - index_sample, 0.0)**p,
+                           axis=1) - margin**p / input.shape[1]
+    else:
+        weight = np.array([weight[label[i]]
+                           for i in range(label.size)]).reshape(-1, 1)
+        expected = np.mean(np.maximum(weight * (margin + input - index_sample), 0.0) ** p, axis=1) - weight*(margin ** p / \
+                   input.shape[1])
+
+    if reduction == 'mean':
+        expected = np.mean(expected)
+    elif reduction == 'sum':
+        expected = np.sum(expected)
+    else:
+        expected = expected
+
+    return expected
+
+
+class TestMultiMarginLoss(unittest.TestCase):
+
+    def test_MultiMarginLoss(self):
+        batch_size = 5
+        num_classes = 2
+        shape = (batch_size, num_classes)
+        input = np.random.uniform(0.1, 0.8, size=shape).astype(np.float64)
+        label = np.random.uniform(0, input.shape[1],
+                                  size=(batch_size, )).astype(np.int64)
+
+        places = [paddle.CPUPlace()]
+        if paddle.device.is_compiled_with_cuda():
+            places.append(paddle.CUDAPlace(0))
+        reductions = ['sum', 'mean', 'none']
+        for place in places:
+            for reduction in reductions:
+                expected = calc_multi_margin_loss(input=input,
+                                                  label=label,
+                                                  reduction=reduction)
+
+                dy_result = test_dygraph(
+                    place=place,
+                    input=input,
+                    label=label,
+                    reduction=reduction,
+                )
+
+                static_result = test_static(
+                    place=place,
+                    input_np=input,
+                    label_np=label,
+                    reduction=reduction,
+                )
+                np.testing.assert_allclose(static_result, expected)
+                np.testing.assert_allclose(static_result, dy_result)
+                np.testing.assert_allclose(dy_result, expected)
+                static_functional = test_static(place=place,
+                                                input_np=input,
+                                                label_np=label,
+                                                reduction=reduction,
+                                                functional=True)
+                dy_functional = test_dygraph(place=place,
+                                             input=input,
+                                             label=label,
+                                             reduction=reduction,
+                                             functional=True)
+                np.testing.assert_allclose(static_functional, expected)
+                np.testing.assert_allclose(static_functional, dy_functional)
+                np.testing.assert_allclose(dy_functional, expected)
+
+    def test_MultiMarginLoss_error(self):
+        paddle.disable_static()
+        self.assertRaises(ValueError,
+                          paddle.nn.MultiMarginLoss,
+                          reduction="unsupport reduction")
+        input = paddle.to_tensor([[0.1, 0.3]], dtype='float32')
+        label = paddle.to_tensor([0], dtype='int32')
+        self.assertRaises(ValueError,
+                          paddle.nn.functional.multi_margin_loss,
+                          input=input,
+                          label=label,
+                          reduction="unsupport reduction")
+        paddle.enable_static()
+
+    def test_MultiMarginLoss_dimension(self):
+        paddle.disable_static()
+
+        input = paddle.to_tensor([[0.1, 0.3], [1, 2]], dtype='float32')
+        label = paddle.to_tensor([0, 1, 1], dtype='int32')
+
+        self.assertRaises(
+            ValueError,
+            paddle.nn.functional.multi_margin_loss,
+            input=input,
+            label=label,
+        )
+        MMLoss = paddle.nn.MultiMarginLoss()
+        self.assertRaises(
+            ValueError,
+            MMLoss,
+            input=input,
+            label=label,
+        )
+        paddle.enable_static()
+
+    def test_MultiMarginLoss_p(self):
+        p = 2
+        batch_size = 5
+        num_classes = 2
+        shape = (batch_size, num_classes)
+        reduction = 'mean'
+        place = paddle.CPUPlace()
+        input = np.random.uniform(0.1, 0.8, size=shape).astype(np.float64)
+        label = np.random.uniform(0, input.shape[1],
+                                  size=(batch_size, )).astype(np.int64)
+        expected = calc_multi_margin_loss(input=input,
+                                          p=p,
+                                          label=label,
+                                          reduction=reduction)
+
+        dy_result = test_dygraph(
+            place=place,
+            p=p,
+            input=input,
+            label=label,
+            reduction=reduction,
+        )
+
+        static_result = test_static(
