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from transformers import PretrainedConfig, PreTrainedModel
import torch, transformers
from typing import List, Optional, Tuple, Union
from transformers.modeling_outputs import CausalLMOutputWithPast
from .VisualTransformer import VisionTransformer, LayerNorm
from functools import partial
from transformers import TextIteratorStreamer
from transformers import StoppingCriteria, GenerationConfig
from threading import Thread
# Model Constants
IGNORE_INDEX = -100
IMAGE_TOKEN_INDEX = -200
DEFAULT_IMAGE_TOKEN = "<image>"
DEFAULT_IMAGE_PATCH_TOKEN = "<im_patch>"
DEFAULT_IM_START_TOKEN = "<im_start>"
DEFAULT_IM_END_TOKEN = "<im_end>"
class AttrDict(dict):
def __init__(self, *args, **kwargs):
super(AttrDict, self).__init__(*args, **kwargs)
self.__dict__ = self
class CXRLLAVAConfig(PretrainedConfig):
model_type = "CXR-LLAVA"
def __init__(self, **kwargs,):
if 'llama' in kwargs:
self.llama = AttrDict(kwargs['llama'])
del kwargs['llama']
self.__dict__.update(kwargs)
super().__init__(**kwargs)
class CXRLLAVAModel(PreTrainedModel):
config_class = CXRLLAVAConfig
def __init__(self, config):
super().__init__(config)
self.tokenizer = transformers.LlamaTokenizer.from_pretrained(config._name_or_path, add_special_tokens=False)
self.tokenizer.pad_token = self.tokenizer.unk_token
self.tokenizer.sep_token = self.tokenizer.unk_token
self.tokenizer.cls_token = self.tokenizer.unk_token
self.tokenizer.mask_token = self.tokenizer.unk_token
from open_clip.model import CLIPVisionCfg
vision_cfg = CLIPVisionCfg(**config.clip_vision_cfg)
self.generation_config = GenerationConfig.from_pretrained(config._name_or_path)
vision_heads = vision_cfg.width // vision_cfg.head_width
norm_layer = LayerNorm
act_layer = torch.nn.GELU
if vision_cfg.norm_kwargs:
norm_layer = partial(norm_layer, **vision_cfg.norm_kwargs)
if vision_cfg.act_kwargs is not None:
act_layer = partial(act_layer, **vision_cfg.act_kwargs)
self.vision_tower = VisionTransformer(
in_channels=1,
image_size=vision_cfg.image_size,
patch_size=vision_cfg.patch_size,
width=vision_cfg.width,
layers=vision_cfg.layers,
heads=vision_heads,
mlp_ratio=vision_cfg.mlp_ratio,
ls_init_value=vision_cfg.ls_init_value,
patch_dropout=vision_cfg.patch_dropout,
attentional_pool=vision_cfg.attentional_pool,
attn_pooler_queries=vision_cfg.attn_pooler_queries,
attn_pooler_heads=vision_cfg.attn_pooler_heads,
pos_embed_type=vision_cfg.pos_embed_type,
no_ln_pre=vision_cfg.no_ln_pre,
final_ln_after_pool=vision_cfg.final_ln_after_pool,
pool_type=vision_cfg.pool_type,
output_tokens=vision_cfg.output_tokens,
output_dim=config.clip_embed_dim,
act_layer=act_layer,
norm_layer=norm_layer,
)
self.vision_tower.image_processor = transformers.CLIPImageProcessor(
do_resize=True,
size={'shortest_edge': config.clip_vision_cfg['image_size']},
resample=True,
do_center_crop=True,
crop_size=config.clip_vision_cfg['image_size'],
do_rescale=True,
rescale_factor=1 / 255,
do_normalize=True,
image_mean=config.image_preprocess_cfg['mean'],
image_std=config.image_preprocess_cfg['std'],
do_convert_rgb=False
)
def convert_dtype(dtype):
if dtype == 'fp32':
dtype = torch.float32
elif dtype == 'fp16':
dtype = torch.float16
elif dtype == 'bf16':
dtype = torch.bfloat16
else:
raise Exception("Unsupported dtype")
return dtype
self.clip_cast_dtype = convert_dtype(config.clip_vision_tower_dtype)
self.mm_projector = torch.nn.Linear(config.mm_projector_dim, config.llama['hidden_size'])
self.lm_head = torch.nn.Linear(config.llama.hidden_size, config.llama.vocab_size, bias=False)
self.llama = transformers.LlamaModel(transformers.LlamaConfig(**config.llama))
self.llama = self.llama.to(torch.bfloat16)
self.lm_head = self.lm_head.to(torch.bfloat16)
self.vision_tower = self.vision_tower.to(torch.bfloat16)
self.mm_projector = self.mm_projector.to(torch.bfloat16)
def get_input_embeddings(self):
return self.llama.get_input_embeddings()
