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import math
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import torch
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import torch.nn as nn
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from transformers import CLIPVisionModel, PretrainedConfig
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from transformers import CLIPVisionConfig
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from transformers.utils import logging
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from datetime import datetime
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logger = logging.get_logger(__name__)
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CLIP_VIT_LARGE_PATCH14_336_CONFIG = CLIPVisionConfig(
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attention_dropout=0.0,
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dropout=0.0,
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hidden_act="quick_gelu",
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hidden_size=1024,
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image_size=336,
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initializer_factor=1.0,
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initializer_range=0.02,
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intermediate_size=4096,
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layer_norm_eps=1e-05,
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num_attention_heads=16,
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num_channels=3,
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num_hidden_layers=24,
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patch_size=14,
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projection_dim=768
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)
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class Phi3ImageEmbedding(nn.Module):
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"""Phi3 Image embedding."""
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def __init__(self, config: PretrainedConfig, wte=None, **kwargs) -> None:
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super().__init__()
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hidden_size = config.n_embd if hasattr(config, 'n_embd') else config.hidden_size
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if hasattr(config, 'embd_pdrop') or hasattr(config, 'embed_pdrop'):
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embd_drop = config.embd_pdrop if hasattr(config, 'embd_pdrop') else config.embed_pdrop
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self.drop = nn.Dropout(embd_drop)
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else:
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self.drop = None
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self.wte = wte
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if isinstance(config.img_processor, dict) and config.img_processor.get('name', None) == 'clip_vision_model':
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assert 'model_name' in config.img_processor, 'model_name must be provided for CLIPVisionModel'
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assert 'image_dim_out' in config.img_processor, 'image_dim_out must be provided for CLIPVisionModel'
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assert 'num_img_tokens' in config.img_processor, 'num_img_tokens must be provided for CLIPVisionModel'
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assert config.img_processor['model_name'] == 'openai/clip-vit-large-patch14-336'
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clip_config = CLIP_VIT_LARGE_PATCH14_336_CONFIG
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self.img_processor = CLIPVisionModel(clip_config)
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image_dim_out = config.img_processor['image_dim_out']
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self.num_img_tokens = config.img_processor['num_img_tokens']
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else:
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raise NotImplementedError(f'img_processor = {config.img_processor}, not implemented')
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self.image_dim_out = image_dim_out
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self.img_sizes = None
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self.use_hd_transform = kwargs.get('use_hd_transform', False)
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self.with_learnable_separator = kwargs.get('with_learnable_separator', False)
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self.hd_transform_order = kwargs.get('hd_transform_order', 'glb_sub')
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assert self.use_hd_transform == self.with_learnable_separator, 'use_hd_transform and with_learnable_separator should have same value'
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if self.with_learnable_separator:
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assert self.use_hd_transform, 'learnable separator is only for hd transform'
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self.glb_GN = nn.Parameter(torch.zeros([1, 1, self.image_dim_out * 4]))
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self.sub_GN = nn.Parameter(torch.zeros([1, 1, 1, self.image_dim_out * 4]))
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logger.info(f'learnable separator enabled for hd transform, hd_transform_order = {self.hd_transform_order}')
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projection_cls = kwargs.get('projection_cls', 'linear')
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if projection_cls == 'linear':
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self.img_projection = nn.Linear(image_dim_out, hidden_size)
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elif projection_cls == 'mlp' and self.use_hd_transform:
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dim_projection = hidden_size
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depth = 2
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layers = [nn.Linear(image_dim_out * 4, dim_projection)]
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for _ in range(1, depth):
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layers.extend([nn.GELU(),
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nn.Linear(dim_projection, dim_projection)])
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self.img_projection = nn.Sequential(*layers)
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elif projection_cls == 'mlp':
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dim_projection = hidden_size
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depth = 2
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layers = [nn.Linear(image_dim_out, dim_projection)]
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for _ in range(1, depth):
