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206 lines
10 KiB
206 lines
10 KiB
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from src/transformers/models/smollm3/modular_smollm3.py.
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# Do NOT edit this file manually as any edits will be overwritten by the generation of
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# the file from the modular. If any change should be done, please apply the change to the
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# modular_smollm3.py file directly. One of our CI enforces this.
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# Copyright 2025 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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from ...configuration_utils import PreTrainedConfig, layer_type_validation
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from ...modeling_rope_utils import RopeParameters
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class SmolLM3Config(PreTrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`SmolLM3Model`]. It is used to instantiate a
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SmolLM3 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 SmolLM3 3B.
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e.g. [HuggingFaceTB/SmolLM3-3B](https://huggingface.co/HuggingFaceTB/SmolLM3-3B)
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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 128256):
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Vocabulary size of the SmolLM3 model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`SmolLM3Model`]
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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 11008):
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Dimension of the MLP representations.
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num_hidden_layers (`int`, *optional*, defaults to 36):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 16):
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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 4):
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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://huggingface.co/papers/2305.13245). If it is not specified, will default to `16`.
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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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max_position_embeddings (`int`, *optional*, defaults to 32768):
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The maximum sequence length that this model might ever be used with.
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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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pad_token_id (`int`, *optional*, defaults to 128004):
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The id of the padding token.
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bos_token_id (`int`, *optional*, defaults to 128000):
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The id of the beginning of sentence token.
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eos_token_id (`int`, *optional*, defaults to 128001):
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The id of the end of sentence token.
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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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use_sliding_window (`bool`, *optional*, defaults to `False`):
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Whether to use sliding window attention.
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sliding_window (`int`, *optional*):
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Sliding window attention (SWA) window size. If not specified, will default to `None`.
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no_rope_layers (`List[int]`, *optional*):
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List with at least the same length as the number of layers in the model.
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A `1` at an index position indicates that the corresponding layer will use RoPE,
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while a `0` indicates that it's a NoPE layer.
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no_rope_layer_interval (`int`, *optional*, defaults to 4):
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If `no_rope_layers` is `None`, it will be created using a NoPE layer every
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`no_rope_layer_interval` layers.
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layer_types (`list`, *optional*):
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Attention pattern for each layer. Automatically computed based on sliding window and NoPE settings.
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attention_bias (`bool`, *optional*, defaults to `False`):
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Whether to use a bias in the query, key, value and output projection layers during self-attention.
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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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```python
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>>> from transformers import SmolLM3Model, SmolLM3Config
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>>> # Initializing a SmolLM3 style configuration
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>>> configuration = SmolLM3Config()
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>>> # Initializing a model from the SmolLM3 style configuration
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>>> model = SmolLM3Model(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 = "smollm3"
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keys_to_ignore_at_inference = ["past_key_values"]
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default_theta = 2000000.0
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base_model_tp_plan = {
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"layers.*.self_attn.q_proj": "colwise",
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"layers.*.self_attn.k_proj": "colwise",
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"layers.*.self_attn.v_proj": "colwise",
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"layers.*.self_attn.o_proj": "rowwise",
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"layers.*.mlp.gate_proj": "colwise",
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"layers.*.mlp.up_proj": "colwise",
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"layers.*.mlp.down_proj": "rowwise",
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}
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base_model_pp_plan = {
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"embed_tokens": (["input_ids"], ["inputs_embeds"]),
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"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
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"norm": (["hidden_states"], ["hidden_states"]),
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}
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def __init__(
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self,
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vocab_size: int | None = 128256,
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hidden_size: int | None = 2048,
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intermediate_size: int | None = 11008,
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num_hidden_layers: int | None = 36,
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num_attention_heads: int | None = 16,
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num_key_value_heads: int | None = 4,
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hidden_act: str | None = "silu",
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max_position_embeddings: int | None = 32768,
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initializer_range: float | None = 0.02,
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rms_norm_eps: int | None = 1e-6,
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use_cache: bool | None = True,
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pad_token_id: int | None = 128004,
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bos_token_id: int | None = 128000,
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eos_token_id: int | None = 128001,
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rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None,
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use_sliding_window: bool | None = False,
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sliding_window: int | None = None,
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no_rope_layers: int | None = None,
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no_rope_layer_interval: int | None = 4,
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layer_types: int | None = None,
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attention_bias: bool | None = False,
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attention_dropout: float | None = 0.0,
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mlp_bias: bool | None = False,
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tie_word_embeddings: bool | None = True,
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**kwargs,
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):
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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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self.tie_word_embeddings = tie_word_embeddings
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.mlp_bias = mlp_bias
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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.use_sliding_window = use_sliding_window
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self.sliding_window = sliding_window
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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.attention_bias = attention_bias
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self.attention_dropout = attention_dropout
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if no_rope_layers is None:
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self.no_rope_layers = [
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int((layer_idx + 1) % no_rope_layer_interval != 0) for layer_idx in range(num_hidden_layers)
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]
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else:
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self.no_rope_layers = no_rope_layers
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self.no_rope_layer_interval = no_rope_layer_interval
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# Update layer_types based on sliding window and NoPE pattern
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if layer_types is None:
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layer_types = []
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for layer_idx in range(num_hidden_layers):
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has_rope = self.no_rope_layers[layer_idx]
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if use_sliding_window and sliding_window is not None and not has_rope:
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layer_types.append("sliding_attention")
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else:
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layer_types.append("full_attention")
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self.layer_types = layer_types
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layer_type_validation(self.layer_types, self.num_hidden_layers)
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self.rope_parameters = rope_parameters
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super().__init__(**kwargs)
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__all__ = ["SmolLM3Config"]
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