# 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. """ YuLanMinimodel configuration""" import math from transformers.configuration_utils import PretrainedConfig from transformers.utils import logging logger = logging.get_logger(__name__) YULANMINI_PRETRAINED_CONFIG_ARCHIVE_MAP = {} class YuLanMiniConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`YuLanMiniModel`]. It is used to instantiate an YuLanMini 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 YuLanMini-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 YuLanMinimodel. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`YuLanMiniModel`] 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. YuLanMini1 supports up to 2048 tokens, YuLanMini2 up to 4096, CodeYuLanMiniup 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/LocalYuLanMini/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. ```python >>> from transformers import YuLanMiniModel, YuLanMiniConfig >>> # Initializing a YuLanMini-7b style configuration >>> configuration = YuLanMiniConfig() >>> # Initializing a model from the YuLanMini-7b style configuration >>> model = YuLanMiniModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "yulanmini" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size=99000, hidden_size=1920, intermediate_size=4800, num_hidden_layers=56, num_attention_heads=30, num_key_value_heads=6, # 不常用变量 hidden_act="silu", max_position_embeddings=4096, rms_norm_eps=1e-6, use_cache=True, pad_token_id=None, # /home/u20140041/pretrain-mini/preprocess/modify_tokenizer/1731 bos_token_id=1, eos_token_id=2, tie_word_embeddings=False, rope_theta=10000.0, use_sliding_window=False, sliding_window=4096, rope_scaling=None, attention_bias=True, # qwen attention_dropout=0.0, # 放缩embedding grad shrink_alpha=1, shrink_alpha2=1, use_liger=False, # 初始化 initializer_range=0.014434, init_scale_o=10.582218, model_reproduce="transformer", # 下面是为了muparam设置的参数，需要保证：默认值是不使用任何muparam的部分 hidden_states_shrink=1, dim_model_base=None, dim_ffn_base_init=None, # 新版muparam没有使用了 dim_model_base_init=None, dim_model_base_attn=None, dim_model_base_lmh=None, dim_model_base_logits=None, dim_model_base_lr=None, scale_emb=1, # qk_layernorm qk_layernorm=False, layer_norm_eps=1e-6, embedding_ln=False, embedding_rmsln=False, ln_scale=1., z_loss=0.0001, # wesar wesar_weights=True, embed_tokens_alpha=1, q_proj_alpha=1, k_proj_alpha=1, v_proj_alpha=1, o_proj_alpha=1, down_proj_alpha=1, gate_up_proj_alpha=1, input_layernorm_alpha=1, post_attention_layernorm_alpha=1, norm_alpha=1, lm_head_alpha=1, use_norm_alpha=True, use_emb_alpha=False, rms_type="llama", num_steps_trained_before_this_epoch=0, num_epochs_trained_before_this_epoch=0, # 加速 gradient_checkpointing_step=7, **kwargs, ): # 训练states，每个epoch更新，epoch内部不会变。比如训练到第4轮数据，这两个的值都是第三轮最后一步的值（epochs=3, steps=xxx），只要是在第4轮，无论是多少步，都是第三轮的值，由update_trained_steps_and_epochs控制是否更新 self.num_steps_trained_before_this_epoch = num_steps_trained_before_this_epoch self.num_epochs_trained_before_this_epoch = num_epochs_trained_before_this_epoch 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.use_sliding_window = use_sliding_window self.sliding_window = sliding_window if use_sliding_window else None # 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.rope_scaling = rope_scaling self._rope_scaling_validation() self.attention_bias = attention_bias self.attention_dropout = attention_dropout self.shrink_alpha = shrink_alpha self.use_liger = use_liger self.init_scale_o = init_scale_o self.hidden_states_shrink = 1 / math.sqrt(num_hidden_layers) if hidden_states_shrink == "muparam" else hidden_states_shrink self.dim_model_base = dim_model_base if dim_model_base is not None else hidden_size self.dim_model_base_init = dim_model_base_init self.dim_model_base_attn = dim_model_base_attn if dim_model_base_attn is not None else (hidden_size // num_attention_heads) # 初始化为1则是使用1/H_dim self.dim_model_base_lmh = dim_model_base_lmh if dim_model_base_lmh is not None else 1 # 初始化为1则是不放缩lm_head的init self.scale_emb = scale_emb if scale_emb is not None else 1 self.model_reproduce=model_reproduce if model_reproduce is not None else "transformer" self.dim_model_base_logits = dim_model_base_logits if dim_model_base_logits is not None else hidden_size self.dim_model_base_lr = dim_model_base_lr if dim_model_base_lr is not None else hidden_size self.qk_layernorm = qk_layernorm self.layer_norm_eps = layer_norm_eps self.embedding_ln = embedding_ln self.embedding_rmsln = embedding_rmsln self.ln_scale = ln_scale self.z_loss = z_loss if embedding_ln and embedding_rmsln: raise ValueError("Only one of embedding_ln and embedding_rmsln should be True") self.wesar_weights = wesar_weights self.embed_tokens_alpha = embed_tokens_alpha self.q_proj_alpha = q_proj_alpha self.k_proj_alpha = k_proj_alpha self.v_proj_alpha = v_proj_alpha self.o_proj_alpha = o_proj_alpha self.down_proj_alpha = down_proj_alpha self.gate_up_proj_alpha = gate_up_proj_alpha self.input_layernorm_alpha = input_layernorm_alpha self.post_attention_layernorm_alpha = post_attention_layernorm_alpha self.norm_alpha = norm_alpha self.lm_head_alpha = lm_head_alpha self.use_norm_alpha = use_norm_alpha self.use_emb_alpha = use_emb_alpha self.rms_type = rms_type self.gradient_checkpointing_step = gradient_checkpointing_step if self.dim_model_base != hidden_size or self.dim_model_base_init is not None or self.dim_model_base_attn != (hidden_size // num_attention_heads) or self.dim_model_base_lmh != 1: if init_scale_o != 1: raise ValueError("When using muparam, init_scale_o should be 1") # multiplier print("Attention放缩：", math.sqrt(self.dim_model_base_attn) / (hidden_size // num_attention_heads)) print("Residual链接处的Hidden States放缩：", hidden_states_shrink) print("Logits放缩：", 1 / (hidden_size / self.dim_model_base)) # initializer if dim_model_base_init is not None: print("o_proj,down_proj初始化STD：", initializer_range / math.sqrt(2 * (hidden_size / dim_model_base_init) * num_hidden_layers)) print("gate_proj,up_proj,q_proj,k_proj,v_proj初始化STD：", initializer_range / math.sqrt(self.hidden_size / self.dim_model_base_init)) else: print("o_proj,down_proj初始化STD：", initializer_range / init_scale_o) print("gate_proj,up_proj,q_proj,k_proj,v_proj初始化STD：", initializer_range) print("lm_head初始化STD：", initializer_range / math.sqrt(self.dim_model_base_lmh)) if not tie_word_embeddings and self.scale_emb != 1: raise ValueError("When using scale_emb, tie_word_embeddings should be False") 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}")