# 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 and/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"]
[docs]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 is 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("Input args parallel_config must be the type OpParallelConfig.")
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