833 lines
38 KiB
Python
833 lines
38 KiB
Python
# coding=utf-8
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# Copyright 2021 The Fairseq Authors The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" PyTorch XGLM model."""
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import math
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from typing import List, Optional, Tuple, Union
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import torch
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import torch.utils.checkpoint
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from torch import nn
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from torch.nn import CrossEntropyLoss
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from ...activations import ACT2FN
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from ...modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask
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from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions
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from ...modeling_utils import PreTrainedModel
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from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
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from .configuration_xglm import XGLMConfig
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logger = logging.get_logger(__name__)
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_CHECKPOINT_FOR_DOC = "facebook/xglm-564M"
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_CONFIG_FOR_DOC = "XGLMConfig"
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from ..deprecated._archive_maps import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402
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XGLM_START_DOCSTRING = r"""
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This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
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library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
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etc.)
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This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
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Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
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and behavior.
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Parameters:
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config ([`XGLMConfig`]):
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Model configuration class with all the parameters of the model. Initializing with a config file does not
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load the weights associated with the model, only the configuration. Check out the
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[`~PreTrainedModel.from_pretrained`] method to load the model weights.
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"""
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XGLM_INPUTS_DOCSTRING = r"""
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Args:
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input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
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Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
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it.
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Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
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[`PreTrainedTokenizer.__call__`] for details.
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[What are input IDs?](../glossary#input-ids)
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attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
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Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
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- 1 for tokens that are **not masked**,
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- 0 for tokens that are **masked**.
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[What are attention masks?](../glossary#attention-mask)
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position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
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Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
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config.max_position_embeddings - 1]`.
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[What are position IDs?](../glossary#position-ids)
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encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
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Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of
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the decoder.
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encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
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Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
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selected in `[0, 1]`:
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- 1 for tokens that are **not masked**,
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- 0 for tokens that are **masked**.
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[What are attention masks?](../glossary#attention-mask)
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head_mask (`torch.Tensor` of shape `(num_layers, attention_heads)`, *optional*):
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Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
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- 1 indicates the head is **not masked**,
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- 0 indicates the head is **masked**.
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cross_attn_head_mask (`torch.Tensor` of shape `(num_layers, attention_heads)`, *optional*):
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Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
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- 1 indicates the head is **not masked**,
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- 0 indicates the head is **masked**.
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past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
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Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
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`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
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`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
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Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
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blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
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If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
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don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
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`decoder_input_ids` of shape `(batch_size, sequence_length)`.
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inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
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Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
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This is useful if you want more control over how to convert `input_ids` indices into associated vectors
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than the model's internal embedding lookup matrix.
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output_attentions (`bool`, *optional*):
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Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
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tensors for more detail.
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output_hidden_states (`bool`, *optional*):
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Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
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more detail.
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return_dict (`bool`, *optional*):
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Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
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"""
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class XGLMSinusoidalPositionalEmbedding(nn.Module):
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"""This module produces sinusoidal positional embeddings of any length."""
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def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None):
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super().__init__()
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self.offset = 2
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self.embedding_dim = embedding_dim
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self.padding_idx = padding_idx
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self.make_weights(num_positions + self.offset, embedding_dim, padding_idx)
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def make_weights(self, num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None):
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emb_weights = self.get_embedding(num_embeddings, embedding_dim, padding_idx)
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if hasattr(self, "weights"):
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# in forward put the weights on the correct dtype and device of the param
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emb_weights = emb_weights.to(dtype=self.weights.dtype, device=self.weights.device)
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self.register_buffer("weights", emb_weights, persistent=False)
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@staticmethod
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def get_embedding(num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None):
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"""
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Build sinusoidal embeddings.
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This matches the implementation in tensor2tensor, but differs slightly from the description in Section 3.5 of
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"Attention Is All You Need".
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"""
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half_dim = embedding_dim // 2
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emb = math.log(10000) / (half_dim - 1)
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emb = torch.exp(torch.arange(half_dim, dtype=torch.int64).float() * -emb)
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emb = torch.arange(num_embeddings, dtype=torch.int64).float().unsqueeze(1) * emb.unsqueeze(0)
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emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1)
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if embedding_dim % 2 == 1:
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# zero pad
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emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
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if padding_idx is not None:
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emb[padding_idx, :] = 0
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return emb.to(torch.get_default_dtype())
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@torch.no_grad()
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def forward(self, position_ids: torch.Tensor = None, past_key_values_length: int = 0):
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bsz, seq_len = position_ids.size()
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position_ids += self.offset
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# Expand embeddings if needed. `position_ids.max()` is NOT used to keep torch.fx compatibility.
