# Copyright 2024 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-2.5 model.""" import math from collections.abc import Callable from dataclasses import dataclass from typing import Any import torch from torch import nn from ... import initialization as init from ...activations import ACT2FN from ...cache_utils import Cache, DynamicCache from ...generation import GenerationMixin from ...masking_utils import create_causal_mask from ...modeling_layers import GradientCheckpointingLayer from ...modeling_outputs import ( BaseModelOutput, BaseModelOutputWithPast, BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPooling, CausalLMOutputWithCrossAttentions, ) from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel from ...processing_utils import Unpack from ...utils import ( ModelOutput, TransformersKwargs, add_start_docstrings, add_start_docstrings_to_model_forward, can_return_tuple, logging, replace_return_docstrings, ) from ...utils.generic import is_flash_attention_requested from .configuration_kosmos2_5 import ( Kosmos2_5Config, Kosmos2_5TextConfig, Kosmos2_5VisionConfig, ) logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = Kosmos2_5Config # Copied from transformers.models.kosmos2.modeling_kosmos2._expand_mask def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: int | None = 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) KOSMOS2_5_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 ([`Kosmos2_5Config`]): 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_5_VISION_INPUTS_DOCSTRING = r""" Args: flattened_patches (`torch.FloatTensor` of shape `(batch_size, max_patches, 2 + patch_height * patch_width * image_channels)`): Flattened patches of the images. `flattened_patches` can be obtained using [`AutoImageProcessor`]. See [`Kosmos2_5ImageProcessor.__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. """ KOSMOS2_5_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). past_key_values (`Cache` 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. """ KOSMOS2_5_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) flattened_patches (`torch.FloatTensor` of shape `(batch_size, max_patches, 2 + patch_height * patch_width * image_channels)`): Flattened patches of the images. `flattened_patches` can be obtained using [`AutoImageProcessor`]. See [`Kosmos2_5ImageProcessor.__call__`] for details. width (`torch.FloatTensor` of shape `(batch_size,)`): The original width (before resizing) of each image in the batch. This can be obtained using [`AutoImageProcessor`]. See [`Kosmos2_5ImageProcessor.__call__`] for details. height (`torch.FloatTensor` of shape `(batch_size,)`): The original height (before resizing) of each image in the batch. This can be obtained using [`AutoImageProcessor`]. See [`Kosmos2_5ImageProcessor.__call__`] for details. 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) past_key_values (`Cache` 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. """ @dataclass class Kosmos2_5ModelOutput(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. width (`torch.FloatTensor` of shape `(batch_size,)`): The original width (before resizing) of each image in the batch. height (`torch.FloatTensor` of shape `(batch_size,)`): The original height (before resizing) of each image in the batch. 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 (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). 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: torch.FloatTensor | None = None past_key_values: Cache | None = None hidden_states: tuple[torch.FloatTensor] | None = None attentions: tuple[torch.FloatTensor] | None = None width: torch.FloatTensor | None = None height: torch.FloatTensor | None = None image_embeds: torch.FloatTensor | None = None projection_attentions: tuple[torch.FloatTensor] | None = None vision_model_output: BaseModelOutputWithPooling = None def to_tuple(self) -> tuple[Any]: return tuple((self[k] if k != "vision_model_output" else getattr(self, k).to_tuple()) for k in self.keys()) @dataclass class Kosmos2_5ForConditionalGenerationModelOutput(ModelOutput): """ Model output class for `Kosmos2_5ForConditionalGeneration`. 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. width (`torch.FloatTensor` of shape `(batch_size,)`): The original width (before resizing) of each image in the batch. height (`torch.FloatTensor` of shape `(batch_size,)`): The original height (before resizing) of each image in the batch. 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 (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). 