1600 lines
68 KiB
Python
1600 lines
68 KiB
Python
# coding=utf-8
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# Copyright 2020-present Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
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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 Funnel Transformer model."""
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import os
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from dataclasses import dataclass
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from typing import List, Optional, Tuple, Union
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import numpy as np
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import torch
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from torch import nn
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from ...activations import ACT2FN
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from ...modeling_outputs import (
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BaseModelOutput,
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MaskedLMOutput,
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MultipleChoiceModelOutput,
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QuestionAnsweringModelOutput,
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SequenceClassifierOutput,
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TokenClassifierOutput,
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)
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from ...modeling_utils import PreTrainedModel
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from ...utils import (
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ModelOutput,
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add_code_sample_docstrings,
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add_start_docstrings,
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add_start_docstrings_to_model_forward,
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logging,
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replace_return_docstrings,
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)
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from .configuration_funnel import FunnelConfig
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logger = logging.get_logger(__name__)
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_CONFIG_FOR_DOC = "FunnelConfig"
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_CHECKPOINT_FOR_DOC = "funnel-transformer/small"
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from ..deprecated._archive_maps import FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402
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INF = 1e6
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def load_tf_weights_in_funnel(model, config, tf_checkpoint_path):
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"""Load tf checkpoints in a pytorch model."""
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try:
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import re
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import numpy as np
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import tensorflow as tf
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except ImportError:
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logger.error(
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"Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
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"https://www.tensorflow.org/install/ for installation instructions."
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)
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raise
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tf_path = os.path.abspath(tf_checkpoint_path)
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logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
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# Load weights from TF model
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init_vars = tf.train.list_variables(tf_path)
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names = []
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arrays = []
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for name, shape in init_vars:
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logger.info(f"Loading TF weight {name} with shape {shape}")
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array = tf.train.load_variable(tf_path, name)
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names.append(name)
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arrays.append(array)
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_layer_map = {
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"k": "k_head",
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"q": "q_head",
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"v": "v_head",
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"o": "post_proj",
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"layer_1": "linear_1",
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"layer_2": "linear_2",
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"rel_attn": "attention",
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"ff": "ffn",
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"kernel": "weight",
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"gamma": "weight",
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"beta": "bias",
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"lookup_table": "weight",
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"word_embedding": "word_embeddings",
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"input": "embeddings",
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}
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for name, array in zip(names, arrays):
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name = name.split("/")
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# adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
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# which are not required for using pretrained model
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if any(
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n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
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for n in name
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):
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logger.info(f"Skipping {'/'.join(name)}")
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continue
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if name[0] == "generator":
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continue
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pointer = model
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skipped = False
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for m_name in name[1:]:
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if not isinstance(pointer, FunnelPositionwiseFFN) and re.fullmatch(r"layer_\d+", m_name):
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layer_index = int(re.search(r"layer_(\d+)", m_name).groups()[0])
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if layer_index < config.num_hidden_layers:
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block_idx = 0
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while layer_index >= config.block_sizes[block_idx]:
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layer_index -= config.block_sizes[block_idx]
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block_idx += 1
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pointer = pointer.blocks[block_idx][layer_index]
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else:
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layer_index -= config.num_hidden_layers
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pointer = pointer.layers[layer_index]
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elif m_name == "r" and isinstance(pointer, FunnelRelMultiheadAttention):
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pointer = pointer.r_kernel
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break
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elif m_name in _layer_map:
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pointer = getattr(pointer, _layer_map[m_name])
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else:
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try:
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pointer = getattr(pointer, m_name)
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except AttributeError:
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print(f"Skipping {'/'.join(name)}", array.shape)
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skipped = True
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break
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if not skipped:
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if len(pointer.shape) != len(array.shape):
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array = array.reshape(pointer.shape)
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if m_name == "kernel":
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array = np.transpose(array)
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pointer.data = torch.from_numpy(array)
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return model
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class FunnelEmbeddings(nn.Module):
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def __init__(self, config: FunnelConfig) -> None:
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super().__init__()
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self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
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self.layer_norm = nn.LayerNorm(config.d_model, eps=config.layer_norm_eps)
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self.dropout = nn.Dropout(config.hidden_dropout)
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def forward(
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self, input_ids: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None
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) -> torch.Tensor:
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if inputs_embeds is None:
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inputs_embeds = self.word_embeddings(input_ids)
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embeddings = self.layer_norm(inputs_embeds)
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embeddings = self.dropout(embeddings)
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return embeddings
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class FunnelAttentionStructure(nn.Module):
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"""
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Contains helpers for `FunnelRelMultiheadAttention `.
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"""
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cls_token_type_id: int = 2
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def __init__(self, config: FunnelConfig) -> None:
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super().__init__()
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self.config = config
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self.sin_dropout = nn.Dropout(config.hidden_dropout)
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self.cos_dropout = nn.Dropout(config.hidden_dropout)
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# Track where we are at in terms of pooling from the original input, e.g., by how much the sequence length was
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# divided.
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self.pooling_mult = None
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def init_attention_inputs(
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self,
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inputs_embeds: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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token_type_ids: Optional[torch.Tensor] = None,
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) -> Tuple[torch.Tensor]:
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"""Returns the attention inputs associated to the inputs of the model."""
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# inputs_embeds has shape batch_size x seq_len x d_model
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# attention_mask and token_type_ids have shape batch_size x seq_len
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self.pooling_mult = 1
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self.seq_len = seq_len = inputs_embeds.size(1)
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position_embeds = self.get_position_embeds(seq_len, inputs_embeds.dtype, inputs_embeds.device)
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token_type_mat = self.token_type_ids_to_mat(token_type_ids) if token_type_ids is not None else None
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cls_mask = (
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nn.functional.pad(inputs_embeds.new_ones([seq_len - 1, seq_len - 1]), (1, 0, 1, 0))
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if self.config.separate_cls
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else None
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)
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return (position_embeds, token_type_mat, attention_mask, cls_mask)
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def token_type_ids_to_mat(self, token_type_ids: torch.Tensor) -> torch.Tensor:
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"""Convert `token_type_ids` to `token_type_mat`."""
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token_type_mat = token_type_ids[:, :, None] == token_type_ids[:, None]
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# Treat <cls> as in the same segment as both A & B
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cls_ids = token_type_ids == self.cls_token_type_id
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cls_mat = cls_ids[:, :, None] | cls_ids[:, None]
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return cls_mat | token_type_mat
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def get_position_embeds(
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self, seq_len: int, dtype: torch.dtype, device: torch.device
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) -> Union[Tuple[torch.Tensor], List[List[torch.Tensor]]]:
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"""
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Create and cache inputs related to relative position encoding. Those are very different depending on whether we
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are using the factorized or the relative shift attention:
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For the factorized attention, it returns the matrices (phi, pi, psi, omega) used in the paper, appendix A.2.2,
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final formula.
