configuration_reformer.py

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""" Reformer model configuration"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)


class ReformerConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`ReformerModel`]. It is used to instantiate a
    Reformer model according to the specified arguments, defining the model architecture. Instantiating a configuration
    with the defaults will yield a similar configuration to that of the ReFormer
    [google/reformer-crime-and-punishment](https://huggingface.co/google/reformer-crime-and-punishment) architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        attention_head_size (`int`, *optional*, defaults to 64):
            Dimensionality of the projected key, query and value vectors
        attn_layers (`List[str]`, *optional*, defaults to `["local", "lsh", "local", "lsh", "local", "lsh"]`):
            List of attention layer types in ascending order. It can be chosen between a LSHSelfAttention layer
            (`"lsh"`) and a LocalSelfAttention layer (`"local"`).

            For more information on LSHSelfAttention layer, see [LSH Self Attention](reformer#lsh-self-attention). For
            more information on LocalSelfAttention layer, see [Local Self Attention](reformer#local-self-attention).
        axial_pos_embds (`bool`, *optional*, defaults to `True`):
            Whether or not to use axial position embeddings. For more information on how axial position embeddings
            work, see [Axial Position Encodings](reformer#axial-positional-encodings).
        axial_norm_std (`float`, *optional*, defaults to 1.0):
            The standard deviation of the normal_initializer for initializing the weight matrices of the axial
            positional encodings.
        axial_pos_shape (`List[int]`, *optional*, defaults to `[64, 64]`):
            The position dims of the axial position encodings. During training, the product of the position dims has to
            be equal to the sequence length.

            For more information on how axial position embeddings work, see [Axial Position
            Encodings](reformer#axial-positional-encodings).
        axial_pos_embds_dim (`List[int]`, *optional*, defaults to `[64, 192]`):
            The embedding dims of the axial position encodings. The sum of the embedding dims has to be equal to the
            hidden size.

            For more information on how axial position embeddings work, see [Axial Position
            Encodings](reformer#axial-positional-encodings).
        chunk_size_lm_head (`int`, *optional*, defaults to 0):
            The chunk size of the final language model feed forward head layer. A chunk size of 0 means that the feed
            forward layer is not chunked. A chunk size of n means that the feed forward layer processes n <
            sequence_length embeddings at a time.

            For more information on feed forward chunking, see [How does Feed Forward Chunking
            work?](../glossary#feed-forward-chunking).
        eos_token_id (`int`, *optional*, defaults to 2):
            The token id for the end-of-sentence token.
        feed_forward_size (`int`, *optional*, defaults to 512):
            Dimensionality of the feed_forward layer in the residual attention block.
        hash_seed (`int`, *optional*):
            Seed that can be used to make local sensitive hashing in `LSHSelfAttention` deterministic. This should only
            be set for testing purposed. For evaluation and training purposes `hash_seed` should be left as `None` to
            ensure fully random rotations in local sensitive hashing scheme.
        hidden_act (`str` or `Callable`, *optional*, defaults to `"relu"`):
            The non-linear activation function (function or string) in the feed forward layer in the residual attention
            block. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported.
        hidden_dropout_prob (`float`, *optional*, defaults to 0.05):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        hidden_size (`int`, *optional*, defaults to 256):
            Dimensionality of the output hidden states of the residual attention blocks.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        is_decoder (`bool`, *optional*, defaults to `False`):
            Whether or not to use a causal mask in addition to the `attention_mask` passed to [`ReformerModel`]. When
            using the Reformer for causal language modeling, this argument should be set to `True`.
        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
            The epsilon used by the layer normalization layers.
        local_chunk_length (`int`, *optional*, defaults to 64):
            Length of chunk which attends to itself in `LocalSelfAttention`. Chunking reduces memory complexity from
            sequence length x sequence length (self attention) to chunk length x chunk length x sequence length / chunk
            length (chunked self attention).
        local_num_chunks_before (`int`, *optional*, defaults to 1):
            Number of previous neighbouring chunks to attend to in `LocalSelfAttention` layer to itself.
        local_num_chunks_after (`int`, *optional*, defaults to 0):
            Number of following neighbouring chunks to attend to in `LocalSelfAttention` layer in addition to itself.
        local_attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout ratio for the attention probabilities in `LocalSelfAttention`.
        lsh_attn_chunk_length (`int`, *optional*, defaults to 64):
            Length of chunk which attends to itself in `LSHSelfAttention`. Chunking reduces memory complexity from
            sequence length x sequence length (self attention) to chunk length x chunk length x sequence length / chunk
            length (chunked self attention).
        lsh_num_chunks_before (`int`, *optional*, defaults to 1):
            Number of previous neighbouring chunks to attend to in `LSHSelfAttention` layer to itself.
        lsh_num_chunks_after (`int`, *optional*, defaults to 0):
            Number of following neighbouring chunks to attend to in `LSHSelfAttention` layer to itself.
        lsh_attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout ratio for the attention probabilities in `LSHSelfAttention`.
        max_position_embeddings (`int`, *optional*, defaults to 4096):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        num_attention_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer encoder.
        num_buckets (`int` or `List[int]`, *optional*):
            Number of buckets, the key query vectors can be "hashed into" using the locality sensitive hashing scheme.
            Each query key vector is hashed into a hash in `1, ..., num_buckets`. The number of buckets can also be
            factorized into a list for improved memory complexity. In this case, each query key vector is hashed into a
            hash in `1-1, 1-2, ..., num_buckets[0]-1, ..., num_buckets[0]-num_buckets[1]` if `num_buckets` is
            factorized into two factors. The number of buckets (or the product the factors) should approximately equal
            sequence length / lsh_chunk_length. If `num_buckets` not set, a good value is calculated on the fly.
        num_hashes (`int`, *optional*, defaults to 1):
            Number of hashing rounds (e.g., number of random rotations) in Local Sensitive Hashing scheme. The higher
            `num_hashes`, the more accurate the `LSHSelfAttention` becomes, but also the more memory and time intensive
            the hashing becomes.
        pad_token_id (`int`, *optional*, defaults to 0):
            The token id for the padding token.
        vocab_size (`int`, *optional*, defaults to 320):\
            Vocabulary size of the Reformer model. Defines the number of different tokens that can be represented by
            the `inputs_ids` passed when calling [`ReformerModel`].
        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
            Whether to tie input and output embeddings.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
        classifier_dropout (`float`, *optional*):
            The dropout ratio for the classification head.

