Create modeling_theta.py

modeling_theta.py

@@ -0,0 +1,117 @@
+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}