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For the relative shift attention, it returns all possible vectors R used in the paper, appendix A.2.1, final
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formula.
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Paper link: https://arxiv.org/abs/2006.03236
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"""
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d_model = self.config.d_model
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if self.config.attention_type == "factorized":
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# Notations from the paper, appending A.2.2, final formula.
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# We need to create and return the matrices phi, psi, pi and omega.
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pos_seq = torch.arange(0, seq_len, 1.0, dtype=torch.int64, device=device).to(dtype)
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freq_seq = torch.arange(0, d_model // 2, 1.0, dtype=torch.int64, device=device).to(dtype)
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inv_freq = 1 / (10000 ** (freq_seq / (d_model // 2)))
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sinusoid = pos_seq[:, None] * inv_freq[None]
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sin_embed = torch.sin(sinusoid)
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sin_embed_d = self.sin_dropout(sin_embed)
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cos_embed = torch.cos(sinusoid)
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cos_embed_d = self.cos_dropout(cos_embed)
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# This is different from the formula on the paper...
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phi = torch.cat([sin_embed_d, sin_embed_d], dim=-1)
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psi = torch.cat([cos_embed, sin_embed], dim=-1)
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pi = torch.cat([cos_embed_d, cos_embed_d], dim=-1)
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omega = torch.cat([-sin_embed, cos_embed], dim=-1)
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return (phi, pi, psi, omega)
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else:
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# Notations from the paper, appending A.2.1, final formula.
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# We need to create and return all the possible vectors R for all blocks and shifts.
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freq_seq = torch.arange(0, d_model // 2, 1.0, dtype=torch.int64, device=device).to(dtype)
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inv_freq = 1 / (10000 ** (freq_seq / (d_model // 2)))
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# Maximum relative positions for the first input
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rel_pos_id = torch.arange(-seq_len * 2, seq_len * 2, 1.0, dtype=torch.int64, device=device).to(dtype)
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zero_offset = seq_len * 2
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sinusoid = rel_pos_id[:, None] * inv_freq[None]
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sin_embed = self.sin_dropout(torch.sin(sinusoid))
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cos_embed = self.cos_dropout(torch.cos(sinusoid))
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pos_embed = torch.cat([sin_embed, cos_embed], dim=-1)
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pos = torch.arange(0, seq_len, dtype=torch.int64, device=device).to(dtype)
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pooled_pos = pos
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position_embeds_list = []
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for block_index in range(0, self.config.num_blocks):
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# For each block with block_index > 0, we need two types position embeddings:
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# - Attention(pooled-q, unpooled-kv)
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# - Attention(pooled-q, pooled-kv)
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# For block_index = 0 we only need the second one and leave the first one as None.
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# First type
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if block_index == 0:
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position_embeds_pooling = None
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else:
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pooled_pos = self.stride_pool_pos(pos, block_index)
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# construct rel_pos_id
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stride = 2 ** (block_index - 1)
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rel_pos = self.relative_pos(pos, stride, pooled_pos, shift=2)
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rel_pos = rel_pos[:, None] + zero_offset
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rel_pos = rel_pos.expand(rel_pos.size(0), d_model)
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position_embeds_pooling = torch.gather(pos_embed, 0, rel_pos)
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# Second type
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pos = pooled_pos
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stride = 2**block_index
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rel_pos = self.relative_pos(pos, stride)
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rel_pos = rel_pos[:, None] + zero_offset
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rel_pos = rel_pos.expand(rel_pos.size(0), d_model)
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position_embeds_no_pooling = torch.gather(pos_embed, 0, rel_pos)
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position_embeds_list.append([position_embeds_no_pooling, position_embeds_pooling])
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return position_embeds_list
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def stride_pool_pos(self, pos_id: torch.Tensor, block_index: int):
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"""
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Pool `pos_id` while keeping the cls token separate (if `config.separate_cls=True`).
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"""
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if self.config.separate_cls:
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# Under separate <cls>, we treat the <cls> as the first token in
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# the previous block of the 1st real block. Since the 1st real
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# block always has position 1, the position of the previous block
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# will be at `1 - 2 ** block_index`.
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cls_pos = pos_id.new_tensor([-(2**block_index) + 1])
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pooled_pos_id = pos_id[1:-1] if self.config.truncate_seq else pos_id[1:]
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return torch.cat([cls_pos, pooled_pos_id[::2]], 0)
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else:
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return pos_id[::2]
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def relative_pos(self, pos: torch.Tensor, stride: int, pooled_pos=None, shift: int = 1) -> torch.Tensor:
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"""
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Build the relative positional vector between `pos` and `pooled_pos`.
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"""
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if pooled_pos is None:
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pooled_pos = pos
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ref_point = pooled_pos[0] - pos[0]
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num_remove = shift * len(pooled_pos)
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max_dist = ref_point + num_remove * stride
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min_dist = pooled_pos[0] - pos[-1]
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return torch.arange(max_dist, min_dist - 1, -stride, dtype=torch.long, device=pos.device)
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def stride_pool(
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self,
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tensor: Union[torch.Tensor, Tuple[torch.Tensor], List[torch.Tensor]],
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axis: Union[int, Tuple[int], List[int]],
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) -> torch.Tensor:
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"""
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Perform pooling by stride slicing the tensor along the given axis.
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"""
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if tensor is None:
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return None
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# Do the stride pool recursively if axis is a list or a tuple of ints.
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if isinstance(axis, (list, tuple)):
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for ax in axis:
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tensor = self.stride_pool(tensor, ax)
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return tensor
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# Do the stride pool recursively if tensor is a list or tuple of tensors.
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if isinstance(tensor, (tuple, list)):
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return type(tensor)(self.stride_pool(x, axis) for x in tensor)
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# Deal with negative axis
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axis %= tensor.ndim
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axis_slice = (
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slice(None, -1, 2) if self.config.separate_cls and self.config.truncate_seq else slice(None, None, 2)
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)
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enc_slice = [slice(None)] * axis + [axis_slice]
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if self.config.separate_cls:
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cls_slice = [slice(None)] * axis + [slice(None, 1)]
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tensor = torch.cat([tensor[cls_slice], tensor], axis=axis)
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return tensor[enc_slice]
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def pool_tensor(
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self, tensor: Union[torch.Tensor, Tuple[torch.Tensor], List[torch.Tensor]], mode: str = "mean", stride: int = 2
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) -> torch.Tensor:
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"""Apply 1D pooling to a tensor of size [B x T (x H)]."""