    Examples:

    ```python
    >>> from transformers import ReformerConfig, ReformerModel

    >>> # Initializing a Reformer configuration
    >>> configuration = ReformerConfig()

    >>> # Initializing a Reformer model (with random weights)
    >>> model = ReformerModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```
"""

    model_type = "reformer"
    keys_to_ignore_at_inference = ["past_buckets_states"]
    attribute_map = {}

    def __init__(
        self,
        attention_head_size=64,
        attn_layers=["local", "lsh", "local", "lsh", "local", "lsh"],
        axial_norm_std=1.0,
        axial_pos_embds=True,
        axial_pos_shape=[64, 64],
        axial_pos_embds_dim=[64, 192],
        chunk_size_lm_head=0,
        eos_token_id=2,
        feed_forward_size=512,
        hash_seed=None,
        hidden_act="relu",
        hidden_dropout_prob=0.05,
        hidden_size=256,
        initializer_range=0.02,
        is_decoder=False,
        layer_norm_eps=1e-12,
        local_num_chunks_before=1,
        local_num_chunks_after=0,
        local_attention_probs_dropout_prob=0.05,
        local_attn_chunk_length=64,
        lsh_attn_chunk_length=64,
        lsh_attention_probs_dropout_prob=0.0,
        lsh_num_chunks_before=1,
        lsh_num_chunks_after=0,
        max_position_embeddings=4096,
        num_attention_heads=12,
        num_buckets=None,
        num_hashes=1,
        pad_token_id=0,
        vocab_size=320,
        tie_word_embeddings=False,
        use_cache=True,
        classifier_dropout=None,
        **kwargs,
    ):
        self.hash_seed = hash_seed
        self.vocab_size = vocab_size
        self.attention_head_size = attention_head_size
        self.hidden_size = hidden_size
        self.num_attention_heads = num_attention_heads
        self.num_hashes = num_hashes
        self.num_hidden_layers = len(attn_layers)
        self.num_buckets = tuple(num_buckets) if isinstance(num_buckets, list) else num_buckets
        self.lsh_attn_chunk_length = lsh_attn_chunk_length
        self.local_attn_chunk_length = local_attn_chunk_length
        self.lsh_num_chunks_after = lsh_num_chunks_after
        self.lsh_num_chunks_before = lsh_num_chunks_before
        self.local_num_chunks_after = local_num_chunks_after
        self.local_num_chunks_before = local_num_chunks_before
        self.hidden_act = hidden_act
        self.feed_forward_size = feed_forward_size
        self.hidden_dropout_prob = hidden_dropout_prob
        self.lsh_attention_probs_dropout_prob = lsh_attention_probs_dropout_prob
        self.local_attention_probs_dropout_prob = local_attention_probs_dropout_prob
        self.max_position_embeddings = max_position_embeddings
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.axial_pos_embds = axial_pos_embds
        self.axial_pos_shape = tuple(axial_pos_shape)
        self.axial_pos_embds_dim = tuple(axial_pos_embds_dim)
        self.axial_norm_std = axial_norm_std
        self.chunk_size_lm_head = chunk_size_lm_head
        self.attn_layers = attn_layers
        self.use_cache = use_cache
        self.classifier_dropout = classifier_dropout
        super().__init__(
            pad_token_id=pad_token_id,
            eos_token_id=eos_token_id,
            is_decoder=is_decoder,
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )