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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} |