1397 lines
64 KiB
Python
1397 lines
64 KiB
Python
""" PyTorch BART model. """
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import random
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from typing import Optional, Tuple, Union, List
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import math
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import torch
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import numpy as np
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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, MSELoss
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from transformers import BartPretrainedModel
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from transformers.activations import ACT2FN
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from transformers.modeling_outputs import (
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BaseModelOutput,
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BaseModelOutputWithPastAndCrossAttentions,
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Seq2SeqLMOutput,
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Seq2SeqModelOutput,
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Seq2SeqSequenceClassifierOutput,
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)
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from .utils import AVSDEncoderOutput, AVSDSeq2SeqModelOutput, AVSDSeq2SeqLMOutput
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from transformers.modeling_utils import PreTrainedModel
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from transformers.models.bart.configuration_bart import BartConfig
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from transformers.utils import logging
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from .utils import ELBO, embed_graphs, diag_tensor, track_features_text, track_features_vis, get_knn_graph, seperate_nextqa_input_modalities
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from .gnns import QNetLocal, PNetLocal, QNetGlobal, PNetGlobal, MLPModule
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logger = logging.get_logger(__name__)
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_CHECKPOINT_FOR_DOC = "facebook/bart-large"
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_CONFIG_FOR_DOC = "BartConfig"
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_TOKENIZER_FOR_DOC = "BartTokenizer"
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BART_PRETRAINED_MODEL_ARCHIVE_LIST = [
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"facebook/bart-large",
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# See all BART models at https://huggingface.co/models?filter=bart
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]
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def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
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"""
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Shift input ids one token to the right.
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"""
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shifted_input_ids = input_ids.new_zeros(input_ids.shape)
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shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
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shifted_input_ids[:, 0] = decoder_start_token_id
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if pad_token_id is None:
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raise ValueError("self.model.config.pad_token_id has to be defined.")
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# replace possible -100 values in labels by `pad_token_id`
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shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
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return shifted_input_ids
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def _make_causal_mask(
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input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
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):
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"""
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Make causal mask used for bi-directional self-attention.
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"""
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bsz, tgt_len = input_ids_shape
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mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
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mask_cond = torch.arange(mask.size(-1), device=device)
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mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
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mask = mask.to(dtype)
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if past_key_values_length > 0:
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mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
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return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
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def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
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"""
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Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
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"""
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bsz, src_len = mask.size()
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tgt_len = tgt_len if tgt_len is not None else src_len
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expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
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inverted_mask = 1.0 - expanded_mask
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return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
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class BartLearnedPositionalEmbedding(nn.Embedding):
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"""
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This module learns positional embeddings up to a fixed maximum size.
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"""
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def __init__(self, num_embeddings: int, embedding_dim: int):
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# Bart is set up so that if padding_idx is specified then offset the embedding ids by 2
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# and adjust num_embeddings appropriately. Other models don't have this hack
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self.offset = 2
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super().__init__(num_embeddings + self.offset, embedding_dim)
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def forward(self, input_ids: torch.Tensor, past_key_values_length: int = 0):
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"""`input_ids' shape is expected to be [bsz x seqlen]."""
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bsz, seq_len = input_ids.shape[:2]
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positions = torch.arange(
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past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device
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).expand(bsz, -1)
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return super().forward(positions + self.offset)
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class BartAttention(nn.Module):
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"""Multi-headed attention from 'Attention Is All You Need' paper"""
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def __init__(
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self,
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embed_dim: int,
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num_heads: int,
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dropout: float = 0.0,
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is_decoder: bool = False,
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bias: bool = True,
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):
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super().__init__()
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self.embed_dim = embed_dim
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self.num_heads = num_heads
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self.dropout = dropout
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self.head_dim = embed_dim // num_heads
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if (self.head_dim * num_heads) != self.embed_dim:
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raise ValueError(
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f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
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f" and `num_heads`: {num_heads})."
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)
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self.scaling = self.head_dim**-0.5
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self.is_decoder = is_decoder
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self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
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self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
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self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
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self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
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def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
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return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
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def forward(
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self,
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hidden_states: torch.Tensor,
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key_value_states: Optional[torch.Tensor] = None,
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past_key_value: Optional[Tuple[torch.Tensor]] = None,
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attention_mask: Optional[torch.Tensor] = None,
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layer_head_mask: Optional[torch.Tensor] = None,
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output_attentions: bool = False,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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"""Input shape: Batch x Time x Channel"""
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# if key_value_states are provided this layer is used as a cross-attention layer
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# for the decoder
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is_cross_attention = key_value_states is not None
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bsz, tgt_len, _ = hidden_states.size()
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# get query proj
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query_states = self.q_proj(hidden_states) * self.scaling
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# get key, value proj
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# `past_key_value[0].shape[2] == key_value_states.shape[1]`
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# is checking that the `sequence_length` of the `past_key_value` is the same as
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# the provided `key_value_states` to support prefix tuning
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if (
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is_cross_attention
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and past_key_value is not None
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and past_key_value[0].shape[2] == key_value_states.shape[1]
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):
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# reuse k,v, cross_attentions
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key_states = past_key_value[0]
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value_states = past_key_value[1]
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elif is_cross_attention:
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# cross_attentions
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key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
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value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
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elif past_key_value is not None:
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# reuse k, v, self_attention
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key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
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value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
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key_states = torch.cat([past_key_value[0], key_states], dim=2)
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value_states = torch.cat([past_key_value[1], value_states], dim=2)
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else:
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# self_attention
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key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
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value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
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if self.is_decoder:
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# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
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# Further calls to cross_attention layer can then reuse all cross-attention
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# key/value_states (first "if" case)
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# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
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# all previous decoder key/value_states. Further calls to uni-directional self-attention
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# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
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# if encoder bi-directional self-attention `past_key_value` is always `None`
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past_key_value = (key_states, value_states)
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proj_shape = (bsz * self.num_heads, -1, self.head_dim)
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query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
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key_states = key_states.reshape(*proj_shape)
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value_states = value_states.reshape(*proj_shape)
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src_len = key_states.size(1)
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attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
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if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
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raise ValueError(
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f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
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f" {attn_weights.size()}"
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)
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if attention_mask is not None:
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if attention_mask.size() != (bsz, 1, tgt_len, src_len):
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raise ValueError(
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f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
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)
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attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
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attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
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attn_weights = nn.functional.softmax(attn_weights, dim=-1)
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if layer_head_mask is not None:
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if layer_head_mask.size() != (self.num_heads,):
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raise ValueError(
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f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
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f" {layer_head_mask.size()}"
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)
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attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
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attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
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if output_attentions:
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# this operation is a bit awkward, but it's required to
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# make sure that attn_weights keeps its gradient.
