import math from dataclasses import dataclass from typing import List, Optional, Tuple, Union import numpy as np import torch import torch.utils.checkpoint from torch import nn from transformers.configuration_utils import PretrainedConfig from .configuration_omchat import OmChatConfig from transformers import Qwen2Config, Qwen2Model, Qwen2ForCausalLM, AutoConfig, AutoModelForCausalLM from transformers.utils import logging from transformers.modeling_outputs import ModelOutput from transformers.utils import ( add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "OmChatConfig" from typing import Optional, Tuple, Union import torch import torch.nn.functional as F import torch.utils.checkpoint from einops import rearrange from timm.models.layers import DropPath from torch import nn from transformers.activations import ACT2FN from transformers.modeling_outputs import (BaseModelOutput, BaseModelOutputWithPooling) from transformers.modeling_utils import PreTrainedModel from transformers.utils import logging from .configuration_omchat import InternVisionConfig #try: #from .flash_attention import FlashAttention has_flash_attn = True #except: # print('FlashAttention is not installed.') # has_flash_attn = False from einops import rearrange try: # v1 from flash_attn.flash_attn_interface import \ flash_attn_unpadded_qkvpacked_func except: # v2 from flash_attn.flash_attn_interface import flash_attn_varlen_qkvpacked_func as flash_attn_unpadded_qkvpacked_func from flash_attn.bert_padding import pad_input, unpad_input logger = logging.get_logger(__name__) class FlashAttention(nn.Module): """Implement the scaled dot product attention with softmax. Arguments --------- softmax_scale: The temperature to use for the softmax attention. (default: 1/sqrt(d_keys) where d_keys is computed at runtime) attention_dropout: The dropout rate to apply to the attention (default: 0.0) """ def __init__(self, softmax_scale=None, attention_dropout=0.0, device=None, dtype=None): super().__init__() self.softmax_scale = softmax_scale self.dropout_p = attention_dropout def forward(self, qkv, key_padding_mask=None, causal=False, cu_seqlens=None, max_s=None, need_weights=False): """Implements the multihead softmax attention. Arguments --------- qkv: The tensor containing the query, key, and value. (B, S, 3, H, D) if key_padding_mask is None if unpadded: (nnz, 3, h, d) key_padding_mask: a bool tensor of shape (B, S) """ assert not need_weights assert qkv.dtype in [torch.float16, torch.bfloat16] assert qkv.is_cuda if cu_seqlens is None: batch_size = qkv.shape[0] seqlen = qkv.shape[1] if key_padding_mask is None: qkv = rearrange(qkv, 'b s ... -> (b s) ...') max_s = seqlen cu_seqlens = torch.arange(0, (batch_size + 1) * seqlen, step=seqlen, dtype=torch.int32, device=qkv.device) output = flash_attn_unpadded_qkvpacked_func( qkv, cu_seqlens, max_s, self.dropout_p if self.training else 0.0, softmax_scale=self.softmax_scale, causal=causal ) output = rearrange(output, '(b s) ... -> b s ...', b=batch_size) else: nheads = qkv.shape[-2] x = rearrange(qkv, 'b s three h d -> b s (three h d)') x_unpad, indices, cu_seqlens, max_s = unpad_input(x, key_padding_mask) x_unpad = rearrange(x_unpad, 'nnz (three h d) -> nnz three h d', three=3, h=nheads) output_unpad = flash_attn_unpadded_qkvpacked_func( x_unpad, cu_seqlens, max_s, self.dropout_p if self.training else 0.0, softmax_scale=self.softmax_scale, causal=causal ) output = rearrange(pad_input(rearrange(output_unpad, 'nnz h d -> nnz (h d)'), indices, batch_size, seqlen), 'b s (h d) -> b s h d', h=nheads) else: assert max_s is not None output = flash_attn_unpadded_qkvpacked_func( qkv, cu_seqlens, max_s, self.dropout_p if self.training else 0.0, softmax_scale=self.softmax_scale, causal=causal ) return output, None class InternRMSNorm(nn.Module): def __init__(self, hidden_size, eps=1e-6): super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, hidden_states): input_dtype = hidden_states.dtype hidden_states = hidden_states.to(torch.float32) variance = hidden_states.pow(2).mean(-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) return self.weight * hidden_states.to(input_dtype) try: from apex.normalization import FusedRMSNorm InternRMSNorm = FusedRMSNorm # noqa logger.info('Discovered apex.normalization.FusedRMSNorm - will use it instead of InternRMSNorm') except ImportError: # using the normal InternRMSNorm pass except Exception: logger.warning('discovered apex but it failed to load, falling back to InternRMSNorm') pass class InternVisionEmbeddings(nn.Module): def __init__(self, config: InternVisionConfig): super().__init__() self.config = config self.embed_dim = config.hidden_size self.image_size = config.image_size self.patch_size = config.patch_size self.class_embedding = nn.Parameter( torch.randn(1, 1, self.embed_dim), ) self.patch_embedding = nn.Conv2d( in_channels=3, out_channels=self.embed_dim, kernel_size=self.patch_size, stride=self.patch_size ) self.num_patches = (self.image_size // self.patch_size) ** 2 self.num_positions = self.num_patches + 1 self.position_embedding = nn.Parameter(torch.randn(1, self.num_positions, self.embed_dim)) def _get_pos_embed(self, pos_embed, H, W): target_dtype = pos_embed.dtype pos_embed = pos_embed.float().reshape( 1, self.image_size // self.patch_size, self.image_size // self.patch_size, -1).permute(0, 3, 1, 2) pos_embed = F.interpolate(pos_embed, size=(H, W), mode='bicubic', align_corners=False).