1589 lines
71 KiB
Python
1589 lines
71 KiB
Python
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# coding=utf-8
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# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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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 Idefics model."""
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from dataclasses import dataclass
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from typing import Any, Dict, List, Optional, Tuple, Union
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import torch
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import torch.nn.functional as F
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import torch.utils.checkpoint
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from torch import nn
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from torch.nn import CrossEntropyLoss
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from ... import PreTrainedModel
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from ...activations import ACT2FN
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from ...modeling_attn_mask_utils import _prepare_4d_causal_attention_mask_for_sdpa
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from ...modeling_outputs import ModelOutput
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from ...modeling_utils import PretrainedConfig
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from ...pytorch_utils import ALL_LAYERNORM_LAYERS
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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 .configuration_idefics import IdeficsConfig
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from .perceiver import IdeficsPerceiverResampler
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from .vision import IdeficsVisionTransformer
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logger = logging.get_logger(__name__)
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_CONFIG_FOR_DOC = "IdeficsConfig"
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from ..deprecated._archive_maps import IDEFICS_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402
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@dataclass
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class IdeficsBaseModelOutputWithPast(ModelOutput):
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"""
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Base class for Idefics model's outputs that may also contain a past key/values (to speed up sequential decoding).
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Args:
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last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
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Sequence of hidden-states at the output of the last layer of the model.
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If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
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hidden_size)` is output.
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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 optionally if
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`config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
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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 optionally if
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`config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
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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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last_hidden_state: torch.FloatTensor = None
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past_key_values: Optional[Tuple[Tuple[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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@dataclass
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class IdeficsCausalLMOutputWithPast(ModelOutput):
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"""
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Base class for Idefics 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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def expand_inputs_for_generation(
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input_ids,
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expand_size=1,
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is_encoder_decoder=False,
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attention_mask=None,
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encoder_outputs=None,
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**model_kwargs,
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):
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expanded_return_idx = (
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torch.arange(input_ids.shape[0]).view(-1, 1).repeat(1, expand_size).view(-1).to(input_ids.device)
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)
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input_ids = input_ids.index_select(0, expanded_return_idx)
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model_kwargs["pixel_values"] = model_kwargs.get("pixel_values", None)
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model_kwargs["image_encoder_embeddings"] = model_kwargs.get("image_encoder_embeddings", None)
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model_kwargs["perceiver_embeddings"] = model_kwargs.get("perceiver_embeddings", None)
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model_kwargs["image_attention_mask"] = model_kwargs.get("image_attention_mask", None)
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if "token_type_ids" in model_kwargs:
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token_type_ids = model_kwargs["token_type_ids"]
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model_kwargs["token_type_ids"] = token_type_ids.index_select(0, expanded_return_idx)
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if attention_mask is not None:
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model_kwargs["attention_mask"] = attention_mask.index_select(0, expanded_return_idx)
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if model_kwargs["image_attention_mask"] is not None:
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model_kwargs["image_attention_mask"] = model_kwargs["image_attention_mask"].index_select(
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0, expanded_return_idx
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)
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if model_kwargs["pixel_values"] is not None:
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model_kwargs["pixel_values"] = model_kwargs["pixel_values"].index_select(0, expanded_return_idx)
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elif model_kwargs["image_encoder_embeddings"] is not None:
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model_kwargs["image_encoder_embeddings"] = model_kwargs["image_encoder_embeddings"].index_select(
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0, expanded_return_idx
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)
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elif model_kwargs["perceiver_embeddings"] is not None:
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model_kwargs["perceiver_embeddings"] = model_kwargs["perceiver_embeddings"].index_select(
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0, expanded_return_idx
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)
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return input_ids, model_kwargs
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def prepare_inputs_for_generation(input_ids, past_key_values=None, **kwargs):
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token_type_ids = kwargs.get("token_type_ids", None)
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# only last token for inputs_ids if past is defined in kwargs
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if past_key_values:
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input_ids = input_ids[:, -1].unsqueeze(-1)
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if token_type_ids is not None:
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token_type_ids = token_type_ids[:, -1].unsqueeze(-1)
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attention_mask = kwargs.get("attention_mask", None)
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position_ids = kwargs.get("position_ids", None)
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if attention_mask is not None and position_ids is None:
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# create position_ids on the fly for batch generation
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position_ids = attention_mask.long().cumsum(-1) - 1
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position_ids.masked_fill_(attention_mask == 0, 1)
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if past_key_values:
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position_ids = position_ids[:, -1].unsqueeze(-1)
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pixel_values = kwargs.get("pixel_values", None)
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image_encoder_embeddings = kwargs.get("image_encoder_embeddings", None)
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perceiver_embeddings = kwargs.get("perceiver_embeddings", None)
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image_attention_mask = kwargs.get("image_attention_mask", None)
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interpolate_pos_encoding = kwargs.get("interpolate_pos_encoding", False)
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return {
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"input_ids": input_ids,
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"past_key_values": past_key_values,
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"use_cache": kwargs.get("use_cache"),
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"position_ids": position_ids,
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"attention_mask": attention_mask,
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"token_type_ids": token_type_ids,
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"pixel_values": pixel_values,
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"image_encoder_embeddings": image_encoder_embeddings,
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"perceiver_embeddings": perceiver_embeddings,
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"image_attention_mask": image_attention_mask,
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"interpolate_pos_encoding": interpolate_pos_encoding,
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}
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def freeze_model(model, module_exceptions=[]):
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mapping = {
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"LayerNorm": nn.LayerNorm,
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"Linear": nn.Linear,
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"Embedding": nn.Embedding,
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}
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module_exceptions_mapped = [mapping[m] for m in module_exceptions]
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for module in model.modules():
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if module_exceptions and any(isinstance(module, t) for t in module_exceptions_mapped):
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module.requires_grad_(True) # Explicitely setting it to true to avoid any mistakes
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else:
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module.requires_grad_(False)
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return model
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class IdeficsDecoupledEmbedding(nn.Embedding):
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# Derived from https://pytorch.org/docs/stable/_modules/torch/nn/modules/sparse.html#Embedding
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"""
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Implements a decoupling of parameters to allow freezing (or not) a subset of the embeddings. In practise, the
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regular `weight` can be trained or frozen (i.e. `partially_freeze=True`), and if `num_additional_embeddings` > 0,
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then it will create `num_additional_embeddings` additional parameters that are always trained. If
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`num_additional_embeddings=0`, then the module defaults back to the regular behavior of `nn.Embedding`.
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"""
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def __init__(
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self,
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num_embeddings,
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num_additional_embeddings,
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embedding_dim,
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partially_freeze: Optional[bool] = False,
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device=None,
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dtype=None,
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padding_idx=None,
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**kwargs,
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) -> None:
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"""
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Args:
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num_embeddings (`int`):
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Size of the dictionary of embeddings
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num_additional_embeddings (`int`):
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Number of additional embeddings. Only useful when you `partially_freeze=True`.
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embedding_dim (`int`):
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The size of each embedding vector
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partially_freeze: (`bool`, *optional*, defaults to `False`):
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If `True`, the regular `weight` will be frozen. `additional_weight` is never frozen.
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padding_idx (`int`, *optional*):
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The padding index (needs to be less than num_embeddings)
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Note: there are a lot of other parameters to initialize a standard `nn.Embedding` such as `padding_idx`,
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`max_norm` or `norm_type`. We are not supporting these.
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"""
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if padding_idx is not None and padding_idx > num_embeddings:
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raise ValueError(f"padding_idx must be within num_embeddings. Got {padding_idx} and {num_embeddings}")
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super().__init__(
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num_embeddings=num_embeddings,
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embedding_dim=embedding_dim,
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device=device,
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dtype=dtype,
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padding_idx=padding_idx,
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**kwargs,
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)
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self.num_embeddings = num_embeddings
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self.padding_idx = padding_idx
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self.num_additional_embeddings = num_additional_embeddings
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self.partially_freeze = partially_freeze
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if partially_freeze:
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self.weight.requires_grad_(False)
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if self.num_additional_embeddings > 0:
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self.additional_embedding = nn.Embedding(
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num_embeddings=self.num_additional_embeddings,
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embedding_dim=embedding_dim,
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device=device,
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dtype=dtype,
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)
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def forward(self, input_ids):
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"""
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we have 2 embeddings, with different indices - one pretrained self.weight and another
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self.additional_embedding.weight that is being trained.
