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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, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import CrossEntropyLoss
from transformers import AutoConfig, AutoModelForCausalLM
from transformers.cache_utils import Cache, DynamicCache
from transformers.generation.utils import GenerateOutput
from transformers.modeling_outputs import (
BaseModelOutputWithPast,
CausalLMOutputWithPast,
)
from transformers.utils import logging
from ..cambrian_arch import CambrianMetaForCausalLM, CambrianMetaModel
IS_XLA_AVAILABLE = False
from transformers import Qwen2Config, Qwen2ForCausalLM, Qwen2Model
logger = logging.get_logger(__name__)
class CambrianConfig(Qwen2Config):
model_type = "cambrian_qwen"
debug = "debug"
class CambrianQwenModel(CambrianMetaModel, Qwen2Model):
config_class = CambrianConfig
def __init__(self, config: Qwen2Config):
super(CambrianQwenModel, self).__init__(config)
def forward(
self,
# pyre-fixme[9]: input_ids has type `LongTensor`; used as `None`.
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,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
vision_tower_aux_feature_list: Optional[List[torch.FloatTensor]] = None,
vision_tower_aux_attention_masks_list: Optional[List[torch.Tensor]] = None,
final_vision_feature_size: Optional[List[tuple]] = None,
global_context_feature: Optional[torch.Tensor] = None,
) -> Union[Tuple, BaseModelOutputWithPast]:
output_attentions = (
output_attentions
if output_attentions is not None
# pyre-fixme[16]: `CambrianQwenModel` has no attribute `config`.
else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError(
"You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
)
# pyre-fixme[16]: `CambrianQwenModel` has no attribute `gradient_checkpointing`.
# pyre-fixme[16]: `CambrianQwenModel` has no attribute `training`.
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
use_legacy_cache = False
if use_cache and not isinstance(past_key_values, Cache):
use_legacy_cache = True
# pyre-fixme[6]: For 1st argument expected
# `Optional[Tuple[Tuple[FloatTensor]]]` but got
# `Optional[List[FloatTensor]]`.
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
logger.warning_once(
"We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. "
"Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)"
)
if inputs_embeds is None:
# pyre-fixme[16]: `CambrianQwenModel` has no attribute `embed_tokens`.
inputs_embeds = self.embed_tokens(input_ids)
if cache_position is None:
past_seen_tokens = (
# pyre-fixme[16]: Item `List` of `Union[List[torch._C.FloatTensor],
# DynamicCache]` has no attribute `get_seq_length`.
past_key_values.get_seq_length() if past_key_values is not None else 0
)
cache_position = torch.arange(
past_seen_tokens,
past_seen_tokens + inputs_embeds.shape[1],
device=inputs_embeds.device,
)
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
# pyre-fixme[16]: `CambrianQwenModel` has no attribute `_update_causal_mask`.
causal_mask = self._update_causal_mask(
attention_mask,
inputs_embeds,
cache_position,
past_key_values,
output_attentions,
)
hidden_states = inputs_embeds
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
next_decoder_cache = None
# pyre-fixme[16]: `CambrianQwenModel` has no attribute `layers`.
for i, decoder_layer in enumerate(self.layers):
if output_hidden_states:
all_hidden_states += (hidden_states,)
if self.gradient_checkpointing and self.training:
# pyre-fixme[16]: `CambrianQwenModel` has no attribute
# `_gradient_checkpointing_func`.
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
causal_mask,
position_ids,
past_key_values,
output_attentions,
use_cache,
cache_position,
)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=causal_mask,
position_ids=position_ids,
past_key_value=past_key_values,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
if output_attentions:
all_self_attns += (layer_outputs[1],)
# pyre-fixme[16]: `CambrianQwenModel` has no attribute `norm`.
hidden_states = self.norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = None
if use_cache:
next_cache = (
next_decoder_cache.to_legacy_cache()
if use_legacy_cache
else next_decoder_cache
)
if not return_dict:
return tuple(
v
for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
if v is not None
)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
)
class CambrianQwenForCausalLM(Qwen2ForCausalLM, CambrianMetaForCausalLM):
config_class = CambrianConfig
def __init__(self, config):
# super(Qwen2ForCausalLM, self).__init__(config)
Qwen2ForCausalLM.__init__(self, config)
config.model_type = "cambrian_qwen"
config.rope_scaling = None
self.model = CambrianQwenModel(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,