\ reshape(1, -1, H * W).permute(0, 2, 1).to(target_dtype) return pos_embed def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor: target_dtype = self.patch_embedding.weight.dtype patch_embeds = self.patch_embedding(pixel_values) # shape = [*, channel, width, height] batch_size, _, height, width = patch_embeds.shape patch_embeds = patch_embeds.flatten(2).transpose(1, 2) class_embeds = self.class_embedding.expand(batch_size, 1, -1).to(target_dtype) embeddings = torch.cat([class_embeds, patch_embeds], dim=1) position_embedding = torch.cat([ self.position_embedding[:, :1, :], self._get_pos_embed(self.position_embedding[:, 1:, :], height, width) ], dim=1) embeddings = embeddings + position_embedding.to(target_dtype) return embeddings class InternAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__(self, config: InternVisionConfig): super().__init__() self.config = config self.embed_dim = config.hidden_size self.num_heads = config.num_attention_heads self.use_flash_attn = config.use_flash_attn and has_flash_attn if config.use_flash_attn and not has_flash_attn: print('Warning: Flash Attention is not available, use_flash_attn is set to False.') self.head_dim = self.embed_dim // self.num_heads if self.head_dim * self.num_heads != self.embed_dim: raise ValueError( f'embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:' f' {self.num_heads}).' ) self.scale = self.head_dim ** -0.5 self.qkv = nn.Linear(self.embed_dim, 3 * self.embed_dim, bias=config.qkv_bias) self.attn_drop = nn.Dropout(config.attention_dropout) self.proj_drop = nn.Dropout(config.dropout) self.qk_normalization = config.qk_normalization if self.qk_normalization: self.q_norm = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps) self.k_norm = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps) if self.use_flash_attn: self.inner_attn = FlashAttention(attention_dropout=config.attention_dropout) self.proj = nn.Linear(self.embed_dim, self.embed_dim) def _naive_attn(self, x): B, N, C = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple) if self.qk_normalization: B_, H_, N_, D_ = q.shape q = self.q_norm(q.transpose(1, 2).flatten(-2, -1)).view(B_, N_, H_, D_).transpose(1, 2) k = self.k_norm(k.transpose(1, 2).flatten(-2, -1)).view(B_, N_, H_, D_).transpose(1, 2) attn = ((q * self.scale) @ k.transpose(-2, -1)) attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) x = (attn @ v).transpose(1, 2).reshape(B, N, C) x = self.proj(x) x = self.proj_drop(x) return x def _flash_attn(self, x, key_padding_mask=None, need_weights=False): qkv = self.qkv(x) qkv = rearrange(qkv, 'b s (three h d) -> b s three h d', three=3, h=self.num_heads) if self.qk_normalization: q, k, v = qkv.unbind(2) q = self.q_norm(q.flatten(-2, -1)).view(q.shape) k = self.k_norm(k.flatten(-2, -1)).view(k.shape) qkv = torch.stack([q, k, v], dim=2) context, _ = self.inner_attn( qkv, key_padding_mask=key_padding_mask, need_weights=need_weights, causal=False ) outs = self.proj(rearrange(context, 'b s h d -> b s (h d)')) outs = self.proj_drop(outs) return outs def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: x = self._naive_attn(hidden_states) if not self.use_flash_attn else self._flash_attn(hidden_states) return x class InternMLP(nn.Module): def __init__(self, config: InternVisionConfig): super().__init__() self.config = config self.act = ACT2FN[config.hidden_act] self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size) self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.fc1(hidden_states) hidden_states = self.act(hidden_states) hidden_states = self.fc2(hidden_states) return hidden_states class InternVisionEncoderLayer(nn.Module): def __init__(self, config: InternVisionConfig, drop_path_rate: float): super().__init__() self.embed_dim = config.hidden_size self.intermediate_size = config.intermediate_size self.attn = InternAttention(config) self.mlp = InternMLP(config) self.norm1 = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps) self.norm2 = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps) self.ls1 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim)) self.ls2 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim)) self.drop_path1 = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity() self.drop_path2 = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity() def forward( self, hidden_states: torch.Tensor, ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor], Optional[Tuple[torch.FloatTensor]]]: """ Args: hidden_states (`Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]`): input to the layer of shape `(batch, seq_len, embed_dim)` """ hidden_states = hidden_states + self.drop_path1(self.attn(self.norm1(hidden_states)) * self.ls1) hidden_states = hidden_states + self.drop_path2(self.mlp(self.norm2(hidden_states)) * self.ls2) return hidden_states class InternVisionEncoder(nn.Module): """ Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a [`InternEncoderLayer`]. Args: config (`InternConfig`): The corresponding vision configuration for the `InternEncoder`. """ def __init__(self, config: InternVisionConfig): super().