# Copyright 2024 IBM 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. """Bamba model configuration""" from ...configuration_utils import PreTrainedConfig from ...modeling_rope_utils import RopeParameters from ...utils import logging logger = logging.get_logger(__name__) class BambaConfig(PreTrainedConfig): r""" This is the configuration class to store the configuration of a [`BambaModel`]. It is used to instantiate a BambaModel model according to the specified arguments, defining the model architecture. Instantiating a configuration with defaults taken from [ibm-fms/Bamba-9.8b-2.2T-hf](https://huggingface.co/ibm-fms/Bamba-9.8b-2.2T-hf). The BambaModel is a hybrid [mamba2](https://github.com/state-spaces/mamba) architecture with SwiGLU. The checkpoints are jointly trained by IBM, Princeton, and UIUC. 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 128000): Vocabulary size of the Bamba model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`BambaModel`] tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether the model's input and output word embeddings should be tied. Note that this is only relevant if the model has an output word embedding layer. hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 14336): 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, check out [this paper](https://huggingface.co/papers/2305.13245). 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. 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. max_position_embeddings (`int`, *optional*, defaults to 262144): Max cached sequence length for the model attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. attn_layer_indices (`list`, *optional*): Specifies the layer indices that will have full attention. Must contain values at most num_hidden_layers. mamba_n_heads (`int`, *optional*, defaults to 128): The number of mamba heads used in the v2 implementation. mamba_d_head (`int`, *optional*, defaults to `"auto"`): Head embedding dimension size mamba_n_groups (`int`, *optional*, defaults to 1): The number of the mamba groups used in the v2 implementation. mamba_d_state (`int`, *optional*, defaults to 256): The dimension the mamba state space latents mamba_d_conv (`int`, *optional*, defaults to 4): The size of the mamba convolution kernel mamba_expand (`int`, *optional*, defaults to 2): Expanding factor (relative to hidden_size) used to determine the mamba intermediate size mamba_chunk_size (`int`, *optional*, defaults to 256): The chunks in which to break the sequence when doing prefill/training mamba_conv_bias (`bool`, *optional*, defaults to `True`): Flag indicating whether or not to use bias in the convolution layer of the mamba mixer block. mamba_proj_bias (`bool`, *optional*, defaults to `False`): Flag indicating whether or not to use bias in the input and output projections (["in_proj", "out_proj"]) of the mamba mixer block 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_limit (`tuple`, *optional*, defaults to `(0.0, inf)`): Accepted range of time step values for clamping. z_loss_coefficient (`float`, *optional*, defaults to 0.0): Coefficient for auxiliary z-loss used to control logit growth during training rope_parameters (`RopeParameters`, *optional*): Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE with longer `max_position_embeddings`. """ model_type = "bamba" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size: int | None = 128000, tie_word_embeddings: bool | None = False, hidden_size: int | None = 4096, intermediate_size: int | None = 14336, num_hidden_layers: int | None = 32, num_attention_heads: int | None = 32, num_key_value_heads: int | None = 8, hidden_act: str | None = "silu", initializer_range: float | None = 0.02, rms_norm_eps: float | None = 1e-5, use_cache: bool | None = True, num_logits_to_keep: int | None = 1, pad_token_id: int | None = 0, bos_token_id: int | None = 1, eos_token_id: int | None = 2, max_position_embeddings: int | None = 262144, attention_dropout: float | None = 0.0, attn_layer_indices: list[int] | None = None, mamba_n_heads: int | None = 128, mamba_d_head: str | None = "auto", mamba_n_groups: int | None = 1, mamba_d_state: int | None = 256, mamba_d_conv: int | None = 4, mamba_expand: int | None = 2, mamba_chunk_size: int | None = 256, mamba_conv_bias: bool | None = True, mamba_proj_bias: bool | None = False, time_step_min: float | None = 0.001, time_step_max: float | None = 0.1, time_step_limit: tuple[float, float] | None = (0.0, float("inf")), z_loss_coefficient: float | None = 0.0, rope_parameters: RopeParameters | None = None, **kwargs, ): self.vocab_size = vocab_size self.tie_word_embeddings = tie_word_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.max_position_embeddings = max_position_embeddings self.attention_dropout = attention_dropout self.attention_bias = False self.mlp_bias = False # 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.num_logits_to_keep = num_logits_to_keep self.attn_layer_indices = attn_layer_indices mamba_intermediate = mamba_expand * hidden_size if mamba_intermediate % mamba_n_heads != 0: raise ValueError("mamba_n_heads must divide mamba_expand * hidden_size") # for the mamba_v2, must satisfy the following if mamba_d_head == "auto": mamba_d_head = mamba_intermediate // mamba_n_heads if mamba_d_head * mamba_n_heads != mamba_intermediate: raise ValueError("The dimensions for the Mamba head state do not match the model intermediate_size") self.mamba_n_heads = mamba_n_heads self.mamba_d_head = mamba_d_head self.mamba_n_groups = mamba_n_groups self.mamba_d_state = mamba_d_state self.mamba_d_conv = mamba_d_conv self.mamba_expand = mamba_expand self.mamba_chunk_size = mamba_chunk_size self.mamba_conv_bias = mamba_conv_bias self.mamba_proj_bias = mamba_proj_bias self.time_step_min = time_step_min self.time_step_max = time_step_max self.time_step_limit = tuple(time_step_limit) if time_step_limit is not None else None self.z_loss_coefficient = z_loss_coefficient self.rope_parameters = rope_parameters kwargs["partial_rotary_factor"] = 0.5 # hardcode for BC self.tie_word_embeddings = tie_word_embeddings self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id super().__init__(**kwargs) @property def layers_block_type(self): return [ "attention" if (self.attn_layer_indices and i in self.attn_layer_indices) else "mamba" for i in range(self.num_hidden_layers) ] __all__ = ["BambaConfig"]