+            place=place,
+            p=p,
+            input_np=input,
+            label_np=label,
+            reduction=reduction,
+        )
+        np.testing.assert_allclose(static_result, expected)
+        np.testing.assert_allclose(static_result, dy_result)
+        np.testing.assert_allclose(dy_result, expected)
+        static_functional = test_static(place=place,
+                                        p=p,
+                                        input_np=input,
+                                        label_np=label,
+                                        reduction=reduction,
+                                        functional=True)
+        dy_functional = test_dygraph(place=place,
+                                     p=p,
+                                     input=input,
+                                     label=label,
+                                     reduction=reduction,
+                                     functional=True)
+        np.testing.assert_allclose(static_functional, expected)
+        np.testing.assert_allclose(static_functional, dy_functional)
+        np.testing.assert_allclose(dy_functional, expected)
+
+    def test_MultiMarginLoss_weight(self):
+        batch_size = 5
+        num_classes = 2
+        shape = (batch_size, num_classes)
+        reduction = 'mean'
+        place = paddle.CPUPlace()
+        input = np.random.uniform(0.1, 0.8, size=shape).astype(np.float64)
+        label = np.random.uniform(0, input.shape[1],
+                                  size=(batch_size, )).astype(np.int64)
+        weight = np.random.uniform(0, 2,
+                                   size=(num_classes, )).astype(np.float64)
+        expected = calc_multi_margin_loss(input=input,
+                                          label=label,
+                                          weight=weight,
+                                          reduction=reduction)
+
+        dy_result = test_dygraph(
+            place=place,
+            input=input,
+            label=label,
+            weight=weight,
+            reduction=reduction,
+        )
+
+        static_result = test_static(
+            place=place,
+            input_np=input,
+            label_np=label,
+            weight_np=weight,
+            reduction=reduction,
+        )
+        np.testing.assert_allclose(static_result, expected)
+        np.testing.assert_allclose(static_result, dy_result)
+        np.testing.assert_allclose(dy_result, expected)
+        static_functional = test_static(place=place,
+                                        input_np=input,
+                                        label_np=label,
+                                        weight_np=weight,
+                                        reduction=reduction,
+                                        functional=True)
+        dy_functional = test_dygraph(place=place,
+                                     input=input,
+                                     label=label,
+                                     weight=weight,
+                                     reduction=reduction,
+                                     functional=True)
+        np.testing.assert_allclose(static_functional, expected)
+        np.testing.assert_allclose(static_functional, dy_functional)
+        np.testing.assert_allclose(dy_functional, expected)
+
+    def test_MultiMarginLoss_static_data_shape(self):
+        batch_size = 5
+        num_classes = 2
+        shape = (batch_size, num_classes)
+        place = paddle.CPUPlace()
+        input = np.random.uniform(0.1, 0.8, size=shape).astype(np.float64)
+        label = np.random.uniform(0, input.shape[1],
+                                  size=(batch_size, )).astype(np.int64)
+        weight = np.random.uniform(0, 2,
+                                   size=(num_classes, )).astype(np.float64)
+
+        self.assertRaises(
+            ValueError,
+            test_static_data_shape,
+            place=place,
+            input_np=input,
+            label_np=label,
+            wrong_label_shape=(10, ),
+            functional=True,
+        )
+        self.assertRaises(
+            ValueError,
+            test_static_data_shape,
+            place=place,
+            input_np=input,
+            label_np=label,
+            wrong_label_shape=(10, ),
+            functional=False,
+        )
+        self.assertRaises(
+            ValueError,
+            test_static_data_shape,
+            place=place,
+            input_np=input,
+            label_np=label,
+            weight_np=weight,
+            wrong_weight_shape=(3, ),
+            functional=True,
+        )
+        self.assertRaises(
+            ValueError,
+            test_static_data_shape,
+            place=place,
+            input_np=input,
+            label_np=label,
+            weight_np=weight,
+            wrong_weight_shape=(3, ),
+            functional=False,
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/nn/__init__.py

