easydel.modules.arctic.__init__

class easydel.modules.arctic.__init__.ArcticConfig(vocab_size=32000, hidden_size=4096, intermediate_size=14336, num_hidden_layers=32, num_attention_heads=32, num_key_value_heads=None, hidden_act='silu', max_position_embeddings=4096, initializer_range=0.02, rms_norm_eps=1e-05, use_cache=True, pad_token_id=None, bos_token_id=1, eos_token_id=2, tie_word_embeddings=False, rope_theta=1000000.0, sliding_window=None, attention_dropout=0.0, num_experts_per_tok=1, num_local_experts=8, router_aux_loss_coef=0.001, moe_layer_frequency=2, parallel_attn_mlp_res=False, moe_train_capacity_factor=1, moe_eval_capacity_factor=1, enable_expert_tensor_parallelism=False, moe_min_capacity=0, moe_token_dropping=True, quantization=None, gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE, use_scan_mlp: bool = False, scan_mlp_chunk_size: int = 1024, bits: Optional[int] = None, rope_scaling: Dict[str, Union[str, float]] = None, **kwargs)

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 32000) – Vocabulary size of the ARCTIC 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 14336) – 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 4096) – 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-5) – 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) – The index of the padding token in the vocabulary. The default value (0) is the same as for GPT2.

• bos_token_id (int, optional) – The index of the beginning of sequence token in the vocabulary. The default value (1) is the same as for GPT2.

• eos_token_id (int, optional) – The index of the end of sequence token in the vocabulary. The default value (2) is the same as for GPT2.

• 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 1e6) – The theta value to use for rotary position embeddings.

• sliding_window (int, optional) – The sliding window size to use for attention. If not specified, no sliding window attention is used.

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

• num_experts_per_tok (int, optional, defaults to 1) – The number of experts per token for mixture of experts.

• num_local_experts (int, optional, defaults to 8) – The number of local experts for mixture of experts.

• router_aux_loss_coef (float, optional, defaults to 0.001) – The auxiliary loss coefficient for the router.

• moe_layer_frequency (int, optional, defaults to 2) – The frequency of MoE layers.

• parallel_attn_mlp_res (bool, optional, defaults to False) – Whether to parallelize attention and MLP residual connections.

• moe_train_capacity_factor (float, optional, defaults to 1) – The capacity factor for MoE layers during training.

• moe_eval_capacity_factor (float, optional, defaults to 1) – The capacity factor for MoE layers during evaluation.

• enable_expert_tensor_parallelism (bool, optional, defaults to False) – Whether to enable expert tensor parallelism.

• moe_min_capacity (int, optional, defaults to 0) – The minimum capacity for MoE layers.

• moe_token_dropping (bool, optional, defaults to True) – Whether to drop tokens in MoE layers.

• quantization (str, optional) – The quantization configuration.

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

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

• scan_mlp_chunk_size (int, optional, defaults to 1024) – The chunk size for scan MLP.

• bits (int, optional) – The number of bits.

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

get_partition_rules(*args, **kwargs)

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

static get_weight_decay_exclusions()

property granted_freq_max_position_embedding: int

property granted_mask_max_position_embedding: int

model_type: str = 'arctic'

static rng_keys()

class easydel.modules.arctic.__init__.ArcticForCausalLM(*args: Any, **kwargs: Any)

Bases: EasyDeLBaseModule

class easydel.modules.arctic.__init__.ArcticForSequenceClassification(*args: Any, **kwargs: Any)

Bases: EasyDeLBaseModule

class easydel.modules.arctic.__init__.ArcticModel(*args: Any, **kwargs: Any)

Bases: EasyDeLBaseModule