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Custom Empathy Classification Model for HuggingFace
This file contains all necessary model architecture code to run independently.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from torch.nn import CrossEntropyLoss
from transformers import PreTrainedModel, PretrainedConfig, RobertaModel, RobertaConfig
# BertLayerNorm is deprecated, use nn.LayerNorm instead
try:
from transformers.models.bert.modeling_bert import BertLayerNorm
except ImportError:
# In newer versions, use LayerNorm directly
BertLayerNorm = nn.LayerNorm
class Norm(nn.Module):
def __init__(self, d_model, eps=1e-6):
super().__init__()
self.size = d_model
self.alpha = nn.Parameter(torch.ones(self.size))
self.bias = nn.Parameter(torch.zeros(self.size))
self.eps = eps
def forward(self, x):
norm = self.alpha * (x - x.mean(dim=-1, keepdim=True)) / (x.std(dim=-1, keepdim=True) + self.eps) + self.bias
return norm
class MultiHeadAttention(nn.Module):
def __init__(self, heads, d_model, dropout=0.1):
super().__init__()
self.d_model = d_model
self.d_k = d_model // heads
self.h = heads
self.q_linear = nn.Linear(d_model, d_model)
self.v_linear = nn.Linear(d_model, d_model)
self.k_linear = nn.Linear(d_model, d_model)
self.dropout = nn.Dropout(dropout)
self.out = nn.Linear(d_model, d_model)
def forward(self, q, k, v, mask=None):
bs = q.size(0)
k = self.k_linear(k).view(bs, -1, self.h, self.d_k)
q = self.q_linear(q).view(bs, -1, self.h, self.d_k)
v = self.v_linear(v).view(bs, -1, self.h, self.d_k)
k = k.transpose(1, 2)
q = q.transpose(1, 2)
v = v.transpose(1, 2)
scores = self.attention(q, k, v, self.d_k, mask, self.dropout)
concat = scores.transpose(1, 2).contiguous().view(bs, -1, self.d_model)
output = self.out(concat)
return output
def attention(self, q, k, v, d_k, mask=None, dropout=None):
scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(d_k)
if mask is not None:
mask = mask.unsqueeze(1)
scores = scores.masked_fill(mask == 0, -1e9)
scores = F.softmax(scores, dim=-1)
if dropout is not None:
scores = dropout(scores)
output = torch.matmul(scores, v)
return output
class RobertaClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, hidden_dropout_prob=0.1, hidden_size=768, empathy_num_labels=3):
super().__init__()
self.dense = nn.Linear(hidden_size, hidden_size)
self.dropout = nn.Dropout(hidden_dropout_prob)
self.out_proj = nn.Linear(hidden_size, empathy_num_labels)
def forward(self, features, **kwargs):
x = features[:, :]
x = self.dropout(x)
x = self.dense(x)
x = torch.relu(x)
x = self.dropout(x)
x = self.out_proj(x)
return x
class SeekerEncoder(nn.Module):
"""Wrapper for seeker encoder to match saved weight structure"""
def __init__(self, config):
super().__init__()
self.roberta = RobertaModel(config, add_pooling_layer=False)
class ResponderEncoder(nn.Module):
"""Wrapper for responder encoder to match saved weight structure"""
def __init__(self, config):
super().__init__()
self.roberta = RobertaModel(config, add_pooling_layer=False)
class BiEncoderAttentionWithRationaleClassification(nn.Module):
def __init__(self, hidden_dropout_prob=0.2, rationale_num_labels=2, empathy_num_labels=3, hidden_size=768, attn_heads=1):
super().__init__()
self.dropout = nn.Dropout(hidden_dropout_prob)
self.rationale_classifier = nn.Linear(hidden_size, rationale_num_labels)
self.attn = MultiHeadAttention(attn_heads, hidden_size)
self.norm = Norm(hidden_size)
self.rationale_num_labels = rationale_num_labels
self.empathy_num_labels = empathy_num_labels
self.empathy_classifier = RobertaClassificationHead(hidden_size=768)
self.apply(self._init_weights)
# Initialize encoders with wrapper to match saved weight structure
roberta_config = RobertaConfig.from_pretrained("roberta-base")
self.seeker_encoder = SeekerEncoder(roberta_config)
self.responder_encoder = ResponderEncoder(roberta_config)
