Update custom_bitnet.py

custom_bitnet.py

@@ -1,14 +1,86 @@
-from transformers import PreTrainedModel
-from transformers.models.bitnet.configuration_bitnet import BitNetConfig
+from transformers import PreTrainedModel, PretrainedConfig
+import torch
+import torch.nn as nn
 
+# BitNetConfig (replace with contents of configuration_bitnet.py)
+class BitNetConfig(PretrainedConfig):
+    model_type = "bitnet"
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=768,
+        num_hidden_layers=12,
+        num_attention_heads=12,
+        intermediate_size=3072,
+        hidden_act="gelu",
+        max_position_embeddings=512,
+        initializer_range=0.02,
+        layer_norm_eps=1e-12,
+        dropout=0.1,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        **kwargs
+    ):
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.max_position_embeddings = max_position_embeddings
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.dropout = dropout
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            **kwargs
+        )
+
+# BitNetForCausalLM (replace with contents of modeling_bitnet.py)
 class BitNetForCausalLM(PreTrainedModel):
     config_class = BitNetConfig
 
     def __init__(self, config):
         super().__init__(config)
-        # Placeholder: Copy implementation from fork's modeling_bitnet.py
-        raise NotImplementedError("Replace with actual BitNetForCausalLM implementation")
+        # Placeholder: Replace with actual implementation
+        # Example structure (based on typical transformer models):
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.layers = nn.ModuleList([
+            # Add BitNet-specific layers (e.g., BitNetLayer)
+            nn.TransformerEncoderLayer(
+                d_model=config.hidden_size,
+                nhead=config.num_attention_heads,
+                dim_feedforward=config.intermediate_size,
+                dropout=config.dropout
+            ) for _ in range(config.num_hidden_layers)
+        ])
+        self.norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.apply(self._init_weights)
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
+            if module.bias is not None:
+                torch.nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.Embedding):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
+
+    def forward(self, input_ids, attention_mask=None, labels=None, **kwargs):
+        # Placeholder: Replace with actual forward pass
+        hidden_states = self.embed_tokens(input_ids)
+        for layer in self.layers:
+            hidden_states = layer(hidden_states)
+        hidden_states = self.norm(hidden_states)
+        logits = self.lm_head(hidden_states)
+        loss = None
+        if labels is not None:
+            loss_fct = nn.CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))
+        return {"logits": logits, "loss": loss} if loss is not None else {"logits": logits}
 
-    def forward(self, *args, **kwargs):
-        # Placeholder: Copy forward pass from fork
-        raise NotImplementedError("Replace with actual forward pass implementation")
+    def prepare_inputs_for_generation(self, input_ids, **kwargs):
+        return {"input_ids": input_ids, **kwargs}