def get_vision_tower(self):
return self.vision_tower
def gradient_checkpointing_enable(self):
return self.llama.gradient_checkpointing_enable()
def encode_images(self, images):
images = images.to(torch.bfloat16)
def _expand_token(token, batch_size: int):
return token.view(1, 1, -1).expand(batch_size, -1, -1)
# open_clip ViT
# https://github.com/mlfoundations/open_clip/blob/main/src/open_clip/transformer.py
x = images
x = self.vision_tower.conv1(x) # shape = [*, width, grid, grid]
x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]
x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]
# class embeddings and positional embeddings
x = torch.cat([_expand_token(self.vision_tower.class_embedding, x.shape[0]).to(x.dtype), x], dim=1)
# shape = [*, grid ** 2 + 1, width]
x = x + self.vision_tower.positional_embedding.to(x.dtype)
x = self.vision_tower.patch_dropout(x)
x = self.vision_tower.ln_pre(x)
x = x.permute(1, 0, 2) # NLD -> LND
x = self.vision_tower.transformer(x)
x = x.permute(1, 0, 2) # LND -> NLD
if self.vision_tower.attn_pool is not None:
if self.vision_tower.attn_pool_contrastive is not None:
# This is untested, WIP pooling that should match paper
x = self.vision_tower.ln_post(x) # TBD LN first or separate one after each pool?
tokens = self.vision_tower.attn_pool(x)
if self.vision_tower.attn_pool_type == 'parallel':
pooled = self.vision_tower.attn_pool_contrastive(x)
else:
assert self.vision_tower.attn_pool_type == 'cascade'
pooled = self.vision_tower.attn_pool_contrastive(tokens)
else:
# this is the original OpenCLIP CoCa setup, does not match paper
x = self.vision_tower.attn_pool(x)
x = self.vision_tower.ln_post(x)
pooled, tokens = self.vision_tower._global_pool(x)
elif self.vision_tower.final_ln_after_pool:
pooled, tokens = self.vision_tower._global_pool(x)
pooled = self.vision_tower.ln_post(pooled)
else:
x = self.vision_tower.ln_post(x)
pooled, tokens = self.vision_tower._global_pool(x)
if self.vision_tower.proj is not None:
pooled = pooled @ self.vision_tower.proj
image_features = tokens
image_features = image_features.to(torch.bfloat16)
image_features = self.mm_projector(image_features)
image_features = image_features.to(torch.bfloat16)
return image_features
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None, # (1,4317)
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
images: Optional[torch.FloatTensor] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithPast]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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.llama(
input_ids=input_ids,
attention_mask=attention_mask,
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)
loss = None
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
# original multimodal code
def prepare_inputs_labels_for_multimodal(
self, input_ids, attention_mask, past_key_values, labels, images
):
vision_tower = self.vision_tower
if vision_tower is None or images is None or input_ids.shape[1] == 1:
if past_key_values is not None and vision_tower is not None and images is not None and input_ids.shape[
1] == 1:
attention_mask = torch.ones((attention_mask.shape[0], past_key_values[-1][-1].shape[-2] + 1),
dtype=attention_mask.dtype, device=attention_mask.device)
return input_ids, attention_mask, past_key_values, None, labels
if type(images) is list or images.ndim == 5:
concat_images = torch.cat([image for image in images], dim=0)
image_features = self.encode_images(concat_images)
split_sizes = [image.shape[0] for image in images]
image_features = torch.split(image_features, split_sizes, dim=0)
image_features = [x.flatten(0, 1) for x in image_features]
else:
image_features = self.encode_images(images)
new_input_embeds = []
new_labels = [] if labels is not None else None
cur_image_idx = 0
for batch_idx, cur_input_ids in enumerate(input_ids):
if (cur_input_ids == IMAGE_TOKEN_INDEX).sum() == 0:
# multimodal LLM, but the current sample is not multimodal
cur_input_embeds = self.llama.embed_tokens(cur_input_ids)
cur_input_embeds = cur_input_embeds + (0. * self.mm_projector(vision_tower.dummy_feature)).sum()
new_input_embeds.append(cur_input_embeds)
if labels is not None:
new_labels.append(labels[batch_idx])
cur_image_idx += 1
continue
image_token_indices = torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0]
cur_new_input_embeds = []
if labels is not None:
cur_labels = labels[batch_idx]
cur_new_labels = []
assert cur_labels.shape == cur_input_ids.shape
while image_token_indices.numel() > 0:
cur_image_features = image_features[cur_image_idx]
image_token_start = image_token_indices[0]
if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end',
False):
cur_new_input_embeds.append(self.llama.embed_tokens(cur_input_ids[:image_token_start - 1]).detach())
cur_new_input_embeds.append(
self.llama.embed_tokens(cur_input_ids[image_token_start - 1:image_token_start]))
cur_new_input_embeds.append(cur_image_features)
cur_new_input_embeds.append(
self.llama.embed_tokens(cur_input_ids[image_token_start + 1:image_token_start + 2]))
if labels is not None:
cur_new_labels.append(cur_labels[:image_token_start])
cur_new_labels.append(
torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=labels.device,
dtype=labels.dtype))
cur_new_labels.append(cur_labels[image_token_start:image_token_start + 1])
cur_labels = cur_labels[image_token_start + 2:]
else:
cur_new_input_embeds.append(self.llama.embed_tokens(cur_input_ids[:image_token_start]))
cur_new_input_embeds.append(cur_image_features)
if labels is not None:
cur_new_labels.append(cur_labels[:image_token_start])
cur_new_labels.append(
torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=labels.device,
dtype=labels.dtype))
cur_labels = cur_labels[image_token_start + 1:]
cur_image_idx += 1
if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end',
False):
cur_input_ids = cur_input_ids[image_token_start + 2:]
else:
cur_input_ids = cur_input_ids[image_token_start + 1:]
image_token_indices = torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0]
if cur_input_ids.numel() > 0:
if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end',
False):
cur_new_input_embeds.append(self.llama.embed_tokens(cur_input_ids).detach())
else:
cur_new_input_embeds.append(self.llama.embed_tokens(cur_input_ids))
if labels is not None:
cur_new_labels.append(cur_labels)
cur_new_input_embeds = [x.to(device=self.device) for x in cur_new_input_embeds]
cur_new_input_embeds = torch.cat(cur_new_input_embeds, dim=0)
new_input_embeds.append(cur_new_input_embeds)
if labels is not None:
cur_new_labels = torch.cat(cur_new_labels, dim=0)
new_labels.append(cur_new_labels)
if any(x.shape != new_input_embeds[0].shape for x in new_input_embeds):
max_len = max(x.shape[0] for x in new_input_embeds)
new_input_embeds_align = []
for cur_new_embed in new_input_embeds:
cur_new_embed = torch.cat((cur_new_embed,
torch.zeros((max_len - cur_new_embed.shape[0], cur_new_embed.shape[1]),
dtype=cur_new_embed.dtype, device=cur_new_embed.device)), dim=0)
new_input_embeds_align.append(cur_new_embed)
new_input_embeds = torch.stack(new_input_embeds_align, dim=0)
if labels is not None:
new_labels_align = []
_new_labels = new_labels
for cur_new_label in new_labels:
cur_new_label = torch.cat((cur_new_label,
torch.full((max_len - cur_new_label.shape[0],), IGNORE_INDEX,
dtype=cur_new_label.dtype, device=cur_new_label.device)),
dim=0)
new_labels_align.append(cur_new_label)
new_labels = torch.stack(new_labels_align, dim=0)
if attention_mask is not None:
new_attention_mask = []
for cur_attention_mask, cur_new_labels, cur_new_labels_align in zip(attention_mask, _new_labels,
new_labels):
new_attn_mask_pad_left = torch.full((cur_new_labels.shape[0] - labels.shape[1],), True,
dtype=attention_mask.dtype, device=attention_mask.device)
