# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """PyTorch Phi-MoE model.""" from ...configuration_utils import PreTrainedConfig from ...modeling_rope_utils import RopeParameters from ...utils import logging logger = logging.get_logger(__name__) class PhimoeConfig(PreTrainedConfig): r""" This is the configuration class to store the configuration of a [`PhimoeModel`]. It is used to instantiate a Phi-moe model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the [microsoft/Phi-3.5-MoE-instruct](https://huggingface.co/microsoft/Phi-3.5-MoE-instruct). Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PreTrainedConfig`] for more information. Args: 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, check out [this paper](https://huggingface.co/papers/2305.13245). 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_parameters (`RopeParameters`, *optional*): Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE with longer `max_position_embeddings`. 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. Enabling this will also allow the model to output the auxiliary loss. See [here]() for more details router_aux_loss_coef (`float`, *optional*, defaults to 0.001): 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. input_jitter_noise (`float`, *optional*, defaults to 0.0): Input jitter noise attention_bias (`bool`, *optional*, defaults to `False`): Attention bias lm_head_bias (`bool`, *optional*, defaults to `False`): LM head bias Example: ```python >>> from transformers import PhimoeModel, PhimoeConfig >>> # Initializing a Phi-3 style configuration >>> configuration = PhimoeConfig.from_pretrained("microsoft/Phi-3.5-MoE-instruct") >>> # Initializing a model from the configuration >>> model = PhimoeModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "phimoe" keys_to_ignore_at_inference = ["past_key_values"] default_theta = 1000000.0 def __init__( self, vocab_size: int | None = 32064, hidden_size: int | None = 4096, intermediate_size: int | None = 6400, num_hidden_layers: int | None = 32, num_attention_heads: int | None = 32, num_key_value_heads: int | None = 8, hidden_act: str | None = "silu", max_position_embeddings: int | None = 4096 * 32, initializer_range: float | None = 0.02, rms_norm_eps: int | None = 1e-5, use_cache: bool | None = True, pad_token_id: int | None = None, bos_token_id: int | None = 1, eos_token_id: int | None = 2, tie_word_embeddings: int | None = False, rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None, sliding_window: int | None = None, attention_dropout: float | None = 0.0, num_experts_per_tok: int | None = 2, num_local_experts: int | None = 16, output_router_logits: bool | None = False, router_aux_loss_coef: float | None = 0.001, router_jitter_noise: float | None = 0.01, input_jitter_noise: float | None = 0.0, attention_bias: bool | None = False, lm_head_bias: bool | None = False, **kwargs, ): self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.sliding_window = sliding_window self.attention_bias = attention_bias self.lm_head_bias = lm_head_bias # for backward compatibility if num_key_value_heads is None: num_key_value_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads self.hidden_act = hidden_act self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.use_cache = use_cache self.attention_dropout = attention_dropout self.num_experts_per_tok = num_experts_per_tok self.num_local_experts = num_local_experts self.output_router_logits = output_router_logits self.router_aux_loss_coef = router_aux_loss_coef self.router_jitter_noise = router_jitter_noise self.input_jitter_noise = input_jitter_noise self.rope_parameters = rope_parameters self.tie_word_embeddings = tie_word_embeddings self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id super().__init__(**kwargs) def validate_rope(self, ignore_keys=None): """ Validate the `rope_parameters` configuration. """ super().validate_rope(ignore_keys=ignore_keys) # Run model-specific rope validation if self.rope_parameters["rope_type"] != "default": if "original_max_position_embeddings" in self.rope_parameters: self.original_max_position_embeddings = self.rope_parameters["original_max_position_embeddings"] rope_parameters_short_mscale = self.rope_parameters.get("short_mscale", None) rope_parameters_long_mscale = self.rope_parameters.get("long_mscale", None) if not isinstance(rope_parameters_short_mscale, (int, float)): raise TypeError( f"`rope_parameters`'s short_mscale field must be a number, got {rope_parameters_short_mscale}" ) if not isinstance(rope_parameters_long_mscale, (int, float)): raise TypeError( f"`rope_parameters`'s long_mscale field must be a number, got {rope_parameters_long_mscale}" ) __all__ = ["PhimoeConfig"]