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470 lines
23 KiB
Python
470 lines
23 KiB
Python
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from examples/modular-transformers/modular_new_task_model.py.
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# Do NOT edit this file manually as any edits will be overwritten by the generation of
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# the file from the modular. If any change should be done, please apply the change to the
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# modular_new_task_model.py file directly. One of our CI enforces this.
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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from dataclasses import dataclass
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from typing import ClassVar, Optional, Union
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import torch
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from torch import nn
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from ...cache_utils import Cache, HybridCache, StaticCache
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from ...generation import GenerationMixin
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from ...modeling_utils import PreTrainedModel
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from ...utils import (
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ModelOutput,
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add_start_docstrings,
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add_start_docstrings_to_model_forward,
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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_new_task_model import NewTaskModelConfig
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_CONFIG_FOR_DOC = "NewTaskModelConfig"
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@dataclass
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class NewTaskModelCausalLMOutputWithPast(ModelOutput):
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"""
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Base class for NewTaskModelcausal 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.text_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 after projecting 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[Union[list[torch.FloatTensor], Cache]] = 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 NewTaskModelMultiModalProjector(nn.Module):
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def __init__(self, config: NewTaskModelConfig):
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super().__init__()
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self.linear = nn.Linear(config.vision_config.hidden_size, config.vision_config.projection_dim, bias=True)
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def forward(self, image_features):
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hidden_states = self.linear(image_features)
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return hidden_states
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NEW_TASK_MODEL_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 ([`NewTaskModelConfig`] or [`NewTaskModelVisionConfig`]):
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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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NEW_TASK_MODEL_START_DOCSTRING,
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)
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class NewTaskModelPreTrainedModel(PreTrainedModel):
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config_class = NewTaskModelConfig
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base_model_prefix = "model"
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supports_gradient_checkpointing = True
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_no_split_modules = ["NewTaskModelMultiModalProjector"]
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_skip_keys_device_placement = "past_key_values"
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_supports_cache_class = True
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_supports_quantized_cache = True
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_supports_static_cache = 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 NewTaskModelisn't meant for training from scratch - only
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# inference and fine-tuning
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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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NEW_TASK_MODEL_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 [`SiglipImageProcessor.__call__`] for details ([]`NewTaskModelProcessor`] uses
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[`SiglipImageProcessor`] 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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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 NEW_TASK_MODEL model which consists of a vision backbone and a language model.""",
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NEW_TASK_MODEL_START_DOCSTRING,
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)
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class NewTaskModelForNewTask(NewTaskModelPreTrainedModel, GenerationMixin):
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main_input_name: ClassVar[str] = "doc_input_ids" # transformers-related
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def __init__(self, config):
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super().__init__(config)
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self.vision_tower = AutoModel.from_config(config=config.vision_config)
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self.multi_modal_projector = NewTaskModelMultiModalProjector(config)
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self.vocab_size = config.text_config.vocab_size
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language_model = AutoModelForCausalLM.from_config(config=config.text_config)
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if language_model._tied_weights_keys is not None:
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self._tied_weights_keys = [f"language_model.{k}" for k in language_model._tied_weights_keys]
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self.language_model = language_model
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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.embedding_dim = self.config.embedding_dim
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self.custom_text_proj = nn.Linear(self.config.text_config.hidden_size, self.embedding_dim)
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if self.language_model._tied_weights_keys is not None:
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self._tied_weights_keys = [f"model.language_model.{k}" for k in self.language_model._tied_weights_keys]
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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 _update_causal_mask(
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self,
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attention_mask,
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token_type_ids,
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past_key_values,
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cache_position,
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input_tensor,
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is_training: bool = False,
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):
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if self.config.text_config._attn_implementation == "flash_attention_2":
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if attention_mask is not None and 0.0 in attention_mask:
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return attention_mask
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return None
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using_static_cache = isinstance(past_key_values, StaticCache)
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min_dtype = torch.finfo(self.dtype).min
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inputs_lead_dim, sequence_length = input_tensor.shape[:2]
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if using_static_cache:
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target_length = past_key_values.get_max_cache_shape()
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elif isinstance(past_key_values, HybridCache):
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target_length = past_key_values.get_max_cache_shape()
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else:
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target_length = (
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attention_mask.shape[-1]
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if isinstance(attention_mask, torch.Tensor)
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else cache_position[0] + sequence_length + 1
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)
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if attention_mask is not None and attention_mask.dim() == 4:
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# In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
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return attention_mask
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causal_mask = torch.full(
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(sequence_length, target_length), fill_value=min_dtype, dtype=self.dtype, device=cache_position.device
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)
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# Causal diagonal mask only if training, otherwise attend to the whole prefix. Training-specific attn for prefix is handled below
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if sequence_length != 1:
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if is_training:
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causal_mask = torch.triu(causal_mask, diagonal=1)
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else:
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causal_mask[:, :sequence_length] = 0.0
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causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(-1, 1)
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causal_mask = causal_mask[None, None, :, :].expand(inputs_lead_dim, 1, -1, -1)
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if attention_mask is not None:
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causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
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mask_length = attention_mask.shape[-1]
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# First unmask prefix tokens during training
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if is_training:
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causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
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token_type_ids[:, None, None, :].to(causal_mask.device) == 0, 0
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)
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# Then apply padding mask (will mask pad tokens)
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padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(causal_mask.device)
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padding_mask = padding_mask == 0
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causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
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padding_mask, min_dtype
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)
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return causal_mask
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def get_image_features(self, pixel_values: torch.FloatTensor):
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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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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)
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selected_image_feature = image_outputs.last_hidden_state
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image_features = self.multi_modal_projector(selected_image_feature)
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image_features = image_features / (self.config.text_config.hidden_size**0.5)
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return image_features
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@add_start_docstrings_to_model_forward(NEW_TASK_MODEL_INPUTS_DOCSTRING)
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@replace_return_docstrings(output_type=NewTaskModelCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
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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[Union[list[torch.FloatTensor], Cache]] = None,
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token_type_ids: Optional[torch.LongTensor] = None,
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cache_position: Optional[torch.LongTensor] = None,
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inputs_embeds: Optional[torch.FloatTensor] = 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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num_logits_to_keep: int = 0,
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) -> Union[tuple, NewTaskModelCausalLMOutputWithPast]:
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r"""
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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.text_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.text_config.vocab_size]`.