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: torch.FloatTensor | None = None logits: torch.FloatTensor | None = None past_key_values: Cache | None = None hidden_states: tuple[torch.FloatTensor] | None = None attentions: tuple[torch.FloatTensor] | None = None width: torch.FloatTensor | None = None height: torch.FloatTensor | None = None image_embeds: torch.FloatTensor | None = None projection_attentions: tuple[torch.FloatTensor] | None = None vision_model_output: BaseModelOutputWithPooling = None def to_tuple(self) -> tuple[Any]: return tuple((self[k] if k != "vision_model_output" else getattr(self, k).to_tuple()) for k in self.keys()) # Copied from transformers.models.pix2struct.modeling_pix2struct.Pix2StructLayerNorm with Pix2Struct->Kosmos2_5 class Kosmos2_5LayerNorm(nn.Module): def __init__(self, hidden_size, eps=1e-6): """ Construct a layernorm module in the T5 style. No bias and no subtraction of mean. """ super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, hidden_states): # T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean # Square Layer Normalization https://huggingface.co/papers/1910.07467 thus variance is calculated # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for # half-precision inputs is done in fp32 variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) # convert into half-precision if necessary if self.weight.dtype in [torch.float16, torch.bfloat16]: hidden_states = hidden_states.to(self.weight.dtype) return self.weight * hidden_states # similar to transformers.models.pix2struct.modeling_pix2struct.Pix2StructVisionEmbeddings but with `inplace=False` # TODO: check with krip class Kosmos2_5VisionEmbeddings(nn.Module): def __init__(self, config: Kosmos2_5VisionConfig) -> None: super().__init__() self.config = config self.patch_projection = nn.Linear(config.patch_embed_hidden_size, config.hidden_size) self.row_embedder = nn.Embedding(config.max_num_patches, config.hidden_size) self.column_embedder = nn.Embedding(config.max_num_patches, config.hidden_size) self.dropout = nn.Dropout(config.dropout_rate, inplace=False) def forward(self, flattened_patches: torch.Tensor) -> torch.Tensor: # the row and column indices are stored in the first and second position of the flattened_patches # flattened_patches: `batch_size`, `seq_len`, `hidden_size` + 2 row_indices = flattened_patches[:, :, 0].long() col_indices = flattened_patches[:, :, 1].long() flattened_patches = flattened_patches[:, :, 2:] embeddings = self.patch_projection(flattened_patches) row_embeddings = self.row_embedder(row_indices).to(embeddings.device) col_embeddings = self.column_embedder(col_indices).to(embeddings.device) # sum all embeddings together embeddings = embeddings + row_embeddings + col_embeddings embeddings = self.dropout(embeddings) return embeddings # Copied from transformers.models.t5.modeling_t5.T5DenseGatedActDense with T5DenseGatedActDense->Pix2StructVisionMlp,T5Config->Pix2StructVisionConfig,config.d_model->config.hidden_size,dropout_rate->dropout_rate class Kosmos2_5VisionMlp(nn.Module): def __init__(self, config: Kosmos2_5VisionConfig): super().__init__() self.wi_0 = nn.Linear(config.hidden_size, config.intermediate_size, bias=False) self.wi_1 = nn.Linear(config.hidden_size, config.intermediate_size, bias=False) self.wo = nn.Linear(config.intermediate_size, config.hidden_size, bias=False) self.dropout = nn.Dropout(config.dropout_rate) self.act = ACT2FN[config.dense_act_fn] # Ignore copy self.config = config def forward(self, hidden_states): hidden_gelu = self.act(self.wi_0(hidden_states)) hidden_linear = self.wi_1(hidden_states) hidden_states = hidden_gelu * hidden_linear hidden_states = self.dropout(hidden_states) # To make 8bit quantization work for google/flan-t5-xxl, self.wo is kept in float32. # See https://github.com/huggingface/transformers/issues/20287 # we also make sure the weights are not in `int8` in case users will force `_keep_in_fp32_modules` to be `None`` if ( isinstance(self.wo.weight, torch.Tensor) and hidden_states.dtype != self.wo.weight.dtype and self.wo.weight.dtype != torch.int8 ): hidden_states = hidden_states.to(self.wo.weight.dtype) hidden_states = self.wo(hidden_states) return hidden_states def eager_attention_forward( module: nn.Module, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, attention_mask: torch.Tensor | None, scaling: float, dropout: float = 0.0, **kwargs, ): # this weight maybe overflow with fp16 attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling if attention_mask is not None: attn_weights = attn_weights + attention_mask attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) attn_output = torch.matmul(attn_weights, value) attn_output = attn_output.transpose(1, 2).contiguous() return attn_output, attn_weights class Kosmos2_5VisionAttention(nn.Module): def __init__(self, config): super().