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- # Copyright 2021 Huawei Technologies Co., Ltd
- #
- # 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.
- # ============================================================================
- """
- Parallel Loss for the Parallel Training
- This is an experimental interface that is subject to change or deletion.
- """
- from mindspore.common.tensor import Tensor
- import mindspore.common.dtype as mstype
- from mindspore.ops import operations as P
- from mindspore.ops import functional as F
- from mindspore.nn import Cell
- from mindspore.nn.loss.loss import _check_is_tensor
- from mindspore.parallel.nn.layers import _check_input_dtype, _check_input_shape
- from .op_parallel_config import default_dpmp_config, OpParallelConfig
-
- __all__ = ["CrossEntropyLoss"]
-
-
- class CrossEntropyLoss(Cell):
- """
- Calculate the cross entropy loss.
-
- Args:
- parallel_config (OpParallelConfig): The parallel configure. Default `default_dpmp_config`,
- an instance of `OpParallelConfig` with default args.
-
- Inputs:
- - **logits** (Tensor) - Tensor of shape (N, C). Data type must be float16 or float32. The output logits of
- the backbone.
-
- - **labels** (Tensor) - Tensor of shape (N, ). The ground truth label of the sample.
-
- - **input_mask** (Tensor) - Tensor of shape (N, ). input_mask indicates whether there are padded inputs and for
- padded inputs it will not be counted into loss.
-
- Outputs:
- Tensor. The corresponding cross entropy loss
-
- Examples:
- >>> import numpy as np
- >>> from mindspore import dtype as mstype
- >>> from mindspore.parallel.nn import CrossEntropyLoss
- >>> from mindspore import Tensor
- >>> loss = CrossEntropyLoss()
- >>> logits = Tensor(np.array([[3, 5, 6, 9, 12, 33, 42, 12, 32, 72]]), mstype.float32)
- >>> labels_np = np.array([1]).astype(np.int32)
- >>> input_mask = Tensor(np.ones(1).astype(np.float32))
- >>> labels = Tensor(labels_np)
- >>> output = loss(logits, labels, input_mask)
- >>> print(output.shape)
- (1,)
- """
-
- def __init__(self, parallel_config=default_dpmp_config):
- super(CrossEntropyLoss, self).__init__()
- if not isinstance(parallel_config, OpParallelConfig):
- raise TypeError("For 'CrossEntropyLoss', the class variable 'parallel_config' must be OpParallelConfig, "
- "but got the type: {}.".format(type(parallel_config)))
- dp = parallel_config.data_parallel
- mp = parallel_config.model_parallel
- self.sum = P.ReduceSum().shard(((dp, mp),))
- self.onehot = P.OneHot().shard(((dp, mp), (), ()))
- # on/off value for onehot, for smooth labeling, modify the off_value
- self.on_value = Tensor(1.0, mstype.float32)
- self.off_value = Tensor(0.0, mstype.float32)
- self.max = P.ArgMaxWithValue(axis=-1, keep_dims=True).shard(
- ((dp, mp),))
- self.eps_const = Tensor(1e-24, mstype.float32)
- self.sub = P.Sub().shard(((dp, mp), (dp, 1)))
- self.exp = P.Exp().shard(((dp, mp),))
- self.div = P.RealDiv().shard(((dp, mp), (dp, 1)))
- self.log = P.Log().shard(((dp, mp),))
- self.add = P.Add().shard(((dp, mp), ()))
- self.mul = P.Mul().shard(
- ((dp, mp), (dp, mp)))
- self.neg = P.Neg().shard(((dp, mp),))
- self.sum2 = P.ReduceSum().shard(((1,),))
-
- self.mul2 = P.Mul().shard(((1,), (1,)))
- self.add2 = P.Add()
- self.div2 = P.RealDiv()
-
- def construct(self, logits, label, input_mask):
- self._check_input(logits, label, input_mask)
-
- # the shape is [bs*seq_length, vocab_size]
- logits = F.cast(logits, mstype.float32)
- # LogSoftmax for logits over last dimension
- _, logit_max = self.max(logits)
- logit_sub = self.sub(logits, logit_max)
- logit_exp = self.exp(logit_sub)
- exp_sum = self.sum(logit_exp, -1)
- exp_sum = P.Reshape()(exp_sum, (F.shape(exp_sum)[0], 1))
- softmax_result = self.div(logit_exp, exp_sum)
- log_softmax_result = self.log(self.add(softmax_result, self.eps_const))
-
- # Flatten label to [bs*seq_length]
- label = P.Reshape()(label, (-1,))
- # Get onehot label [bs*seq_length, vocab_size]
- one_hot_label = self.onehot(label, F.shape(logits)[-1], self.on_value,
- self.off_value)
- # Cross-Entropy loss
- loss = self.mul(log_softmax_result, one_hot_label)
- loss_unsum = self.neg(loss)
- loss_reduce = self.sum(loss_unsum, -1)
- # input_mask indicates whether there is padded inputs and for padded inputs it will not be counted into loss
- input_mask = P.Reshape()(input_mask, (-1,))
- numerator = self.sum2(self.mul2(loss_reduce, input_mask))
-
- denominator = self.add2(
- self.sum2(input_mask),
- P.Cast()(F.tuple_to_array((1e-5,)), mstype.float32))
- loss = self.div2(numerator, denominator)
- return loss
-
- def _check_input(self, logits, label, input_mask):
- r"""Check the input tensor shape and type"""
- _check_is_tensor('logits', logits, self.cls_name)
- _check_is_tensor('label', label, self.cls_name)
- _check_is_tensor('input_mask', input_mask, self.cls_name)
- _check_input_dtype(F.dtype(logits), "logits", [mstype.float32, mstype.float16], self.cls_name)
- _check_input_dtype(F.dtype(label), "label", [mstype.int32], self.cls_name)
- _check_input_dtype(F.dtype(input_mask), "input_mask", [mstype.float32], self.cls_name)
- _check_input_shape(F.shape(logits), "logits", self.cls_name, 2)
- _check_input_shape(F.shape(label), "label", self.cls_name, 1)
- _check_input_shape(F.shape(input_mask), "input_mask", self.cls_name, 1)
- return True
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