# 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 abc import ABC, abstractmethod import math import torch import torch.nn as nn import torch.nn.functional as F import torch.distributed as dist from .multimodal_encoder.builder import build_vision_tower from .multimodal_projector.builder import build_vision_projector #This has been modified for hf implementation from .constants import (IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, DEFAULT_REGION_FEA_TOKEN) #This has been modified for hf implementation from .mm_utils import get_anyres_image_grid_shape import os def rand_sample(x, max_len): if x.shape[0] <= max_len: return x else: rand_idx = torch.randperm(x.shape[0])[:max_len] return x[rand_idx, :] def rand_sample_repeat(x, max_len): if x.shape[0] < max_len: indices = torch.randint(0, x.shape[0], (max_len-x.shape[0],)) # pdb.set_trace() return torch.cat((x, x[indices]), dim=0) elif x.shape[0] == max_len: return x else: rand_idx = torch.randperm(x.shape[0])[:max_len] return x[rand_idx, :] def point_sample(input, point_coords, return_dtype, **kwargs): """ A wrapper around :function:`torch.nn.functional.grid_sample` to support 3D point_coords tensors. Unlike :function:`torch.nn.functional.grid_sample` it assumes `point_coords` to lie inside [0, 1] x [0, 1] square. Args: input (Tensor): A tensor of shape (N, C, H, W) that contains features map on a H x W grid. point_coords (Tensor): A tensor of shape (N, P, 2) or (N, Hgrid, Wgrid, 2) that contains [0, 1] x [0, 1] normalized point coordinates. Returns: output (Tensor): A tensor of shape (N, C, P) or (N, C, Hgrid, Wgrid) that contains features for points in `point_coords`. The features are obtained via bilinear interplation from `input` the same way as :function:`torch.nn.functional.grid_sample`. """ add_dim = False if point_coords.dim() == 3: add_dim = True point_coords = point_coords.unsqueeze(2) # output = F.grid_sample(input, 2.0 * point_coords - 1.0, **kwargs) output = F.grid_sample(input.float(), (2.0 * point_coords - 1.0).float(), **kwargs) output = output.to(return_dtype) if add_dim: output = output.squeeze(3) return output def farthest_point_sample(xyz, npoint): """ Input: xyz: pointcloud data, [B, N, 2] npoint: number of samples Return: centroids: sampled pointcloud index, [B, npoint] """ device = xyz.device B, N, C = xyz.shape centroids = torch.zeros(B, npoint, dtype=torch.long).to(device) distance = torch.ones(B, N).to(device) * 1e10 farthest = torch.randint(0, N, (B,), dtype=torch.long).to(device) batch_indices = torch.arange(B, dtype=torch.long).to(device) for i in range(npoint): centroids[:, i] = farthest centroid = xyz[batch_indices, farthest, :].view(B, 1, 2) dist = torch.sum((xyz - centroid) ** 2, -1) distance = torch.min(distance, dist) farthest = torch.max(distance, -1)[1] return centroids def index_points(points, idx): """ Input: points: input points data, [B, N, C] idx: sample index data, [B, S] Return: new_points:, indexed points data, [B, S, C] """ device = points.device B = points.shape[0] view_shape = list(idx.shape) view_shape[1:] = [1] * (len(view_shape) - 1) repeat_shape = list(idx.shape) repeat_shape[0] = 1 batch_indices = torch.arange(B, dtype=torch.long).to(device).view(view_shape).repeat(repeat_shape) new_points = points[batch_indices, idx, :] return new_points def square_distance(src, dst): """ Calculate Euclid distance between each two points. src^T * dst = xn * xm + yn * ym + zn * zm； sum(src^2, dim=-1) = xn*xn + yn*yn + zn*zn; sum(dst^2, dim=-1) = xm*xm + ym*ym + zm*zm; dist = (xn - xm)^2 + (yn - ym)^2 + (zn - zm)^2 = sum(src**2,dim=-1)+sum(dst**2,dim=-1)-2*src^T*dst Input: src: source points, [B, N, C] dst: target points, [B, M, C] Output: dist: per-point square distance, [B, N, M] """ B, N, _ = src.shape _, M, _ = dst.shape dist = -2 * torch.matmul(src, dst.permute(0, 2, 1)) dist += torch.sum(src ** 2, -1).view(B, N, 1) dist += torch.sum(dst ** 2, -1).view(B, 1, M) return dist def knn_point(nsample, xyz, new_xyz): """ Input: nsample: max sample number in local region xyz: all points, [B, N, C] new_xyz: query points, [B, S, C] Return: group_idx: grouped points index, [B, S, nsample] """ sqrdists = square_distance(new_xyz, xyz) _, group_idx = torch.topk(sqrdists, nsample, dim=-1, largest=False, sorted=False) return group_idx class ConvReLULN1D(nn.Module): def __init__(self, in_channels, out_channels, kernel_size=1, bias=True): super(ConvReLULN1D, self).