new_attn_mask_pad_right = torch.full((cur_new_labels_align.shape[0] - cur_new_labels.shape[0],),
False, dtype=attention_mask.dtype,
device=attention_mask.device)
cur_new_attention_mask = torch.cat(
(new_attn_mask_pad_left, cur_attention_mask, new_attn_mask_pad_right), dim=0)
new_attention_mask.append(cur_new_attention_mask)
attention_mask = torch.stack(new_attention_mask, dim=0)
assert attention_mask.shape == new_labels.shape
else:
new_input_embeds = torch.stack(new_input_embeds, dim=0)
if labels is not None:
new_labels = torch.stack(new_labels, dim=0)
if attention_mask is not None:
new_attn_mask_pad_left = torch.full(
(attention_mask.shape[0], new_input_embeds.shape[1] - input_ids.shape[1]), True,
dtype=attention_mask.dtype, device=attention_mask.device)
attention_mask = torch.cat((new_attn_mask_pad_left, attention_mask), dim=1)
assert attention_mask.shape == new_input_embeds.shape[:2]
return None, attention_mask, past_key_values, new_input_embeds, new_labels
# sw-modified code
def prepare_inputs_labels_for_multimodal_use_final_vector(
self, input_ids, attention_mask, past_key_values, labels, images
):
vision_tower = self.vision_tower
if vision_tower is None or images is None or input_ids.shape[1] == 1:
if past_key_values is not None and vision_tower is not None and images is not None and input_ids.shape[
1] == 1:
attention_mask = torch.ones((attention_mask.shape[0], past_key_values[-1][-1].shape[-2] + 1),
dtype=attention_mask.dtype, device=attention_mask.device)
return input_ids, attention_mask, past_key_values, None, labels
if type(images) is list or images.ndim == 5:
concat_images = torch.cat([image for image in images], dim=0)
image_features = self.encode_images(concat_images)
split_sizes = [image.shape[0] for image in images]
image_features = torch.split(image_features, split_sizes, dim=0)
image_features = [x.flatten(0, 1) for x in image_features]
else:
image_features = self.encode_images(images)
new_input_embeds = []
new_labels = [] if labels is not None else None
cur_image_idx = 0
for batch_idx, cur_input_ids in enumerate(input_ids):
if (cur_input_ids == IMAGE_TOKEN_INDEX).sum() == 0:
# multimodal LLM, but the current sample is not multimodal
cur_input_embeds = self.llama.embed_tokens(cur_input_ids)
cur_input_embeds = cur_input_embeds + (0. * self.mm_projector(vision_tower.dummy_feature)).sum()
new_input_embeds.append(cur_input_embeds)
if labels is not None:
new_labels.append(labels[batch_idx])
cur_image_idx += 1
continue
image_token_indices = torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0]
cur_new_input_embeds = []
if labels is not None:
cur_labels = labels[batch_idx]
cur_new_labels = []
assert cur_labels.shape == cur_input_ids.shape
while image_token_indices.numel() > 0:
cur_image_features = image_features[cur_image_idx]
image_token_start = image_token_indices[0]
if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end',
False):
cur_new_input_embeds.append(self.llama.embed_tokens(cur_input_ids[:image_token_start - 1]).detach())
cur_new_input_embeds.append(
self.llama.embed_tokens(cur_input_ids[image_token_start - 1:image_token_start]))
cur_new_input_embeds.append(cur_image_features)
cur_new_input_embeds.append(
self.llama.embed_tokens(cur_input_ids[image_token_start + 1:image_token_start + 2]))
if labels is not None:
cur_new_labels.append(cur_labels[:image_token_start])
cur_new_labels.append(
torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=labels.device,
dtype=labels.dtype))
cur_new_labels.append(cur_labels[image_token_start:image_token_start + 1])
cur_labels = cur_labels[image_token_start + 2:]
else:
cur_new_input_embeds.append(
self.llama.embed_tokens(cur_input_ids[:image_token_start].to(self.device)))
cur_new_input_embeds.append(cur_image_features)
if labels is not None:
cur_new_labels.append(cur_labels[:image_token_start])
cur_new_labels.append(
torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=labels.device,
