# coding=utf-8 # 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 transformers.configuration_utils import PretrainedConfig from transformers.utils import logging logger = logging.get_logger(__name__) PHIMOE_PRETRAINED_CONFIG_ARCHIVE_MAP = { "microsoft/Phi-3.5-MoE-instruct": "https://huggingface.co/microsoft/Phi-3.5-MoE-instruct/resolve/main/config.json", } 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 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. ```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"] def __init__( self, 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=4096 * 32, initializer_range=0.02, rms_norm_eps=1e-5, use_cache=True, pad_token_id=None, bos_token_id=1, eos_token_id=2, tie_word_embeddings=False, rope_theta=1e6, 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, lm_head_bias = 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.rope_theta = rope_theta 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_scaling = rope_scaling self._rope_scaling_validation() super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs, ) def _rope_scaling_validation(self): """ Validate the `rope_scaling` configuration. """ if self.rope_scaling is None: return if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 6: raise ValueError( "`rope_scaling` must be a dictionary with three fields, `type`, `short_factor`, `long_factor`, " f"`short_mscale`, `long_mscale` and `original_max_position_embeddings`, got {self.rope_scaling}" ) rope_scaling_type = self.rope_scaling.get("type", None) rope_scaling_short_factor = self.rope_scaling.get("short_factor", None) rope_scaling_long_factor = self.rope_scaling.get("long_factor", None) rope_scaling_short_mscale = self.rope_scaling.get("short_mscale", None) rope_scaling_long_mscale = self.rope_scaling.get("long_mscale", None) original_max_position_embeddings = self.rope_scaling.get("original_max_position_embeddings", None) if rope_scaling_type is None or rope_scaling_type not in ["longrope"]: raise ValueError(f"`rope_scaling`'s type field must be one of ['longrope'], got {rope_scaling_type}") if not ( isinstance(rope_scaling_short_factor, list) and all(isinstance(x, (int, float)) for x in rope_scaling_short_factor) ): raise ValueError( f"`rope_scaling`'s short_factor field must be a list of numbers, got {rope_scaling_short_factor}" ) if not len(rope_scaling_short_factor) == self.hidden_size // self.num_attention_heads // 2: raise ValueError( f"`rope_scaling`'s short_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_short_factor)}" ) if not ( isinstance(rope_scaling_long_factor, list) and all(isinstance(x, (int, float)) for x in rope_scaling_long_factor) ): raise ValueError( f"`rope_scaling`'s long_factor field must be a list of numbers, got {rope_scaling_long_factor}" ) if not len(rope_scaling_long_factor) == self.hidden_size // self.num_attention_heads // 2: raise ValueError( f"`rope_scaling`'s long_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_long_factor)}" ) if not isinstance(rope_scaling_short_mscale, (int, float)): raise ValueError( f"`rope_scaling`'s short_mscale field must be a number, got {rope_scaling_short_mscale}" ) if not isinstance(rope_scaling_long_mscale, (int, float)): raise ValueError( f"`rope_scaling`'s long_mscale field must be a number, got {rope_scaling_long_mscale}" ) if not isinstance(original_max_position_embeddings, int): raise ValueError( f"`rope_scaling`'s original_max_position_embeddings field must be an integer, got {original_max_position_embeddings}" )