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# In order to do so, attn_weights have to be reshaped
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# twice and have to be reused in the following
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attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
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attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
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else:
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attn_weights_reshaped = None
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attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
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attn_output = torch.bmm(attn_probs, value_states)
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if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
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raise ValueError(
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f"`attn_output` should be of size {(bsz * self.num_heads, tgt_len, self.head_dim)}, but is"
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f" {attn_output.size()}"
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)
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attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
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attn_output = attn_output.transpose(1, 2)
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# Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
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# partitioned across GPUs when using tensor-parallelism.
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attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
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attn_output = self.out_proj(attn_output)
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return attn_output, attn_weights_reshaped, past_key_value
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class BartEncoderLayer(nn.Module):
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def __init__(self, config: BartConfig):
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super().__init__()
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self.embed_dim = config.d_model
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self.self_attn = BartAttention(
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embed_dim=self.embed_dim,
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num_heads=config.encoder_attention_heads,
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dropout=config.attention_dropout,
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)
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self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
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self.dropout = config.dropout
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self.activation_fn = ACT2FN[config.activation_function]
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self.activation_dropout = config.activation_dropout
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self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
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self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
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self.final_layer_norm = nn.LayerNorm(self.embed_dim)
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def forward(
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self,
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hidden_states: torch.FloatTensor,
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attention_mask: torch.FloatTensor,
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layer_head_mask: torch.FloatTensor,
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output_attentions: Optional[bool] = False,
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) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]:
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"""
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Args:
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hidden_states (`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)`
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attention_mask (`torch.FloatTensor`): attention mask of size
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`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
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layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
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`(encoder_attention_heads,)`.
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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
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returned tensors for more detail.
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"""
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residual = hidden_states
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hidden_states, attn_weights, _ = self.self_attn(
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hidden_states=hidden_states,
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attention_mask=attention_mask,
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layer_head_mask=layer_head_mask,
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output_attentions=output_attentions,
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)
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hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
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hidden_states = residual + hidden_states
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hidden_states = self.self_attn_layer_norm(hidden_states)
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residual = hidden_states
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hidden_states = self.activation_fn(self.fc1(hidden_states))
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hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
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hidden_states = self.fc2(hidden_states)
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hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
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hidden_states = residual + hidden_states
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hidden_states = self.final_layer_norm(hidden_states)
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if hidden_states.dtype == torch.float16 and (
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torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()
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):
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clamp_value = torch.finfo(hidden_states.dtype).max - 1000
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hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
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outputs = (hidden_states,)
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if output_attentions:
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outputs += (attn_weights,)
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return outputs
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class BartDecoderLayer(nn.Module):
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def __init__(self, config: BartConfig):
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super().__init__()
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self.embed_dim = config.d_model
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self.self_attn = BartAttention(
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embed_dim=self.embed_dim,
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num_heads=config.decoder_attention_heads,
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dropout=config.attention_dropout,
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is_decoder=True,
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)
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self.dropout = config.dropout
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self.activation_fn = ACT2FN[config.activation_function]
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self.activation_dropout = config.activation_dropout
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self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
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self.encoder_attn = BartAttention(
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self.embed_dim,
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config.decoder_attention_heads,
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dropout=config.attention_dropout,
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is_decoder=True,
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)
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self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
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self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
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self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
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self.final_layer_norm = nn.LayerNorm(self.embed_dim)
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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encoder_hidden_states: Optional[torch.Tensor] = None,
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encoder_attention_mask: Optional[torch.Tensor] = None,
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layer_head_mask: Optional[torch.Tensor] = None,
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cross_attn_layer_head_mask: Optional[torch.Tensor] = None,
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past_key_value: Optional[Tuple[torch.Tensor]] = None,
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output_attentions: Optional[bool] = False,
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use_cache: Optional[bool] = True,
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) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
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"""
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Args:
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hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
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attention_mask (`torch.FloatTensor`): attention mask of size
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|
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
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encoder_hidden_states (`torch.FloatTensor`):
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cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
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encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
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`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
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layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
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`(encoder_attention_heads,)`.
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cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of
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size `(decoder_attention_heads,)`.
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past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states
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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
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returned tensors for more detail.
