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| 1 | +# coding=utf-8 |
| 2 | +# Copyright 2024 AI21 Labs Ltd. and the HuggingFace Inc. team. All rights reserved. |
| 3 | +# |
| 4 | +# Licensed under the Apache License, Version 2.0 (the "License"); |
| 5 | +# you may not use this file except in compliance with the License. |
| 6 | +# You may obtain a copy of the License at |
| 7 | +# |
| 8 | +# http://www.apache.org/licenses/LICENSE-2.0 |
| 9 | +# |
| 10 | +# Unless required by applicable law or agreed to in writing, software |
| 11 | +# distributed under the License is distributed on an "AS IS" BASIS, |
| 12 | +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 13 | +# See the License for the specific language governing permissions and |
| 14 | +# limitations under the License. |
| 15 | +""" Jamba model configuration""" |
| 16 | +import math |
| 17 | + |
| 18 | +from ..configuration_utils import PretrainedConfig |
| 19 | + |
| 20 | +__all__ = [ |
| 21 | + "JambaConfig", |
| 22 | +] |
| 23 | + |
| 24 | + |
| 25 | +class JambaConfig(PretrainedConfig): |
| 26 | + r""" |
| 27 | + This is the configuration class to store the configuration of a [`JambaModel`]. It is used to instantiate a |
| 28 | + Jamba model according to the specified arguments, defining the model architecture. Instantiating a configuration |
| 29 | + with the defaults will yield a similar configuration to that of the Jamba-v0.1 model. |
| 30 | +
|
| 31 | + [ai21labs/Jamba-v0.1](https://huggingface.co/ai21labs/Jamba-v0.1) |
| 32 | +
|
| 33 | + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the |
| 34 | + documentation from [`PretrainedConfig`] for more information. |
| 35 | +
|
| 36 | +
|
| 37 | + Args: |
| 38 | + vocab_size (`int`, *optional*, defaults to 65536): |
| 39 | + Vocabulary size of the Jamba model. Defines the number of different tokens that can be represented by the |
| 40 | + `inputs_ids` passed when calling [`JambaModel`] |
| 41 | + tie_word_embeddings (`bool`, *optional*, defaults to `False`): |
| 42 | + Whether the model's input and output word embeddings should be tied. Note that this is only relevant if the |
| 43 | + model has a output word embedding layer. |
| 44 | + hidden_size (`int`, *optional*, defaults to 4096): |
| 45 | + Dimension of the hidden representations. |
| 46 | + intermediate_size (`int`, *optional*, defaults to 14336): |
| 47 | + Dimension of the MLP representations. |
| 48 | + num_hidden_layers (`int`, *optional*, defaults to 32): |
| 49 | + Number of hidden layers in the Transformer encoder. |
| 50 | + num_attention_heads (`int`, *optional*, defaults to 32): |
| 51 | + Number of attention heads for each attention layer in the Transformer encoder. |
| 52 | + num_key_value_heads (`int`, *optional*, defaults to 8): |
| 53 | + This is the number of key_value heads that should be used to implement Grouped Query Attention. If |
| 54 | + `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if |
| 55 | + `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When |
| 56 | + converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed |
| 57 | + by meanpooling all the original heads within that group. For more details checkout [this |
| 58 | + paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `8`. |
| 59 | + hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): |
| 60 | + The non-linear activation function (function or string) in the decoder. |
| 61 | + initializer_range (`float`, *optional*, defaults to 0.02): |
| 62 | + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. |
| 63 | + rms_norm_eps (`float`, *optional*, defaults to 1e-06): |
| 64 | + The epsilon used by the rms normalization layers. |
| 65 | + use_cache (`bool`, *optional*, defaults to `True`): |
| 66 | + Whether or not the model should return the last key/values attentions (not used by all models). Only |
| 67 | + relevant if `config.is_decoder=True`. |
| 68 | + num_logits_to_keep (`int` or `None`, *optional*, defaults to 1): |