__init__() self.act = nn.ReLU(inplace=True) self.net = nn.Sequential( nn.Conv1d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, bias=bias), self.act ) self.norm = nn.LayerNorm(out_channels) def forward(self, x): # (B, C, N) -> (B, C_1, N) x = self.net(x) x = x.permute(0, 2, 1) x = self.norm(x) x = x.permute(0, 2, 1) return x def normal_init(module, mean=0, std=1, bias=0): if hasattr(module, 'weight') and module.weight is not None: nn.init.normal_(module.weight, mean, std) if hasattr(module, 'bias') and module.bias is not None: nn.init.constant_(module.bias, bias) class GeoRegionSampler(nn.Module): def __init__(self, input_dim, output_dim, num_init_point, num_sub_point, num_neighbor, pooler_mode='mean'): super(GeoRegionSampler, self).__init__() self.input_dim = input_dim self.output_dim = output_dim self.num_init_point = num_init_point self.num_sub_point = num_sub_point self.num_neighbor = num_neighbor self.diff_projector_list = nn.ModuleList() self.agg_projector_list = nn.ModuleList() self.pooler_list = nn.ModuleList() for ii in range(len(num_sub_point)): self.diff_projector_list.append(nn.Linear(self.input_dim + 2, self.input_dim + 2)) self.agg_projector_list.append(ConvReLULN1D(in_channels=2*(self.input_dim + 2), out_channels=self.input_dim, )) if pooler_mode == 'mean': self.pooler_list.append(nn.AvgPool1d(kernel_size=num_neighbor[ii])) elif pooler_mode =='max': self.pooler_list.append(nn.AdaptiveMaxPool1d(output_size=1)) else: raise NotImplementedError(f'{self.pooler_mode} is not supported.') self.flatten_projector = nn.Linear(self.input_dim * num_sub_point[-1], self.input_dim) self.dim_projector = nn.Linear(self.input_dim, self.output_dim) # self.dim_projector = nn.Sequential(*[ # nn.Linear(self.input_dim, self.output_dim), # nn.GELU(), # nn.Linear(self.output_dim, self.output_dim) # ]) self.norm_init_weights() # self.dtype = torch.float32 def norm_init_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): normal_init(m, 0, 0.01) def forward(self, feature_map, region_masks, original_dtype, return_dtype): assert len(feature_map) == len(region_masks) all_points = [] all_points_fea = [] all_points_img_ids = [] # Sample points and their features for img_idx, (region_feature_map_i, region_masks_list_i) in enumerate(zip(feature_map, region_masks)): if len(region_masks_list_i) != 0: # (w, h) ori_image_wh = torch.tensor([region_masks_list_i[0].shape[0], region_masks_list_i[0].shape[1]], device=region_masks_list_i[0].device)[None,] # list of elements of shape [num_sample_point, 2] cur_non_zero_pos = [rand_sample_repeat((m.nonzero()/ori_image_wh), self.num_init_point) for m in region_masks_list_i] # list -> [num_mask, num_sample_point, 2] cur_non_zero_pos = torch.stack(cur_non_zero_pos) # [HxW, C] -> [H, W, C] -> [C, H, W] -> [N, C, H, W] if region_feature_map_i.ndim == 2: h = w = int(math.sqrt(region_feature_map_i.shape[0])) c = region_feature_map_i.shape[-1] region_feature_map_i = region_feature_map_i.reshape(h, w, c) else: assert region_feature_map_i.ndim == 3 dup_region_feature_map_i = region_feature_map_i.permute(2, 0, 1) dup_region_feature_map_i = dup_region_feature_map_i.unsqueeze(0).repeat(cur_non_zero_pos.shape[0], 1, 1, 1) # [num_mask, C, H, W] x [num_mask, num_sample_point, 2] -> [num_mask, C, num_sample_point] -> [num_mask, num_sample_point, C] # F.grid_sample doesn't support BF16. Need to tranform into float32 then transform back. dup_region_feature_map_i_ori_type = dup_region_feature_map_i.to(original_dtype) region_feature_i = point_sample(dup_region_feature_map_i_ori_type, cur_non_zero_pos.flip(dims=(2,)).type(original_dtype), return_dtype, align_corners=True, ) # region_feature_i = region_feature_i.to(dup_region_feature_map_i.dtype) region_feature_i = region_feature_i.transpose(-2, -1) cur_img_ids = [img_idx] * len(cur_non_zero_pos) # save to global list all_points.append(cur_non_zero_pos) all_points_fea.append(region_feature_i) all_points_img_ids.extend(cur_img_ids) # No region found, return list of None. if len(all_points) == 0: return [None] * len(region_masks) all_points = torch.cat(all_points, dim=0).to(return_dtype) # [B*num_mask, num_sample_point, 2] all_points_fea = torch.cat(all_points_fea, dim=0) # [B*num_mask, num_sample_point, C] all_points_img_ids = torch.tensor(all_points_img_ids, device=all_points_fea.device) assert all_points_fea.shape[:-1] == all_points_fea.shape[:-1] # Processing. for stage_i in range(len(self.num_sub_point)): cur_num_sub_point = self.num_sub_point[stage_i] cur_num_neighbor = self.num_neighbor[stage_i] all_points = all_points.contiguous() # xy [btach, points, xy] fps_idx = farthest_point_sample(all_points, cur_num_sub_point).long() new_points = index_points(all_points, fps_idx) # [B, npoint, 2] new_points_fea = index_points(all_points_fea, fps_idx) # [B, npoint, d] idx = knn_point(cur_num_neighbor, all_points, new_points) grouped_points = index_points(all_points, idx) # [B, npoint, k, 2] grouped_points_fea = index_points(all_points_fea, idx) # [B, npoint, k, d] local_points_fea = torch.cat([grouped_points_fea, grouped_points],dim=-1) # [B, npoint, k, d+2] anchor_points_fea = torch.cat([new_points_fea, new_points],dim=-1).unsqueeze(-2) diff_points_fea = local_points_fea-anchor_points_fea diff_points_fea = self.diff_projector_list[stage_i](diff_points_fea) gather_points_fea = torch.cat([diff_points_fea, anchor_points_fea.repeat(1, 1, cur_num_neighbor, 1)], dim=-1) # [B, npoint, k, 2(d+2)] b, n, s, d = gather_points_fea.size() gather_points_fea = gather_points_fea.permute(0, 1, 3, 2) # [B, npoint, 2(d+2), k] gather_points_fea = gather_points_fea.reshape(-1, d, s) # [B*npoint, 2(d+2), k] gather_points_fea = self.agg_projector_list[stage_i](gather_points_fea) # [B*npoint, d, k] batch_size, new_dim, _ = gather_points_fea.size() gather_points_fea = self.pooler_list[stage_i](gather_points_fea).view(batch_size, new_dim) # [B*npoint, d] gather_points_fea = gather_points_fea.reshape(b, n, -1) # [B, npoint, d] all_points = new_points all_points_fea = gather_points_fea x = all_points_fea.flatten(1, -1) # [B, npoint x d] x = self.flatten_projector(x) all_region_fea = self.dim_projector(x) # [B, d] output_region_fea = [] for img_idx in range(len(region_masks)): cur_mask = all_points_img_ids == img_idx if not cur_mask.any(): output_region_fea.append(None) else: output_region_fea.append(all_region_fea[cur_mask]) return output_region_fea class FerretMetaModel: def __init__(self, config): super(FerretMetaModel, self).__init__(config) self.max_sample_point = 512 if hasattr(config, "mm_vision_tower"): self.vision_tower = build_vision_tower(config, delay_load=True) self.mm_projector = build_vision_projector(config) if 'unpad' in getattr(config, 'mm_patch_merge_type', ''): self.image_newline = nn.Parameter( torch.empty(config.hidden_size, dtype=self.dtype) ) if hasattr(config, "region_fea_adapter"): self.region_fea_adapter = nn.Linear(config.mm_hidden_size, config.hidden_size) if hasattr(config, "region_geo_sampler"): if getattr(config, 'mm_patch_merge_type', 'flat').startswith('spatial'): self.region_geo_sampler = GeoRegionSampler(input_dim=config.mm_hidden_size, output_dim=config.hidden_size, num_init_point=self.max_sample_point, num_sub_point=[128, 32], num_neighbor=[24, 24], pooler_mode=config.sampler_pooler_mode ) else: self.region_geo_sampler = GeoRegionSampler(input_dim=config.mm_hidden_size, output_dim=config.hidden_size, num_init_point=self.max_sample_point, num_sub_point=[128, 32], num_neighbor=[24, 24], pooler_mode=config.sampler_pooler_mode ) def get_vision_tower(self): vision_tower = getattr(self, 'vision_tower', None) if type(vision_tower) is list: vision_tower = vision_tower[0] return vision_tower def initialize_vision_modules(self, model_args, fsdp=None, add_region_feature=False, region_geo_sampler=False, sampler_pooler_mode='mean', ): vision_tower = model_args.vision_tower mm_vision_select_layer = model_args.mm_vision_select_layer mm_vision_select_feature = model_args.mm_vision_select_feature pretrain_mm_mlp_adapter = model_args.pretrain_mm_mlp_adapter mm_patch_merge_type = model_args.mm_patch_merge_type self.config.mm_vision_tower = vision_tower if self.get_vision_tower() is None: vision_tower = build_vision_tower(model_args) if fsdp is not None and len(fsdp) > 0: self.vision_tower = [vision_tower] else: self.vision_tower = vision_tower else: if fsdp is not None and len(fsdp) > 0: vision_tower = self.vision_tower[0] else: vision_tower = self.vision_tower vision_tower.load_model() self.config.use_mm_proj = True self.config.mm_projector_type = getattr(model_args, 'mm_projector_type', 'linear') self.config.mm_hidden_size = vision_tower.hidden_size self.config.mm_vision_select_layer = mm_vision_select_layer self.config.mm_vision_select_feature = mm_vision_select_feature self.config.mm_patch_merge_type = mm_patch_merge_type if getattr(self, 'mm_projector', None) is None: self.mm_projector = build_vision_projector(self.config) if 'unpad' in mm_patch_merge_type: embed_std = 1 / torch.sqrt(torch.tensor(self.config.hidden_size, dtype=self.dtype)) self.image_newline = nn.Parameter( torch.randn(self.config.hidden_size, dtype=self.dtype) * embed_std ) if add_region_feature: if region_geo_sampler: self.config.region_geo_sampler = True self.config.sampler_pooler_mode = sampler_pooler_mode if not hasattr(self, 'region_geo_sampler'): if mm_patch_merge_type.startswith('spatial'): # === if feature is concated === # self.region_geo_sampler = GeoRegionSampler(input_dim=self.config.mm_hidden_size * 2, # output_dim=self.config.hidden_size, # num_init_point=self.max_sample_point, # num_sub_point=[128, 32], # num_neighbor=[24, 24], # pooler_mode=sampler_pooler_mode # ) # === if feature is added === self.region_geo_sampler = GeoRegionSampler(input_dim=self.config.mm_hidden_size, output_dim=self.config.hidden_size, num_init_point=self.max_sample_point, num_sub_point=[128, 32], num_neighbor=[24, 24], pooler_mode=sampler_pooler_mode ) else: self.region_geo_sampler = GeoRegionSampler(input_dim=self.config.mm_hidden_size, output_dim=self.config.hidden_size, num_init_point=self.max_sample_point, num_sub_point=[128, 32], num_neighbor=[24, 24], pooler_mode=sampler_pooler_mode ) else: self.config.region_fea_adapter = True if not hasattr(self, 'region_fea_adapter'): self.region_fea_adapter = nn.Linear(self.config.mm_hidden_size, self.config.hidden_size) else: # In case it is frozen by LoRA for p in self.mm_projector.parameters(): p.requires_grad = True # print(f"pretrain mm mlp adapter: {type(pretrain_mm_mlp_adapter)}") # String if pretrain_mm_mlp_adapter is not None and pretrain_mm_mlp_adapter != "None": mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu') def get_w(weights, keyword): return {k.split(keyword + '.')[1]: v for k, v in weights.items() if keyword in k} self.mm_projector.load_state_dict(get_w(mm_projector_weights, 'mm_projector')) def unpad_image(tensor, original_size): """ Unpads a PyTorch tensor of a padded and resized image. Args: tensor (torch.Tensor): The image tensor, assumed to be in CxHxW format. original_size (tuple): The original size of PIL image (width, height). Returns: torch.Tensor: The unpadded image tensor. """ original_width, original_height = original_size current_height, current_width = tensor.shape[1:] original_aspect_ratio = original_width / original_height current_aspect_ratio = current_width / current_height if original_aspect_ratio > current_aspect_ratio: scale_factor = current_width / original_width new_height = int(original_height * scale_factor) padding = (current_height - new_height) // 2 unpadded_tensor = tensor[:, padding:current_height - padding, :] else: scale_factor = current_height / original_height new_width = int(original_width * scale_factor) padding = (current_width - new_width) // 2 unpadded_tensor = tensor[:, :, padding:current_width - padding] return unpadded_tensor class FerretMetaForCausalLM(ABC): @abstractmethod def get_model(self): pass def get_vision_tower(self): return self.get_model().get_vision_tower() def encode_images(self, images, region_flag=False, region_geo_sampler=False): image_features = self.get_model().get_vision_tower()(images) projected_image_features = self.get_model().mm_projector(image_features) if region_flag: if region_geo_sampler: new_region_feature_map = image_features else: new_region_feature_map = self.get_model().region_fea_adapter(image_features) else: new_region_feature_map = None return image_features, projected_image_features, new_region_feature_map def extract_region_feature(self, region_feature_map, region_masks, original_dtype, return_dtype): all_region_features = [] assert len(region_feature_map) == len(region_masks) for region_feature_map_i, region_masks_list_i in zip(region_feature_map, region_masks): if len(region_masks_list_i) == 0: all_region_features.append(None) else: # (w, h) ori_image_wh = torch.tensor([region_masks_list_i[0].shape[0], region_masks_list_i[0].shape[1]], device=region_masks_list_i[0].device)[None,] # list of elements of shape [num_sample_point, 2] non_zero_pos = [rand_sample((m.nonzero()/ori_image_wh), self.get_model().max_sample_point) for m in region_masks_list_i] # [num_mask, num_sample_point(padded), 2] non_zero_pos = nn.utils.rnn.pad_sequence(non_zero_pos, padding_value=-1, batch_first=True) non_zero_pos_mask = ~(non_zero_pos.sum(dim=-1) < 0) # [HxW, C] -> [H, W, C] -> [C, H, W] -> [N, C, H, W] h = w = int(math.sqrt(region_feature_map_i.shape[0])) c = region_feature_map_i.shape[-1] dup_region_feature_map_i = region_feature_map_i.reshape(h, w, c).permute(2, 0, 1) dup_region_feature_map_i = dup_region_feature_map_i.unsqueeze(0).repeat(non_zero_pos.shape[0], 1, 1, 1) # [num_mask, C, H, W] x [num_mask, num_sample_point(padded), 2] -> [num_mask, C, num_sample_point(padded)] # F.grid_sample doesn't support BF16. Need to tranform into float32 then transform back. dup_region_feature_map_i_ori_type = dup_region_feature_map_i.to(original_dtype) # pdb.set_trace() region_feature_i = point_sample(dup_region_feature_map_i_ori_type, non_zero_pos.flip(dims=(2,)).type(original_dtype), return_dtype, align_corners=True ) region_feature_i = region_feature_i.to(dup_region_feature_map_i.dtype) # [num_mask, C] region_feature_i = torch.stack([x[m].mean(dim=0) for x, m in zip(region_feature_i.transpose(1,2), non_zero_pos_mask)]).nan_to_num() all_region_features.append(region_feature_i) return all_region_features def prepare_inputs_labels_for_multimodal( self, input_ids, position_ids, attention_mask, past_key_values, labels, images, image_sizes=None, region_masks=None ): if region_masks is not None: region_flag = True else: region_flag = False region_geo_sampler = region_flag and getattr(self.config, 'region_geo_sampler', False) vision_tower = self.get_vision_tower() if vision_tower is None or images is None or input_ids.shape[1] == 1: return input_ids, position_ids, attention_mask, past_key_values, None, labels if type(images) is list or images.ndim == 5: if type(images) is list: images = [x.unsqueeze(0) if x.ndim == 3 else x for x in images] concat_images = torch.cat([image for image in images], dim=0) raw_image_features, image_features, region_feature_map = self.encode_images(concat_images, region_flag=region_flag, region_geo_sampler=region_geo_sampler) split_sizes = [image.shape[0] for image in images] image_features = torch.split(image_features, split_sizes, dim=0) if region_flag: region_feature_maps = torch.split(region_feature_map, split_sizes, dim=0) # (#images, #patches, h*w, c) # ======== This is for only taking the global image feature map for referring ====== # region_feature_map = torch.split(region_feature_map, split_sizes, dim=0) # first_region_feature_map = [x[0:1] for x in region_feature_map] # region_feature_map = torch.cat(first_region_feature_map, dim=0) mm_patch_merge_type = getattr(self.config, 'mm_patch_merge_type', 'flat') image_aspect_ratio = getattr(self.config, 'image_aspect_ratio', 'square_nocrop') if mm_patch_merge_type == 'flat': image_features = [x.flatten(0, 1) for x in image_features] # TODO: here we use the first feature map default for each batch (global feaure map) for referring first_region_feature_map = [x[0:1] for x in region_feature_map] region_feature_map = torch.cat(first_region_feature_map, dim=0) # (#images, h, w, c) elif mm_patch_merge_type.startswith('spatial'): new_image_features = [] new_region_features = [] for image_idx, image_feature in enumerate(image_features): if image_feature.shape[0] > 1: base_image_feature = image_feature[0] image_feature = image_feature[1:] height = width = self.get_vision_tower().num_patches_per_side assert height * width == base_image_feature.shape[0] if region_flag: cur_region_feature_map = region_feature_maps[image_idx] # (#patches, h*w, c) cur_region_feature_map = cur_region_feature_map.view(cur_region_feature_map.shape[0], height, width, cur_region_feature_map.shape[-1]) # (#patches, h, w, c) base_region_feature = cur_region_feature_map[0] region_feature = cur_region_feature_map[1:] # pdb.set_trace() if image_aspect_ratio == 'anyres': num_patch_width, num_patch_height = get_anyres_image_grid_shape(image_sizes[image_idx], self.config.image_grid_pinpoints, self.get_vision_tower().config.image_size) image_feature = image_feature.view(num_patch_height, num_patch_width, height, width, -1) if region_flag: region_feature = region_feature.view(num_patch_height, num_patch_width, height, width, -1) else: raise NotImplementedError if 'unpad' in mm_patch_merge_type: image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous() image_feature = image_feature.flatten(1, 2).flatten(2, 3) image_feature = unpad_image(image_feature, image_sizes[image_idx]) image_feature = torch.cat(( image_feature, self.model.image_newline[:, None, None].expand(*image_feature.shape[:-1], 1).to(image_feature.device) ), dim=-1) image_feature = image_feature.flatten(1, 2).transpose(0, 1) else: image_feature = image_feature.permute(0, 2, 1, 3, 