custom_bitnet.py

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: 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 prepare_inputs_for_generation(self, input_ids, **kwargs):
        return {"input_ids": input_ids, **kwargs}