# pyre-fixme[9]: input_ids has type `LongTensor`; used as `None`.
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_aux_attention_masks_list: Optional[List[torch.Tensor]] = None,
image_sizes: Optional[List[List[int]]] = None,
return_dict: Optional[bool] = None,
modalities: Optional[List[str]] = ["image"],
dpo_forward: Optional[bool] = False,
cache_position=None,
) -> Union[Tuple, CausalLMOutputWithPast]:
input_image_features = None
highres_image_features = None
frame_split_sizes = None
if inputs_embeds is None:
(
input_ids,
position_ids,
attention_mask,
past_key_values,
inputs_embeds,
labels,
vision_tower_aux_feature_list,
vision_tower_aux_attention_masks_list,
final_vision_feature_size,
global_context_feature,
) = self.prepare_inputs_labels_for_multimodal(
input_ids,
position_ids,
attention_mask,
past_key_values,
labels,
images,
image_aux_attention_masks_list,
image_sizes,
)
if dpo_forward:
# pyre-fixme[29]: `CambrianQwenModel` is not a function.
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,
return_dict=return_dict,
)
hidden_states = outputs[0]
logits = self.lm_head(hidden_states)
return logits, labels
else:
if hasattr(self, "vision_tower_aux_feature_list"):
# pyre-fixme[29]: `CambrianQwenModel` is not a function.
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,
return_dict=return_dict,
vision_tower_aux_feature_list=(
# pyre-fixme[61]: `vision_tower_aux_feature_list` is
# undefined, or not always defined.
vision_tower_aux_feature_list
if inputs_embeds is None
# pyre-fixme[16]: `CambrianQwenForCausalLM` has no attribute
# `vision_tower_aux_feature_list`.
else self.vision_tower_aux_feature_list
),
vision_tower_aux_attention_masks_list=(
# pyre-fixme[61]: `vision_tower_aux_attention_masks_list` is
# undefined, or not always defined.
vision_tower_aux_attention_masks_list
if inputs_embeds is None
# pyre-fixme[16]: `CambrianQwenForCausalLM` has no attribute
# `vision_tower_aux_attention_masks_list`.
else self.vision_tower_aux_attention_masks_list
),
final_vision_feature_size=(
# pyre-fixme[61]: `final_vision_feature_size` is undefined,
# or not always defined.
final_vision_feature_size
if inputs_embeds is None
# pyre-fixme[16]: `CambrianQwenForCausalLM` has no attribute
# `final_vision_feature_size`.
else self.final_vision_feature_size
),
global_context_feature=(
# pyre-fixme[61]: `global_context_feature` is undefined, or
# not always defined.
global_context_feature
if inputs_embeds is None
# pyre-fixme[16]: `CambrianQwenForCausalLM` has no attribute
# `global_context_feature`.
else self.global_context_feature
),
)
else:
# pyre-fixme[29]: `CambrianQwenModel` is not a function.
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,
return_dict=return_dict,
# final_vision_feature_size=final_vision_feature_size,
)
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()
# pyre-fixme[16]: `CambrianQwenForCausalLM` has no attribute `config`.
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)
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@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,
_,
vision_tower_aux_feature_list,
vision_tower_aux_attention_masks_list,
final_vision_feature_size,
global_context_feature,
) = self.prepare_inputs_labels_for_multimodal(
inputs,
position_ids,
attention_mask,
None,
None,
images,
image_sizes=image_sizes,
)
# pyre-fixme[16]: `CambrianQwenForCausalLM` has no attribute
# `vision_tower_aux_feature_list`.
self.vision_tower_aux_feature_list = vision_tower_aux_feature_list
# pyre-fixme[16]: `CambrianQwenForCausalLM` has no attribute
# `vision_tower_aux_attention_masks_list`.
self.vision_tower_aux_attention_masks_list = (
vision_tower_aux_attention_masks_list
)
# pyre-fixme[16]: `CambrianQwenForCausalLM` has no attribute
# `final_vision_feature_size`.
self.final_vision_feature_size = final_vision_feature_size
# pyre-fixme[16]: `CambrianQwenForCausalLM` has no attribute
# `global_context_feature`.
self.global_context_feature = global_context_feature
else:
inputs_embeds = self.get_model().embed_tokens(inputs)
# pyre-fixme[16]: `Qwen2ForCausalLM` has no attribute `generate`.
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("cambrian_qwen", CambrianConfig)
AutoModelForCausalLM.register(CambrianConfig, CambrianQwenForCausalLM)
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