dtype=labels.dtype))
cur_labels = cur_labels[image_token_start + 1:]
cur_image_idx += 1
if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end',
False):
cur_input_ids = cur_input_ids[image_token_start + 2:]
else:
cur_input_ids = cur_input_ids[image_token_start + 1:]
image_token_indices = torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0]
if cur_input_ids.numel() > 0:
if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end',
False):
cur_new_input_embeds.append(self.llama.embed_tokens(cur_input_ids).detach())
else:
cur_new_input_embeds.append(self.llama.embed_tokens(cur_input_ids.to(self.device)))
if labels is not None:
# seowoo-edit
cur_labels = labels[batch_idx]
cur_new_labels.append(cur_labels)
# [5120] -> [1, 5120]
cur_new_input_embeds[1] = torch.unsqueeze(cur_new_input_embeds[1], dim=0)
cur_new_input_embeds = [x.to(device=self.device) for x in cur_new_input_embeds]
cur_new_input_embeds = torch.cat(cur_new_input_embeds, dim=0)
new_input_embeds.append(cur_new_input_embeds)
if labels is not None:
cur_new_labels = torch.cat(cur_new_labels, dim=0)
new_labels.append(cur_new_labels)
if any(x.shape != new_input_embeds[0].shape for x in new_input_embeds):
# print("if 204")
max_len = max(x.shape[0] for x in new_input_embeds)
new_input_embeds_align = []
for cur_new_embed in new_input_embeds:
cur_new_embed = torch.cat((cur_new_embed,
torch.zeros((max_len - cur_new_embed.shape[0], cur_new_embed.shape[1]),
dtype=cur_new_embed.dtype, device=cur_new_embed.device)), dim=0)
new_input_embeds_align.append(cur_new_embed)
new_input_embeds = torch.stack(new_input_embeds_align, dim=0)
if labels is not None:
new_labels_align = []
_new_labels = new_labels
for cur_new_label in new_labels:
cur_new_label = torch.cat((cur_new_label,
torch.full((max_len - cur_new_label.shape[0],), IGNORE_INDEX,
dtype=cur_new_label.dtype, device=cur_new_label.device)),
dim=0)
new_labels_align.append(cur_new_label)
new_labels = torch.stack(new_labels_align, dim=0)
if attention_mask is not None:
new_attention_mask = []
for cur_attention_mask, cur_new_labels, cur_new_labels_align in zip(attention_mask, _new_labels,
new_labels):
new_attn_mask_pad_left = torch.full((cur_new_labels.shape[0] - labels.shape[1],), True,
dtype=attention_mask.dtype, device=attention_mask.device)
new_attn_mask_pad_right = torch.full((cur_new_labels_align.shape[0] - cur_new_labels.shape[0],),
False, dtype=attention_mask.dtype,
device=attention_mask.device)
cur_new_attention_mask = torch.cat(
(new_attn_mask_pad_left, cur_attention_mask, new_attn_mask_pad_right), dim=0)
new_attention_mask.append(cur_new_attention_mask)
attention_mask = torch.stack(new_attention_mask, dim=0)
assert attention_mask.shape == new_labels.shape
else:
new_input_embeds = torch.stack(new_input_embeds, dim=0)
if labels is not None:
new_labels = torch.stack(new_labels, dim=0)
if attention_mask is not None:
new_attn_mask_pad_left = torch.full(
(attention_mask.shape[0], new_input_embeds.shape[1] - input_ids.shape[1]), True,
dtype=attention_mask.dtype, device=attention_mask.device)
attention_mask = torch.cat((new_attn_mask_pad_left, attention_mask), dim=1)
assert attention_mask.shape == new_input_embeds.shape[:2]
return None, attention_mask, past_key_values, new_input_embeds, labels
def prepare_inputs_for_generation(
self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
):
if past_key_values:
input_ids = input_ids[:, -1:]
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
if inputs_embeds is not None and past_key_values is None:
model_inputs = {"inputs_embeds": inputs_embeds}
else:
model_inputs = {"input_ids": input_ids}
model_inputs.update(
{
"past_key_values": past_key_values,
"use_cache": kwargs.get("use_cache"),
"attention_mask": attention_mask,
"images": kwargs.get("images", None),
}
)
return model_inputs
def apply_chat_template(self, chat):
return self.tokenizer.apply_chat_template(chat, tokenize=False)
