# 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.
# ============================================================================
"""
Note: Mixture of Expert (MoE) structure. This is an experimental interface that is subject to change or deletion.
"""
from __future__ import absolute_import
from __future__ import division
import math
import numpy as np
from mindspore.common.tensor import Tensor
import mindspore.common.dtype as mstype
import mindspore.communication.management as D
from mindspore._checkparam import Validator
from mindspore.ops import operations as P
from mindspore.ops import functional as F
from mindspore.ops.primitive import constexpr
from mindspore.nn.cell import Cell
from mindspore.nn.layer import Dense
from mindspore.context import ParallelMode
from mindspore.parallel._utils import _get_parallel_mode, _is_sharding_propagation
from mindspore.nn.transformer.op_parallel_config import default_moeparallel_config
__all__ = [
"MoEConfig"]
[文档]class MoEConfig:
r"""
The configuration of MoE (Mixture of Expert).
Args:
expert_num (int): The number of experts employed. Default: 1
capacity_factor (float): The factor is used to indicate how much to expand expert capacity,
which is >=1.0. Default: 1.1.
aux_loss_factor (float): The factor is used to indicate how much the load balance loss (produced by the
router) to be added to the entire model loss, which is < 1.0. Default: 0.05.
num_experts_chosen (int): The number of experts is chosen by each token and it should not be larger
than expert_num. Default: 1.
expert_group_size (int): The number of tokens in each data parallel group. Default: None. This parameter is
effective only when in AUTO_PARALLEL mode, and NOT SHARDING_PROPAGATION.
Supported Platforms:
``Ascend``
Examples:
>>> from mindspore.nn.transformer import MoEConfig
>>> moe_config = MoEConfig(expert_num=4, capacity_factor=5.0, aux_loss_factor=0.05, num_experts_chosen=1,
... expert_group_size=64)
"""
def __init__(self, expert_num=1, capacity_factor=1.1, aux_loss_factor=0.05,
num_experts_chosen=1, expert_group_size=None):
Validator.check_positive_int(expert_num, "expert_num")
Validator.check_positive_float(capacity_factor, "capacity_factor")
Validator.check_positive_float(aux_loss_factor, "aux_loss_factor")
Validator.check_positive_int(num_experts_chosen, "num_experts_chosen")
if expert_group_size is not None:
Validator.check_positive_int(expert_group_size, "expert_group_size")
if capacity_factor < 1.0:
raise ValueError(f"'capacity_factor' must be equal to or greater than 1.0, "
f"but got {capacity_factor}.")
if aux_loss_factor >= 1.0:
raise ValueError(f"'aux_loss_factor' must be less than 1.0, "
f"but got {aux_loss_factor}.")
if num_experts_chosen > expert_num:
raise ValueError(f"'num_experts_chosen' must not be larger than 'expert_num', "
f"but got {num_experts_chosen}.")
self.expert_num = expert_num
self.capacity_factor = capacity_factor
self.aux_loss_factor = aux_loss_factor
self.num_experts_chosen = num_experts_chosen
self.expert_group_size = expert_group_size
default_moe_config = MoEConfig()
def _check_moe_config(moe_config=None, parallel_config=None):
"""
check if MoE with right configuration.
"""
if not isinstance(moe_config, MoEConfig):
raise TypeError(f"'moe_config' must be an instance of MoEConfig, but got {type(moe_config).__name__}.")
use_moe = (moe_config.expert_num > 1)
if use_moe is False:
return
if moe_config.expert_num % parallel_config.expert_parallel != 0:
raise ValueError(f"When using MoE, the 'expert_num' in {type(moe_config).__name__} must be a multiple "
f"of 'expert_parallel' value in {type(parallel_config).__name__}, but got "
f"{moe_config.expert_num} for 'expert_num' and {parallel_config.expert_parallel} for "
f"'expert_parallel'.")
device_num = D.get_group_size()
if device_num % parallel_config.expert_parallel != 0:
raise ValueError(f"device_num: {device_num} must be a multiple of expert_parallel: "
f"{parallel_config.expert_parallel}.")
if parallel_config.data_parallel % parallel_config.expert_parallel != 0:
raise ValueError(f"data parallel: {parallel_config.data_parallel} must be a multiple of "
f"expert_parallel: {parallel_config.expert_parallel} when using MoE.")
if parallel_config.data_parallel * parallel_config.model_parallel > device_num:
raise ValueError(f"The product of the data parallel: {parallel_config.data_parallel} and "
f"model parallel: {parallel_config.model_parallel} "
f"should be less than device_num: {device_num}.")
