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239 lines
12 KiB
239 lines
12 KiB
# 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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from ...modeling_rope_utils import RopeParameters
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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_parameters (`RopeParameters`, *optional*):
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Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain
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a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE
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with longer `max_position_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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tie_word_embeddings (`bool`, *optional*, defaults to `True`):
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Whether to tie weight embeddings
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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: int | None = 32000,
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max_position_embeddings: int | None = 4096,
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hidden_size: int | None = 2560,
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num_hidden_layers: int | None = 54,
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layers_block_type: list[str] | None = None,
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mamba_d_state: int | None = 64,
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mamba_d_conv: int | None = 4,
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mamba_expand: int | None = 2,
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mamba_ngroups: int | None = 1,
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time_step_min: float | None = 0.001,
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time_step_max: float | None = 0.1,
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time_step_floor: int | None = 1e-4,
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time_step_limit: int | None = None,
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n_mamba_heads: int | None = 8,
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use_conv_bias: bool | None = True,
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chunk_size: int | None = 256,
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use_mem_eff_path: bool | None = False,
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add_bias_linear: bool | None = False,
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intermediate_size: int | None = None,
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hidden_act: str | None = "gelu",
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num_attention_heads: int | None = 32,
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num_key_value_heads: int | None = None,
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attention_dropout: float | None = 0.0,
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num_mem_blocks: int | None = 1,
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use_shared_attention_adapter: bool | None = False,
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adapter_rank: int | None = 128,
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use_mem_rope: bool | None = False,
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rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None,
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initializer_range: float | None = 0.02,
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rms_norm_eps: int | None = 1e-5,
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use_cache: bool | None = True,
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num_logits_to_keep: int | None = 1,
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pad_token_id: int | None = 0,
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bos_token_id: int | None = 1,
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eos_token_id: int | None = 2,
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use_long_context: bool | None = False,
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tie_word_embeddings: bool | None = True,
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**kwargs,
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):
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self.pad_token_id = pad_token_id
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self.bos_token_id = bos_token_id
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self.eos_token_id = eos_token_id
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self.tie_word_embeddings = tie_word_embeddings
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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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self.rope_parameters = rope_parameters
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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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super().__init__(**kwargs)
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__all__ = ["Zamba2Config"]
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