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in order to make a lookup of the input ids, we:
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1. find out the indices of the entries belonging to the 2nd embedding
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2. extract those values while subtracting the size of the first embedding (num_embeddings), since the 2nd
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embedding starts from 0 and not num_embeddings
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3. perform the 2nd embedding lookup
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4. now we handle the 1st embedding, we overwrite indices belonging to the 2nd embedding with a padding index
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5. perform the 1st embedding lookup
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6. now we overwrite the values in the 1st embedding lookup with the values of the 2nd embedding lookup
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note: for the 1st embedding lookup we could have looked up only the low indices and not do the padding, but
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then we have to create a new tensor and populate it with 2 tensors that are spread out across various indices -
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i.e. not a simple concat - I haven't benchmarked the complex case if it's any faster, given that seqlens are
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usually relatively short it's probably not faster or if faster not by much - but might be a good idea to
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measure.
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"""
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if self.num_additional_embeddings == 0:
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return F.embedding(input_ids, self.weight)
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# Clone so that we don't modify the original input_ids later on
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input_ids = input_ids.clone()
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additional_vocab_indices = torch.where(input_ids >= self.num_embeddings)
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input_ids_additional_vocab = input_ids[additional_vocab_indices]
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additional_embeddings = self.additional_embedding(input_ids_additional_vocab - self.num_embeddings)
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# for successful lookup replace input_ids with 0, the results of these will be discarded anyway
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input_ids[additional_vocab_indices] = 0
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full_vector = F.embedding(input_ids, self.weight)
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# overwrite the records with high indices
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full_vector[additional_vocab_indices] = additional_embeddings
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return full_vector
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def extra_repr(self) -> str:
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return "num_embeddings={}, num_additional_embeddings={}, embedding_dim={}, partially_freeze={}".format(
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self.num_embeddings,
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self.num_additional_embeddings,
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self.embedding_dim,
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self.partially_freeze,
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)
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class IdeficsDecoupledLinear(nn.Linear):
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# Derived from https://pytorch.org/docs/stable/_modules/torch/nn/modules/linear.html#Linear
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"""
|
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Implements a decoupling of parameters to allow freezing (or not) a subset of the parameters. In practise, the
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regular `weight` can be trained or frozen (i.e. `partially_freeze=True`), and if `out_additional_features` > 0,
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then it will create `out_additional_features * in_features` additional parameters that are always trained. If
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`out_additional_features=0`, then the module defaults back to the regular behavior of `nn.Linear`.
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"""
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def __init__(
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self,
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in_features: int,
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out_features: int,
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out_additional_features: int = 0,
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bias: bool = True,
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partially_freeze: bool = True,
|
||
|
device=None,
|
||
|
dtype=None,
|
||
|
) -> None:
|
||
|
"""
|
||
|
out_additional_features: int. Number of additional trainable dimensions. Only makes sense when
|
||
|
`partially_freeze=True`. partially_freeze: bool. If True, the regular `weight` will be frozen and extra
|
||
|
parameters (if any) will be trainable. If False, default to the regular behavior of nn.Linear.
|
||
|
"""
|
||
|
super().__init__(in_features, out_features, bias, device, dtype)
|
||
|
self.out_additional_features = out_additional_features
|
||
|
self.partially_freeze = partially_freeze
|
||
|
|
||
|
self.in_features = in_features
|
||
|
self.out_features = out_features
|
||
|
|
||
|
if partially_freeze:
|
||
|
self.weight.requires_grad_(False)
|
||
|
if bias:
|
||
|
self.bias.requires_grad_(False)
|
||
|
|
||
|
if out_additional_features > 0:
|
||
|
self.additional_fc = nn.Linear(
|
||
|
in_features=in_features,
|
||
|
out_features=out_additional_features,
|
||
|
bias=bias,
|
||
|
device=device,
|
||
|
dtype=dtype,
|
||
|
)
|
||
|
|
||
|
def forward(self, input: torch.Tensor) -> torch.Tensor:
|
||
|
output = F.linear(input, self.weight, self.bias)
|
||
|
|
||
|
if self.out_additional_features > 0:
|
||
|
additional_features = self.additional_fc(input)
|
||
|
output = torch.cat((output, additional_features), -1)
|
||
|
|
||
|
return output
|
||
|
|
||
|
def extra_repr(self) -> str:
|
||
|
"""Overwriting `nn.Linear.extra_repr` to include new parameters."""
|
||
|
return "in_features={}, out_features={}, out_additional_features={}, bias={}, partially_freeze={}".format(
|
||
|
self.in_features,
|
||
|
self.out_features,
|
||
|
self.out_additional_features,
|
||
|
self.bias is not None,
|
||
|
self.partially_freeze,
|
||
|
)
|
||
|
|
||
|
|
||
|
# this was adapted from LlamaRMSNorm
|
||
|
class IdeficsRMSNorm(nn.Module):
|
||
|
def __init__(self, hidden_size, eps=1e-6):
|
||
|
"""
|
||
|
IdeficsRMSNorm is equivalent to T5LayerNorm
|
||
|
"""
|
||
|
super().__init__()
|
||
|
self.weight = nn.Parameter(torch.ones(hidden_size))
|
||
|
self.variance_epsilon = eps
|
||
|
|
||
|
def forward(self, hidden_states):
|
||
|
variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
|
||
|
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
|
||
|
|
||
|
# convert into half-precision if necessary
|
||
|
if self.weight.dtype in [torch.float16, torch.bfloat16]:
|
||
|
hidden_states = hidden_states.to(self.weight.dtype)
|
||
|
|
||
|
return self.weight * hidden_states
|
||
|
|
||
|
|
||
|
ALL_LAYERNORM_LAYERS.append(IdeficsRMSNorm)
|
||
|
|
||
|
|
||
|
# this was adapted from LlamaRotaryEmbedding
|
||
|
class IdeficsEmbedding(torch.nn.Module):
|
||
|
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
|
||
|
super().__init__()
|
||
|
|
||
|
self.dim = dim
|
||
|
self.max_position_embeddings = max_position_embeddings
|
||
|
self.base = base
|
||
|
inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
|
||
|
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
||
|
|
||
|
# Build here to make `torch.jit.trace` work.
|
||
|
self._set_cos_sin_cache(
|
||
|
seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
|
||
|
)
|
||
|
|
||
|
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
||
|
self.max_seq_len_cached = seq_len
|
||
|
t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq)
|
||
|
|
||
|
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
|
||
|
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
||
|
emb = torch.cat((freqs, freqs), dim=-1)
|
||
|
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
|
||
|
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
|
||
|
|
||
|
def forward(self, x, seq_len=None):
|
||
|
# x: [bs, num_attention_heads, seq_len, head_size]
|
||
|
if seq_len > self.max_seq_len_cached:
|
||
|
self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
|
||
|
|
||
|
return (
|
||
|
self.cos_cached[:seq_len].to(dtype=x.dtype),
|
||
|
self.sin_cached[:seq_len].to(dtype=x.dtype),
|
||
|
)
|
||
|
|
||
|
|
||
|
def rotate_half(x):
|
||
|
"""Rotates half the hidden dims of the input."""
|
||
|
x1 = x[..., : x.shape[-1] // 2]
|
||
|
x2 = x[..., x.shape[-1] // 2 :]
|
||
|
return torch.cat((-x2, x1), dim=-1)
|
||
|
|
||
|
|
||
|
# Copied from transformers.models.mistral.modeling_mistral.apply_rotary_pos_emb
|
||
|
def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
|
||
|
"""Applies Rotary Position Embedding to the query and key tensors.
|
||
|
|
||
|
Args:
|
||
|
q (`torch.Tensor`): The query tensor.
|
||
|
k (`torch.Tensor`): The key tensor.
|
||
|
cos (`torch.Tensor`): The cosine part of the rotary embedding.
