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# modeling_mixture_of_recursions.py
# Create this file in your repository root
import torch
from transformers import PreTrainedModel
from transformers.modeling_outputs import CausalLMOutputWithPast
from typing import Optional, Tuple
# Import your existing model
try:
from model_slm import * # Import everything from your existing model file
except:
pass # Will work when uploaded to HF
from .configuration_mixture_of_recursions import MixtureOfRecursionsConfig
class MixtureOfRecursionsPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models.
"""
config_class = MixtureOfRecursionsConfig
base_model_prefix = "model"
supports_gradient_checkpointing = False
_no_split_modules = []
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, torch.nn.Linear):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, torch.nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, torch.nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
class MixtureOfRecursionsModel(MixtureOfRecursionsPreTrainedModel):
"""
Wrapper around your existing model to make it compatible with Transformers
"""
def __init__(self, config):
super().__init__(config)
self.config = config
# This should match your actual model initialization from model_slm.py
# Replace this with your actual model class name
# For example: self.model = YourModelClass(config)
# Placeholder - update with your actual model architecture
self.vocab_size = config.vocab_size
self.hidden_size = config.hidden_size
self.post_init()
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
# Your forward pass logic from model_slm.py
# This is a placeholder - replace with your actual forward implementation
pass
class MixtureOfRecursionsForCausalLM(MixtureOfRecursionsPreTrainedModel):
"""
Causal LM head wrapper for your model
"""
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.model = MixtureOfRecursionsModel(config)
self.vocab_size = config.vocab_size
self.lm_head = torch.nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.post_init()
def get_input_embeddings(self):
return self.model.embed_tokens if hasattr(self.model, 'embed_tokens') else None
def set_input_embeddings(self, value):
if hasattr(self.model, 'embed_tokens'):
self.model.embed_tokens = value
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# Forward pass through model
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0] if isinstance(outputs, tuple) else outputs.last_hidden_state
logits = self.lm_head(hidden_states)
loss = None
if labels is not None:
# Shift for causal language modeling
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
loss_fct = torch.nn.CrossEntropyLoss()
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
if not return_dict:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values if hasattr(outputs, 'past_key_values') else None,
hidden_states=outputs.hidden_states if hasattr(outputs, 'hidden_states') else None,
attentions=outputs.attentions if hasattr(outputs, 'attentions') else None,
)
def prepare_inputs_for_generation(
self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
):
if past_key_values:
input_ids = input_ids[:, -1:]
position_ids = kwargs.get("position_ids", None)
if attention_mask is not None and position_ids is None:
position_ids = attention_mask.long().cumsum(-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
if past_key_values:
position_ids = position_ids[:, -1].unsqueeze(-1)
if inputs_embeds is not None and past_key_values is None:
model_inputs = {"inputs_embeds": inputs_embeds}
else:
model_inputs = {"input_ids": input_ids}
model_inputs.update(
{
"position_ids": position_ids,
"past_key_values": past_key_values,
"use_cache": kwargs.get("use_cache"),
"attention_mask": attention_mask,
}
)
return model_inputs |