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logits_to_keep (`int` or `torch.Tensor`, *optional*):
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If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
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`input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
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token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
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If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
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This is useful when using packed tensor format (single dimension for batch and sequence length).
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Returns:
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Example:
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```python
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>>> from PIL import Image
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>>> import requests
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>>> from transformers import AutoProcessor, NewTaskModelForNewTask
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>>> model = NewTaskModelForNewTask.from_pretrained("google/NewTaskModel-test-224px-hf")
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>>> processor = AutoProcessor.from_pretrained("google/NewTaskModel-test-224px-hf")
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>>> prompt = "answer en Where is the cow standing?"
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>>> url = "https://huggingface.co/gv-hf/NewTaskModel-test-224px-hf/resolve/main/cow_beach_1.png"
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>>> image = Image.open(requests.get(url, stream=True).raw)
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>>> inputs = processor(images=image, text=prompt, return_tensors="pt")
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>>> # Generate
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>>> generate_ids = model.generate(**inputs, max_length=30)
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>>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
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"answer en Where is the cow standing?\nbeach"
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```
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Returns:
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"""
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vlm_outputs = super().forward(
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input_ids=input_ids,
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pixel_values=pixel_values,
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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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token_type_ids=token_type_ids,
|
|
cache_position=cache_position,
|
|
inputs_embeds=inputs_embeds,
|
|
labels=labels,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=True,
|
|
return_dict=True,
|
|
num_logits_to_keep=num_logits_to_keep,
|
|
)
|
|
last_hidden_states = vlm_outputs.hidden_states[-1] # (batch_size, sequence_length, hidden_size)
|
|
proj = self.custom_text_proj(last_hidden_states) # (batch_size, sequence_length, dim)
|
|
|
|
# L2 normalization
|
|
embeddings = proj / proj.norm(dim=-1, keepdim=True) # (batch_size, sequence_length, dim)
|
|
|
|
embeddings = embeddings * attention_mask.unsqueeze(-1) # (batch_size, sequence_length, dim)
|
|
|
|
return (embeddings,) + vlm_outputs
|
|
|
|
def prepare_inputs_for_generation(
|
|
self,
|
|
input_ids,
|
|
past_key_values=None,
|
|
inputs_embeds=None,
|
|
cache_position=None,
|
|
position_ids=None,
|
|
pixel_values=None,
|
|
attention_mask=None,
|
|
token_type_ids=None,
|
|
use_cache=True,
|
|
logits_to_keep=None,
|
|
labels=None,
|
|
**kwargs,
|
|
):
|
|
# Overwritten -- custom `position_ids` and `pixel_values` handling
|
|
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,
|
|
position_ids=position_ids,
|
|
cache_position=cache_position,
|
|
use_cache=use_cache,
|
|
logits_to_keep=logits_to_keep,
|
|
token_type_ids=token_type_ids,
|
|
**kwargs,
|
|
)
|
|
|
|
# position_ids in NewTaskModel are 1-indexed
|
|
if model_inputs.get("position_ids") is not None:
|
|
model_inputs["position_ids"] += 1
|
|
# 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. NOTE: use_cache=False needs pixel_values always
|
|
if cache_position[0] == 0:
|
|
model_inputs["pixel_values"] = pixel_values
|
|
is_training = token_type_ids is not None and labels is not None
|
|
if cache_position[0] == 0 and isinstance(past_key_values, HybridCache):
|
|
input_tensor = inputs_embeds if inputs_embeds is not None else input_ids
|
|
causal_mask = self._update_causal_mask(
|
|
attention_mask, token_type_ids, past_key_values, cache_position, input_tensor, is_training
|
|
)
|
|
model_inputs["attention_mask"] = causal_mask
|
|
|
|
return model_inputs
|
|
|
|
def resize_token_embeddings(
|
|
self, new_num_tokens: Optional[int] = None, pad_to_multiple_of=None, mean_resizing=True
|
|
) -> nn.Embedding:
|
|
model_embeds = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of, mean_resizing)
|
|
|
|
# Update vocab size
|
|
self.config.text_config.vocab_size = model_embeds.num_embeddings
|
|
self.config.vocab_size = model_embeds.num_embeddings
|
|
self.vocab_size = model_embeds.num_embeddings
|
|
|
|
return model_embeds
|