1854 lines
82 KiB
Python
1854 lines
82 KiB
Python
# coding=utf-8
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# Copyright 2022 The REALM 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 REALM model."""
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import math
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import os
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from dataclasses import dataclass
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from typing import Optional, Tuple, Union
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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 ...activations import ACT2FN
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from ...modeling_outputs import (
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BaseModelOutputWithPastAndCrossAttentions,
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BaseModelOutputWithPoolingAndCrossAttentions,
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MaskedLMOutput,
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ModelOutput,
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)
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from ...modeling_utils import PreTrainedModel
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from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
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from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
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from .configuration_realm import RealmConfig
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logger = logging.get_logger(__name__)
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_EMBEDDER_CHECKPOINT_FOR_DOC = "google/realm-cc-news-pretrained-embedder"
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_ENCODER_CHECKPOINT_FOR_DOC = "google/realm-cc-news-pretrained-encoder"
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_SCORER_CHECKPOINT_FOR_DOC = "google/realm-cc-news-pretrained-scorer"
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_CONFIG_FOR_DOC = "RealmConfig"
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from ..deprecated._archive_maps import REALM_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402
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def load_tf_weights_in_realm(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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for name, array in zip(names, arrays):
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if isinstance(model, RealmReader) and "reader" not in name:
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logger.info(f"Skipping {name} as it is not {model.__class__.__name__}'s parameter")
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continue
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# For pretrained openqa reader
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if (name.startswith("bert") or name.startswith("cls")) and isinstance(model, RealmForOpenQA):
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name = name.replace("bert/", "reader/realm/")
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name = name.replace("cls/", "reader/cls/")
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# For pretrained encoder
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if (name.startswith("bert") or name.startswith("cls")) and isinstance(model, RealmKnowledgeAugEncoder):
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name = name.replace("bert/", "realm/")
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# For finetuned reader
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if name.startswith("reader"):
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reader_prefix = "" if isinstance(model, RealmReader) else "reader/"
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name = name.replace("reader/module/bert/", f"{reader_prefix}realm/")
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name = name.replace("reader/module/cls/", f"{reader_prefix}cls/")
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name = name.replace("reader/dense/", f"{reader_prefix}qa_outputs/dense_intermediate/")
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name = name.replace("reader/dense_1/", f"{reader_prefix}qa_outputs/dense_output/")
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name = name.replace("reader/layer_normalization", f"{reader_prefix}qa_outputs/layer_normalization")
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# For embedder and scorer
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if name.startswith("module/module/module/"): # finetuned
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embedder_prefix = "" if isinstance(model, RealmEmbedder) else "embedder/"
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name = name.replace("module/module/module/module/bert/", f"{embedder_prefix}realm/")
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name = name.replace("module/module/module/LayerNorm/", f"{embedder_prefix}cls/LayerNorm/")
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name = name.replace("module/module/module/dense/", f"{embedder_prefix}cls/dense/")
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name = name.replace("module/module/module/module/cls/predictions/", f"{embedder_prefix}cls/predictions/")
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name = name.replace("module/module/module/bert/", f"{embedder_prefix}realm/")
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name = name.replace("module/module/module/cls/predictions/", f"{embedder_prefix}cls/predictions/")
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elif name.startswith("module/module/"): # pretrained
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embedder_prefix = "" if isinstance(model, RealmEmbedder) else "embedder/"
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name = name.replace("module/module/LayerNorm/", f"{embedder_prefix}cls/LayerNorm/")
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name = name.replace("module/module/dense/", f"{embedder_prefix}cls/dense/")
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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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pointer = model
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for m_name in name:
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if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
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scope_names = re.split(r"_(\d+)", m_name)
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else:
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scope_names = [m_name]
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if scope_names[0] == "kernel" or scope_names[0] == "gamma":
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pointer = getattr(pointer, "weight")
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elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
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pointer = getattr(pointer, "bias")
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else:
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try:
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pointer = getattr(pointer, scope_names[0])
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except AttributeError:
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logger.info(f"Skipping {'/'.join(name)}")
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continue
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if len(scope_names) >= 2:
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num = int(scope_names[1])
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pointer = pointer[num]
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if m_name[-11:] == "_embeddings":
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pointer = getattr(pointer, "weight")
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elif m_name == "kernel":
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array = np.transpose(array)
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try:
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assert (
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pointer.shape == array.shape
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), f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"
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except AssertionError as e:
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e.args += (pointer.shape, array.shape)
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raise
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logger.info(f"Initialize PyTorch weight {name}")
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pointer.data = torch.from_numpy(array)
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return model
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# Copied from transformers.models.bert.modeling_bert.BertEmbeddings with Bert->Realm
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class RealmEmbeddings(nn.Module):
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"""Construct the embeddings from word, position and token_type embeddings."""
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def __init__(self, config):
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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.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
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self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
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# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
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# any TensorFlow checkpoint file
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self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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# position_ids (1, len position emb) is contiguous in memory and exported when serialized
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self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
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self.register_buffer(
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"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
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)
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self.register_buffer(
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"token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False
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)
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def forward(
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self,
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input_ids: Optional[torch.LongTensor] = None,
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token_type_ids: Optional[torch.LongTensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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past_key_values_length: int = 0,
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) -> torch.Tensor:
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if input_ids is not None:
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input_shape = input_ids.size()
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else:
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input_shape = inputs_embeds.size()[:-1]
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seq_length = input_shape[1]
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if position_ids is None:
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position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
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# Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
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# when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
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# issue #5664
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if token_type_ids is None:
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if hasattr(self, "token_type_ids"):
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buffered_token_type_ids = self.token_type_ids[:, :seq_length]
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buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
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token_type_ids = buffered_token_type_ids_expanded
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else:
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token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
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if inputs_embeds is None:
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inputs_embeds = self.word_embeddings(input_ids)
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token_type_embeddings = self.token_type_embeddings(token_type_ids)
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embeddings = inputs_embeds + token_type_embeddings
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if self.position_embedding_type == "absolute":
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position_embeddings = self.position_embeddings(position_ids)
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embeddings += position_embeddings
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embeddings = self.LayerNorm(embeddings)
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embeddings = self.dropout(embeddings)
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return embeddings
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# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->Realm
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class RealmSelfAttention(nn.Module):
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def __init__(self, config, position_embedding_type=None):
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super().__init__()
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if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
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raise ValueError(
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f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
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f"heads ({config.num_attention_heads})"
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)
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self.num_attention_heads = config.num_attention_heads
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self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
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self.all_head_size = self.num_attention_heads * self.attention_head_size
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self.query = nn.Linear(config.hidden_size, self.all_head_size)
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self.key = nn.Linear(config.hidden_size, self.all_head_size)
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self.value = nn.Linear(config.hidden_size, self.all_head_size)
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self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
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self.position_embedding_type = position_embedding_type or getattr(
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config, "position_embedding_type", "absolute"
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)
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if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
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self.max_position_embeddings = config.max_position_embeddings
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self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
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self.is_decoder = config.is_decoder
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def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
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new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
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x = x.view(new_x_shape)
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return x.permute(0, 2, 1, 3)
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.FloatTensor] = None,
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head_mask: Optional[torch.FloatTensor] = None,
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encoder_hidden_states: Optional[torch.FloatTensor] = None,
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encoder_attention_mask: Optional[torch.FloatTensor] = None,
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past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
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output_attentions: Optional[bool] = False,
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) -> Tuple[torch.Tensor]:
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mixed_query_layer = self.query(hidden_states)
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# If this is instantiated as a cross-attention module, the keys
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# and values come from an encoder; the attention mask needs to be
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# such that the encoder's padding tokens are not attended to.