4).contiguous() image_feature = image_feature.flatten(0, 3) image_feature = torch.cat((base_image_feature, image_feature), dim=0) if region_flag: region_feature = region_feature.permute(0, 2, 1, 3, 4).contiguous() # (patch_h, patch_w, h, w, c) -> (patch_h, h, patch_w, w, c) region_feature = region_feature.flatten(0, 1).flatten(1, 2) # (patch_h, h, patch_w, w, c) -> (all_h, all_w, c) # Tranform dtype, if using pytorch2.1+, no need to do this. base_region_feature = base_region_feature.to(dtype=torch.float32) base_region_feature_resized = F.interpolate(base_region_feature.unsqueeze(0).permute(0, 3, 1, 2), (region_feature.shape[0], region_feature.shape[1])) # (1, c, all_h, all_w) base_region_feature_resized = base_region_feature_resized.to(region_feature.dtype) base_region_feature_resized = base_region_feature_resized.squeeze(0).permute(1, 2, 0) # (all_h, all_w, c) # === Add: new_region_feature = base_region_feature_resized + region_feature # === Concat: A bit lower, 1/3 more GPU memory consumption. # new_region_feature = torch.cat((base_region_feature_resized, region_feature), dim=2) # (all_h, all_w, 2c) else: image_feature = image_feature[0] if 'unpad' in mm_patch_merge_type: image_feature = torch.cat(( image_feature, self.model.image_newline[None].to(image_feature.device) ), dim=0) if region_flag: new_region_feature = region_feature_maps[image_idx][0] # (h, w, c) new_image_features.append(image_feature) if region_flag: new_region_features.append(new_region_feature) # pdb.set_trace() image_features = new_image_features if region_flag: # region_feature_map = torch.stack(new_region_features, dim=0) # (#images, h, w, c or 2c) region_feature_map = new_region_features # pdb.set_trace() else: raise ValueError(f"Unexpected mm_patch_merge_type: {self.config.mm_patch_merge_type}") else: raw_image_features, image_features, region_feature_map = self.encode_images(images, region_flag=region_flag, region_geo_sampler=region_geo_sampler) if region_flag: assert len(region_masks) == len(input_ids) for img_idx, (cur_input_id, cur_region_mask) in enumerate(zip(input_ids, region_masks)): cur_region_token_num = (cur_input_id == self.config.im_region_fea_token).sum() if cur_region_token_num != len(cur_region_mask): print('Found regions cropped because of text beyond max_len, removed them.') region_masks[img_idx] = cur_region_mask[:cur_region_token_num] # dump_region_mask = torch.zeros(100, 100).to(device='cuda') dump_region_mask = torch.zeros(100, 100, device='cuda') dump_region_mask[10:20, 10:20] = 1 dump_region_masks = [[dump_region_mask.clone()]] for _ in range(len(region_feature_map)-1): dump_region_masks.append([]) if region_geo_sampler: if type(image_features) is list: region_features = self.get_model().region_geo_sampler(region_feature_map, region_masks, original_dtype=raw_image_features.dtype, return_dtype=image_features[0].dtype) dump_region_features = self.get_model().region_geo_sampler(region_feature_map, dump_region_masks, original_dtype=raw_image_features.dtype, return_dtype=image_features[0].dtype) else: region_features = self.get_model().region_geo_sampler(region_feature_map, region_masks, original_dtype=raw_image_features.dtype, return_dtype=image_features.dtype) dump_region_features = self.get_model().region_geo_sampler(region_feature_map, dump_region_masks, original_dtype=raw_image_features.dtype, return_dtype=image_features.dtype) else: if type(image_features) is list: region_features = self.extract_region_feature(region_feature_map, region_masks, original_dtype=raw_image_features.dtype, return_dtype=image_features[0].dtype) dump_region_features = self.extract_region_feature(region_feature_map, dump_region_masks, original_dtype=raw_image_features.dtype, return_dtype=image_features[0].dtype) else: region_features = self.extract_region_feature(region_feature_map, region_masks, original_dtype=raw_image_features.dtype, return_dtype=image_features.dtype) dump_region_features = self.extract_region_feature(region_feature_map, dump_region_masks, original_dtype=raw_image_features.dtype, return_dtype=image_features.dtype) # assert len(dump_region_features) == 1 assert len([df for df in dump_region_features if df is not None]) == 1 assert len(dump_region_features[0]) == 1 assert len(region_features) == len(input_ids) # TODO: image start / end is not implemented here to support pretraining. if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False): raise NotImplementedError # Let's just add dummy tensors if they do