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layers.extend([nn.GELU(),
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nn.Linear(dim_projection, dim_projection)])
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self.img_projection = nn.Sequential(*layers)
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else:
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raise NotImplementedError(f'projection_cls = {projection_cls}, not implemented')
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self.vocab_size = config.vocab_size
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self.img_features = None
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if isinstance(config.img_processor, dict):
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self.layer_idx = config.img_processor.get('layer_idx', -2)
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self.type_feature = config.img_processor.get('type_feature', 'patch')
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else:
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self.layer_idx = -2
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self.type_feature = 'patch'
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def set_img_features(self, img_features: torch.FloatTensor) -> None:
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self.img_features = img_features
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def set_img_sizes(self, img_sizes: torch.LongTensor) -> None:
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self.img_sizes = img_sizes
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def get_img_features(self, img_embeds: torch.FloatTensor) -> torch.FloatTensor:
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LAYER_IDX = self.layer_idx
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TYPE_FEATURE = self.type_feature
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img_processor_output = self.img_processor(img_embeds, output_hidden_states=True)
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img_feature = img_processor_output.hidden_states[LAYER_IDX]
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if TYPE_FEATURE == "patch":
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patch_feature = img_feature[:, 1:]
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return patch_feature
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if TYPE_FEATURE == "cls_patch":
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return img_feature
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raise NotImplementedError
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def forward(self, input_ids: torch.LongTensor, pixel_values: torch.FloatTensor, image_sizes=None) -> torch.FloatTensor:
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MAX_INPUT_ID = int(1e9)
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img_embeds = pixel_values
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img_sizes = image_sizes
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if self.img_features is not None:
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img_embeds = self.img_features.clone()
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self.img_features = None
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if self.img_sizes is not None:
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img_sizes = self.img_sizes
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input_shape = input_ids.size()
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input_ids = input_ids.view(-1, input_shape[-1])
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with torch.no_grad():
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positions = torch.nonzero((input_ids < 0) & (input_ids > -MAX_INPUT_ID), as_tuple=False)
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select = False
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if isinstance(self.img_projection, nn.Sequential):
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target_device = self.img_projection[0].bias.device
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target_dtype = self.img_projection[0].bias.dtype
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else:
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target_device = self.img_projection.bias.device
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target_dtype = self.img_projection.bias.dtype
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if len(positions.tolist()) > 0:
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with torch.no_grad():
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g_values = abs(input_ids[positions[:, 0], positions[:, 1]])
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if self.use_hd_transform and img_sizes is not None and len(img_sizes):
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hd_transform = True
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assert img_embeds.ndim == 5, f'img_embeds size: {img_embeds.size()}, expect 5D tensor for hd transform'
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start_time = datetime.now()
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bs = img_embeds.shape[0]
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img_features = self.get_img_features(img_embeds.flatten(0, 1))
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base_feat_height = base_feat_width = int(img_features.shape[1] ** 0.5)
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assert base_feat_height == 24 and base_feat_width == 24, f'base_feat_height: {base_feat_height}, base_feat_width: {base_feat_width}, expect 24x24 features for hd transform'
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img_features = img_features.view(bs, -1, base_feat_height * base_feat_width, self.image_dim_out)
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C = self.image_dim_out
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H = base_feat_height
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output_imgs = []
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output_len = []
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if isinstance(img_sizes, torch.Tensor):
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img_sizes = img_sizes.view(-1, 2)
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for _bs in range(bs):
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h, w = img_sizes[_bs]
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h = h // 336
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w = w // 336
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B_ = h * w
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global_img_feature = img_features[_bs, :1]
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glb_img = global_img_feature.reshape(1,H,H,C).reshape(1,H//2,2,H//2,2,C).contiguous().permute(0,1,3,2,4,5).reshape(1,H//2,H//2,4*C).contiguous()
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temp_glb_GN = self.sub_GN.repeat(1, H//2, 1, 1)
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glb_img = torch.cat([glb_img, temp_glb_GN], dim=2).reshape(1,-1,4*C)
