211 lines
9.9 KiB
Python
211 lines
9.9 KiB
Python
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# coding=utf-8
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# Copyright 2024 IBM and the HuggingFace Inc. team. All rights reserved.
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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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"""Bamba model configuration"""
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from ...configuration_utils import PretrainedConfig
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from ...utils import logging
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logger = logging.get_logger(__name__)
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class BambaConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`BambaModel`]. It is used to instantiate a
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BambaModel model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with defaults taken from [ibm-fms/Bamba-9.8b-2.2T-hf](https://huggingface.co/ibm-fms/Bamba-9.8b-2.2T-hf).
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The BambaModel is a hybrid [mamba2](https://github.com/state-spaces/mamba) architecture with SwiGLU.
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The checkpoints are jointly trained by IBM, Princeton, and UIUC.
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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 128000):
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Vocabulary size of the Bamba model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`BambaModel`]
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether the model's input and output word embeddings should be tied. Note that this is only relevant if the
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model has an output word embedding layer.
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hidden_size (`int`, *optional*, defaults to 4096):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 14336):
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Dimension of the MLP representations.
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num_hidden_layers (`int`, *optional*, defaults to 32):
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Number of hidden layers in the Transformer encoder.
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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 encoder.
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num_key_value_heads (`int`, *optional*, defaults to 8):
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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=num_attention_heads`, 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). If it is not specified, will default to `8`.
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hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
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The non-linear activation function (function or string) in the decoder.
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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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max_position_embeddings (`int`, *optional*, defaults to 262144):
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Max cached sequence length for the model
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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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attn_layer_indices (`list`, *optional*):
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Specifies the layer indices that will have full attention. Must contain values at most num_hidden_layers.
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mamba_n_heads (`int`, *optional*, defaults to 128):
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The number of mamba heads used in the v2 implementation.
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mamba_d_head (`int`, *optional*, defaults to `"auto"`):
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Head embedding dimension size
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mamba_n_groups (`int`, *optional*, defaults to 1):
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The number of the mamba groups used in the v2 implementation.
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mamba_d_state (`int`, *optional*, defaults to 256):
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The dimension the mamba state space latents
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mamba_d_conv (`int`, *optional*, defaults to 4):
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The size of the mamba convolution kernel
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mamba_expand (`int`, *optional*, defaults to 2):
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Expanding factor (relative to hidden_size) used to determine the mamba intermediate size
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mamba_chunk_size (`int`, *optional*, defaults to 256):
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The chunks in which to break the sequence when doing prefill/training
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mamba_conv_bias (`bool`, *optional*, defaults to `True`):
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Flag indicating whether or not to use bias in the convolution layer of the mamba mixer block.
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mamba_proj_bias (`bool`, *optional*, defaults to `False`):
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Flag indicating whether or not to use bias in the input and output projections (["in_proj", "out_proj"]) of the mamba mixer block
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z_loss_coefficient (`float`, *optional*, defaults to 0.0):
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Coefficient for auxiliary z-loss used to control logit growth during training
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"""
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model_type = "bamba"
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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=128000,
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tie_word_embeddings=False,
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hidden_size=4096,
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intermediate_size=14336,
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num_hidden_layers=32,
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num_attention_heads=32,
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num_key_value_heads=8,
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hidden_act="silu",
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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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max_position_embeddings=262144,
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attention_dropout=0.0,
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attn_layer_indices=None,
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mamba_n_heads=128,
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mamba_d_head="auto",
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mamba_n_groups=1,
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mamba_d_state=256,
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mamba_d_conv=4,
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mamba_expand=2,
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mamba_chunk_size=256,
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mamba_conv_bias=True,
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mamba_proj_bias=False,
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z_loss_coefficient=0.0,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.tie_word_embeddings = tie_word_embeddings
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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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.max_position_embeddings = max_position_embeddings
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self.attention_dropout = attention_dropout
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self.attention_bias = False
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self.mlp_bias = False
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# for backward compatibility
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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.hidden_act = hidden_act
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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.attn_layer_indices = attn_layer_indices
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self.rope_theta = 10000.0
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self.rope_scaling = None
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self.partial_rotary_factor = 0.5
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mamba_intermediate = mamba_expand * hidden_size
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if mamba_intermediate % mamba_n_heads != 0:
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raise ValueError("mamba_n_heads must divide mamba_expand * hidden_size")
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# for the mamba_v2, must satisfy the following
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if mamba_d_head == "auto":
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mamba_d_head = mamba_intermediate // mamba_n_heads
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if mamba_d_head * mamba_n_heads != mamba_intermediate:
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raise ValueError("The dimensions for the Mamba head state do not match the model intermediate_size")
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self.mamba_n_heads = mamba_n_heads
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self.mamba_d_head = mamba_d_head
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self.mamba_n_groups = mamba_n_groups
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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.mamba_chunk_size = mamba_chunk_size
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self.mamba_conv_bias = mamba_conv_bias
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self.mamba_proj_bias = mamba_proj_bias
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self.z_loss_coefficient = z_loss_coefficient
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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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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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@property
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def layers_block_type(self):
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return [
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"attention" if (self.attn_layer_indices and i in self.attn_layer_indices) else "mamba"
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for i in range(self.num_hidden_layers)
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]
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__all__ = ["BambaConfig"]
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