567 lines
29 KiB
Python
567 lines
29 KiB
Python
# 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 VipLlava 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 ... import PreTrainedModel
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from ...activations import ACT2FN
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from ...cache_utils import Cache
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from ...modeling_outputs import ModelOutput
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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_vipllava import VipLlavaConfig
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logger = logging.get_logger(__name__)
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_CONFIG_FOR_DOC = "VipLlavaConfig"
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from ..deprecated._archive_maps import VIPLLAVA_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402
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@dataclass
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# Copied from transformers.models.idefics.modeling_idefics.IdeficsCausalLMOutputWithPast with Idefics->VipLlava
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class VipLlavaCausalLMOutputWithPast(ModelOutput):
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"""
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Base class for VipLlava 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 (`tuple(torch.FloatTensor)`, *optional*):
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Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, num_images,
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sequence_length, hidden_size)`.
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image_hidden_states of the model produced by the vision encoder, and optionally by the perceiver
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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[Tuple[torch.FloatTensor]] = None
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class VipLlavaMultiModalProjector(nn.Module):
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def __init__(self, config: VipLlavaConfig):
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super().__init__()
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self.projector_layernorm = nn.LayerNorm(
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len(config.vision_feature_layers) * config.vision_config.hidden_size, eps=config.projector_layernorm_eps
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)
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self.linear_1 = nn.Linear(
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len(config.vision_feature_layers) * config.vision_config.hidden_size,
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config.text_config.hidden_size,
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bias=True,
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)
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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, hidden_states):
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hidden_states = self.projector_layernorm(hidden_states)
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hidden_states = self.linear_1(hidden_states)
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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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VIPLLAVA_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 ([`VipLlavaConfig`] or [`VipLlavaVisionConfig`]):
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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 VipLlava Model outputting raw hidden-states without any specific head on top.",
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VIPLLAVA_START_DOCSTRING,
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)
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# Copied from transformers.models.llava.modeling_llava.LlavaPreTrainedModel with Llava->VipLlava,llava->vipllava
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class VipLlavaPreTrainedModel(PreTrainedModel):
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config_class = VipLlavaConfig
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base_model_prefix = "model"
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supports_gradient_checkpointing = True
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_no_split_modules = ["VipLlavaVisionAttention"]
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_skip_keys_device_placement = "past_key_values"
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_supports_flash_attn_2 = True
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def _init_weights(self, module):
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# important: this ported version of VipLlava 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/vipllava 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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@property
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def _supports_sdpa(self):
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"""
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Retrieve language_model's attribute to check whether the model supports
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SDPA or not.
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"""
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return self.language_model._supports_sdpa
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VIPLLAVA_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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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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"""
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@add_start_docstrings(
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"""The VIPLLAVA model which consists of a vision backbone and a language model.""",
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VIPLLAVA_START_DOCSTRING,
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)
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# Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration with LLAVA->VIPLLAVA,Llava->VipLlava
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class VipLlavaForConditionalGeneration(VipLlavaPreTrainedModel):
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def __init__(self, config: VipLlavaConfig):
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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 = VipLlavaMultiModalProjector(config)
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self.vocab_size = config.text_config.vocab_size
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self.language_model = AutoModelForCausalLM.from_config(
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config.text_config, attn_implementation=config._attn_implementation
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)
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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 _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
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# Calculate new positions for text tokens in merged image-text sequence.
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# `special_image_token_mask` identifies image tokens. Each image token will be replaced by `nb_text_tokens_per_images - 1` text tokens.
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# `torch.cumsum` computes how each image token shifts subsequent text token positions.
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# - 1 to adjust for zero-based indexing, as `cumsum` inherently increases indices by one.
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new_token_positions = torch.cumsum((special_image_token_mask * (num_image_patches - 1) + 1), -1) - 1
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nb_image_pad = max_embed_dim - 1 - new_token_positions[:, -1]
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if left_padding:
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new_token_positions += nb_image_pad[:, None] # offset for left padding
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text_to_overwrite = new_token_positions[batch_indices, non_image_indices]
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# 3. Create the full embedding, already padded to the maximum position
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final_embedding = torch.zeros(
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batch_size, max_embed_dim, embed_dim, dtype=inputs_embeds.dtype, device=inputs_embeds.device
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)
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final_attention_mask = torch.zeros(
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batch_size, max_embed_dim, dtype=attention_mask.dtype, device=inputs_embeds.device
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)
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if labels is not None:
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final_labels = torch.full(
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(batch_size, max_embed_dim), self.config.ignore_index, dtype=input_ids.dtype, device=input_ids.device
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)
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# In case the Vision model or the Language model has been offloaded to CPU, we need to manually
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# set the corresponding tensors into their correct target device.