|
||
|
sin (`torch.Tensor`): The sine part of the rotary embedding.
|
||
|
position_ids (`torch.Tensor`):
|
||
|
The position indices of the tokens corresponding to the query and key tensors. For example, this can be
|
||
|
used to pass offsetted position ids when working with a KV-cache.
|
||
|
unsqueeze_dim (`int`, *optional*, defaults to 1):
|
||
|
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
|
||
|
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
|
||
|
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
|
||
|
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
|
||
|
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
|
||
|
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
|
||
|
Returns:
|
||
|
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
|
||
|
"""
|
||
|
cos = cos[position_ids].unsqueeze(unsqueeze_dim)
|
||
|
sin = sin[position_ids].unsqueeze(unsqueeze_dim)
|
||
|
q_embed = (q * cos) + (rotate_half(q) * sin)
|
||
|
k_embed = (k * cos) + (rotate_half(k) * sin)
|
||
|
return q_embed, k_embed
|
||
|
|
||
|
|
||
|
# this was adapted from LlamaMLP
|
||
|
class IdeficsMLP(nn.Module):
|
||
|
def __init__(
|
||
|
self,
|
||
|
hidden_size: int,
|
||
|
intermediate_size: int,
|
||
|
hidden_act: str,
|
||
|
):
|
||
|
super().__init__()
|
||
|
self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
|
||
|
self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
|
||
|
self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
|
||
|
self.act_fn = ACT2FN[hidden_act]
|
||
|
|
||
|
def forward(self, x):
|
||
|
return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
|
||
|
|
||
|
|
||
|
# this was adapted from LlamaAttention
|
||
|
class IdeficsAttention(nn.Module):
|
||
|
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
||
|
|
||
|
def __init__(
|
||
|
self,
|
||
|
hidden_size: int,
|
||
|
num_heads: int,
|
||
|
dropout: float = 0.0,
|
||
|
is_cross_attention: bool = False,
|
||
|
config: PretrainedConfig = None,
|
||
|
qk_layer_norms: bool = False,
|
||
|
):
|
||
|
super().__init__()
|
||
|
self.hidden_size = hidden_size
|
||
|
self.num_heads = num_heads
|
||
|
self.head_dim = hidden_size // num_heads
|
||
|
self.dropout = dropout
|
||
|
self.is_causal = True
|
||
|
|
||
|
if (self.head_dim * num_heads) != self.hidden_size:
|
||
|
raise ValueError(
|
||
|
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
|
||
|
f" and `num_heads`: {num_heads})."
|
||
|
)
|
||
|
|
||
|
self.is_cross_attention = is_cross_attention
|
||
|
|
||
|
if not hasattr(nn.functional, "scaled_dot_product_attention"):
|
||
|
raise ValueError("this model requires pytorch 2.0 or higher")
|
||
|
|
||
|
if self.is_cross_attention:
|
||
|
kv_input_dim = (
|
||
|
self.hidden_size if not hasattr(config.vision_config, "embed_dim") else config.vision_config.embed_dim
|
||
|
)
|
||
|
self.q_proj = nn.Linear(
|
||
|
self.hidden_size,
|
||
|
num_heads * self.head_dim,
|
||
|
bias=False,
|
||
|
)
|
||
|
self.k_proj = nn.Linear(kv_input_dim, num_heads * self.head_dim, bias=False)
|
||
|
self.v_proj = nn.Linear(
|
||
|
kv_input_dim,
|
||
|
num_heads * self.head_dim,
|
||
|
bias=False,
|
||
|
)
|
||
|
else:
|
||
|
self.q_proj = nn.Linear(
|
||
|
self.hidden_size,
|
||
|
num_heads * self.head_dim,
|
||
|
bias=False,
|
||
|
)
|
||
|
self.k_proj = nn.Linear(
|
||
|
self.hidden_size,
|
||
|
num_heads * self.head_dim,
|
||
|
bias=False,
|
||
|
)
|
||
|
self.v_proj = nn.Linear(
|
||
|
self.hidden_size,
|
||
|
num_heads * self.head_dim,
|
||
|
bias=False,
|
||
|
)
|
||
|
self.o_proj = nn.Linear(
|
||
|
num_heads * self.head_dim,
|
||
|
hidden_size,
|
||
|
bias=False,
|
||
|
)
|
||
|
self.rotary_emb = IdeficsEmbedding(self.head_dim)
|
||
|
|
||
|
self.qk_layer_norms = qk_layer_norms
|
||
|
if self.qk_layer_norms:
|
||
|
self.q_layer_norm = IdeficsRMSNorm(self.head_dim, eps=config.rms_norm_eps)
|
||
|
self.k_layer_norm = IdeficsRMSNorm(self.head_dim, eps=config.rms_norm_eps)
|
||
|
|
||
|
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
|
||
|
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
|
||
|
|
||
|
def forward(
|
||
|
self,
|
||
|
hidden_states: torch.Tensor,
|
||
|
key_value_states: Optional[torch.Tensor] = None,
|
||
|
attention_mask: Optional[torch.Tensor] = None,
|
||
|
position_ids: Optional[torch.LongTensor] = None,
|
||
|
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
||
|
output_attentions: bool = False,
|
||
|
use_cache: bool = False,
|
||
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
||
|
# if key_value_states are provided this layer is used as a cross-attention layer
|
||
|
is_cross_attention = self.is_cross_attention or key_value_states is not None
|
||
|
|
||
|
bsz, q_len, _ = hidden_states.size()
|
||
|
|
||
|
query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||
|
if not is_cross_attention:
|
||
|
key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||
|
value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||
|
else:
|
||
|
_, kv_len, _ = key_value_states.size() # Note that, in this case, `kv_len` == `kv_seq_len`
|
||
|
key_states = self.k_proj(key_value_states).view(bsz, kv_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||
|
value_states = (
|
||
|
self.v_proj(key_value_states).view(bsz, kv_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||
|
)
|
||
|
|
||
|
kv_seq_len = key_states.shape[-2]
|
||
|
if past_key_value is not None:
|
||
|
kv_seq_len += past_key_value[0].shape[-2]
|
||
|
if not is_cross_attention:
|
||
|
cos, sin = self.rotary_emb(value_states, seq_len=max(kv_seq_len, q_len))
|
||
|
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
|
||
|
# [bsz, nh, t, hd]
|
||
|
|
||
|
if past_key_value is not None:
|
||
|
# reuse k, v, self_attention
|
||
|
key_states = torch.cat([past_key_value[0], key_states], dim=2)
|
||
|
value_states = torch.cat([past_key_value[1], value_states], dim=2)
|
||
|
|
||
|
past_key_value = (key_states, value_states) if use_cache else None
|
||
|
|
||
|
if self.qk_layer_norms:
|
||
|
query_states = self.q_layer_norm(query_states)
|
||
|
key_states = self.k_layer_norm(key_states)
|
||
|
|
||
|
if attention_mask is not None:
|
||
|
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
|
||
|
raise ValueError(
|
||
|
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
|
||
|
)