@@ -109,6 +109,7 @@ from .layer.loss import CTCLoss  # noqa: F401
 from .layer.loss import SmoothL1Loss  # noqa: F401
 from .layer.loss import HingeEmbeddingLoss  # noqa: F401
 from .layer.loss import CosineEmbeddingLoss  # noqa: F401
+from .layer.loss import MultiMarginLoss
 from .layer.loss import TripletMarginWithDistanceLoss
 from .layer.loss import TripletMarginLoss
 from .layer.loss import SoftMarginLoss
@@ -319,6 +320,7 @@ __all__ = [  # noqa
     'Identity',
     'CosineEmbeddingLoss',
     'RReLU',
+    'MultiMarginLoss',
     'TripletMarginWithDistanceLoss',
     'TripletMarginLoss',
     'SoftMarginLoss',

python/paddle/nn/functional/__init__.py

@@ -92,6 +92,7 @@ from .loss import square_error_cost  # noqa: F401
 from .loss import ctc_loss  # noqa: F401
 from .loss import hinge_embedding_loss  # noqa: F401
 from .loss import cosine_embedding_loss  # noqa: F401
+from .loss import multi_margin_loss
 from .loss import multi_label_soft_margin_loss
 from .loss import triplet_margin_with_distance_loss
 from .loss import triplet_margin_loss
@@ -241,5 +242,6 @@ __all__ = [  # noqa
     'rrelu',
     'triplet_margin_with_distance_loss',
     'triplet_margin_loss',
+    'multi_margin_loss',
     'soft_margin_loss',
 ]