__init__() self.config = config # stochastic depth decay rule dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, config.num_hidden_layers)] self.layers = nn.ModuleList([ InternVisionEncoderLayer(config, dpr[idx]) for idx in range(config.num_hidden_layers)]) self.gradient_checkpointing = True def forward( self, inputs_embeds, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutput]: r""" Args: inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Embedded representation of the inputs. Should be float, not int tokens. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ 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 encoder_states = () if output_hidden_states else None hidden_states = inputs_embeds for idx, encoder_layer in enumerate(self.layers): if output_hidden_states: encoder_states = encoder_states + (hidden_states,) if self.gradient_checkpointing and self.training: layer_outputs = torch.utils.checkpoint.checkpoint( encoder_layer, hidden_states) else: layer_outputs = encoder_layer( hidden_states, ) hidden_states = layer_outputs if output_hidden_states: encoder_states = encoder_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, encoder_states] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=encoder_states ) class InternVisionModel(PreTrainedModel): main_input_name = 'pixel_values' config_class = InternVisionConfig _no_split_modules=["InternVisionEncoderLayer"] def __init__(self, config: InternVisionConfig): super().__init__(config) self.config = config self.embeddings = InternVisionEmbeddings(config) self.encoder = InternVisionEncoder(config) def resize_pos_embeddings(self, old_size, new_size, patch_size): pos_emb = self.embeddings.position_embedding _, num_positions, embed_dim = pos_emb.shape cls_emb = pos_emb[:, :1, :] pos_emb = pos_emb[:, 1:, :].reshape(1, old_size // patch_size, old_size // patch_size, -1).permute(0, 3, 1, 2) pos_emb = F.interpolate(pos_emb.float(), size=new_size // patch_size, mode='bicubic', align_corners=False) pos_emb = pos_emb.to(cls_emb.dtype).reshape(1, embed_dim, -1).permute(0, 2, 1) pos_emb = torch.cat([cls_emb, pos_emb], dim=1) self.embeddings.position_embedding = nn.Parameter(pos_emb) logger.info('Resized position embeddings from {} to {}'.format(old_size, new_size)) def get_input_embeddings(self): return self.embeddings def forward( self, pixel_values: Optional[torch.FloatTensor] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, pixel_embeds: Optional[torch.FloatTensor] = None, ) -> Union[Tuple, BaseModelOutputWithPooling]: 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 if pixel_values is None and pixel_embeds is None: raise ValueError('You have to specify pixel_values or pixel_embeds') if pixel_embeds is not None: hidden_states = pixel_embeds else: if len(pixel_values.shape) == 4: hidden_states = self.embeddings(pixel_values) else: raise ValueError(f'wrong pixel_values size: {pixel_values.shape}') encoder_outputs = self.encoder( inputs_embeds=hidden_states, output_hidden_states=output_hidden_states, return_dict=return_dict, ) last_hidden_state = encoder_outputs.last_hidden_state pooled_output = last_hidden_state[:, 0, :] if not return_dict: return (last_hidden_state, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=last_hidden_state, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size): """ Calculate the shape of the image patch grid after the preprocessing for images of any resolution. Args: image_size (`tuple`): The size of the input image in the format (width, height). grid_pinpoints (`List`): A list containing possible resolutions. Each item in the list should be a tuple or list of the form `(height, width)`. patch_size (`int`): The size of each image patch. Returns: tuple: The shape of the image patch grid in the format (width, height). """ if not isinstance(grid_pinpoints, list): raise TypeError("grid_pinpoints should be a list of tuples or lists") # ! VERY IMPORTANT if image_size is tensor, must convert to into tuple, otherwise it will cause wrong calculate if not isinstance(image_size, (list, tuple)): if not isinstance(image_size, (torch.Tensor, np.ndarray)): raise TypeError( f"image_size invalid type: {type(image_size)} not valid, should be either list, tuple, np.ndarray or tensor" ) image_size = image_size.tolist() height, width = select_best_resolution(image_size, grid_pinpoints) return height // patch_size, width // patch_size def image_size_to_num_patches(image_size, grid_pinpoints, patch_size: int): """ Calculate the number of patches after the preprocessing for images of any resolution. Args: image_size (`torch.LongTensor` or `np.ndarray` or `Tuple[int, int]`): The size of the input image in the format (height, width). ? grid_pinpoints (`List`): A list containing possible resolutions. Each item in the list should be a tuple or list of the form `(height, width)`. patch_size (`int`): The size of each image patch. Returns: int: the number of patches """ if not isinstance(grid_pinpoints, list): raise TypeError("grid_pinpoints should be a list of tuples or lists") # ! VERY IMPORTANT if image_size is tensor, must convert to into tuple, otherwise it