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is_cross_attention = encoder_hidden_states is not None
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if is_cross_attention and past_key_value is not None:
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# reuse k,v, cross_attentions
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key_layer = past_key_value[0]
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value_layer = past_key_value[1]
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attention_mask = encoder_attention_mask
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elif is_cross_attention:
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key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
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value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
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attention_mask = encoder_attention_mask
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elif past_key_value is not None:
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key_layer = self.transpose_for_scores(self.key(hidden_states))
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value_layer = self.transpose_for_scores(self.value(hidden_states))
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key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
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value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
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else:
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key_layer = self.transpose_for_scores(self.key(hidden_states))
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value_layer = self.transpose_for_scores(self.value(hidden_states))
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query_layer = self.transpose_for_scores(mixed_query_layer)
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use_cache = past_key_value is not None
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if self.is_decoder:
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# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
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# Further calls to cross_attention layer can then reuse all cross-attention
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# key/value_states (first "if" case)
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# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
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# all previous decoder key/value_states. Further calls to uni-directional self-attention
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# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
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# if encoder bi-directional self-attention `past_key_value` is always `None`
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past_key_value = (key_layer, value_layer)
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# Take the dot product between "query" and "key" to get the raw attention scores.
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attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
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if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
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query_length, key_length = query_layer.shape[2], key_layer.shape[2]
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if use_cache:
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position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view(
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-1, 1
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)
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else:
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position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
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position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
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distance = position_ids_l - position_ids_r
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positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
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positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
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if self.position_embedding_type == "relative_key":
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relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
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attention_scores = attention_scores + relative_position_scores
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elif self.position_embedding_type == "relative_key_query":
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relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
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relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
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attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
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attention_scores = attention_scores / math.sqrt(self.attention_head_size)
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if attention_mask is not None:
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# Apply the attention mask is (precomputed for all layers in RealmModel forward() function)
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attention_scores = attention_scores + attention_mask
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# Normalize the attention scores to probabilities.
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attention_probs = nn.functional.softmax(attention_scores, dim=-1)
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# This is actually dropping out entire tokens to attend to, which might
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# seem a bit unusual, but is taken from the original Transformer paper.
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attention_probs = self.dropout(attention_probs)
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# Mask heads if we want to
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if head_mask is not None:
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attention_probs = attention_probs * head_mask
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context_layer = torch.matmul(attention_probs, value_layer)
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context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
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new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
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context_layer = context_layer.view(new_context_layer_shape)
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outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
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if self.is_decoder:
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outputs = outputs + (past_key_value,)
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return outputs
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# Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->Realm
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class RealmSelfOutput(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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hidden_states = self.LayerNorm(hidden_states + input_tensor)
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return hidden_states
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# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Realm
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class RealmAttention(nn.Module):
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def __init__(self, config, position_embedding_type=None):
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super().__init__()
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self.self = RealmSelfAttention(config, position_embedding_type=position_embedding_type)
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self.output = RealmSelfOutput(config)
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self.pruned_heads = set()
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def prune_heads(self, heads):
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if len(heads) == 0:
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return
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heads, index = find_pruneable_heads_and_indices(
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heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
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)
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# Prune linear layers
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self.self.query = prune_linear_layer(self.self.query, index)
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self.self.key = prune_linear_layer(self.self.key, index)
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self.self.value = prune_linear_layer(self.self.value, index)
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self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
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# Update hyper params and store pruned heads
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self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
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self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
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self.pruned_heads = self.pruned_heads.union(heads)
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.FloatTensor] = None,
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head_mask: Optional[torch.FloatTensor] = None,
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encoder_hidden_states: Optional[torch.FloatTensor] = None,
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encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
