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# ------------------------------------------------------------------------
# Modified from MoE-LLaVA(https://github.com/PKU-YuanGroup/MoE-LLaVA)
# ------------------------------------------------------------------------
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss
from transformers.modeling_outputs import CausalLMOutputWithPast
from dataclasses import dataclass
from einops import rearrange, repeat, reduce, pack, unpack
from transformers.utils import ModelOutput
from transformers.activations import ACT2FN
def MixtralDecoderLayerMOEBlock_forward(self):
def forward(hidden_states: torch.Tensor):
batch_size, sequence_length, hidden_dim = hidden_states.shape
hidden_states = hidden_states.view(-1, hidden_dim)
# router_logits: (batch * sequence_length, n_experts)
router_logits = self.gate(hidden_states)
router_z_loss = torch.logsumexp(router_logits, dim = -1)
router_z_loss = torch.square(router_z_loss)
router_z_loss = router_z_loss.mean()
routing_weights = nn.functional.softmax(router_logits, dim=1, dtype=torch.float)
density_1_proxy = reduce(routing_weights, '... n e -> ... e', 'mean')
routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
one_hot_gate_indices = nn.functional.one_hot(rearrange(selected_experts, '... k -> k ...'), self.num_experts).float()[0]
density_1 = reduce(one_hot_gate_indices, '... n e -> ... e', 'mean')
balance_loss = (density_1_proxy * density_1).mean() * float(self.num_experts ** 2)
routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
# we cast back to the input dtype
routing_weights = routing_weights.to(hidden_states.dtype)
final_hidden_states = torch.zeros(
(batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
)
# One hot encode the selected experts to create an expert mask
# this will be used to easily index which expert is going to be sollicitated
expert_mask = nn.functional.one_hot(selected_experts, num_classes=self.num_experts).permute(2, 1, 0)
# Loop over all available experts in the model and perform the computation on each expert
for expert_idx in range(self.num_experts):
expert_layer = self.experts[expert_idx]
idx, top_x = torch.where(expert_mask[expert_idx])
if top_x.shape[0] == 0:
continue
# in torch it is faster to index using lists than torch tensors
top_x_list = top_x.tolist()
idx_list = idx.tolist()
# Index the correct hidden states and compute the expert hidden state for
# the current expert. We need to make sure to multiply the output hidden
# states by `routing_weights` on the corresponding tokens (top-1 and top-2)
current_state = hidden_states[None, top_x_list].reshape(-1, hidden_dim)
current_hidden_states = expert_layer(current_state) * routing_weights[top_x_list, idx_list, None]
# However `index_add_` only support torch tensors for indexing so we'll use
# the `top_x` tensor here.
final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
return final_hidden_states, (balance_loss, router_z_loss)
return forward
@dataclass
class SMoECausalLMOutputWithPast(ModelOutput):
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
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