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if tensor is None:
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return None
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# Do the pool recursively if tensor is a list or tuple of tensors.
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if isinstance(tensor, (tuple, list)):
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return type(tensor)(self.pool_tensor(tensor, mode=mode, stride=stride) for x in tensor)
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if self.config.separate_cls:
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suffix = tensor[:, :-1] if self.config.truncate_seq else tensor
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tensor = torch.cat([tensor[:, :1], suffix], dim=1)
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ndim = tensor.ndim
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if ndim == 2:
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tensor = tensor[:, None, :, None]
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elif ndim == 3:
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tensor = tensor[:, None, :, :]
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# Stride is applied on the second-to-last dimension.
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stride = (stride, 1)
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if mode == "mean":
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tensor = nn.functional.avg_pool2d(tensor, stride, stride=stride, ceil_mode=True)
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elif mode == "max":
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tensor = nn.functional.max_pool2d(tensor, stride, stride=stride, ceil_mode=True)
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elif mode == "min":
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tensor = -nn.functional.max_pool2d(-tensor, stride, stride=stride, ceil_mode=True)
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else:
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raise NotImplementedError("The supported modes are 'mean', 'max' and 'min'.")
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if ndim == 2:
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return tensor[:, 0, :, 0]
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elif ndim == 3:
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return tensor[:, 0]
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return tensor
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def pre_attention_pooling(
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self, output, attention_inputs: Tuple[torch.Tensor]
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) -> Tuple[torch.Tensor, Tuple[torch.Tensor]]:
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"""Pool `output` and the proper parts of `attention_inputs` before the attention layer."""
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position_embeds, token_type_mat, attention_mask, cls_mask = attention_inputs
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if self.config.pool_q_only:
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if self.config.attention_type == "factorized":
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position_embeds = self.stride_pool(position_embeds[:2], 0) + position_embeds[2:]
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token_type_mat = self.stride_pool(token_type_mat, 1)
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cls_mask = self.stride_pool(cls_mask, 0)
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output = self.pool_tensor(output, mode=self.config.pooling_type)
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else:
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self.pooling_mult *= 2
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if self.config.attention_type == "factorized":
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position_embeds = self.stride_pool(position_embeds, 0)
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token_type_mat = self.stride_pool(token_type_mat, [1, 2])
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cls_mask = self.stride_pool(cls_mask, [1, 2])
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attention_mask = self.pool_tensor(attention_mask, mode="min")
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output = self.pool_tensor(output, mode=self.config.pooling_type)
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attention_inputs = (position_embeds, token_type_mat, attention_mask, cls_mask)
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return output, attention_inputs
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def post_attention_pooling(self, attention_inputs: Tuple[torch.Tensor]) -> Tuple[torch.Tensor]:
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"""Pool the proper parts of `attention_inputs` after the attention layer."""
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position_embeds, token_type_mat, attention_mask, cls_mask = attention_inputs
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if self.config.pool_q_only:
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self.pooling_mult *= 2
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if self.config.attention_type == "factorized":
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position_embeds = position_embeds[:2] + self.stride_pool(position_embeds[2:], 0)
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token_type_mat = self.stride_pool(token_type_mat, 2)
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cls_mask = self.stride_pool(cls_mask, 1)
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attention_mask = self.pool_tensor(attention_mask, mode="min")
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attention_inputs = (position_embeds, token_type_mat, attention_mask, cls_mask)
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return attention_inputs
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def _relative_shift_gather(positional_attn: torch.Tensor, context_len: int, shift: int) -> torch.Tensor:
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batch_size, n_head, seq_len, max_rel_len = positional_attn.shape
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# max_rel_len = 2 * context_len + shift -1 is the numbers of possible relative positions i-j
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# What's next is the same as doing the following gather, which might be clearer code but less efficient.