) -> Tuple[torch.Tensor]:
|
|
self_outputs = self.self(
|
|
hidden_states,
|
|
attention_mask,
|
|
head_mask,
|
|
encoder_hidden_states,
|
|
encoder_attention_mask,
|
|
past_key_value,
|
|
output_attentions,
|
|
)
|
|
attention_output = self.output(self_outputs[0], hidden_states)
|
|
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
|
|
return outputs
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->Realm
|
|
class RealmIntermediate(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
|
|
if isinstance(config.hidden_act, str):
|
|
self.intermediate_act_fn = ACT2FN[config.hidden_act]
|
|
else:
|
|
self.intermediate_act_fn = config.hidden_act
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.intermediate_act_fn(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->Realm
|
|
class RealmOutput(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
|
|
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
|
|
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.dropout(hidden_states)
|
|
hidden_states = self.LayerNorm(hidden_states + input_tensor)
|
|
return hidden_states
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Realm
|
|
class RealmLayer(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.chunk_size_feed_forward = config.chunk_size_feed_forward
|
|
self.seq_len_dim = 1
|
|
self.attention = RealmAttention(config)
|
|
self.is_decoder = config.is_decoder
|
|
self.add_cross_attention = config.add_cross_attention
|
|
if self.add_cross_attention:
|
|
if not self.is_decoder:
|
|
raise ValueError(f"{self} should be used as a decoder model if cross attention is added")
|
|
self.crossattention = RealmAttention(config, position_embedding_type="absolute")
|
|
self.intermediate = RealmIntermediate(config)
|
|
self.output = RealmOutput(config)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
) -> Tuple[torch.Tensor]:
|
|
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
|
|
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
|
|
self_attention_outputs = self.attention(
|
|
hidden_states,
|
|
attention_mask,
|
|
head_mask,
|
|
output_attentions=output_attentions,
|
|
past_key_value=self_attn_past_key_value,
|
|
)
|
|
attention_output = self_attention_outputs[0]
|
|
|
|
# if decoder, the last output is tuple of self-attn cache
|
|
if self.is_decoder:
|
|
outputs = self_attention_outputs[1:-1]
|
|
present_key_value = self_attention_outputs[-1]
|
|
else:
|
|
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
|
|
|
|
cross_attn_present_key_value = None
|
|
if self.is_decoder and encoder_hidden_states is not None:
|
|
if not hasattr(self, "crossattention"):
|
|
raise ValueError(
|
|
f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers"
|
|
" by setting `config.add_cross_attention=True`"
|
|
)
|
|
|
|
# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
|
|
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
|
|
cross_attention_outputs = self.crossattention(
|
|
attention_output,
|
|
attention_mask,
|
|
head_mask,
|
|
encoder_hidden_states,
|
|
encoder_attention_mask,
|
|
cross_attn_past_key_value,
|
|
output_attentions,
|
|
)
|
|
attention_output = cross_attention_outputs[0]
|
|
outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights
|
|
|
|
# add cross-attn cache to positions 3,4 of present_key_value tuple
|
|
cross_attn_present_key_value = cross_attention_outputs[-1]
|
|
present_key_value = present_key_value + cross_attn_present_key_value
|
|
|
|
layer_output = apply_chunking_to_forward(
|
|
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
|
|
)
|
|
outputs = (layer_output,) + outputs
|
|
|
|
# if decoder, return the attn key/values as the last output
|
|
if self.is_decoder:
|
|
outputs = outputs + (present_key_value,)
|
|
|
|
return outputs
|
|
|
|
def feed_forward_chunk(self, attention_output):
|
|
intermediate_output = self.intermediate(attention_output)
|
|
layer_output = self.output(intermediate_output, attention_output)
|
|
return layer_output
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Realm
|
|
class RealmEncoder(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.config = config
|
|
self.layer = nn.ModuleList([RealmLayer(config) for _ in range(config.num_hidden_layers)])
|
|
self.gradient_checkpointing = False
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
output_hidden_states: Optional[bool] = False,
|
|
return_dict: Optional[bool] = True,
|
|
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_self_attentions = () if output_attentions else None
|
|
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
if use_cache:
|
|
logger.warning_once(
|
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
|
)
|
|
use_cache = False
|
|
|
|
next_decoder_cache = () if use_cache else None
|
|
for i, layer_module in enumerate(self.layer):
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
layer_head_mask = head_mask[i] if head_mask is not None else None
|
|
past_key_value = past_key_values[i] if past_key_values is not None else None
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
layer_outputs = self._gradient_checkpointing_func(
|
|
layer_module.__call__,
|
|
hidden_states,
|
|
attention_mask,
|
|
layer_head_mask,
|
|
encoder_hidden_states,
|
|
encoder_attention_mask,
|
|
past_key_value,
|
|
output_attentions,
|
|
)
|
|
else:
|
|
layer_outputs = layer_module(
|
|
hidden_states,
|
|
attention_mask,
|
|
layer_head_mask,
|
|
encoder_hidden_states,
|
|
encoder_attention_mask,
|
|
past_key_value,
|
|
output_attentions,
|
|
)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
if use_cache:
|
|
next_decoder_cache += (layer_outputs[-1],)
|
|
if output_attentions:
|
|
all_self_attentions = all_self_attentions + (layer_outputs[1],)
|
|
if self.config.add_cross_attention:
|
|
all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
|
|
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
if not return_dict:
|
|
return tuple(
|
|
v
|
|
for v in [
|
|
hidden_states,
|
|
next_decoder_cache,
|
|
all_hidden_states,
|
|
all_self_attentions,
|
|
all_cross_attentions,
|
|
]
|
|
if v is not None
|
|
)
|
|
return BaseModelOutputWithPastAndCrossAttentions(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=next_decoder_cache,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attentions,
|
|
cross_attentions=all_cross_attentions,
|
|
)
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertPooler with Bert->Realm
|
|
class RealmPooler(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
self.activation = nn.Tanh()
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
# We "pool" the model by simply taking the hidden state corresponding
|
|
# to the first token.
|
|
first_token_tensor = hidden_states[:, 0]
|
|
pooled_output = self.dense(first_token_tensor)
|
|
pooled_output = self.activation(pooled_output)
|
|
return pooled_output
|
|
|
|
|
|
@dataclass
|
|
class RealmEmbedderOutput(ModelOutput):
|
|
"""
|
|
Outputs of [`RealmEmbedder`] models.
|
|
|
|
Args:
|
|
projected_score (`torch.FloatTensor` of shape `(batch_size, config.retriever_proj_size)`):
|
|
|
|
Projected score.
|
|
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.
|
|
"""
|
|
|
|
projected_score: torch.FloatTensor = None
|
|
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
|
|
attentions: Optional[Tuple[torch.FloatTensor]] = None
|
|
|
|
|
|
@dataclass
|
|
class RealmScorerOutput(ModelOutput):
|
|
"""
|
|
Outputs of [`RealmScorer`] models.
|
|
|
|
Args:
|
|
relevance_score (`torch.FloatTensor` of shape `(batch_size, config.num_candidates)`):
|
|
The relevance score of document candidates (before softmax).
|
|
query_score (`torch.FloatTensor` of shape `(batch_size, config.retriever_proj_size)`):
|
|
Query score derived from the query embedder.
|
|
candidate_score (`torch.FloatTensor` of shape `(batch_size, config.num_candidates, config.retriever_proj_size)`):
|
|
Candidate score derived from the embedder.
|
|
"""
|
|
|
|
relevance_score: torch.FloatTensor = None
|
|
query_score: torch.FloatTensor = None
|
|
candidate_score: torch.FloatTensor = None
|
|
|
|
|
|
@dataclass
|
|
class RealmReaderOutput(ModelOutput):
|
|
"""
|
|
Outputs of [`RealmReader`] models.
|
|
|
|
Args:
|
|
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `start_positions`, `end_positions`, `has_answers` are provided):
|
|
Total loss.
|
|
retriever_loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `start_positions`, `end_positions`, `has_answers` are provided):
|
|
Retriever loss.
|
|
reader_loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `start_positions`, `end_positions`, `has_answers` are provided):
|
|
Reader loss.
|
|
retriever_correct (`torch.BoolTensor` of shape `(config.searcher_beam_size,)`, *optional*):
|
|
Whether or not an evidence block contains answer.
|
|
reader_correct (`torch.BoolTensor` of shape `(config.reader_beam_size, num_candidates)`, *optional*):
|
|
Whether or not a span candidate contains answer.
|
|
block_idx (`torch.LongTensor` of shape `()`):
|
|
The index of the retrieved evidence block in which the predicted answer is most likely.
|
|
candidate (`torch.LongTensor` of shape `()`):
|
|
The index of the retrieved span candidates in which the predicted answer is most likely.
|
|
start_pos (`torch.IntTensor` of shape `()`):
|
|
Predicted answer starting position in *RealmReader*'s inputs.
|
|
end_pos (`torch.IntTensor` of shape `()`):
|
|
Predicted answer ending position in *RealmReader*'s inputs.
|
|
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: torch.FloatTensor = None
|
|
retriever_loss: torch.FloatTensor = None
|
|
reader_loss: torch.FloatTensor = None
|
|
retriever_correct: torch.BoolTensor = None
|
|
reader_correct: torch.BoolTensor = None
|
|
block_idx: torch.LongTensor = None
|
|
candidate: torch.LongTensor = None
|
|
start_pos: torch.int32 = None
|
|
end_pos: torch.int32 = None
|
|
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
|
|
attentions: Optional[Tuple[torch.FloatTensor]] = None
|
|
|
|
|
|
@dataclass
|
|
class RealmForOpenQAOutput(ModelOutput):
|
|
"""
|
|
|
|
Outputs of [`RealmForOpenQA`] models.
|
|
|
|
Args:
|
|
reader_output (`dict`):
|
|
Reader output.
