616 lines
32 KiB
Python
616 lines
32 KiB
Python
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# coding=utf-8
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# Copyright 2023 the HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""PyTorch Llava model."""
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from dataclasses import dataclass
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from typing import List, Optional, Tuple, Union
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import torch
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import torch.utils.checkpoint
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from torch import nn
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from ...activations import ACT2FN
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from ...generation import GenerationMixin
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from ...modeling_outputs import ModelOutput
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from ...modeling_utils import PreTrainedModel
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from ...utils import (
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add_start_docstrings,
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add_start_docstrings_to_model_forward,
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logging,
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replace_return_docstrings,
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)
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from ..auto import AutoModel, AutoModelForCausalLM
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from .configuration_llava import LlavaConfig
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logger = logging.get_logger(__name__)
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_CONFIG_FOR_DOC = "LlavaConfig"
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# Base docstring
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_CHECKPOINT_FOR_DOC = "llava-hf/llava-1.5-7b-hf"
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@dataclass
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class LlavaCausalLMOutputWithPast(ModelOutput):
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"""
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Base class for Llava causal language model (or autoregressive) outputs.
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Args:
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loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
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Language modeling loss (for next-token prediction).
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logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
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Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
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past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
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Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
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`(batch_size, num_heads, sequence_length, embed_size_per_head)`)
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Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
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`past_key_values` input) to speed up sequential decoding.
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hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
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one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
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Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
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attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
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sequence_length)`.
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Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
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heads.
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image_hidden_states (`torch.FloatTensor`, *optional*):
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A `torch.FloatTensor` of size (batch_size, num_images, sequence_length, hidden_size)`.
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image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
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"""
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loss: Optional[torch.FloatTensor] = None
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logits: torch.FloatTensor = None
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past_key_values: Optional[List[torch.FloatTensor]] = None
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hidden_states: Optional[Tuple[torch.FloatTensor]] = None
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attentions: Optional[Tuple[torch.FloatTensor]] = None
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image_hidden_states: Optional[torch.FloatTensor] = None
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class LlavaMultiModalProjector(nn.Module):
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def __init__(self, config: LlavaConfig):
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super().__init__()
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self.linear_1 = nn.Linear(config.vision_config.hidden_size, config.text_config.hidden_size, bias=True)
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self.act = ACT2FN[config.projector_hidden_act]
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self.linear_2 = nn.Linear(config.text_config.hidden_size, config.text_config.hidden_size, bias=True)
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def forward(self, image_features):
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hidden_states = self.linear_1(image_features)
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hidden_states = self.act(hidden_states)
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hidden_states = self.linear_2(hidden_states)
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return hidden_states
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LLAVA_START_DOCSTRING = r"""
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This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
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library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
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etc.)
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This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
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Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
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and behavior.
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Parameters:
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config ([`LlavaConfig`] or [`LlavaVisionConfig`]):
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Model configuration class with all the parameters of the model. Initializing with a config file does not
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load the weights associated with the model, only the configuration. Check out the
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[`~PreTrainedModel.from_pretrained`] method to load the model weights.
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"""
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@add_start_docstrings(
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"The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
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LLAVA_START_DOCSTRING,
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)
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class LlavaPreTrainedModel(PreTrainedModel):
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config_class = LlavaConfig
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base_model_prefix = "model"
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supports_gradient_checkpointing = True
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_no_split_modules = ["LlavaVisionAttention"]
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_skip_keys_device_placement = "past_key_values"
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_supports_cache_class = True
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_supports_flash_attn_2 = True
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_supports_sdpa = True
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def _init_weights(self, module):
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# important: this ported version of Llava isn't meant for training from scratch - only
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# inference and fine-tuning - so the proper init weights code has been removed - the original codebase
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# https://github.com/haotian-liu/LLaVA/tree/main/llava should serve for that purpose
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std = (
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self.config.initializer_range
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if hasattr(self.config, "initializer_range")
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else self.config.text_config.initializer_range
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)
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if hasattr(module, "class_embedding"):
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module.class_embedding.data.normal_(mean=0.0, std=std)
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if isinstance(module, (nn.Linear, nn.Conv2d)):
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module.weight.data.normal_(mean=0.0, std=std)
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if module.bias is not None:
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module.bias.data.zero_()
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elif isinstance(module, nn.Embedding):
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module.weight.data.normal_(mean=0.0, std=std)
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if module.padding_idx is not None:
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module.weight.data[module.padding_idx].zero_()
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LLAVA_INPUTS_DOCSTRING = r"""
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Args:
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input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
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Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
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it.
