# coding=utf-8 # Copyright 2023 Microsoft Research and The HuggingFace Inc. team. All rights reserved. # # 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. """PyTorch KOSMOS-2 model.""" import math from dataclasses import dataclass from typing import Any, List, Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import CrossEntropyLoss from ...activations import ACT2FN from ...generation import GenerationMixin from ...modeling_outputs import ( BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPooling, CausalLMOutputWithCrossAttentions, ) from ...modeling_utils import PreTrainedModel from ...utils import ( ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, torch_int, ) from .configuration_kosmos2 import Kosmos2Config, Kosmos2TextConfig, Kosmos2VisionConfig logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = Kosmos2Config def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): """ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. """ bsz, src_len = mask.size() tgt_len = tgt_len if tgt_len is not None else src_len expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) inverted_mask = 1.0 - expanded_mask return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) def _make_causal_mask( input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0 ): """ Make causal mask used for bi-directional self-attention. """ bsz, tgt_len = input_ids_shape mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) mask_cond = torch.arange(mask.size(-1), device=device) mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) mask = mask.to(dtype) if past_key_values_length > 0: mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1) return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) # Copied from transformers.models.roberta.modeling_roberta.create_position_ids_from_input_ids def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0): """ Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols are ignored. This is modified from fairseq's `utils.make_positions`. Args: x: torch.Tensor x: Returns: torch.Tensor """ # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. mask = input_ids.ne(padding_idx).int() incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask return incremental_indices.long() + padding_idx KOSMOS2_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 ([`Kosmos2Config`]): 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. """ KOSMOS2_VISION_INPUTS_DOCSTRING = r""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details. 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. interpolate_pos_encoding (`bool`, *optional*, defaults `False`): Whether to interpolate the pre-trained position encodings. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ KOSMOS2_TEXT_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) 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) image_embeds: (`torch.FloatTensor` of shape `(batch_size, latent_query_num, hidden_size)`, *optional*): Sequence of hidden-states at the output of `Kosmos2ImageToTextProjection`. image_embeds_position_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to indicate the location in a sequence to insert the image features . Mask values selected in `[0, 1]`: - 1 for places where to put the image features, - 0 for places that are not for image features (i.e. for text tokens). encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up 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. 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.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) 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. """ KOSMOS2_INPUTS_DOCSTRING = r""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details. 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) image_embeds_position_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to indicate the location in a sequence to insert the image features . Mask values selected in `[0, 1]`: - 1 for places where to put the image features, - 0 for places that are not for image features (i.e. for text tokens). 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) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up 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)`. image_embeds: (`torch.FloatTensor` of shape `(batch_size, latent_query_num, hidden_size)`, *optional*): Sequence of hidden-states at the output of `Kosmos2ImageToTextProjection`. 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. 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.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) 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. interpolate_pos_encoding (`bool`, *optional*, defaults `False`): Whether to interpolate the pre-trained position encodings. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @dataclass class Kosmos2ModelOutput(ModelOutput): """ Base class for text model's outputs that also contains a pooling of the last hidden states. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. 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_embeds (`torch.FloatTensor` of shape `(batch_size, latent_query_num, hidden_size)`, *optional*): Sequence of hidden-states at the output of `Kosmos2ImageToTextProjection`. projection_attentions (`tuple(torch.FloatTensor)`, *optional*): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights given by `Kosmos2ImageToTextProjection`, after the attention softmax, used to compute the weighted average in the self-attention heads. vision_model_output(`BaseModelOutputWithPooling`, *optional*): The output of the [`Kosmos2VisionModel`]. 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 optionally if `config.is_encoder_decoder=True` 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 optionally if `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. """ last_hidden_state: Optional[torch.FloatTensor] = None past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None image_embeds: Optional[torch.FloatTensor] = None projection_attentions: Optional[Tuple[torch.FloatTensor]] = None vision_model_output: BaseModelOutputWithPooling = None def to_tuple(self) -> Tuple[Any]: return tuple( self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple() for k in self.keys() ) @dataclass class Kosmos2ForConditionalGenerationModelOutput(ModelOutput): """ Model output class for `Kosmos2ForConditionalGeneration`. 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). 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_embeds (`torch.FloatTensor` of shape `(batch_size, latent_query_num, hidden_size)`, *optional*): Sequence of hidden-states at the output of `Kosmos2ImageToTextProjection`. projection_attentions (`tuple(torch.FloatTensor)`, *optional*): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights given by `Kosmos2ImageToTextProjection`, after the attention softmax, used to compute the weighted average in the self-attention heads. vision_model_output(`BaseModelOutputWithPooling`, *optional*): The output of the [`Kosmos2VisionModel`]. 