from typing import List, Optional, Tuple import warnings import torch import torch.nn.functional as F import math from transformers import AutoConfig, AutoModelForCausalLM from transformers.modeling_outputs import CausalLMOutputWithPast from mpt.modeling_mpt import MPTConfig, MPTForCausalLM, MPTModel from mmalaya_arch import MMAlayaMetaModel, MMAlayaMetaForCausalLM from configuration_mmalaya import MMAlayaMPTConfig class MMAlayaMPTModel(MMAlayaMetaModel, MPTModel): config_class = MMAlayaMPTConfig def __init__(self, config: MPTConfig): config.hidden_size = config.d_model super(MMAlayaMPTModel, self).__init__(config) def embed_tokens(self, x): return self.wte(x) class MMAlayaMPTForCausalLM(MPTForCausalLM, MMAlayaMetaForCausalLM): config_class = MMAlayaMPTConfig supports_gradient_checkpointing = True def __init__(self, config): super(MPTForCausalLM, self).__init__(config) if not config.tie_word_embeddings: raise ValueError('MPTForCausalLM only supports tied word embeddings') self.transformer = MMAlayaMPTModel(config) self.logit_scale = None if config.logit_scale is not None: logit_scale = config.logit_scale if isinstance(logit_scale, str): if logit_scale == 'inv_sqrt_d_model': logit_scale = 1 / math.sqrt(config.d_model) else: raise ValueError(f"logit_scale={logit_scale!r} is not recognized as an option; use numeric value or 'inv_sqrt_d_model'.") self.logit_scale = logit_scale def get_model(self): return self.transformer def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, MMAlayaMPTModel): module.gradient_checkpointing = value def forward(self, input_ids: torch.LongTensor, past_key_values: Optional[List[Tuple[torch.FloatTensor]]]=None, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None, labels: Optional[torch.LongTensor]=None, return_dict: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, use_cache: Optional[bool]=None, images=None): return_dict = return_dict if return_dict is not None else self.config.return_dict use_cache = use_cache if use_cache is not None else self.config.use_cache input_ids, _, attention_mask, past_key_values, inputs_embeds, labels = self.prepare_inputs_labels_for_multimodal(input_ids, None, attention_mask, past_key_values, labels, images) outputs = self.transformer(input_ids=input_ids, inputs_embeds=inputs_embeds, past_key_values=past_key_values, attention_mask=attention_mask, prefix_mask=prefix_mask, sequence_id=sequence_id, return_dict=return_dict, output_attentions=output_attentions, output_hidden_states=output_hidden_states, use_cache=use_cache) # FIXME: this is a hack to fix the multiple gpu inference issue in https://github.com/haotian-liu/LLaVA/issues/338 logits = F.linear(outputs.last_hidden_state.to(self.transformer.wte.weight.device), self.transformer.wte.weight) if self.logit_scale is not None: if self.logit_scale == 0: warnings.warn(f'Multiplying logits by self.logit_scale={self.logit_scale!r}. This will produce uniform (uninformative) outputs.') logits *= self.logit_scale loss = None if labels is not None: labels = torch.roll(labels, shifts=-1) labels[:, -1] = -100 loss = F.cross_entropy(logits.view(-1, logits.size(-1)), labels.to(logits.device).view(-1)) return CausalLMOutputWithPast(loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states) def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs): if inputs_embeds is not None: raise NotImplementedError('inputs_embeds is not implemented for MPT yet') attention_mask = kwargs['attention_mask'].bool() if attention_mask[:, -1].sum() != attention_mask.shape[0]: raise NotImplementedError('MPT does not support generation with right padding.') if self.transformer.attn_uses_sequence_id and self.training: sequence_id = torch.zeros_like(input_ids[:1]) else: sequence_id = None if past_key_values is not None: input_ids = input_ids[:, -1].unsqueeze(-1) if self.transformer.prefix_lm: prefix_mask = torch.ones_like(attention_mask) if kwargs.get('use_cache') == False: raise NotImplementedError('MPT with prefix_lm=True does not support use_cache=False.') else: prefix_mask = None return {'input_ids': input_ids, 'attention_mask': attention_mask, 'prefix_mask': prefix_mask, 'sequence_id': sequence_id, 'past_key_values': past_key_values, 'use_cache': kwargs.get('use_cache', True), "images": kwargs.get("images", None)} AutoConfig.register("mmalaya", MMAlayaMPTConfig) AutoModelForCausalLM.register(MMAlayaMPTConfig, MMAlayaMPTForCausalLM)