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Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
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[`PreTrainedTokenizer.__call__`] for details.
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[What are input IDs?](../glossary#input-ids)
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pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)):
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The tensors corresponding to the input images. Pixel values can be obtained using
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[`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details ([]`LlavaProcessor`] uses
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[`CLIPImageProcessor`] for processing images).
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attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
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Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
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- 1 for tokens that are **not masked**,
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- 0 for tokens that are **masked**.
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[What are attention masks?](../glossary#attention-mask)
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Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
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[`PreTrainedTokenizer.__call__`] for details.
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If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
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`past_key_values`).
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If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
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and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
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information on the default strategy.
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- 1 indicates the head is **not masked**,
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- 0 indicates the head is **masked**.
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position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
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Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
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config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
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past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
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Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
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`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
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`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
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Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
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blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
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If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
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don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
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`decoder_input_ids` of shape `(batch_size, sequence_length)`.
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inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
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Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
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is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
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model's internal embedding lookup matrix.
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vision_feature_layer (`int`, *optional*, defaults to -2):
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The index of the layer to select the vision feature.
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vision_feature_select_strategy (`str`, *optional*, defaults to `"default"`):
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The feature selection strategy used to select the vision feature from the vision backbone.
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Can be one of `"default"` or `"full"`.
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use_cache (`bool`, *optional*):
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If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
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`past_key_values`).
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output_attentions (`bool`, *optional*):
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Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
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tensors for more detail.
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output_hidden_states (`bool`, *optional*):
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Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
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more detail.
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return_dict (`bool`, *optional*):
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Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
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cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
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Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
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this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
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the complete sequence length.
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"""
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@add_start_docstrings(
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"""The LLAVA model which consists of a vision backbone and a language model.""",
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LLAVA_START_DOCSTRING,
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)
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class LlavaForConditionalGeneration(LlavaPreTrainedModel, GenerationMixin):
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def __init__(self, config: LlavaConfig):
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super().__init__(config)
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self.vision_tower = AutoModel.from_config(config.vision_config)
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self.multi_modal_projector = LlavaMultiModalProjector(config)
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self.vocab_size = config.text_config.vocab_size
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self.language_model = AutoModelForCausalLM.from_config(config.text_config)
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self.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else -1
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self.post_init()
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def get_input_embeddings(self):
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return self.language_model.get_input_embeddings()
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def set_input_embeddings(self, value):
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self.language_model.set_input_embeddings(value)
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def get_output_embeddings(self):
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return self.language_model.get_output_embeddings()
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def set_output_embeddings(self, new_embeddings):
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self.language_model.set_output_embeddings(new_embeddings)
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def set_decoder(self, decoder):
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self.language_model.set_decoder(decoder)
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def get_decoder(self):
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return self.language_model.get_decoder()
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def tie_weights(self):
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return self.language_model.tie_weights()
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def resize_token_embeddings(self, new_num_tokens: Optional[int] = None, pad_to_multiple_of=None) -> nn.Embedding:
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model_embeds = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
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# update vocab size
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self.config.text_config.vocab_size = model_embeds.num_embeddings
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self.vocab_size = model_embeds.num_embeddings
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return model_embeds
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def get_image_features(
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self, pixel_values: torch.FloatTensor, vision_feature_layer: int, vision_feature_select_strategy: str
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):
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"""
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Obtains image last hidden states from the vision tower and apply multimodal projection.