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 optionally if `config.is_encoder_decoder=True` 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 optionally if `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. """ loss: Optional[torch.FloatTensor] = None logits: Optional[torch.FloatTensor] = None past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None image_embeds: Optional[torch.FloatTensor] = None projection_attentions: Optional[Tuple[torch.FloatTensor]] = None vision_model_output: BaseModelOutputWithPooling = None def to_tuple(self) -> Tuple[Any]: return tuple( self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple() for k in self.keys() ) # Copied from transformers.models.clip.modeling_clip.CLIPVisionEmbeddings with CLIP->Kosmos2 class Kosmos2VisionEmbeddings(nn.Module): def __init__(self, config: Kosmos2VisionConfig): 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(self.embed_dim)) self.patch_embedding = nn.Conv2d( in_channels=config.num_channels, out_channels=self.embed_dim, kernel_size=self.patch_size, stride=self.patch_size, bias=False, ) self.num_patches = (self.image_size // self.patch_size) ** 2 self.num_positions = self.num_patches + 1 self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim) self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False) def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor: """ This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution images. This method is also adapted to support torch.jit tracing. Adapted from: - https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and - https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211 """ num_patches = embeddings.shape[1] - 1 position_embedding = self.position_embedding.weight.unsqueeze(0) num_positions = position_embedding.shape[1] - 1 # always interpolate when tracing to ensure the exported model works for dynamic input shapes if not torch.jit.is_tracing() and num_patches == num_positions and height == width: return self.position_embedding(self.position_ids) class_pos_embed = position_embedding[:, :1] patch_pos_embed = position_embedding[:, 1:] dim = embeddings.shape[-1] new_height = height // self.patch_size new_width = width // self.patch_size sqrt_num_positions = torch_int(num_positions**0.5) patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim) patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2) patch_pos_embed = nn.functional.interpolate( patch_pos_embed, size=(new_height, new_width), mode="bicubic", align_corners=False, ) patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) return torch.cat((class_pos_embed, patch_pos_embed), dim=1) def forward(self, pixel_values: torch.FloatTensor, interpolate_pos_encoding=False) -> torch.Tensor: batch_size, _, height, width = pixel_values.shape if not interpolate_pos_encoding and (height != self.image_size or width != self.image_size): raise ValueError( f"Input image size ({height}*{width}) doesn't match model ({self.image_size}*{self.image_size})." ) target_dtype = self.patch_embedding.weight.dtype patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype)) # shape = [*, width, grid, grid] patch_embeds = patch_embeds.flatten(2).transpose(1, 2) class_embeds = self.class_embedding.expand(batch_size, 1, -1) embeddings = torch.cat([class_embeds, patch_embeds], dim=1) if interpolate_pos_encoding: embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width) else: embeddings = embeddings + self.position_embedding(self.position_ids) return embeddings # Copied from transformers.models.clip.modeling_clip.CLIPAttention with CLIP->Kosmos2Vision class Kosmos2VisionAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__(self, config): super().__init__() self.config = config self.embed_dim = config.hidden_size self.num_heads = config.num_attention_heads 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.dropout = config.attention_dropout self.k_proj = nn.Linear(self.embed_dim, self.embed_dim) self.v_proj = nn.Linear(self.embed_dim, self.embed_dim) self.q_proj = nn.Linear(self.embed_dim, self.embed_dim) self.out_proj = nn.Linear(self.embed_dim, self.embed_dim) def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, causal_attention_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: """Input shape: Batch x Time x Channel""" bsz, tgt_len, embed_dim = hidden_states.size() # get query proj query_states = self.q_proj(hidden_states) * self.scale key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) proj_shape = (bsz * self.num_heads, -1, self.head_dim) query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) key_states = key_states.view(*proj_shape) value_states = value_states.view(*proj_shape) src_len = key_states.size(1) attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): raise ValueError( f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is" f" {attn_weights.size()}" ) # apply the causal_attention_mask first if causal_attention_mask is not None: if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is" f" {causal_attention_mask.size()}" ) attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + causal_attention_mask attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) if attention_mask is not None: if attention_mask.size() != (bsz, 1, tgt_len, src_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}" ) attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) attn_weights = nn.functional.softmax(attn_weights, dim=-1) if output_attentions: # this operation is a bit awkward, but it's required to # make sure that attn_weights keeps its gradient. # In order to do so, attn_weights have to reshaped # twice and have to be reused in the following attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len) else: attn_weights_reshaped = None attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) attn_output = torch.bmm(attn_probs, value_states) if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is" f" {attn_output.size()}" ) attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) attn_output = attn_output.transpose(1, 2) attn_output = attn_output.reshape(bsz, tgt_len, embed_dim) attn_output = self.out_proj(attn_output) return attn_output, attn_weights_reshaped # Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->Kosmos2Vision class Kosmos2VisionMLP(nn.Module): def __init__(self, config): super().__init__() self.config = config self.activation_fn = 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.activation_fn(hidden_states) hidden_states = self.fc2(hidden_states) return hidden_states # Copied from transformers.models.altclip.modeling_altclip.AltCLIPEncoderLayer with AltCLIP->Kosmos2Vision class Kosmos2VisionEncoderLayer(nn.Module): def __init__(self, config: Kosmos2VisionConfig): super().