160 lines
7.1 KiB
Python
160 lines
7.1 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from transformers.configuration_utils import PretrainedConfig
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class Lfm2MoeConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Lfm2MoeModel`]. It is used to instantiate a LFM2 Moe
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to that of the LFM2-8B-A1B model.
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e.g. [LiquidAI/LFM2-8B-A1B](https://huggingface.co/LiquidAI/LFM2-8B-A1B)
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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 LLaMA model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`Lfm2Model`]
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hidden_size (`int`, *optional*, defaults to 2048):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 7168):
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Dimension of the MLP representations.
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moe_intermediate_size (`int`, *optional*, defaults to 1792):
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Intermediate size of the routed expert.
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num_hidden_layers (`int`, *optional*, defaults to 32):
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Number of hidden layers in the Transformer decoder.
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pad_token_id (`int`, *optional*, defaults to 0):
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Padding token id.
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bos_token_id (`int`, *optional*, defaults to 1):
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Beginning of stream token id.
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eos_token_id (`int`, *optional*, defaults to 2):
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End of stream token id.
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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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rope_theta (`float`, *optional*, defaults to 1000000.0):
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The base period of the RoPE embeddings.
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max_position_embeddings (`int`, *optional*, defaults to 128000):
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The maximum sequence length that this model might ever be used with.
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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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norm_eps (`float`, *optional*, defaults to 1e-05):
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The epsilon used by the rms normalization layers.
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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*, 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
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`num_attention_heads`.
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conv_bias (`bool`, *optional*, defaults to `False`):
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Whether to use bias in the conv layers.
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conv_L_cache (`int`, *optional*, defaults to 3):
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L_cache dim in the conv layers.
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num_dense_layers (`int`, *optional*, defaults to 2):
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Number of dense Lfm2MoeMLP layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).
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num_experts_per_tok (`int`, *optional*, defaults to 4):
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Number of selected experts.
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num_experts (`int`, *optional*, defaults to 32):
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Number of routed experts.
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use_expert_bias (`bool`, *optional*, defaults to `True`):
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Whether to use the expert bias on the routing weights.
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routed_scaling_factor (`float`, *optional*, defaults to 1.0):
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Scaling factor for routed experts in MoE models.
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norm_topk_prob (`bool`, *optional*, defaults to `True`):
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Whether to normalize the topk probabilities.
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layer_types (`Optional`, *optional*):
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Type of each layers.
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```python
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>>> from transformers import Lfm2MoeModel, Lfm2MoeConfig
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>>> # Initializing a LFM2 Moe model
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>>> configuration = Lfm2MoeConfig()
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>>> # Initializing a model from the LFM2-8B-A1B style configuration
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>>> model = Lfm2MoeModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```""" # noqa: E501
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model_type = "lfm2_moe"
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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 = 65536,
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hidden_size: int = 2048,
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intermediate_size: int = 7168,
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moe_intermediate_size: int = 1792,
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num_hidden_layers: int = 32,
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pad_token_id: int = 0,
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bos_token_id: int = 1,
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eos_token_id: int = 2,
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tie_word_embeddings: bool = True,
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rope_theta: float = 1000000.0,
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max_position_embeddings: int = 128_000,
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use_cache: bool = True,
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norm_eps: float = 0.00001,
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num_attention_heads: int = 32,
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num_key_value_heads: int = 8,
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conv_bias: bool = False,
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conv_L_cache: int = 3,
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num_dense_layers: int = 2,
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num_experts_per_tok: int = 4,
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num_experts: int = 32,
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use_expert_bias: bool = True,
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routed_scaling_factor: float = 1.0,
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norm_topk_prob: bool = True,
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layer_types: list[str] | None = None,
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**kwargs,
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):
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self.vocab_size = vocab_size
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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.rope_theta = rope_theta
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self.max_position_embeddings = max_position_embeddings
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self.use_cache = use_cache
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self.norm_eps = norm_eps
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# attn operator config
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self.num_attention_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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# custom operator config
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self.conv_bias = conv_bias
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self.conv_L_cache = conv_L_cache
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# moe config
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self.num_dense_layers = num_dense_layers
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self.moe_intermediate_size = moe_intermediate_size
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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.use_expert_bias = use_expert_bias
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self.routed_scaling_factor = routed_scaling_factor
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self.norm_topk_prob = norm_topk_prob
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self.layer_types = layer_types
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tie_word_embeddings = kwargs.get(
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"tie_embedding", tie_word_embeddings
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) # to fit original config keys
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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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__all__ = ["Lfm2MoeConfig"]
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