@constexpr
def calculate_expert_capacity(k, tokens_per_group, capacity_factor, expert_dim):
return math.ceil(k * tokens_per_group * capacity_factor / expert_dim)
class MoE(Cell):
"""
The mixture of experts (MoE) implementation. The implementation includes a router and a FeedForward layer.
The router dispatches tokens to experts in FeedForward, then FeedForward does computation, and the final output is
obtained by multiplying FeedForward's output and router's combine weight.
Args:
hidden_size (int): The dimension of the inputs.
ffn_hidden_size (int): The intermediate hidden size.
dropout_rate (float): The dropout rate for the second linear's output.
hidden_act (str): The activation of the internal feedforward layer. Supports 'relu',
'relu6', 'tanh', 'gelu', 'fast_gelu', 'elu', 'sigmoid', 'prelu', 'leakyrelu', 'hswish',
'hsigmoid', 'logsigmoid' and so on. Default: gelu.
param_init_type (dtype.Number): The parameter initialization type. Can be dtype.float32 or dtype.float16.
moe_config(MoEConfig): The configuration of MoE (Mixture of Expert). Default is an instance of MoEConfig with
default values. Please see `MoEConfig`.
parallel_config(MoEParallelConfig): The parallel config for MoE, see `MoEParallelConfig`.
Default `default_moeparallel_config`, an instance of `MoEParallelConfig` with default args.
Inputs:
- **x** (Tensor) - should be `[batch, seq_length, hidden_size]`. Float tensor.
Outputs:
Tensor, the output of this layer after mapping. The shape is `[batch, seq_length, hidden_size]`.
"""
def __init__(self, hidden_size,
ffn_hidden_size,
dropout_rate,
hidden_act='gelu',
param_init_type=mstype.float32,
moe_config=default_moe_config,
parallel_config=default_moeparallel_config):
super(MoE, self).__init__()
self.dp = parallel_config.data_parallel
self.ep = parallel_config.expert_parallel
self.hidden_size = hidden_size
self.expert_dim = moe_config.expert_num
self.capacity_factor = moe_config.capacity_factor
self.aux_loss_factor = moe_config.aux_loss_factor
self.num_experts_chosen = moe_config.num_experts_chosen
self.dp_group = parallel_config.data_parallel
from mindspore.nn.transformer import FeedForward
self.reshape = P.Reshape()
self.shape = P.Shape()
self.transpose_2dim = P.Transpose().shard(((self.dp, 1),))
self.transpose_2dim_ep = P.Transpose().shard(((self.ep, 1),))
self.transpose_3dim = P.Transpose().shard(((self.dp, 1, 1),))
self.transpose_4dim_ep = P.Transpose().shard(((self.ep, 1, 1, 1),))
self.batch_mm = P.BatchMatMul().shard(((self.dp, 1, 1), (self.dp, 1, 1)))
self.batch_mm2 = P.BatchMatMul().shard(((self.dp, 1, 1), (self.dp, 1, 1)))
self.router = Router(d_model=hidden_size, moe_config=moe_config, routing_policy=None,
training=True, parallel_config=parallel_config)
self.cast = P.Cast()
if _get_parallel_mode() in (ParallelMode.AUTO_PARALLEL,) and _is_sharding_propagation():
self.expert_group_size = moe_config.expert_group_size
self.ffn = FeedForward(hidden_size=hidden_size,
ffn_hidden_size=ffn_hidden_size,
dropout_rate=dropout_rate,
hidden_act=hidden_act,
expert_group_size=self.expert_group_size,
expert_num=self.expert_dim,
param_init_type=param_init_type,
parallel_config=parallel_config)
self.mul = P.Mul()
else:
self.ffn = FeedForward(hidden_size=hidden_size,
ffn_hidden_size=ffn_hidden_size,
dropout_rate=dropout_rate,
hidden_act=hidden_act,
expert_num=self.expert_dim,
param_init_type=param_init_type,
parallel_config=parallel_config)
self.mul = P.Mul().shard(((), ()))
def construct(self, input_tensor):
input_shape = F.shape(input_tensor)
input_tensor = self.reshape(input_tensor, (-1, self.hidden_size))
bs_and_dmodel = self.shape(input_tensor)
tokens_per_group = bs_and_dmodel[0] // self.dp_group
input_tensor = self.reshape(input_tensor, (self.dp_group, tokens_per_group, self.hidden_size))
expert_capacity = calculate_expert_capacity(self.num_experts_chosen, tokens_per_group,
self.capacity_factor, self.expert_dim)
