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# Copyright 2023 Haotian Liu
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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 llava.model.llava_arch import LlavaMetaModel, LlavaMetaForCausalLM
class LlavaMPTConfig(MPTConfig):
model_type = "llava_mpt"
class LlavaMPTModel(LlavaMetaModel, MPTModel):
config_class = LlavaMPTConfig
def __init__(self, config: MPTConfig):
config.hidden_size = config.d_model
super(LlavaMPTModel, self).__init__(config)
def embed_tokens(self, x):
return self.wte(x)
class LlavaMPTForCausalLM(MPTForCausalLM, LlavaMetaForCausalLM):
config_class = LlavaMPTConfig
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 = LlavaMPTModel(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, LlavaMPTModel):
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, 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("llava_mpt", LlavaMPTConfig)
AutoModelForCausalLM.register(LlavaMPTConfig, LlavaMPTForCausalLM)
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