__init__() self.embed_dim = config.hidden_size self.self_attn = Kosmos2VisionAttention(config) self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) self.mlp = Kosmos2VisionMLP(config) self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) def forward( self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, causal_attention_mask: torch.Tensor, output_attentions: Optional[bool] = False, ) -> Tuple[torch.FloatTensor]: """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. `(config.encoder_attention_heads,)`. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. """ residual = hidden_states hidden_states = self.layer_norm1(hidden_states) hidden_states, attn_weights = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, causal_attention_mask=causal_attention_mask, output_attentions=output_attentions, ) hidden_states = residual + hidden_states residual = hidden_states hidden_states = self.layer_norm2(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (attn_weights,) return outputs # Copied from transformers.models.altclip.modeling_altclip.AltCLIPEncoder with AltCLIP->Kosmos2Vision class Kosmos2VisionEncoder(nn.Module): """ Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a [`Kosmos2VisionEncoderLayer`]. Args: config: Kosmos2VisionConfig """ def __init__(self, config: Kosmos2VisionConfig): super().__init__() self.config = config self.layers = nn.ModuleList([Kosmos2VisionEncoderLayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, inputs_embeds, attention_mask: Optional[torch.Tensor] = None, causal_attention_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, 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)`): 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. 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) causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Causal mask for the text model. 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) 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. """ 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 encoder_states = () if output_hidden_states else None all_attentions = () if output_attentions 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 = self._gradient_checkpointing_func( encoder_layer.__call__, hidden_states, attention_mask, causal_attention_mask, output_attentions, ) else: layer_outputs = encoder_layer( hidden_states, attention_mask, causal_attention_mask, output_attentions=output_attentions, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) if output_hidden_states: encoder_states = encoder_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions ) # Similar to `transformers.models.clip.modeling_clip.CLIPVisionTransformer` but without docstring for `forward` class Kosmos2VisionTransformer(nn.Module): # Copied from transformers.models.altclip.modeling_altclip.AltCLIPVisionTransformer.__init__ with AltCLIPVision->Kosmos2Vision,ALTCLIP_VISION->KOSMOS2_VISION,AltCLIP->Kosmos2Vision def __init__(self, config: Kosmos2VisionConfig): super().__init__() self.config = config embed_dim = config.hidden_size self.embeddings = Kosmos2VisionEmbeddings(config) self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) self.encoder = Kosmos2VisionEncoder(config) self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) def forward( self, pixel_values: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, interpolate_pos_encoding: bool = False, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPooling]: 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 if pixel_values is None: raise ValueError("You have to specify pixel_values") hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding) hidden_states = self.pre_layrnorm(hidden_states) encoder_outputs = self.encoder( inputs_embeds=hidden_states, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) last_hidden_state = encoder_outputs[0] pooled_output = last_hidden_state[:, 0, :] pooled_output = self.post_layernorm(pooled_output) 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, ) # Similar to `transformers.models.m2m_100.modeling_m2m_100.M2M100SinusoidalPositionalEmbedding` but allowing to pass `position_ids` class Kosmos2TextSinusoidalPositionalEmbedding(nn.Module): """This module produces sinusoidal positional embeddings of any length.""" # Copied from transformers.models.m2m_100.modeling_m2m_100.M2M100SinusoidalPositionalEmbedding.__init__ def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None): super().__init__() self.offset = 2 self.embedding_dim = embedding_dim self.padding_idx = padding_idx self.make_weights(num_positions + self.offset, embedding_dim, padding_idx) # Copied from transformers.models.m2m_100.modeling_m2m_100.M2M100SinusoidalPositionalEmbedding.make_weights def make_weights(self, num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None): emb_weights = self.get_embedding(num_embeddings, embedding_dim, padding_idx) if hasattr(self, "weights"): # in forward put the weights on the correct dtype and device of the param emb_weights = emb_weights.to(dtype=self.weights.dtype, device=self.weights.device) self.register_buffer("weights", emb_weights, persistent=False) @staticmethod # Copied from transformers.models.m2m_100.modeling_m2m_100.M2M100SinusoidalPositionalEmbedding.get_embedding def get_embedding(num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None): """ Build sinusoidal embeddings. This matches the implementation in tensor2tensor, but differs slightly from the description in Section 3.5 of "Attention Is All You Need". """ half_dim = embedding_dim // 2 emb = math.log(10000) / (half_dim - 1) emb = torch.exp(torch.arange(half_dim, dtype=torch.int64).float() * -emb) emb = torch.arange(num_embeddings, dtype=torch.int64).float().unsqueeze(1) * emb.unsqueeze(0) emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1) if embedding_dim % 2 == 1: # zero pad emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1) if padding_idx is not None: emb[padding_idx, :] = 0 return emb.to(torch.get_default_dtype()) @torch.no_grad() def forward( self, input_ids: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, past_key_values_length: int = 0, position_ids: Optional[torch.Tensor] = None, ): if input_ids is not None: bsz, seq_len = input_ids.size() if position_ids is None: # Create the position ids from the input token ids. Any padded tokens remain padded. position_ids = create_position_ids_from_input_ids( input_ids, self.padding_idx, past_key_values_length ).to(input_ids.device) else: bsz, seq_len = inputs_embeds.size()[:-1] if position_ids is None: position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds, past_key_values_length) # expand embeddings if needed max_pos = self.padding_idx + 1 + seq_len + past_key_values_length if max_pos > self.weights.size(0): self.make_weights(max_pos + self.offset, self.embedding_dim, self.padding_idx) return self.weights.index_select(0, position_ids.view(-1)).view(bsz, seq_len, self.weights.shape[-1]).detach() # Copied from transformers.models.m2m_100.modeling_m2m_100.M2M100SinusoidalPositionalEmbedding.create_position_ids_from_inputs_embeds def create_position_ids_from_inputs_embeds(self, inputs_embeds, past_key_values_length): """ We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. Args: inputs_embeds: torch.Tensor Returns: torch.Tensor """ input_shape = inputs_embeds.size()[:-1] sequence_length = input_shape[1] position_ids = torch.arange( self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device ) return position_ids.unsqueeze(0).expand(input_shape).contiguous() + past_key_values_length class KosmosTextAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" # Similar to transformers.models.bart.modeling_bart.BartAttention.