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418 lines
18 KiB
418 lines
18 KiB
# Copyright 2022, UCLA NLP, The Facebook AI Research Team and 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 PLBART model."""
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import math
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import torch
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from torch import nn
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from torch.nn import CrossEntropyLoss
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from ... import initialization as init
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from ...cache_utils import Cache
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from ...generation import GenerationMixin
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from ...modeling_outputs import (
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BaseModelOutput,
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Seq2SeqLMOutput,
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Seq2SeqModelOutput,
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)
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from ...modeling_utils import PreTrainedModel
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from ...utils import auto_docstring
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from ..bart.modeling_bart import (
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BartClassificationHead,
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BartDecoder,
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BartEncoder,
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BartForCausalLM,
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BartScaledWordEmbedding,
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)
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from ..bigbird_pegasus.modeling_bigbird_pegasus import BigBirdPegasusForSequenceClassification
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from ..mbart.modeling_mbart import shift_tokens_right
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from .configuration_plbart import PLBartConfig
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class PLBartScaledWordEmbedding(BartScaledWordEmbedding):
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pass
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@auto_docstring
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class PLBartPreTrainedModel(PreTrainedModel):
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config: PLBartConfig
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base_model_prefix = "model"
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supports_gradient_checkpointing = True
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_no_split_modules = ["PLBartDecoderLayer", "PLBartEncoderLayer"]
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_supports_flash_attn = True
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_supports_sdpa = True
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_supports_flex_attn = True
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def _init_weights(self, module):
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super()._init_weights(module)
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if isinstance(module, PLBartForConditionalGeneration):
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init.zeros_(module.final_logits_bias)
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class PLBartEncoder(BartEncoder):
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pass
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class PLBartDecoder(BartDecoder):
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pass
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@auto_docstring
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class PLBartModel(PLBartPreTrainedModel):
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_tied_weights_keys = {
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"encoder.embed_tokens.weight": "shared.weight",
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"decoder.embed_tokens.weight": "shared.weight",
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}
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def __init__(self, config: PLBartConfig):
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super().__init__(config)
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padding_idx, vocab_size = config.pad_token_id, config.vocab_size
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embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
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self.shared = PLBartScaledWordEmbedding(vocab_size, config.d_model, padding_idx, embed_scale=embed_scale)
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self.encoder = PLBartEncoder(config)
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self.decoder = PLBartDecoder(config)
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self.post_init()
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def get_input_embeddings(self):
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return self.shared
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def set_input_embeddings(self, value):
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self.shared = value
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self.encoder.embed_tokens = self.shared
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self.decoder.embed_tokens = self.shared
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@auto_docstring
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def forward(
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self,
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input_ids: torch.LongTensor | None = None,
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attention_mask: torch.LongTensor | None = None,
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decoder_input_ids: torch.LongTensor | None = None,
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decoder_attention_mask: torch.Tensor | None = None,
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encoder_outputs: list[torch.FloatTensor] | None = None,
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past_key_values: Cache | None = None,
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inputs_embeds: torch.FloatTensor | None = None,
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decoder_inputs_embeds: torch.FloatTensor | None = None,
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use_cache: bool | None = None,
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output_attentions: bool | None = None,
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output_hidden_states: bool | None = None,
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return_dict: bool | None = None,
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cache_position: torch.LongTensor | None = None,
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**kwargs,
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) -> tuple[torch.Tensor] | Seq2SeqModelOutput:
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r"""
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decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
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Indices of decoder input sequence tokens in the vocabulary.
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Indices can be obtained using [`AutoTokenizer`] or [`PLBartMultiTokenizer`] depending on the checkpoint.
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See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details.
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[What are decoder input IDs?](../glossary#decoder-input-ids)
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PLBart uses a specific language id token as the starting token for `decoder_input_ids` generation that
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varies according to source and target language, *e.g.* 50003 for *en_XX*, and 50001 for *java*. If
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`past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
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`past_key_values`).
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For translation and summarization training, `decoder_input_ids` should be provided. If no
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`decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
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for denoising pre-training following the paper.
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decoder_attention_mask (:
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obj:*torch.LongTensor* of shape `(batch_size, target_sequence_length)`, *optional*):
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Default behavior:
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generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default.