# dispatch_tensor's shape: (self.dp_group, tokens_per_group, self.expert_dim, expert_capacity)
# combine_tensor's shape: (self.dp_group, tokens_per_group, self.expert_dim, expert_capacity)
dispatch_tensor, combine_tensor, aux_loss = self.router(input_tensor)
# after transpose, input_tensor's shape: (self.dp_group, self.hidden_size, tokens_per_group)
input_tensor = self.transpose_3dim(input_tensor, (0, 2, 1))
dispatch_tensor = self.reshape(dispatch_tensor, (self.dp_group, tokens_per_group,
self.expert_dim * expert_capacity))
dispatch_tensor = self.cast(dispatch_tensor, F.dtype(input_tensor))
# expert_input's shape: (self.dp_group, self.hidden_size, self.expert_dim * expert_capacity)
expert_input = self.batch_mm(input_tensor, dispatch_tensor)
expert_input = self.reshape(expert_input, (self.dp_group, self.hidden_size, self.expert_dim,
expert_capacity))
# The following four ops are to implement transpose(expert_input, (2, 0, 3, 1)), for that a single transpose
# has bad performance
expert_input = self.reshape(expert_input, (self.dp_group * self.hidden_size,
self.expert_dim * expert_capacity))
expert_input = self.transpose_2dim(expert_input, (1, 0))
expert_input = self.reshape(expert_input, (self.expert_dim, expert_capacity, self.dp_group,
self.hidden_size))
# expert_input's shape: (self.expert_dim, self.dp_group, expert_capacity, self.hidden_size)
expert_input = self.transpose_4dim_ep(expert_input, (0, 2, 1, 3))
expert_input = self.reshape(expert_input, (self.expert_dim * self.dp_group * expert_capacity,
self.hidden_size))
# expert_output's shape: (self.expert_dim, self.dp_group*expert_capacity, self.hidden_size)
expert_output = self.ffn(expert_input)
expert_output = self.reshape(expert_output, (self.expert_dim, self.dp_group,
expert_capacity, self.hidden_size))
# The following five ops are to implement transpose(expert_output, (1, 3, 0, 2)), for that a single transpose
# has bad performance
expert_output = self.reshape(expert_output, (self.expert_dim,
self.dp_group * expert_capacity * self.hidden_size))
expert_output = self.transpose_2dim_ep(expert_output, (1, 0))
expert_output = self.reshape(expert_output, (self.dp_group, expert_capacity,
self.hidden_size * self.expert_dim))
expert_output = self.transpose_3dim(expert_output, (0, 2, 1))
# expert_output's shape: (self.dp_group, self.hidden_size, self.expert_dim, expert_capacity)
expert_output = self.reshape(expert_output, (self.dp_group, self.hidden_size, self.expert_dim,
expert_capacity))
expert_output = self.reshape(expert_output, (self.dp_group, self.hidden_size,
self.expert_dim * expert_capacity))
combine_tensor = self.reshape(combine_tensor, (self.dp_group, tokens_per_group,
self.expert_dim * expert_capacity))
# combine_tensor's shape: (self.dp_group, self.expert_dim*expert_capacity, tokens_per_group)
combine_tensor = self.transpose_3dim(combine_tensor, (0, 2, 1))
combine_tensor = self.cast(combine_tensor, F.dtype(expert_output))
# combined_output's shape: (self.dp_group, self.hidden_size, tokens_per_group)
combined_output = self.batch_mm2(expert_output, combine_tensor)
# combined_output's shape: (self.dp_group, tokens_per_group, self.hidden_size)
combined_output = self.transpose_3dim(combined_output, (0, 2, 1))
combined_output = self.reshape(combined_output, (bs_and_dmodel[0], bs_and_dmodel[1]))
combined_output = self.reshape(combined_output, input_shape)
aux_loss = self.mul(self.aux_loss_factor, aux_loss)
return combined_output, aux_loss
class Router(Cell):
r"""
A router backbone used to calculate logits of each token, which should be cascaded by router implementations
mapping tokens to experts.
when moe_config.num_experts_chosen = 1, use top1 routing;
when moe_config.num_experts_chosen > 1, use topk routing
Args:
d_model (int): The hidden size of each token.
moe_config(MoEConfig): The configuration of MoE (Mixture of Expert).
routing_policy: The policy of mapping tokens to experts. Default: topkRouter
training (bool): The value indicating whether is in training phase.
parallel_config: The parallel-related configuration.