__init__ except an additional `inner_attn_ln`. def __init__( self, config, embed_dim: int, num_heads: int, dropout: float = 0.0, is_decoder: bool = False, add_inner_attn_layernorm: bool = False, bias: bool = True, ): super().__init__() self.embed_dim = embed_dim self.num_heads = num_heads self.dropout = dropout self.head_dim = embed_dim // num_heads if (self.head_dim * num_heads) != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" f" and `num_heads`: {num_heads})." ) self.scaling = self.head_dim**-0.5 self.is_decoder = is_decoder self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) # End opy self.inner_attn_ln = None if add_inner_attn_layernorm: self.inner_attn_ln = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) def _shape(self, projection: torch.Tensor) -> torch.Tensor: new_projection_shape = projection.size()[:-1] + (self.num_heads, self.head_dim) # move heads to 2nd position (B, T, H * D) -> (B, T, H, D) -> (B, H, T, D) new_projection = projection.view(new_projection_shape).permute(0, 2, 1, 3) return new_projection def forward( self, hidden_states: torch.Tensor, encoder_hidden_states: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, attention_mask: Optional[torch.Tensor] = None, layer_head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: """Input shape: Batch x Time x Channel""" # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = encoder_hidden_states is not None batch_size, seq_length = hidden_states.shape[:2] # use encoder_hidden_states if cross attention current_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states # checking that the `sequence_length` of the `past_key_value` is the same as the he provided # `encoder_hidden_states` to support prefix tuning if is_cross_attention and past_key_value and past_key_value[0].shape[2] == current_states.shape[1]: # reuse k,v, cross_attentions key_states = past_key_value[0] value_states = past_key_value[1] else: key_states = self._shape(self.k_proj(current_states)) value_states = self._shape(self.v_proj(current_states)) if past_key_value is not None and not is_cross_attention: # reuse k, v, self_attention key_states = torch.cat([past_key_value[0], key_states], dim=2) value_states = torch.cat([past_key_value[1], value_states], dim=2) query_states = self._shape(self.q_proj(hidden_states) * self.scaling) attn_weights = torch.matmul(query_states, key_states.transpose(-1, -2)) if self.is_decoder: # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_states, value_states) src_len = key_states.size(2) if attention_mask is not None: if attention_mask.size() != (batch_size, 1, seq_length, src_len): raise ValueError( f"Attention mask should be of size {(batch_size, 1, seq_length, src_len)}, but is {attention_mask.size()}" ) attn_weights = attn_weights + attention_mask attn_weights = nn.functional.softmax(attn_weights, dim=-1) # Mask heads if we want to if layer_head_mask is not None: attn_weights = attn_weights * layer_head_mask attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) # attn_output = torch.bmm(attn_probs, value_states) ? context_states = torch.matmul(attn_weights, value_states) # attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) ? context_states = context_states.permute(0, 2, 1, 3).contiguous().view(batch_size, seq_length, -1) if self.inner_attn_ln is not None: context_states = self.inner_attn_ln(context_states) attn_output = self.out_proj(context_states) return attn_output, attn_weights, past_key_value class Kosmos2TextFFN(nn.Module): def __init__(self, config: Kosmos2TextConfig): super().__init__() self.dropout = config.dropout self.activation_fn = ACT2FN[config.activation_function] self.activation_dropout = config.activation_dropout self.fc1 = nn.Linear(config.embed_dim, config.ffn_dim) self.fc2 = nn.Linear(config.ffn_dim, config.embed_dim) self.ffn_layernorm = nn.LayerNorm(config.ffn_dim, eps=config.layer_norm_eps) def forward(self, hidden_states): hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) hidden_states = self.ffn_layernorm(hidden_states) hidden_states = self.fc2(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) return hidden_states class Kosmos2TextBlock(nn.Module): def __init__(self, config: Kosmos2TextConfig): super().__init__() self.embed_dim = config.embed_dim self.self_attn = KosmosTextAttention( config, embed_dim=self.embed_dim, num_heads=config.attention_heads, dropout=config.attention_dropout, is_decoder=True, add_inner_attn_layernorm=True, ) self.dropout = config.dropout self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) if config.add_cross_attention: self.encoder_attn = KosmosTextAttention( config, embed_dim=self.embed_dim, num_heads=config.attention_heads, dropout=config.attention_dropout, is_decoder=True, add_inner_attn_layernorm=False, ) self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) self.ffn = Kosmos2TextFFN(config) self.final_layer_norm = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, layer_head_mask: Optional[torch.Tensor] = None, cross_attn_layer_head_mask: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, output_attentions: Optional[bool] = False, use_cache: Optional[bool] = True, ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: residual = hidden_states # Self Attention # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None hidden_states = self.self_attn_layer_norm(hidden_states) # add present self-attn cache to positions 1,2 of present_key_value tuple hidden_states, self_attn_weights, present_key_value = self.self_attn( hidden_states=hidden_states, past_key_value=self_attn_past_key_value, attention_mask=attention_mask, layer_head_mask=layer_head_mask, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states # Cross-Attention Block cross_attn_present_key_value = None cross_attn_weights = None if encoder_hidden_states is not None: if not hasattr(self, "encoder_attn"): raise ValueError( f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers" " by setting `config.add_cross_attention=True`" ) residual = hidden_states hidden_states = self.encoder_attn_layer_norm(hidden_states) # cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn( hidden_states=hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=encoder_attention_mask, layer_head_mask=cross_attn_layer_head_mask, past_key_value=cross_attn_past_key_value, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states # add cross-attn to positions 3,4 of present_key_value tuple present_key_value = present_key_value + cross_attn_present_key_value # Fully Connected residual = hidden_states hidden_states = self.final_layer_norm(hidden_states) # FFN hidden_states = self.ffn(hidden_states) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights, cross_attn_weights) if use_cache: outputs += (present_key_value,) return outputs class Kosmos2TextTransformer(nn.Module): """ Transformer decoder consisting of `config.layers` layers. Each layer is a [`Kosmos2TextBlock`]. Args: config: Kosmos2TextConfig """ def __init__(self, config: Kosmos2TextConfig): super().__init__() self.config = config self.dropout = config.dropout self.layerdrop = config.layerdrop self.embed_scale = math.sqrt(config.embed_dim) if config.scale_embedding else 1.0 self.embed_tokens = nn.Embedding(config.vocab_size, config.embed_dim, padding_idx=config.pad_token_id) self.embed_positions = Kosmos2TextSinusoidalPositionalEmbedding( num_positions=config.max_position_embeddings, embedding_dim=config.embed_dim, padding_idx=config.pad_token_id, ) self.layers = nn.ModuleList([Kosmos2TextBlock(config) for _ in range(config.layers)]) self.layer_norm = nn.LayerNorm(config.embed_dim, config.layer_norm_eps) self.gradient_checkpointing = False def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length): # create causal mask # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] combined_attention_mask = None if input_shape[-1] > 1: combined_attention_mask = _make_causal_mask( input_shape, inputs_embeds.dtype, device=inputs_embeds.device, past_key_values_length=past_key_values_length, ) if attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to( inputs_embeds.device ) combined_attention_mask = ( expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask ) return combined_attention_mask def forward_embedding( self, input_ids, inputs_embeds: Optional[torch.Tensor] = None, image_embeds: Optional[torch.Tensor] = None, img_input_mask: Optional[torch.Tensor] = None, past_key_values_length: int = 0, position_ids: Optional[torch.Tensor] = None, ): # The argument `inputs_embeds` should be the one without being multiplied by `self.embed_scale`. if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) if image_embeds is not None: inputs_embeds[img_input_mask.to(dtype=torch.bool)] = image_embeds.to(inputs_embeds.device).view( -1, image_embeds.size(-1) ) inputs_embeds = inputs_embeds * self.embed_scale # embed positions positions = self.embed_positions( input_ids=input_ids, inputs_embeds=inputs_embeds, past_key_values_length=past_key_values_length, position_ids=position_ids, ) positions = positions.to(inputs_embeds.device) hidden_states = inputs_embeds + positions hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) return hidden_states def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, image_embeds: Optional[torch.Tensor] = None, image_embeds_position_mask: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]: 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 ) use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = input_ids.shape input_ids = input_ids.view(-1, input_shape[-1]) elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") # past_key_values_length past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 # We don't need img info. when `past_key_values_length` > 0 if past_key_values_length > 0: image_embeds = None image_embeds_position_mask = None hidden_states = self.forward_embedding( input_ids=input_ids, inputs_embeds=inputs_embeds, image_embeds=image_embeds, img_input_mask=image_embeds_position_mask, past_key_values_length=past_key_values_length, position_ids=position_ids, ) attention_mask = self._prepare_decoder_attention_mask( attention_mask, input_shape, hidden_states, past_key_values_length ) # expand encoder attention mask if encoder_hidden_states is not None and encoder_attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] encoder_attention_mask = _expand_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) if self.gradient_checkpointing and self.training: if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None present_key_value_states = () if use_cache else None # check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]): if attn_mask is not None: if attn_mask.size()[0] != (len(self.layers)): raise ValueError( f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for" f" {head_mask.size()[0]}." ) for idx, decoder_layer in enumerate(self.layers): # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) if output_hidden_states: all_hidden_states += (hidden_states,) if self.training: dropout_probability = torch.rand([]) if dropout_probability < self.layerdrop: continue past_key_value = past_key_values[idx] if past_key_values is not None else None if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( decoder_layer.__call__, hidden_states, attention_mask, encoder_hidden_states, encoder_attention_mask, head_mask[idx] if head_mask is not None else None, cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None, None, output_attentions, use_cache, ) else: layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, layer_head_mask=(head_mask[idx] if head_mask is not None else None), cross_attn_layer_head_mask=( cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None ), past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, ) hidden_states = layer_outputs[0] if use_cache: present_key_value_states += (layer_outputs[3 if output_attentions else 1],) if output_attentions: all_self_attns += (layer_outputs[1],) if encoder_hidden_states is not None: all_cross_attentions += (layer_outputs[2],) # add final layer norm hidden_states = self.layer_norm(hidden_states) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) if not return_dict: return tuple( v for v in [ hidden_states, present_key_value_states, all_hidden_states, all_self_attns, all_cross_attentions, ] if v is not None ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=present_key_value_states, hidden_states=all_hidden_states, attentions=all_self_attns, cross_attentions=all_cross_attentions, ) class Kosmos2PreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = Kosmos2Config supports_gradient_checkpointing = True _no_split_modules = ["Kosmos2VisionEncoderLayer", "Kosmos2TextBlock"] def _init_weights(self, module): """Initialize the weights""" if isinstance(self, Kosmos2VisionModel): factor = self.config.initializer_factor elif isinstance(self, (Kosmos2Model, Kosmos2ForConditionalGeneration)): factor = self.config.vision_config.initializer_factor if isinstance(self, (Kosmos2TextModel, Kosmos2TextForCausalLM)): std = self.config.init_std elif isinstance(self, (Kosmos2Model, Kosmos2ForConditionalGeneration)): std = self.config.text_config.init_std if isinstance(module, Kosmos2VisionEmbeddings): nn.init.normal_(module.class_embedding, mean=0.0, std=module.embed_dim**-0.5 * factor) nn.init.normal_(module.patch_embedding.weight, std=module.config.initializer_range * factor) nn.init.normal_(module.position_embedding.weight, std=module.config.initializer_range * factor) elif isinstance(module, Kosmos2VisionAttention): in_proj_std = (module.embed_dim**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor out_proj_std = (module.embed_dim**-0.5) * factor nn.init.normal_(module.q_proj.weight, std=in_proj_std) nn.init.normal_(module.k_proj.weight, std=in_proj_std) nn.init.normal_(module.v_proj.weight, std=in_proj_std) nn.init.normal_(module.out_proj.weight, std=out_proj_std) if module.q_proj.bias is not None: module.q_proj.bias.data.zero_() if module.k_proj.bias is not None: module.k_proj.bias.data.zero_() if module.v_proj.bias is not None: module.v_proj.bias.data.zero_() if module.out_proj.bias is not None: module.out_proj.bias.data.zero_() elif isinstance(module, Kosmos2VisionMLP): in_proj_std = (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor fc_std = (2 * module.config.hidden_size) ** -0.5 * factor nn.init.normal_(module.fc1.weight, std=fc_std) nn.init.normal_(module.fc2.weight, std=in_proj_std) if module.fc1.bias is not None: module.fc1.bias.data.zero_() if module.fc2.bias is not None: module.fc2.bias.data.zero_() elif isinstance(module, Kosmos2VisionEncoderLayer): module.layer_norm1.bias.data.zero_() module.layer_norm1.weight.data.fill_(1.0) module.layer_norm2.bias.data.zero_() module.layer_norm2.weight.data.fill_(1.0) elif isinstance(module, Kosmos2VisionTransformer): module.pre_layrnorm.bias.data.zero_() module.pre_layrnorm.weight.data.fill_(1.0) module.post_layernorm.bias.data.zero_() module.post_layernorm.weight.data.fill_(1.0) elif isinstance(module, KosmosTextAttention): nn.init.normal_(module.q_proj.weight, std=std) nn.init.normal_(module.k_proj.weight, std=std) nn.init.normal_(module.v_proj.weight, std=std) nn.init.normal_(module.out_proj.weight, std=std) if module.q_proj.bias is not None: module.q_proj.bias.data.zero_() if module.k_proj.bias is not None: module.k_proj.bias.data.zero_() if module.v_proj.bias is not None: module.v_proj.bias.data.zero_() if module.out_proj.bias is not None: module.out_proj.bias.data.zero_() elif isinstance(module, Kosmos2TextFFN): nn.init.normal_(module.fc1.weight, std=std) nn.init.normal_(module.fc2.weight, std=std) if module.fc1.bias is not None: module.fc1.bias.data.zero_() if module.fc2.bias is not None: module.fc2.bias.data.zero_() elif isinstance(module, Kosmos2TextForCausalLM): nn.init.normal_(module.lm_head.weight, std=std) if module.lm_head.bias is not None: module.lm_head.bias.data.zero_() elif isinstance(module, Kosmos2ImageToTextProjection): nn.init.normal_(module.dense.weight, std=std) if module.dense.bias is not None: module.dense.bias.data.zero_() elif isinstance(module, Kosmos2TextTransformer): module.embed_tokens.weight.data.normal_(mean=0.0, std=std) if module.embed_tokens.padding_idx is not None: module.embed_tokens.weight.data[module.embed_tokens.padding_idx].zero_() class Kosmos2VisionModel(Kosmos2PreTrainedModel): config_class = Kosmos2VisionConfig main_input_name = "pixel_values" # Copied from transformers.models.clip.modeling_clip.CLIPVisionModel.__init__ with CLIP_VISION->KOSMOS2_VISION,CLIP->Kosmos2,self.vision_model->self.model def __init__(self, config: Kosmos2VisionConfig): super().__init__(config) self.model = Kosmos2VisionTransformer(config) # Initialize weights and apply final processing self.post_init() # Copied from transformers.models.clip.modeling_clip.CLIPVisionModel.get_input_embeddings with CLIP_VISION->KOSMOS2_VISION,CLIP->Kosmos2,self.vision_model->self.model def get_input_embeddings(self) -> nn.Module: return self.model.embeddings.patch_embedding @add_start_docstrings_to_model_forward(KOSMOS2_VISION_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=Kosmos2VisionConfig) def forward( self, pixel_values: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, interpolate_pos_encoding: bool = False, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPooling]: r""" Returns: """ return self.model( pixel_values=pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, interpolate_pos_encoding=interpolate_pos_encoding, return_dict=return_dict, ) class Kosmos2TextModel(Kosmos2PreTrainedModel): config_class = Kosmos2TextConfig def __init__(self, config: Kosmos2TextConfig): super().__init__(config) self.model = Kosmos2TextTransformer(config) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self) -> nn.Module: return self.model.embed_tokens def set_input_embeddings(self, value): self.model.embed_tokens = value @add_start_docstrings_to_model_forward(KOSMOS2_TEXT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutputWithPastAndCrossAttentions, config_class=Kosmos2TextConfig) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, image_embeds: Optional[torch.Tensor] = None, image_embeds_position_mask: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]: r""" Returns: """ return self.model( input_ids=input_ids, attention_mask=attention_mask, image_embeds=image_embeds, image_embeds_position_mask=image_embeds_position_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, head_mask=head_mask, cross_attn_head_mask=cross_attn_head_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, position_ids=position_ids, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) @add_start_docstrings( """ The text model from KOSMOS-2 with a language modeling head on top (linear layer with weights tied to the input embeddings). """, KOSMOS2_START_DOCSTRING, ) class Kosmos2TextForCausalLM(Kosmos2PreTrainedModel, GenerationMixin): config_class = Kosmos2TextConfig _tied_weights_keys = ["lm_head.weight"] def __init__(self, config: Kosmos2TextConfig): super().