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"""
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
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)
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use_cache = use_cache if use_cache is not None else self.config.use_cache
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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# different to other models, PLBart automatically creates decoder_input_ids from
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# input_ids if no decoder_input_ids are provided
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if decoder_input_ids is None and decoder_inputs_embeds is None:
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decoder_input_ids = shift_tokens_right(input_ids, self.config.pad_token_id)
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if encoder_outputs is None:
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encoder_outputs = self.encoder(
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input_ids=input_ids,
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attention_mask=attention_mask,
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inputs_embeds=inputs_embeds,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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)
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# If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
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elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
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encoder_outputs = BaseModelOutput(
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last_hidden_state=encoder_outputs[0],
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hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
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attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
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)
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# decoder outputs consists of (dec_features, past_key_values, dec_hidden, dec_attn)
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decoder_outputs = self.decoder(
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input_ids=decoder_input_ids,
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attention_mask=decoder_attention_mask,
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encoder_hidden_states=encoder_outputs[0],
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encoder_attention_mask=attention_mask,
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past_key_values=past_key_values,
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inputs_embeds=decoder_inputs_embeds,
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use_cache=use_cache,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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cache_position=cache_position,
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)
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if not return_dict:
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return decoder_outputs + encoder_outputs
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return Seq2SeqModelOutput(
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last_hidden_state=decoder_outputs.last_hidden_state,
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past_key_values=decoder_outputs.past_key_values,
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decoder_hidden_states=decoder_outputs.hidden_states,
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decoder_attentions=decoder_outputs.attentions,
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cross_attentions=decoder_outputs.cross_attentions,
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encoder_last_hidden_state=encoder_outputs.last_hidden_state,
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encoder_hidden_states=encoder_outputs.hidden_states,
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encoder_attentions=encoder_outputs.attentions,
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)
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@auto_docstring(
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custom_intro="""
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The PLBART Model with a language modeling head. Can be used for code-to-text, text-to-code and code-to-code.
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"""
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)
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class PLBartForConditionalGeneration(PLBartPreTrainedModel, GenerationMixin):
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base_model_prefix = "model"
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_keys_to_ignore_on_load_missing = ["final_logits_bias"]
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_tied_weights_keys = {
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"lm_head.weight": "model.shared.weight",
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}
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def __init__(self, config: PLBartConfig):
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super().__init__(config)
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self.model = PLBartModel(config)
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self.register_buffer("final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings)))
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self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False)
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self.post_init()
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def resize_token_embeddings(
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self, new_num_tokens: int, pad_to_multiple_of: int | None = None, mean_resizing: bool = True
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) -> nn.Embedding:
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new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of, mean_resizing)
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self._resize_final_logits_bias(new_embeddings.weight.shape[0])
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return new_embeddings
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def _resize_final_logits_bias(self, new_num_tokens: int) -> None:
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old_num_tokens = self.final_logits_bias.shape[-1]
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if new_num_tokens <= old_num_tokens:
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new_bias = self.final_logits_bias[:, :new_num_tokens]
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else:
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extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device)
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new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1)
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self.register_buffer("final_logits_bias", new_bias)
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@auto_docstring
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def forward(
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self,
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input_ids: torch.LongTensor | None = None,
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attention_mask: torch.LongTensor | None = None,
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decoder_input_ids: torch.LongTensor | None = None,
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decoder_attention_mask: torch.Tensor | None = None,
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encoder_outputs: list[torch.FloatTensor] | None = None,
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past_key_values: Cache | None = None,
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inputs_embeds: torch.FloatTensor | None = None,
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decoder_inputs_embeds: torch.FloatTensor | None = None,
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labels: torch.Tensor | None = None,
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use_cache: bool | None = None,
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output_attentions: bool | None = None,
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output_hidden_states: bool | None = None,
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return_dict: bool | None = None,
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cache_position: torch.LongTensor | None = None,
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**kwargs,
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) -> tuple[torch.Tensor] | Seq2SeqLMOutput:
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r"""
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decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
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Indices of decoder input sequence tokens in the vocabulary.
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Indices can be obtained using [`AutoTokenizer`] or [`PLBartMultiTokenizer`] depending on the checkpoint.
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See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details.
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[What are decoder input IDs?](../glossary#decoder-input-ids)
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PLBart uses a specific language id token as the starting token for `decoder_input_ids` generation that
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varies according to source and target language, *e.g.* 50003 for *en_XX*, and 50001 for *java*. If
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`past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
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`past_key_values`).
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For translation and summarization training, `decoder_input_ids` should be provided. If no
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`decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
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for denoising pre-training following the paper.
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decoder_attention_mask (:
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obj:*torch.LongTensor* of shape `(batch_size, target_sequence_length)`, *optional*):
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Default behavior:
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generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default.