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max_pos = 2 + seq_len + past_key_values_length
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if max_pos > self.weights.size(0):
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self.make_weights(max_pos, self.embedding_dim, self.padding_idx)
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return self.weights.index_select(0, position_ids.view(-1)).view(bsz, seq_len, self.weights.shape[-1]).detach()
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class XGLMAttention(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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if is_cross_attention and past_key_value is not None:
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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.view(*proj_shape)
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value_states = value_states.view(*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 = torch.max(
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attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min, device=attn_weights.device)
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)
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attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
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# upcast to fp32 if the weights are in fp16. Please see https://github.com/huggingface/transformers/pull/17437
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if attn_weights.dtype == torch.float16:
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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(torch.float16)
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else:
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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 aross 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 XGLMDecoderLayer(nn.Module):
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def __init__(self, config: XGLMConfig):
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super().__init__()
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self.embed_dim = config.d_model
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self.self_attn = XGLMAttention(
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embed_dim=self.embed_dim,
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num_heads=config.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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if config.add_cross_attention:
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self.encoder_attn = XGLMAttention(
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embed_dim=self.embed_dim,
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num_heads=config.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.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
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self.fc1 = nn.Linear(self.embed_dim, config.ffn_dim)
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self.fc2 = nn.Linear(config.ffn_dim, self.embed_dim)
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self.final_layer_norm = nn.LayerNorm(self.embed_dim)
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# Copied from transformers.models.mbart.modeling_mbart.MBartDecoderLayer.forward
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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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) -> torch.Tensor:
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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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hidden_states = self.self_attn_layer_norm(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
|
|
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
|
|
# add present self-attn cache to positions 1,2 of present_key_value tuple
|
|
hidden_states, self_attn_weights, present_key_value = self.self_attn(
|
|
hidden_states=hidden_states,
|
|
past_key_value=self_attn_past_key_value,
|
|
attention_mask=attention_mask,
|
|
layer_head_mask=layer_head_mask,
|
|
output_attentions=output_attentions,
|
|
)
|
|
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
|
|
hidden_states = residual + hidden_states
|
|
|
|
# Cross-Attention Block
|
|
cross_attn_present_key_value = None
|
|
cross_attn_weights = None
|
|
if encoder_hidden_states is not None:
|
|
residual = hidden_states
|
|
hidden_states = self.encoder_attn_layer_norm(hidden_states)
|
|
|
|
# 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
|
|
|
|
# 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.final_layer_norm(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
|
|
|
|
outputs = (hidden_states,)
|
|
|
|
if output_attentions:
|
|
outputs += (self_attn_weights, cross_attn_weights)
|
|
|
|
if use_cache:
|
|
outputs += (present_key_value,)
|
|
|
|
return outputs
|
|
|
|
|
|
class XGLMPreTrainedModel(PreTrainedModel):
|
|
config_class = XGLMConfig
|
|
base_model_prefix = "model"
|
|
supports_gradient_checkpointing = True
|
|
_no_split_modules = ["XGLMDecoderLayer"]
|
|
|
|
def _init_weights(self, module):
|
|
std = self.config.init_std
|
|
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_()
|
|
|
|
|
|
@add_start_docstrings(
|
|
"The bare XGLM Model transformer outputting raw hidden-states without any specific head on top.",
|
|
XGLM_START_DOCSTRING,
|
|
)
|
|
class XGLMModel(XGLMPreTrainedModel):
|
|
"""
|
|
Transformer decoder consisting of *config.num_layers* layers. Each layer is a [`XGLMDecoderLayer`]
|
|
|
|
Args:
|
|
config: XGLMConfig
|
|
embed_tokens (nn.Embedding): output embedding
|
|
"""
|
|
|
|
def __init__(self, config: XGLMConfig, embed_tokens: Optional[nn.Embedding] = None):
|
|
super().__init__(config)
|
|
self.dropout = config.dropout
|
|
self.layerdrop = config.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
|
|
|
|
if embed_tokens is not None:
|
|
self.embed_tokens = embed_tokens
|
|
else:
|
|
self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model, self.padding_idx)