Inputs:
- **input_tensor** (Tensor) - Tensor of shape :math:`(expert\_parallel, tokens\_per\_device,
hidden\_size)`.
Outputs:
Tensor of shape :math:`(expert\_parallel, tokens\_per\_device, expert\_dim)`.
"""
def __init__(self,
d_model,
moe_config,
routing_policy=None,
training=True,
parallel_config=None):
super(Router, self).__init__()
dp = parallel_config.data_parallel
self.d_model = d_model
self.expert_dim = moe_config.expert_num
self.capacity_factor = moe_config.capacity_factor
self.num_experts_chosen = moe_config.num_experts_chosen
self.training = training
self.routing_policy = routing_policy
self.noisy_policy = None # candidate: ["jitter", "rsample", "None"]
self.noisy_epsilon = 1e-2
self.noise = Tensor(np.random.uniform(1 - self.noisy_epsilon, 1 + self.noisy_epsilon, (d_model,)))
self.dense = Dense(in_channels=self.d_model, out_channels=self.expert_dim, has_bias=False)
self.router = routing_policy
if self.routing_policy is None:
self.router = TopkRouter(d_model=d_model, moe_config=moe_config, training=training,
parallel_config=parallel_config)
if _get_parallel_mode() in (ParallelMode.AUTO_PARALLEL,) and _is_sharding_propagation():
self.dense.matmul.shard(((dp, 1), (1, 1)))
self.mul = P.Mul()
self.cast = P.Cast()
else:
self.dense.matmul.shard(((dp, 1), (1, 1)))
self.mul = P.Mul().shard(((dp, 1, 1), (dp,)))
self.cast = P.Cast()
def construct(self, input_tensor):
input_tensor = self.cast(input_tensor, mstype.float32)
if self.noisy_policy == "jitter" and self.training:
# Here, we temporarily implement the multiplicative jitter this way,
# for the lack of UniforReal parallel operator.
input_tensor = self.mul(input_tensor, self.noise)
router_logits = self.dense(input_tensor)
return self.router(router_logits)
class TopkRouter(Cell):
r"""
A router implementation which maps each tokens to the topk expert.
Args:
d_model (int): The hidden size of each token.
moe_config(MoEConfig): The configuration of MoE (Mixture of Expert).
training (bool): The value indicating whether is in training phase.
config: The parallel-related configuration.
Inputs:
- **input_tensor** (Tensor) - Tensor of shape :math:`(expert\_parallel, tokens\_per\_device,
hidden\_size)`.
Outputs:
Tensor of shape :math:`(expert\_parallel, tokens\_per\_device, expert\_dim, expert\_capacity)`,
Tensor of shape :math:`(expert\_parallel, tokens\_per\_device, expert\_dim, expert\_capacity)`,
Tensor of shape :math:`(1)`.
"""
def __init__(self,
d_model,
moe_config,
training=True,
parallel_config=None):
super(TopkRouter, self).__init__()
dp = parallel_config.data_parallel
self.d_model = d_model
self.expert_dim = moe_config.expert_num
self.capacity_factor = moe_config.capacity_factor
self.training = training
self.dp_group = dp
self.noisy_policy = None
self.cast = P.Cast()
self.reshape = P.Reshape()
self.shape = P.Shape()
self.on_value = Tensor(1.0, mstype.float32)
self.off_value = Tensor(0.0, mstype.float32)
self.num_experts_chosen = moe_config.num_experts_chosen
if _get_parallel_mode() in (ParallelMode.AUTO_PARALLEL,) and _is_sharding_propagation():
self.softmax = P.Softmax(axis=-1)
self.argmax = P.ArgMaxWithValue(axis=-1, keep_dims=False)
self.onehot = P.OneHot()
self.onehot2 = P.OneHot()
self.onehot3 = P.OneHot()
self.reduce_mean = P.ReduceMean(keep_dims=False)
self.reduce_mean2 = P.ReduceMean(keep_dims=False)
self.reduce_mean3 = P.ReduceMean(keep_dims=False)
self.mul = P.Mul()
self.mul2 = P.Mul()
self.mul3 = P.Mul()
self.mul4 = P.Mul()
self.mul5 = P.Mul()