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Args:
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pixel_values (`torch.FloatTensor]` of shape `(batch_size, channels, height, width)`)
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The tensors corresponding to the input images.
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vision_feature_layer (`int`):
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The index of the layer to select the vision feature.
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vision_feature_select_strategy (`str`):
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The feature selection strategy used to select the vision feature from the vision backbone.
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Can be one of `"default"` or `"full"`
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Returns:
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image_features (`torch.Tensor`): Image feature tensor of shape `(num_images, image_length, embed_dim)`).
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"""
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image_outputs = self.vision_tower(pixel_values, output_hidden_states=True)
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# this is not memory efficient at all (output_hidden_states=True) will save all the hidden stated.
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selected_image_feature = image_outputs.hidden_states[vision_feature_layer]
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if vision_feature_select_strategy == "default":
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selected_image_feature = selected_image_feature[:, 1:]
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elif vision_feature_select_strategy == "full":
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selected_image_feature = selected_image_feature
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else:
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raise ValueError(f"Unexpected select feature strategy: {self.config.vision_feature_select_strategy}")
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image_features = self.multi_modal_projector(selected_image_feature)
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return image_features
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def _merge_input_ids_with_image_features(self, image_features, inputs_embeds, input_ids, attention_mask, labels):
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num_images, num_image_patches, embed_dim = image_features.shape
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batch_size, sequence_length = input_ids.shape
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left_padding = not torch.sum(input_ids[:, -1] == torch.tensor(self.pad_token_id))
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# 1. Create a mask to know where special image tokens are
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special_image_token_mask = input_ids == self.config.image_token_index
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num_special_image_tokens = torch.sum(special_image_token_mask, dim=-1)
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# Compute the maximum embed dimension
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max_embed_dim = (num_special_image_tokens.max() * (num_image_patches - 1)) + sequence_length
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batch_indices, non_image_indices = torch.where(input_ids != self.config.image_token_index)
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|
||
|
|
# 2. Compute the positions where text should be written
|
||
|
|
# Calculate new positions for text tokens in merged image-text sequence.
|
||
|
|
# `special_image_token_mask` identifies image tokens. Each image token will be replaced by `nb_text_tokens_per_images - 1` text tokens.
|
||
|
|
# `torch.cumsum` computes how each image token shifts subsequent text token positions.
|
||
|
|
# - 1 to adjust for zero-based indexing, as `cumsum` inherently increases indices by one.
|
||
|
|
new_token_positions = torch.cumsum((special_image_token_mask * (num_image_patches - 1) + 1), -1) - 1
|
||
|
|
nb_image_pad = max_embed_dim - 1 - new_token_positions[:, -1]
|
||
|
|
if left_padding:
|
||
|
|
new_token_positions += nb_image_pad[:, None] # offset for left padding
|
||
|
|
text_to_overwrite = new_token_positions[batch_indices, non_image_indices]
|
||
|
|
|
||
|
|
# 3. Create the full embedding, already padded to the maximum position
|
||
|
|
final_embedding = torch.zeros(
|
||
|
|
batch_size, max_embed_dim, embed_dim, dtype=inputs_embeds.dtype, device=inputs_embeds.device
|
||
|
|
)
|
||
|
|
final_attention_mask = torch.zeros(
|
||
|
|
batch_size, max_embed_dim, dtype=attention_mask.dtype, device=inputs_embeds.device
|
||
|
|
)
|
||
|
|
if labels is not None:
|
||
|
|
final_labels = torch.full(
|
||
|
|
(batch_size, max_embed_dim), self.config.ignore_index, dtype=input_ids.dtype, device=input_ids.device
|
||
|
|
)