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, (nn.Linear, nn.Embedding)):
initializer_range = 0.02
module.weight.data.normal_(mean=0.0, std=initializer_range)
elif isinstance(module, BertLayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
if isinstance(module, nn.Linear) and module.bias is not None:
module.bias.data.zero_()
def forward(
self,
input_ids_SP=None,
input_ids_RP=None,
attention_mask_SP=None,
attention_mask_RP=None,
token_type_ids_SP=None,
token_type_ids_RP=None,
position_ids_SP=None,
position_ids_RP=None,
head_mask_SP=None,
head_mask_RP=None,
inputs_embeds_SP=None,
inputs_embeds_RP=None,
empathy_labels=None,
rationale_labels=None,
lambda_EI=1,
lambda_RE=0.1
):
outputs_SP = self.seeker_encoder.roberta(
input_ids_SP,
attention_mask=attention_mask_SP,
token_type_ids=token_type_ids_SP,
position_ids=position_ids_SP,
head_mask=head_mask_SP,
inputs_embeds=inputs_embeds_SP,
)
outputs_RP = self.responder_encoder.roberta(
input_ids_RP,
attention_mask=attention_mask_RP,
token_type_ids=token_type_ids_RP,
position_ids=position_ids_RP,
head_mask=head_mask_RP,
inputs_embeds=inputs_embeds_RP,
)
sequence_output_SP = outputs_SP[0]
sequence_output_RP = outputs_RP[0]
sequence_output_RP = sequence_output_RP + self.dropout(self.attn(sequence_output_RP, sequence_output_SP, sequence_output_SP))
logits_empathy = self.empathy_classifier(sequence_output_RP[:, 0, :])
sequence_output = self.dropout(sequence_output_RP)
logits_rationales = self.rationale_classifier(sequence_output)
outputs = (logits_empathy, logits_rationales) + outputs_RP[2:]
loss_rationales = 0.0
loss_empathy = 0.0
if rationale_labels is not None:
loss_fct = CrossEntropyLoss()
if attention_mask_RP is not None:
active_loss = attention_mask_RP.view(-1) == 1
active_logits = logits_rationales.view(-1, self.rationale_num_labels)
active_labels = torch.where(
active_loss, rationale_labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(rationale_labels)
)
loss_rationales = loss_fct(active_logits, active_labels)
else:
loss_rationales = loss_fct(logits_rationales.view(-1, self.rationale_num_labels), rationale_labels.view(-1))
if empathy_labels is not None:
loss_fct = CrossEntropyLoss()
loss_empathy = loss_fct(logits_empathy.view(-1, self.empathy_num_labels), empathy_labels.view(-1))
loss = lambda_EI * loss_empathy + lambda_RE * loss_rationales
outputs = (loss, loss_empathy, loss_rationales) + outputs
return outputs
class EmpathyModelConfig(PretrainedConfig):
"""Configuration for Empathy Model"""
model_type = "empathy_classifier"
def __init__(
self,
hidden_dropout_prob=0.2,
rationale_num_labels=2,
empathy_num_labels=3,
hidden_size=768,
attn_heads=1,
max_length=64,
**kwargs
):
super().__init__(**kwargs)
self.hidden_dropout_prob = hidden_dropout_prob
self.rationale_num_labels = rationale_num_labels
self.empathy_num_labels = empathy_num_labels
self.hidden_size = hidden_size
self.attn_heads = attn_heads
self.max_length = max_length
class EmpathyModel(PreTrainedModel):
"""HuggingFace wrapper for Empathy Model"""
config_class = EmpathyModelConfig
base_model_prefix = "model" # Important: matches the saved state_dict structure
def __init__(self, config):
super().__init__(config)
self.config = config
self.model = BiEncoderAttentionWithRationaleClassification(
hidden_dropout_prob=config.hidden_dropout_prob,
rationale_num_labels=config.rationale_num_labels,
empathy_num_labels=config.empathy_num_labels,
hidden_size=config.hidden_size,
attn_heads=config.attn_heads
)
def forward(
self,
input_ids_SP=None,
input_ids_RP=None,
attention_mask_SP=None,
attention_mask_RP=None,
**kwargs
):
return self.model(
input_ids_SP=input_ids_SP,
input_ids_RP=input_ids_RP,
attention_mask_SP=attention_mask_SP,
attention_mask_RP=attention_mask_RP,
**kwargs
)
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