__init__(config) self.model = Kosmos2TextTransformer(config) self.lm_head = nn.Linear(in_features=config.embed_dim, out_features=config.vocab_size, bias=False) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self) -> nn.Module: return self.model.embed_tokens def set_input_embeddings(self, value): self.model.embed_tokens = value def get_output_embeddings(self) -> nn.Module: return self.lm_head def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings @add_start_docstrings_to_model_forward(KOSMOS2_TEXT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=Kosmos2TextConfig) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, image_embeds: Optional[torch.Tensor] = None, image_embeds_position_mask: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, CausalLMOutputWithCrossAttentions]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in `[-100, 0, ..., config.vocab_size]` (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: """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict if labels is not None: if use_cache: logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.") use_cache = False outputs = self.model( input_ids=input_ids, attention_mask=attention_mask, image_embeds=image_embeds, image_embeds_position_mask=image_embeds_position_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, head_mask=head_mask, cross_attn_head_mask=cross_attn_head_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, position_ids=position_ids, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) lm_logits = self.lm_head(outputs[0]) loss = None if labels is not None: # move labels to correct device to enable model parallelism labels = labels.to(lm_logits.device) # Shift so that tokens < n predict n shift_logits = lm_logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() batch_size, seq_length, vocab_size = shift_logits.shape # Flatten the tokens loss_fct = CrossEntropyLoss() loss = loss_fct( shift_logits.view(batch_size * seq_length, vocab_size), shift_labels.view(batch_size * seq_length) ) if not return_dict: output = (lm_logits,) + outputs[1:] return (loss,) + output if loss is not None else output return CausalLMOutputWithCrossAttentions( loss=loss, logits=lm_logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) def prepare_inputs_for_generation( self, input_ids, image_embeds=None, image_embeds_position_mask=None, past_key_values=None, attention_mask=None, use_cache=None, cache_position=None, **model_kwargs, ): # Overwritten -- in specific circumstances we don't want to forward image inputs to the model # Kosmos2 has offset for position ids, so we need to create them correctly position_ids = create_position_ids_from_input_ids( input_ids, padding_idx=self.config.pad_token_id, past_key_values_length=0, ) if past_key_values is not None: image_embeds = None image_embeds_position_mask = None # appending `False` to `image_embeds_position_mask` (because `input_ids` grows during generation) elif image_embeds_position_mask is not None: batch_size, seq_len = input_ids.size() mask_len = image_embeds_position_mask.size()[-1] image_embeds_position_mask = torch.cat( ( image_embeds_position_mask, torch.zeros(size=(batch_size, seq_len - mask_len), dtype=torch.bool, device=input_ids.device), ), dim=1, ) model_inputs = super().prepare_inputs_for_generation( input_ids, past_key_values=past_key_values, attention_mask=attention_mask, image_embeds=image_embeds, image_embeds_position_mask=image_embeds_position_mask, use_cache=use_cache, position_ids=position_ids, cache_position=cache_position, **model_kwargs, ) return model_inputs @staticmethod # Copied from transformers.models.umt5.modeling_umt5.UMT5ForConditionalGeneration._reorder_cache def _reorder_cache(past_key_values, beam_idx): reordered_past = () for layer_past in past_key_values: reordered_past += ( tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past), ) return reordered_past class Kosmos2ImageToTextProjection(nn.Module): """The layer that transforms the image model's output to part of the text model's input (namely, image features)""" def __init__(self, config: Kosmos2Config): super().__init__() self.dense = nn.Linear(config.vision_config.hidden_size, config.text_config.embed_dim) self.latent_query = nn.Parameter(torch.randn(config.latent_query_num, config.text_config.embed_dim)) self.x_attn = KosmosTextAttention( config.text_config, config.text_config.embed_dim, config.text_config.attention_heads, dropout=config.text_config.attention_dropout, is_decoder=False, add_inner_attn_layernorm=False, ) def forward(self, features): hidden_states = self.dense(features) # shape = [batch, latent_query_num, h_dim] latent_query = self.latent_query.unsqueeze(0).expand(hidden_states.size(0), -1, -1) key_value_states = torch.cat([hidden_states, latent_query], dim=1) hidden_states, attn_weights, _ = self.x_attn( hidden_states=latent_query, encoder_hidden_states=key_value_states, past_key_value=None, attention_mask=None, output_attentions=None, ) return hidden_states, attn_weights @add_start_docstrings( """ KOSMOS-2 Model for generating text and image features. The model consists of a vision encoder and a language model. """, KOSMOS2_START_DOCSTRING, ) class Kosmos2Model(Kosmos2PreTrainedModel): config_class = Kosmos2Config main_input_name = "pixel_values" def __init__(self, config: Kosmos2Config): super().__init__(config) self.text_model = Kosmos2TextModel(config.text_config) self.vision_model = Kosmos2VisionModel(config.vision_config) self.image_to_text_projection = Kosmos2ImageToTextProjection(config) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self) -> nn.Module: return self.text_model.model.embed_tokens def set_input_embeddings(self, value): self.text_model.model.embed_tokens = value @add_start_docstrings_to_model_forward(KOSMOS2_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=Kosmos2ModelOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values: Optional[torch.Tensor] = None, input_ids: Optional[torch.Tensor] = None, image_embeds_position_mask: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, image_embeds: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, interpolate_pos_encoding: bool = False, return_dict: Optional[bool] = None, ) -> Union[Tuple, Kosmos2ModelOutput]: r""" Returns: Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoProcessor, Kosmos2Model >>> model = Kosmos2Model.from_pretrained("microsoft/kosmos-2-patch14-224") >>> processor = AutoProcessor.from_pretrained("microsoft/kosmos-2-patch14-224") >>> url = "https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/snowman.