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"""
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residual = hidden_states
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# Self Attention
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# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
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self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
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# add present self-attn cache to positions 1,2 of present_key_value tuple
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hidden_states, self_attn_weights, present_key_value = self.self_attn(
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hidden_states=hidden_states,
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past_key_value=self_attn_past_key_value,
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attention_mask=attention_mask,
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layer_head_mask=layer_head_mask,
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output_attentions=output_attentions,
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)
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hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
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hidden_states = residual + hidden_states
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hidden_states = self.self_attn_layer_norm(hidden_states)
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# Cross-Attention Block
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cross_attn_present_key_value = None
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cross_attn_weights = None
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if encoder_hidden_states is not None:
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residual = hidden_states
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|
|
# cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
|
|
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
|
|
hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
|
|
hidden_states=hidden_states,
|
|
key_value_states=encoder_hidden_states,
|
|
attention_mask=encoder_attention_mask,
|
|
layer_head_mask=cross_attn_layer_head_mask,
|
|
past_key_value=cross_attn_past_key_value,
|
|
output_attentions=output_attentions,
|
|
)
|
|
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
|
|
hidden_states = residual + hidden_states
|
|
hidden_states = self.encoder_attn_layer_norm(hidden_states)
|
|
|
|
# add cross-attn to positions 3,4 of present_key_value tuple
|
|
present_key_value = present_key_value + cross_attn_present_key_value
|
|
|
|
# Fully Connected
|
|
residual = hidden_states
|
|
hidden_states = self.activation_fn(self.fc1(hidden_states))
|
|
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
|
|
hidden_states = self.fc2(hidden_states)
|
|
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
|
|
hidden_states = residual + hidden_states
|
|
hidden_states = self.final_layer_norm(hidden_states)
|
|
|
|
outputs = (hidden_states,)
|
|
|
|
if output_attentions:
|
|
outputs += (self_attn_weights, cross_attn_weights)
|
|
|
|
if use_cache:
|
|
outputs += (present_key_value,)
|
|
|
|
return outputs
|
|
|
|
|
|
class BartEncoder(BartPretrainedModel):
|
|
"""
|
|
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
|
|
[`BartEncoderLayer`].
|
|
|
|
Args:
|
|
config: BartConfig
|
|
embed_tokens (nn.Embedding): output embedding
|
|
"""
|
|
|
|
def __init__(self, config: BartConfig, embed_tokens: Optional[nn.Embedding] = None):
|
|
super().__init__(config)
|
|
|
|
self.dropout = config.dropout
|
|
self.layerdrop = config.encoder_layerdrop
|
|
|
|
embed_dim = config.d_model
|
|
self.padding_idx = config.pad_token_id
|
|
self.max_source_positions = config.max_position_embeddings
|
|
self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
|
|
|
|
self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim, self.padding_idx)
|
|
|
|
if embed_tokens is not None:
|
|
self.embed_tokens.weight = embed_tokens.weight
|
|
|
|
self.embed_positions = BartLearnedPositionalEmbedding(
|
|
config.max_position_embeddings,
|
|
embed_dim,
|
|
)
|
|
self.layers = nn.ModuleList([BartEncoderLayer(config) for _ in range(config.encoder_layers)])
|
|
self.layernorm_embedding = nn.LayerNorm(embed_dim)
|
|
|
|
self.gradient_checkpointing = False
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.embed_tokens
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.embed_tokens = value
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor = None,
|
|
i3d_rgb_interval: list = None,
|
|
i3d_flow_interval: list = None,
|
|
question_intervals: list = None,
|
|
vis_state_vector_idx: list = None,
|
|
question_state_vector_idx: list = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
qnet_local: Optional[nn.Module] = None,
|
|
pnet_local: Optional[nn.Module] = None,
|
|
qnet_global: Optional[nn.Module] = None,
|
|
pnet_global: Optional[nn.Module] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, BaseModelOutput]:
|
|
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)
|
|
head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
|
|
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicates the head is **masked**.
|
|
|
|
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.
|
|
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.
|
|
"""
|
|
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
|
|
|
|
# retrieve input_ids and inputs_embeds
|
|
if input_ids is not None and inputs_embeds is not None:
|
|
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
|
|
elif input_ids is not None:
|
|
input = input_ids
|
|
input_ids = input_ids.view(-1, input_ids.shape[-1])
|
|
elif inputs_embeds is not None:
|
|
input = inputs_embeds[:, :, -1]
|
|
else:
|
|
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
|
|
|
|
embed_pos = self.embed_positions(input)
|
|
embed_pos = embed_pos.to(inputs_embeds.device)
|
|
device = inputs_embeds.device
|
|
|
|
hidden_states = inputs_embeds + embed_pos
|
|
hidden_states = self.layernorm_embedding(hidden_states)
|
|
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
|
|
|
|
# expand attention_mask
|
|
if attention_mask is not None:
|
|
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
|
attention_mask = _expand_mask(attention_mask, inputs_embeds.dtype)
|
|
|
|
encoder_states = () if output_hidden_states else None
|
|
all_attentions = () if output_attentions else None
|
|
|
|
# check if head_mask has a correct number of layers specified if desired
|
|
if head_mask is not None:
|
|
if head_mask.size()[0] != (len(self.layers)):
|
|
raise ValueError(
|
|
f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
|
|
f" {head_mask.size()[0]}."
