easydel.modules.deepseek_v2.__init__

class easydel.modules.deepseek_v2.__init__.DeepseekV2Config(vocab_size=102400, hidden_size=4096, intermediate_size=11008, moe_intermediate_size=1407, num_hidden_layers=30, num_attention_heads=32, num_key_value_heads=32, n_shared_experts=None, n_routed_experts=None, ep_size=1, routed_scaling_factor=1.0, kv_lora_rank=512, q_lora_rank=1536, qk_rope_head_dim=64, v_head_dim=128, qk_nope_head_dim=128, topk_method='gready', n_group=None, topk_group=None, num_experts_per_tok=None, moe_layer_freq=1, first_k_dense_replace=0, norm_topk_prob=False, scoring_func='softmax', aux_loss_alpha=0.001, seq_aux=True, hidden_act='silu', max_position_embeddings=2048, initializer_range=0.02, rms_norm_eps=1e-06, use_cache=True, pad_token_id=None, bos_token_id=100000, eos_token_id=100001, pretraining_tp=1, tie_word_embeddings=False, rope_theta=10000.0, attention_bias=False, attention_dropout=0.0, 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 102400) – Vocabulary size of the DeepseekV2 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.

• moe_intermediate_size (int, optional, defaults to 1407) – Dimensionality of the “intermediate” (i.e., feed-forward) layer in the MoE layer.

• num_hidden_layers (int, optional, defaults to 30) – 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 32) – Number of key and value heads for each attention layer in the Transformer encoder.

• n_shared_experts (int, optional) – Number of shared experts.

• n_routed_experts (int, optional) – Number of routed experts.

• ep_size (int, optional, defaults to 1) – Expert parallel size.

• routed_scaling_factor (float, optional, defaults to 1.0) – Routed scaling factor.

• kv_lora_rank (int, optional, defaults to 512) – KV LoRA rank.

• q_lora_rank (int, optional, defaults to 1536) – Q LoRA rank.

• qk_rope_head_dim (int, optional, defaults to 64) – QK rope head dimension.

• v_head_dim (int, optional, defaults to 128) – V head dimension.

• qk_nope_head_dim (int, optional, defaults to 128) – QK nope head dimension.

• topk_method (str, optional, defaults to “gready”) – Top-k method.

• n_group (int, optional) – Number of groups.

• topk_group (int, optional) – Top-k group.

• num_experts_per_tok (int, optional) – Number of experts per token.

• moe_layer_freq (int, optional, defaults to 1) – MoE layer frequency.

• first_k_dense_replace (int, optional, defaults to 0) – First k dense replace.

• norm_topk_prob (bool, optional, defaults to False) – Whether to normalize top-k probabilities.

• scoring_func (str, optional, defaults to “softmax”) – Scoring function.

• aux_loss_alpha (float, optional, defaults to 0.001) – Auxiliary loss alpha.

• seq_aux (bool, optional, defaults to True) – Whether to use sequence auxiliary loss.

• 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.

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

• bos_token_id (int, optional, defaults to 100000) – The index of the beginning of sequence token in the vocabulary.

• eos_token_id (int, optional, defaults to 100001) – The index of the end of sequence token in the vocabulary.

• pretraining_tp (int, optional, defaults to 1) – Pretraining TP.

• 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.

• 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.

• 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 to quantize the model to.

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

add_jax_args(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)

The add_jax_args function adds the following arguments to the model:

Parameters

• self – Bind the attributes and methods of a class to an instance of that class

• gradient_checkpointing – str: Determine whether to use gradient checkpointing

• use_scan_mlp – bool: Determine whether to use the scan_mlp function or not

• scan_mlp_chunk_size – int: Chunk the input to the mlp

• bits – tp.Optional[int]: Specify the number of bits to use for quantization

Return type

A tuple of the following

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 = 'deepseek_v2'

static rng_keys()

class easydel.modules.deepseek_v2.__init__.DeepseekV2ForCausalLM(*args: Any, **kwargs: Any)

Bases: EasyDeLBaseModule

class easydel.modules.deepseek_v2.__init__.DeepseekV2Model(*args: Any, **kwargs: Any)

Bases: EasyDeLBaseModule

property frequencies

Returns frequency values from the config.