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target_device = inputs_embeds.device
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batch_indices, non_image_indices, text_to_overwrite = (
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batch_indices.to(target_device),
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non_image_indices.to(target_device),
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text_to_overwrite.to(target_device),
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)
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attention_mask = attention_mask.to(target_device)
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# 4. Fill the embeddings based on the mask. If we have ["hey" "<image>", "how", "are"]
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# we need to index copy on [0, 577, 578, 579] for the text and [1:576] for the image features
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final_embedding[batch_indices, text_to_overwrite] = inputs_embeds[batch_indices, non_image_indices]
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final_attention_mask[batch_indices, text_to_overwrite] = attention_mask[batch_indices, non_image_indices]
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if labels is not None:
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final_labels[batch_indices, text_to_overwrite] = labels[batch_indices, non_image_indices]
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# 5. Fill the embeddings corresponding to the images. Anything that is still zeros needs filling
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image_to_overwrite = torch.all(final_embedding == 0, dim=-1)
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image_to_overwrite &= image_to_overwrite.cumsum(-1) - 1 >= nb_image_pad[:, None].to(target_device)
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if image_to_overwrite.sum() != image_features.shape[:-1].numel():
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raise ValueError(
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f"The input provided to the model are wrong. The number of image tokens is {torch.sum(special_image_token_mask)} while"
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f" the number of image given to the model is {num_images}. This prevents correct indexing and breaks batch generation."
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)
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final_embedding[image_to_overwrite] = image_features.contiguous().reshape(-1, embed_dim).to(target_device)
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final_attention_mask |= image_to_overwrite
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position_ids = (final_attention_mask.cumsum(-1) - 1).masked_fill_((final_attention_mask == 0), 1)
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# 6. Mask out the embedding at padding positions, as we later use the past_key_value value to determine the non-attended tokens.
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batch_indices, pad_indices = torch.where(input_ids == self.pad_token_id)
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indices_to_mask = new_token_positions[batch_indices, pad_indices]
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final_embedding[batch_indices, indices_to_mask] = 0
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if labels is None:
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final_labels = None
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return final_embedding, final_attention_mask, final_labels, position_ids
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@add_start_docstrings_to_model_forward(VIPLLAVA_INPUTS_DOCSTRING)
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@replace_return_docstrings(output_type=VipLlavaCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
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# Ignore copy
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def forward(
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self,
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input_ids: torch.LongTensor = None,
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pixel_values: torch.FloatTensor = None,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_values: Optional[List[torch.FloatTensor]] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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vision_feature_layers: Optional[List[int]] = None,
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labels: Optional[torch.LongTensor] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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) -> Union[Tuple, VipLlavaCausalLMOutputWithPast]:
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r"""
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Args:
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labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
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Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
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config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
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(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
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Returns:
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Example:
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```python
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>>> import torch
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>>> from PIL import Image
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>>> import requests
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>>> from transformers import AutoProcessor, VipLlavaForConditionalGeneration
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>>> model = VipLlavaForConditionalGeneration.from_pretrained("llava-hf/vip-llava-7b-hf", device_map="auto", torch_dtype=torch.float16)
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>>> processor = AutoProcessor.from_pretrained("llava-hf/vip-llava-7b-hf")
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>>> prompt = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.###Human: <image>\n{}###Assistant:"
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>>> question = "Can you please describe this image?"
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>>> prompt = prompt.format(question)
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>>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/compel-neg.png"
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>>> image = Image.open(requests.get(url, stream=True).raw)
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>>> inputs = processor(text=text, images=image, return_tensors="pt").to(0, torch.float16)
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>>> # Generate
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>>> generate_ids = model.generate(**inputs, max_new_tokens=20)
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>>> processor.decode(generate_ids[0][len(inputs["input_ids"][0]):], skip_special_tokens=True)
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The image features a brown and white cat sitting on a green surface, with a red ball in its
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```"""
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|
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
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)
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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vision_feature_layers = (
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vision_feature_layers if vision_feature_layers is not None else self.config.vision_feature_layers
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|
)
|
|
|
|
if inputs_embeds is None:
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# 1. Extra the input embeddings
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inputs_embeds = self.get_input_embeddings()(input_ids)