self.mul6 = P.Mul()
self.mul7 = P.Mul()
self.mul8 = P.Mul().shard(((dp, 1, 1), (dp, 1, 1)))
self.mul9 = P.Mul().shard(((dp, 1, 1, 1), (dp, 1, 1, 1)))
self.not_equal = P.NotEqual()
self.div1 = P.RealDiv()
self.div2 = P.RealDiv()
self.add = P.Add()
self.add1 = P.Add()
self.add2 = P.Add()
self.add3 = P.Add()
self.add4 = P.Add()
self.sub = P.Sub()
self.cumsum = P.CumSum(exclusive=True)
self.less = P.Less()
self.reduce_sum = P.ReduceSum(keep_dims=False)
self.reduce_sum_keep = P.ReduceSum(keep_dims=True)
self.reduce_sum_keep2 = P.ReduceSum(keep_dims=True)
self.expand = P.ExpandDims()
self.expand2 = P.ExpandDims()
self.add_scala = P.Add()
self.init_loss = Tensor(0.0, mstype.float32)
else:
self.softmax = P.Softmax(axis=-1).shard(((dp, 1, 1,),))
self.argmax = P.ArgMaxWithValue(axis=-1, keep_dims=False).shard(((dp, 1, 1),))
self.onehot = P.OneHot().shard(((dp, 1, 1), (), ()))
self.onehot2 = P.OneHot().shard(((dp, 1, 1), (), ()))
self.onehot3 = P.OneHot().shard(((dp, 1, 1, 1), (), ()))
self.reduce_mean = P.ReduceMean(keep_dims=False).shard(((dp, 1, 1),))
self.reduce_mean2 = P.ReduceMean(keep_dims=False).shard(((dp, 1, 1),))
self.reduce_mean3 = P.ReduceMean(keep_dims=False).shard(((dp, 1),))
self.mul = P.Mul().shard(((dp, 1), (dp, 1)))
self.mul2 = P.Mul().shard(((), ()))
self.mul3 = P.Mul().shard(((), ()))
self.mul4 = P.Mul().shard(((dp, 1, 1), (dp, 1, 1)))
self.mul5 = P.Mul().shard(((dp, 1, 1), (dp, 1, 1)))
self.mul6 = P.Mul().shard(((dp, 1), (dp, 1)))
self.mul7 = P.Mul().shard(((dp, 1), (dp, 1)))
self.mul8 = P.Mul().shard(((dp, 1, 1), (dp, 1, 1)))
self.mul9 = P.Mul().shard(((dp, 1, 1, 1), (dp, 1, 1, 1)))
self.not_equal = P.NotEqual().shard(((dp, 1, 1, 1), ()))
self.div1 = P.RealDiv().shard(((dp, 1, 1), (dp, 1, 1)))
self.div2 = P.RealDiv().shard(((dp, 1, 1, 1), (dp, 1, 1, 1)))
self.add = P.Add().shard(((dp, 1, 1), (dp, 1, 1)))
self.add1 = P.Add().shard(((dp, 1, 1), ()))
self.add2 = P.Add().shard(((dp, 1, 1, 1), (dp, 1, 1, 1)))
self.add3 = P.Add().shard(((dp, 1), (dp, 1)))
self.add4 = P.Add().shard(((dp, 1, 1, 1), ()))
self.sub = P.Sub().shard(((), (dp, 1, 1)))
self.cumsum = P.CumSum(exclusive=True).shard(((dp, 1, 1),))
self.less = P.Less().shard(((dp, 1, 1), ()))
self.reduce_sum = P.ReduceSum(keep_dims=False).shard(((dp, 1, 1),))
self.reduce_sum_keep = P.ReduceSum(keep_dims=True).shard(((dp, 1, 1),))
self.reduce_sum_keep2 = P.ReduceSum(keep_dims=True).shard(((dp, 1, 1, 1),))
self.expand = P.ExpandDims().shard(((dp, 1),))
self.expand2 = P.ExpandDims().shard(((dp, 1, 1),))
self.add_scala = P.Add().shard(((), ()))
self.init_loss = Tensor(0.0, mstype.float32)
def construct(self, router_logits):
router_logits_shape = self.shape(router_logits)
router_logits = self.reshape(router_logits, (-1, router_logits_shape[-1]))
logits_shape = self.shape(router_logits)
tokens_per_group = logits_shape[0] // self.dp_group
expert_capacity = calculate_expert_capacity(self.num_experts_chosen, tokens_per_group, self.capacity_factor,
self.expert_dim)
router_logits = self.reshape(router_logits, (self.dp_group, tokens_per_group, self.expert_dim))
accum_expert_mask = 0
accum_expert_gate = 0
loss = self.init_loss
mask_count = 0
accum_combine_tensor = 0
# Probabilities for each token of what expert is should be sent to
router_prob = self.softmax(router_logits)
for expert_chosen_index in range(self.num_experts_chosen):
# for each token, set the router_prob of the selected experts to zero
router_prob = self.mul4(router_prob, self.sub(self.on_value, accum_expert_mask))