not exist, # it is a headache to deal with None all the time. # But it is not ideal, and if you have a better idea, # please open an issue / submit a PR, thanks. _labels = labels _position_ids = position_ids _attention_mask = attention_mask if attention_mask is None: attention_mask = torch.ones_like(input_ids, dtype=torch.bool) else: attention_mask = attention_mask.bool() if position_ids is None: position_ids = torch.arange(0, input_ids.shape[1], dtype=torch.long, device=input_ids.device) if labels is None: labels = torch.full_like(input_ids, IGNORE_INDEX) # remove the padding using attention_mask -- FIXME _input_ids = input_ids input_ids = [cur_input_ids[cur_attention_mask] for cur_input_ids, cur_attention_mask in zip(input_ids, attention_mask)] labels = [cur_labels[cur_attention_mask] for cur_labels, cur_attention_mask in zip(labels, attention_mask)] new_input_embeds = [] new_labels = [] cur_image_idx = 0 for batch_idx, cur_input_ids in enumerate(input_ids): num_images = (cur_input_ids == IMAGE_TOKEN_INDEX).sum() if num_images == 0: cur_image_features = image_features[cur_image_idx] cur_input_embeds_1 = self.get_model().embed_tokens(cur_input_ids) cur_input_embeds = torch.cat([cur_input_embeds_1, cur_image_features[0:0]], dim=0) new_input_embeds.append(cur_input_embeds) new_labels.append(labels[batch_idx]) cur_image_idx += 1 continue image_token_indices = [-1] + torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0].tolist() + [cur_input_ids.shape[0]] cur_input_id_with_im = [] cur_input_ids_noim = [] cur_labels = labels[batch_idx] cur_labels_noim = [] for i in range(len(image_token_indices) - 1): cur_input_ids_noim.append(cur_input_ids[image_token_indices[i]+1:image_token_indices[i+1]]) cur_labels_noim.append(cur_labels[image_token_indices[i]+1:image_token_indices[i+1]]) split_sizes = [x.shape[0] for x in cur_labels_noim] cur_input_embeds = self.get_model().embed_tokens(torch.cat(cur_input_ids_noim)) cur_input_embeds_no_im = torch.split(cur_input_embeds, split_sizes, dim=0) cur_new_input_embeds = [] cur_new_labels = [] assert len(cur_input_ids_noim) == len(cur_input_embeds_no_im) for i in range(num_images + 1): cur_input_id_with_im.append(cur_input_ids_noim[i]) cur_new_input_embeds.append(cur_input_embeds_no_im[i]) cur_new_labels.append(cur_labels_noim[i]) if i < num_images: cur_image_features = image_features[cur_image_idx] cur_image_idx += 1 cur_input_id_with_im.append(torch.full((cur_image_features.shape[0],), IMAGE_TOKEN_INDEX, device=cur_labels.device, dtype=cur_labels.dtype)) cur_new_input_embeds.append(cur_image_features) cur_new_labels.append(torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=cur_labels.device, dtype=cur_labels.dtype)) cur_new_input_embeds = [x.to(self.device) for x in cur_new_input_embeds] cur_new_input_embeds = torch.cat(cur_new_input_embeds) cur_new_labels = torch.cat(cur_new_labels) cur_input_id_with_im = torch.cat(cur_input_id_with_im) assert len(cur_input_id_with_im) == len(cur_new_input_embeds) # Add region feature into text feature embeddings. # Currently only support one image in each input. assert batch_idx+1 == cur_image_idx if region_flag and region_features[batch_idx] is not None: region_embs = torch.zeros_like(cur_new_input_embeds) region_replace_mask = (cur_input_id_with_im == self.config.im_region_fea_token) # region_embs[region_replace_mask] = region_features[batch_idx].to(cur_new_input_embeds.dtype) if len(region_embs[region_replace_mask]) != len(region_features[batch_idx]): # ("Found a region cropped in text") region_embs[region_replace_mask] = region_features[batch_idx][:len(region_embs[region_replace_mask])].to(cur_new_input_embeds.dtype) else: region_embs[region_replace_mask] = region_features[batch_idx].to(cur_new_input_embeds.dtype) cur_new_input_embeds = cur_new_input_embeds * (~region_replace_mask).to(cur_new_input_embeds.dtype)[:, None] + region_embs else: if hasattr(self.config, 'im_region_fea_token'): assert (cur_input_id_with_im == self.config.im_region_fea_token).sum() == 0 # Add dump region feature to input embedding, to make sure the gradient for region sampler always exist when open region_flag. if region_flag: # cur_new_input_embeds[0] = cur_new_input_embeds[0] + 0 * dump_region_features[0, 0].to(cur_new_input_embeds.dtype) cur_new_input_embeds[0] = cur_new_input_embeds[0] + 0.0 * dump_region_features[0][0].to(cur_new_input_embeds.dtype) new_input_embeds.append(cur_new_input_embeds) new_labels.append(cur_new_labels) # Truncate