241 lines
12 KiB
Python
241 lines
12 KiB
Python
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from src/transformers/models/zamba2/modular_zamba2.py.
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# Do NOT edit this file manually as any edits will be overwritten by the generation of
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# the file from the modular. If any change should be done, please apply the change to the
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# modular_zamba2.py file directly. One of our CI enforces this.
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# coding=utf-8
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# Copyright 2024 Zyphra Technologies and the HuggingFace Inc. team. All rights reserved.
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#
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from ...configuration_utils import PretrainedConfig
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class Zamba2Config(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Zamba2Model`]. It is used to instantiate a
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Zamba2 model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the Zamba2 model.
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[Zyphra/Zamba2-2.7B](https://huggingface.co/Zyphra/Zamba2-2.7B)
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 32000):
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Vocabulary size of the Zamba2 model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`Zamba2Model`]
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max_position_embeddings (`int`, *optional*, defaults to 4096):
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The maximum sequence length that this model might ever be used with.
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hidden_size (`int`, *optional*, defaults to 2560):
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Dimension of the hidden representations.
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num_hidden_layers (`int`, *optional*, defaults to 54):
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Number of hidden layers in the model.
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layers_block_type (`list`, *optional*):
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List of layer types, which can be either "mamba" or "hybrid".
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mamba_d_state (`int`, *optional*, defaults to 64): shape of the state space latents.
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mamba_d_conv (`int`, *optional*, defaults to 4): Size of the convolution kernel.
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mamba_expand (`int`, *optional*, defaults to 2): Expanding factor used to determine the intermediate size.
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mamba_ngroups (`int`, *optional*, defaults to 1):
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Number of groups for the evolution matrices of mamba 2.
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time_step_min (`float`, *optional*, defaults to 0.001):
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Minimum `time_step` used to bound `dt_proj.bias`.
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time_step_max (`float`, *optional*, defaults to 0.1):
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Maximum `time_step` used to bound `dt_proj.bias`.
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time_step_floor (`float`, *optional*, defaults to 0.0001):
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Minimum clamping value of the `dt_proj.bias` layer initialization.
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time_step_limit (`tuple`, *optional*):
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Accepted range of time step values.
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n_mamba_heads (`int`, *optional*, defaults to 8):
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Number of heads for the evolution matrices of mamba 2.
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use_conv_bias (`bool`, *optional*, defaults to `True`):
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Whether or not to use bias in the convolution layer of the mixer block.
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chunk_size (`int`, *optional*, defaults to 256):
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Size of the chunks that will comprise the sequence.
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use_mem_eff_path (`bool`, *optional*, defaults to `False`):
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Whether or not to use the fused conv1d and scan in mamba2 layers.
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add_bias_linear (`bool`, *optional*, defaults to `False`):
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Flag indicating whether or not to use bias in various layers
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intermediate_size (`int`, *optional*, defaults to 4 * hidden_size):
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Dimension of the MLP representations.
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hidden_act (`str`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the MLP.
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num_attention_heads (`int`, *optional*, defaults to 32):
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Number of attention heads for each attention layer in the Transformer decoder.
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num_key_value_heads (`int`, *optional*):
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This is the number of key_value heads that should be used to implement Grouped Query Attention. If
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`num_key_value_heads=None`, the model will use Multi Head Attention (MHA), if
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`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
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converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
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by meanpooling all the original heads within that group. For more details, check out [this
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paper](https://huggingface.co/papers/2305.13245).
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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num_mem_blocks (`int`, *optional*, defaults to 1):
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Number of unshared transformer blocks.
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use_shared_attention_adapter (`bool`, *optional*, defaults to `False`):
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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.
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adapter_rank (`int`, *optional*, defaults to 128):
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Rank of the adapter in the shared MLP and shared attention layers.
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use_mem_rope (`bool`, *optional*, defaults to `False`):
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If True, includes RoPE in the shared attention layers.
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rope_theta (`float`, *optional*, defaults to `10000.0`):
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The base period of the RoPE embeddings.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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rms_norm_eps (`float`, *optional*, defaults to 1e-05):
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The epsilon used by the rms normalization layers.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models). Only
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relevant if `config.is_decoder=True`.
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num_logits_to_keep (`int` or `None`, *optional*, defaults to 1):
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Number of prompt logits to calculate during generation. If `None`, all logits will be calculated. If an
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integer value, only last `num_logits_to_keep` logits will be calculated. Default is 1 because only the
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logits of the last prompt token are needed for generation. For long sequences, the logits for the entire
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sequence may use a lot of memory so, setting `num_logits_to_keep=1` will reduce memory footprint
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significantly.
