# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/zamba2/modular_zamba2.py. # Do NOT edit this file manually as any edits will be overwritten by the generation of # the file from the modular. If any change should be done, please apply the change to the # modular_zamba2.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # coding=utf-8 # Copyright 2024 Zyphra Technologies 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. from ...configuration_utils import PretrainedConfig class Zamba2Config(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Zamba2Model`]. It is used to instantiate a Zamba2 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 Zamba2 model. [Zyphra/Zamba2-2.7B](https://huggingface.co/Zyphra/Zamba2-2.7B) 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 Zamba2 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Zamba2Model`] max_position_embeddings (`int`, *optional*, defaults to 4096): The maximum sequence length that this model might ever be used with. hidden_size (`int`, *optional*, defaults to 2560): Dimension of the hidden representations. num_hidden_layers (`int`, *optional*, defaults to 54): Number of hidden layers in the model. layers_block_type (`list`, *optional*): List of layer types, which can be either "mamba" or "hybrid". mamba_d_state (`int`, *optional*, defaults to 64): shape of the state space latents. mamba_d_conv (`int`, *optional*, defaults to 4): Size of the convolution kernel. mamba_expand (`int`, *optional*, defaults to 2): Expanding factor used to determine the intermediate size. mamba_ngroups (`int`, *optional*, defaults to 1): Number of groups for the evolution matrices of mamba 2. time_step_min (`float`, *optional*, defaults to 0.001): Minimum `time_step` used to bound `dt_proj.bias`. time_step_max (`float`, *optional*, defaults to 0.1): Maximum `time_step` used to bound `dt_proj.bias`. time_step_floor (`float`, *optional*, defaults to 0.0001): Minimum clamping value of the `dt_proj.bias` layer initialization. time_step_limit (`tuple`, *optional*): Accepted range of time step values. n_mamba_heads (`int`, *optional*, defaults to 8): Number of heads for the evolution matrices of mamba 2. use_conv_bias (`bool`, *optional*, defaults to `True`): Whether or not to use bias in the convolution layer of the mixer block. chunk_size (`int`, *optional*, defaults to 256): Size of the chunks that will comprise the sequence. use_mem_eff_path (`bool`, *optional*, defaults to `False`): Whether or not to use the fused conv1d and scan in mamba2 layers. add_bias_linear (`bool`, *optional*, defaults to `False`): Flag indicating whether or not to use bias in various layers intermediate_size (`int`, *optional*, defaults to 4 * hidden_size): Dimension of the MLP representations. hidden_act (`str`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the MLP. 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=None`, 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). attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. num_mem_blocks (`int`, *optional*, defaults to 1): Number of unshared transformer blocks. use_shared_attention_adapter (`bool`, *optional*, defaults to `False`): If True, unshared adapters (formally the same as LoRA but used in the base model) will be added to the q, k, v projectors in the shared attention layers. adapter_rank (`int`, *optional*, defaults to 128): Rank of the adapter in the shared MLP and shared attention layers. use_mem_rope (`bool`, *optional*, defaults to `False`): If True, includes RoPE in the shared attention layers. rope_theta (`float`, *optional*, defaults to `10000.0`): The base period of the RoPE embeddings. 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`. num_logits_to_keep (`int` or `None`, *optional*, defaults to 1): Number of prompt logits to calculate during generation. If `None`, all logits will be calculated. If an integer value, only last `num_logits_to_keep` logits will be calculated. Default is 1 because only the logits of the last prompt token are needed for generation. For long sequences, the logits for the entire sequence may use a lot of memory so, setting `num_logits_to_keep=1` will reduce memory footprint significantly. pad_token_id (`int`, *optional*, defaults to 0): 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. use_long_context (`bool`, *optional*, defaults to `False`): Activates the context-extended version of Zamba by modifying RoPE. ```python >>> from transformers import Zamba2Model, Zamba2Config >>> # Initializing a Zamba2-2.7B style configuration >>> configuration = Zamba2Config() >>> # Initializing a model from the Zamba2-2.7B style configuration >>> model = Zamba2Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "zamba2" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size=32000, max_position_embeddings=4096, hidden_size=2560, num_hidden_layers=54, layers_block_type=None, mamba_d_state=64, mamba_d_conv=4, mamba_expand=2, mamba_ngroups=1, time_step_min=0.001, time_step_max=0.1, time_step_floor=1e-4, time_step_limit=None, n_mamba_heads=8, use_conv_bias=True, chunk_size=256, use_mem_eff_path=False, add_bias_linear=False, intermediate_size=None, hidden_act="gelu", num_attention_heads=32, num_key_value_heads=None, attention_dropout=0.0, num_mem_blocks=1, use_shared_attention_adapter=False, adapter_rank=128, use_mem_rope=False, rope_theta=10000, initializer_range=0.02, rms_norm_eps=1e-5, use_cache=True, num_logits_to_keep=1, pad_token_id=0, bos_token_id=1, eos_token_id=2, use_long_context=False, **kwargs, ): super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs, ) self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size if intermediate_size is None: self.intermediate_size = 4 * hidden_size else: self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.num_mem_blocks = num_mem_blocks self.attention_hidden_size = 2 * hidden_size self.attention_head_dim = 2 * self.hidden_size // self.num_attention_heads self.attention_dropout = attention_dropout self.use_mem_rope = use_mem_rope self.use_long_context = use_long_context if use_mem_rope and use_long_context: a = 8 rope_theta = rope_theta * a ** (self.attention_head_dim / (self.attention_head_dim - 2)) self.rope_theta = rope_theta self.mamba_d_state = mamba_d_state self.mamba_d_conv = mamba_d_conv self.mamba_expand = mamba_expand self.add_bias_linear = add_bias_linear self.mamba_ngroups = mamba_ngroups self.n_mamba_heads = n_mamba_heads self.mamba_headdim = int(mamba_expand * hidden_size) // n_mamba_heads self.use_conv_bias = use_conv_bias self.chunk_size = chunk_size self.time_step_limit = time_step_limit self.use_shared_attention_adapter = use_shared_attention_adapter self.adapter_rank = adapter_rank self.time_step_min = time_step_min self.time_step_max = time_step_max self.time_step_floor = time_step_floor if use_long_context: self.max_position_embeddings = 16384 if num_key_value_heads is None: num_key_value_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads self.num_attention_heads = num_attention_heads self.kv_channels = self.hidden_size // self.num_attention_heads self.num_query_groups = self.num_attention_heads # Below, "mamba" stands for mamba layer, "hybrid" stands for hybrid layer (composed by a shared transformer followed by mamba layer) if layers_block_type is None: self.layers_block_type = ( ["mamba"] + (["mamba"] * 5 + ["hybrid"]) * 7 + ["mamba"] * 4 + ["hybrid"] + ["mamba"] * 3 + ["hybrid"] + ["mamba"] * 2 ) else: self.layers_block_type = layers_block_type self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.use_cache = use_cache self.num_logits_to_keep = num_logits_to_keep self.hybrid_layer_ids = [index for index, type in enumerate(self.layers_block_type) if type == "hybrid"] self.use_mem_eff_path = use_mem_eff_path __all__ = ["Zamba2Config"]