# Copyright (c) 2025 Baidu, Inc. and 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. """Ernie 4.5 MoE model configuration""" from ...configuration_utils import PretrainedConfig from ...modeling_rope_utils import rope_config_validation from ...utils import logging logger = logging.get_logger(__name__) class Ernie4_5_MoeConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Ernie4_5_MoeModel`]. It is used to instantiate a Ernie 4.5 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 [baidu/ERNIE-4.5-21B-A3B-PT](https://huggingface.co/baidu/ERNIE-4.5-21B-A3B-PT). 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 103424): Vocabulary size of the Ernie 4.5 MoE model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Ernie4_5_MoeModel`] pad_token_id (`int`, *optional*, defaults to 0): Padding token id. bos_token_id (`int`, *optional*, defaults to 1): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 2): End of stream token id. hidden_size (`int`, *optional*, defaults to 2560): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 12288): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 28): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 20): Number of attention heads for each attention layer in the Transformer encoder. num_key_value_heads (`int`, *optional*, defaults to 4): 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 `32`. 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 131072): The maximum sequence length that this model might ever be used with. 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`. tie_word_embeddings (`bool`, *optional*, defaults to `True`): Whether the model's input and output word embeddings should be tied. rope_theta (`float`, *optional*, defaults to 500000.0): The base period of the RoPE embeddings. rope_scaling (`Dict`, *optional*): Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value accordingly. Expected contents: `rope_type` (`str`): The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope', 'llama3'], with 'default' being the original RoPE implementation. `factor` (`float`, *optional*): Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In most scaling types, a `factor` of x will enable the model to handle sequences of length x * original maximum pre-trained length. `original_max_position_embeddings` (`int`, *optional*): Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during pretraining. `attention_factor` (`float`, *optional*): Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention computation. If unspecified, it defaults to value recommended by the implementation, using the `factor` field to infer the suggested value. `beta_fast` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear ramp function. If unspecified, it defaults to 32. `beta_slow` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear ramp function. If unspecified, it defaults to 1. `short_factor` (`list[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to short contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `long_factor` (`list[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to long contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `low_freq_factor` (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE `high_freq_factor` (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE use_bias (`bool`, *optional*, defaults to `False`): Whether to use a bias in any of the projections including mlp and attention for example. moe_intermediate_size (`int`, *optional*, defaults to 1536): Intermediate size of the routed expert. moe_k (`int`, *optional*, defaults to 6): Number of selected experts. moe_num_experts (`int`, *optional*, defaults to 64): Number of routed experts. moe_num_shared_experts (`int`, *optional*, defaults to 2): The number of experts that are shared for all MoE forwards. moe_layer_start_index (`int`, *optional*, defaults to 1): The first index at which MoE layers start to appear. moe_layer_end_index (`int`, *optional*, defaults to -1): The last possible index for a MoE layer. moe_layer_interval (`int`, *optional*, defaults to 1): The intervals between MoE layers to appear. moe_norm_min (`float`, *optional*, defaults to 1e-12): Minimum division value during routing normalization. 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, including load balancing loss and router z-loss. router_aux_loss_coef (`float`, *optional*, defaults to 0.001): The aux loss factor for the total loss. ```python >>> from transformers import Ernie4_5_MoeModel, Ernie4_5_MoEConfig >>> # Initializing a Ernie4_5_MoE style configuration >>> configuration = Ernie4_5_MoEConfig() >>> # Initializing a model from the ERNIE-4.5-21B-A3B style configuration >>> model = Ernie4_5_MoeModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "ernie4_5_moe" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"num_experts": "moe_num_experts", "num_experts_per_tok": "moe_k"} # Default tensor parallel plan for base model `Ernie4_5_MoE` base_model_tp_plan = { "layers.*.self_attn.q_proj": "colwise", "layers.*.self_attn.k_proj": "colwise", "layers.*.self_attn.v_proj": "colwise", "layers.*.self_attn.o_proj": "rowwise", # sequence parallel is pretty slow # "norm.weight": "sequence_parallel", # "layers.*.input_layernorm.weight": "sequence_parallel", # "layers.*.post_attention_layernorm.weight": "sequence_parallel", "layers.*.mlp.shared_experts.gate_proj": "local_colwise", "layers.*.mlp.shared_experts.up_proj": "local_colwise", "layers.*.mlp.shared_experts.down_proj": "local_rowwise", "layers.*.mlp.experts.*.gate_proj": "local_colwise", "layers.*.mlp.experts.*.up_proj": "local_colwise", "layers.*.mlp.experts.*.down_proj": "local_rowwise", "layers.*.mlp.experts": "local", "layers.*.mlp.gate_proj": "local_colwise", "layers.*.mlp.up_proj": "local_colwise", "layers.*.mlp.down_proj": "local_rowwise", "layers.*.mlp": "gather", } base_model_pp_plan = { "embed_tokens": (["input_ids"], ["inputs_embeds"]), "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), "norm": (["hidden_states"], ["hidden_states"]), } def __init__( self, vocab_size=103424, pad_token_id=0, bos_token_id=1, eos_token_id=2, hidden_size=2560, intermediate_size=12288, num_hidden_layers=28, num_attention_heads=20, num_key_value_heads=4, hidden_act="silu", max_position_embeddings=131072, initializer_range=0.02, rms_norm_eps=1e-5, use_cache=True, tie_word_embeddings=True, rope_theta=500000.0, rope_scaling=None, use_bias=False, moe_intermediate_size=1536, moe_k=6, moe_num_experts=64, moe_num_shared_experts=2, moe_layer_start_index=1, moe_layer_end_index=-1, moe_layer_interval=1, moe_norm_min=1e-12, output_router_logits=False, router_aux_loss_coef=0.001, **kwargs, ): self.vocab_size = vocab_size 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.num_key_value_heads = num_key_value_heads self.hidden_act = hidden_act self.max_position_embeddings = max_position_embeddings self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.use_cache = use_cache self.use_bias = use_bias self.rope_theta = rope_theta self.rope_scaling = rope_scaling # Validate the correctness of rotary position embeddings parameters # BC: if there is a 'type' field, move it to 'rope_type'. if self.rope_scaling is not None and "type" in self.rope_scaling: self.rope_scaling["rope_type"] = self.rope_scaling["type"] rope_config_validation(self) # MoE arguments self.moe_intermediate_size = moe_intermediate_size self.moe_k = moe_k self.moe_num_experts = moe_num_experts self.moe_num_shared_experts = moe_num_shared_experts self.moe_layer_start_index = moe_layer_start_index self.moe_layer_end_index = self.num_hidden_layers - 1 if moe_layer_end_index == -1 else moe_layer_end_index self.moe_layer_interval = moe_layer_interval self.moe_norm_min = moe_norm_min self.output_router_logits = output_router_logits self.router_aux_loss_coef = router_aux_loss_coef 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, ) __all__ = ["Ernie4_5_MoeConfig"]