170 lines
7.5 KiB
Python
170 lines
7.5 KiB
Python
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# 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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""" REALM model configuration."""
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from ...configuration_utils import PretrainedConfig
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from ...utils import logging
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logger = logging.get_logger(__name__)
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from ..deprecated._archive_maps import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class RealmConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of
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1. [`RealmEmbedder`]
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2. [`RealmScorer`]
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3. [`RealmKnowledgeAugEncoder`]
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4. [`RealmRetriever`]
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5. [`RealmReader`]
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6. [`RealmForOpenQA`]
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It is used to instantiate an REALM model according to the specified arguments, defining the model architecture.
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Instantiating a configuration with the defaults will yield a similar configuration to that of the REALM
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[google/realm-cc-news-pretrained-embedder](https://huggingface.co/google/realm-cc-news-pretrained-embedder)
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architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 30522):
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Vocabulary size of the REALM model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`RealmEmbedder`], [`RealmScorer`], [`RealmKnowledgeAugEncoder`], or
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[`RealmReader`].
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hidden_size (`int`, *optional*, defaults to 768):
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Dimension of the encoder layers and the pooler layer.
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retriever_proj_size (`int`, *optional*, defaults to 128):
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Dimension of the retriever(embedder) projection.
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num_hidden_layers (`int`, *optional*, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 12):
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Number of attention heads for each attention layer in the Transformer encoder.
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num_candidates (`int`, *optional*, defaults to 8):
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Number of candidates inputted to the RealmScorer or RealmKnowledgeAugEncoder.
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intermediate_size (`int`, *optional*, defaults to 3072):
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Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu_new"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"selu"` and `"gelu_new"` are supported.
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hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the attention probabilities.
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max_position_embeddings (`int`, *optional*, defaults to 512):
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The maximum sequence length that this model might ever be used with. Typically set this to something large
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just in case (e.g., 512 or 1024 or 2048).
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type_vocab_size (`int`, *optional*, defaults to 2):
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The vocabulary size of the `token_type_ids` passed when calling [`RealmEmbedder`], [`RealmScorer`],
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[`RealmKnowledgeAugEncoder`], or [`RealmReader`].
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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layer_norm_eps (`float`, *optional*, defaults to 1e-12):
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The epsilon used by the layer normalization layers.
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span_hidden_size (`int`, *optional*, defaults to 256):
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Dimension of the reader's spans.
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max_span_width (`int`, *optional*, defaults to 10):
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Max span width of the reader.
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reader_layer_norm_eps (`float`, *optional*, defaults to 1e-3):
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The epsilon used by the reader's layer normalization layers.
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reader_beam_size (`int`, *optional*, defaults to 5):
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Beam size of the reader.
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reader_seq_len (`int`, *optional*, defaults to 288+32):
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Maximum sequence length of the reader.
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num_block_records (`int`, *optional*, defaults to 13353718):
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Number of block records.
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searcher_beam_size (`int`, *optional*, defaults to 5000):
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Beam size of the searcher. Note that when eval mode is enabled, *searcher_beam_size* will be the same as
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*reader_beam_size*.
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Example:
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```python
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>>> from transformers import RealmConfig, RealmEmbedder
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>>> # Initializing a REALM realm-cc-news-pretrained-* style configuration
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>>> configuration = RealmConfig()
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>>> # Initializing a model (with random weights) from the google/realm-cc-news-pretrained-embedder style configuration
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>>> model = RealmEmbedder(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "realm"
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def __init__(
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self,
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vocab_size=30522,
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hidden_size=768,
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retriever_proj_size=128,
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num_hidden_layers=12,
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num_attention_heads=12,
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num_candidates=8,
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intermediate_size=3072,
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hidden_act="gelu_new",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=2,
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initializer_range=0.02,
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layer_norm_eps=1e-12,
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span_hidden_size=256,
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max_span_width=10,
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reader_layer_norm_eps=1e-3,
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reader_beam_size=5,
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reader_seq_len=320, # 288 + 32
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num_block_records=13353718,
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searcher_beam_size=5000,
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pad_token_id=1,
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bos_token_id=0,
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eos_token_id=2,
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**kwargs,
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):
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super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
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# Common config
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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self.retriever_proj_size = retriever_proj_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.num_candidates = num_candidates
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.initializer_range = initializer_range
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self.type_vocab_size = type_vocab_size
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self.layer_norm_eps = layer_norm_eps
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# Reader config
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self.span_hidden_size = span_hidden_size
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self.max_span_width = max_span_width
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self.reader_layer_norm_eps = reader_layer_norm_eps
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self.reader_beam_size = reader_beam_size
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self.reader_seq_len = reader_seq_len
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# Retrieval config
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self.num_block_records = num_block_records
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self.searcher_beam_size = searcher_beam_size
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