|
|
predicted_answer_ids (`torch.LongTensor` of shape `(answer_sequence_length)`):
|
|
Predicted answer ids.
|
|
"""
|
|
|
|
reader_output: dict = None
|
|
predicted_answer_ids: torch.LongTensor = None
|
|
|
|
|
|
class RealmPredictionHeadTransform(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
if isinstance(config.hidden_act, str):
|
|
self.transform_act_fn = ACT2FN[config.hidden_act]
|
|
else:
|
|
self.transform_act_fn = config.hidden_act
|
|
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
|
|
def forward(self, hidden_states):
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.transform_act_fn(hidden_states)
|
|
hidden_states = self.LayerNorm(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class RealmLMPredictionHead(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.transform = RealmPredictionHeadTransform(config)
|
|
|
|
# The output weights are the same as the input embeddings, but there is
|
|
# an output-only bias for each token.
|
|
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
|
|
|
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
|
|
|
|
# Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
|
|
self.decoder.bias = self.bias
|
|
|
|
def forward(self, hidden_states):
|
|
hidden_states = self.transform(hidden_states)
|
|
hidden_states = self.decoder(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class RealmOnlyMLMHead(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.predictions = RealmLMPredictionHead(config)
|
|
|
|
def forward(self, sequence_output):
|
|
prediction_scores = self.predictions(sequence_output)
|
|
return prediction_scores
|
|
|
|
|
|
class RealmScorerProjection(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.predictions = RealmLMPredictionHead(config)
|
|
self.dense = nn.Linear(config.hidden_size, config.retriever_proj_size)
|
|
self.LayerNorm = nn.LayerNorm(config.retriever_proj_size, eps=config.layer_norm_eps)
|
|
|
|
def forward(self, hidden_states):
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.LayerNorm(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class RealmReaderProjection(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.config = config
|
|
self.dense_intermediate = nn.Linear(config.hidden_size, config.span_hidden_size * 2)
|
|
self.dense_output = nn.Linear(config.span_hidden_size, 1)
|
|
self.layer_normalization = nn.LayerNorm(config.span_hidden_size, eps=config.reader_layer_norm_eps)
|
|
self.relu = nn.ReLU()
|
|
|
|
def forward(self, hidden_states, block_mask):
|
|
def span_candidates(masks):
|
|
"""
|
|
Generate span candidates.
|
|
|
|
Args:
|
|
masks: <bool> [num_retrievals, max_sequence_len]
|
|
|
|
Returns:
|
|
starts: <int32> [num_spans] ends: <int32> [num_spans] span_masks: <int32> [num_retrievals, num_spans]
|
|
whether spans locate in evidence block.
|
|
"""
|
|
_, max_sequence_len = masks.shape
|
|
|
|
def _spans_given_width(width):
|
|
current_starts = torch.arange(max_sequence_len - width + 1, device=masks.device)
|
|
current_ends = torch.arange(width - 1, max_sequence_len, device=masks.device)
|
|
return current_starts, current_ends
|
|
|
|
starts, ends = zip(*(_spans_given_width(w + 1) for w in range(self.config.max_span_width)))
|
|
|
|
# [num_spans]
|
|
starts = torch.cat(starts, 0)
|
|
ends = torch.cat(ends, 0)
|
|
|
|
# [num_retrievals, num_spans]
|
|
start_masks = torch.index_select(masks, dim=-1, index=starts)
|
|
end_masks = torch.index_select(masks, dim=-1, index=ends)
|
|
span_masks = start_masks * end_masks
|
|
|
|
return starts, ends, span_masks
|
|
|
|
def mask_to_score(mask, dtype=torch.float32):
|
|
return (1.0 - mask.type(dtype)) * torch.finfo(dtype).min
|
|
|
|
# [reader_beam_size, max_sequence_len, span_hidden_size * 2]
|
|
hidden_states = self.dense_intermediate(hidden_states)
|
|
# [reader_beam_size, max_sequence_len, span_hidden_size]
|
|
start_projection, end_projection = hidden_states.chunk(2, dim=-1)
|
|
|
|
candidate_starts, candidate_ends, candidate_mask = span_candidates(block_mask)
|
|
|
|
candidate_start_projections = torch.index_select(start_projection, dim=1, index=candidate_starts)
|
|
candidate_end_projections = torch.index_select(end_projection, dim=1, index=candidate_ends)
|
|
candidate_hidden = candidate_start_projections + candidate_end_projections
|
|
|
|
# [reader_beam_size, num_candidates, span_hidden_size]
|
|
candidate_hidden = self.relu(candidate_hidden)
|
|
# [reader_beam_size, num_candidates, span_hidden_size]
|
|
candidate_hidden = self.layer_normalization(candidate_hidden)
|
|
# [reader_beam_size, num_candidates]
|
|
reader_logits = self.dense_output(candidate_hidden).squeeze(-1)
|
|
# [reader_beam_size, num_candidates]
|
|
reader_logits += mask_to_score(candidate_mask, dtype=reader_logits.dtype)
|
|
|
|
return reader_logits, candidate_starts, candidate_ends
|
|
|
|
|
|
REALM_START_DOCSTRING = r"""
|
|
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
|
|
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
|
|
behavior.
|
|
|
|
Parameters:
|
|
config ([`RealmConfig`]): 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.
|
|
"""
|
|
|
|
REALM_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)
|
|
position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
|
|
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
|
config.max_position_embeddings - 1]`.
|
|
|
|
[What are position IDs?](../glossary#position-ids)
|
|
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
|
|
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicates the head is **masked**.
|
|
|
|
inputs_embeds (`torch.FloatTensor` of shape `({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.
|
|
"""
|
|
|
|
|
|
class RealmPreTrainedModel(PreTrainedModel):
|
|
"""
|
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
|
models.
|
|
"""
|
|
|
|
config_class = RealmConfig
|
|
load_tf_weights = load_tf_weights_in_realm
|
|
base_model_prefix = "realm"
|
|
|
|
def _init_weights(self, module):
|
|
"""Initialize the weights"""
|
|
if isinstance(module, nn.Linear):
|
|
# Slightly different from the TF version which uses truncated_normal for initialization
|
|
# cf https://github.com/pytorch/pytorch/pull/5617
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
|
if module.bias is not None:
|
|
module.bias.data.zero_()
|
|
elif isinstance(module, nn.Embedding):
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
|
if module.padding_idx is not None:
|
|
module.weight.data[module.padding_idx].zero_()
|
|
elif isinstance(module, nn.LayerNorm):
|
|
module.bias.data.zero_()
|
|
module.weight.data.fill_(1.0)
|
|
|
|
def _flatten_inputs(self, *inputs):
|
|
"""Flatten inputs' shape to (-1, input_shape[-1])"""
|
|
flattened_inputs = []
|
|
for tensor in inputs:
|
|
if tensor is None:
|
|
flattened_inputs.append(None)
|
|
else:
|
|
input_shape = tensor.shape
|
|
if len(input_shape) > 2:
|
|
tensor = tensor.view((-1, input_shape[-1]))
|
|
flattened_inputs.append(tensor)
|
|
return flattened_inputs
|
|
|
|
|
|
class RealmBertModel(RealmPreTrainedModel):
|
|
"""
|
|
Same as the original BertModel but remove docstrings.