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sub_img = img_features[_bs, 1:]
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sub_img = sub_img[:B_]
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sub_img = sub_img.reshape(B_,H,H,C).reshape(B_,H//2,2,H//2,2,C).contiguous().permute(0,1,3,2,4,5).reshape(B_,-1,4*C).contiguous()
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sub_img = sub_img.reshape(1, h, w, 12, 12, -1).permute(0,1,3,2,4,5).reshape(1,h*12,w*12,4*C)
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temp_sub_GN = self.sub_GN.repeat(1, h*12, 1, 1)
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sub_img = torch.cat([sub_img, temp_sub_GN], dim=2).reshape(1,-1,4*C)
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if self.hd_transform_order == 'glb_sub':
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output_imgs.append(torch.cat([glb_img, self.glb_GN, sub_img], dim=1))
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elif self.hd_transform_order == 'sub_glb':
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output_imgs.append(torch.cat([sub_img, self.glb_GN, glb_img], dim=1))
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else:
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raise NotImplementedError(f'hd_transform_order = {self.hd_transform_order}, not implemented')
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temp_len = int((h*w+1)*144 + 1 + (h+1)*12)
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assert temp_len == output_imgs[-1].shape[1], f'temp_len: {temp_len}, output_imgs[-1].shape[1]: {output_imgs[-1].shape[1]}'
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output_len.append(temp_len)
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num_img_tokens = output_len
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img_set_tensor = []
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for _output_img in output_imgs:
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img_feature_proj = self.img_projection(_output_img.to(target_device).to(target_dtype))
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img_set_tensor.append(img_feature_proj)
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logger.info(f'img_embeds size: {img_embeds.size()}, image sizes: {img_sizes} loading time {datetime.now() - start_time}')
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elif img_embeds.ndim == 4:
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selected_g_values = g_values[::self.num_img_tokens]
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assert len(img_embeds) == len(selected_g_values), f'img_embeds size: {img_embeds.size()}, selected_g_values size: {len(selected_g_values)}, selected_g_value {selected_g_values}'
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start_time = datetime.now()
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tt = (
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self.get_img_features(img_embeds)
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.to(target_device)
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.to(target_dtype)
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.reshape(-1, self.image_dim_out)
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)
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logger.info(f'img_embeds size: {img_embeds.size()}, loading time {datetime.now() - start_time}')
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img_set_tensor = self.img_projection(tt)
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elif img_embeds.ndim == 3:
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selected_g_values = g_values[::self.num_img_tokens]
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assert len(img_embeds) == len(selected_g_values), f'img_embeds size: {img_embeds.size()}, selected_g_values size: {len(selected_g_values)}, selected_g_value {selected_g_values}'
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tt = (
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img_embeds
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.to(target_device)
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.to(target_dtype)
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.view(-1, self.image_dim_out)
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)
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img_set_tensor = self.img_projection(tt)
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else:
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raise NotImplementedError
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select = True
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with torch.no_grad():
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input_ids.clamp_min_(0).clamp_max_(self.vocab_size)
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hidden_states = self.wte(input_ids)
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if select:
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if hd_transform:
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idx = 0
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for i, cnt in enumerate(num_img_tokens):
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hidden_states[positions[idx, 0], positions[idx, 1] : positions[idx, 1] + cnt] = (
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img_set_tensor[i]
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.to(hidden_states.dtype)
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.to(hidden_states.device)
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)
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idx += cnt
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else:
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idx = 0
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assert len(selected_g_values) * self.num_img_tokens == len(img_set_tensor), f'len(selected_g_values) * self.num_img_tokens = {len(selected_g_values) * self.num_img_tokens}, len(img_set_tensor) = {len(img_set_tensor)}'
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for i, g in enumerate(selected_g_values):
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cnt = self.num_img_tokens
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hidden_states[positions[idx, 0], positions[idx, 1] : positions[idx, 1] + cnt] = (
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img_set_tensor[i * cnt : (i + 1) * cnt]
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.to(hidden_states.dtype)
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.to(hidden_states.device)
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)
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idx += cnt
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if self.drop is not None:
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hidden_states = self.drop(hidden_states)
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return hidden_states
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