sequences to max length as image embeddings can make the sequence longer tokenizer_model_max_length = getattr(self.config, 'tokenizer_model_max_length', None) if tokenizer_model_max_length is not None: new_input_embeds = [x[:tokenizer_model_max_length] for x in new_input_embeds] new_labels = [x[:tokenizer_model_max_length] for x in new_labels] # Combine them max_len = max(x.shape[0] for x in new_input_embeds) batch_size = len(new_input_embeds) new_input_embeds_padded = [] new_labels_padded = torch.full((batch_size, max_len), IGNORE_INDEX, dtype=new_labels[0].dtype, device=new_labels[0].device) attention_mask = torch.zeros((batch_size, max_len), dtype=attention_mask.dtype, device=attention_mask.device) position_ids = torch.zeros((batch_size, max_len), dtype=position_ids.dtype, device=position_ids.device) for i, (cur_new_embed, cur_new_labels) in enumerate(zip(new_input_embeds, new_labels)): cur_len = cur_new_embed.shape[0] if getattr(self.config, 'tokenizer_padding_side', 'right') == "left": new_input_embeds_padded.append(torch.cat(( torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device), cur_new_embed ), dim=0)) if cur_len > 0: new_labels_padded[i, -cur_len:] = cur_new_labels attention_mask[i, -cur_len:] = True position_ids[i, -cur_len:] = torch.arange(0, cur_len, dtype=position_ids.dtype, device=position_ids.device) else: new_input_embeds_padded.append(torch.cat(( cur_new_embed, torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device) ), dim=0)) if cur_len > 0: new_labels_padded[i, :cur_len] = cur_new_labels attention_mask[i, :cur_len] = True position_ids[i, :cur_len] = torch.arange(0, cur_len, dtype=position_ids.dtype, device=position_ids.device) new_input_embeds = torch.stack(new_input_embeds_padded, dim=0) if _labels is None: new_labels = None else: new_labels = new_labels_padded if _attention_mask is None: attention_mask = None else: attention_mask = attention_mask.to(dtype=_attention_mask.dtype) if _position_ids is None: position_ids = None return None, position_ids, attention_mask, past_key_values, new_input_embeds, new_labels def initialize_vision_tokenizer(self, model_args, tokenizer, add_region_feature=False): if model_args.mm_use_im_patch_token: tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True) self.resize_token_embeddings(len(tokenizer)) if add_region_feature: region_token_id = tokenizer.convert_tokens_to_ids([DEFAULT_REGION_FEA_TOKEN])[0] # If region_token doesn't exist, add it. if region_token_id == tokenizer.unk_token_id: num_region_fea_tokens = tokenizer.add_tokens([DEFAULT_REGION_FEA_TOKEN], special_tokens=True) self.config.im_region_fea_token = tokenizer.convert_tokens_to_ids([DEFAULT_REGION_FEA_TOKEN])[0] self.resize_token_embeddings(len(tokenizer)) if model_args.mm_use_im_start_end: num_new_tokens = tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True) self.resize_token_embeddings(len(tokenizer)) if add_region_feature: num_new_tokens = num_new_tokens + num_region_fea_tokens if num_new_tokens > 0: input_embeddings = self.get_input_embeddings().weight.data output_embeddings = self.get_output_embeddings().weight.data input_embeddings_avg = input_embeddings[:-num_new_tokens].mean( dim=0, keepdim=True) output_embeddings_avg = output_embeddings[:-num_new_tokens].mean( dim=0, keepdim=True) input_embeddings[-num_new_tokens:] = input_embeddings_avg output_embeddings[-num_new_tokens:] = output_embeddings_avg if model_args.tune_mm_mlp_adapter: for p in self.get_input_embeddings().parameters(): p.requires_grad = True for p in self.get_output_embeddings().parameters(): p.requires_grad = False if model_args.pretrain_mm_mlp_adapter: mm_projector_weights = torch.load(model_args.pretrain_mm_mlp_adapter, map_location='cpu') embed_tokens_weight = mm_projector_weights['model.embed_tokens.weight'] assert num_new_tokens == 2 if input_embeddings.shape == embed_tokens_weight.shape: input_embeddings[-num_new_tokens:] = embed_tokens_weight[-num_new_tokens:] elif embed_tokens_weight.shape[0] == num_new_tokens: input_embeddings[-num_new_tokens:] = embed_tokens_weight else: raise ValueError(f"Unexpected embed_tokens_weight shape. Pretrained: {embed_tokens_weight.shape}. Current: {input_embeddings.shape}. Numer of new tokens: {num_new_tokens}.") elif model_args.mm_use_im_patch_token: if model_args.tune_mm_mlp_adapter: for p in self.get_input_embeddings().parameters(): p.requires_grad = False for p in self.get_output_embeddings().parameters(): p.requires_grad = False