|
|
"""
|
|
|
|
def __init__(self, config, add_pooling_layer=True):
|
|
super().__init__(config)
|
|
self.config = config
|
|
|
|
self.embeddings = RealmEmbeddings(config)
|
|
self.encoder = RealmEncoder(config)
|
|
|
|
self.pooler = RealmPooler(config) if add_pooling_layer else None
|
|
|
|
# Weights initialization is mostly managed by other Realm models,
|
|
# but we also have them initialized here to keep a consistency.
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.embeddings.word_embeddings
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.embeddings.word_embeddings = value
|
|
|
|
def _prune_heads(self, heads_to_prune):
|
|
"""
|
|
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
|
|
class PreTrainedModel
|
|
"""
|
|
for layer, heads in heads_to_prune.items():
|
|
self.encoder.layer[layer].attention.prune_heads(heads)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids=None,
|
|
attention_mask=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
head_mask=None,
|
|
inputs_embeds=None,
|
|
encoder_hidden_states=None,
|
|
encoder_attention_mask=None,
|
|
past_key_values=None,
|
|
use_cache=None,
|
|
output_attentions=None,
|
|
output_hidden_states=None,
|
|
return_dict=None,
|
|
):
|
|
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 self.config.is_decoder:
|
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
|
else:
|
|
use_cache = False
|
|
|
|
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")
|
|
|
|
batch_size, seq_length = input_shape
|
|
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
|
|
|
# past_key_values_length
|
|
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
|
|
|
|
if attention_mask is None:
|
|
attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
|
|
|
|
if token_type_ids is None:
|
|
if hasattr(self.embeddings, "token_type_ids"):
|
|
buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
|
|
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
|
|
token_type_ids = buffered_token_type_ids_expanded
|
|
else:
|
|
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
|
|
|
|
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
|
|
# ourselves in which case we just need to make it broadcastable to all heads.
|
|
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
|
|
|
|
# If a 2D or 3D attention mask is provided for the cross-attention
|
|
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
|
|
if self.config.is_decoder and encoder_hidden_states is not None:
|
|
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
|
|
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
|
|
if encoder_attention_mask is None:
|
|
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
|
|
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
|
|
else:
|
|
encoder_extended_attention_mask = None
|
|
|
|
# Prepare head mask if needed
|
|
# 1.0 in head_mask indicate we keep the head
|
|
# attention_probs has shape bsz x n_heads x N x N
|
|
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
|
|
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
|
|
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
|
|
|
|
embedding_output = self.embeddings(
|
|
input_ids=input_ids,
|
|
position_ids=position_ids,
|
|
token_type_ids=token_type_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
past_key_values_length=past_key_values_length,
|
|
)
|
|
encoder_outputs = self.encoder(
|
|
embedding_output,
|
|
attention_mask=extended_attention_mask,
|
|
head_mask=head_mask,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
encoder_attention_mask=encoder_extended_attention_mask,
|
|
past_key_values=past_key_values,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
sequence_output = encoder_outputs[0]
|
|
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
|
|
|
|
if not return_dict:
|
|
return (sequence_output, pooled_output) + encoder_outputs[1:]
|
|
|
|
return BaseModelOutputWithPoolingAndCrossAttentions(
|
|
last_hidden_state=sequence_output,
|
|
pooler_output=pooled_output,
|
|
past_key_values=encoder_outputs.past_key_values,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
cross_attentions=encoder_outputs.cross_attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"The embedder of REALM outputting projected score that will be used to calculate relevance score.",
|
|
REALM_START_DOCSTRING,
|
|
)
|
|
class RealmEmbedder(RealmPreTrainedModel):
|
|
_tied_weights_keys = ["cls.predictions.decoder.bias"]
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.realm = RealmBertModel(self.config)
|
|
self.cls = RealmScorerProjection(self.config)
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.realm.embeddings.word_embeddings
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.realm.embeddings.word_embeddings = value
|
|
|
|
@add_start_docstrings_to_model_forward(REALM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@replace_return_docstrings(output_type=RealmEmbedderOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, RealmEmbedderOutput]:
|
|
r"""
|
|
Returns:
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, RealmEmbedder
|
|
>>> import torch
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("google/realm-cc-news-pretrained-embedder")
|
|
>>> model = RealmEmbedder.from_pretrained("google/realm-cc-news-pretrained-embedder")
|
|
|
|
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
|
|
>>> outputs = model(**inputs)
|
|
|
|
>>> projected_score = outputs.projected_score
|
|
```
|
|
"""
|
|
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
realm_outputs = self.realm(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
# [batch_size, hidden_size]
|
|
pooler_output = realm_outputs[1]
|
|
# [batch_size, retriever_proj_size]
|
|
projected_score = self.cls(pooler_output)
|
|
|
|
if not return_dict:
|
|
return (projected_score,) + realm_outputs[2:4]
|
|
else:
|
|
return RealmEmbedderOutput(
|
|
projected_score=projected_score,
|
|
hidden_states=realm_outputs.hidden_states,
|
|
attentions=realm_outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"The scorer of REALM outputting relevance scores representing the score of document candidates (before softmax).",
|
|
REALM_START_DOCSTRING,
|
|
)
|
|
class RealmScorer(RealmPreTrainedModel):
|
|
r"""
|
|
Args:
|
|
query_embedder ([`RealmEmbedder`]):
|
|
Embedder for input sequences. If not specified, it will use the same embedder as candidate sequences.
|
|
"""
|
|
|
|
def __init__(self, config, query_embedder=None):
|
|
super().__init__(config)
|
|
|
|
self.embedder = RealmEmbedder(self.config)
|
|
|
|
self.query_embedder = query_embedder if query_embedder is not None else self.embedder
|
|
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(REALM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@replace_return_docstrings(output_type=RealmScorerOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
candidate_input_ids: Optional[torch.LongTensor] = None,
|
|
candidate_attention_mask: Optional[torch.FloatTensor] = None,
|
|
candidate_token_type_ids: Optional[torch.LongTensor] = None,
|
|
candidate_inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, RealmScorerOutput]:
|
|
r"""
|
|
candidate_input_ids (`torch.LongTensor` of shape `(batch_size, num_candidates, sequence_length)`):
|
|
Indices of candidate 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)
|
|
candidate_attention_mask (`torch.FloatTensor` of shape `(batch_size, num_candidates, sequence_length)`, *optional*):
|
|
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
[What are attention masks?](../glossary#attention-mask)
|
|
candidate_token_type_ids (`torch.LongTensor` of shape `(batch_size, num_candidates, sequence_length)`, *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)
|
|
candidate_inputs_embeds (`torch.FloatTensor` of shape `(batch_size * num_candidates, sequence_length, hidden_size)`, *optional*):
|
|
Optionally, instead of passing `candidate_input_ids` you can choose to directly pass an embedded
|
|
representation. This is useful if you want more control over how to convert *candidate_input_ids* indices
|
|
into associated vectors than the model's internal embedding lookup matrix.
