# coding=utf-8 # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. # # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX # and OPT implementations in this library. It has been modified from its # original forms to accommodate minor architectural differences compared # to GPT-NeoX and OPT used by the Meta AI team that trained the model. # # 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. """ MiniCPM model configuration""" from transformers.configuration_utils import PretrainedConfig from transformers.utils import logging logger = logging.get_logger(__name__) MINICPM_PRETRAINED_CONFIG_ARCHIVE_MAP = {} class MiniCPMConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`MiniCPMModel`]. It is used to instantiate an MiniCPM 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 MiniCPM-7B. 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 32000): Vocabulary size of the MiniCPM model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`MiniCPMModel`] hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 11008): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer in the Transformer decoder. num_key_value_heads (`int`, *optional*): 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 `num_attention_heads`. 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 2048): The maximum sequence length that this model might ever be used with. MiniCPM 1 supports up to 2048 tokens, MiniCPM 2 up to 4096, CodeMiniCPM up to 16384. 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-06): 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*): 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. pretraining_tp (`int`, *optional*, defaults to 1): Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is necessary to ensure exact reproducibility of the pretraining results. Please refer to [this issue](https://github.com/pytorch/pytorch/issues/76232). tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie weight embeddings rope_theta (`float`, *optional*, defaults to 10000.0): The base period of the RoPE embeddings. rope_scaling (`Dict`, *optional*): Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update `max_position_embeddings` to the expected new maximum. See the following thread for more information on how these scaling strategies behave: https://www.reddit.com/r/LocalMiniCPM/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an experimental feature, subject to breaking API changes in future versions. attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`): Whether to use a bias in the query, key, value and output projection layers during self-attention. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. """ model_type = "minicpm" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size=32000, hidden_size=4096, intermediate_size=11008, num_hidden_layers=32, attention_type="vanilla", num_attention_heads=32, num_key_value_heads=None, qk_nope_head_dim=64, qk_rope_head_dim=32, q_lora_rank=768, kv_lora_rank=256, v_head_dim=None, ffn_type = "vanilla", hidden_act="silu", router_act="relu", expert_size=128, num_experts=40, moe_top_k=2, moe_top_p=0.3, moe_routing_strategy="topk", max_position_embeddings=4096, initializer_range=0.02, rms_norm_eps=1e-6, use_cache=True, pad_token_id=None, bos_token_id=1, eos_token_id=2, pretraining_tp=1, tie_word_embeddings=True, rope_theta=10000.0, rope_scaling=None, attention_bias=False, attention_dropout=0.0, scale_emb=1, dim_model_base=1, scale_depth=1, **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.attention_type = attention_type self.num_attention_heads = num_attention_heads # for backward compatibility if num_key_value_heads is None: num_key_value_heads = num_attention_heads self.qk_nope_head_dim = qk_nope_head_dim self.qk_rope_head_dim = qk_rope_head_dim self.q_lora_rank = q_lora_rank self.kv_lora_rank = kv_lora_rank if v_head_dim is None: v_head_dim = qk_nope_head_dim self.v_head_dim = v_head_dim self.num_key_value_heads = num_key_value_heads self.ffn_type = ffn_type self.hidden_act = hidden_act self.router_act = router_act self.expert_size = expert_size self.num_experts = num_experts self.moe_top_k = moe_top_k self.moe_top_p = moe_top_p self.moe_routing_strategy = moe_routing_strategy self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.pretraining_tp = pretraining_tp self.use_cache = use_cache self.rope_theta = rope_theta self.rope_scaling = rope_scaling self._rope_scaling_validation() self.attention_bias = attention_bias self.attention_dropout = attention_dropout self.scale_emb = scale_emb self.dim_model_base = dim_model_base self.scale_depth = scale_depth 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, ) try: import flash_attn self._attn_implementation = "flash_attention_2" except: pass 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) != 2: raise ValueError( "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, " f"got {self.rope_scaling}" ) rope_scaling_type = self.rope_scaling.get("type", None) rope_scaling_factor = self.rope_scaling.get("factor", None) if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]: raise ValueError( f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}" ) if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0: raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")