|
|
)
|
|
|
|
for idx, encoder_layer in enumerate(self.layers):
|
|
if output_hidden_states:
|
|
encoder_states = encoder_states + (hidden_states,)
|
|
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
|
|
dropout_probability = random.uniform(0, 1)
|
|
if self.training and (dropout_probability < self.layerdrop): # skip the layer
|
|
layer_outputs = (None, None)
|
|
else:
|
|
if self.gradient_checkpointing and self.training:
|
|
|
|
def create_custom_forward(module):
|
|
def custom_forward(*inputs):
|
|
return module(*inputs, output_attentions)
|
|
|
|
return custom_forward
|
|
|
|
layer_outputs = torch.utils.checkpoint.checkpoint(
|
|
create_custom_forward(encoder_layer),
|
|
hidden_states,
|
|
attention_mask,
|
|
(head_mask[idx] if head_mask is not None else None),
|
|
)
|
|
else:
|
|
# NOTE: ---------------> Here we implement our logic
|
|
if idx % self.config.gnns_every == 0 or idx == len(self.layers) - 1:
|
|
hidden_states_copy = hidden_states.clone()
|
|
attention_values = None if idx == 0 else all_attentions[-1]
|
|
|
|
features, att = seperate_nextqa_input_modalities(
|
|
hidden_states,
|
|
i3d_rgb_interval,
|
|
i3d_flow_interval,
|
|
question_intervals,
|
|
vis_state_vector_idx,
|
|
question_state_vector_idx,
|
|
attention_values=attention_values
|
|
)
|
|
i3d_rgb_hidden, i3d_flow_hidden, question_hidden = features
|
|
i3d_rgb_att, i3d_flow_att, question_att = att
|
|
|
|
i3d_rgb_X, i3d_rgb_node_idx = track_features_vis(i3d_rgb_hidden, i3d_rgb_att, self.config.top_k, device)
|
|
i3d_flow_X, i3d_flow_node_idx = track_features_vis(i3d_flow_hidden, i3d_flow_att, self.config.top_k, device)
|
|
|
|
question_X, question_node_idx = track_features_text(question_hidden, question_att, self.config.top_k, device)
|
|
|
|
# NOTE: The indices need to be adjusted to match the global input
|
|
i3d_rgb_node_idx = i3d_rgb_node_idx + 1
|
|
i3d_flow_node_idx = i3d_flow_node_idx + i3d_flow_interval[0] + 1
|
|
|
|
question_node_idx = [x + qi[0] + 1 for x, qi in zip(question_node_idx, question_intervals)]
|
|
|
|
X = [i3d_rgb_X, i3d_flow_X, question_X]
|
|
QZ_global = torch.cat(X, dim=1)
|
|
PZ_global = torch.cat(X, dim=1)
|
|
|
|
if self.config.use_elbo_local:
|
|
QAs_local, QZs_local = qnet_local(X)
|
|
PAs_local, PZs_local = pnet_local(X)
|
|
|
|
QA_global, delimiters = diag_tensor(QAs_local)
|
|
PA_global, _ = diag_tensor(PAs_local)
|
|
else:
|
|
QA_global = get_knn_graph(QZ_global, self.config.num_nn, QZ_global.device)
|
|
PA_global = get_knn_graph(PZ_global, self.config.num_nn, PZ_global.device)
|
|
QAs_local = None
|
|
PAs_local = None
|
|
delimiters = [feat.size(1) for feat in X]
|
|
delimiters.insert(0, 0)
|
|
delimiters = np.cumsum(delimiters).tolist()
|
|
|
|
if self.config.use_elbo_global:
|
|
QA_global, QZ_global = qnet_global(QZ_global, QA_global)
|
|
PA_global, PZ_global = pnet_global(PZ_global, PA_global)
|
|
else:
|
|
QZ_global = torch.cat(QZs_local, dim=1)
|
|
PZ_global = torch.cat(PZs_local, dim=1)
|
|
QA_global = None
|
|
PA_global = None
|
|
|
|
# Embed the local graphs and get the updated state representations
|
|
Z = 0.5 * QZ_global + 0.5 * PZ_global
|
|
state_vectors = embed_graphs(Z, delimiters)
|
|
for sv, idx in zip(state_vectors[:2], vis_state_vector_idx): # Only the visual state vectors
|
|
hidden_states[:, idx, :] = sv
|
|
|
|
question_state_vector_idx_expanded = torch.tensor(question_state_vector_idx).unsqueeze(0).unsqueeze(-1).repeat(1, 1, hidden_states.size(-1)).to(device)
|
|
hidden_states = hidden_states.scatter(1, question_state_vector_idx_expanded, state_vectors[2].unsqueeze(0))
|
|
|
|
if self.config.integrate_all_gnn_features:
|
|
# Now integrate the GNN node features into the hidden states
|
|
vis_node_idx = [i3d_rgb_node_idx, i3d_flow_node_idx]
|
|
for i in range(len(vis_node_idx)):
|
|
hidden_states = hidden_states.scatter(1, vis_node_idx[i], Z[:, delimiters[i]:delimiters[i+1], :])
|
|
|
|
question_node_idx = torch.cat(question_node_idx, dim=0)
|
|
hidden_states = hidden_states.scatter(1, question_node_idx, Z[:, delimiters[-2]:delimiters[-1], :])
|
|
|
|
hidden_states = (1 - self.config.alpha) * hidden_states + self.config.alpha * hidden_states_copy
|
|
# NOTE: <--------------- END
|
|
|
|
layer_outputs = encoder_layer(
|
|
hidden_states,
|
|
attention_mask,
|
|
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
|
|
output_attentions=output_attentions,
|
|
)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
|
|
if output_attentions:
|
|
all_attentions = all_attentions + (layer_outputs[1],)
|
|
|
|
if output_hidden_states:
|
|
encoder_states = encoder_states + (hidden_states,)
|
|
|
|
if not return_dict:
|
|
return tuple(v for v in [hidden_states, encoder_states, all_attentions, QAs_local, PAs_local, QA_global, PA_global] if v is not None)
|
|
return AVSDEncoderOutput(
|
|
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions,
|
|
QAs_local=QAs_local, PAs_local=PAs_local, QA_global=QA_global, PA_global=PA_global,