__init__() self.config = config self.hidden_size = config.hidden_size self.head_dim = config.head_dim self.n_heads = config.num_attention_heads self.dropout = config.attention_dropout self.inner_dim = self.n_heads * self.head_dim self.is_causal = False self.scaling = self.head_dim**-0.5 self.query = nn.Linear(self.hidden_size, self.inner_dim, bias=False) self.key = nn.Linear(self.hidden_size, self.inner_dim, bias=False) self.value = nn.Linear(self.hidden_size, self.inner_dim, bias=False) self.output = nn.Linear(self.inner_dim, self.hidden_size, bias=False) self.gradient_checkpointing = False def forward( self, hidden_states, attention_mask=None, **kwargs: Unpack[TransformersKwargs], ): """ Self-attention block """ input_shape = hidden_states.shape[:-1] hidden_shape = (*input_shape, -1, self.head_dim) query_states = self.query(hidden_states).view(hidden_shape).transpose(1, 2) key_states = self.key(hidden_states).view(hidden_shape).transpose(1, 2) value_states = self.value(hidden_states).view(hidden_shape).transpose(1, 2) attention_interface: Callable = getattr( ALL_ATTENTION_FUNCTIONS, self.config._attn_implementation, eager_attention_forward ) attn_output, attn_weights = attention_interface( self, query_states, key_states, value_states, attention_mask, dropout=0.0 if not self.training else self.dropout, scaling=self.scaling, **kwargs, ) attn_output = attn_output.reshape(*input_shape, -1) attn_output = self.output(attn_output) return attn_output, attn_weights class Kosmos2_5VisionLayer(GradientCheckpointingLayer): def __init__(self, config: Kosmos2_5VisionConfig) -> None: super().__init__() self.config = config self.attention = Kosmos2_5VisionAttention(config) self.mlp = Kosmos2_5VisionMlp(config) self.pre_mlp_layer_norm = Kosmos2_5LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.pre_attention_layer_norm = Kosmos2_5LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward( self, hidden_states: torch.Tensor, attention_mask: torch.Tensor | None = None, output_attentions: bool = False, **kwargs: Unpack[TransformersKwargs], ) -> tuple[torch.Tensor, torch.Tensor] | tuple[torch.Tensor]: residual = hidden_states # in Kosmos2_5Vision, layernorm is applied before self-attention hidden_states = self.pre_attention_layer_norm(hidden_states) attention_output, self_attn_weights = self.attention( hidden_states, attention_mask=attention_mask, output_attentions=output_attentions, **kwargs, ) # first residual connection hidden_states = attention_output + residual # in Kosmos2_5Vision, layernorm is also applied after self-attention layer_output = self.pre_mlp_layer_norm(hidden_states) layer_output = self.mlp(layer_output) + hidden_states # second residual connection outputs = (layer_output,) if output_attentions: outputs += (self_attn_weights,) return outputs # Adapted from transformers.models.pix2struct.modeling_pix2struct.Pix2StructVisionEncoder with Pix2Struct->Kosmos2_5 class Kosmos2_5VisionEncoder(nn.Module): def __init__(self, config: Kosmos2_5VisionConfig) -> None: super().__init__() self.config = config self.layer = nn.ModuleList([Kosmos2_5VisionLayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def _prepare_attention_mask(self, attention_mask, input_shape, inputs_embeds): if is_flash_attention_requested(self.config): if attention_mask is not None and 0.0 in attention_mask: return attention_mask return None 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 ) return expanded_attn_mask def forward( self, hidden_states: torch.Tensor, attention_mask: torch.Tensor | None = None, output_attentions: bool = False, output_hidden_states: bool = False, **kwargs: Unpack[TransformersKwargs], ) -> BaseModelOutput: all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None attention_mask = self._prepare_attention_mask(attention_mask, hidden_states.shape[:2], hidden_states) for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_outputs = layer_module(hidden_states, attention_mask, output_attentions, **kwargs) hidden_states = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions, ) # Copied from transformers.models.kosmos2.modeling_kosmos2.Kosmos2TextSinusoidalPositionalEmbedding with Kosmos2->Kosmos2_5 class Kosmos2_5TextSinusoidalPositionalEmbedding(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: int | None = None): super().__init__() self.offset = 2 self.num_positions = num_positions 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: int | None = 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: int | None = 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: torch.Tensor | None = None, inputs_embeds: torch.Tensor | None = None, past_key_values_length: int = 0, position_ids: torch.Tensor | None = 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 = self.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, self.padding_idx ) # 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() @staticmethod # Copied from transformers.models.m2m_100.modeling_m2m_100.M2M100SinusoidalPositionalEmbedding.create_position_ids_from_inputs_embeds def create_position_ids_from_inputs_embeds(inputs_embeds, past_key_values_length, padding_idx): """ 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( padding_idx + 1, sequence_length + padding_idx + 1, dtype=torch.long, device=inputs_embeds.device ) return position_ids.unsqueeze(0).expand(input_shape).contiguous() + past_key_values_length @staticmethod # Copied from transformers.models.roberta.modeling_roberta.RobertaEmbeddings.