|
||
|
|
||
|
# SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
|
||
|
# Reference: https://github.com/pytorch/pytorch/issues/112577.
|
||
|
if query_states.device.type == "cuda" and attention_mask is not None:
|
||
|
query_states = query_states.contiguous()
|
||
|
key_states = key_states.contiguous()
|
||
|
value_states = value_states.contiguous()
|
||
|
|
||
|
attn_output = nn.functional.scaled_dot_product_attention(
|
||
|
query_states,
|
||
|
key_states,
|
||
|
value_states,
|
||
|
attn_mask=attention_mask,
|
||
|
dropout_p=self.dropout,
|
||
|
# The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
|
||
|
is_causal=self.is_causal and attention_mask is None and q_len > 1,
|
||
|
)
|
||
|
|
||
|
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
|
||
|
raise ValueError(
|
||
|
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
|
||
|
f" {attn_output.size()}"
|
||
|
)
|
||
|
|
||
|
attn_output = attn_output.transpose(1, 2)
|
||
|
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
||
|
|
||
|
attn_output = self.o_proj(attn_output)
|
||
|
|
||
|
attn_weights = None
|
||
|
if output_attentions:
|
||
|
logger.warning_once(
|
||
|
"attn_weights are not extracted in scaled_dot_product_attention. The model returns None instead"
|
||
|
)
|
||
|
|
||
|
return attn_output, attn_weights, past_key_value
|
||
|
|
||
|
|
||
|
# this was adapted from LlamaDecoderLayer
|
||
|
class IdeficsDecoderLayer(nn.Module):
|
||
|
def __init__(self, config: IdeficsConfig):
|
||
|
super().__init__()
|
||
|
self.hidden_size = config.hidden_size
|
||
|
self.self_attn = IdeficsAttention(
|
||
|
hidden_size=self.hidden_size,
|
||
|
num_heads=config.num_attention_heads,
|
||
|
dropout=config.dropout,
|
||
|
config=config,
|
||
|
)
|
||
|
self.mlp = IdeficsMLP(
|
||
|
hidden_size=self.hidden_size,
|
||
|
intermediate_size=config.intermediate_size,
|
||
|
hidden_act=config.hidden_act,
|
||
|
)
|
||
|
self.input_layernorm = IdeficsRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||
|
self.post_attention_layernorm = IdeficsRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||
|
self.dropout = config.dropout
|
||
|
|
||
|
def forward(
|
||
|
self,
|
||
|
hidden_states: torch.Tensor,
|
||
|
attention_mask: Optional[torch.Tensor] = None,
|
||
|
position_ids: Optional[torch.LongTensor] = None,
|
||
|
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
||
|
output_attentions: Optional[bool] = False,
|
||
|
use_cache: Optional[bool] = False,
|
||
|
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
||
|
"""
|
||
|
Args:
|
||
|
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
|
||
|
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
|
||
|
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
|
||
|
output_attentions (`bool`, *optional*):
|
||
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
||
|
returned tensors for more detail.
|
||
|
use_cache (`bool`, *optional*):
|
||
|
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
|
||
|
(see `past_key_values`).
|
||
|
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
|
||
|
"""
|
||
|
|
||
|
residual = hidden_states
|
||
|
|
||
|
hidden_states = self.input_layernorm(hidden_states)
|
||
|
|
||
|
# Self Attention
|
||
|
hidden_states, self_attn_weights, present_key_value = self.self_attn(
|
||
|
hidden_states=hidden_states,
|
||
|
attention_mask=attention_mask,
|
||
|
position_ids=position_ids,
|
||
|
past_key_value=past_key_value,
|
||
|
output_attentions=output_attentions,
|
||
|
use_cache=use_cache,
|
||
|
)
|
||
|
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
|
||
|
hidden_states = residual + hidden_states
|
||
|
|
||
|
# Fully Connected
|
||
|
residual = hidden_states
|
||
|
hidden_states = self.post_attention_layernorm(hidden_states)
|
||
|
hidden_states = self.mlp(hidden_states)
|
||
|
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
|
||
|
hidden_states = residual + hidden_states
|
||
|
|
||
|
outputs = (hidden_states,)
|
||
|
|
||
|
if output_attentions:
|
||
|
outputs += (self_attn_weights,)
|
||
|
|
||
|
if use_cache:
|
||
|
outputs += (present_key_value,)
|
||
|
|
||
|
return outputs
|
||
|
|
||
|
|
||
|
class IdeficsGatedCrossAttentionLayer(nn.Module):
|
||
|
def __init__(self, config: IdeficsConfig):
|
||
|
super().__init__()
|
||
|
self.hidden_size = config.hidden_size
|
||
|
self.cross_attn = IdeficsAttention(
|
||
|
hidden_size=self.hidden_size,
|
||
|
num_heads=config.num_attention_heads,
|
||
|
is_cross_attention=True,
|
||
|
dropout=config.dropout,
|
||
|
config=config,
|
||
|
qk_layer_norms=config.qk_layer_norms,
|
||
|
)
|
||
|
self.mlp = IdeficsMLP(
|
||
|
hidden_size=self.hidden_size,
|
||
|
intermediate_size=config.intermediate_size,
|
||
|
hidden_act=config.hidden_act,
|
||
|
)
|
||
|
self.input_layernorm = IdeficsRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||
|
self.post_attention_layernorm = IdeficsRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||
|
self.config = config.dropout
|
||
|
|
||
|
self.act_cross_attn = nn.Tanh()
|
||
|
self.act_dense = nn.Tanh()
|
||
|
|
||
|
if config.alpha_initializer == "zeros":
|
||
|
if config.alpha_type == "vector":
|
||
|
self.alpha_cross_attn = nn.Parameter(torch.zeros(1, 1, self.hidden_size))
|
||
|
self.alpha_dense = nn.Parameter(torch.zeros(1, 1, self.hidden_size))
|
||
|
elif config.alpha_type == "float":
|
||
|
self.alpha_cross_attn = nn.Parameter(torch.zeros(1))
|
||
|
self.alpha_dense = nn.Parameter(torch.zeros(1))
|
||
|
else:
|
||
|
raise ValueError(f"Unknown value for `alpha_type` ({config.alpha_type})")
|
||
|
|
||
|
elif config.alpha_initializer == "ones":
|
||
|
if config.alpha_type == "vector":
|
||
|
self.alpha_cross_attn = nn.Parameter(torch.ones(1, 1, self.hidden_size))
|
||
|
self.alpha_dense = nn.Parameter(torch.ones(1, 1, self.hidden_size))
|
||
|
elif config.alpha_type == "float":
|
||
|
self.alpha_cross_attn = nn.Parameter(torch.ones(1))
|
||
|
self.alpha_dense = nn.Parameter(torch.ones(1))
|
||
|
else:
|
||
|
raise ValueError(f"Unknown value for `alpha_type` ({config.alpha_type})")
|
||
|
|
||
|
elif config.alpha_initializer in {"normal", "gaussian", "random"}:
|
||
|
if config.alpha_type == "vector":
|
||
|
self.alpha_cross_attn = nn.Parameter(
|
||
|
torch.normal(mean=0.0, std=config.alphas_initializer_range, size=(1, 1, self.hidden_size))
|
||
|
)
|
||
|
self.alpha_dense = nn.Parameter(
|
||
|
torch.normal(mean=0.0, std=config.alphas_initializer_range, size=(1, 1, self.hidden_size))
|
||
|
)
|
||
|
elif config.alpha_type == "float":
|
||
|
self.alpha_cross_attn = nn.Parameter(
|
||
|
torch.normal(mean=0.0, std=config.alphas_initializer_range, size=(1))
|
||
|
)
|
||
|
self.alpha_dense = nn.Parameter(torch.normal(mean=0.0, std=config.alphas_initializer_range, size=(1)))
|
||
|
else:
|
||
|
raise ValueError(f"Unknown value for `alpha_type` ({config.alpha_type})")
|
||
|
|
||
|
else:
|
||
|
raise NotImplementedError(f"Alpha initialization scheme {config.alpha_initializer} not yet implemented!")
|
||
|
|
||
|
if not (hasattr(self, "alpha_cross_attn") and hasattr(self, "alpha_dense")):
|
||
|
raise ValueError("Alpha parameters not initialized correctly!")
|
||
|
|
||
|
def forward(
|
||
|
self,
|
||
|
hidden_states: torch.Tensor,
|
||
|
attention_mask: Optional[torch.Tensor] = None,
|
||
|
image_hidden_states: Optional[torch.Tensor] = None,
|
||
|
image_attention_mask: Optional[torch.Tensor] = None,
|
||
|
cross_attention_gate: Optional[torch.Tensor] = None,
|
||
|
output_attentions: Optional[bool] = False,
|
||
|
use_cache: Optional[bool] = False,
|
||
|
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
||
|
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
||
|
"""
|
||
|
Args:
|
||
|
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
|
||
|
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
|
||
|
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
|
||
|
image_attention_mask (`torch.FloatTensor`, *optional*): image attention mask of size
|
||
|
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
|
||
|
cross_attention_gate (`torch.FloatTensor`, *optional*):
|
||
|
gate of size `(batch, seq_len)` used to zero-out cross-attention output for tokens attending no images.