python/paddle/nn/functional/loss.py

@@ -3457,6 +3457,117 @@ def triplet_margin_loss(input,
         return loss
 
 
+def multi_margin_loss(input,
+                      label,
+                      p: int = 1,
+                      margin: float = 1.0,
+                      weight=None,
+                      reduction='mean',
+                      name=None):
+    r"""
+        Measures a multi-class classification hinge loss between input :math:`input` and label :math:`label`:
+
+        For i-th mini-batch sample, the loss in terms of the 1D input :math:`input_i` and scalar
+        output :math:`label_i` is:
+
+        .. math::
+            \text{loss}(input_i, label_i) = \frac{\sum_{j} \max(0, \text{margin} - input_i[label_i] + input_i[j])^p}{\text{C}}
+
+        where :math:`0 \leq j \leq \text{C}-1`, :math:`0 \leq i \leq \text{N}-1` and :math:`j \neq label_i`.
+
+        Optionally, you can give non-equal weighting on the classes by passing
+        a 1D :attr:`weight` tensor into the constructor.
+
+        The loss function for i-th sample then becomes:
+
+        .. math::
+            \text{loss}(input_i, label_i) = \frac{\sum_{j} \max(0, weight[label_i] * (\text{margin} - input_i[label_i] + input_i[j]))^p}{\text{C}}
+
+
+    Parameters:
+        input (Tensor): Input tensor, the data type is float32 or float64. Shape is (N, C), where C is number of classes.
+
+        label (Tensor): Label tensor, the data type is int32 or int64. The shape of label is (N,)
+
+        p (int, Optional): The power num. Default: :math:`1`.
+
+        margin (float, Optional): Default: :math:`1`.
+
+        weight (Tensor,optional): a manual rescaling weight given to each class.
+                If given, has to be a Tensor of shape (C,) and the data type is float32, float64.
+                Default is ``'None'`` .
+
+
+        reduction (str, Optional):Indicate how to calculate the loss by batch_size.
+            the candidates are ``'none'`` | ``'mean'`` | ``'sum'``.
+            If :attr:`reduction` is ``'none'``, the unreduced loss is returned;
+            If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned;
+            If :attr:`reduction` is ``'sum'``, the summed loss is returned.
+            Default: ``'mean'``
+
+        name (str, Optional): Name for the operation (optional, default is None).
+            For more information, please refer to :ref:`api_guide_Name`.
+
+    Returns:
+        Output: Tensor. The tensor variable storing the multi_margin_loss of input and label.
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+            import paddle.nn.functional as F
+
+            input = paddle.to_tensor([[1, 5, 3], [0, 3, 2], [1, 4, 1]], dtype=paddle.float32)
+            label = paddle.to_tensor([1, 2, 1], dtype=paddle.int32)
+            loss = F.multi_margin_loss(input, label, margin=1.0, reduction='none')
+            print(loss)
+
+    """
+    if reduction not in ['sum', 'mean', 'none']:
+        raise ValueError(
+            "'reduction' in 'multi_margin_loss' should be 'sum', 'mean' or 'none', "
+            "but received {}.".format(reduction))
+
+    if not _non_static_mode():
+        check_variable_and_dtype(input, 'input', ['float32', 'float64'],
+                                 'multi_margin_loss')
+        check_variable_and_dtype(label, 'label', ['int32', 'int64'],
+                                 'multi_margin_loss')
+    if not (input.shape[0] == label.shape[0]):
+        raise ValueError(
+            "The label's shape[0] should be equal to input's shape[0], "
+            "but received input's shape[0] {} and label's shape[0]:{}. ".format(
+                input.shape[0], label.shape[0]))
+    label = label.reshape((-1, 1))
+    index_sample = paddle.index_sample(input, label)
+    if weight is not None:
+        if not _non_static_mode():
+            check_variable_and_dtype(weight, 'weight', ['float32', 'float64'],
+                                     'multi_margin_loss')
+        if not (input.shape[1] == weight.shape[0]):
+            raise ValueError(
+                "The weight's shape[0] should be equal to input's shape[1]"
+                "but received weight's shape[0]: {} and input's shape[1]: {}".
+                format(weight.shape[0], input.shape[1]))
+        weight = paddle.gather(weight, label, axis=0).reshape((-1, 1))
+        loss = paddle.mean(
+            paddle.pow(
+                paddle.clip(weight *
+                            (margin - index_sample + input), min=0.0), p),
+            axis=1) - weight * (margin**p / paddle.shape(input)[1])
+    else:
+        loss = paddle.mean(paddle.pow(
+            paddle.clip(margin - index_sample + input, min=0.0), p),
+                           axis=1) - margin**p / paddle.shape(input)[1]
+
+    if reduction == 'mean':
+        return paddle.mean(loss, name=name)
+    elif reduction == 'sum':
+        return paddle.sum(loss, name=name)
+    elif reduction == 'none':
+        return loss
+
+
 def soft_margin_loss(input, label, reduction='mean', name=None):
     """
     The API measures the soft margin loss between input predictions ``input``

python/paddle/nn/layer/__init__.py

@@ -83,6 +83,7 @@ from .loss import HingeEmbeddingLoss  # noqa: F401
 from .loss import TripletMarginWithDistanceLoss
 from .loss import TripletMarginLoss
 from .loss import SoftMarginLoss
+from .loss import MultiMarginLoss
 from .norm import BatchNorm1D  # noqa: F401
 from .norm import BatchNorm2D  # noqa: F401
 from .norm import BatchNorm3D  # noqa: F401

python/paddle/nn/layer/loss.py

@@ -1674,6 +1674,103 @@ class TripletMarginLoss(Layer):
                                      name=self.name)
 