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> text = ( ... " An image of a snowman" ... " warming himself by a fire" ... "" ... ) >>> inputs = processor(text=text, images=image, return_tensors="pt", add_eos_token=True) >>> last_hidden_state = model( ... pixel_values=inputs["pixel_values"], ... input_ids=inputs["input_ids"], ... attention_mask=inputs["attention_mask"], ... image_embeds_position_mask=inputs["image_embeds_position_mask"], ... ).last_hidden_state >>> list(last_hidden_state.shape) [1, 91, 2048] ```""" 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_model_output = None projection_attentions = None if image_embeds is None: if pixel_values is None: raise ValueError("You have to specify either `pixel_values` or `image_embeds`.") vision_model_output = self.vision_model( pixel_values=pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, interpolate_pos_encoding=interpolate_pos_encoding, return_dict=return_dict, ) # The whole `last_hidden_state` through `post_layernorm` instead of just `pooled_output`. image_embeds = self.vision_model.model.post_layernorm(vision_model_output[0]) # normalized features image_embeds = nn.functional.normalize(image_embeds, dim=-1) image_embeds, projection_attentions = self.image_to_text_projection(image_embeds) outputs = self.text_model( input_ids=input_ids, attention_mask=attention_mask, image_embeds=image_embeds, image_embeds_position_mask=image_embeds_position_mask, head_mask=head_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, position_ids=position_ids, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if not return_dict: outputs = outputs + (image_embeds, projection_attentions, vision_model_output) return tuple(output for output in outputs if output is not None) return Kosmos2ModelOutput( last_hidden_state=outputs.last_hidden_state, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, image_embeds=image_embeds, projection_attentions=projection_attentions, vision_model_output=vision_model_output, ) @add_start_docstrings( """ KOSMOS-2 Model for generating text and bounding boxes given an image. The model consists of a vision encoder and a language model. """, KOSMOS2_START_DOCSTRING, ) class Kosmos2ForConditionalGeneration(Kosmos2PreTrainedModel, GenerationMixin): config_class = Kosmos2Config main_input_name = "pixel_values" _tied_weights_keys = ["text_model.lm_head.weight"] def __init__(self, config: Kosmos2Config): super().__init__(config) self.text_model = Kosmos2TextForCausalLM(config.text_config) self.vision_model = Kosmos2VisionModel(config.vision_config) self.image_to_text_projection = Kosmos2ImageToTextProjection(config) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self) -> nn.Module: return self.text_model.model.embed_tokens def set_input_embeddings(self, value): self.text_model.model.embed_tokens = value def get_output_embeddings(self) -> nn.Module: return self.text_model.get_output_embeddings() def set_output_embeddings(self, new_embeddings): self.text_model.set_output_embeddings(new_embeddings) @add_start_docstrings_to_model_forward(KOSMOS2_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=Kosmos2ForConditionalGenerationModelOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values: Optional[torch.Tensor] = None, input_ids: Optional[torch.Tensor] = None, image_embeds_position_mask: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, image_embeds: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, Kosmos2ForConditionalGenerationModelOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in `[-100, 0, ..., config.vocab_size]` (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: Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoProcessor, Kosmos2ForConditionalGeneration >>> model = Kosmos2ForConditionalGeneration.from_pretrained("microsoft/kosmos-2-patch14-224") >>> processor = AutoProcessor.from_pretrained("microsoft/kosmos-2-patch14-224") >>> url = "https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/snowman.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> prompt = " An image of" >>> inputs = processor(text=prompt, images=image, return_tensors="pt") >>> generated_ids = model.generate( ... pixel_values=inputs["pixel_values"], ... input_ids=inputs["input_ids"], ... attention_mask=inputs["attention_mask"], ... image_embeds=None, ... image_embeds_position_mask=inputs["image_embeds_position_mask"], ... use_cache=True, ... max_new_tokens=64, ... ) >>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] >>> processed_text = processor.post_process_generation(generated_text, cleanup_and_extract=False) >>> processed_text ' An image of a snowman warming himself by a fire.' >>> caption, entities = processor.post_process_generation(generated_text) >>> caption 'An image of a snowman warming himself by a fire.' >>> entities [('a snowman', (12, 21), [(0.390625, 0.046875, 0.984375, 0.828125)]), ('a fire', (41, 47), [(0.171875, 0.015625, 0.484375, 0.890625)])] ```""" 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_model_output = None projection_attentions = None if image_embeds is None: if pixel_values is None: raise ValueError("You have to specify either `pixel_values` or `image_embeds`.") vision_model_output = self.vision_model( pixel_values=pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) # The whole `last_hidden_state` through `post_layernorm` instead of just `pooled_output`. image_embeds = self.vision_model.model.post_layernorm(vision_model_output[0]) # normalized features image_embeds = nn.functional.normalize(image_embeds, dim=-1) image_embeds, projection_attentions = self.image_to_text_projection(image_embeds) lm_outputs = self.text_model( input_ids=input_ids, attention_mask=attention_mask, image_embeds=image_embeds, image_embeds_position_mask=image_embeds_position_mask, head_mask=head_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, position_ids=position_ids, labels=labels, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if not return_dict: outputs = lm_outputs + (image_embeds, projection_attentions, vision_model_output) return tuple(output for output in outputs if output is not None) return Kosmos2ForConditionalGenerationModelOutput( loss=lm_outputs.loss, logits=lm_outputs.logits, past_key_values=lm_outputs.past_key_values, hidden_states=lm_outputs.hidden_states, attentions=lm_outputs.attentions, image_embeds=image_embeds, projection_attentions=projection_attentions, vision_model_output=vision_model_output, ) def generate( self, pixel_values: Optional[torch.Tensor] = None, image_embeds_position_mask: Optional[torch.Tensor] = None, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, image_embeds: Optional[torch.Tensor] = None, **kwargs, ): # in order to allow `inputs` argument (as in `GenerationMixin`) inputs = kwargs.pop("inputs", None) if pixel_values is not None and inputs is not None: raise ValueError( f"`inputs`: {inputs} were passed alongside `pixel_values` which is not allowed." f"Make sure to either pass `inputs` or pixel_values=..." ) if pixel_values is None and inputs is not None: pixel_values = inputs if image_embeds is None: vision_model_output = self.vision_model(pixel_values) # The whole `last_hidden_state` through `post_layernorm` instead of just `pooled_output`. image_embeds = self.vision_model.model.post_layernorm(vision_model_output[0]) # normalized features image_embeds = nn.functional.normalize(image_embeds, dim=-1) image_embeds, projection_attentions = self.image_to_text_projection(image_embeds) output = self.text_model.generate( input_ids=input_ids, attention_mask=attention_mask, image_embeds=image_embeds, image_embeds_position_mask=image_embeds_position_mask, **kwargs, ) return output __all__ = ["Kosmos2ForConditionalGeneration", "Kosmos2Model", "Kosmos2PreTrainedModel"]