|
|
import torch |
|
|
from torch import nn |
|
|
from typing import Optional |
|
|
from dataclasses import dataclass |
|
|
from transformers import PreTrainedModel |
|
|
from .configuration_mlp import MLPConfig |
|
|
from transformers.utils import ModelOutput |
|
|
from transformers.activations import ACT2FN |
|
|
|
|
|
|
|
|
@dataclass |
|
|
class MLPOutput(ModelOutput): |
|
|
loss: Optional[torch.FloatTensor] = None |
|
|
logits: Optional[torch.FloatTensor] = None |
|
|
|
|
|
|
|
|
class MLPPreTrainedModel(PreTrainedModel): |
|
|
config_class = MLPConfig |
|
|
|
|
|
def _init_weights(self, module): |
|
|
"""Initialize the weights""" |
|
|
if isinstance(module, nn.Linear): |
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) |
|
|
if module.bias is not None: |
|
|
module.bias.data.zero_() |
|
|
|
|
|
|
|
|
class MLPModel(MLPPreTrainedModel): |
|
|
def __init__(self, config): |
|
|
super().__init__(config) |
|
|
self.act_fn = ACT2FN[config.hidden_act] |
|
|
iho = [config.input_size, *config.hidden_size, config.output_size] |
|
|
self.linears = nn.ModuleList([ |
|
|
nn.Linear(iho[i], iho[i+1]) |
|
|
for i in range(config.num_hidden_layers + 1) |
|
|
]) |
|
|
self.loss_fn = nn.CrossEntropyLoss() |
|
|
|
|
|
self.post_init() |
|
|
|
|
|
def forward(self, inputs, labels=None): |
|
|
for i in range(len(self.linears) - 1): |
|
|
inputs = self.act_fn(self.linears[i](inputs)) |
|
|
logits = self.linears[-1](inputs) |
|
|
|
|
|
loss = None |
|
|
if labels is None: |
|
|
return ModelOutput(loss=loss, logits=logits) |
|
|
else: |
|
|
loss = self.loss_fn(logits, labels) |
|
|
return ModelOutput(loss=loss, logits=logits) |