will cause wrong calculate if not isinstance(image_size, (list, tuple)): if not isinstance(image_size, (torch.Tensor, np.ndarray)): raise TypeError(f"image_size invalid type {type(image_size)} with value {image_size}") image_size = image_size.tolist() best_resolution = select_best_resolution(image_size, grid_pinpoints) height, width = best_resolution num_patches = 0 # consider change to ceil(height/patch_size)*ceil(width/patch_size) + 1 for i in range(0, height, patch_size): for j in range(0, width, patch_size): num_patches += 1 # add the base patch num_patches += 1 return num_patches 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 of shape (num_channels, height, width). original_size (`tuple`): The original size of the image (height, width). Returns: `torch.Tensor`: The unpadded image tensor. """ original_height, original_width = 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 @dataclass # Copied from transformers.models.idefics.modeling_idefics.IdeficsCausalLMOutputWithPast with Idefics->OmChat class OmChatCausalLMOutputWithPast(ModelOutput): """ Base class for OmChat causal language model (or autoregressive) outputs. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Language modeling loss (for next-token prediction). logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. image_hidden_states (`tuple(torch.FloatTensor)`, *optional*): Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, num_images, sequence_length, hidden_size)`. image_hidden_states of the model produced by the vision encoder, and optionally by the perceiver """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None past_key_values: Optional[List[torch.FloatTensor]] = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None image_hidden_states: Optional[Tuple[torch.FloatTensor]] = None # Copied from transformers.models.llava.modeling_llava.LlavaMultiModalProjector with Llava->OmChat class OmChatMultiModalProjector(nn.Module): def __init__(self, config: OmChatConfig): super().__init__() self.linear_1 = nn.Linear(config.vision_config.hidden_size, config.text_config.hidden_size, bias=True) self.act = nn.GELU() self.linear_2 = nn.Linear(config.text_config.hidden_size, config.text_config.hidden_size, bias=True) def forward(self, image_features): hidden_states = self.linear_1(image_features) hidden_states = self.act(hidden_states) hidden_states = self.linear_2(hidden_states) return hidden_states OMCHAT_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`OmChatConfig`] or [`OmChatVisionConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ @add_start_docstrings( "The bare LLaMA Model outputting raw hidden-states without any specific head on top.", OMCHAT_START_DOCSTRING, ) # Copied from transformers.models.llava.modeling_llava.LlavaPreTrainedModel with Llava->OmChat,llava->omchat class OmChatPreTrainedModel(PreTrainedModel): config_class = OmChatConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["OmChatVisionAttention"] _skip_keys_device_placement = "past_key_values" _supports_flash_attn_2 = True _supports_cache_class = True def _init_weights(self, module): # important: this ported version of OmChat isn't meant for training from scratch - only # inference and fine-tuning - so the proper init weights code has been removed - the original codebase # https://github.com/haotian-liu/LLaVA/tree/main/omchat should serve for that purpose std = ( self.config.initializer_range if hasattr(self.config, "initializer_range") else self.config.text_config.initializer_range ) if hasattr(module, "class_embedding"): module.class_embedding.data.normal_(mean=0.0, std=std) if isinstance(module, (nn.Linear, nn.Conv2d)): 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_() @property def _supports_sdpa(self): """ Retrieve language_model's attribute to check whether the model supports SDPA or not. """ return self.language_model._supports_sdpa OMCHAT_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)): The tensors corresponding to the input images. Pixel values can be obtained using [`AutoImageProcessor`]. See [`OmChatImageProcessor.__call__`] for details. [`LlavaProcessor`] uses [`OmChatImageProcessor`] for processing images. image_sizes (`torch.LongTensor` of shape `(batch_size, 2)`, *optional*): The sizes of the images in the batch, being (height, width) for each image. attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`). If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy. - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. vision_feature_layer (`int`, *optional*, defaults to -2): The index of the layer to select the vision feature. vision_feature_select_strategy (`str`, *optional*, defaults to `"default"`): The feature selection strategy used to select the vision feature from the vision backbone. Can be one of `"default"` or `"full"`. If `"default"`, the CLS token is removed from the vision features. If `"full"`, the full vision features are used. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( """The OmChat model which consists of a vision backbone and a language model.""", OMCHAT_START_DOCSTRING, ) class OmChatForConditionalGeneration(OmChatPreTrainedModel): def __init__(self, config: OmChatConfig): super().