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# idxs = context_len + torch.arange(0, context_len).unsqueeze(0) - torch.arange(0, seq_len).unsqueeze(1)
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# # matrix of context_len + i-j
|
|
# return positional_attn.gather(3, idxs.expand([batch_size, n_head, context_len, context_len]))
|
|
|
|
positional_attn = torch.reshape(positional_attn, [batch_size, n_head, max_rel_len, seq_len])
|
|
positional_attn = positional_attn[:, :, shift:, :]
|
|
positional_attn = torch.reshape(positional_attn, [batch_size, n_head, seq_len, max_rel_len - shift])
|
|
positional_attn = positional_attn[..., :context_len]
|
|
return positional_attn
|
|
|
|
|
|
class FunnelRelMultiheadAttention(nn.Module):
|
|
def __init__(self, config: FunnelConfig, block_index: int) -> None:
|
|
super().__init__()
|
|
self.config = config
|
|
self.block_index = block_index
|
|
d_model, n_head, d_head = config.d_model, config.n_head, config.d_head
|
|
|
|
self.hidden_dropout = nn.Dropout(config.hidden_dropout)
|
|
self.attention_dropout = nn.Dropout(config.attention_dropout)
|
|
|
|
self.q_head = nn.Linear(d_model, n_head * d_head, bias=False)
|
|
self.k_head = nn.Linear(d_model, n_head * d_head)
|
|
self.v_head = nn.Linear(d_model, n_head * d_head)
|
|
|
|
self.r_w_bias = nn.Parameter(torch.zeros([n_head, d_head]))
|
|
self.r_r_bias = nn.Parameter(torch.zeros([n_head, d_head]))
|
|
self.r_kernel = nn.Parameter(torch.zeros([d_model, n_head, d_head]))
|
|
self.r_s_bias = nn.Parameter(torch.zeros([n_head, d_head]))
|
|
self.seg_embed = nn.Parameter(torch.zeros([2, n_head, d_head]))
|
|
|
|
self.post_proj = nn.Linear(n_head * d_head, d_model)
|
|
self.layer_norm = nn.LayerNorm(d_model, eps=config.layer_norm_eps)
|
|
self.scale = 1.0 / (d_head**0.5)
|
|
|
|
def relative_positional_attention(self, position_embeds, q_head, context_len, cls_mask=None):
|
|
"""Relative attention score for the positional encodings"""
|
|
# q_head has shape batch_size x sea_len x n_head x d_head
|
|
if self.config.attention_type == "factorized":
|
|
# Notations from the paper, appending A.2.2, final formula (https://arxiv.org/abs/2006.03236)
|
|
# phi and pi have shape seq_len x d_model, psi and omega have shape context_len x d_model
|
|
phi, pi, psi, omega = position_embeds
|
|
# Shape n_head x d_head
|
|
u = self.r_r_bias * self.scale
|
|
# Shape d_model x n_head x d_head
|
|
w_r = self.r_kernel
|
|
|
|
# Shape batch_size x sea_len x n_head x d_model
|
|
q_r_attention = torch.einsum("binh,dnh->bind", q_head + u, w_r)
|
|
q_r_attention_1 = q_r_attention * phi[:, None]
|
|
q_r_attention_2 = q_r_attention * pi[:, None]
|
|
|
|
# Shape batch_size x n_head x seq_len x context_len
|
|
positional_attn = torch.einsum("bind,jd->bnij", q_r_attention_1, psi) + torch.einsum(
|
|
"bind,jd->bnij", q_r_attention_2, omega
|
|
)
|
|
else:
|
|
shift = 2 if q_head.shape[1] != context_len else 1
|
|
# Notations from the paper, appending A.2.1, final formula (https://arxiv.org/abs/2006.03236)
|
|
# Grab the proper positional encoding, shape max_rel_len x d_model
|
|
r = position_embeds[self.block_index][shift - 1]
|
|
# Shape n_head x d_head
|
|
v = self.r_r_bias * self.scale
|
|
# Shape d_model x n_head x d_head
|
|
w_r = self.r_kernel
|
|
|
|
# Shape max_rel_len x n_head x d_model
|
|
r_head = torch.einsum("td,dnh->tnh", r, w_r)
|
|
# Shape batch_size x n_head x seq_len x max_rel_len
|
|
positional_attn = torch.einsum("binh,tnh->bnit", q_head + v, r_head)
|
|
# Shape batch_size x n_head x seq_len x context_len
|
|
positional_attn = _relative_shift_gather(positional_attn, context_len, shift)
|
|
|
|
if cls_mask is not None:
|
|
positional_attn *= cls_mask
|
|
return positional_attn
|
|
|
|
def relative_token_type_attention(self, token_type_mat, q_head, cls_mask=None):
|
|
"""Relative attention score for the token_type_ids"""
|
|
if token_type_mat is None:
|
|
return 0
|
|
batch_size, seq_len, context_len = token_type_mat.shape
|
|
# q_head has shape batch_size x seq_len x n_head x d_head
|
|
# Shape n_head x d_head
|
|
r_s_bias = self.r_s_bias * self.scale
|
|
|
|
# Shape batch_size x n_head x seq_len x 2
|
|
token_type_bias = torch.einsum("bind,snd->bnis", q_head + r_s_bias, self.seg_embed)
|
|
# Shape batch_size x n_head x seq_len x context_len
|
|
token_type_mat = token_type_mat[:, None].expand([batch_size, q_head.shape[2], seq_len, context_len])
|
|
# Shapes batch_size x n_head x seq_len
|
|
diff_token_type, same_token_type = torch.split(token_type_bias, 1, dim=-1)
|
|
# Shape batch_size x n_head x seq_len x context_len
|
|
token_type_attn = torch.where(
|
|
token_type_mat, same_token_type.expand(token_type_mat.shape), diff_token_type.expand(token_type_mat.shape)
|
|
)
|
|
|
|
if cls_mask is not None:
|
|
token_type_attn *= cls_mask
|
|
return token_type_attn
|
|
|
|
def forward(
|
|
self,
|
|
query: torch.Tensor,
|
|
key: torch.Tensor,
|
|
value: torch.Tensor,
|
|
attention_inputs: Tuple[torch.Tensor],
|
|
output_attentions: bool = False,
|
|
) -> Tuple[torch.Tensor, ...]:
|
|
# query has shape batch_size x seq_len x d_model
|
|
# key and value have shapes batch_size x context_len x d_model
|
|
position_embeds, token_type_mat, attention_mask, cls_mask = attention_inputs
|
|
|
|
batch_size, seq_len, _ = query.shape
|
|
context_len = key.shape[1]
|
|
n_head, d_head = self.config.n_head, self.config.d_head
|
|
|
|
# Shape batch_size x seq_len x n_head x d_head
|
|
q_head = self.q_head(query).view(batch_size, seq_len, n_head, d_head)
|
|
# Shapes batch_size x context_len x n_head x d_head
|
|
k_head = self.k_head(key).view(batch_size, context_len, n_head, d_head)
|
|
v_head = self.v_head(value).view(batch_size, context_len, n_head, d_head)
|
|
|
|
q_head = q_head * self.scale
|
|
# Shape n_head x d_head
|
|
r_w_bias = self.r_w_bias * self.scale
|
|
# Shapes batch_size x n_head x seq_len x context_len
|
|
content_score = torch.einsum("bind,bjnd->bnij", q_head + r_w_bias, k_head)
|
|
positional_attn = self.relative_positional_attention(position_embeds, q_head, context_len, cls_mask)
|
|
token_type_attn = self.relative_token_type_attention(token_type_mat, q_head, cls_mask)
|
|
|
|
# merge attention scores
|
|
attn_score = content_score + positional_attn + token_type_attn
|
|
|
|
# precision safe in case of mixed precision training
|
|
dtype = attn_score.dtype
|
|
attn_score = attn_score.float()
|
|
# perform masking
|
|
if attention_mask is not None:
|
|
attn_score = attn_score - INF * (1 - attention_mask[:, None, None].float())
|
|
# attention probability
|
|
attn_prob = torch.softmax(attn_score, dim=-1, dtype=dtype)
|
|
attn_prob = self.attention_dropout(attn_prob)
|
|
|
|
# attention output, shape batch_size x seq_len x n_head x d_head
|
|