def tokenizer_image_token(self, prompt, tokenizer, image_token_index=IMAGE_TOKEN_INDEX, return_tensors=None):
prompt_chunks = [tokenizer(chunk).input_ids for chunk in prompt.split('<image>')]
def insert_separator(X, sep):
return [ele for sublist in zip(X, [sep] * len(X)) for ele in sublist][:-1]
input_ids = []
offset = 0
if len(prompt_chunks) > 0 and len(prompt_chunks[0]) > 0 and prompt_chunks[0][0] == tokenizer.bos_token_id:
offset = 1
input_ids.append(prompt_chunks[0][0])
for x in insert_separator(prompt_chunks, [image_token_index] * (offset + 1)):
input_ids.extend(x[offset:])
if return_tensors is not None:
if return_tensors == 'pt':
return torch.tensor(input_ids, dtype=torch.long)
raise ValueError(f'Unsupported tensor type: {return_tensors}')
return input_ids
def generate_cxr_repsonse(self, chat, pil_image, temperature=0.2, top_p=0.8):
with torch.no_grad():
streamer = TextIteratorStreamer(self.tokenizer, skip_prompt=True, skip_special_tokens=True, timeout=15)
import numpy as np
pil_image = np.expand_dims(pil_image,axis=-1)
prompt = self.apply_chat_template(chat)
images = self.vision_tower.image_processor(pil_image, return_tensors='pt')['pixel_values']
images = images.to(self.device)
input_ids = self.tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
stopping_criteria = KeywordsStoppingCriteria(["</s>"], self.tokenizer, input_ids)
image_args = {"images": images}
do_sample = True if temperature > 0.001 else False
num_image_tokens = 1
max_context_length = getattr(self.config, 'max_position_embeddings', 2048)
max_new_tokens = min(512, max_context_length - input_ids.shape[-1] - num_image_tokens)
thread = Thread(target=self.generate, kwargs=dict(
inputs=input_ids,
do_sample=do_sample,
temperature=temperature,
top_p=top_p,
max_new_tokens=max_new_tokens,
streamer=streamer,
stopping_criteria=[stopping_criteria],
use_cache=True,
generation_config=self.generation_config,
**image_args
))
thread.start()
generated_text = ""
for new_text in streamer:
generated_text += new_text
return generated_text
def tokenizer_image_token(self, prompt, tokenizer, image_token_index=IMAGE_TOKEN_INDEX, return_tensors=None):
prompt_chunks = [tokenizer(chunk).input_ids for chunk in prompt.split('<image>')]
def insert_separator(X, sep):
return [ele for sublist in zip(X, [sep] * len(X)) for ele in sublist][:-1]
input_ids = []
offset = 0
if len(prompt_chunks) > 0 and len(prompt_chunks[0]) > 0 and prompt_chunks[0][0] == tokenizer.bos_token_id:
offset = 1
input_ids.append(prompt_chunks[0][0])
for x in insert_separator(prompt_chunks, [image_token_index] * (offset + 1)):
input_ids.extend(x[offset:])
if return_tensors is not None:
if return_tensors == 'pt':
return torch.tensor(input_ids, dtype=torch.long)
raise ValueError(f'Unsupported tensor type: {return_tensors}')
return input_ids
class KeywordsStoppingCriteria(StoppingCriteria):
def __init__(self, keywords, tokenizer, input_ids):
self.keywords = keywords
self.keyword_ids = []
for keyword in keywords:
cur_keyword_ids = tokenizer(keyword).input_ids
if len(cur_keyword_ids) > 1 and cur_keyword_ids[0] == tokenizer.bos_token_id:
cur_keyword_ids = cur_keyword_ids[1:]
self.keyword_ids.append(torch.tensor(cur_keyword_ids))
self.tokenizer = tokenizer
self.start_len = input_ids.shape[1]
def __call__(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
assert output_ids.shape[0] == 1, "Only support batch size 1 (yet)" # TODO
offset = min(output_ids.shape[1] - self.start_len, 3)
self.keyword_ids = [keyword_id.to(output_ids.device) for keyword_id in self.keyword_ids]
for keyword_id in self.keyword_ids:
if output_ids[0, -keyword_id.shape[0]:] == keyword_id:
return True
outputs = self.tokenizer.batch_decode(output_ids[:, -offset:], skip_special_tokens=True)[0]
for keyword in self.keywords:
if keyword in outputs:
return True
return False
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