easydel.modules.mixtral.mixtral_configuration

easydel.modules.mixtral.mixtral_configuration#

class easydel.modules.mixtral.mixtral_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.mixtral.mixtral_configuration.MixtralConfig(vocab_size=32000, hidden_size=4096, intermediate_size=14336, num_hidden_layers=32, num_attention_heads=32, num_key_value_heads=8, hidden_act='silu', max_position_embeddings=131072, 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=4096, attention_dropout=0.0, num_experts_per_tok=2, num_local_experts=8, output_router_logits=False, router_aux_loss_coef=0.001, gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE, use_scan_mlp: bool = False, scan_mlp_chunk_size: int = 1024, number_rep_kv: int = 1, bits: int | None = None, rope_scaling: dict[str, str | float] | None = None, attention_bias: bool = False, initialization_of_moe: bool = False, router_jitter_noise=0.0, head_dim: int | None = None, layer_types: list[str] | 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 32000) – Vocabulary size of the Mixtral 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, defaults to 8) – Number of key and value heads for each attention layer in the Transformer encoder.

  • 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 * 32) – 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.

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

  • eos_token_id (int, optional, defaults to 2) – 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 1e6) – The theta value to use for rotary position embeddings.

  • sliding_window (int, optional, defaults to 4096) – The sliding window size.

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

  • num_experts_per_tok (int, optional, defaults to 2) – The number of experts per token.

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

  • output_router_logits (bool, optional, defaults to False) – Whether to output router logits.

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

  • 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.

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

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

  • 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 bias in the attention layer.

  • initialization_of_moe (bool, optional, defaults to False) – Whether to initialize the MoE layers.

  • router_jitter_noise (float, optional, defaults to 0.0) – The jitter noise for the router.

get_mask_details() → dict[int, easydel.infra.utils.AttnMaskDetail][source]#

Retrieve attention mask details for each layer in the model.

This method generates a dictionary mapping layer indices to their corresponding attention mask details. If a sliding window is defined, each layer is assigned a sliding window attention mask with the specified size.

Returns

A dictionary where keys are layer indices (int) and values are AttnMaskDetail objects specifying the attention mask type and size for each layer.

Return type

dict[int, AttnMaskDetail]

Notes

  • If self.sliding_window is None, an empty dictionary is returned.

  • The method iterates over self.num_hidden_layers to assign mask details for each layer.

  • The attention mask type is set to AttnMaskType.SLIDING when a sliding window is defined.

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

Get the partition rules for the model. :returns: The partition rules. :rtype: 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 = 'mixtral'#
Contents