attn_vec = torch.einsum("bnij,bjnd->bind", attn_prob, v_head)
|
|
|
|
# Shape shape batch_size x seq_len x d_model
|
|
attn_out = self.post_proj(attn_vec.reshape(batch_size, seq_len, n_head * d_head))
|
|
attn_out = self.hidden_dropout(attn_out)
|
|
|
|
output = self.layer_norm(query + attn_out)
|
|
return (output, attn_prob) if output_attentions else (output,)
|
|
|
|
|
|
class FunnelPositionwiseFFN(nn.Module):
|
|
def __init__(self, config: FunnelConfig) -> None:
|
|
super().__init__()
|
|
self.linear_1 = nn.Linear(config.d_model, config.d_inner)
|
|
self.activation_function = ACT2FN[config.hidden_act]
|
|
self.activation_dropout = nn.Dropout(config.activation_dropout)
|
|
self.linear_2 = nn.Linear(config.d_inner, config.d_model)
|
|
self.dropout = nn.Dropout(config.hidden_dropout)
|
|
self.layer_norm = nn.LayerNorm(config.d_model, config.layer_norm_eps)
|
|
|
|
def forward(self, hidden: torch.Tensor) -> torch.Tensor:
|
|
h = self.linear_1(hidden)
|
|
h = self.activation_function(h)
|
|
h = self.activation_dropout(h)
|
|
h = self.linear_2(h)
|
|
h = self.dropout(h)
|
|
return self.layer_norm(hidden + h)
|
|
|
|
|
|
class FunnelLayer(nn.Module):
|
|
def __init__(self, config: FunnelConfig, block_index: int) -> None:
|
|
super().__init__()
|
|
self.attention = FunnelRelMultiheadAttention(config, block_index)
|
|
self.ffn = FunnelPositionwiseFFN(config)
|
|
|
|
def forward(
|
|
self,
|
|
query: torch.Tensor,
|
|
key: torch.Tensor,
|
|
value: torch.Tensor,
|
|
attention_inputs,
|
|
output_attentions: bool = False,
|
|
) -> Tuple:
|
|
attn = self.attention(query, key, value, attention_inputs, output_attentions=output_attentions)
|
|
output = self.ffn(attn[0])
|
|
return (output, attn[1]) if output_attentions else (output,)
|
|
|
|
|
|
class FunnelEncoder(nn.Module):
|
|
def __init__(self, config: FunnelConfig) -> None:
|
|
super().__init__()
|
|
self.config = config
|
|
self.attention_structure = FunnelAttentionStructure(config)
|
|
self.blocks = nn.ModuleList(
|
|
[
|
|
nn.ModuleList([FunnelLayer(config, block_index) for _ in range(block_size)])
|
|
for block_index, block_size in enumerate(config.block_sizes)
|
|
]
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
inputs_embeds: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
output_attentions: bool = False,
|
|
output_hidden_states: bool = False,
|
|
return_dict: bool = True,
|
|
) -> Union[Tuple, BaseModelOutput]:
|
|
# The pooling is not implemented on long tensors, so we convert this mask.
|
|
attention_mask = attention_mask.type_as(inputs_embeds)
|
|
attention_inputs = self.attention_structure.init_attention_inputs(
|
|
inputs_embeds,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
)
|
|
hidden = inputs_embeds
|
|
|
|
all_hidden_states = (inputs_embeds,) if output_hidden_states else None
|
|
all_attentions = () if output_attentions else None
|
|
|
|
for block_index, block in enumerate(self.blocks):
|
|
pooling_flag = hidden.size(1) > (2 if self.config.separate_cls else 1)
|
|
pooling_flag = pooling_flag and block_index > 0
|
|
if pooling_flag:
|
|
pooled_hidden, attention_inputs = self.attention_structure.pre_attention_pooling(
|
|
hidden, attention_inputs
|
|
)
|
|
for layer_index, layer in enumerate(block):
|
|
for repeat_index in range(self.config.block_repeats[block_index]):
|
|
do_pooling = (repeat_index == 0) and (layer_index == 0) and pooling_flag
|
|
if do_pooling:
|
|
query = pooled_hidden
|
|
key = value = hidden if self.config.pool_q_only else pooled_hidden
|
|
else:
|
|
query = key = value = hidden
|
|
layer_output = layer(query, key, value, attention_inputs, output_attentions=output_attentions)
|
|
hidden = layer_output[0]
|
|
if do_pooling:
|
|
attention_inputs = self.attention_structure.post_attention_pooling(attention_inputs)
|
|
|
|
if output_attentions:
|
|
all_attentions = all_attentions + layer_output[1:]
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden,)
|
|
|
|
if not return_dict:
|
|
return tuple(v for v in [hidden, all_hidden_states, all_attentions] if v is not None)
|
|
return BaseModelOutput(last_hidden_state=hidden, hidden_states=all_hidden_states, attentions=all_attentions)
|
|
|
|
|
|
def upsample(
|
|
x: torch.Tensor, stride: int, target_len: int, separate_cls: bool = True, truncate_seq: bool = False
|
|
) -> torch.Tensor:
|
|
"""
|
|
Upsample tensor `x` to match `target_len` by repeating the tokens `stride` time on the sequence length dimension.
|
|
"""
|
|
if stride == 1:
|
|
return x
|
|
if separate_cls:
|
|
cls = x[:, :1]
|
|
x = x[:, 1:]
|
|
output = torch.repeat_interleave(x, repeats=stride, dim=1)
|
|
if separate_cls:
|
|
if truncate_seq:
|
|
output = nn.functional.pad(output, (0, 0, 0, stride - 1, 0, 0))
|
|
output = output[:, : target_len - 1]
|
|
output = torch.cat([cls, output], dim=1)
|
|
else:
|
|
output = output[:, :target_len]
|
|
return output
|
|
|
|
|
|
class FunnelDecoder(nn.Module):
|
|
def __init__(self, config: FunnelConfig) -> None:
|
|
super().__init__()
|
|
self.config = config
|
|
self.attention_structure = FunnelAttentionStructure(config)
|
|
self.layers = nn.ModuleList([FunnelLayer(config, 0) for _ in range(config.num_decoder_layers)])
|
|
|
|
def forward(
|
|
self,
|
|
final_hidden: torch.Tensor,
|
|
first_block_hidden: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
output_attentions: bool = False,
|
|
output_hidden_states: bool = False,
|
|
return_dict: bool = True,
|
|
) -> Union[Tuple, BaseModelOutput]:
|
|
upsampled_hidden = upsample(
|
|
final_hidden,
|
|
stride=2 ** (len(self.config.block_sizes) - 1),
|
|
target_len=first_block_hidden.shape[1],
|
|
separate_cls=self.config.separate_cls,
|
|
truncate_seq=self.config.truncate_seq,
|
|
)
|
|
|
|
hidden = upsampled_hidden + first_block_hidden
|
|
all_hidden_states = (hidden,) if output_hidden_states else None
|
|
all_attentions = () if output_attentions else None
|
|
|
|
attention_inputs = self.attention_structure.init_attention_inputs(
|
|
hidden,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
)
|
|
|
|
for layer in self.layers:
|
|
layer_output = layer(hidden, hidden, hidden, attention_inputs, output_attentions=output_attentions)
|
|
hidden = layer_output[0]
|
|
|
|
if output_attentions:
|
|
all_attentions = all_attentions + layer_output[1:]
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden,)
|
|
|
|
if not return_dict:
|
|
return tuple(v for v in [hidden, all_hidden_states, all_attentions] if v is not None)
|
|
return BaseModelOutput(last_hidden_state=hidden, hidden_states=all_hidden_states, attentions=all_attentions)
|
|
|
|
|
|
class FunnelDiscriminatorPredictions(nn.Module):
|
|
"""Prediction module for the discriminator, made up of two dense layers."""
