easydel.modules.xerxes2.xerxes2_configuration

easydel.modules.xerxes2.xerxes2_configuration#

class easydel.modules.xerxes2.xerxes2_configuration.ExpertTensorParallel(axes: Sequence[str | None], mode: Union[Literal['__autoregressive__', '__prefill__', '__train__', '__insert__'], int])[source]#

Bases: DynamicShardingAxes

Expert Tensor Parallelism (EPxTP) sharding axes.

axes: ClassVar = ['__TENSOR_PARALLEL__', '_', '_']#
mode: ClassVar = '__train__'#
class easydel.modules.xerxes2.xerxes2_configuration.Xerxes2Config(vocab_size: int = 256128, hidden_size: int = 4096, intermediate_size: int = 16384, moe_intermediate_size: int = 8192, decoder_sparse_step: int = 1, num_experts_per_tok: int = 8, num_experts: int = 128, norm_topk_prob: int = False, output_router_logits: int = False, router_aux_loss_coef: int = 0.001, num_hidden_layers: int = 32, num_attention_heads: int = 32, max_position_embeddings: int = 16384, initializer_range: float = 0.02, rms_norm_eps: float = 1e-06, use_cache: bool = True, pad_token_id: int = 0, eos_token_id: int = 1, bos_token_id: int = 2, tie_word_embeddings: bool = False, rope_theta: float = 10000.0, bits: int | None = None, scan_layers: bool = False, q_lora_dim: int | None = 1536, kv_lora_dim: int = 512, qk_rope_head_dim: int = 64, qk_nope_head_dim: int = 128, vhead_dim: int = 128, mlp_only_layers: list[int] | None = None, hidden_act: str | None = None, rope_scaling: dict | None = 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 256128) – Vocabulary size of the xerxes 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 16384) – 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 16) – Number of attention heads for each attention layer in the Transformer encoder.

  • num_key_value_heads (int, optional, defaults to 16) – Number of key and value heads for each attention layer in the Transformer encoder.

  • head_dim (int, optional, defaults to 256) – Dimensionality of the attention head.

  • max_position_embeddings (int, optional, defaults to 6144) – 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.

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

  • 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 0) – The index of the padding token in the vocabulary.

  • eos_token_id (int, optional, defaults to 1) – The index of the end of sequence token in the vocabulary.

  • bos_token_id (int, optional, defaults to 2) – The index of the beginning of sequence token in the vocabulary.

  • tie_word_embeddings (bool, optional, defaults to True) – 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.

  • softmax_scale (float, optional, defaults to 14.9666295471) – softmax scale for attention module.

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

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

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

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

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

Get the partition rules for the model.

Parameters

fully_sharded_data_parallel (bool, optional, defaults to True) – Whether to use fully sharded data parallelism.

Returns

The partition rules.

Return type

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

model_type: str = 'xerxes2'#
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