|
||
|
output_attentions (`bool`, *optional*):
|
||
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
||
|
returned tensors for more detail.
|
||
|
use_cache (`bool`, *optional*):
|
||
|
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
|
||
|
(see `past_key_values`).
|
||
|
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
|
||
|
"""
|
||
|
if image_hidden_states is None:
|
||
|
raise ValueError(
|
||
|
"`image_hidden_states` is required for Idefics cross attention module which are visual features to be"
|
||
|
" conditioned on."
|
||
|
)
|
||
|
|
||
|
if cross_attention_gate is None:
|
||
|
raise ValueError(
|
||
|
"`cross_attention_gate` is required for Idefics cross attention module to zero-out the cross-attention hidden_states attending to no images."
|
||
|
)
|
||
|
|
||
|
if past_key_value is not None:
|
||
|
raise NotImplementedError("Past key value states are not implemented for Idefics cross attention module.")
|
||
|
|
||
|
residual = hidden_states
|
||
|
|
||
|
hidden_states = self.input_layernorm(hidden_states)
|
||
|
|
||
|
# Self Attention
|
||
|
hidden_states, self_attn_weights, present_key_value = self.cross_attn(
|
||
|
hidden_states=hidden_states,
|
||
|
key_value_states=image_hidden_states,
|
||
|
attention_mask=image_attention_mask,
|
||
|
output_attentions=output_attentions,
|
||
|
)
|
||
|
hidden_states = nn.functional.dropout(hidden_states, p=self.config, training=self.training)
|
||
|
# Fill in zeros for cross_attention hidden_states of tokens attending to no images
|
||
|
hidden_states[cross_attention_gate == 0] = hidden_states[cross_attention_gate == 0].fill_(0)
|
||
|
hidden_states = residual + self.act_cross_attn(self.alpha_cross_attn) * hidden_states
|
||
|
|
||
|
# Fully Connected
|
||
|
residual = hidden_states
|
||
|
hidden_states = self.post_attention_layernorm(hidden_states)
|
||
|
hidden_states = self.mlp(hidden_states)
|
||
|
hidden_states = nn.functional.dropout(hidden_states, p=self.config, training=self.training)
|
||
|
hidden_states = residual + self.act_dense(self.alpha_dense) * hidden_states
|
||
|
|
||
|
outputs = (hidden_states,)
|
||
|
|
||
|
if output_attentions:
|
||
|
outputs += (self_attn_weights,)
|
||
|
|
||
|
if use_cache:
|
||
|
outputs += (present_key_value,)
|
||
|
|
||
|
return outputs
|
||
|
|
||
|
|
||
|
LLAMA_START_DOCSTRING = r"""
|
||
|
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
||
|
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
|
||
|
etc.)
|
||
|
|
||
|
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
||
|
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
||
|
and behavior.
|
||
|
|
||
|
Parameters:
|
||
|
config ([`IdeficsConfig`]):
|
||
|
Model configuration class with all the parameters of the model. Initializing with a config file does not
|
||
|
load the weights associated with the model, only the configuration. Check out the
|
||
|
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
||
|
"""
|
||
|
|
||
|
|
||
|
@add_start_docstrings(
|
||
|
"The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
|
||
|
LLAMA_START_DOCSTRING,
|
||
|
)
|
||
|
class IdeficsPreTrainedModel(PreTrainedModel):
|
||
|
config_class = IdeficsConfig
|
||
|
base_model_prefix = "model"
|
||
|
supports_gradient_checkpointing = True
|
||
|
_no_split_modules = ["IdeficsDecoderLayer", "IdeficsGatedCrossAttentionLayer"]
|
||
|
_supports_sdpa = True
|
||
|
|
||
|
def _init_weights(self, module):
|
||
|
# important: this ported version of Idefics isn't meant for training from scratch - only
|
||
|
# inference and fine-tuning - so the proper init weights code has been removed - the m4 code
|
||
|
# base should be used for training from scratch and it contains the correct code.
|
||
|
std = self.config.initializer_range
|
||
|
if isinstance(module, nn.Linear):
|
||
|
module.weight.data.normal_(mean=0.0, std=std)
|
||
|
if module.bias is not None:
|
||
|
module.bias.data.zero_()
|
||
|
elif isinstance(module, nn.Embedding):
|
||
|
module.weight.data.normal_(mean=0.0, std=std)
|
||
|
if module.padding_idx is not None:
|
||
|
module.weight.data[module.padding_idx].zero_()
|
||
|
|
||
|
# Adapted from transformers.modeling_utils.PreTrainedModel._check_and_enable_sdpa
|
||
|
@classmethod
|
||
|
def _check_and_enable_sdpa(cls, config, hard_check_only: bool = False) -> PretrainedConfig:
|
||
|
# We remove the checks on `is_torch_sdpa_available()` and `cls._supports_sdpa` as Falcon supports SDPA from torch==2.0.0 (no requirement on 2.1).
|
||
|
_is_bettertransformer = getattr(cls, "use_bettertransformer", False)
|
||
|
if _is_bettertransformer:
|
||
|
return config
|
||
|
|
||
|
if not hard_check_only:
|
||
|
config._attn_implementation = "sdpa"
|
||
|
return config
|
||
|
|
||
|
|
||
|
LLAMA_INPUTS_DOCSTRING = r"""
|
||
|
Args:
|
||
|
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
||
|
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
|
||
|
it.
|
||
|
|
||
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
||
|
[`PreTrainedTokenizer.__call__`] for details.
|
||
|
|
||
|
[What are input IDs?](../glossary#input-ids)
|
||
|
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
||
|
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
||
|
|
||
|
- 1 for tokens that are **not masked**,
|
||
|
- 0 for tokens that are **masked**.
|
||
|
|
||
|
[What are attention masks?](../glossary#attention-mask)
|
||
|
|
||
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
||
|
[`PreTrainedTokenizer.__call__`] for details.
|
||
|
|
||
|
If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
|
||
|
`past_key_values`).
|
||
|
|
||
|
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
|
||
|
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
|
||
|
information on the default strategy.
|
||
|
|
||
|
- 1 indicates the head is **not masked**,
|
||
|
- 0 indicates the head is **masked**.
|
||
|
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
||
|
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
||
|
config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
|
||
|
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
|
||
|
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
|
||
|
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
|
||
|
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
|
||
|
|
||
|
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
|
||
|
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
|
||
|
|
||
|
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
|
||
|
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
|
||
|
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
|
||
|
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
||
|
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
|
||
|
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
|
||
|
model's internal embedding lookup matrix.
|
||
|
use_cache (`bool`, *optional*):
|
||
|
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
|
||
|
`past_key_values`).
|
||
|
output_attentions (`bool`, *optional*):
|
||
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
||
|
tensors for more detail.
|
||
|
output_hidden_states (`bool`, *optional*):
|
||
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
||
|
more detail.