|
|
|
|
def __init__(self, config: FunnelConfig) -> None:
|
|
super().__init__()
|
|
self.config = config
|
|
self.dense = nn.Linear(config.d_model, config.d_model)
|
|
self.dense_prediction = nn.Linear(config.d_model, 1)
|
|
|
|
def forward(self, discriminator_hidden_states: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(discriminator_hidden_states)
|
|
hidden_states = ACT2FN[self.config.hidden_act](hidden_states)
|
|
logits = self.dense_prediction(hidden_states).squeeze(-1)
|
|
return logits
|
|
|
|
|
|
class FunnelPreTrainedModel(PreTrainedModel):
|
|
"""
|
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
|
models.
|
|
"""
|
|
|
|
config_class = FunnelConfig
|
|
load_tf_weights = load_tf_weights_in_funnel
|
|
base_model_prefix = "funnel"
|
|
|
|
def _init_weights(self, module):
|
|
classname = module.__class__.__name__
|
|
if classname.find("Linear") != -1:
|
|
if getattr(module, "weight", None) is not None:
|
|
if self.config.initializer_std is None:
|
|
fan_out, fan_in = module.weight.shape
|
|
std = np.sqrt(1.0 / float(fan_in + fan_out))
|
|
else:
|
|
std = self.config.initializer_std
|
|
nn.init.normal_(module.weight, std=std)
|
|
if getattr(module, "bias", None) is not None:
|
|
nn.init.constant_(module.bias, 0.0)
|
|
elif classname == "FunnelRelMultiheadAttention":
|
|
nn.init.uniform_(module.r_w_bias, b=self.config.initializer_range)
|
|
nn.init.uniform_(module.r_r_bias, b=self.config.initializer_range)
|
|
nn.init.uniform_(module.r_kernel, b=self.config.initializer_range)
|
|
nn.init.uniform_(module.r_s_bias, b=self.config.initializer_range)
|
|
nn.init.uniform_(module.seg_embed, b=self.config.initializer_range)
|
|
elif classname == "FunnelEmbeddings":
|
|
std = 1.0 if self.config.initializer_std is None else self.config.initializer_std
|
|
nn.init.normal_(module.word_embeddings.weight, std=std)
|
|
if module.word_embeddings.padding_idx is not None:
|
|
module.word_embeddings.weight.data[module.padding_idx].zero_()
|
|
|
|
|
|
class FunnelClassificationHead(nn.Module):
|
|
def __init__(self, config: FunnelConfig, n_labels: int) -> None:
|
|
super().__init__()
|
|
self.linear_hidden = nn.Linear(config.d_model, config.d_model)
|
|
self.dropout = nn.Dropout(config.hidden_dropout)
|
|
self.linear_out = nn.Linear(config.d_model, n_labels)
|
|
|
|
def forward(self, hidden: torch.Tensor) -> torch.Tensor:
|
|
hidden = self.linear_hidden(hidden)
|
|
hidden = torch.tanh(hidden)
|
|
hidden = self.dropout(hidden)
|
|
return self.linear_out(hidden)
|
|
|
|
|
|
@dataclass
|
|
class FunnelForPreTrainingOutput(ModelOutput):
|
|
"""
|
|
Output type of [`FunnelForPreTraining`].
|
|
|
|
Args:
|
|
loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
|
|
Total loss of the ELECTRA-style objective.
|
|
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
|
|
Prediction scores of the head (scores for each token before SoftMax).
|
|
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
|
|
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
|
|
shape `(batch_size, sequence_length, hidden_size)`.
|
|
|
|
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
|
|
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
|
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
|
|
sequence_length)`.
|
|
|
|
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
|
|
heads.
|
|
"""
|
|
|
|
loss: Optional[torch.FloatTensor] = None
|
|
logits: torch.FloatTensor = None
|
|
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
|
|
attentions: Optional[Tuple[torch.FloatTensor]] = None
|
|
|
|
|
|
FUNNEL_START_DOCSTRING = r"""
|
|
|
|
The Funnel Transformer model was proposed in [Funnel-Transformer: Filtering out Sequential Redundancy for Efficient
|
|
Language Processing](https://arxiv.org/abs/2006.03236) by Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
|
|
|
|
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
|
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
|
|
etc.)
|
|
|
|
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
|
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
|
and behavior.
|
|
|
|
Parameters:
|
|
config ([`FunnelConfig`]): Model configuration class with all the parameters of the model.
|
|
Initializing with a config file does not load the weights associated with the model, only the
|
|
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
|
"""
|
|
|
|
FUNNEL_INPUTS_DOCSTRING = r"""
|
|
Args:
|
|
input_ids (`torch.LongTensor` of shape `({0})`):
|
|
Indices of input sequence tokens in the vocabulary.
|
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
|
[`PreTrainedTokenizer.__call__`] for details.
|
|
|
|
[What are input IDs?](../glossary#input-ids)
|
|
attention_mask (`torch.FloatTensor` of shape `({0})`, *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)
|
|
token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
|
|
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
|
|
1]`:
|
|
|
|
- 0 corresponds to a *sentence A* token,
|
|
- 1 corresponds to a *sentence B* token.
|
|
|
|
[What are token type IDs?](../glossary#token-type-ids)
|
|
inputs_embeds (`torch.FloatTensor` of shape `({0}, 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.
|
|
"""
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
The base Funnel Transformer Model transformer outputting raw hidden-states without upsampling head (also called
|
|
decoder) or any task-specific head on top.
