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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.
# ------------------------------------------------------------------------
# Modified from LLaVA (https://github.com/haotian-liu/LLaVA) and MoE-LLaVA(https://github.com/PKU-YuanGroup/MoE-LLaVA)
# Copyright 2024 Jiachen Li
# ------------------------------------------------------------------------
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss
from transformers import AutoConfig, AutoModelForCausalLM, \
MixtralConfig, MixtralModel, MixtralForCausalLM
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.generation.utils import GenerateOutput
from ..llava_arch import LlavaMetaModel, LlavaMetaForCausalLM
from .smoe_mixtral_helper import SMoECausalLMOutputWithPast, MixtralDecoderLayerMOEBlock_forward
class LlavaMixtralConfig(MixtralConfig):
model_type = "llava_mixtral"
class LlavaMixtralModel(LlavaMetaModel, MixtralModel):
config_class = LlavaMixtralConfig
def __init__(self, config: MixtralConfig):
super(LlavaMixtralModel, self).__init__(config)
class LlavaMixtralForCausalLM(MixtralForCausalLM, LlavaMetaForCausalLM):
config_class = LlavaMixtralConfig
def __init__(self, config):
super(MixtralForCausalLM, self).__init__(config)
self.model = LlavaMixtralModel(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_model(self):
return self.model
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[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,
images: Optional[torch.FloatTensor] = None,
image_sizes: Optional[List[List[int]]] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithPast]:
if inputs_embeds is None:
(
input_ids,
position_ids,
attention_mask,
past_key_values,
inputs_embeds,
labels,
clip_balance_loss,
clip_router_z_loss,
mlp_balance_loss,
mlp_router_z_loss
) = self.prepare_inputs_labels_for_multimodal(
input_ids,
position_ids,
attention_mask,
past_key_values,
labels,
images,
image_sizes
)
output_router_logits = True
### We set output_router_logits to True and squeeze bzloss into outputs.router_logits. This hack implementation needs to be fixed
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,
output_router_logits=output_router_logits,
return_dict=return_dict,
)
hidden_states = outputs[0]
logits = self.lm_head(hidden_states)
logits = logits.float()
loss = None
if labels is not None:
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
shift_logits = shift_logits.view(-1, self.config.vocab_size)
shift_labels = shift_labels.view(-1)
# Enable model parallelism
shift_labels = shift_labels.to(shift_logits.device)
loss = loss_fct(shift_logits, shift_labels)
b_loss = None
z_loss = None
if self.config.training:
if self.config.mlp_smoe or self.config.clip_smoe:
if self.config.local_rank == 0:
print('language loss: ', loss.item())
if self.config.mlp_smoe:
mlp_balance_loss = mlp_balance_loss.sum(dim=-1).mean()
mlp_balance_loss = self.config.balance_loss_coef * mlp_balance_loss
loss += mlp_balance_loss
mlp_router_z_loss = mlp_router_z_loss.sum(dim=-1).mean()
mlp_router_z_loss = self.config.router_z_loss_coef * mlp_router_z_loss
loss += mlp_router_z_loss
if self.config.local_rank == 0:
print('mlp balance loss: ', mlp_balance_loss.item(), 'mlp router z loss: ', mlp_router_z_loss.item())
if self.config.clip_smoe:
clip_balance_loss = clip_balance_loss.sum(dim=-1).mean()
clip_balance_loss = self.config.balance_loss_coef * clip_balance_loss
loss += clip_balance_loss
clip_router_z_loss = clip_router_z_loss.sum(dim=-1).mean()
clip_router_z_loss = self.config.router_z_loss_coef * clip_router_z_loss
loss += clip_router_z_loss
if self.config.local_rank == 0:
print('clip balance loss: ', clip_balance_loss.item(), 'clip router z loss: ', clip_router_z_loss.item())
balance_loss = [loss_pair[0] for loss_pair in outputs.router_logits]
b_loss = sum(balance_loss) / len(balance_loss)
b_loss = self.config.balance_loss_coef * b_loss
loss += b_loss
router_z_loss = [loss_pair[1] for loss_pair in outputs.router_logits]
z_loss = sum(router_z_loss) / len(balance_loss)
z_loss = self.config.router_z_loss_coef * z_loss
loss += z_loss
if self.config.local_rank == 0:
print('llm balance loss: ', b_loss.item(), 'llm router z loss: ', z_loss.item())
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return SMoECausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def initialize_smoe_modules(self, model_args):
for m in self.model.layers:
m.block_sparse_moe.forward = MixtralDecoderLayerMOEBlock_forward(m.block_sparse_moe)
@torch.no_grad()
def generate(
self,
inputs: Optional[torch.Tensor] = None,
images: Optional[torch.Tensor] = None,
image_sizes: Optional[torch.Tensor] = None,
**kwargs,
) -> Union[GenerateOutput, torch.LongTensor]:
position_ids = kwargs.pop("position_ids", None)
attention_mask = kwargs.pop("attention_mask", None)
if "inputs_embeds" in kwargs:
raise NotImplementedError("`inputs_embeds` is not supported")
if images is not None:
(
inputs,
position_ids,
attention_mask,
_,
inputs_embeds,
_,
_,
_,
_,
_
) = self.prepare_inputs_labels_for_multimodal(
inputs,
position_ids,
attention_mask,
None,
None,
images,
image_sizes=image_sizes
)
else:
inputs_embeds = self.get_model().embed_tokens(inputs)
return super().generate(
position_ids=position_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
**kwargs
)
def prepare_inputs_for_generation(self, input_ids, past_key_values=None,
inputs_embeds=None, **kwargs):
images = kwargs.pop("images", None)
image_sizes = kwargs.pop("image_sizes", None)
inputs = super().prepare_inputs_for_generation(
input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, **kwargs
)
if images is not None:
inputs['images'] = images
if image_sizes is not None:
inputs['image_sizes'] = image_sizes
return inputs
AutoConfig.register("llava_mixtral", LlavaMixtralConfig)
AutoModelForCausalLM.register(LlavaMixtralConfig, LlavaMixtralForCausalLM)
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