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# modeling_theta.py
import torch
import torch.nn as nn
from transformers.modeling_outputs import BaseModelOutputWithPast
from transformers import PreTrainedModel
from configuration_theta import ThetaConfig
class ThetaAttention(nn.Module):
def __init__(self, config: ThetaConfig):
super().__init__()
self.num_heads = config.num_attention_heads
self.head_dim = config.hidden_size // config.num_attention_heads
self.scale = self.head_dim ** -0.5
self.q_proj = nn.Linear(config.hidden_size, config.hidden_size)
self.k_proj = nn.Linear(config.hidden_size, config.hidden_size)
self.v_proj = nn.Linear(config.hidden_size, config.hidden_size)
self.out_proj = nn.Linear(config.hidden_size, config.hidden_size)
def forward(self, hidden_states):
batch_size, seq_length, embed_dim = hidden_states.size()
query = self.q_proj(hidden_states)
key = self.k_proj(hidden_states)
value = self.v_proj(hidden_states)
query = query.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
key = key.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
value = value.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
attn_scores = torch.matmul(query, key.transpose(-2, -1)) * self.scale
attn_probs = nn.functional.softmax(attn_scores, dim=-1)
attn_output = torch.matmul(attn_probs, value)
attn_output = attn_output.transpose(1, 2).contiguous().view(batch_size, seq_length, embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output
class ThetaMLP(nn.Module):
def __init__(self, config: ThetaConfig):
super().__init__()
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
self.act = nn.SiLU()
self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
def forward(self, hidden_states):
hidden_states = self.fc1(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.fc2(hidden_states)
return hidden_states
class ThetaBlock(nn.Module):
def __init__(self, config: ThetaConfig):
super().__init__()
self.attention = ThetaAttention(config)
self.mlp = ThetaMLP(config)
self.norm1 = nn.LayerNorm(config.hidden_size, eps=config.rms_norm_eps)
self.norm2 = nn.LayerNorm(config.hidden_size, eps=config.rms_norm_eps)
def forward(self, hidden_states):
hidden_states = hidden_states + self.attention(self.norm1(hidden_states))
hidden_states = hidden_states + self.mlp(self.norm2(hidden_states))
return hidden_states
class ThetaModel(PreTrainedModel):
config_class = ThetaConfig
def __init__(self, config: ThetaConfig):
super().__init__(config)
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
self.layers = nn.ModuleList([ThetaBlock(config) for _ in range(config.num_hidden_layers)])
self.norm = nn.LayerNorm(config.hidden_size, eps=config.rms_norm_eps)
def forward(
self,
input_ids=None,
attention_mask=None,
**kwargs,
):
hidden_states = self.embed_tokens(input_ids)
for layer in self.layers:
hidden_states = layer(hidden_states)
hidden_states = self.norm(hidden_states)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
hidden_states=None,
past_key_values=None,
)
class ThetaForCausalLM(PreTrainedModel):
config_class = ThetaConfig
def __init__(self, config: ThetaConfig):
super().__init__(config)
self.model = ThetaModel(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
def forward(
self,
input_ids=None,
labels=None,
**kwargs,
):
outputs = self.model(input_ids=input_ids, **kwargs)
logits = self.lm_head(outputs.last_hidden_state)
loss = None
if labels is not None:
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
loss_fct = nn.CrossEntropyLoss()
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
return {"loss": loss, "logits": logits} if loss is not None else {"logits": logits}
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