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 # Copied from transformers.models.kosmos2.modeling_kosmos2.Kosmos2TextFFN with Kosmos2->Kosmos2_5 class Kosmos2_5TextFFN(nn.Module): def __init__(self, config: Kosmos2_5TextConfig): 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 Kosmos2_5TextAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__( self, config, embed_dim: int, num_heads: int, dropout: float = 0.0, is_decoder: bool = False, bias: bool = True, is_causal=True, layer_idx: int | None = None, ): super().__init__() self.config = config self.layer_idx = layer_idx 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) self.is_causal = is_causal def forward( self, hidden_states: torch.Tensor, # text part encoder_hidden_states: torch.Tensor | None = None, # image part attention_mask: torch.Tensor | None = None, past_key_values: Cache | None = None, cache_position: torch.LongTensor | None = None, **kwargs: Unpack[TransformersKwargs], ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: input_shape = hidden_states.shape[:-1] hidden_shape = (*input_shape, -1, self.head_dim) # use encoder_hidden_states if cross attention is_cross_attention = encoder_hidden_states is not None current_states = encoder_hidden_states if is_cross_attention else hidden_states current_input_shape = current_states.shape[:-1] current_hidden_shape = (*current_input_shape, -1, self.head_dim) key_states = self.k_proj(current_states).view(current_hidden_shape).transpose(1, 2) value_states = self.v_proj(current_states).view(current_hidden_shape).transpose(1, 2) query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) # Apply `self.scaling` query_states = self.scaling * query_states if past_key_values is not None: # sin and cos are specific to RoPE models; cache_position needed for the static cache cache_kwargs = {"cache_position": cache_position} key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs) attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( self.config._attn_implementation, eager_attention_forward ) attn_output, attn_weights = attention_interface( self, query_states, key_states, value_states, attention_mask, dropout=0.0 if not self.training else self.dropout, scaling=1.0, # We don't use `self.scaling` as it's already applied to `query_states` above . **kwargs, ) attn_output = attn_output.reshape(*input_shape, -1).contiguous() attn_output = self.out_proj(attn_output) return attn_output, attn_weights class Kosmos2_5TextBlock(GradientCheckpointingLayer): def __init__(self, config: Kosmos2_5TextConfig, layer_idx: int): super().__init__() self.embed_dim = config.embed_dim self.layer_idx = layer_idx self.self_attn = Kosmos2_5TextAttention( config, embed_dim=self.embed_dim, num_heads=config.attention_heads, dropout=config.attention_dropout, is_decoder=True, is_causal=True, layer_idx=layer_idx, ) self.dropout = config.dropout self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) self.ffn = Kosmos2_5TextFFN(config) self.final_layer_norm = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) # Adapted from transformers.models.kosmos2.modeling_kosmos2.Kosmos2TextBlock.forward def forward( self, hidden_states: torch.Tensor, attention_mask: torch.Tensor | None = None, past_key_values: Cache | None = None, output_attentions: bool | None = False, use_cache: bool | None = True, cache_position: torch.LongTensor | None = None, **kwargs: Unpack[TransformersKwargs], ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]: residual = hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) # Self Attention hidden_states, self_attn_weights = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, past_key_values=past_key_values, output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position, **kwargs, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states # Fully Connected residual = hidden_states hidden_states = self.final_layer_norm(hidden_states) hidden_states = self.ffn(hidden_states) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights,) return outputs # Adapted from transformers.models.kosmos2.modeling_kosmos2.Kosmos2TextTransformer with Kosmos2->Kosmos2_5 class Kosmos2_5TextTransformer(nn.Module): """ Transformer decoder consisting of `config.layers` layers. Each layer is a [`Kosmos2_5TextBlock`]. Here we doesn't have cross attention. Args: config: Kosmos2_5TextConfig """ def __init__(self, config: Kosmos2_5TextConfig): 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 = Kosmos2_5TextSinusoidalPositionalEmbedding( num_positions=config.max_position_embeddings, embedding_dim=config.embed_dim, padding_idx=config.pad_token_id, ) # Ignore copy self.segment_emb = nn.Embedding(2, config.embed_dim) self.layers = nn.ModuleList([Kosmos2_5TextBlock(config, layer_idx) for