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labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
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Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
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config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
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(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
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Example Mask-filling:
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```python
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>>> from transformers import AutoTokenizer, PLBartForConditionalGeneration
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>>> model = PLBartForConditionalGeneration.from_pretrained("uclanlp/plbart-base")
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>>> tokenizer = AutoTokenizer.from_pretrained("uclanlp/plbart-base")
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>>> # en_XX is the language symbol id <LID> for English
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>>> TXT = "<s> Is 0 the <mask> Fibonacci number ? </s> en_XX"
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>>> input_ids = tokenizer([TXT], add_special_tokens=False, return_tensors="pt").input_ids
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>>> logits = model(input_ids).logits
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>>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item()
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>>> probs = logits[0, masked_index].softmax(dim=0)
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>>> values, predictions = probs.topk(5)
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>>> tokenizer.decode(predictions).split()
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['first', 'same', 'highest', 'result', 'number']
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```
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"""
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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if labels is not None:
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if decoder_input_ids is None and decoder_inputs_embeds is None:
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decoder_input_ids = shift_tokens_right(labels, self.config.pad_token_id)
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outputs = self.model(
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input_ids,
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attention_mask=attention_mask,
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decoder_input_ids=decoder_input_ids,
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encoder_outputs=encoder_outputs,
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decoder_attention_mask=decoder_attention_mask,
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past_key_values=past_key_values,
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inputs_embeds=inputs_embeds,
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decoder_inputs_embeds=decoder_inputs_embeds,
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use_cache=use_cache,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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cache_position=cache_position,
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)
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lm_logits = self.lm_head(outputs[0])
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lm_logits = lm_logits + self.final_logits_bias.to(lm_logits.device)
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masked_lm_loss = None
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if labels is not None:
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loss_fct = CrossEntropyLoss()
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masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))
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if not return_dict:
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output = (lm_logits,) + outputs[1:]
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return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
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return Seq2SeqLMOutput(
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loss=masked_lm_loss,
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logits=lm_logits,
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past_key_values=outputs.past_key_values,
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decoder_hidden_states=outputs.decoder_hidden_states,
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decoder_attentions=outputs.decoder_attentions,
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cross_attentions=outputs.cross_attentions,
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encoder_last_hidden_state=outputs.encoder_last_hidden_state,
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encoder_hidden_states=outputs.encoder_hidden_states,
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encoder_attentions=outputs.encoder_attentions,
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)
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def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
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return shift_tokens_right(labels, self.config.pad_token_id)
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class PLBartClassificationHead(BartClassificationHead):
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pass
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class PLBartForSequenceClassification(BigBirdPegasusForSequenceClassification):
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def forward(**super_kwargs):
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r"""
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decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
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Indices of decoder input sequence tokens in the vocabulary.
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Indices can be obtained using [`AutoTokenizer`] or [`PLBartMultiTokenizer`] depending on the checkpoint.
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See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details.
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[What are decoder input IDs?](../glossary#decoder-input-ids)
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PLBart uses a specific language id token as the starting token for `decoder_input_ids` generation that
|
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varies according to source and target language, *e.g.* 50003 for *en_XX*, and 50001 for *java*. If
|
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`past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
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`past_key_values`).
|
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For translation and summarization training, `decoder_input_ids` should be provided. If no
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`decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
|
|
for denoising pre-training following the paper.
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decoder_attention_mask (:
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obj:*torch.LongTensor* of shape `(batch_size, target_sequence_length)`, *optional*):
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|
Default behavior:
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generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default.
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labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
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Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
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config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
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"""
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super().forward(**super_kwargs)
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class PLBartForCausalLM(BartForCausalLM):
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@auto_docstring
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def forward(**super_kwargs):
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r"""
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labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
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Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
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config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
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(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
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Example:
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```python
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>>> from transformers import AutoTokenizer, PLBartForCausalLM
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>>> tokenizer = AutoTokenizer.from_pretrained("uclanlp/plbart-base")
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>>> model = PLBartForCausalLM.from_pretrained("uclanlp/plbart-base")
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>>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
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>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
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>>> outputs = model(**inputs)
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>>> logits = outputs.logits
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>>> expected_shape = [1, inputs.input_ids.shape[-1], model.config.vocab_size]
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>>> list(logits.shape) == expected_shape
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True
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```"""
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super().forward(**super_kwargs)
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__all__ = [
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"PLBartForCausalLM",
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"PLBartForConditionalGeneration",
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"PLBartForSequenceClassification",
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"PLBartModel",
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"PLBartPreTrainedModel",
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]
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