|
||
|
return_dict (`bool`, *optional*):
|
||
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
||
|
"""
|
||
|
|
||
|
|
||
|
@add_start_docstrings(
|
||
|
"The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
|
||
|
LLAMA_START_DOCSTRING,
|
||
|
)
|
||
|
class IdeficsModel(IdeficsPreTrainedModel):
|
||
|
"""
|
||
|
Transformer decoder consisting of `config.num_hidden_layers` layers. Each layer is a [`IdeficsDecoderLayer`]
|
||
|
|
||
|
Args:
|
||
|
config: IdeficsConfig
|
||
|
"""
|
||
|
|
||
|
def __init__(self, config: IdeficsConfig):
|
||
|
super().__init__(config)
|
||
|
self.config = config
|
||
|
self.padding_idx = config.pad_token_id
|
||
|
self.vocab_size = config.vocab_size
|
||
|
|
||
|
self.embed_tokens = IdeficsDecoupledEmbedding(
|
||
|
num_embeddings=config.vocab_size,
|
||
|
num_additional_embeddings=config.additional_vocab_size,
|
||
|
embedding_dim=config.hidden_size,
|
||
|
partially_freeze=config.freeze_text_layers,
|
||
|
padding_idx=self.padding_idx,
|
||
|
)
|
||
|
|
||
|
self.image_size = config.vision_config.image_size
|
||
|
self.vision_config = config.vision_config
|
||
|
self.vision_model = IdeficsVisionTransformer(config.vision_config)
|
||
|
|
||
|
# Perceiver Resampler
|
||
|
if config.use_resampler:
|
||
|
perceiver_config = config.perceiver_config
|
||
|
self.perceiver_resampler = IdeficsPerceiverResampler(
|
||
|
config,
|
||
|
config.vision_config.embed_dim,
|
||
|
perceiver_config.resampler_depth,
|
||
|
perceiver_config.resampler_n_heads,
|
||
|
perceiver_config.resampler_head_dim,
|
||
|
perceiver_config.resampler_n_latents,
|
||
|
)
|
||
|
|
||
|
self.layers = nn.ModuleList([IdeficsDecoderLayer(config) for _ in range(config.num_hidden_layers)])
|
||
|
|
||
|
self.cross_layer_interval = config.cross_layer_interval
|
||
|
num_cross_layers = config.num_hidden_layers // self.cross_layer_interval
|
||
|
self.gated_cross_attn_layers = nn.ModuleList(
|
||
|
[IdeficsGatedCrossAttentionLayer(config) for _ in range(num_cross_layers)]
|
||
|
)
|
||
|
self.gradient_checkpointing = False
|
||
|
|
||
|
self.norm = IdeficsRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||
|
|
||
|
# Initialize weights and apply final processing
|
||
|
self.post_init()
|
||
|
|
||
|
self.freeze_relevant_params(config)
|
||
|
|
||
|
def freeze_relevant_params(self, config=None):
|
||
|
if config is None:
|
||
|
config = self.config
|
||
|
|
||
|
if config.freeze_text_layers:
|
||
|
self.freeze_text_layers(config.freeze_text_module_exceptions)
|
||
|
|
||
|
if config.freeze_vision_layers:
|
||
|
freeze_model(self.vision_model, module_exceptions=config.freeze_vision_module_exceptions)
|
||
|
|
||
|
def freeze_text_layers(self, module_exceptions=[]):
|
||
|
for module in [self.layers, self.norm]:
|
||
|
freeze_model(module, module_exceptions=module_exceptions)
|
||
|
|
||
|
def freeze_vision_layers(self, module_exceptions=[]):
|
||
|
freeze_model(self.vision_model, module_exceptions=module_exceptions)
|
||
|
|
||
|
def get_input_embeddings(self):
|
||
|
return self.embed_tokens
|
||
|
|
||
|
def set_input_embeddings(self, value):
|
||
|
self.embed_tokens = value
|
||
|
|
||
|
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
|
||
|
def forward(
|
||
|
self,
|
||
|
input_ids: torch.LongTensor = 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,
|
||
|
pixel_values: Optional[torch.FloatTensor] = None,
|
||
|
image_encoder_embeddings: Optional[torch.FloatTensor] = None,
|
||
|
perceiver_embeddings: Optional[torch.FloatTensor] = None,
|
||
|
image_attention_mask: Optional[torch.Tensor] = None,
|
||
|
use_cache: Optional[bool] = None,
|
||
|
output_attentions: Optional[bool] = None,
|
||
|
output_hidden_states: Optional[bool] = None,
|
||
|
interpolate_pos_encoding: Optional[bool] = False,
|
||
|
return_dict: Optional[bool] = None,
|
||
|
) -> Union[Tuple, IdeficsBaseModelOutputWithPast]:
|
||
|
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
||
|
|
||
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
||
|
output_hidden_states = (
|
||
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
||
|
)
|
||
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
||
|
|
||
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||
|
|
||
|
# retrieve input_ids and inputs_embeds
|
||
|
if input_ids is not None and inputs_embeds is not None:
|
||
|
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
|
||
|
elif input_ids is not None:
|
||
|
batch_size, seq_length = input_ids.shape
|
||
|
elif inputs_embeds is not None:
|
||
|
batch_size, seq_length, _ = inputs_embeds.shape
|
||
|
else:
|
||
|
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
|
||
|
|
||
|
seq_length_with_past = seq_length
|
||
|
past_key_values_length = 0
|
||
|
|
||
|
if past_key_values is not None:
|
||
|
past_key_values_length = past_key_values[0][0].shape[2]
|
||
|
seq_length_with_past = seq_length_with_past + past_key_values_length
|
||
|
|
||
|
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)
|
||
|
elif position_ids is None:
|
||
|
position_ids = torch.arange(
|
||
|
past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
|
||
|
)
|
||
|
position_ids = position_ids.unsqueeze(0)
|
||
|
|
||
|
if (pixel_values, image_encoder_embeddings, perceiver_embeddings).count(None) != 2:
|
||
|
raise ValueError(
|
||
|
"Exactly 1 of pixel_values, image_encoder_embeddings or perceiver_embeddings has to be not-None."
|
||
|
)
|
||
|
|
||
|
elif pixel_values is not None:
|
||
|
pixel_values = pixel_values.to(dtype=self.dtype, device=device) # fp16 compatibility
|
||
|
batch_size, num_images = pixel_values.shape[:2]
|
||
|
pixel_values = pixel_values.contiguous().view(batch_size * num_images, *pixel_values.shape[2:])
|
||
|
|
||
|
# Get sequence from the vision encoder
|
||
|
image_hidden_states = self.vision_model(
|
||
|
pixel_values=pixel_values, interpolate_pos_encoding=interpolate_pos_encoding
|
||
|
).last_hidden_state
|
||
|
|
||
|
elif image_encoder_embeddings is not None:
|
||
|
batch_size, num_images, image_seq_len, image_hidden_size = image_encoder_embeddings.size()
|
||
|
image_hidden_states = image_encoder_embeddings.to(dtype=self.dtype, device=device)
|
||
|
image_hidden_states = image_hidden_states.view(batch_size * num_images, image_seq_len, image_hidden_size)
|
||
|
|
||
|
if self.config.use_resampler:
|
||
|
if perceiver_embeddings is None:
|
||
|
perceiver_embeddings = self.perceiver_resampler(image_hidden_states)
|
||
|
image_seq_len, image_hidden_size = perceiver_embeddings.size(1), perceiver_embeddings.size(2)
|
||
|
else:
|
||
|
batch_size, num_images, image_seq_len, image_hidden_size = perceiver_embeddings.size()
|
||
|
image_hidden_states = perceiver_embeddings
|
||
|
elif perceiver_embeddings is None:
|
||
|
image_seq_len, image_hidden_size = image_hidden_states.size(1), image_hidden_states.size(2)
|
||
|
else:
|
||
|
raise ValueError("If `perceiver_embeddings` are passed, use_resampler should be True")
|
||
|
|
||
|
image_hidden_states = image_hidden_states.view(batch_size, num_images * image_seq_len, image_hidden_size)
|
||
|
# # Hack to use the model in full language modeling mode
|
||
|
# image_attention_mask = torch.zeros(batch_size, seq_length, 1, dtype=torch.long, device=image_hidden_states.device)
|
||
|
# Make image_attention_mask compatible with hidden states
|
||
|
text_seq_len = image_attention_mask.size(1)
|
||
|
image_attention_mask = image_attention_mask.unsqueeze(-1)
|
||
|
image_attention_mask = image_attention_mask.repeat(1, 1, 1, image_seq_len)
|
||
|
image_attention_mask = image_attention_mask.view(batch_size, text_seq_len, num_images * image_seq_len)
|
||
|
|
||
|
if image_hidden_states is not None:
|
||
|
image_batch_size, image_sequence_length, _ = image_hidden_states.size()
|
||
|
image_hidden_shape = (image_batch_size, image_sequence_length)
|
||
|
if image_attention_mask is None:
|
||
|
image_attention_mask = torch.ones(image_hidden_shape, device=device)
|
||
|
image_attention_mask = self.invert_attention_mask(image_attention_mask)
|
||
|
else:
|
||
|
image_attention_mask = None
|
||
|
|
||
|
# cross_attention_gate:
|
||
|
# For any tokens attending to no images, the hidden_states comming out of the cross-attention should be zeroed-out.
|
||
|
# `image_attention_mask` has shape [bsz, 1, num_images, hidden_size] with elements equal to either 0.0 or a very negative number.
