|
from functools import partial |
|
from typing import List, Optional |
|
from argparse import Namespace |
|
import torch |
|
from torch import nn |
|
import torch.nn.functional as F |
|
from transformers import PreTrainedModel, PreTrainedTokenizer |
|
|
|
from .configuration_emu import EmuConfig |
|
from .constants import * |
|
from .modeling_llama import LlamaForCausalLM |
|
from .visual import EVAVisionTransformer |
|
|
|
|
|
class EmuPreTrainedModel(PreTrainedModel): |
|
config_class = EmuConfig |
|
base_model_prefix = "model" |
|
supports_gradient_checkpointing = False |
|
_no_split_modules = ["LlamaDecoderLayer", "Block"] |
|
_skip_keys_device_placement = "past_key_values" |
|
|
|
def _init_weights(self, module): |
|
std = self.config.initializer_range |
|
if isinstance(module, nn.Linear): |
|
module.weight.data.normal_(mean=0.0, std=std) |
|
if module.bias is not None: |
|
module.bias.data.zero_() |
|
elif isinstance(module, nn.Embedding): |
|
module.weight.data.normal_(mean=0.0, std=std) |
|
if module.padding_idx is not None: |
|
module.weight.data[module.padding_idx].zero_() |
|
|
|
class EmuForClsAndRegression(EmuPreTrainedModel): |
|
|
|
def __init__(self, config): |
|
super(EmuForClsAndRegression, self).__init__(config) |
|
|
|
self.lm = LlamaForCausalLM(config=config) |
|
|
|
self.lm.model.embed_tokens.padding_idx = config.pad_token_id |
|
|
|
def get_num_layers(self): |
|
return len(self.lm.model.layers) |
|
|
|
class EmuModel(EmuPreTrainedModel): |
|
|
|
def __init__(self, config): |
|
super().__init__(config) |
|
|
|
vision_config = Namespace(**config.vision_config) |
|
|
|
self.visual = EVAVisionTransformer( |
|
img_size=vision_config.image_size, |
|
patch_size=vision_config.patch_size, |
|
embed_dim=vision_config.width, |
|
depth=vision_config.layers, |
|
num_heads=vision_config.width // vision_config.head_width, |
|
mlp_ratio=vision_config.mlp_ratio, |
|
qkv_bias=vision_config.qkv_bias, |
|
drop_path_rate=vision_config.drop_path_rate, |
|
norm_layer=partial(nn.LayerNorm, eps=vision_config.layer_norm_eps), |
|
xattn=vision_config.xattn, |
|
postnorm=vision_config.postnorm, |
|
) |
|
|
|
self.decoder = EmuForClsAndRegression(config) |
|
|
|
self.gradient_checkpointing = False |
|
|
|
self.n_query = vision_config.n_query |
|
self.v_query = vision_config.v_query |
|
|
|
@property |
|
def device(self): |
|
return next(iter(self.parameters())).device |
|
|
|
@property |
|
def dtype(self): |
|
return next(iter(self.parameters())).dtype |
|
|
|
@torch.no_grad() |
|
def encode_image(self, image: torch.Tensor, *, n_query=None): |
|
n_query = n_query if n_query is not None else self.n_query |
|
|
|
image_embeds = self.visual(image) |
|
image_embeds = image_embeds[:, 1:, :] |
|
b, n, c = image_embeds.shape |
|
sqrt_n = int(n**0.5) |
|
image_embeds = image_embeds.permute(0, 2, 1).view(b, c, sqrt_n, sqrt_n) |
|
|
|
stride = int(sqrt_n // (n_query ** 0.5)) |
|
image_embeds = F.avg_pool2d(image_embeds, kernel_size=(stride, stride), stride=stride) |
|
image_embeds = image_embeds.view(b, c, -1).permute(0, 2, 1).contiguous() |
|
return image_embeds |
|
|
|
|
|
class EmuForCausalLM(EmuPreTrainedModel): |
|
_auto_class = "AutoModelForCausalLM" |
|
|
|
def __init__(self, config): |
|
super().__init__(config) |
|
|