| 69 | + Number of prompt logits to calculate during generation. If `None`, all logits will be calculated. If an |
| 70 | + integer value, only last `num_logits_to_keep` logits will be calculated. Default is 1 because only the |
| 71 | + logits of the last prompt token are needed for generation. For long sequences, the logits for the entire |
| 72 | + sequence may use a lot of memory so, setting `num_logits_to_keep=1` will reduce memory footprint |
| 73 | + significantly. |
| 74 | + output_router_logits (`bool`, *optional*, defaults to `False`): |
| 75 | + Whether or not the router logits should be returned by the model. Enabling this will also |
| 76 | + allow the model to output the auxiliary loss. See [here]() for more details |
| 77 | + router_aux_loss_coef (`float`, *optional*, defaults to 0.001): |
| 78 | + The aux loss factor for the total loss. |
| 79 | + pad_token_id (`int`, *optional*, defaults to 0): |
| 80 | + The id of the padding token. |
| 81 | + bos_token_id (`int`, *optional*, defaults to 1): |
| 82 | + The id of the "beginning-of-sequence" token. |
| 83 | + eos_token_id (`int`, *optional*, defaults to 2): |
| 84 | + The id of the "end-of-sequence" token. |
| 85 | + sliding_window (`int`, *optional*): |
| 86 | + Sliding window attention window size. If not specified, will default to `None`. |
| 87 | + max_position_embeddings (`int`, *optional*, defaults to 262144): |
| 88 | + This value doesn't have any real effect. The maximum sequence length that this model is intended to be |
| 89 | + used with. It can be used with longer sequences, but performance may degrade. |
| 90 | + attention_dropout (`float`, *optional*, defaults to 0.0): |
| 91 | + The dropout ratio for the attention probabilities. |
| 92 | + num_experts_per_tok (`int`, *optional*, defaults to 2): |
| 93 | + The number of experts to root per-token, can be also interpreted as the `top-p` routing |
| 94 | + parameter |
| 95 | + num_experts (`int`, *optional*, defaults to 16): |
| 96 | + Number of experts per Sparse MLP layer. |
| 97 | + expert_layer_period (`int`, *optional*, defaults to 2): |
| 98 | + Once in this many layers, we will have an expert layer |
| 99 | + expert_layer_offset (`int`, *optional*, defaults to 1): |
| 100 | + The first layer index that contains an expert mlp layer |
| 101 | + attn_layer_period (`int`, *optional*, defaults to 8): |
| 102 | + Once in this many layers, we will have a vanilla attention layer |
| 103 | + attn_layer_offset (`int`, *optional*, defaults to 4): |
| 104 | + The first layer index that contains a vanilla attention mlp layer |
| 105 | + use_mamba_kernels (`bool`, *optional*, defaults to `True`): |
| 106 | + Flag indicating whether or not to use the fast mamba kernels. These are available only if `mamba-ssm` and |
| 107 | + `causal-conv1d` are installed, and the mamba modules are running on a CUDA device. Raises ValueError if |
| 108 | + `True` and kernels are not available |
| 109 | + mamba_d_state (`int`, *optional*, defaults to 16): |
| 110 | + The dimension the mamba state space latents |
| 111 | + mamba_d_conv (`int`, *optional*, defaults to 4): |
| 112 | + The size of the mamba convolution kernel |
| 113 | + mamba_expand (`int`, *optional*, defaults to 2): |
| 114 | + Expanding factor (relative to hidden_size) used to determine the mamba intermediate size |
| 115 | + mamba_dt_rank (`Union[int,str]`, *optional*, defaults to `"auto"`): |
| 116 | + Rank of the the mamba discretization projection matrix. `"auto"` means that it will default to `math.ceil(self.hidden_size / 16)` |
| 117 | + mamba_conv_bias (`bool`, *optional*, defaults to `True`): |
| 118 | + Flag indicating whether or not to use bias in the convolution layer of the mamba mixer block. |
| 119 | + mamba_proj_bias (`bool`, *optional*, defaults to `False`): |
| 120 | + Flag indicating whether or not to use bias in the input and output projections (["in_proj", "out_proj"]) of the mamba mixer block |
| 121 | +
|
| 122 | + """ |
| 123 | + |