|
||
|
# If any of the elements are 0.0, then the token is attending to at least one image and the gate value is 1. Otherwise the gate value is 0.
|
||
|
# `cross_attention_gate` has shape [bsz, seq_len] with elements equal to either 0.0 or 1.0.
|
||
|
cross_attention_gate = ((((image_attention_mask == 0.0).any(dim=-1)).to(dtype=self.dtype)).squeeze(dim=1)).to(
|
||
|
device
|
||
|
)
|
||
|
|
||
|
if inputs_embeds is None:
|
||
|
inputs_embeds = self.embed_tokens(input_ids)
|
||
|
# embed positions
|
||
|
if attention_mask is None:
|
||
|
attention_mask = torch.ones(
|
||
|
(batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
|
||
|
)
|
||
|
attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
|
||
|
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
|
||
|
)
|
||
|
|
||
|
hidden_states = inputs_embeds
|
||
|
|
||
|
if self.gradient_checkpointing and self.training:
|
||
|
if use_cache:
|
||
|
logger.warning_once(
|
||
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
||
|
)
|
||
|
use_cache = False
|
||
|
|
||
|
# decoder layers
|
||
|
all_hidden_states = () if output_hidden_states else None
|
||
|
all_self_attns = () if output_attentions else None
|
||
|
next_decoder_cache = () if use_cache else None
|
||
|
|
||
|
for idx, decoder_layer in enumerate(self.layers):
|
||
|
if output_hidden_states:
|
||
|
all_hidden_states += (hidden_states,)
|
||
|
|
||
|
past_key_value = past_key_values[idx] if past_key_values is not None else None
|
||
|
|
||
|
def vblock(
|
||
|
main_block,
|
||
|
hidden_states,
|
||
|
attention_mask,
|
||
|
position_ids,
|
||
|
past_key_value,
|
||
|
image_hidden_states,
|
||
|
image_attention_mask,
|
||
|
cross_attention_gate,
|
||
|
output_attentions,
|
||
|
use_cache,
|
||
|
layer_idx,
|
||
|
cross_layer_interval,
|
||
|
gated_cross_attn_layers,
|
||
|
):
|
||
|
# TODO(ls): Add cross attention values to respective lists
|
||
|
if layer_idx % cross_layer_interval == 0:
|
||
|
xblock = gated_cross_attn_layers[layer_idx // cross_layer_interval]
|
||
|
outputs = xblock(
|
||
|
hidden_states,
|
||
|
attention_mask=attention_mask,
|
||
|
image_hidden_states=image_hidden_states,
|
||
|
image_attention_mask=image_attention_mask,
|
||
|
cross_attention_gate=cross_attention_gate,
|
||
|
output_attentions=output_attentions,
|
||
|
use_cache=use_cache,
|
||
|
past_key_value=None, # not implemented
|
||
|
)
|
||
|
hidden_states = outputs[0]
|
||
|
|
||
|
layer_outputs = main_block(
|
||
|
hidden_states,
|
||
|
attention_mask=attention_mask,
|
||
|
position_ids=position_ids,
|
||
|
past_key_value=past_key_value,
|
||
|
output_attentions=output_attentions,
|
||
|
use_cache=use_cache,
|
||
|
)
|
||
|
|
||
|
return layer_outputs
|
||
|
|
||
|
if self.gradient_checkpointing and self.training:
|
||
|
past_key_value = None
|
||
|
if use_cache:
|
||
|
logger.warning_once(
|
||
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
||
|
)
|
||
|
use_cache = False
|
||
|
|
||
|
layer_outputs = self._gradient_checkpointing_func(
|
||
|
vblock,
|
||
|
decoder_layer,
|
||
|
hidden_states,
|
||
|
attention_mask,
|
||
|
position_ids,
|
||
|
past_key_value,
|
||
|
image_hidden_states,
|
||
|
image_attention_mask,
|
||
|
cross_attention_gate,
|
||
|
output_attentions,
|
||
|
use_cache,
|
||
|
idx,
|
||
|
self.cross_layer_interval,
|
||
|
self.gated_cross_attn_layers,
|
||
|
)
|
||
|
else:
|
||
|
layer_outputs = vblock(
|
||
|
decoder_layer,
|
||
|
hidden_states,
|
||
|
attention_mask=attention_mask,
|
||
|
position_ids=position_ids,
|
||
|
past_key_value=past_key_value,
|
||
|
image_hidden_states=image_hidden_states,
|
||
|
image_attention_mask=image_attention_mask,
|
||
|
cross_attention_gate=cross_attention_gate,
|
||
|
output_attentions=output_attentions,
|
||
|
use_cache=use_cache,
|
||
|
layer_idx=idx,
|
||
|
cross_layer_interval=self.cross_layer_interval,
|
||
|
gated_cross_attn_layers=self.gated_cross_attn_layers,
|
||
|
)
|
||
|
|
||
|
hidden_states = layer_outputs[0]
|
||
|
|
||
|
if use_cache:
|
||
|
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
|
||
|
|
||
|
if output_attentions:
|
||
|
all_self_attns += (layer_outputs[1],)
|
||
|
|
||
|
hidden_states = self.norm(hidden_states)
|
||
|
|
||
|
# add hidden states from the last decoder layer
|
||
|
if output_hidden_states:
|
||
|
all_hidden_states += (hidden_states,)
|
||
|
|
||
|
next_cache = next_decoder_cache if use_cache else None
|
||
|
image_hidden_states = image_hidden_states.view(batch_size, num_images, image_seq_len, image_hidden_size)
|
||
|
if not return_dict:
|
||
|
return tuple(
|
||
|
v
|
||
|
for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, image_hidden_states]
|
||
|
if v is not None
|
||
|
)
|
||
|
return IdeficsBaseModelOutputWithPast(
|
||
|
last_hidden_state=hidden_states,
|
||
|
past_key_values=next_cache,
|
||
|
hidden_states=all_hidden_states,
|
||
|
attentions=all_self_attns,
|
||
|
image_hidden_states=image_hidden_states,
|
||
|
)
|
||
|
|
||
|
|
||
|
class IdeficsForVisionText2Text(IdeficsPreTrainedModel):
|
||
|
_keys_to_ignore_on_load_missing = [r"lm_head.weight"]
|
||
|
_tied_weights_keys = ["model.embed_tokens.weight", "lm_head.weight"]
|
||
|
|
||
|
def __init__(self, config, vision_model=None):
|
||
|
super().__init__(config)
|
||
|
self.model = IdeficsModel(config)
|
||
|
|
||
|
self.lm_head = IdeficsDecoupledLinear(
|
||
|
in_features=config.hidden_size,
|
||
|
out_features=config.vocab_size,
|
||
|
out_additional_features=config.additional_vocab_size,
|
||
|
bias=False,
|
||
|
partially_freeze=config.freeze_lm_head,
|
||
|
)
|
||
|
|
||
|
# Initialize weights and apply final processing
|
||
|
self.post_init()
|
||
|
|
||
|
def get_input_embeddings(self):
|
||
|
return self.model.embed_tokens
|
||
|
|
||
|
def set_input_embeddings(self, value):
|
||
|
self.model.embed_tokens = value
|
||
|
|
||
|
def get_output_embeddings(self):
|
||
|
return self.lm_head
|
||
|
|
||
|
def set_output_embeddings(self, new_embeddings):
|
||
|
self.lm_head = new_embeddings
|
||
|
|
||
|
def set_decoder(self, decoder):
|
||
|
self.model = decoder
|
||
|
|
||
|
def get_decoder(self):
|
||
|
return self.model
|
||
|
|
||
|
def tie_weights(self):
|
||
|
"""
|
||
|
Overwrite `transformers.modeling_utils.PreTrainedModel.tie_weights` to handle the case of
|
||
|
IdeficsDecoupledLinear and IdeficsDecoupledEmbedding.