|
|
state_vectors=state_vectors
|
|
|
|
)
|
|
|
|
|
|
class BartDecoder(BartPretrainedModel):
|
|
"""
|
|
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`BartDecoderLayer`]
|
|
|
|
Args:
|
|
config: BartConfig
|
|
embed_tokens (nn.Embedding): output embedding
|
|
"""
|
|
|
|
def __init__(self, config: BartConfig, embed_tokens: Optional[nn.Embedding] = None):
|
|
super().__init__(config)
|
|
self.dropout = config.dropout
|
|
self.layerdrop = config.decoder_layerdrop
|
|
self.padding_idx = config.pad_token_id
|
|
self.max_target_positions = config.max_position_embeddings
|
|
self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
|
|
|
|
self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model, self.padding_idx)
|
|
|
|
if embed_tokens is not None:
|
|
self.embed_tokens.weight = embed_tokens.weight
|
|
|
|
self.embed_positions = BartLearnedPositionalEmbedding(
|
|
config.max_position_embeddings,
|
|
config.d_model,
|
|
)
|
|
self.layers = nn.ModuleList([BartDecoderLayer(config) for _ in range(config.decoder_layers)])
|
|
|
|
self.layernorm_embedding = nn.LayerNorm(config.d_model)
|
|
|
|
self.gradient_checkpointing = False
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.embed_tokens
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.embed_tokens = value
|
|
|
|
def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
|
|
# create causal mask
|
|
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
|
combined_attention_mask = None
|
|
if input_shape[-1] > 1:
|
|
combined_attention_mask = _make_causal_mask(
|
|
input_shape,
|
|
inputs_embeds.dtype,
|
|
device=inputs_embeds.device,
|
|
past_key_values_length=past_key_values_length,
|
|
)
|
|
|
|
if attention_mask is not None:
|
|
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
|
expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
|
|
inputs_embeds.device
|
|
)
|
|
combined_attention_mask = (
|
|
expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
|
|
)
|
|
|
|
return combined_attention_mask
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
|
encoder_attention_mask: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
cross_attn_head_mask: Optional[torch.Tensor] = None,
|
|
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
|
|
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)
|
|
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
|
|
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
|
|
of the decoder.
|
|
encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
|
|
Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. 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)
|
|
head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
|
|
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicates the head is **masked**.
|
|
|
|
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
|
|
Mask to nullify selected heads of the cross-attention modules in the decoder to avoid performing
|
|
cross-attention on hidden heads. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicates the head is **masked**.
|
|
|
|
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.
|
|
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.
|
|
"""
|
|
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:
|
|
input = input_ids
|
|
input_shape = input.shape
|
|
input_ids = input_ids.view(-1, input_shape[-1])
|
|
elif inputs_embeds is not None:
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
input = inputs_embeds[:, :, -1]
|
|
else:
|
|
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
|
|
|
|
# past_key_values_length
|
|
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.embed_tokens(input) * self.embed_scale
|
|
|
|
attention_mask = self._prepare_decoder_attention_mask(
|
|
attention_mask, input_shape, inputs_embeds, past_key_values_length
|
|
)
|
|
|
|
# expand encoder attention mask
|
|
if encoder_hidden_states is not None and encoder_attention_mask is not None:
|
|
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
|
encoder_attention_mask = _expand_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])
|
|
|
|
# embed positions
|
|
positions = self.embed_positions(input, past_key_values_length)
|
|
positions = positions.to(inputs_embeds.device)
|
|
|
|
hidden_states = inputs_embeds + positions
|
|
hidden_states = self.layernorm_embedding(hidden_states)
|
|
|
|
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
|
|
|
|
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
|
|
all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
|
|
next_decoder_cache = () if use_cache else None
|
|
|
|
# check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
|
|
for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
|
|
if attn_mask is not None:
|
|
if attn_mask.size()[0] != (len(self.layers)):
|
|
raise ValueError(
|
|
f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
|
|
f" {head_mask.size()[0]}."
