Source code for cerebras.modelzoo.losses.LoadBalancingLoss

# Copyright 2022 Cerebras Systems.
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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.
# You may obtain a copy of the License at
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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import torch
import torch.nn as nn

from cerebras.modelzoo.common.registry import registry
from cerebras.modelzoo.trainer import summarize_scalar


[docs]@registry.register_loss("LoadBalancingLoss") class LoadBalancingLoss(nn.Module): def __init__( self, num_experts, top_k, ): super(LoadBalancingLoss, self).__init__() self.num_experts = num_experts self.top_k = top_k
[docs] def forward( self, router_weights_list, expert_mask_list, attention_mask=None, ): """ router_weights: Num hidden layers * [[batch_size, seq_len, experts]] expert_mask: Num hidden layers * [[batch_size, seq_len, experts]]. """ tokens_per_expert = torch.zeros_like(router_weights_list[0][:, 0, :]) router_prob_per_expert = torch.zeros_like( router_weights_list[0][:, 0, :] ) for router_weights, expert_mask in zip( router_weights_list, expert_mask_list ): if attention_mask is not None: extended_attention_mask = ( attention_mask[:, :, None] .broadcast_to(expert_mask.shape) .to(router_weights.dtype) ) # Compute the percentage of tokens routed to each experts tokens_per_expert += torch.sum( expert_mask * extended_attention_mask, dim=1 ) / torch.sum(extended_attention_mask, dim=1) router_prob_per_expert += torch.sum( router_weights * extended_attention_mask, dim=1 ) / torch.sum(extended_attention_mask, dim=1) else: # Compute the percentage of tokens routed to each experts tokens_per_expert += torch.mean(expert_mask, dim=1) # Compute the average probability of routing to these experts router_prob_per_expert += torch.mean(router_weights, dim=1) tokens_per_expert /= len(router_weights_list) router_prob_per_expert /= len(router_weights_list) for expert_idx in range(self.num_experts): summarize_scalar( f"expert_stats/tokens_per_expert/expert_{expert_idx}", torch.mean(tokens_per_expert[:, expert_idx]), ) summarize_scalar( f"expert_stats/router_prob_per_expert/expert_{expert_idx}", torch.mean(router_prob_per_expert[:, expert_idx]), ) return (self.num_experts**2) * torch.mean( router_prob_per_expert * tokens_per_expert )