|
|
|
|
Returns:
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> import torch
|
|
>>> from transformers import AutoTokenizer, RealmScorer
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("google/realm-cc-news-pretrained-scorer")
|
|
>>> model = RealmScorer.from_pretrained("google/realm-cc-news-pretrained-scorer", num_candidates=2)
|
|
|
|
>>> # batch_size = 2, num_candidates = 2
|
|
>>> input_texts = ["How are you?", "What is the item in the picture?"]
|
|
>>> candidates_texts = [["Hello world!", "Nice to meet you!"], ["A cute cat.", "An adorable dog."]]
|
|
|
|
>>> inputs = tokenizer(input_texts, return_tensors="pt")
|
|
>>> candidates_inputs = tokenizer.batch_encode_candidates(candidates_texts, max_length=10, return_tensors="pt")
|
|
|
|
>>> outputs = model(
|
|
... **inputs,
|
|
... candidate_input_ids=candidates_inputs.input_ids,
|
|
... candidate_attention_mask=candidates_inputs.attention_mask,
|
|
... candidate_token_type_ids=candidates_inputs.token_type_ids,
|
|
... )
|
|
>>> relevance_score = outputs.relevance_score
|
|
```"""
|
|
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if input_ids is None and inputs_embeds is None:
|
|
raise ValueError("You have to specify either input_ids or input_embeds.")
|
|
|
|
if candidate_input_ids is None and candidate_inputs_embeds is None:
|
|
raise ValueError("You have to specify either candidate_input_ids or candidate_inputs_embeds.")
|
|
|
|
query_outputs = self.query_embedder(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
# [batch_size * num_candidates, candidate_seq_len]
|
|
(flattened_input_ids, flattened_attention_mask, flattened_token_type_ids) = self._flatten_inputs(
|
|
candidate_input_ids, candidate_attention_mask, candidate_token_type_ids
|
|
)
|
|
|
|
candidate_outputs = self.embedder(
|
|
flattened_input_ids,
|
|
attention_mask=flattened_attention_mask,
|
|
token_type_ids=flattened_token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=candidate_inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
# [batch_size, retriever_proj_size]
|
|
query_score = query_outputs[0]
|
|
# [batch_size * num_candidates, retriever_proj_size]
|
|
candidate_score = candidate_outputs[0]
|
|
# [batch_size, num_candidates, retriever_proj_size]
|
|
candidate_score = candidate_score.view(-1, self.config.num_candidates, self.config.retriever_proj_size)
|
|
# [batch_size, num_candidates]
|
|
relevance_score = torch.einsum("bd,bnd->bn", query_score, candidate_score)
|
|
|
|
if not return_dict:
|
|
return relevance_score, query_score, candidate_score
|
|
|
|
return RealmScorerOutput(
|
|
relevance_score=relevance_score, query_score=query_score, candidate_score=candidate_score
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"The knowledge-augmented encoder of REALM outputting masked language model logits and marginal log-likelihood"
|
|
" loss.",
|
|
REALM_START_DOCSTRING,
|
|
)
|
|
class RealmKnowledgeAugEncoder(RealmPreTrainedModel):
|
|
_tied_weights_keys = ["cls.predictions.decoder"]
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.realm = RealmBertModel(self.config)
|
|
self.cls = RealmOnlyMLMHead(self.config)
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.realm.embeddings.word_embeddings
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.realm.embeddings.word_embeddings = value
|
|
|
|
def get_output_embeddings(self):
|
|
return self.cls.predictions.decoder
|
|
|
|
def set_output_embeddings(self, new_embeddings):
|
|
self.cls.predictions.decoder = new_embeddings
|
|
|
|
@add_start_docstrings_to_model_forward(
|
|
REALM_INPUTS_DOCSTRING.format("batch_size, num_candidates, sequence_length")
|
|
)
|
|
@replace_return_docstrings(output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
relevance_score: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
mlm_mask: Optional[torch.LongTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, MaskedLMOutput]:
|
|
r"""
|
|
relevance_score (`torch.FloatTensor` of shape `(batch_size, num_candidates)`, *optional*):
|
|
Relevance score derived from RealmScorer, must be specified if you want to compute the masked language
|
|
modeling loss.
|
|
|
|
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]`
|
|
|
|
mlm_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Mask to avoid calculating joint loss on certain positions. If not specified, the loss will not be masked.
|
|
Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
Returns:
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> import torch
|
|
>>> from transformers import AutoTokenizer, RealmKnowledgeAugEncoder
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("google/realm-cc-news-pretrained-encoder")
|
|
>>> model = RealmKnowledgeAugEncoder.from_pretrained(
|
|
... "google/realm-cc-news-pretrained-encoder", num_candidates=2
|
|
... )
|
|
|
|
>>> # batch_size = 2, num_candidates = 2
|
|
>>> text = [["Hello world!", "Nice to meet you!"], ["The cute cat.", "The adorable dog."]]
|
|
|
|
>>> inputs = tokenizer.batch_encode_candidates(text, max_length=10, return_tensors="pt")
|
|
>>> outputs = model(**inputs)
|
|
>>> logits = outputs.logits
|
|
```"""
|
|
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
(flattened_input_ids, flattened_attention_mask, flattened_token_type_ids) = self._flatten_inputs(
|
|
input_ids, attention_mask, token_type_ids
|
|
)
|
|
|
|
joint_outputs = self.realm(
|
|
flattened_input_ids,
|
|
attention_mask=flattened_attention_mask,
|
|
token_type_ids=flattened_token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
# [batch_size * num_candidates, joint_seq_len, hidden_size]
|
|
joint_output = joint_outputs[0]
|
|
# [batch_size * num_candidates, joint_seq_len, vocab_size]
|
|
prediction_scores = self.cls(joint_output)
|
|
# [batch_size, num_candidates]
|
|
candidate_score = relevance_score
|
|
|
|
masked_lm_loss = None
|
|
if labels is not None:
|
|
if candidate_score is None:
|
|
raise ValueError(
|
|
"You have to specify `relevance_score` when `labels` is specified in order to compute loss."