|
|
)
|
|
|
|
for idx, decoder_layer in enumerate(self.layers):
|
|
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
|
|
dropout_probability = random.uniform(0, 1)
|
|
if self.training and (dropout_probability < self.layerdrop):
|
|
continue
|
|
|
|
past_key_value = past_key_values[idx] if past_key_values is not None else None
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
|
|
def create_custom_forward(module):
|
|
def custom_forward(*inputs):
|
|
# None for past_key_value
|
|
return module(*inputs, output_attentions, use_cache)
|
|
|
|
return custom_forward
|
|
|
|
layer_outputs = torch.utils.checkpoint.checkpoint(
|
|
create_custom_forward(decoder_layer),
|
|
hidden_states,
|
|
attention_mask,
|
|
encoder_hidden_states,
|
|
encoder_attention_mask,
|
|
head_mask[idx] if head_mask is not None else None,
|
|
cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None,
|
|
None,
|
|
)
|
|
else:
|
|
layer_outputs = decoder_layer(
|
|
hidden_states,
|
|
attention_mask=attention_mask,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
|
|
cross_attn_layer_head_mask=(
|
|
cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None
|
|
),
|
|
past_key_value=past_key_value,
|
|
output_attentions=output_attentions,
|
|
use_cache=use_cache,
|
|
)
|
|
hidden_states = layer_outputs[0]
|
|
|
|
if use_cache:
|
|
next_decoder_cache += (layer_outputs[3 if output_attentions else 1],)
|
|
|
|
if output_attentions:
|
|
all_self_attns += (layer_outputs[1],)
|
|
|
|
if encoder_hidden_states is not None:
|
|
all_cross_attentions += (layer_outputs[2],)
|
|
|
|
# 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
|
|
if not return_dict:
|
|
return tuple(
|
|
v
|
|
for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions]
|
|
if v is not None
|
|
)
|
|
return BaseModelOutputWithPastAndCrossAttentions(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=next_cache,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attns,
|
|
cross_attentions=all_cross_attentions,
|
|
)
|
|
|
|
|
|
class BartModel(BartPretrainedModel):
|
|
_keys_to_ignore_on_load_missing = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
|
|
|
|
def __init__(self, config: BartConfig):
|
|
super().__init__(config)
|
|
|
|
padding_idx, vocab_size = config.pad_token_id, config.vocab_size
|
|
self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx)
|
|
|
|
self.encoder = BartEncoder(config, self.shared)
|
|
self.decoder = BartDecoder(config, self.shared)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.shared
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.shared = value
|
|
self.encoder.embed_tokens = self.shared
|
|
self.decoder.embed_tokens = self.shared
|
|
|
|
def get_encoder(self):
|
|
return self.encoder
|
|
|
|
def get_decoder(self):
|
|
return self.decoder
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
i3d_rgb_interval: list = None,
|
|
i3d_flow_interval: list = None,
|
|
question_intervals: list = None,
|
|
vis_state_vector_idx: list = None,
|
|
question_state_vector_idx: list = None,
|
|
decoder_input_ids: Optional[torch.LongTensor] = None,
|
|
decoder_attention_mask: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
decoder_head_mask: Optional[torch.Tensor] = None,
|
|
cross_attn_head_mask: Optional[torch.Tensor] = None,
|
|
encoder_outputs: Optional[List[torch.FloatTensor]] = None,
|
|
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
qnet_local: Optional[nn.Module] = None,
|
|
pnet_local: Optional[nn.Module] = None,
|
|
qnet_global: Optional[nn.Module] = None,
|
|
pnet_global: Optional[nn.Module] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, Seq2SeqModelOutput]:
|
|
# different to other models, Bart automatically creates decoder_input_ids from
|
|
# input_ids if no decoder_input_ids are provided
|
|
if decoder_input_ids is None and decoder_inputs_embeds is None:
|
|
if input_ids is None:
|
|
raise ValueError(
|
|
"If no `decoder_input_ids` or `decoder_inputs_embeds` are "
|
|
"passed, `input_ids` cannot be `None`. Please pass either "
|
|
"`input_ids` or `decoder_input_ids` or `decoder_inputs_embeds`."
|
|
)
|
|
|
|
decoder_input_ids = shift_tokens_right(
|
|
input_ids, self.config.pad_token_id, self.config.decoder_start_token_id
|
|
)
|
|
|
|
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
|
|
|
|
if encoder_outputs is None:
|
|
encoder_outputs = self.encoder(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
i3d_rgb_interval=i3d_rgb_interval,
|
|
i3d_flow_interval=i3d_flow_interval,
|
|
question_intervals=question_intervals,
|
|
vis_state_vector_idx=vis_state_vector_idx,
|
|
question_state_vector_idx=question_state_vector_idx,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
qnet_local=qnet_local,
|
|
pnet_local=pnet_local,
|
|
qnet_global=qnet_global,
|
|
pnet_global=pnet_global,
|
|
return_dict=return_dict,
|
|
)
|
|
# If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
|
|
elif return_dict and not isinstance(encoder_outputs, AVSDEncoderOutput):
|
|
|
|
encoder_outputs = AVSDEncoderOutput(
|
|
last_hidden_state=encoder_outputs[0],
|
|
hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
|
|
attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
|
|
QAs_local=encoder_outputs[3] if len(encoder_outputs) > 3 else None,
|
|
PAs_local=encoder_outputs[4] if len(encoder_outputs) > 4 else None,
|
|
QA_global=encoder_outputs[5] if len(encoder_outputs) > 5 else None,
|
|
PA_global=encoder_outputs[6] if len(encoder_outputs) > 6 else None,
|
|