|
|
""",
|
|
FUNNEL_START_DOCSTRING,
|
|
)
|
|
class FunnelBaseModel(FunnelPreTrainedModel):
|
|
def __init__(self, config: FunnelConfig) -> None:
|
|
super().__init__(config)
|
|
|
|
self.embeddings = FunnelEmbeddings(config)
|
|
self.encoder = FunnelEncoder(config)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self) -> nn.Embedding:
|
|
return self.embeddings.word_embeddings
|
|
|
|
def set_input_embeddings(self, new_embeddings: nn.Embedding) -> None:
|
|
self.embeddings.word_embeddings = new_embeddings
|
|
|
|
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@add_code_sample_docstrings(
|
|
checkpoint="funnel-transformer/small-base",
|
|
output_type=BaseModelOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, BaseModelOutput]:
|
|
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
|
|
|
|
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()
|
|
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")
|
|
|
|
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
|
|
|
if attention_mask is None:
|
|
attention_mask = torch.ones(input_shape, device=device)
|
|
if token_type_ids is None:
|
|
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
|
|
|
|
# TODO: deal with head_mask
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.embeddings(input_ids)
|
|
|
|
encoder_outputs = self.encoder(
|
|
inputs_embeds,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
return encoder_outputs
|
|
|
|
|
|
@add_start_docstrings(
|
|
"The bare Funnel Transformer Model transformer outputting raw hidden-states without any specific head on top.",
|
|
FUNNEL_START_DOCSTRING,
|
|
)
|
|
class FunnelModel(FunnelPreTrainedModel):
|
|
def __init__(self, config: FunnelConfig) -> None:
|
|
super().__init__(config)
|
|
self.config = config
|
|
self.embeddings = FunnelEmbeddings(config)
|
|
self.encoder = FunnelEncoder(config)
|
|
self.decoder = FunnelDecoder(config)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self) -> nn.Embedding:
|
|
return self.embeddings.word_embeddings
|
|
|
|
def set_input_embeddings(self, new_embeddings: nn.Embedding) -> None:
|
|
self.embeddings.word_embeddings = new_embeddings
|
|
|
|
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=BaseModelOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, BaseModelOutput]:
|
|
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
|
|
|
|
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()
|
|
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")
|
|
|
|
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
|
|
|
if attention_mask is None:
|
|
attention_mask = torch.ones(input_shape, device=device)
|
|
if token_type_ids is None:
|
|
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
|
|
|
|
# TODO: deal with head_mask
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.embeddings(input_ids)
|
|
|
|
encoder_outputs = self.encoder(
|
|
inputs_embeds,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=True,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
decoder_outputs = self.decoder(
|
|
final_hidden=encoder_outputs[0],
|
|
first_block_hidden=encoder_outputs[1][self.config.block_sizes[0]],
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
if not return_dict:
|
|
idx = 0
|
|
outputs = (decoder_outputs[0],)
|
|
if output_hidden_states:
|
|
idx += 1
|
|
outputs = outputs + (encoder_outputs[1] + decoder_outputs[idx],)
|
|
if output_attentions:
|
|
idx += 1
|
|
outputs = outputs + (encoder_outputs[2] + decoder_outputs[idx],)
|
|
return outputs
|
|
|
|
return BaseModelOutput(
|
|
last_hidden_state=decoder_outputs[0],
|
|
hidden_states=(encoder_outputs.hidden_states + decoder_outputs.hidden_states)
|
|
if output_hidden_states
|
|
else None,
|
|
attentions=(encoder_outputs.attentions + decoder_outputs.attentions) if output_attentions else None,
|
|
)
|
|
|
|
|
|
add_start_docstrings(
|
|
"""
|
|
Funnel Transformer model with a binary classification head on top as used during pretraining for identifying
|
|
generated tokens.
|
|
""",
|
|
FUNNEL_START_DOCSTRING,
|
|
)
|
|
|
|
|
|
class FunnelForPreTraining(FunnelPreTrainedModel):
|
|
def __init__(self, config: FunnelConfig) -> None:
|
|
super().__init__(config)
|
|
|
|
self.funnel = FunnelModel(config)
|
|
self.discriminator_predictions = FunnelDiscriminatorPredictions(config)
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@replace_return_docstrings(output_type=FunnelForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, FunnelForPreTrainingOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for computing the ELECTRA-style loss. Input should be a sequence of tokens (see `input_ids`
|
|
docstring) Indices should be in `[0, 1]`:
|
|
|
|
- 0 indicates the token is an original token,
|
|
- 1 indicates the token was replaced.
|
|
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, FunnelForPreTraining
|
|
>>> import torch
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("funnel-transformer/small")
|
|
>>> model = FunnelForPreTraining.from_pretrained("funnel-transformer/small")
|
|
|
|
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
|
|
>>> logits = model(**inputs).logits
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
discriminator_hidden_states = self.funnel(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
discriminator_sequence_output = discriminator_hidden_states[0]
|
|
|
|
logits = self.discriminator_predictions(discriminator_sequence_output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss_fct = nn.BCEWithLogitsLoss()
|
|
if attention_mask is not None:
|
|
active_loss = attention_mask.view(-1, discriminator_sequence_output.shape[1]) == 1
|
|
active_logits = logits.view(-1, discriminator_sequence_output.shape[1])[active_loss]
|
|
active_labels = labels[active_loss]
|
|
loss = loss_fct(active_logits, active_labels.float())
|
|
else:
|
|
loss = loss_fct(logits.view(-1, discriminator_sequence_output.shape[1]), labels.float())
|
|
|
|
if not return_dict:
|
|
output = (logits,) + discriminator_hidden_states[1:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return FunnelForPreTrainingOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=discriminator_hidden_states.hidden_states,
|
|
attentions=discriminator_hidden_states.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings("""Funnel Transformer Model with a `language modeling` head on top.""", FUNNEL_START_DOCSTRING)
|
|
class FunnelForMaskedLM(FunnelPreTrainedModel):
|
|
_tied_weights_keys = ["lm_head.weight"]
|
|
|
|
def __init__(self, config: FunnelConfig) -> None:
|
|
super().__init__(config)
|
|
|
|
self.funnel = FunnelModel(config)
|
|
self.lm_head = nn.Linear(config.d_model, config.vocab_size)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_output_embeddings(self) -> nn.Linear:
|
|
return self.lm_head
|
|
|
|
def set_output_embeddings(self, new_embeddings: nn.Embedding) -> None:
|
|
self.lm_head = new_embeddings
|
|
|
|
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=MaskedLMOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
mask="<mask>",
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, MaskedLMOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
|
|
config.vocab_size]` (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]`
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.funnel(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
last_hidden_state = outputs[0]
|
|
prediction_logits = self.lm_head(last_hidden_state)
|
|
|
|
masked_lm_loss = None
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss() # -100 index = padding token
|
|
masked_lm_loss = loss_fct(prediction_logits.view(-1, self.config.vocab_size), labels.view(-1))
|
|
|
|
if not return_dict:
|
|
output = (prediction_logits,) + outputs[1:]
|
|
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
|
|
|
|
return MaskedLMOutput(
|
|
loss=masked_lm_loss,
|
|
logits=prediction_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
Funnel Transformer Model with a sequence classification/regression head on top (two linear layer on top of the
|
|
first timestep of the last hidden state) e.g. for GLUE tasks.