|
|
)
|
|
|
|
batch_size, seq_length = labels.size()
|
|
|
|
if mlm_mask is None:
|
|
mlm_mask = torch.ones_like(labels, dtype=torch.float32)
|
|
else:
|
|
mlm_mask = mlm_mask.type(torch.float32)
|
|
|
|
# Compute marginal log-likelihood
|
|
loss_fct = CrossEntropyLoss(reduction="none") # -100 index = padding token
|
|
|
|
# [batch_size * num_candidates * joint_seq_len, vocab_size]
|
|
mlm_logits = prediction_scores.view(-1, self.config.vocab_size)
|
|
# [batch_size * num_candidates * joint_seq_len]
|
|
mlm_targets = labels.tile(1, self.config.num_candidates).view(-1)
|
|
# [batch_size, num_candidates, joint_seq_len]
|
|
masked_lm_log_prob = -loss_fct(mlm_logits, mlm_targets).view(
|
|
batch_size, self.config.num_candidates, seq_length
|
|
)
|
|
# [batch_size, num_candidates, 1]
|
|
candidate_log_prob = candidate_score.log_softmax(-1).unsqueeze(-1)
|
|
# [batch_size, num_candidates, joint_seq_len]
|
|
joint_gold_log_prob = candidate_log_prob + masked_lm_log_prob
|
|
# [batch_size, joint_seq_len]
|
|
marginal_gold_log_probs = joint_gold_log_prob.logsumexp(1)
|
|
# []
|
|
masked_lm_loss = -torch.nansum(torch.sum(marginal_gold_log_probs * mlm_mask) / torch.sum(mlm_mask))
|
|
|
|
if not return_dict:
|
|
output = (prediction_scores,) + joint_outputs[2:4]
|
|
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
|
|
|
|
return MaskedLMOutput(
|
|
loss=masked_lm_loss,
|
|
logits=prediction_scores,
|
|
hidden_states=joint_outputs.hidden_states,
|
|
attentions=joint_outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings("The reader of REALM.", REALM_START_DOCSTRING)
|
|
class RealmReader(RealmPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.realm = RealmBertModel(config)
|
|
self.cls = RealmOnlyMLMHead(config)
|
|
self.qa_outputs = RealmReaderProjection(config)
|
|
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(REALM_INPUTS_DOCSTRING.format("reader_beam_size, sequence_length"))
|
|
@replace_return_docstrings(output_type=RealmReaderOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
relevance_score: Optional[torch.FloatTensor] = None,
|
|
block_mask: Optional[torch.BoolTensor] = None,
|
|
start_positions: Optional[torch.LongTensor] = None,
|
|
end_positions: Optional[torch.LongTensor] = None,
|
|
has_answers: Optional[torch.BoolTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, RealmReaderOutput]:
|
|
r"""
|
|
relevance_score (`torch.FloatTensor` of shape `(searcher_beam_size,)`, *optional*):
|
|
Relevance score, which must be specified if you want to compute the logits and marginal log loss.
|
|
block_mask (`torch.BoolTensor` of shape `(searcher_beam_size, sequence_length)`, *optional*):
|
|
The mask of the evidence block, which must be specified if you want to compute the logits and marginal log
|
|
loss.
|
|
start_positions (`torch.LongTensor` of shape `(searcher_beam_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 `(searcher_beam_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.
|
|
has_answers (`torch.BoolTensor` of shape `(searcher_beam_size,)`, *optional*):
|
|
Whether or not the evidence block has answer(s).
|
|
|
|
Returns:
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if relevance_score is None:
|
|
raise ValueError("You have to specify `relevance_score` to calculate logits and loss.")
|
|
if block_mask is None:
|
|
raise ValueError("You have to specify `block_mask` to separate question block and evidence block.")
|
|
if token_type_ids.size(1) < self.config.max_span_width:
|
|
raise ValueError("The input sequence length must be greater than or equal to config.max_span_width.")
|
|
outputs = self.realm(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
# [reader_beam_size, joint_seq_len, hidden_size]
|
|
sequence_output = outputs[0]
|
|
|
|
# [reader_beam_size, num_candidates], [num_candidates], [num_candidates]
|
|
reader_logits, candidate_starts, candidate_ends = self.qa_outputs(
|
|
sequence_output, block_mask[0 : self.config.reader_beam_size]
|
|
)
|
|
# [searcher_beam_size, 1]
|
|
retriever_logits = torch.unsqueeze(relevance_score[0 : self.config.reader_beam_size], -1)
|
|
# [reader_beam_size, num_candidates]
|
|
reader_logits += retriever_logits
|
|
# []
|
|
predicted_block_index = torch.argmax(torch.max(reader_logits, dim=1).values)
|
|
# []
|
|
predicted_candidate = torch.argmax(torch.max(reader_logits, dim=0).values)
|
|
# [1]
|
|
predicted_start = torch.index_select(candidate_starts, dim=0, index=predicted_candidate)
|
|
# [1]
|
|
predicted_end = torch.index_select(candidate_ends, dim=0, index=predicted_candidate)
|
|
|
|
total_loss = None
|
|
retriever_loss = None
|
|
reader_loss = None
|
|
retriever_correct = None
|
|
reader_correct = None
|
|
if start_positions is not None and end_positions is not None and has_answers is not None:
|
|
|
|
def compute_correct_candidates(candidate_starts, candidate_ends, gold_starts, gold_ends):
|
|
"""Compute correct span."""
|
|
# [reader_beam_size, num_answers, num_candidates]
|
|
is_gold_start = torch.eq(
|
|
torch.unsqueeze(torch.unsqueeze(candidate_starts, 0), 0), torch.unsqueeze(gold_starts, -1)
|
|
)
|
|
is_gold_end = torch.eq(
|
|
torch.unsqueeze(torch.unsqueeze(candidate_ends, 0), 0), torch.unsqueeze(gold_ends, -1)
|
|
)
|
|
|
|
# [reader_beam_size, num_candidates]
|
|
return torch.any(torch.logical_and(is_gold_start, is_gold_end), 1)
|
|
|
|
def marginal_log_loss(logits, is_correct):
|
|
"""Loss based on the negative marginal log-likelihood."""
|
|
|
|
def mask_to_score(mask, dtype=torch.float32):
|
|
return (1.0 - mask.type(dtype)) * torch.finfo(dtype).min
|
|
|
|
# []
|
|
log_numerator = torch.logsumexp(logits + mask_to_score(is_correct, dtype=logits.dtype), dim=-1)
|
|
log_denominator = torch.logsumexp(logits, dim=-1)
|
|
return log_denominator - log_numerator
|
|
|
|
# sometimes the start/end positions are outside our model inputs, we ignore these terms
|
|
# `-1` is reserved for no answer.
|
|
ignored_index = sequence_output.size(1)
|
|
start_positions = start_positions.clamp(-1, ignored_index)
|
|
end_positions = end_positions.clamp(-1, ignored_index)
|
|
|
|
retriever_correct = has_answers
|
|
any_retriever_correct = torch.any(retriever_correct)
|
|
|
|
reader_correct = compute_correct_candidates(
|
|
candidate_starts=candidate_starts,
|
|
candidate_ends=candidate_ends,
|
|
gold_starts=start_positions[0 : self.config.reader_beam_size],
|
|
gold_ends=end_positions[0 : self.config.reader_beam_size],
|
|
)
|
|
any_reader_correct = torch.any(reader_correct)
|
|
|
|
retriever_loss = marginal_log_loss(relevance_score, retriever_correct)
|
|
reader_loss = marginal_log_loss(reader_logits.view(-1), reader_correct.view(-1))
|
|
retriever_loss *= any_retriever_correct.type(torch.float32)
|
|
reader_loss *= any_reader_correct.type(torch.float32)
|
|
|
|
total_loss = (retriever_loss + reader_loss).mean()
|
|
|
|
if not return_dict:
|
|
output = (predicted_block_index, predicted_candidate, predicted_start, predicted_end) + outputs[2:]
|
|
return (
|
|
((total_loss, retriever_loss, reader_loss, retriever_correct, reader_correct) + output)
|
|
if total_loss is not None
|
|
else output
|
|
)
|
|
|
|
return RealmReaderOutput(
|
|
loss=total_loss,
|
|
retriever_loss=retriever_loss,
|
|
reader_loss=reader_loss,
|
|
retriever_correct=retriever_correct,
|
|
reader_correct=reader_correct,
|
|
block_idx=predicted_block_index,
|
|
candidate=predicted_candidate,
|
|
start_pos=predicted_start,
|
|
end_pos=predicted_end,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
REALM_FOR_OPEN_QA_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 (should not be used in this model by design).
