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167 lines
8.0 KiB
167 lines
8.0 KiB
# Copyright 2025 The HuggingFace 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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from ...configuration_utils import PreTrainedConfig
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from ...modeling_rope_utils import RopeParameters
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class Lfm2Config(PreTrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Lfm2Model`]. It is used to instantiate a LFM2
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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 LFM2-1.2B model.
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e.g. [LiquidAI/LFM2-1.2B](https://huggingface.co/LiquidAI/LFM2-1.2B)
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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 2560):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 12288):
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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 decoder.
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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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max_position_embeddings (`int`, *optional*, defaults to 128000):
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The maximum sequence length that this model might ever be used with. Lfm2 1 supports up to 2048 tokens,
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Lfm2 2 up to 4096, CodeLfm2 up to 16384.
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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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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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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_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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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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block_multiple_of (`int`, *optional*, defaults to 256):
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Multiple for the `intermediate_size`.
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block_ffn_dim_multiplier (`float`, *optional*, defaults to 1.0):
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Multiplier for the `intermediate_size`.
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block_auto_adjust_ff_dim (`bool`, *optional*, defaults to `True`):
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Whether to adjust the dim of the `intermediate_size`.
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full_attn_idxs (`Optional`, *optional*):
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Index of the layers which use attention.
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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 Lfm2Model, Lfm2Config
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>>> # Initializing a LFM2 model
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>>> configuration = Lfm2Config()
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>>> # Initializing a model from the LFM2-1.2B style configuration
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>>> model = Lfm2Model(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 = "lfm2"
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keys_to_ignore_at_inference = ["past_key_values"]
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default_theta = 1000000.0
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def __init__(
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self,
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vocab_size: int | None = 65536,
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hidden_size: int | None = 2560,
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intermediate_size: int | None = 12288,
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num_hidden_layers: int | None = 32,
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num_attention_heads: int | None = 32,
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num_key_value_heads: int | None = 8,
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max_position_embeddings: int | None = 128_000,
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initializer_range: float | None = 0.02,
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norm_eps: float | None = 0.00001,
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use_cache: bool | None = True,
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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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tie_word_embeddings: bool | None = True,
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rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None,
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conv_bias: bool | None = False,
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conv_L_cache: int | None = 3,
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block_multiple_of: int | None = 256,
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block_ffn_dim_multiplier: float | None = 1.0,
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block_auto_adjust_ff_dim: bool | None = True,
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full_attn_idxs: list[int] | None = None,
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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.num_hidden_layers = num_hidden_layers
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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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self.initializer_range = initializer_range
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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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# MLP config
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self.intermediate_size = kwargs.get("block_ff_dim", intermediate_size) # to fit original config keys
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self.block_multiple_of = block_multiple_of
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self.block_ffn_dim_multiplier = block_ffn_dim_multiplier
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self.block_auto_adjust_ff_dim = block_auto_adjust_ff_dim
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self.layer_types = layer_types
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if self.layer_types is None:
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full_attn_idxs = full_attn_idxs if full_attn_idxs is not None else list(range(num_hidden_layers))
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self.layer_types = ["full_attention" if i in full_attn_idxs else "conv" for i in range(num_hidden_layers)]
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self.rope_parameters = rope_parameters
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tie_word_embeddings = kwargs.get("tie_embedding", tie_word_embeddings) # to fit original config keys
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self.tie_word_embeddings = tie_word_embeddings
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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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super().__init__(**kwargs)
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__all__ = ["Lfm2Config"]
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