|
||
|
|
# In case the Vision model or the Language model has been offloaded to CPU, we need to manually
|
||
|
|
# set the corresponding tensors into their correct target device.
|
||
|
|
target_device = inputs_embeds.device
|
||
|
|
batch_indices, non_image_indices, text_to_overwrite = (
|
||
|
|
batch_indices.to(target_device),
|
||
|
|
non_image_indices.to(target_device),
|
||
|
|
text_to_overwrite.to(target_device),
|
||
|
|
)
|
||
|
|
attention_mask = attention_mask.to(target_device)
|
||
|
|
|
||
|
|
# 4. Fill the embeddings based on the mask. If we have ["hey" "<image>", "how", "are"]
|
||
|
|
# we need to index copy on [0, 577, 578, 579] for the text and [1:576] for the image features
|
||
|
|
final_embedding[batch_indices, text_to_overwrite] = inputs_embeds[batch_indices, non_image_indices]
|
||
|
|
final_attention_mask[batch_indices, text_to_overwrite] = attention_mask[batch_indices, non_image_indices]
|
||
|
|
if labels is not None:
|
||
|
|
final_labels[batch_indices, text_to_overwrite] = labels[batch_indices, non_image_indices]
|
||
|
|
|
||
|
|
# 5. Fill the embeddings corresponding to the images. Anything that is not `text_positions` needs filling (#29835)
|
||
|
|
image_to_overwrite = torch.full(
|
||
|
|
(batch_size, max_embed_dim), True, dtype=torch.bool, device=inputs_embeds.device
|
||
|
|
)
|
||
|
|
image_to_overwrite[batch_indices, text_to_overwrite] = False
|
||
|
|
image_to_overwrite &= image_to_overwrite.cumsum(-1) - 1 >= nb_image_pad[:, None].to(target_device)
|
||
|
|
|
||
|
|
if image_to_overwrite.sum() != image_features.shape[:-1].numel():
|
||
|
|
raise ValueError(
|
||
|
|
f"The input provided to the model are wrong. The number of image tokens is {torch.sum(special_image_token_mask)} while"
|
||
|
|
f" the number of image given to the model is {num_images}. This prevents correct indexing and breaks batch generation."
|
||
|
|
)
|
||
|
|
|
||
|
|
final_embedding[image_to_overwrite] = image_features.contiguous().reshape(-1, embed_dim).to(target_device)
|
||
|
|
final_attention_mask |= image_to_overwrite
|
||
|
|
position_ids = (final_attention_mask.cumsum(-1) - 1).masked_fill_((final_attention_mask == 0), 1)
|
||
|
|
|
||
|
|
# 6. Mask out the embedding at padding positions, as we later use the past_key_value value to determine the non-attended tokens.
|
||
|
|
batch_indices, pad_indices = torch.where(input_ids == self.pad_token_id)
|
||
|
|
indices_to_mask = new_token_positions[batch_indices, pad_indices]
|
||
|
|
|
||
|
|
final_embedding[batch_indices, indices_to_mask] = 0
|
||
|
|
|
||
|
|
if labels is None:
|
||
|
|
final_labels = None
|
||
|
|
|
||
|
|
return final_embedding, final_attention_mask, final_labels, position_ids
|
||
|
|
|
||
|
|
@add_start_docstrings_to_model_forward(LLAVA_INPUTS_DOCSTRING)
|
||
|
|
@replace_return_docstrings(output_type=LlavaCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
|
||
|
|
def forward(
|
||
|
|
self,
|
||
|
|
input_ids: torch.LongTensor = None,
|
||
|
|
pixel_values: torch.FloatTensor = None,
|
||
|
|
attention_mask: Optional[torch.Tensor] = None,
|
||
|
|
position_ids: Optional[torch.LongTensor] = None,
|
||
|
|
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
||
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||
|
|
vision_feature_layer: Optional[int] = None,
|
||
|
|
vision_feature_select_strategy: Optional[str] = None,
|
||
|
|
labels: Optional[torch.LongTensor] = None,
|
||
|
|
use_cache: Optional[bool] = None,
|
||
|
|
output_attentions: Optional[bool] = None,
|
||
|
|
output_hidden_states: Optional[bool] = None,
|
||
|
|
return_dict: Optional[bool] = None,
|
||
|
|
cache_position: Optional[torch.LongTensor] = None,
|
||
|
|
num_logits_to_keep: int = 0,
|
||
|
|
) -> Union[Tuple, LlavaCausalLMOutputWithPast]:
|
||
|
|
r"""
|
||
|
|
Args:
|
||
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
||
|
|
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
