224 lines
11 KiB
Python
224 lines
11 KiB
Python
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# coding=utf-8
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# Copyright 2024 AI21 Labs Ltd. 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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""" Jamba model configuration"""
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import math
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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 JambaConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`JambaModel`]. It is used to instantiate a
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Jamba 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 Jamba-v0.1 model.
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[ai21labs/Jamba-v0.1](https://huggingface.co/ai21labs/Jamba-v0.1)
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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 65536):
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Vocabulary size of the Jamba model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`JambaModel`]
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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 a 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 checkout [this
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paper](https://arxiv.org/pdf/2305.13245.pdf). 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-06):
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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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output_router_logits (`bool`, *optional*, defaults to `False`):
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Whether or not the router logits should be returned by the model. Enabling this will also
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allow the model to output the auxiliary loss. See [here]() for more details
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router_aux_loss_coef (`float`, *optional*, defaults to 0.001):
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The aux loss factor for the total loss.
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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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sliding_window (`int`, *optional*):
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Sliding window attention window size. If not specified, will default to `None`.
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max_position_embeddings (`int`, *optional*, defaults to 262144):
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This value doesn't have any real effect. The maximum sequence length that this model is intended to be
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used with. It can be used with longer sequences, but performance may degrade.
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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_experts_per_tok (`int`, *optional*, defaults to 2):
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The number of experts to root per-token, can be also interpreted as the `top-p` routing
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parameter
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num_experts (`int`, *optional*, defaults to 16):
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Number of experts per Sparse MLP layer.
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expert_layer_period (`int`, *optional*, defaults to 2):
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Once in this many layers, we will have an expert layer
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expert_layer_offset (`int`, *optional*, defaults to 1):
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The first layer index that contains an expert mlp layer
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attn_layer_period (`int`, *optional*, defaults to 8):
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Once in this many layers, we will have a vanilla attention layer
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attn_layer_offset (`int`, *optional*, defaults to 4):
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The first layer index that contains a vanilla attention mlp layer
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use_mamba_kernels (`bool`, *optional*, defaults to `True`):
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Flag indicating whether or not to use the fast mamba kernels. These are available only if `mamba-ssm` and
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`causal-conv1d` are installed, and the mamba modules are running on a CUDA device. Raises ValueError if
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`True` and kernels are not available
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mamba_d_state (`int`, *optional*, defaults to 16):
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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_dt_rank (`Union[int,str]`, *optional*, defaults to `"auto"`):
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Rank of the the mamba discretization projection matrix. `"auto"` means that it will default to `math.ceil(self.hidden_size / 16)`
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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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"""
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model_type = "jamba"
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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=65536,
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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-6,
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use_cache=True,
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num_logits_to_keep=1,
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output_router_logits=False,
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router_aux_loss_coef=0.001,
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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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sliding_window=None,
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max_position_embeddings=262144,
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attention_dropout=0.0,
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num_experts_per_tok=2,
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num_experts=16,
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expert_layer_period=2,
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expert_layer_offset=1,
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attn_layer_period=8,
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attn_layer_offset=4,
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use_mamba_kernels=True,
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mamba_d_state=16,
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mamba_d_conv=4,
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mamba_expand=2,
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mamba_dt_rank="auto",
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mamba_conv_bias=True,
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mamba_proj_bias=False,
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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.sliding_window = sliding_window
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self.max_position_embeddings = max_position_embeddings
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self.attention_dropout = attention_dropout
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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.output_router_logits = output_router_logits
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self.router_aux_loss_coef = router_aux_loss_coef
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self.num_experts_per_tok = num_experts_per_tok
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self.num_experts = num_experts
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self.expert_layer_period = expert_layer_period
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self.expert_layer_offset = expert_layer_offset
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self.attn_layer_period = attn_layer_period
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self.attn_layer_offset = attn_layer_offset
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self.use_mamba_kernels = use_mamba_kernels
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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_dt_rank = math.ceil(self.hidden_size / 16) if mamba_dt_rank == "auto" else mamba_dt_rank
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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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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 i % self.attn_layer_period == self.attn_layer_offset else "mamba"
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for i in range(self.num_hidden_layers)
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]
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@property
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def layers_num_experts(self):
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return [
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self.num_experts if i % self.expert_layer_period == self.expert_layer_offset else 1
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for i in range(self.num_hidden_layers)
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]
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