Source code for mindspore.nn.transformer.loss

# Copyright 2021 Huawei Technologies Co., Ltd
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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# ============================================================================
"""
Parallel Loss for the Parallel Training
This is an experimental interface that is subject to change or deletion.
"""
from __future__ import absolute_import

from mindspore.parallel import set_algo_parameters
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._utils import _get_parallel_mode, _is_sharding_propagation
from mindspore.context import ParallelMode
from mindspore.parallel._utils import _get_device_num, _get_pipeline_stages
from mindspore.log import _LogActionOnce
from mindspore import log as logger
from mindspore.nn.transformer.layers import _check_input_dtype, _check_input_shape
from mindspore.nn.transformer.op_parallel_config import default_dpmp_config, OpParallelConfig

__all__ = ["CrossEntropyLoss"]


class _Softmax(Cell):
    """
    Calculate the softmax results with given logits.

    Note:
        The bprop of the cell is rewritten, just returns the accepted dout as returns. This cell should be used
        together with _NLLoss, to optimize the bprop of the cross entroy 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.


    Outputs:
        Tensor. The corresponding softmax results.
    """
    def __init__(self, parallel_config=default_dpmp_config):
        super(_Softmax, 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
        # 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.sum = P.ReduceSum().shard(((dp, mp),))
        self.max = P.ArgMaxWithValue(axis=-1, keep_dims=True).shard(
            ((dp, mp),))
        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.onehot = P.OneHot().shard(((dp, mp), (), ()))

    def construct(self, logits, label):
        # LogSoftmax for logits over last dimension
        logits = F.cast(logits, mstype.float32)
        _, 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)

        one_hot_label = self.onehot(label, F.shape(logits)[-1], self.on_value, self.off_value)
        return softmax_result, one_hot_label

    def bprop(self, logits, label, out, dout):
        """just return the loss of the dout. Note this should be used together with _NLLLoss"""
        d_logits = F.cast(dout[0], F.dtype(logits))
        return d_logits, F.zeros_like(label)


class _NLLLoss(Cell):
    """
    Calculate the NLLLoss results with given softmax results and the label.

    Note:
        The bprop of the cell is rewritten. This cell should be used
        together with _Softmax, to optimize the bprop of the cross entroy loss.

    Args:
        parallel_config (OpParallelConfig): The parallel configure. Default `default_dpmp_config`,
            an instance of `OpParallelConfig` with default args.

    Inputs:
        - **loss** (Tensor) - Tensor of shape (N, C). Data type is float32.

    Outputs:
        Tensor. The corresponding loss results.
    """
    def __init__(self, parallel_config=default_dpmp_config):
        super(_NLLLoss, 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.repeat_loss = 1
        self.eps_const = Tensor(1e-24, mstype.float32)
        # In auto parallel, there will be a virtual div in the back propagation begins. As we use custom bprop function
        # we need to eliminate this virtual div by adding a factor "mp".
        if _get_parallel_mode() in (ParallelMode.AUTO_PARALLEL, ParallelMode.SEMI_AUTO_PARALLEL):
            self.repeat_loss = mp
        if _get_parallel_mode() in (ParallelMode.AUTO_PARALLEL,) and _is_sharding_propagation():
            self.sum = P.ReduceSum()
            self.mul = P.Mul()
            self.neg = P.Neg()
            self.log = P.Log()
            self.add = P.Add().shard(((dp, mp), ()))
        else:
            self.sum = P.ReduceSum().shard(((dp, mp),))
            self.mul = P.Mul().shard(((dp, mp), (dp, mp)))
            self.neg = P.Neg().shard(((dp, mp),))
            self.log = P.Log().shard(((dp, mp),))
            self.add = P.Add().shard(((dp, mp), ()))

    def construct(self, softmax_result, one_hot_label):
        log_softmax_result = self.log(self.add(softmax_result, self.eps_const))
        loss = self.mul(log_softmax_result, one_hot_label)
        loss_unsum = self.neg(loss)
        loss_reduce = self.sum(loss_unsum, -1)
        return loss_reduce

    def bprop(self, softmax_result, one_hot_label, out, dout):
        """A simplified function. Note this should be used together with _Softmax"""
        logits = softmax_result - one_hot_label
        logits = logits * P.ExpandDims()(dout, -1) * self.repeat_loss

        return logits, F.zeros_like(one_hot_label)


[文档]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.nn.transformer 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,) """ @_LogActionOnce(logger=logger, key='CrossEntropyLoss', no_warning=_get_parallel_mode() in (ParallelMode.STAND_ALONE,)) 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.enable_force_redistribute = False if _get_parallel_mode() in (ParallelMode.AUTO_PARALLEL, ParallelMode.SEMI_AUTO_PARALLEL): self.enable_force_redistribute = True self.add = P.Add().shard(((dp, mp), ())).add_prim_attr("keep_alive", True) self.add_label = P.Add().shard(((dp,), ())).add_prim_attr("keep_alive", True) self._check_and_modify_sharding_context(dp) self.sum2 = P.ReduceSum().shard(((1,),)) self.mul2 = P.Mul().shard(((1,), (1,))) self.add2 = P.Add() self.div2 = P.RealDiv() self.relu = P.ReLU().shard(((1,),)) self._softmax = _Softmax(parallel_config) self._nllloss = _NLLLoss(parallel_config) @staticmethod def _check_and_modify_sharding_context(dp): device_num = _get_device_num() stages = _get_pipeline_stages() if _get_parallel_mode() in (ParallelMode.AUTO_PARALLEL,) and dp * stages != device_num: set_algo_parameters(fully_use_devices=False) def construct(self, logits, label, input_mask): self._check_input(logits, label, input_mask) # The add is used for forcing the redistribution before stepping in sub graphs, when semi/auto parallel enabled. if self.enable_force_redistribute: logits = self.add(logits, 0) label = self.add_label(label, 0) softmax, one_hot_label = self._softmax(logits, label) loss_reduce = self._nllloss(softmax, one_hot_label) # Using input_mask to mask the 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