easydel.modules.olmo.olmo_configuration

easydel.modules.olmo.olmo_configuration#

class easydel.modules.olmo.olmo_configuration.OlmoConfig(vocab_size=50304, hidden_size=4096, intermediate_size=11008, num_hidden_layers=32, num_attention_heads=32, num_key_value_heads=None, hidden_act='silu', max_position_embeddings=2048, initializer_range=0.02, use_cache=True, pad_token_id=1, bos_token_id=None, eos_token_id=50279, tie_word_embeddings=False, rope_theta=10000.0, rope_scaling=None, attention_bias=False, attention_dropout=0.0, clip_qkv=None, gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE, use_scan_mlp: bool = False, scan_mlp_chunk_size: int = 1024, bits: Optional[int] = None, **kwargs)[source]#

Bases: EasyDeLBaseConfig

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

Parameters

  • vocab_size (int, optional, defaults to 50304) – Vocabulary size of the Olmo model. Defines the number of different tokens that can be represented by the inputs_ids passed to the forward method.

  • hidden_size (int, optional, defaults to 4096) – Dimensionality of the encoder layers and the pooler layer.

  • intermediate_size (int, optional, defaults to 11008) – Dimensionality of the “intermediate” (i.e., feed-forward) layer in the Transformer encoder.

  • num_hidden_layers (int, optional, defaults to 32) – Number of hidden layers in the Transformer encoder.

  • num_attention_heads (int, optional, defaults to 32) – Number of attention heads for each attention layer in the Transformer encoder.

  • num_key_value_heads (int, optional) – Number of key and value heads for each attention layer in the Transformer encoder. Will default to num_attention_heads if not set.

  • hidden_act (str or function, optional, defaults to “silu”) – The non-linear activation function (function or string) to use in the encoder and pooler. If string, “gelu”, “relu”, “swish” and “gelu_new” are supported.

  • max_position_embeddings (int, optional, defaults to 2048) – The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 2048 or 4096).

  • initializer_range (float, optional, defaults to 0.02) – The standard deviation of the truncated_normal_initializer for initializing all weight matrices.

  • use_cache (bool, optional, defaults to True) – Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if config.is_decoder=True.

  • pad_token_id (int, optional, defaults to 1) – The index of the padding token in the vocabulary.

  • bos_token_id (int, optional) – The id of the beginning-of-sequence token.

  • eos_token_id (int, optional, defaults to 50279) – The id of the end-of-sequence token.

  • tie_word_embeddings (bool, optional, defaults to False) – Whether to tie the weights of the input embeddings and the output embeddings.

  • rope_theta (float, optional, defaults to 10000.0) – The theta value to use for rotary position embeddings.

  • rope_scaling (tp.Dict[str, tp.Union[str, float]], optional) – The configuration for rope scaling.

  • attention_bias (bool, optional, defaults to False) – Whether to use attention bias.

  • attention_dropout (float, optional, defaults to 0.0) – The dropout ratio for the attention probabilities.

  • clip_qkv (float, optional) – The clip value applied to the query, key, and value tensors.

  • gradient_checkpointing (str, optional, defaults to “nothing_saveable”) – The gradient checkpointing configuration.

  • use_scan_mlp (bool, optional, defaults to False) – Whether to use the scan implementation for the MLP.

  • scan_mlp_chunk_size (int, optional, defaults to 1024) – The chunk size to use when scanning the MLP.

  • bits (int, optional) – The number of bits to quantize the model to.

attach_custom_arguments(gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE, use_scan_mlp: bool = False, scan_mlp_chunk_size: int = 1024, bits: Optional[int] = None)[source]#

Attaches custom arguments to the configuration object.

This method allows adding or overriding configuration attributes dynamically. It primarily sets attributes related to gradient checkpointing, MLP scanning, and quantization bits.

Parameters

  • gradient_checkpointing (EasyDeLGradientCheckPointers, optional) – Gradient checkpointing strategy. Defaults to EasyDeLGradientCheckPointers.NONE.

  • use_scan_mlp (bool, optional) – Whether to use scan for MLP layers. Defaults to False.

  • scan_mlp_chunk_size (int, optional) – Chunk size for scan MLP. Defaults to 1024.

  • bits (tp.Optional[int], optional) – Quantization bits. Defaults to None.

get_partition_rules(*args, **kwargs)[source]#

Get the partition rules for the model. This method defines how the model’s parameters are partitioned across devices for distributed training and inference.

Parameters

  • *args – Additional positional arguments (unused).

  • **kwargs – Additional keyword arguments (unused).

Returns

A tuple of partition rules, where each rule is a tuple

containing a regex pattern for parameter names and the corresponding PartitionSpec.

Return type

tp.Tuple[tp.Tuple[str, PartitionSpec]]

property granted_freq_max_position_embedding: int#

Returns the maximum position embedding size specifically for frequency-based position embeddings.

If freq_max_position_embeddings is set, it returns that value. Otherwise, it falls back to max_position_embeddings.

Returns

The granted maximum position embedding size for frequency encoding.

Return type

int

property granted_mask_max_position_embedding: int#

Returns the maximum position embedding size specifically for mask-based position embeddings.

If mask_max_position_embeddings is set, it returns that value. Otherwise, it falls back to max_position_embeddings.

Returns

The granted maximum position embedding size for mask encoding.

Return type

int

model_type: str = 'olmo'#
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