__init__(config) self.vision_tower = InternVisionModel(InternVisionConfig()) self.multi_modal_projector = OmChatMultiModalProjector(config) self.vocab_size = config.text_config.vocab_size self.language_model = Qwen2ForCausalLM._from_config( config.text_config, attn_implementation=config._attn_implementation ) self.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else -1 self._padding_side = "left" # set it to left by default, user can use setter to change padding_sides self.post_init() @property def padding_side(self): return self._padding_side @padding_side.setter def padding_side(self, padding_side: str): if padding_side not in ["left", "right"]: raise ValueError(f"{padding_side} is not `left` or `right`.") self._padding_side = padding_side # Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.get_input_embeddings def get_input_embeddings(self): return self.language_model.get_input_embeddings() # Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.set_input_embeddings def set_input_embeddings(self, value): self.language_model.set_input_embeddings(value) # Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.get_output_embeddings def get_output_embeddings(self): return self.language_model.get_output_embeddings() # Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.set_output_embeddings def set_output_embeddings(self, new_embeddings): self.language_model.set_output_embeddings(new_embeddings) # Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.set_decoder def set_decoder(self, decoder): self.language_model.set_decoder(decoder) # Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.get_decoder def get_decoder(self): return self.language_model.get_decoder() # Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.tie_weights def tie_weights(self): return self.language_model.tie_weights() # Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.resize_token_embeddings def resize_token_embeddings(self, new_num_tokens: Optional[int] = None, pad_to_multiple_of=None) -> nn.Embedding: model_embeds = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of) # update vocab size self.config.text_config.vocab_size = model_embeds.num_embeddings self.vocab_size = model_embeds.num_embeddings return model_embeds def get_vision_tower(self): if isinstance(self.vision_tower, list): return self.vision_tower[0] return self.vision_tower def get_model(self): return self.language_model.model def encode_images(self, images): vision_tower = self.get_vision_tower() image_features = self.vision_tower_forward(images) return self.multi_modal_projector(image_features.to(torch.float16)) def feature_select(self, image_forward_outs): image_features = image_forward_outs.hidden_states[-1] image_features = image_features[:, 1:] return image_features def vision_tower_forward(self, images): if type(images) is list: image_features = [] for image in images: image_forward_out = self.vision_tower(image.to(device=self.device, dtype=self.dtype).unsqueeze(0), output_hidden_states=True) image_feature = self.feature_select(image_forward_out).to(image.dtype) image_features.append(image_feature) else: image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=torch.float16), output_hidden_states=True) #image_forward_outs = self.vision_tower(images, output_hidden_states=True) image_features = self.feature_select(image_forward_outs) return image_features def prepare_inputs_labels_for_multimodal( self, input_ids, position_ids, attention_mask, past_key_values, labels, images ): vision_tower = self.get_vision_tower() video_tower = self.get_vision_tower() if (vision_tower is None and video_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 or video_tower is not None) and images is not None and input_ids.shape[1] == 1: target_shape = past_key_values[-1][-1].shape[-2] + 1 attention_mask = torch.cat((attention_mask, torch.ones( (attention_mask.shape[0], target_shape - attention_mask.shape[1]), dtype=attention_mask.dtype, device=attention_mask.device )), dim=1) position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1 return input_ids, position_ids, attention_mask, past_key_values, None, labels image_idx = [idx for idx, img in enumerate(images) if img.ndim == 3] is_all_image = len(image_idx) == len(images) video_idx = [idx for idx, vid in enumerate(images) if vid.ndim == 4] images_minibatch = torch.stack([images[idx] for idx in image_idx]) if len(image_idx) > 0 else [] # mini_b c h w videos_minibatch = torch.stack([images[idx] for idx in video_idx]) if len(video_idx) > 0 else [] # mini_b c t h w tmp_image_features = [None] * (len(image_idx) + len(video_idx)) if getattr(images_minibatch, 'ndim', 0) == 4: # batch consists of images, [mini_b, c, h, w] if vision_tower is not None: image_features_minibatch = self.encode_images(images_minibatch) # [mini_b, l, c] else: image_features_minibatch = torch.randn(1).to(self.device) # dummy feature for video-only training under tuning for i, pos in enumerate(image_idx): tmp_image_features[pos] = image_features_minibatch[i] if getattr(videos_minibatch, 'ndim', 0) == 5: # batch consists of videos, [mini_b, c, t, h, w] video_features_minibatch = self.encode_images(videos_minibatch) # fake list [mini_b, t, l, c] for i, pos in enumerate(video_idx): tmp_image_features[pos] = video_features_minibatch[i] new_tmp = [] for image in tmp_image_features: if isinstance(image, list): t = len(image) for i in range(t): new_tmp.append(image[i]) else: new_tmp.append(image) image_features = new_tmp if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False): raise NotImplementedError _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, -100) 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 == -200).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 == -200)[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].to(self.device) cur_image_idx += 1 cur_new_input_embeds.append(cur_image_features) cur_new_labels.append(torch.full((cur_image_features.shape[0],), -100, device=cur_labels.device, dtype=cur_labels.dtype)) 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] 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), -100, 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 _merge_input_ids_with_image_features( self, image_features, feature_lens, inputs_embeds, input_ids, attention_mask, position_ids=None, labels=None, image_token_index=None, ignore_index=-100, ): """ Merge input_ids with with image features into final embeddings Args: image_features (`torch.Tensor` of shape `(all_feature_lens, embed_dim)`): All vision vectors of all images in the batch feature_lens (`torch.LongTensor` of shape `(num_images)`): The length of visual embeddings of each image as stacked in `image_features` inputs_embeds (`torch.Tensor` of shape `(batch_size, sequence_length, embed_dim)`): Token embeddings before merging with visual embeddings input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Input_ids of tokens, possibly filled with image token attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Mask to avoid performing attention on padding token indices. position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`. labels (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) :abels need to be recalculated to support training (if provided) image_token_index (`int`, *optional*) Token id used to indicate the special "image" token. Defaults to `config.image_token_index` ignore_index (`int`, *optional*) Value that is used to pad `labels` and will be ignored when calculated loss. Default: -100. Returns: final_embedding, final_attention_mask, position_ids, final_labels Explanation: each image has variable length embeddings, with length specified by feature_lens image_features is concatenation of all visual embed vectors task: fill each with the correct number of visual embeddings Example: X (5 patches), Y (3 patches), Z (8) X, Y are in the same sequence (in-context learning) if right padding input_ids: [ a b c d e f X g h i j k Y l m o p q r Z s t u v _ _ _ _ _ _ ] input_ids should be: [ a b c d e f X X X X X g h i j k Y Y Y l m o p q r Z Z Z Z Z Z Z Z s t u v _ _ _ _ _ ] labels should be: [ a b c d e f _ _ _ _ _ g h i j k _ _ _ l m o p q r _ _ _ _ _ _ _ _ s t u v _ _ _ _ _ ] elif left padding input_ids: [ a b c d e f X g h i j k Y l m _ _ _ _ _ _ o p q r Z s t u v ] input_ids should be: [ a b c d e f X X X X X g h i j k Y Y Y l m _ _ _ _ _ o p q r Z Z Z Z Z Z Z Z s t u v ] labels should be: [ a b c d e f _ _ _ _ _ g h i j k _ _ _ l m _ _ _ _ _ o p q r _ _ _ _ _ _ _ _ s t u v ] Edge cases: * If tokens are same but image token sizes are different, then cannot infer left or right padding ```python cat_img = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw) chart_img = Image.open(requests.get("https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true", stream=True).raw) prompts = [ "[INST] \nWhat is shown in this image? [/INST]", "[INST] \nWhat is shown in this image? [/INST]", ] inputs = processor(prompts, [chart_img, cat_img], return_tensors='pt', padding=True).to("cuda") chart_img has 2634 tokens, while cat_img has 2340 tokens ``` input_ids: [ a b c d X g h i j Y k l m n ] where X is 3 tokens while Y is 5, this mean after merge if left-padding (batched generation) input_ids should be: [ _ _ a b c d X X X g h i j Y Y Y Y Y k l m n ] elif (right padding) (training) input_ids should be: [ a b c d X X X g h _ _ i j Y Y Y Y Y k l m n ] """ image_token_index = image_token_index if image_token_index is not None else self.config.image_token_index ignore_index = ignore_index if ignore_index is not None else self.config.ignore_index with torch.no_grad(): # ! in llava 1.6, number of patches is variable num_images = feature_lens.size(0) num_image_features, embed_dim = image_features.shape if feature_lens.sum() != num_image_features: raise ValueError(f"{feature_lens=} / {feature_lens.sum()} != {image_features.shape=}") batch_size = input_ids.shape[0] _left_padding = torch.any(attention_mask[:, 0] == 0) _right_padding = torch.any(attention_mask[:, -1] == 0) left_padding = True if not self.training else False if batch_size > 1 and not self.training: if _left_padding and not _right_padding: left_padding = True elif not _left_padding and _right_padding: left_padding = False elif not _left_padding and not _right_padding: # both side is 1, so cannot tell left_padding = self.padding_side == "left" else: # invalid attention_mask raise ValueError(f"both side of attention_mask has zero, invalid. {attention_mask}") # Whether to turn off right padding # 1. Create a mask to know where special image tokens are special_image_token_mask = input_ids == image_token_index # special_image_token_mask: [bsz, seqlen] num_special_image_tokens = torch.sum(special_image_token_mask, dim=-1) # num_special_image_tokens: [bsz] # Reserve for padding of num_images total_num_special_image_tokens = torch.sum(special_image_token_mask) if total_num_special_image_tokens != num_images: raise ValueError( f"Number of image tokens in input_ids ({total_num_special_image_tokens}) different from num_images ({num_images})." ) # Compute the maximum embed dimension # max_image_feature_lens is max_feature_lens per batch feature_lens = feature_lens.to(input_ids.device) feature_lens_batch = feature_lens.split(num_special_image_tokens.tolist(), dim=0) feature_lens_batch_sum = torch.tensor([x.sum() for x in feature_lens_batch], device=input_ids.device) embed_sequence_lengths = ( (attention_mask == 1).long().sum(-1) - num_special_image_tokens + feature_lens_batch_sum ) max_embed_dim = embed_sequence_lengths.max() batch_indices, non_image_indices = torch.where((input_ids != image_token_index) & (attention_mask == 1)) # 2. Compute the positions where text should be written # Calculate new positions for text tokens in merged image-text sequence. # `special_image_token_mask` identifies image tokens. Each image token will be replaced by `nb_text_tokens_per_images` text tokens. # `torch.cumsum` computes how each image token shifts subsequent text token positions. # - 1 to adjust for zero-based indexing, as `cumsum` inherently increases indices by one. # ! instead of special_image_token_mask * (num_image_patches - 1) # special_image_token_mask * (num_feature_len - 1) special_image_token_mask = special_image_token_mask.long() special_image_token_mask[special_image_token_mask == 1] = feature_lens - 1 new_token_positions = torch.cumsum((special_image_token_mask + 1), -1) - 1 if left_padding: # shift right token positions so that they are ending at the same number # the below here was incorrect? new_token_positions += new_token_positions[:, -1].max() - new_token_positions[:, -1:] new_token_positions += max_embed_dim - 1 - new_token_positions[:, -1:] text_to_overwrite = new_token_positions[batch_indices, non_image_indices] # 3. Create the full embedding, already padded to the maximum position final_embedding = torch.zeros( batch_size, max_embed_dim, embed_dim, dtype=inputs_embeds.dtype, device=inputs_embeds.device ) final_attention_mask = torch.zeros( batch_size, max_embed_dim, dtype=attention_mask.dtype, device=inputs_embeds.device ) final_input_ids = torch.full( (batch_size, max_embed_dim), self.pad_token_id, dtype=input_ids.dtype, device=inputs_embeds.device ) # In case the Vision model or the Language model has been offloaded to CPU, we need to manually # set the corresponding tensors into their correct target device. target_device = inputs_embeds.device batch_indices, non_image_indices, text_to_overwrite = ( batch_indices.to(target_device), non_image_indices.to(target_device), text_to_overwrite.to(target_device), ) attention_mask = attention_mask.to(target_device) input_ids = input_ids.to(target_device) # 4. Fill the embeddings based on the mask. If we have ["hey" "", "how", "are"] # we need to index copy on [0, 577, 578, 579] for the text and [1:576] for the image features final_embedding[batch_indices, text_to_overwrite] = inputs_embeds[batch_indices, non_image_indices] final_attention_mask[batch_indices, text_to_overwrite] = attention_mask[batch_indices, non_image_indices] final_input_ids[batch_indices, text_to_overwrite] = input_ids[batch_indices, non_image_indices] final_labels = None if labels is not None: labels = labels.to(target_device) final_labels = torch.full_like(final_attention_mask, ignore_index).to(torch.long) final_labels[batch_indices, text_to_overwrite] = labels[batch_indices, non_image_indices] # 5. Fill the embeddings corresponding to the images. Anything that is not `text_positions` needs filling (#29835) with torch.no_grad(): image_to_overwrite = torch.full( (batch_size, max_embed_dim), True, dtype=torch.bool, device=inputs_embeds.device ) image_to_overwrite[batch_indices, text_to_overwrite] = False embed_indices = torch.arange(max_embed_dim).unsqueeze(0).to(target_device) embed_indices = embed_indices.expand(batch_size, max_embed_dim) embed_seq_lens = embed_sequence_lengths[:, None].to(target_device) if left_padding: # exclude padding on the left max_embed_dim = max_embed_dim.to(target_device) val = (max_embed_dim - embed_indices) <= embed_seq_lens else: # exclude padding on the right val = embed_indices < embed_seq_lens image_to_overwrite &= val if image_to_overwrite.sum() != num_image_features: raise ValueError( f"{image_to_overwrite.sum()=} != {num_image_features=} The input provided to the model are wrong. " f"The number of image tokens is {torch.sum(special_image_token_mask)} while" f" the number of image given to the model is {num_images}. " f"This prevents correct indexing and breaks batch generation." ) final_embedding[image_to_overwrite] = image_features.contiguous().reshape(-1, embed_dim).to(target_device) final_attention_mask |= image_to_overwrite position_ids = (final_attention_mask.cumsum(-1) - 1).masked_fill_((final_attention_mask == 0), 1) return final_embedding, final_attention_mask, position_ids, final_labels, final_input_ids def pack_image_features(self, image_features, image_sizes, image_newline=None): """ Reshape, unpad and then pack each image_feature into a single image_features tensor containing all visual vectors. Args: image_features (`List[torch.Tensor]` of length num_images, each of shape `(num_patches, image_length, embed_dim)`) List of image feature tensor, each contains all the visual feature of all patches. image_sizes (`torch.Tensor` of shape `(num_images, 2)`) Actual image size of each images (H, W). image_newline (`torch.Tensor` of shape `(embed_dim)`) New line embedding vector. Returns: image_features (`torch.Tensor` of shape `(all_feat_len, embed_dim)`) feature_lens (`List[int]`) token length of each image in image_features """ new_image_features = [] feature_lens = [] 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.config.vision_config.image_size // self.config.vision_config.patch_size if height * width != base_image_feature.shape[0]: raise ValueError("The number of patches is not consistent with the image size.") num_patch_height, num_patch_width = get_anyres_image_grid_shape( image_sizes[image_idx], self.config.image_grid_pinpoints, self.config.vision_config.image_size, ) image_feature = image_feature.view(num_patch_height, num_patch_width, height, width, -1) 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]) if image_newline is not None: image_feature = torch.cat( ( image_feature, image_newline[:, None, None].expand(*image_feature.shape[:-1], 1).to(image_feature.dtype), ), dim=-1, ) image_feature = image_feature.flatten(1, 2).transpose(0, 1) image_feature = torch.cat((base_image_feature, image_feature), dim=0) else: image_feature = image_feature[0] if image_newline is not None: image_feature = torch.cat((image_feature, image_newline[None].to(image_feature)), dim=0) new_image_features.append(image_feature) feature_lens.append(image_feature.size(0)) image_features = torch.cat(new_image_features, dim=0) feature_lens = torch.tensor(feature_lens, dtype=torch.long, device=image_features.device) return image_features, feature_lens @add_start_docstrings_to_model_forward(OMCHAT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=OmChatCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, vision_feature_layer: Optional[int] = None, vision_feature_select_strategy: Optional[str] = None, labels: Optional[torch.LongTensor] = None, 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, OmChatCausalLMOutputWithPast]: r""" Args: labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. Returns: Example: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoProcessor, OmChatForConditionalGeneration >>> model = OmChatForConditionalGeneration.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf") >>> processor = AutoProcessor.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf") >>> prompt = "[INST] \nWhat is shown in this image? [/INST]" >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor(text=prompt, images=image, return_tensors="pt") >>> # Generate >>> generate_ids = model.generate(**inputs, max_length=30) >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] "[INST] \nWhat is shown in this image? [/INST] The image appears to be a radar chart, which is a type of multi-dimensional plot (...)" ```""" 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 vision_feature_layer = ( vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer ) vision_feature_select_strategy = ( vision_feature_select_strategy if vision_feature_select_strategy is not None else self.config.vision_feature_select_strategy ) if inputs_embeds is None: ( input_ids, position_ids, attention_mask, past_key_values, inputs_embeds, labels ) = self.prepare_inputs_labels_for_multimodal( input_ids, position_ids, attention_mask, past_key_values, labels, images ) outputs = self.language_model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, 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 ) return outputs logits = outputs[0] loss = None if labels is not None: # Shift so that tokens < n predict n if attention_mask is not None: shift_attention_mask = attention_mask[..., 1:] shift_logits = logits[..., :-1, :][shift_attention_mask.to(logits.device) != 0].contiguous() shift_labels = labels[..., 1:][shift_attention_mask.to(labels.device) != 0].contiguous() else: shift_logits = logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() # Flatten the tokens loss_fct = nn.CrossEntropyLoss() loss = loss_fct( shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1).to(shift_logits.device) ) if not return_dict: output = (logits,) + outputs[1:] return (loss,) + output if loss is not None else output return OmChatCausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) 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 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 # Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration._reorder_cache def _reorder_cache(self, *args, **kwargs): return self.language_model._reorder_cache(*args, **kwargs)