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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/zamba2/modular_zamba2.py.
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# 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, check out [this
paper](https://huggingface.co/papers/2305.13245).
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"
attribute_map = {"head_dim": "attention_head_dim"}
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"]