|
self.config = config |
|
self.model = EmuModel(config) |
|
|
|
self.project_down = nn.Linear(config.hidden_size, config.d_model, bias=False) |
|
|
|
self.project_up = nn.Linear(config.d_model, config.hidden_size, bias=False) |
|
|
|
self.n_query = self.model.n_query |
|
self.image_placeholder = DEFAULT_IMG_TOKEN + DEFAULT_IMAGE_TOKEN * self.n_query + DEFAULT_IMG_END_TOKEN |
|
|
|
def device(self, module=None): |
|
if module is None: |
|
return next(self.parameters()).device |
|
return next(module.parameters()).device |
|
|
|
def dtype(self, module): |
|
if module is None: |
|
return next(self.parameters()).dtype |
|
return next(module.parameters()).dtype |
|
|
|
@torch.no_grad() |
|
def generate_image( |
|
self, |
|
text: List[str], |
|
tokenizer: PreTrainedTokenizer, |
|
image: Optional[torch.Tensor] = None, |
|
placeholder: str = DEFAULT_IMG_PLACEHOLDER, |
|
): |
|
IMAGE, BOI = tokenizer.convert_tokens_to_ids([DEFAULT_IMAGE_TOKEN, DEFAULT_IMG_TOKEN]) |
|
if image is not None: |
|
prompt_image_embeds = self.model.encode_image(image) |
|
_, _, c = prompt_image_embeds.shape |
|
prompt_image_embeds = prompt_image_embeds.view(-1, c) |
|
prompt_image_embeds = self.project_up(prompt_image_embeds) |
|
|
|
text = [t.replace(placeholder, self.image_placeholder) for t in text] |
|
|
|
target_image_embeds = None |
|
for num_img_token in range(self.n_query): |
|
if num_img_token == 0: |
|
text = [f"{t}{DEFAULT_IMG_TOKEN}" for t in text] |
|
else: |
|
text = [f"{t}{DEFAULT_IMAGE_TOKEN}" for t in text] |
|
|
|
inputs = tokenizer(text, padding="longest", return_tensors="pt") |
|
device = self.device(self.model.decoder.lm.model.embed_tokens) |
|
attention_mask = inputs.attention_mask.to(device) |
|
input_ids = inputs.input_ids.to(device) |
|
|
|
text_embeds = self.model.decoder.lm.model.embed_tokens(input_ids) |
|
|
|
image_idx = (input_ids == IMAGE) |
|
cumsum_idx = torch.flip(torch.cumsum(torch.flip(image_idx, dims=[1]), dim=1), dims=[1]) |
|
if image is not None: |
|
prompt_idx = torch.logical_and(image_idx, cumsum_idx > num_img_token) |
|
text_embeds[prompt_idx] = prompt_image_embeds.to(text_embeds.device) |
|
|
|
if target_image_embeds is not None: |
|
target_idx = torch.logical_and(image_idx, torch.logical_and(cumsum_idx > 0, cumsum_idx <= num_img_token)) |
|
text_embeds[target_idx] = self.project_up(target_image_embeds).to(text_embeds.device) |
|
|
|
outputs = self.model.decoder.lm.model( |
|
inputs_embeds=text_embeds, |
|
attention_mask=attention_mask, |
|
output_hidden_states=True, |
|
return_dict=True, |
|
) |
|
|
|
image_idx = (input_ids == IMAGE) + (input_ids == BOI) |
|
cumsum_idx = torch.flip(torch.cumsum(torch.flip(image_idx, dims=[1]), dim=1), dims=[1]) |
|
target_idx = torch.logical_and(image_idx, torch.logical_and(cumsum_idx > 0, cumsum_idx <= num_img_token+1)) |
|
|
|
hidden_states = outputs.hidden_states[-1] |
|
target_image_embeds = hidden_states[target_idx.to(hidden_states.device)] |
|
target_image_embeds = target_image_embeds.view(-1, target_image_embeds.shape[-1]) |
|
target_image_embeds = self.project_down(target_image_embeds) |
|
|
|
_, C = target_image_embeds.shape |
|
B = hidden_states.shape[0] |
|
target_image_embeds = target_image_embeds.view(B, -1, C) |
|
|
|
return target_image_embeds |
|
|
|
|