|
|
|
|
self.embed_positions = XGLMSinusoidalPositionalEmbedding(
|
|
config.max_position_embeddings,
|
|
config.d_model,
|
|
config.pad_token_id,
|
|
)
|
|
self.layers = nn.ModuleList([XGLMDecoderLayer(config) for _ in range(config.num_layers)])
|
|
self.layer_norm = 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
|
|
|
|
@add_start_docstrings_to_model_forward(XGLM_INPUTS_DOCSTRING)
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=BaseModelOutputWithPastAndCrossAttentions,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
encoder_attention_mask: Optional[torch.Tensor] = 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.Tensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
|
|
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 input_ids and inputs_embeds at the same time")
|
|
elif input_ids is not None:
|
|
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
|
|
input_shape = input_ids.size()
|
|
input_ids = input_ids.view(-1, input_shape[-1])
|
|
elif inputs_embeds is not None:
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
else:
|
|
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
|
|
|
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
|
|
|
|
if position_ids is None:
|
|
position_ids = torch.arange(
|
|
past_key_values_length,
|
|
input_shape[-1] + past_key_values_length,
|
|
dtype=torch.long,
|
|
device=input_ids.device if input_ids is not None else inputs_embeds.device,
|
|
)
|
|
position_ids = position_ids.unsqueeze(0)
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
|
|
|
|
attention_mask = _prepare_4d_causal_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 = _prepare_4d_attention_mask(
|
|
encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
|
|
)
|
|
|
|
hidden_states = inputs_embeds + self.embed_positions(position_ids, past_key_values_length)
|
|
hidden_states = nn.functional.dropout(hidden_states, p=float(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,)
|
|
if self.training:
|
|
dropout_probability = torch.rand([])
|
|
if 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:
|
|
layer_outputs = self._gradient_checkpointing_func(
|
|
decoder_layer.__call__,
|
|
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,
|
|
output_attentions,
|
|
use_cache,
|
|
)
|
|
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],)
|
|
|
|
hidden_states = self.layer_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
|
|
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,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
The XGLM Model transformer with a language modeling head on top (linear layer with weights tied to the input
|
|
embeddings).
|
|
""",
|
|
XGLM_START_DOCSTRING,
|
|
)
|
|
class XGLMForCausalLM(XGLMPreTrainedModel):
|
|
base_model_prefix = "model"
|
|
_tied_weights_keys = ["lm_head.weight"]
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.model = XGLMModel(config)
|
|
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
|
|
|
# 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
|
|
|
|
@add_start_docstrings_to_model_forward(XGLM_INPUTS_DOCSTRING)
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=CausalLMOutputWithCrossAttentions,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
encoder_attention_mask: Optional[torch.Tensor] = 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.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
|
|
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]`.
|
|
"""
|
|
|
|
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,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
head_mask=head_mask,
|
|
cross_attn_head_mask=cross_attn_head_mask,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
logits = self.lm_head(outputs[0])
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
# shift labels and add a pad token to the end
|
|
shift_labels = labels.new_zeros(labels.shape)
|
|
shift_labels[:, :-1] = labels[:, 1:].clone()
|
|
shift_labels[:, -1] = self.config.pad_token_id
|
|
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(logits.view(-1, self.config.vocab_size), shift_labels.view(-1))
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[1:]
|
|
return (loss,) + output if loss is not None else output
|
|
|
|
return CausalLMOutputWithCrossAttentions(
|
|
loss=loss,
|
|
logits=logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
cross_attentions=outputs.cross_attentions,
|
|
)
|
|
|
|
def prepare_inputs_for_generation(
|
|
self, input_ids, past_key_values=None, attention_mask=None, use_cache=None, **kwargs
|
|
):
|
|
if past_key_values is not None:
|
|
past_length = past_key_values[0][0].shape[2]
|
|
|
|
# Some generation methods already pass only the last input ID
|
|
if input_ids.shape[1] > past_length:
|
|
remove_prefix_length = past_length
|
|
else:
|
|
# Default to old behavior: keep only final ID
|
|
remove_prefix_length = input_ids.shape[1] - 1
|
|
|
|
input_ids = input_ids[:, remove_prefix_length:]
|
|
|
|
position_ids = kwargs.get("position_ids", None)
|
|
if attention_mask is not None and position_ids is None:
|
|
# create position_ids on the fly for batch generation
|
|
position_ids = attention_mask.long().cumsum(-1) - 1
|
|
position_ids.masked_fill_(attention_mask == 0, 1)
|
|
if past_key_values:
|
|
position_ids = position_ids[:, -input_ids.shape[1] :]
|
|
else:
|
|
position_ids = None
|
|
# if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
|
|
if attention_mask is None:
|
|
attention_mask = input_ids.new_ones(input_ids.shape)
|
|
# first step, decoder_cached_states are empty
|
|
return {
|
|
"input_ids": input_ids, # encoder_outputs is defined. input_ids not needed
|
|
"attention_mask": attention_mask,
|
|
"position_ids": position_ids,
|
|
"past_key_values": past_key_values,
|
|
"use_cache": use_cache,
|
|
}
|
|
|
|
@staticmethod
|
|
def _reorder_cache(past_key_values, beam_idx):
|
|
reordered_past = ()
|
|
for layer_past in past_key_values:
|
|
reordered_past += (
|
|
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
|
|
)
|
|
return reordered_past
|