113 lines
5.7 KiB
Python
113 lines
5.7 KiB
Python
# 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)
|