easydel.modules.gidd.gidd_configuration

easydel.modules.gidd.gidd_configuration#

class easydel.modules.gidd.gidd_configuration.GiddConfig(vocab_size: int = 131072, hidden_size: int = 768, intermediate_size: int = 3072, num_hidden_layers: int = 12, num_attention_heads: int = 12, head_dim: int | None = None, max_position_embeddings: int = 1024, resid_scale: float = 4.0, rms_norm_eps: float = 1e-06, use_qk_norm: bool = True, qk_norm_eps: float = 1e-06, init_scale: float = 0.4, emb_init_scale: float = 0.1, head_init_scale: float = 0.0, bos_token_id: int = 0, eos_token_id: int = 1, rope_theta: float = 10000.0, tie_word_embeddings: bool = False, gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE, rope_scaling: dict[str, str | float] | None = None, scan_mlp_chunk_size: int = 1024, bits: int | None = None, pretraining_tp: int = 1, attention_bias: bool = False, mlp_bias: bool = False, scan_layers: bool = False, **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 131072) – Vocabulary size of the Gidd 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.

  • number_rep_kv (int, optional, defaults to 1) – Number of repetitions for the key and value vectors.

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

  • 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). head_dim (int, optional):

    head_dim for attention qkv.

  • rms_norm_eps (float, optional, defaults to 1e-6) – The epsilon used by the rms normalization layers.

  • 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.

  • bos_token_id (int, optional, defaults to 0) – The id of the beginning-of-sequence token.

  • eos_token_id (int, optional, defaults to 1) – The id of the end-of-sequence token.

  • resid_pdrop (float, optional, defaults to 0.0) – The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.

  • embd_pdrop (float, optional, defaults to 0.0) – The dropout ratio for the embeddings.

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

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

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

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

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

  • fcm_min_ratio (float, optional, defaults to -1) – The minimum ratio for Flash Attention.

  • fcm_max_ratio (float, optional, defaults to -1) – The maximum ratio for Flash Attention.

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

  • 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.

  • pretraining_tp (int, optional, defaults to 1) – The tensor parallelism degree used during pretraining.

  • mlp_bias (bool, optional, defaults to False) – Whether to use bias in the MLP.

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

attach_custom_arguments(tie_word_embeddings: bool = False, gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE, bits: int | None = None, rope_theta: float = 10000.0, attention_bias: bool = False, mlp_bias: bool = False, scan_layers: bool = True, **kwargs)[source]#

The attach_custom_arguments function adds the following arguments to the Transformer class:

Parameters

  • self – Refer to the current object

  • resid_pdrop – float: Set the dropout rate for residual connections

  • embd_pdrop – float: Set the probability of dropping an embedding

  • attention_dropout – float: Set the probability of dropping out the attention layer

  • tie_word_embeddings – bool: Tie the word embeddings to the decoder

  • gradient_checkpointing – str: Control the amount of memory used by jax

  • fcm_min_ratio – float: Control the minimum ratio of the number of chunks to be used in flash-based computation

  • fcm_max_ratio – float: Set the maximum ratio of the number of input tokens to output tokens

  • number_rep_kv – int: Determine how many times the key and value vectors are repeated

  • bits – tp.Optional[int]: Determine the number of bits used in the quantization

  • rope_theta – float : rope_theta for compute rope

  • attention_bias – bool : whenever to use attention bias or no

  • mlp_bias – bool : whenever to use bias in mlp

  • scan_layers – bool: Determine whether to use scan layers or not

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

Get the partition rules for the model. :returns: The partition rules. :rtype: tp.Tuple[tp.Tuple[str, PartitionSpec]]

static get_weight_decay_exclusions()[source]#
property granted_freq_max_position_embedding: int#

Return the max position embedding allowed for frequency-based caches.

property granted_mask_max_position_embedding: int#

Return the max position embedding allowed for mask precomputation.

model_type: str = 'gidd'#
static rng_keys()[source]#
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