| 124 | + model_type = "jamba" |
| 125 | + keys_to_ignore_at_inference = ["past_key_values"] |
| 126 | + |
| 127 | + def __init__( |
| 128 | + self, |
| 129 | + vocab_size=65536, |
| 130 | + tie_word_embeddings=False, |
| 131 | + hidden_size=4096, |
| 132 | + intermediate_size=14336, |
| 133 | + num_hidden_layers=32, |
| 134 | + num_attention_heads=32, |
| 135 | + num_key_value_heads=8, |
| 136 | + hidden_act="silu", |
| 137 | + initializer_range=0.02, |
| 138 | + rms_norm_eps=1e-6, |
| 139 | + use_cache=True, |
| 140 | + num_logits_to_keep=1, |
| 141 | + output_router_logits=False, |
| 142 | + router_aux_loss_coef=0.001, |
| 143 | + pad_token_id=0, |
| 144 | + bos_token_id=1, |
| 145 | + eos_token_id=2, |
| 146 | + sliding_window=None, |
| 147 | + max_position_embeddings=262144, |
| 148 | + attention_dropout=0.0, |
| 149 | + num_experts_per_tok=2, |
| 150 | + num_experts=16, |
| 151 | + expert_layer_period=2, |
| 152 | + expert_layer_offset=1, |
| 153 | + attn_layer_period=8, |
| 154 | + attn_layer_offset=4, |
| 155 | + use_mamba_kernels=True, |
| 156 | + mamba_d_state=16, |
| 157 | + mamba_d_conv=4, |
| 158 | + mamba_expand=2, |
| 159 | + mamba_dt_rank="auto", |
| 160 | + mamba_conv_bias=True, |
| 161 | + mamba_proj_bias=False, |
| 162 | + **kwargs, |
| 163 | + ): |
| 164 | + kwargs["return_dict"] = kwargs.pop("return_dict", True) |
| 165 | + super().__init__( |
| 166 | + pad_token_id=pad_token_id, |
| 167 | + bos_token_id=bos_token_id, |
| 168 | + eos_token_id=eos_token_id, |
| 169 | + tie_word_embeddings=tie_word_embeddings, |
| 170 | + **kwargs, |
| 171 | + ) |
| 172 | + self.vocab_size = vocab_size |
| 173 | + self.tie_word_embeddings = tie_word_embeddings |
| 174 | + self.hidden_size = hidden_size |
| 175 | + self.intermediate_size = intermediate_size |
| 176 | + self.num_hidden_layers = num_hidden_layers |
| 177 | + self.num_attention_heads = num_attention_heads |
| 178 | + self.sliding_window = sliding_window |
| 179 | + self.max_position_embeddings = max_position_embeddings |
| 180 | + self.attention_dropout = attention_dropout |
| 181 | + |
| 182 | + # for backward compatibility |
| 183 | + if num_key_value_heads is None: |
| 184 | + num_key_value_heads = num_attention_heads |
| 185 | + |
| 186 | + self.num_key_value_heads = num_key_value_heads |
| 187 | + self.hidden_act = hidden_act |
| 188 | + self.initializer_range = initializer_range |
| 189 | + self.rms_norm_eps = rms_norm_eps |
| 190 | + |
| 191 | + self.use_cache = use_cache |
| 192 | + self.num_logits_to_keep = num_logits_to_keep |
| 193 | + self.output_router_logits = output_router_logits |
| 194 | + self.router_aux_loss_coef = router_aux_loss_coef |
| 195 | + |
| 196 | + self.num_experts_per_tok = num_experts_per_tok |
| 197 | + self.num_experts = num_experts |
| 198 | + self.expert_layer_period = expert_layer_period |
| 199 | + self.expert_layer_offset = expert_layer_offset |
| 200 | + self.attn_layer_period = attn_layer_period |
| 201 | + self.attn_layer_offset = attn_layer_offset |
| 202 | + |
| 203 | + self.use_mamba_kernels = use_mamba_kernels |
| 204 | + self.mamba_d_state = mamba_d_state |
| 205 | + self.mamba_d_conv = mamba_d_conv |
| 206 | + self.mamba_expand = mamba_expand |
| 207 | + self.mamba_dt_rank = math.ceil(self.hidden_size / 16) if mamba_dt_rank == "auto" else mamba_dt_rank |
| 208 | + self.mamba_conv_bias = mamba_conv_bias |
| 209 | + self.mamba_proj_bias = mamba_proj_bias |
| 210 | + |
| 211 | + @property |
| 212 | + def layers_block_type(self): |
| 213 | + return [ |
| 214 | + "attention" if i % self.attn_layer_period == self.attn_layer_offset else "mamba" |
| 215 | + for i in range(self.num_hidden_layers) |
| 216 | + ] |
| 217 | + |
| 218 | + @property |
| 219 | + def layers_num_experts(self): |
| 220 | + return [ |
| 221 | + self.num_experts if i % self.expert_layer_period == self.expert_layer_offset else 1 |
| 222 | + for i in range(self.num_hidden_layers) |
| 223 | + ] |
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