|
|
""",
|
|
FUNNEL_START_DOCSTRING,
|
|
)
|
|
class FunnelForSequenceClassification(FunnelPreTrainedModel):
|
|
def __init__(self, config: FunnelConfig) -> None:
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.config = config
|
|
|
|
self.funnel = FunnelBaseModel(config)
|
|
self.classifier = FunnelClassificationHead(config, config.num_labels)
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@add_code_sample_docstrings(
|
|
checkpoint="funnel-transformer/small-base",
|
|
output_type=SequenceClassifierOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, SequenceClassifierOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
|
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
|
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.funnel(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
last_hidden_state = outputs[0]
|
|
pooled_output = last_hidden_state[:, 0]
|
|
logits = self.classifier(pooled_output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
if self.config.problem_type is None:
|
|
if self.num_labels == 1:
|
|
self.config.problem_type = "regression"
|
|
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
|
self.config.problem_type = "single_label_classification"
|
|
else:
|
|
self.config.problem_type = "multi_label_classification"
|
|
|
|
if self.config.problem_type == "regression":
|
|
loss_fct = MSELoss()
|
|
if self.num_labels == 1:
|
|
loss = loss_fct(logits.squeeze(), labels.squeeze())
|
|
else:
|
|
loss = loss_fct(logits, labels)
|
|
elif self.config.problem_type == "single_label_classification":
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
|
elif self.config.problem_type == "multi_label_classification":
|
|
loss_fct = BCEWithLogitsLoss()
|
|
loss = loss_fct(logits, labels)
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[1:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return SequenceClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
Funnel Transformer Model with a multiple choice classification head on top (two linear layer on top of the first
|
|
timestep of the last hidden state, and a softmax) e.g. for RocStories/SWAG tasks.
|
|
""",
|
|
FUNNEL_START_DOCSTRING,
|
|
)
|
|
class FunnelForMultipleChoice(FunnelPreTrainedModel):
|
|
def __init__(self, config: FunnelConfig) -> None:
|
|
super().__init__(config)
|
|
|
|
self.funnel = FunnelBaseModel(config)
|
|
self.classifier = FunnelClassificationHead(config, 1)
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
|
|
@add_code_sample_docstrings(
|
|
checkpoint="funnel-transformer/small-base",
|
|
output_type=MultipleChoiceModelOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, MultipleChoiceModelOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
|
|
num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
|
|
`input_ids` above)
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
|
|
|
|
input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
|
|
attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
|
|
token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
|
|
inputs_embeds = (
|
|
inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
|
|
if inputs_embeds is not None
|
|
else None
|
|
)
|
|
|
|
outputs = self.funnel(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
last_hidden_state = outputs[0]
|
|
pooled_output = last_hidden_state[:, 0]
|
|
logits = self.classifier(pooled_output)
|
|
reshaped_logits = logits.view(-1, num_choices)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(reshaped_logits, labels)
|
|
|
|
if not return_dict:
|
|
output = (reshaped_logits,) + outputs[1:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return MultipleChoiceModelOutput(
|
|
loss=loss,
|
|
logits=reshaped_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
Funnel Transformer Model with a token classification head on top (a linear layer on top of the hidden-states
|
|
output) e.g. for Named-Entity-Recognition (NER) tasks.
|
|
""",
|
|
FUNNEL_START_DOCSTRING,
|
|
)
|
|
class FunnelForTokenClassification(FunnelPreTrainedModel):
|
|
def __init__(self, config: FunnelConfig) -> None:
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.funnel = FunnelModel(config)
|
|
self.dropout = nn.Dropout(config.hidden_dropout)
|
|
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=TokenClassifierOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, TokenClassifierOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.funnel(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
last_hidden_state = outputs[0]
|
|
last_hidden_state = self.dropout(last_hidden_state)
|
|
logits = self.classifier(last_hidden_state)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[1:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return TokenClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
Funnel Transformer Model with a span classification head on top for extractive question-answering tasks like SQuAD
|
|
(a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
|
|
""",
|
|
FUNNEL_START_DOCSTRING,
|
|
)
|
|
class FunnelForQuestionAnswering(FunnelPreTrainedModel):
|
|
def __init__(self, config: FunnelConfig) -> None:
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.funnel = FunnelModel(config)
|
|
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=QuestionAnsweringModelOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
start_positions: Optional[torch.Tensor] = None,
|
|
end_positions: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, QuestionAnsweringModelOutput]:
|
|
r"""
|
|
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for position (index) of the start of the labelled span for computing the token classification loss.
|
|
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
|
are not taken into account for computing the loss.
|
|
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for position (index) of the end of the labelled span for computing the token classification loss.
|
|
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
|
are not taken into account for computing the loss.
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.funnel(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
last_hidden_state = outputs[0]
|
|
|
|
logits = self.qa_outputs(last_hidden_state)
|
|
start_logits, end_logits = logits.split(1, dim=-1)
|
|
start_logits = start_logits.squeeze(-1).contiguous()
|
|
end_logits = end_logits.squeeze(-1).contiguous()
|
|
|
|
total_loss = None
|
|
if start_positions is not None and end_positions is not None:
|
|
# If we are on multi-GPU, split add a dimension
|
|
if len(start_positions.size()) > 1:
|
|
start_positions = start_positions.squeze(-1)
|
|
if len(end_positions.size()) > 1:
|
|
end_positions = end_positions.squeeze(-1)
|
|
# sometimes the start/end positions are outside our model inputs, we ignore these terms
|
|
ignored_index = start_logits.size(1)
|
|
start_positions = start_positions.clamp(0, ignored_index)
|
|
end_positions = end_positions.clamp(0, ignored_index)
|
|
|
|
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
|
|
start_loss = loss_fct(start_logits, start_positions)
|
|
end_loss = loss_fct(end_logits, end_positions)
|
|
total_loss = (start_loss + end_loss) / 2
|
|
|
|
if not return_dict:
|
|
output = (start_logits, end_logits) + outputs[1:]
|
|
return ((total_loss,) + output) if total_loss is not None else output
|
|
|
|
return QuestionAnsweringModelOutput(
|
|
loss=total_loss,
|
|
start_logits=start_logits,
|
|
end_logits=end_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|