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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.
import math
import warnings
from typing import List, Optional, Tuple
import torch
import torch.nn.functional as F
from transformers import AutoConfig, AutoModelForCausalLM
from transformers.modeling_outputs import CausalLMOutputWithPast
from ..llava_arch import LlavaMetaForCausalLM, LlavaMetaModel
from .mpt.modeling_mpt import MPTConfig, MPTForCausalLM, MPTModel
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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