layer_idx in range(config.layers)]) self.layer_norm = nn.LayerNorm(config.embed_dim, config.layer_norm_eps) self.gradient_checkpointing = False def forward( self, input_ids: torch.Tensor | None = None, attention_mask: torch.Tensor | None = None, image_embeds: torch.Tensor | None = None, image_embeds_position_mask: torch.Tensor | None = None, past_key_values: Cache | None = None, inputs_embeds: torch.Tensor | None = None, position_ids: torch.Tensor | None = None, use_cache: bool | None = None, output_attentions: bool | None = None, output_hidden_states: bool | None = None, cache_position: torch.LongTensor | None = None, **kwargs: Unpack[TransformersKwargs], ) -> 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 if (input_ids is None) ^ (inputs_embeds is not None): raise ValueError( "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one" ) if self.gradient_checkpointing and self.training and use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`." ) use_cache = False # 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) # Ignore copy if image_embeds is not None: inputs_embeds = inputs_embeds.clone() inputs_embeds[image_embeds_position_mask == 1] = image_embeds.to(inputs_embeds.device).view( -1, image_embeds.shape[-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=0, position_ids=position_ids, ) positions = positions.to(inputs_embeds.device) # Ignore copy if image_embeds_position_mask is not None: # make every not equal 0 be 1 image_embeds_position_mask = image_embeds_position_mask.ne(0).long() segment_embeds = self.segment_emb(image_embeds_position_mask).to(positions.device) positions += segment_embeds else: # add zero embedding for padding tokens bsz, seq_len, dim = positions.size() zero_emb = self.segment_emb( torch.zeros((bsz, 1), dtype=torch.long, device=self.segment_emb.weight.device) ).to(positions.device) positions += zero_emb hidden_states = inputs_embeds + positions hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) if use_cache and past_key_values is None: past_key_values = DynamicCache(config=self.config) if cache_position is None: past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 cache_position = torch.arange( past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device ) causal_mask = create_causal_mask( config=self.config, inputs_embeds=inputs_embeds, attention_mask=attention_mask, cache_position=cache_position, past_key_values=past_key_values, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None for decoder_layer in self.layers: if output_hidden_states: all_hidden_states += (hidden_states,) layer_outputs = decoder_layer( hidden_states, attention_mask=causal_mask, past_key_values=past_key_values, output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position, **kwargs, ) hidden_states = layer_outputs[0] if output_attentions: all_self_attns += (layer_outputs[1],) # 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,) output = BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=past_key_values if use_cache else None, hidden_states=all_hidden_states, attentions=all_self_attns, ) return output class Kosmos2_5ImageToTextProjection(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: Kosmos2_5Config): 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)) # Ignore copy self.x_attn = Kosmos2_5TextAttention( config.text_config, config.text_config.embed_dim, config.text_config.attention_heads, dropout=config.text_config.attention_dropout, is_decoder=False, is_causal=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_values=None, attention_mask=None, output_attentions=None, is_causal=False, ) return hidden_states, attn_weights class Kosmos2_5PreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = Kosmos2_5Config input_modalities = ("image", "text") supports_gradient_checkpointing = True _no_split_modules = ["Kosmos2_5VisionLayer", "Kosmos2_5TextBlock"] _supports_flash_attn = True _supports_cache_class = True _supports_sdpa = True _supports_attention_backend = True @torch.no_grad() def _init_weights(self, module): """Initialize the weights""" if isinstance(self, Kosmos2_5VisionModel): init_factor = self.config.initializer_factor std = self.config.initializer_range * init_factor elif isinstance(self, (Kosmos2_5TextModel, Kosmos2_5TextForCausalLM)): std = self.config.init_std elif isinstance(self, (Kosmos2_5Model, Kosmos2_5ForConditionalGeneration)): std = self.config.text_config.init_std if isinstance(module, nn.Linear): init.normal_(module.weight, mean=0.0, std=std) if module.bias is not None: init.zeros_(module.bias) elif isinstance(module, nn.Embedding): init.normal_(module.weight, mean=0.0, std=std) # Here we need the check explicitly, as we slice the weight in the `zeros_` call, so it looses the flag if module.padding_idx is not None and not getattr(module.weight, "_is_hf_initialized", False): init.zeros_(module.weight[module.padding_idx]) elif isinstance(module, (nn.LayerNorm, Kosmos2_5LayerNorm)): init.ones_(module.weight) if getattr(module, "bias", None) is not None: init.zeros_(module.bias) elif isinstance(module, Kosmos2_5ImageToTextProjection): init.normal_(module.latent_query, mean=0.0, std=1.0) elif isinstance(module, Kosmos2_5TextSinusoidalPositionalEmbedding): emb_weights = module.get_embedding( module.num_positions + module.offset, module.embedding_dim, module.padding_idx ) init.copy_(module.weights, emb_weights) class Kosmos2_5VisionModel(Kosmos2_5PreTrainedModel): config_class = Kosmos2_5VisionConfig input_modalities = ("text",) # Copied from transformers.models.pix2struct.modeling_pix2struct.Pix2StructVisionModel.