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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 abc import ABC, abstractmethod | |
import torch | |
import torch.nn as nn | |
from .multimodal_encoder.builder import build_vision_tower | |
from .multimodal_resampler.builder import build_vision_resampler | |
from .multimodal_projector.builder import build_vision_projector | |
from llava.constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN | |
from llava.mm_utils import get_anyres_image_grid_shape | |
from llava.utils import rank0_print | |
class LlavaMetaModel: | |
def __init__(self, config): | |
super(LlavaMetaModel, self).__init__(config) | |
if hasattr(config, "mm_vision_tower"): | |
delay_load = getattr(config, "delay_load", False) | |
self.vision_tower = build_vision_tower(config, delay_load=delay_load) | |
self.vision_resampler = build_vision_resampler(config, vision_tower=self.vision_tower) | |
self.mm_projector = build_vision_projector(config, vision_cfg=self.vision_tower.config) | |
if "unpad" in getattr(config, "mm_patch_merge_type", ""): | |
self.image_newline = nn.Parameter(torch.empty(config.hidden_size, dtype=self.dtype)) | |
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): | |
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 | |
self.config.vision_tower_pretrained = getattr(model_args, "vision_tower_pretrained", "") | |
if self.get_vision_tower() is None: | |
vision_tower = build_vision_tower(model_args) | |
vision_resampler = build_vision_resampler(model_args, vision_tower=vision_tower) | |
for k, v in vision_resampler.config.items(): | |
setattr(self.config, k, v) | |
if fsdp is not None and len(fsdp) > 0: | |
self.vision_tower = [vision_tower] | |
self.vision_resampler = [vision_resampler] | |
else: | |
self.vision_tower = vision_tower | |
self.vision_resampler = vision_resampler | |
else: | |
if fsdp is not None and len(fsdp) > 0: | |
vision_resampler = self.vision_resampler[0] | |
vision_tower = self.vision_tower[0] | |
else: | |
vision_resampler = self.vision_resampler | |
vision_tower = self.vision_tower | |
vision_tower.load_model() | |
# In case it is frozen by LoRA | |
for p in self.vision_resampler.parameters(): | |
p.requires_grad = True | |
self.config.use_mm_proj = True | |
self.config.mm_projector_type = getattr(model_args, "mm_projector_type", "linear") | |
self.config.mm_hidden_size = getattr(vision_resampler, "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, vision_cfg=vision_tower.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) | |
else: | |
# In case it is frozen by LoRA | |
for p in self.mm_projector.parameters(): | |
p.requires_grad = True | |
if pretrain_mm_mlp_adapter is not 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} | |
incompatible_keys = self.mm_projector.load_state_dict(get_w(mm_projector_weights, "mm_projector")) | |
rank0_print(f"Loaded mm projector weights from {pretrain_mm_mlp_adapter}. Incompatible keys: {incompatible_keys}") | |
incompatible_keys = self.vision_resampler.load_state_dict(get_w(mm_projector_weights, "vision_resampler"), strict=False) | |
rank0_print(f"Loaded vision resampler weights from {pretrain_mm_mlp_adapter}. Incompatible keys: {incompatible_keys}") | |
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 the image (height, width). | |
Returns: | |
torch.Tensor: The unpadded image tensor. | |
""" | |
original_width, original_height = original_size | |
current_height, current_width = tensor.shape[1:] | |
# Compute aspect ratios | |
original_aspect_ratio = original_width / original_height | |
current_aspect_ratio = current_width / current_height | |
# Determine padding size and direction | |
if original_aspect_ratio > current_aspect_ratio: | |
# Padding was added to the height | |
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: | |
# Padding was added to the width | |
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 LlavaMetaForCausalLM(ABC): | |
def get_model(self): | |
pass | |
def get_vision_tower(self): | |
return self.get_model().get_vision_tower() | |
def encode_images(self, images): | |
image_features = self.get_model().get_vision_tower()(images) | |
image_features = self.get_model().vision_resampler(image_features, images=images) | |
image_features = self.get_model().mm_projector(image_features) | |
return image_features | |
def prepare_inputs_labels_for_multimodal(self, input_ids, position_ids, attention_mask, past_key_values, labels, images, image_sizes=None): | |
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) | |
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) | |
mm_patch_merge_type = getattr(self.config, "mm_patch_merge_type", "flat") | |
image_aspect_ratio = getattr(self.config, "image_aspect_ratio", "square") | |
if mm_patch_merge_type == "flat": | |
image_features = [x.flatten(0, 1) for x in image_features] | |
elif mm_patch_merge_type.startswith("spatial"): | |
new_image_features = [] | |
for image_idx, image_feature in enumerate(image_features): | |
# FIXME: now assume the image is square, and split to 2x2 patches | |
# num_patches = h * w, where h = w = sqrt(num_patches) | |
# currently image_feature is a tensor of shape (4, num_patches, hidden_size) | |
# we want to first unflatten it to (2, 2, h, w, hidden_size) | |
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 image_aspect_ratio == "anyres": | |
if hasattr(self.get_vision_tower(), "image_size"): | |
vision_tower_image_size = self.get_vision_tower().image_size | |
else: | |
raise ValueError("vision_tower_image_size is not found in the vision tower.") | |
num_patch_width, num_patch_height = get_anyres_image_grid_shape(image_sizes[image_idx], self.config.image_grid_pinpoints, vision_tower_image_size) | |
image_feature = image_feature.view(num_patch_height, num_patch_width, height, width, -1) | |
else: | |
image_feature = image_feature.view(2, 2, height, width, -1) | |
if "maxpool2x2" 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 = nn.functional.max_pool2d(image_feature, 2) | |
image_feature = image_feature.flatten(1, 2).transpose(0, 1) | |
elif "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) | |
if "nobase" in mm_patch_merge_type: | |
pass | |
else: | |
image_feature = torch.cat((base_image_feature, image_feature), dim=0) | |
else: | |
image_feature = image_feature[0] | |
if "unpad" in mm_patch_merge_type: | |
image_feature = torch.cat((image_feature, self.model.image_newline[None]), dim=0) | |
new_image_features.append(image_feature) | |
image_features = new_image_features | |
else: | |
raise ValueError(f"Unexpected mm_patch_merge_type: {self.config.mm_patch_merge_type}") | |
else: | |
image_features = self.encode_images(images) | |
# 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_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 = [] | |
for i in range(num_images + 1): | |
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_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) | |
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): | |
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 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 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 | |