 
+class MultiMarginLoss(Layer):
+    r"""Creates a criterion that optimizes a multi-class classification hinge loss (margin-based loss) between
+        input :math:`input` and label :math:`label`:
+
+        For i-th mini-batch sample, the loss in terms of the 1D input :math:`input_i` and scalar
+        output :math:`label_i` is:
+
+        .. math::
+            \text{loss}(input_i, label_i) = \frac{\sum_{j} \max(0, \text{margin} - input_i[label_i] + input_i[j])^p}{\text{C}}
+
+        where :math:`0 \leq j \leq \text{C}-1`, :math:`0 \leq i \leq \text{N}-1` and :math:`j \neq label_i`.
+
+        Optionally, you can give non-equal weighting on the classes by passing
+        a 1D :attr:`weight` tensor into the constructor.
+
+        The loss function for i-th sample then becomes:
+
+        .. math::
+            \text{loss}(input_i, label_i) = \frac{\sum_{j} \max(0, weight[label_i] * (\text{margin} - input_i[label_i] + input_i[j]))^p}{\text{C}}
+
+
+        Parameters:
+
+            p (int, Optional):The norm degree for pairwise distance. Default: :math:`1`.
+
+            margin (float, Optional):Default: :math:`1`.
+
+            weight (Tensor,optional): a manual rescaling weight given to each class.
+                    If given, has to be a Tensor of shape (C,) and the data type is float32, float64.
+                    Default is ``'None'`` .
+
+            reduction (str, optional): Indicate how to calculate the loss by batch_size,
+                    the candidates are ``'none'`` | ``'mean'`` | ``'sum'``.
+                    If :attr:`reduction` is ``'none'``, the unreduced loss is returned;
+                    If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned;
+                    If :attr:`reduction` is ``'sum'``, the summed loss is returned.
+                    Default: ``'mean'``
+
+            name (str, optional): Name for the operation (optional, default is None).
+                For more information, please refer to :ref:`api_guide_Name`.
+
+        Call parameters:
+            input (Tensor): Input tensor, the data type is float32 or float64.
+
+            label (Tensor): Label tensor, 0<= label < input.shape[1], the data type is int32 or int64.
+
+        Shape:
+            input: 2-D Tensor, the shape is [N, C], N is batch size and `C` means number of classes.
+
+            label: 1-D Tensor, the shape is [N,].
+
+            output: scalar. If :attr:`reduction` is ``'none'``, then same shape as the label.
+
+        Returns:
+            A callable object of MultiMarginLoss.
+
+        Examples:
+            .. code-block:: python
+
+                import paddle
+                import paddle.nn as nn
+
+                input = paddle.to_tensor([[1, -2, 3], [0, -1, 2], [1, 0, 1]], dtype=paddle.float32)
+                label = paddle.to_tensor([0, 1, 2], dtype=paddle.int32)
+
+                multi_margin_loss = nn.MultiMarginLoss(reduction='mean')
+                loss = multi_margin_loss(input, label)
+                print(loss)
+        """
+
+    def __init__(self,
+                 p: int = 1,
+                 margin: float = 1.0,
+                 weight=None,
+                 reduction="mean",
+                 name=None):
+        super(MultiMarginLoss, self).__init__()
+        if reduction not in ['sum', 'mean', 'none']:
+            raise ValueError(
+                "'reduction' in 'MultiMarginLoss' should be 'sum', 'mean' or 'none', "
+                "but received {}.".format(reduction))
+        self.p = p
+        self.margin = margin
+        self.weight = weight
+        self.reduction = reduction
+        self.name = name
+
+    def forward(self, input, label):
+        return F.multi_margin_loss(input,
+                                   label,
+                                   p=self.p,
+                                   margin=self.margin,
+                                   weight=self.weight,
+                                   reduction=self.reduction,
+                                   name=self.name)
+
+
 class SoftMarginLoss(Layer):
     r"""
     Creates a criterion that measures a two-class soft margin loss between input predictions ``input``