__init__ with Pix2Struct->Kosmos2_5 def __init__(self, config: Kosmos2_5VisionConfig): super().__init__(config) self.config = config self.embeddings = Kosmos2_5VisionEmbeddings(config) self.encoder = Kosmos2_5VisionEncoder(config) self.layernorm = Kosmos2_5LayerNorm(config.hidden_size, eps=config.layer_norm_eps) # Initialize weights and apply final processing self.post_init() # Copied from transformers.models.pix2struct.modeling_pix2struct.Pix2StructVisionModel.get_input_embeddings def get_input_embeddings(self): return self.embeddings.patch_projection # Similar to transformers.models.pix2struct.modeling_pix2struct.Pix2StructVisionModel.forward without docstring def forward( self, flattened_patches: torch.Tensor | None = None, attention_mask: torch.Tensor | None = None, output_attentions: bool | None = None, output_hidden_states: bool | None = None, **kwargs: Unpack[TransformersKwargs], ) -> 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 ) if flattened_patches is None: raise ValueError("You have to specify flattened_patches") if attention_mask is None: # check where `flattened_patches` is not 0 attention_mask = (flattened_patches.sum(dim=-1) != 0).float() embedding_output = self.embeddings(flattened_patches) encoder_outputs = self.encoder( embedding_output, attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, **kwargs, ) sequence_output = encoder_outputs.last_hidden_state sequence_output = self.layernorm(sequence_output) return BaseModelOutput( last_hidden_state=sequence_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) # Adapted from transformers.models.kosmos2.modeling_kosmos2.Kosmos2TextModel with KOSMOS2->KOSMOS2_5 class Kosmos2_5TextModel(Kosmos2_5PreTrainedModel): config_class = Kosmos2_5TextConfig input_modalities = ("text",) def __init__(self, config: Kosmos2_5TextConfig): super().__init__(config) self.model = Kosmos2_5TextTransformer(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_5_TEXT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutputWithPastAndCrossAttentions, config_class=Kosmos2_5TextConfig) def forward( self, input_ids: torch.Tensor | None = None, attention_mask: torch.Tensor | None = None, image_embeds: torch.Tensor | None = None, image_embeds_position_mask: torch.Tensor | None = None, past_key_values: Cache | None = None, inputs_embeds: torch.Tensor | None = None, position_ids: torch.Tensor | None = None, use_cache: bool | None = None, output_attentions: bool | None = None, output_hidden_states: bool | None = None, cache_position: torch.LongTensor | None = None, **kwargs: Unpack[TransformersKwargs], ) -> 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, 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, cache_position=cache_position, **kwargs, ) @add_start_docstrings( """ KOSMOS-2.5 Model for generating text and image features. The model consists of a vision encoder and a language model. """, KOSMOS2_5_START_DOCSTRING, ) class Kosmos2_5Model(Kosmos2_5PreTrainedModel): config_class = Kosmos2_5Config def __init__(self, config: Kosmos2_5Config): super().__init__(config) self.text_model = Kosmos2_5TextModel._from_config(config.text_config) self.vision_model = Kosmos2_5VisionModel._from_config(config.vision_config) self.image_to_text_projection = Kosmos2_5ImageToTextProjection(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 @can_return_tuple @add_start_docstrings_to_model_forward(KOSMOS2_5_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=Kosmos2_5ModelOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: torch.Tensor | None = None, flattened_patches: torch.Tensor | None = None, width: torch.Tensor | None = None, height: torch.Tensor | None = None, image_embeds_position_mask: torch.Tensor | None = None, attention_mask: torch.Tensor | None = None, past_key_values: Cache | None = None, image_embeds: torch.Tensor | None = None, inputs_embeds: torch.Tensor | None = None, position_ids: torch.Tensor | None = None, use_cache: bool | None = None, output_attentions: bool | None = None, output_hidden_states: bool | None = None, cache_position: torch.LongTensor | None = None, **kwargs: Unpack[TransformersKwargs], ) -> Kosmos2_5ModelOutput: r""" Returns: Examples: ```python >>> from PIL import Image >>> import httpx >>> from io import BytesIO >>> from transformers import AutoProcessor, Kosmos2_5Model >>> model = Kosmos2_5Model.from_pretrained("microsoft/kosmos2.5") >>> processor = AutoProcessor.from_pretrained("microsoft/kosmos2.5") >>> url = "https://huggingface.co/microsoft/kosmos2.5/resolve/main/snowman.jpg" >>> with httpx.stream("GET", url) as response: ... image = Image.open(BytesIO(response.read())) >>> 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 ) vision_model_output = None projection_attentions = None if image_embeds is None: if flattened_patches is not None: vision_model_output = self.vision_model( flattened_patches=flattened_patches, output_attentions=output_attentions, output_hidden_states=output_hidden_states, **kwargs, ) # normalized features image_embeds = nn.functional.normalize(vision_model_output.last_hidden_state, 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, 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, cache_position=cache_position, **kwargs, ) return Kosmos2_5ModelOutput( last_hidden_state=outputs.last_hidden_state, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, width=width, height=height, image_embeds=image_embeds, projection_attentions=projection_attentions, vision_model_output=vision_model_output, ) @add_start_docstrings( """ The text model from KOSMOS-2.5 with a language modeling head on top (linear layer with weights tied to the input embeddings). """, KOSMOS2_5_START_DOCSTRING, ) class Kosmos2_5TextForCausalLM(Kosmos2_5PreTrainedModel, GenerationMixin): config_class = Kosmos2_5TextConfig input_modalities = ("text",) _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"} def __init__(self, config: Kosmos2_5TextConfig): super().__init__(config) self.model = Kosmos2_5TextTransformer(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_5_TEXT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=Kosmos2_5TextConfig) def forward( self, input_ids: torch.Tensor | None = None, attention_mask: torch.Tensor | None = None, image_embeds: torch.Tensor | None = None, image_embeds_position_mask: torch.Tensor | None = None, position_ids: torch.Tensor | None = None, past_key_values: Cache | None = None, inputs_embeds: torch.Tensor | None = None, labels: torch.LongTensor | None = None, use_cache: bool | None = None, output_attentions: bool | None = None, output_hidden_states: bool | None = None, logits_to_keep: int | torch.Tensor = 0, **kwargs: Unpack[TransformersKwargs], ) -> 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: """ 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: BaseModelOutputWithPastAndCrossAttentions = self.model( input_ids=input_ids, attention_mask=attention_mask, image_embeds=image_embeds, image_embeds_position_mask=image_embeds_position_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, **kwargs, ) hidden_states = outputs.last_hidden_state # Only compute necessary logits, and do not upcast them to float if we are not computing the loss slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep logits = self.lm_head(hidden_states[:, slice_indices, :]) loss = None if labels is not None: loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs) return CausalLMOutputWithCrossAttentions( 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, inputs_embeds=None, image_embeds=None, image_embeds_position_mask=None, past_key_values=None, attention_mask=None, use_cache=None, cache_position=None, position_ids=None, is_first_iteration=False, **model_kwargs, ): # Overwritten -- in specific circumstances we don't want to forward image inputs to the model model_inputs = super().prepare_inputs_for_generation( input_ids, inputs_embeds=inputs_embeds, image_embeds=image_embeds, image_embeds_position_mask=image_embeds_position_mask, past_key_values=past_key_values, attention_mask=attention_mask, use_cache=use_cache, cache_position=cache_position, position_ids=position_ids, is_first_iteration=is_first_iteration, **model_kwargs, ) # Pixel values are used only in the first iteration if available # In subsequent iterations, they are already cached if past_key_values is not None and past_key_values.get_seq_length() > 0: model_inputs["image_embeds"] = None model_inputs["image_embeds_position_mask"] = None model_inputs["position_ids"] = ( Kosmos2_5TextSinusoidalPositionalEmbedding.create_position_ids_from_input_ids( input_ids, padding_idx=self.config.pad_token_id, past_key_values_length=0, )[:, -cache_position.shape[0] :] ) # 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 = inputs_embeds.size()[:-1] if inputs_embeds is not None else input_ids.size() mask_len = image_embeds_position_mask.size()[-1] model_inputs["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, ) # Kosmos2.5 has offset for position ids, so we need to create them correctly in PositionEmbedding layer model_inputs.pop("position_ids", None) return model_inputs @add_start_docstrings( """ KOSMOS-2.5 Model for generating text and bounding boxes given an image. The model consists of a vision encoder and a language model. """, KOSMOS2_5_START_DOCSTRING, ) class Kosmos2_5ForConditionalGeneration(Kosmos2_5PreTrainedModel, GenerationMixin): config_class = Kosmos2_5Config def __init__(self, config: Kosmos2_5Config): super().