# shape is : (dp_group, tokens_per_group)
expert_index, expert_gate = self.argmax(router_prob)
# expert_mask's shape: (dp_group, tokens_per_group, self.expert_dim)
expert_mask = self.onehot(expert_index, self.expert_dim, self.on_value, self.off_value)
# renormalize the rest prob to be of sum 1
router_prob_normal = self.div1(router_prob, self.add1(self.reduce_sum_keep(router_prob, -1), 1e-9))
# the balance loss is computed at each routing step
loss = self.add_scala(loss, self._auxiliary_loss(expert_mask, router_prob_normal))
output = self._maskout_overflowed_tokens(expert_mask, expert_capacity, expert_gate,
mask_count, expert_chosen_index)
expert_mask, expert_gate, expert_mask_flat, position_in_expert = output[0], output[1], output[2], output[3]
accum_expert_mask = self.add(accum_expert_mask, expert_mask)
accum_expert_gate = self.add3(accum_expert_gate, expert_gate)
mask_count = self.add(mask_count, self.reduce_sum_keep(expert_mask, 1))
# combine_tensor's shape: (dp_group, tokens_per_group)
combine_tensor = self.mul7(expert_gate, expert_mask_flat)
# combine_tensor's shape: (dp_group, tokens_per_group, self.expert_dim)
combine_tensor = self.mul8(self.expand(combine_tensor, -1),
self.onehot2(expert_index, self.expert_dim, self.on_value, self.off_value))
# combine_tensor's shape: (dp_group, tokens_per_group, self.expert_dim, self.expert_capacity)
combine_tensor = self.mul9(self.expand2(combine_tensor, -1),
self.onehot3(self.cast(position_in_expert, mstype.int32), expert_capacity,
self.on_value, self.off_value))
accum_combine_tensor = self.add2(accum_combine_tensor, combine_tensor)
# expert weights normalization
combine_tensor_sum = self.reduce_sum_keep2(self.reduce_sum_keep2(accum_combine_tensor, -1), -2)
accum_combine_tensor = self.div2(accum_combine_tensor, self.add4(combine_tensor_sum, 1e-9))
# dispatch_tensor is of boolean type. Here, using NotEqual instead of Cast, for that 'Cast to bool' has
# bad performance
dispatch_tensor = self.not_equal(accum_combine_tensor, 0.0)
return dispatch_tensor, accum_combine_tensor, loss
def _auxiliary_loss(self, expert_mask, router_prob):
"""
Computing the load balance loss.
"""
# density_1's shape: (dp_group, self.expert_dim)
density_1 = self.reduce_mean(expert_mask, 1)
# density_1_proxy's shape: (dp_group, self.expert_dim)
density_1_proxy = self.reduce_mean2(router_prob, 1)
loss = self.mul(density_1, density_1_proxy)
loss = self.reduce_mean3(loss)
loss = self.mul3(self.mul2(loss, self.expert_dim), self.expert_dim)
return loss
def _maskout_overflowed_tokens(self, expert_mask, expert_capacity, expert_gate, last_num, expert_chosen_index):
"""
Keeping only the tokens that fit within expert_capacity.
"""
cumsum = self.cumsum(expert_mask, 1)
if expert_chosen_index > 0:
cumsum = self.add(cumsum, last_num)
# position_in_expert's shape: (dp_group, tokens_per_group, self.expert_dim)
position_in_expert = self.mul4(cumsum, expert_mask)
less_result = self.less(position_in_expert, expert_capacity)
# expert_mask's shape: (dp_group, tokens_per_group, self.expert_dim)
expert_mask = self.mul5(less_result, expert_mask)
# expert_mask_flat's shape: (dp_group, tokens_per_group)
expert_mask_flat = self.reduce_sum(expert_mask, -1)
# Mask out the experts that have overflowed the expert_capacity.
# expert_gate's shape: (dp_group, tokens_per_group)
expert_gate = self.mul6(expert_gate, expert_mask_flat)
output = (expert_mask, expert_gate, expert_mask_flat, position_in_expert)
return output