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|
|
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# 2. Merge text and images
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if pixel_values is not None and input_ids.shape[1] != 1:
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|
image_outputs = self.vision_tower(pixel_values, output_hidden_states=True)
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|
# For VIP-llava, the image features are computed this way
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|
# We select the features from index 1: for the layers -2, -5, -8, -11 and 6
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image_features = [image_outputs.hidden_states[index][:, 1:] for index in vision_feature_layers]
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image_features = torch.cat(image_features, dim=-1)
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|
|
|
image_features = self.multi_modal_projector(image_features)
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|
inputs_embeds, attention_mask, labels, position_ids = self._merge_input_ids_with_image_features(
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image_features, inputs_embeds, input_ids, attention_mask, labels
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|
)
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|
if labels is None:
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labels = torch.full_like(attention_mask, self.config.ignore_index).to(torch.long)
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|
else:
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|
# In case input_ids.shape[1] == 1 & pixel_values==None & past_key_values != None, we are in the case of
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|
# generation with cache
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|
if past_key_values is not None and pixel_values is not None and input_ids.shape[1] == 1:
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|
# Retrieve the first layer to inspect the logits and mask out the hidden states
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|
# that are set to 0
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|
first_layer_past_key_value = past_key_values[0][0][:, :, :, 0]
|
|
|
|
# Sum all dimensions of head_dim (-1) to avoid random errors such as: https://github.com/huggingface/transformers/pull/28032#issuecomment-1863691941
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|
batch_index, non_attended_tokens = torch.where(first_layer_past_key_value.float().sum(-2) == 0)
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|
|
|
target_length = input_ids.shape[1]
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|
past_length = first_layer_past_key_value.shape[-1]
|
|
|
|
extended_attention_mask = torch.ones(
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|
(attention_mask.shape[0], past_length),
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|
dtype=attention_mask.dtype,
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|
device=attention_mask.device,
|
|
)
|
|
|
|
# Filter out only the tokens that can be un-attended, this can happen
|
|
# in the case one uses Llava + Fused modules where the cache on the
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|
# first iteration is already big enough, or if one passes custom cache
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|
valid_indices = non_attended_tokens < extended_attention_mask.size(-1)
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|
new_batch_index = batch_index[valid_indices]
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|
new_non_attended_tokens = non_attended_tokens[valid_indices]
|
|
|
|
# Zero-out the places where we don't need to attend
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|
extended_attention_mask[new_batch_index, new_non_attended_tokens] = 0
|
|
|
|
attention_mask = torch.cat((extended_attention_mask, attention_mask[:, -target_length:]), dim=1)
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|
position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1
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|
|
|
outputs = self.language_model(
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|
attention_mask=attention_mask,
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|
position_ids=position_ids,
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|
past_key_values=past_key_values,
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|
inputs_embeds=inputs_embeds,
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|
use_cache=use_cache,
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|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
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|
return_dict=return_dict,
|
|
)
|
|
|
|
logits = outputs[0]
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
# Shift so that tokens < n predict n
|
|
if attention_mask is not None:
|
|
shift_attention_mask = attention_mask[..., 1:]
|
|
shift_logits = logits[..., :-1, :][shift_attention_mask.to(logits.device) != 0].contiguous()
|
|
shift_labels = labels[..., 1:][shift_attention_mask.to(labels.device) != 0].contiguous()
|
|
else:
|
|
shift_logits = logits[..., :-1, :].contiguous()
|
|
shift_labels = labels[..., 1:].contiguous()
|
|
# Flatten the tokens
|
|
loss_fct = nn.CrossEntropyLoss()
|
|
loss = loss_fct(
|
|
shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1).to(shift_logits.device)
|
|
)
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[1:]
|
|
return (loss,) + output if loss is not None else output
|
|
|
|
return VipLlavaCausalLMOutputWithPast(
|
|
loss=loss,
|
|
logits=logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
def prepare_inputs_for_generation(
|
|
self, input_ids, past_key_values=None, inputs_embeds=None, pixel_values=None, attention_mask=None, **kwargs
|
|
):
|
|
if past_key_values is not None:
|
|
if isinstance(past_key_values, Cache):
|
|
cache_length = past_key_values.get_seq_length()
|
|
past_length = past_key_values.seen_tokens
|
|
else:
|
|
cache_length = past_length = past_key_values[0][0].shape[2]
|
|
|
|
# Keep only the unprocessed tokens:
|
|
# 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
|
|
# some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
|
|
# input)
|
|
if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
|
|
input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
|
|
# 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
|
|
# input_ids based on the past_length.
|
|
elif past_length < input_ids.shape[1]:
|
|
input_ids = input_ids[:, past_length:]
|
|
# 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
|
|
elif self.config.image_token_index in input_ids:
|
|
input_ids = input_ids[:, input_ids.shape[1] - 1 :]
|
|
# If the cache has seen more tokens than it can hold, then the cache has a size limit. Let's discard the
|
|
# older attention values, as their corresponding values are not part of the input.
|
|
if cache_length < past_length and attention_mask is not None:
|
|
attention_mask = attention_mask[:, -(cache_length + input_ids.shape[1]) :]
|
|
|
|
position_ids = kwargs.get("position_ids", None)
|
|
if attention_mask is not None and position_ids is None:
|
|
# create position_ids on the fly for batch generation
|
|
position_ids = attention_mask.long().cumsum(-1) - 1
|
|
position_ids.masked_fill_(attention_mask == 0, 1)
|
|
if past_key_values:
|
|
position_ids = position_ids[:, -input_ids.shape[1] :]
|
|
|
|
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
|
if inputs_embeds is not None and past_key_values is None:
|
|
model_inputs = {"inputs_embeds": inputs_embeds}
|
|
else:
|
|
model_inputs = {"input_ids": input_ids}
|
|
|
|
model_inputs.update(
|
|
{
|
|
"position_ids": position_ids,
|
|
"past_key_values": past_key_values,
|
|
"use_cache": kwargs.get("use_cache"),
|
|
"attention_mask": attention_mask,
|
|
"pixel_values": pixel_values,
|
|
}
|
|
)
|
|
return model_inputs
|
|
|
|
def _reorder_cache(self, *args, **kwargs):
|
|
return self.language_model._reorder_cache(*args, **kwargs)
|