|
|
|
|
[What are token type IDs?](../glossary#token-type-ids)
|
|
answer_ids (`list` of shape `(num_answers, answer_length)`, *optional*):
|
|
Answer ids for computing the marginal log-likelihood loss. Indices should be in `[-1, 0, ...,
|
|
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-1` are ignored (masked), the
|
|
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
|
|
return_dict (`bool`, *optional*):
|
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
|
"""
|
|
|
|
|
|
@add_start_docstrings(
|
|
"`RealmForOpenQA` for end-to-end open domain question answering.",
|
|
REALM_START_DOCSTRING,
|
|
)
|
|
class RealmForOpenQA(RealmPreTrainedModel):
|
|
def __init__(self, config, retriever=None):
|
|
super().__init__(config)
|
|
self.embedder = RealmEmbedder(config)
|
|
self.reader = RealmReader(config)
|
|
self.register_buffer(
|
|
"block_emb",
|
|
torch.zeros(()).new_empty(
|
|
size=(config.num_block_records, config.retriever_proj_size),
|
|
dtype=torch.float32,
|
|
device=torch.device("cpu"),
|
|
),
|
|
)
|
|
self.retriever = retriever
|
|
|
|
self.post_init()
|
|
|
|
@property
|
|
def searcher_beam_size(self):
|
|
if self.training:
|
|
return self.config.searcher_beam_size
|
|
return self.config.reader_beam_size
|
|
|
|
def block_embedding_to(self, device):
|
|
"""Send `self.block_emb` to a specific device.
|
|
|
|
Args:
|
|
device (`str` or `torch.device`):
|
|
The device to which `self.block_emb` will be sent.
|
|
"""
|
|
|
|
self.block_emb = self.block_emb.to(device)
|
|
|
|
@add_start_docstrings_to_model_forward(REALM_FOR_OPEN_QA_DOCSTRING.format("1, sequence_length"))
|
|
@replace_return_docstrings(output_type=RealmForOpenQAOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor],
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
answer_ids: Optional[torch.LongTensor] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, RealmForOpenQAOutput]:
|
|
r"""
|
|
Returns:
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> import torch
|
|
>>> from transformers import RealmForOpenQA, RealmRetriever, AutoTokenizer
|
|
|
|
>>> retriever = RealmRetriever.from_pretrained("google/realm-orqa-nq-openqa")
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("google/realm-orqa-nq-openqa")
|
|
>>> model = RealmForOpenQA.from_pretrained("google/realm-orqa-nq-openqa", retriever=retriever)
|
|
|
|
>>> question = "Who is the pioneer in modern computer science?"
|
|
>>> question_ids = tokenizer([question], return_tensors="pt")
|
|
>>> answer_ids = tokenizer(
|
|
... ["alan mathison turing"],
|
|
... add_special_tokens=False,
|
|
... return_token_type_ids=False,
|
|
... return_attention_mask=False,
|
|
... ).input_ids
|
|
|
|
>>> reader_output, predicted_answer_ids = model(**question_ids, answer_ids=answer_ids, return_dict=False)
|
|
>>> predicted_answer = tokenizer.decode(predicted_answer_ids)
|
|
>>> loss = reader_output.loss
|
|
```"""
|
|
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if input_ids is not None and input_ids.shape[0] != 1:
|
|
raise ValueError("The batch_size of the inputs must be 1.")
|
|
|
|
question_outputs = self.embedder(
|
|
input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask, return_dict=True
|
|
)
|
|
# [1, projection_size]
|
|
question_projection = question_outputs[0]
|
|
|
|
# CPU computation starts.
|
|
# [1, block_emb_size]
|
|
batch_scores = torch.einsum("BD,QD->QB", self.block_emb, question_projection.to(self.block_emb.device))
|
|
# [1, searcher_beam_size]
|
|
_, retrieved_block_ids = torch.topk(batch_scores, k=self.searcher_beam_size, dim=-1)
|
|
# [searcher_beam_size]
|
|
retrieved_block_ids = retrieved_block_ids.squeeze()
|
|
# [searcher_beam_size, projection_size]
|
|
retrieved_block_emb = torch.index_select(self.block_emb, dim=0, index=retrieved_block_ids)
|
|
# CPU computation ends.
|
|
|
|
# Retrieve possible answers
|
|
has_answers, start_pos, end_pos, concat_inputs = self.retriever(
|
|
retrieved_block_ids.cpu(), input_ids, answer_ids, max_length=self.config.reader_seq_len
|
|
)
|
|
|
|
concat_inputs = concat_inputs.to(self.reader.device)
|
|
block_mask = concat_inputs.special_tokens_mask.type(torch.bool).to(device=self.reader.device)
|
|
block_mask.logical_not_().logical_and_(concat_inputs.token_type_ids.type(torch.bool))
|
|
|
|
if has_answers is not None:
|
|
has_answers = torch.tensor(has_answers, dtype=torch.bool, device=self.reader.device)
|
|
start_pos = torch.tensor(start_pos, dtype=torch.long, device=self.reader.device)
|
|
end_pos = torch.tensor(end_pos, dtype=torch.long, device=self.reader.device)
|
|
|
|
# [searcher_beam_size]
|
|
retrieved_logits = torch.einsum(
|
|
"D,BD->B", question_projection.squeeze(), retrieved_block_emb.to(self.reader.device)
|
|
)
|
|
|
|
reader_output = self.reader(
|
|
input_ids=concat_inputs.input_ids[0 : self.config.reader_beam_size],
|
|
attention_mask=concat_inputs.attention_mask[0 : self.config.reader_beam_size],
|
|
token_type_ids=concat_inputs.token_type_ids[0 : self.config.reader_beam_size],
|
|
relevance_score=retrieved_logits,
|
|
block_mask=block_mask,
|
|
has_answers=has_answers,
|
|
start_positions=start_pos,
|
|
end_positions=end_pos,
|
|
return_dict=True,
|
|
)
|
|
|
|
predicted_block = concat_inputs.input_ids[reader_output.block_idx]
|
|
predicted_answer_ids = predicted_block[reader_output.start_pos : reader_output.end_pos + 1]
|
|
|
|
if not return_dict:
|
|
return reader_output, predicted_answer_ids
|
|
|
|
return RealmForOpenQAOutput(
|
|
reader_output=reader_output,
|
|
predicted_answer_ids=predicted_answer_ids,
|
|
)
|