__init__(config) self.text_model = Kosmos2_5TextForCausalLM(config.text_config) self.vision_model = Kosmos2_5VisionModel(config.vision_config) self.image_to_text_projection = Kosmos2_5ImageToTextProjection(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) @can_return_tuple @add_start_docstrings_to_model_forward(KOSMOS2_5_INPUTS_DOCSTRING) @replace_return_docstrings( output_type=Kosmos2_5ForConditionalGenerationModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: torch.Tensor | None = None, flattened_patches: torch.Tensor | None = None, width: torch.Tensor | None = None, height: torch.Tensor | None = None, image_embeds_position_mask: torch.Tensor | None = None, attention_mask: torch.Tensor | None = None, past_key_values: Cache | None = None, image_embeds: torch.Tensor | None = None, inputs_embeds: torch.Tensor | None = None, position_ids: torch.Tensor | None = None, labels: torch.LongTensor | None = None, use_cache: bool | None = None, output_attentions: bool | None = None, output_hidden_states: bool | None = None, logits_to_keep: int | torch.Tensor = 0, **kwargs: Unpack[TransformersKwargs], ) -> Kosmos2_5ForConditionalGenerationModelOutput: 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 httpx >>> from io import BytesIO >>> import torch >>> from transformers import AutoProcessor, Kosmos2_5ForConditionalGeneration >>> repo = "microsoft/kosmos-2.5" >>> device = "cuda:0" >>> dtype = torch.bfloat16 # torch.float16 >>> model = Kosmos2_5ForConditionalGeneration.from_pretrained(repo, device_map=device, dtype=dtype) >>> processor = AutoProcessor.from_pretrained(repo) >>> url = "https://huggingface.co/microsoft/kosmos-2.5/resolve/main/receipt_00008.png" >>> with httpx.stream("GET", url) as response: ... image = Image.open(BytesIO(response.read())) >>> prompt = "" # >>> inputs = processor(text=prompt, images=image, return_tensors="pt") >>> height, width = inputs.pop("height"), inputs.pop("width") >>> inputs = {k: v.to(device) if v is not None else None for k, v in inputs.items()} >>> inputs["flattened_patches"] = inputs["flattened_patches"].to(dtype) >>> generated_ids = model.generate(**inputs,max_new_tokens=1024) >>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] >>> generated_text '1\n[REG] BLACK SAKURA\n45,455\n1\nCOOKIE DOH SAUCES\n0\n1\nNATA DE COCO\n0\nSub Total 45,455\nPB1 (10%) 4,545\nRounding 0\nTotal 50,000\nCard Payment 50,000\n' ```""" 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 ) vision_model_output = None projection_attentions = None if image_embeds is None: if flattened_patches is not None: vision_model_output = self.vision_model( flattened_patches=flattened_patches, output_attentions=output_attentions, output_hidden_states=output_hidden_states, **kwargs, ) image_embeds = nn.functional.normalize(vision_model_output.last_hidden_state, dim=-1) image_embeds, projection_attentions = self.image_to_text_projection(image_embeds) lm_outputs: CausalLMOutputWithCrossAttentions = self.text_model( input_ids=input_ids, attention_mask=attention_mask, image_embeds=image_embeds, image_embeds_position_mask=image_embeds_position_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, logits_to_keep=logits_to_keep, **kwargs, ) return Kosmos2_5ForConditionalGenerationModelOutput( 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, width=width, height=height, image_embeds=image_embeds, projection_attentions=projection_attentions, vision_model_output=vision_model_output, ) def prepare_inputs_for_generation( self, input_ids, flattened_patches=None, image_embeds=None, image_embeds_position_mask=None, past_key_values=None, attention_mask=None, use_cache=None, cache_position=None, position_ids=None, is_first_iteration=False, **model_kwargs, ): # Overwritten -- in specific circumstances we don't want to forward image inputs to the model model_inputs = self.text_model.prepare_inputs_for_generation( input_ids, image_embeds=image_embeds, image_embeds_position_mask=image_embeds_position_mask, past_key_values=past_key_values, attention_mask=attention_mask, use_cache=use_cache, cache_position=cache_position, position_ids=position_ids, is_first_iteration=is_first_iteration, **model_kwargs, ) if is_first_iteration or not use_cache: # If we're in cached decoding stage, `flattened_patches` should be `None` because `input_ids` do not contain special image token anymore # Otherwise we need `flattened_patches` to be passed to model model_inputs["flattened_patches"] = flattened_patches return model_inputs __all__ = [ "Kosmos2_5ForConditionalGeneration", "Kosmos2_5Model", "Kosmos2_5PreTrainedModel", ]