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pad_token_id (`int`, *optional*, defaults to 0):
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The id of the padding token.
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bos_token_id (`int`, *optional*, defaults to 1):
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The id of the "beginning-of-sequence" token.
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eos_token_id (`int`, *optional*, defaults to 2):
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The id of the "end-of-sequence" token.
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use_long_context (`bool`, *optional*, defaults to `False`):
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Activates the context-extended version of Zamba by modifying RoPE.
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```python
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>>> from transformers import Zamba2Model, Zamba2Config
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>>> # Initializing a Zamba2-2.7B style configuration
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>>> configuration = Zamba2Config()
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>>> # Initializing a model from the Zamba2-2.7B style configuration
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>>> model = Zamba2Model(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "zamba2"
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attribute_map = {"head_dim": "attention_head_dim"}
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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vocab_size=32000,
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max_position_embeddings=4096,
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hidden_size=2560,
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num_hidden_layers=54,
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layers_block_type=None,
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mamba_d_state=64,
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mamba_d_conv=4,
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mamba_expand=2,
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mamba_ngroups=1,
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time_step_min=0.001,
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time_step_max=0.1,
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time_step_floor=1e-4,
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time_step_limit=None,
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n_mamba_heads=8,
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use_conv_bias=True,
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chunk_size=256,
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use_mem_eff_path=False,
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add_bias_linear=False,
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intermediate_size=None,
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hidden_act="gelu",
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num_attention_heads=32,
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num_key_value_heads=None,
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attention_dropout=0.0,
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num_mem_blocks=1,
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use_shared_attention_adapter=False,
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adapter_rank=128,
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use_mem_rope=False,
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rope_theta=10000,
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initializer_range=0.02,
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rms_norm_eps=1e-5,
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use_cache=True,
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num_logits_to_keep=1,
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pad_token_id=0,
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bos_token_id=1,
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eos_token_id=2,
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use_long_context=False,
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**kwargs,
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):
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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**kwargs,
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)
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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if intermediate_size is None:
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self.intermediate_size = 4 * hidden_size
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else:
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.num_mem_blocks = num_mem_blocks
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self.attention_hidden_size = 2 * hidden_size
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self.attention_head_dim = 2 * self.hidden_size // self.num_attention_heads
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self.attention_dropout = attention_dropout
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self.use_mem_rope = use_mem_rope
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self.use_long_context = use_long_context
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if use_mem_rope and use_long_context:
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a = 8
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rope_theta = rope_theta * a ** (self.attention_head_dim / (self.attention_head_dim - 2))
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self.rope_theta = rope_theta
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self.mamba_d_state = mamba_d_state
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self.mamba_d_conv = mamba_d_conv
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self.mamba_expand = mamba_expand
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self.add_bias_linear = add_bias_linear
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self.mamba_ngroups = mamba_ngroups
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self.n_mamba_heads = n_mamba_heads
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self.mamba_headdim = int(mamba_expand * hidden_size) // n_mamba_heads
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self.use_conv_bias = use_conv_bias
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self.chunk_size = chunk_size
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self.time_step_limit = time_step_limit
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self.use_shared_attention_adapter = use_shared_attention_adapter
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self.adapter_rank = adapter_rank
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self.time_step_min = time_step_min
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self.time_step_max = time_step_max
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self.time_step_floor = time_step_floor
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if use_long_context:
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self.max_position_embeddings = 16384
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if num_key_value_heads is None:
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num_key_value_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.num_attention_heads = num_attention_heads
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self.kv_channels = self.hidden_size // self.num_attention_heads
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self.num_query_groups = self.num_attention_heads
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# Below, "mamba" stands for mamba layer, "hybrid" stands for hybrid layer (composed by a shared transformer followed by mamba layer)
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if layers_block_type is None:
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self.layers_block_type = (
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["mamba"]
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+ (["mamba"] * 5 + ["hybrid"]) * 7
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+ ["mamba"] * 4
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+ ["hybrid"]
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+ ["mamba"] * 3
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+ ["hybrid"]
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+ ["mamba"] * 2
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)
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else:
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self.layers_block_type = layers_block_type
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self.initializer_range = initializer_range
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self.rms_norm_eps = rms_norm_eps
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self.use_cache = use_cache
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self.num_logits_to_keep = num_logits_to_keep
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self.hybrid_layer_ids = [index for index, type in enumerate(self.layers_block_type) if type == "hybrid"]
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self.use_mem_eff_path = use_mem_eff_path
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__all__ = ["Zamba2Config"]
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