easydel.modules.phimoe.__init__

class easydel.modules.phimoe.__init__.PhiMoeConfig(vocab_size=32064, hidden_size=4096, intermediate_size=6400, 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, rope_scaling=None, sliding_window=None, attention_dropout=0.0, num_experts_per_tok=2, num_local_experts=16, output_router_logits=False, router_aux_loss_coef=0.001, router_jitter_noise=0.01, input_jitter_noise=0.0, attention_bias=False, embd_pdrop: float = 0.0, lm_head_bias=False, bits: Optional[int] = None, gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.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 32064) – Vocabulary size of the PhiMoE model. Defines the number of different tokens that can be represented by the inputs_ids passed when calling [PhiMoEModel]

• hidden_size (int, optional, defaults to 4096) – Dimension of the hidden representations.

• intermediate_size (int, optional, defaults to 6400) – Dimension of the MLP representations.

• 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) – This is the number of key_value heads that should be used to implement Grouped Query Attention. If num_key_value_heads=num_attention_heads, the model will use Multi Head Attention (MHA), if num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details checkout [this paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `8.

• hidden_act (str or function, optional, defaults to “silu”) – The non-linear activation function (function or string) in the decoder.

• max_position_embeddings (int, optional, defaults to 4096*32) – The maximum sequence length that this model might ever be used with. Mixtral’s sliding window attention allows sequence of up to 4096*32 tokens.

• 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-05) – 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 id of the padding token.

• 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 the model’s input and output word embeddings should be tied.

• rope_theta (float, optional, defaults to 10000.0) – The base period of the RoPE embeddings.

• rope_scaling (dict, optional) – The scaling strategy for the RoPE embeddings. If None, no scaling is applied. If a dictionary, it must contain the following keys: type, short_factor, long_factor, short_mscale, long_mscale and original_max_position_embeddings. The type must be longrope, the short_mscale and long_scale must be numbers, the short_factor and long_factor must be lists of numbers with the same length as half of the attention head size and the original_max_position_embeddings must be an integer.

• sliding_window (int, optional) – Sliding window attention window size. If not specified, will default to 262144.

• 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 to root per-token, can be also interpreted as the top-p routing parameter

• num_local_experts (int, optional, defaults to 16) – Number of experts per Sparse MLP layer.

• output_router_logits (bool, optional, defaults to False) – Whether or not the router logits should be returned by the model. Enabeling this will also allow the model to output the auxiliary loss. See [here]() for more details

• router_aux_loss_coef (float, optional, defaults to 0.0) – The aux loss factor for the total loss.

• router_jitter_noise (float, optional, defaults to 0.01) – Amount of noise to add to the router.

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

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

attach_custom_arguments(bits: Optional[int] = None, embd_pdrop: float = 0.0, gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE, **kwargs)

get_partition_rules(fully_sharded_data_parallel: bool = True)

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]]

property granted_freq_max_position_embedding: int

property granted_mask_max_position_embedding: int

model_type: str = 'phimoe'

class easydel.modules.phimoe.__init__.PhiMoeForCausalLM(*args: Any, **kwargs: Any)

Bases: EasyDeLBaseModule

class easydel.modules.phimoe.__init__.PhiMoeModel(*args: Any, **kwargs: Any)

Bases: EasyDeLBaseModule