state_vectors=encoder_outputs[7] if len(encoder_outputs) > 7 else None,
|
|
)
|
|
|
|
# decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
|
|
decoder_outputs = self.decoder(
|
|
input_ids=decoder_input_ids,
|
|
attention_mask=decoder_attention_mask,
|
|
encoder_hidden_states=encoder_outputs[0],
|
|
encoder_attention_mask=attention_mask,
|
|
head_mask=decoder_head_mask,
|
|
cross_attn_head_mask=cross_attn_head_mask,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=decoder_inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
if not return_dict:
|
|
return decoder_outputs + encoder_outputs
|
|
|
|
return AVSDSeq2SeqModelOutput(
|
|
last_hidden_state=decoder_outputs.last_hidden_state,
|
|
past_key_values=decoder_outputs.past_key_values,
|
|
decoder_hidden_states=decoder_outputs.hidden_states,
|
|
decoder_attentions=decoder_outputs.attentions,
|
|
cross_attentions=decoder_outputs.cross_attentions,
|
|
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
|
|
encoder_hidden_states=encoder_outputs.hidden_states,
|
|
encoder_attentions=encoder_outputs.attentions,
|
|
QAs_local=encoder_outputs.QAs_local,
|
|
PAs_local=encoder_outputs.PAs_local,
|
|
QA_global=encoder_outputs.QA_global,
|
|
PA_global=encoder_outputs.PA_global,
|
|
state_vectors=encoder_outputs.state_vectors
|
|
)
|
|
|
|
class AVSDBart(BartPretrainedModel):
|
|
base_model_prefix = "model"
|
|
_keys_to_ignore_on_load_missing = [
|
|
r"final_logits_bias",
|
|
r"lm_head.weight",
|
|
"encoder.embed_tokens.weight",
|
|
"decoder.embed_tokens.weight",]
|
|
|
|
def __init__(self, config: BartConfig):
|
|
super().__init__(config)
|
|
self.config = config
|
|
self.model = BartModel(config)
|
|
self.register_buffer("final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings)))
|
|
self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False)
|
|
|
|
# Add the linear mapping of the visual features
|
|
self.i3d_rgb_linear = nn.Linear(config.d_i3d_rgb, config.d_model)
|
|
self.i3d_flow_linear = nn.Linear(config.d_i3d_flow, config.d_model)
|
|
|
|
if self.config.use_random_graphs:
|
|
assert (self.config.use_elbo_global and self.config.use_elbo_local)
|
|
|
|
if self.config.use_elbo_local:
|
|
self.qnet_local = QNetLocal(config)
|
|
self.pnet_local = PNetLocal(config)
|
|
else:
|
|
self.qnet_local = None
|
|
self.pnet_local = None
|
|
if self.config.use_elbo_global:
|
|
self.qnet_global = QNetGlobal(config)
|
|
self.pnet_global = PNetGlobal(config)
|
|
else:
|
|
self.qnet_global = None
|
|
self.pnet_global = None
|
|
|
|
self.ce = CrossEntropyLoss() # Ignore the -100 index
|
|
self.elbo = ELBO()
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
if self.config.use_elbo_local:
|
|
self.qnet_local.reset_parameters()
|
|
self.pnet_local.reset_parameters()
|
|
if self.config.use_elbo_global:
|
|
self.qnet_global.reset_parameters()
|
|
self.pnet_global.reset_parameters()
|
|
|
|
def get_encoder(self):
|
|
return self.model.get_encoder()
|
|
|
|
def get_decoder(self):
|
|
return self.model.get_decoder()
|
|
|
|
def resize_token_embeddings(self, new_num_tokens: int) -> nn.Embedding:
|
|
new_embeddings = super().resize_token_embeddings(new_num_tokens)
|
|
self._resize_final_logits_bias(new_num_tokens)
|
|
return new_embeddings
|
|
|
|
def _resize_final_logits_bias(self, new_num_tokens: int) -> None:
|
|
old_num_tokens = self.final_logits_bias.shape[-1]
|
|
if new_num_tokens <= old_num_tokens:
|
|
new_bias = self.final_logits_bias[:, :new_num_tokens]
|
|
else:
|
|
extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device)
|
|
new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1)
|
|
self.register_buffer("final_logits_bias", new_bias)
|
|
|
|
def get_output_embeddings(self):
|
|
return self.lm_head
|
|
|
|
def set_output_embeddings(self, new_embeddings):
|
|
self.lm_head = new_embeddings
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor = None,
|
|
video_place_holder_ids: torch.LongTensor = None,
|
|
i3d_rgb: torch.LongTensor = None,
|
|
i3d_flow: torch.LongTensor = None,
|
|
i3d_rgb_interval: list = None,
|
|
i3d_flow_interval: list = None,
|
|
question_intervals: list = None,
|
|
vis_state_vector_idx: list = None,
|
|
question_state_vector_idx: list = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
text_attention_mask: Optional[torch.Tensor] = None,
|
|
decoder_input_ids: Optional[torch.LongTensor] = None,
|
|
decoder_attention_mask: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
decoder_head_mask: Optional[torch.Tensor] = None,
|
|
cross_attn_head_mask: Optional[torch.Tensor] = None,
|
|
encoder_outputs: Optional[List[torch.FloatTensor]] = None,
|
|
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
generate: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, Seq2SeqLMOutput]:
|
|
r"""
|
|
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:
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
# First map the video features
|
|
i3d_rgb = self.i3d_rgb_linear(i3d_rgb)
|
|
i3d_flow = self.i3d_flow_linear(i3d_flow)
|
|
video_embeds = self.model.encoder.embed_tokens(video_place_holder_ids)
|
|
|
|
# inject the real features
|
|
num_vid_feat = i3d_rgb.size(1)
|
|
video_embeds[:, 1:num_vid_feat+1] = i3d_rgb
|
|
video_embeds[:, num_vid_feat + 2: 2*num_vid_feat + 2] = i3d_flow
|
|
|
|
text_embeds = self.model.encoder.embed_tokens(input_ids)
|
|
inputs_embeds = torch.cat([video_embeds, text_embeds], dim=1)
|
|
|
|
if labels is not None:
|
|
if use_cache:
|
|
logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.")