|
||
|
"""
|
||
|
output_embeddings = self.get_output_embeddings()
|
||
|
input_embeddings = self.get_input_embeddings()
|
||
|
|
||
|
if getattr(self.config, "tie_word_embeddings", True):
|
||
|
output_embeddings.weight = input_embeddings.weight
|
||
|
if input_embeddings.num_additional_embeddings > 0:
|
||
|
assert output_embeddings.out_additional_features == input_embeddings.num_additional_embeddings
|
||
|
output_embeddings.additional_fc.weight = input_embeddings.additional_embedding.weight
|
||
|
|
||
|
if hasattr(output_embeddings, "out_features") and hasattr(input_embeddings, "num_embeddings"):
|
||
|
output_embeddings.out_features = input_embeddings.num_embeddings
|
||
|
if hasattr(output_embeddings, "out_additional_features") and hasattr(
|
||
|
input_embeddings, "num_additional_embeddings"
|
||
|
):
|
||
|
output_embeddings.out_additional_features = input_embeddings.num_additional_embeddings
|
||
|
|
||
|
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
|
||
|
@replace_return_docstrings(output_type=IdeficsCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
|
||
|
def forward(
|
||
|
self,
|
||
|
input_ids: torch.LongTensor = 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,
|
||
|
pixel_values: Optional[torch.FloatTensor] = None,
|
||
|
image_encoder_embeddings: Optional[torch.FloatTensor] = None,
|
||
|
perceiver_embeddings: Optional[torch.FloatTensor] = None,
|
||
|
image_attention_mask: Optional[torch.Tensor] = None,
|
||
|
labels: Optional[torch.LongTensor] = None,
|
||
|
use_cache: Optional[bool] = None,
|
||
|
output_attentions: Optional[bool] = None,
|
||
|
output_hidden_states: Optional[bool] = None,
|
||
|
interpolate_pos_encoding: Optional[bool] = False,
|
||
|
return_dict: Optional[bool] = None,
|
||
|
) -> Union[Tuple, IdeficsCausalLMOutputWithPast]:
|
||
|
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]`.
|
||
|
|
||
|
Returns:
|
||
|
|
||
|
Example:
|
||
|
|
||
|
```python
|
||
|
>>> from transformers import AutoProcessor, IdeficsForVisionText2Text
|
||
|
|
||
|
>>> model = IdeficsForVisionText2Text.from_pretrained("HuggingFaceM4/idefics-9b")
|
||
|
>>> processor = AutoProcessor.from_pretrained("HuggingFaceM4/idefics-9b")
|
||
|
|
||
|
>>> dogs_image_url_1 = "https://huggingface.co/datasets/hf-internal-testing/fixtures_nlvr2/raw/main/image1.jpeg"
|
||
|
>>> dogs_image_url_2 = "https://huggingface.co/datasets/hf-internal-testing/fixtures_nlvr2/raw/main/image2.jpeg"
|
||
|
|
||
|
>>> prompts = [
|
||
|
... [
|
||
|
... "User:",
|
||
|
... dogs_image_url_1,
|
||
|
... "Describe this image.\nAssistant: An image of two dogs.\n",
|
||
|
... "User:",
|
||
|
... dogs_image_url_2,
|
||
|
... "Describe this image.\nAssistant:",
|
||
|
... ]
|
||
|
... ]
|
||
|
>>> inputs = processor(prompts, return_tensors="pt")
|
||
|
>>> generate_ids = model.generate(**inputs, max_new_tokens=6)
|
||
|
>>> processor.batch_decode(generate_ids, skip_special_tokens=True)
|
||
|
```"""
|
||
|
|
||
|
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
|
||
|
|
||
|
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
||
|
outputs = self.model(
|
||
|
input_ids=input_ids,
|
||
|
attention_mask=attention_mask,
|
||
|
position_ids=position_ids,
|
||
|
past_key_values=past_key_values,
|
||
|
inputs_embeds=inputs_embeds,
|
||
|
pixel_values=pixel_values,
|
||
|
image_encoder_embeddings=image_encoder_embeddings,
|
||
|
perceiver_embeddings=perceiver_embeddings,
|
||
|
image_attention_mask=image_attention_mask,
|
||
|
use_cache=use_cache,
|
||
|
output_attentions=output_attentions,
|
||
|
output_hidden_states=output_hidden_states,
|
||
|
interpolate_pos_encoding=interpolate_pos_encoding,
|
||
|
return_dict=return_dict,
|
||
|
)
|
||
|
|
||
|
hidden_states = outputs[0]
|
||
|
logits = self.lm_head(hidden_states)
|
||
|
|
||
|
loss = None
|
||
|
if labels is not None:
|
||
|
labels = labels.to(logits.device)
|
||
|
# Shift so that tokens < n predict n
|
||
|
if attention_mask is not None:
|
||
|
shift_attention_mask = attention_mask[..., 1:].to(logits.device)
|
||
|
shift_logits = logits[..., :-1, :][shift_attention_mask != 0].contiguous()
|
||
|
shift_labels = labels[..., 1:][shift_attention_mask != 0].contiguous()
|
||
|
else:
|
||
|
shift_logits = logits[..., :-1, :].contiguous()
|
||
|
shift_labels = labels[..., 1:].contiguous()
|
||
|
# Flatten the tokens
|
||
|
loss_fct = CrossEntropyLoss()
|
||
|
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
|
||
|
|
||
|
if not return_dict:
|
||
|
output = (logits,) + outputs[1:]
|
||
|
return (loss,) + output if loss is not None else output
|
||
|
|
||
|
return IdeficsCausalLMOutputWithPast(
|
||
|
loss=loss,
|
||
|
logits=logits,
|
||
|
past_key_values=outputs.past_key_values,
|
||
|
hidden_states=outputs.hidden_states,
|
||
|
attentions=outputs.attentions,
|
||
|
image_hidden_states=outputs.image_hidden_states,
|
||
|
)
|
||
|
|
||
|
def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs):
|
||
|
image_hidden_states = kwargs.pop("image_hidden_states", None)
|
||
|
if image_hidden_states is not None:
|
||
|
if self.config.use_resampler:
|
||
|
kwargs["perceiver_embeddings"] = image_hidden_states
|
||
|
else:
|
||
|
kwargs["image_encoder_embeddings"] = image_hidden_states
|
||
|
kwargs["pixel_values"] = None
|
||
|
inputs = prepare_inputs_for_generation(input_ids, past=past, **kwargs)
|
||
|
unwanted_kwargs = ["token_type_ids"]
|
||
|
for kwarg in unwanted_kwargs:
|
||
|
inputs.pop(kwarg, None)
|
||
|
return inputs
|
||
|
|
||
|
@staticmethod
|
||
|
def _expand_inputs_for_generation(
|
||
|
*args,
|
||
|
**model_kwargs,
|
||
|
):
|
||
|
return expand_inputs_for_generation(*args, **model_kwargs)
|
||
|
|
||
|
def _update_model_kwargs_for_generation(
|
||
|
self,
|
||
|
outputs: ModelOutput,
|
||
|
model_kwargs: Dict[str, Any],
|
||
|
is_encoder_decoder: bool = False,
|
||
|
standardize_cache_format: bool = False,
|
||
|
) -> Dict[str, Any]:
|
||
|
model_kwargs = super()._update_model_kwargs_for_generation(
|
||
|
outputs,
|
||
|
model_kwargs,
|
||
|
is_encoder_decoder,
|
||
|
standardize_cache_format,
|
||
|
)
|
||
|
|
||
|
if "image_attention_mask" in model_kwargs:
|
||
|
image_attention_mask = model_kwargs["image_attention_mask"]
|
||
|
last_mask = image_attention_mask[:, -1, :].unsqueeze(1)
|
||
|
model_kwargs["image_attention_mask"] = last_mask
|
||
|
|
||
|
# Get the precomputed image_hidden_states
|
||
|
model_kwargs["image_hidden_states"] = outputs.image_hidden_states
|
||
|
return model_kwargs
|
||
|
|
||
|
@staticmethod
|
||
|
def _reorder_cache(past, beam_idx):
|
||
|
reordered_past = ()
|
||
|
for layer_past in past:
|
||
|
reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
|
||
|
return reordered_past
|