||
|
|
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
||
|
|
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
||
|
|
|
||
|
|
num_logits_to_keep (`int`, *optional*):
|
||
|
|
Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all
|
||
|
|
`input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
|
||
|
|
token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
|
||
|
|
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
|
||
|
|
Example:
|
||
|
|
|
||
|
|
```python
|
||
|
|
>>> from PIL import Image
|
||
|
|
>>> import requests
|
||
|
|
>>> from transformers import AutoProcessor, LlavaForConditionalGeneration
|
||
|
|
|
||
|
|
>>> model = LlavaForConditionalGeneration.from_pretrained("llava-hf/llava-1.5-7b-hf")
|
||
|
|
>>> processor = AutoProcessor.from_pretrained("llava-hf/llava-1.5-7b-hf")
|
||
|
|
|
||
|
|
>>> prompt = "USER: <image>\nWhat's the content of the image? ASSISTANT:"
|
||
|
|
>>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
|
||
|
|
>>> image = Image.open(requests.get(url, stream=True).raw)
|
||
|
|
|
||
|
|
>>> inputs = processor(images=image, text=prompt, return_tensors="pt")
|
||
|
|
|
||
|
|
>>> # Generate
|
||
|
|
>>> generate_ids = model.generate(**inputs, max_new_tokens=15)
|
||
|
|
>>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
||
|
|
"USER: \nWhat's the content of the image? ASSISTANT: The image features a busy city street with a stop sign prominently displayed"
|
||
|
|
```"""
|
||
|
|
|
||
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
||
|
|
output_hidden_states = (
|
||
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
||
|
|
)
|
||
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||
|
|
vision_feature_layer = (
|
||
|
|
vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
|
||
|
|
)
|
||
|
|
vision_feature_select_strategy = (
|
||
|
|
vision_feature_select_strategy
|
||
|
|
if vision_feature_select_strategy is not None
|
||
|
|
else self.config.vision_feature_select_strategy
|
||
|
|
)
|
||
|
|
|
||
|
|
if (input_ids is None) ^ (inputs_embeds is not None):
|
||
|
|
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
||
|
|
|
||
|
|
if pixel_values is not None and inputs_embeds is not None:
|
||
|
|
raise ValueError(
|
||
|
|
"You cannot specify both pixel_values and inputs_embeds at the same time, and must specify either one"
|
||
|
|
)
|
||
|
|
|
||
|
|
legacy_processing = False
|
||
|
|
if inputs_embeds is None:
|
||
|
|
inputs_embeds = self.get_input_embeddings()(input_ids)
|
||
|
|
|
||
|
|
# if the number of image tokens is more than image embeddings seq length, then prob we expanded it in processing
|
||
|
|
# not very reliable, but we don't expect one to actually pass 500+ images for one prompt
|
||
|
|
# In case we're in decoding stage, legacy behavior is checked by presence of pixel values even if use_cache=True
|
||
|
|
legacy_processing = (
|
||
|
|
(input_ids == self.config.image_token_index).sum(1).max() < self.config.image_seq_length
|
||
|
|
) or (input_ids.shape[-1] == 1 and pixel_values is not None)
|
||
|
|
|
||
|
|
image_features = None
|
||
|
|
if pixel_values is not None:
|
||
|
|
image_features = self.get_image_features(
|
||
|
|
pixel_values=pixel_values,
|
||
|
|
vision_feature_layer=vision_feature_layer,
|
||
|
|
vision_feature_select_strategy=vision_feature_select_strategy,
|
||
|
|
)
|
||
|
|
|
||
|
|
if legacy_processing:
|
||
|
|
logger.warning_once(
|
||
|
|
"Expanding inputs for image tokens in LLaVa should be done in processing. "
|
||
|
|
"Please add `patch_size` and `vision_feature_select_strategy` to the model's processing config or set directly "
|
||
|
|
"with `processor.patch_size = {{patch_size}}` and processor.vision_feature_select_strategy = {{vision_feature_select_strategy}}`. "
|
||
|
|
"Using processors without these attributes in the config is deprecated and will throw an error in v4.47."
|
||
|
|
)
|
||
|
|
# prefill stage vs decoding stage (legacy behavior copied)
|
||
|
|
if input_ids.shape[1] != 1:
|
||
|
|
inputs_embeds, attention_mask, labels, position_ids = self._merge_input_ids_with_image_features(
|
||
|
|
image_features, inputs_embeds, input_ids, attention_mask, labels
|
||
|
|
)
|
||
|
|
cache_position = torch.arange(attention_mask.shape[1], device=attention_mask.device)
|
||
|
|
else:
|
||
|
|
# Retrieve the first layer to inspect the logits and mask out the hidden states
|
||
|
|
# that are set to 0
|
||
|
|
first_layer_past_key_value = past_key_values[0][0][:, :, :, 0]
|
||
|
|
|
||
|
|
# Sum all dimensions of head_dim (-2) to avoid random errors such as: https://github.com/huggingface/transformers/pull/28032#issuecomment-1863691941
|
||
|
|
batch_index, non_attended_tokens = torch.where(first_layer_past_key_value.float().sum(-2) == 0)
|
||
|
|
|
||
|
|
# Get the target length
|
||
|
|
target_length = input_ids.shape[1]
|
||
|
|
past_length = first_layer_past_key_value.shape[-1]
|
||
|
|
|
||
|
|
extended_attention_mask = torch.ones(
|
||
|
|
(attention_mask.shape[0], past_length),
|
||
|
|
dtype=attention_mask.dtype,
|
||
|
|
device=attention_mask.device,
|
||
|
|
)
|
||
|
|
|
||
|
|
# Filter out only the tokens that can be un-attended, this can happen
|
||
|
|
# if one uses Llava + Fused modules where the cache on the
|
||
|
|
# first iteration is already big enough, or if one passes custom cache
|
||
|
|
valid_indices = non_attended_tokens < extended_attention_mask.size(-1)
|
||
|
|
new_batch_index = batch_index[valid_indices]
|
||
|
|
new_non_attended_tokens = non_attended_tokens[valid_indices]
|
||
|
|
|
||
|
|
# Zero-out the places where we don't need to attend
|
||
|
|
extended_attention_mask[new_batch_index, new_non_attended_tokens] = 0
|
||
|
|
|
||
|
|
attention_mask = torch.cat((extended_attention_mask, attention_mask[:, -target_length:]), dim=1)
|
||
|
|
position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1
|
||
|
|
cache_position = torch.arange(attention_mask.shape[1], device=attention_mask.device)[-target_length:]
|
||
|
|
|
||
|
|
# TODO: @raushan retain only the new behavior after v4.47
|
||
|
|
elif image_features is not None:
|
||
|
|
n_image_tokens = (input_ids == self.config.image_token_index).sum().item()
|
||
|
|
n_image_features = image_features.shape[0] * image_features.shape[1]
|
||
|
|
|
||
|
|
if n_image_tokens != n_image_features:
|
||
|
|
raise ValueError(
|
||
|
|
f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
|
||
|
|
)
|
||
|
|
special_image_mask = (
|
||
|
|
(input_ids == self.config.image_token_index)
|
||
|
|
.unsqueeze(-1)
|
||
|
|
.expand_as(inputs_embeds)
|
||
|
|
.to(inputs_embeds.device)
|
||
|
|
)
|
||
|
|
image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
|
||
|
|
inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)
|
||
|
|
|
||
|
|
outputs = self.language_model(
|
||
|
|
attention_mask=attention_mask,
|
||
|
|
position_ids=position_ids,
|
||
|
|
past_key_values=past_key_values,
|
||
|
|
inputs_embeds=inputs_embeds,
|
||
|
|
use_cache=use_cache,
|
||
|
|
output_attentions=output_attentions,
|
||
|
|
output_hidden_states=output_hidden_states,
|
||
|
|
return_dict=return_dict,
|
||
|
|
cache_position=cache_position,
|
||
|
|
num_logits_to_keep=num_logits_to_keep,
|
||
|
|
)
|
||
|
|
|
||
|
|
logits = outputs[0]
|
||
|
|
|
||
|
|
loss = None
|
||
|
|
if labels is not None:
|
||
|
|
# Shift so that tokens < n predict n
|
||
|
|
if attention_mask is not None:
|
||
|
|
# we use the input attention mask to shift the logits and labels, because it is 2D.
|
||
|
|
# we also crop attn mask in case it is longer, which happens in PrefixTuning with peft
|
||
|
|
shift_attention_mask = attention_mask[:, -(logits.shape[1] - 1) :].to(logits.device)
|
||
|
|
shift_logits = logits[..., :-1, :][shift_attention_mask.to(logits.device) != 0].contiguous()
|
||
|
|
shift_labels = labels[..., 1:][shift_attention_mask.to(labels.device) != 0].contiguous()
|
||
|
|
else:
|
||
|
|
shift_logits = logits[..., :-1, :].contiguous()
|
||
|
|
shift_labels = labels[..., 1:].contiguous()
|
||
|
|
# Flatten the tokens
|
||
|
|
loss_fct = nn.CrossEntropyLoss()
|
||
|
|
loss = loss_fct(
|
||
|
|
shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1).to(shift_logits.device)
|
||
|
|
)
|
||
|
|
|
||
|
|
if not return_dict:
|
||
|
|
output = (logits,) + outputs[1:]
|
||
|
|
return (loss,) + output if loss is not None else output
|
||
|
|
|
||
|
|
return LlavaCausalLMOutputWithPast(
|
||
|
|
loss=loss,
|
||
|
|
logits=logits,
|
||
|
|
past_key_values=outputs.past_key_values,
|
||
|
|
hidden_states=outputs.hidden_states,
|
||
|
|
attentions=outputs.attentions,
|
||
|
|
image_hidden_states=image_features if pixel_values is not None else None,
|
||
|
|
)
|
||
|
|
|
||
|
|
def prepare_inputs_for_generation(
|
||
|
|
self,
|
||
|
|
input_ids,
|
||
|
|
past_key_values=None,
|
||
|
|
inputs_embeds=None,
|
||
|
|
pixel_values=None,
|
||
|
|
attention_mask=None,
|
||
|
|
cache_position=None,
|
||
|
|
num_logits_to_keep=None,
|
||
|
|
**kwargs,
|
||
|
|
):
|
||
|
|
# Overwritten -- in specific circumstances we don't want to forward image inputs to the model
|
||
|
|
|
||
|
|
model_inputs = self.language_model.prepare_inputs_for_generation(
|
||
|
|
input_ids,
|
||
|
|
past_key_values=past_key_values,
|
||
|
|
inputs_embeds=inputs_embeds,
|
||
|
|
attention_mask=attention_mask,
|
||
|
|
cache_position=cache_position,
|
||
|
|
num_logits_to_keep=num_logits_to_keep,
|
||
|
|
**kwargs,
|
||
|
|
)
|
||
|
|
|
||
|
|
if cache_position[0] == 0:
|
||
|
|
# If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore
|
||
|
|
# Otherwise we need pixel values to be passed to model
|
||
|
|
model_inputs["pixel_values"] = pixel_values
|
||
|
|
|
||
|
|
return model_inputs
|