|
|
use_cache = False
|
|
if decoder_input_ids is None and decoder_inputs_embeds is None:
|
|
decoder_input_ids = shift_tokens_right(
|
|
labels, self.config.pad_token_id, self.config.decoder_start_token_id
|
|
)
|
|
|
|
outputs = self.model(
|
|
inputs_embeds=inputs_embeds,
|
|
attention_mask=attention_mask,
|
|
decoder_input_ids=decoder_input_ids,
|
|
encoder_outputs=encoder_outputs,
|
|
decoder_attention_mask=text_attention_mask,
|
|
head_mask=head_mask,
|
|
decoder_head_mask=decoder_head_mask,
|
|
cross_attn_head_mask=cross_attn_head_mask,
|
|
past_key_values=past_key_values,
|
|
decoder_inputs_embeds=decoder_inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
i3d_rgb_interval=i3d_rgb_interval,
|
|
i3d_flow_interval=i3d_flow_interval,
|
|
question_intervals=question_intervals,
|
|
vis_state_vector_idx=vis_state_vector_idx,
|
|
question_state_vector_idx=question_state_vector_idx,
|
|
qnet_local=self.qnet_local,
|
|
pnet_local=self.pnet_local,
|
|
qnet_global=self.qnet_global,
|
|
pnet_global=self.pnet_global,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
PA=outputs['PA_global']
|
|
QA=outputs['QA_global']
|
|
|
|
PAs=outputs['PAs_local']
|
|
QAs=outputs['QAs_local']
|
|
|
|
|
|
lm_logits = self.lm_head(outputs[0])
|
|
|
|
lm_logits = lm_logits + self.final_logits_bias.to(lm_logits.device)
|
|
|
|
masked_lm_loss = None
|
|
elbo_loss_global = None
|
|
elbo_loss_local = None
|
|
if labels is not None and generate is None:
|
|
labels = labels.to(lm_logits.device)
|
|
masked_lm_loss = self.ce(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))
|
|
|
|
if not generate:
|
|
if self.config.use_elbo_global:
|
|
elbo_loss_global = self.elbo(QA, PA)
|
|
|
|
if self.config.use_elbo_local:
|
|
elbo_loss_local = 0.0
|
|
for qa, pa in zip(QAs, PAs):
|
|
elbo_loss_local += self.elbo(qa, pa)
|
|
elbo_loss_local /= self.config.num_modalities
|
|
|
|
loss_output = []
|
|
if masked_lm_loss is not None:
|
|
loss_output.append(masked_lm_loss)
|
|
if not generate:
|
|
if elbo_loss_global is not None:
|
|
loss_output.append(elbo_loss_global)
|
|
if elbo_loss_local is not None:
|
|
loss_output.append(elbo_loss_local)
|
|
|
|
if not return_dict:
|
|
output = (lm_logits,) + outputs[1:]
|
|
|
|
return (tuple(loss_output) + output) if len(loss_output) > 0 else output
|
|
|
|
return AVSDSeq2SeqLMOutput(
|
|
gen_loss=masked_lm_loss,
|
|
elbo_loss_global=elbo_loss_global,
|
|
elbo_loss_local=elbo_loss_local,
|
|
logits=lm_logits,
|
|
past_key_values=outputs.past_key_values,
|
|
decoder_hidden_states=outputs.decoder_hidden_states,
|
|
decoder_attentions=outputs.decoder_attentions,
|
|
cross_attentions=outputs.cross_attentions,
|
|
encoder_last_hidden_state=outputs.encoder_last_hidden_state,
|
|
encoder_hidden_states=outputs.encoder_hidden_states,
|
|
encoder_attentions=outputs.encoder_attentions,
|
|
encoder_QAs_local=outputs.QAs_local,
|
|
encoder_PAs_local=outputs.PAs_local,
|
|
encoder_QA_global=outputs.QA_global,
|
|
encoder_PA_global=outputs.PA_global,
|
|
encoder_state_vectors=outputs.state_vectors
|
|
)
|
|
|
|
def prepare_inputs_for_generation(
|
|
self,
|
|
decoder_input_ids,
|
|
past_key_values=None,
|
|
attention_mask=None,
|
|
decoder_attention_mask=None,
|
|
head_mask=None,
|
|
decoder_head_mask=None,
|
|
cross_attn_head_mask=None,
|
|
use_cache=None,
|
|
encoder_outputs=None,
|
|
**kwargs,
|
|
):
|
|
# cut decoder_input_ids if past_key_values is used
|
|
if past_key_values is not None:
|
|
decoder_input_ids = decoder_input_ids[:, -1:]
|
|
|
|
return {
|
|
"input_ids": None, # encoder_outputs is defined. input_ids not needed
|
|
"encoder_outputs": encoder_outputs,
|
|
"past_key_values": past_key_values,
|
|
"decoder_input_ids": decoder_input_ids,
|
|
"attention_mask": attention_mask,
|
|
"decoder_attention_mask": decoder_attention_mask,
|
|
"head_mask": head_mask,
|
|
"decoder_head_mask": decoder_head_mask,
|
|
"cross_attn_head_mask": cross_attn_head_mask,
|
|
"use_cache": use_cache, # change this to avoid caching (presumably for debugging)
|
|
}
|
|
|
|
def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
|
|
return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
|