# coding=utf-8 # Copyright 2025 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Callable, Optional import torch from ...cache_utils import Cache from ...configuration_utils import PretrainedConfig, layer_type_validation from ...modeling_flash_attention_utils import FlashAttentionKwargs from ...modeling_rope_utils import rope_config_validation from ...modeling_utils import ALL_ATTENTION_FUNCTIONS from ...processing_utils import Unpack from ...utils import logging from ..llama.modeling_llama import ( LlamaAttention, LlamaDecoderLayer, LlamaForCausalLM, LlamaForQuestionAnswering, LlamaForSequenceClassification, LlamaForTokenClassification, LlamaPreTrainedModel, apply_rotary_pos_emb, eager_attention_forward, ) from ..qwen2.modeling_qwen2 import Qwen2Model logger = logging.get_logger(__name__) class SmolLM3Config(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`SmolLM3Model`]. It is used to instantiate a SmolLM3 model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the SmolLM3 3B. e.g. [HuggingFaceTB/SmolLM3-3B](https://huggingface.co/HuggingFaceTB/SmolLM3-3B) Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 128256): Vocabulary size of the SmolLM3 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`SmolLM3Model`] hidden_size (`int`, *optional*, defaults to 2048): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 11008): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 36): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. num_key_value_heads (`int`, *optional*, defaults to 4): This is the number of key_value heads that should be used to implement Grouped Query Attention. If `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details checkout [this paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `16`. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 32768): The maximum sequence length that this model might ever be used with. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. rms_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon used by the rms normalization layers. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. pad_token_id (`int`, *optional*, defaults to 128004): The id of the padding token. bos_token_id (`int`, *optional*, defaults to 128000): The id of the beginning of sentence token. eos_token_id (`int`, *optional*, defaults to 128001): The id of the end of sentence token. rope_theta (`float`, *optional*, defaults to 2000000.0): The base period of the RoPE embeddings. rope_scaling (`Dict`, *optional*): Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value accordingly. Expected contents: `rope_type` (`str`): The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope', 'llama3'], with 'default' being the original RoPE implementation. `factor` (`float`, *optional*): Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In most scaling types, a `factor` of x will enable the model to handle sequences of length x * original maximum pre-trained length. `original_max_position_embeddings` (`int`, *optional*): Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during pretraining. `attention_factor` (`float`, *optional*): Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention computation. If unspecified, it defaults to value recommended by the implementation, using the `factor` field to infer the suggested value. `beta_fast` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear ramp function. If unspecified, it defaults to 32. `beta_slow` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear ramp function. If unspecified, it defaults to 1. `short_factor` (`List[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to short contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `long_factor` (`List[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to long contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `low_freq_factor` (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE `high_freq_factor` (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE use_sliding_window (`bool`, *optional*, defaults to `False`): Whether to use sliding window attention. sliding_window (`int`, *optional*): Sliding window attention (SWA) window size. If not specified, will default to `None`. no_rope_layers (`List[int]`, *optional*): List with at least the same length as the number of layers in the model. A `1` at an index position indicates that the corresponding layer will use RoPE, while a `0` indicates that it's a NoPE layer. no_rope_layer_interval (`int`, *optional*, defaults to 4): If `no_rope_layers` is `None`, it will be created using a NoPE layer every `no_rope_layer_interval` layers. layer_types (`list`, *optional*): Attention pattern for each layer. Automatically computed based on sliding window and NoPE settings. attention_bias (`bool`, *optional*, defaults to `False`): Whether to use a bias in the query, key, value and output projection layers during self-attention. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. ```python >>> from transformers import SmolLM3Model, SmolLM3Config >>> # Initializing a SmolLM3 style configuration >>> configuration = SmolLM3Config() >>> # Initializing a model from the SmolLM3 style configuration >>> model = SmolLM3Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "smollm3" keys_to_ignore_at_inference = ["past_key_values"] base_model_tp_plan = { "layers.*.self_attn.q_proj": "colwise", "layers.*.self_attn.k_proj": "colwise", "layers.*.self_attn.v_proj": "colwise", "layers.*.self_attn.o_proj": "rowwise", "layers.*.mlp.gate_proj": "colwise", "layers.*.mlp.up_proj": "colwise", "layers.*.mlp.down_proj": "rowwise", } base_model_pp_plan = { "embed_tokens": (["input_ids"], ["inputs_embeds"]), "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), "norm": (["hidden_states"], ["hidden_states"]), } def __init__( self, vocab_size=128256, hidden_size=2048, intermediate_size=11008, num_hidden_layers=36, num_attention_heads=16, num_key_value_heads=4, hidden_act="silu", max_position_embeddings=32768, initializer_range=0.02, rms_norm_eps=1e-6, use_cache=True, pad_token_id=128004, bos_token_id=128000, eos_token_id=128001, rope_theta=2000000.0, rope_scaling=None, use_sliding_window=False, sliding_window=None, no_rope_layers=None, no_rope_layer_interval=4, layer_types=None, attention_bias=False, attention_dropout=0.0, mlp_bias=False, **kwargs, ): super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs, ) self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.mlp_bias = mlp_bias self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.use_sliding_window = use_sliding_window self.sliding_window = sliding_window # for backward compatibility if num_key_value_heads is None: num_key_value_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads self.hidden_act = hidden_act self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.use_cache = use_cache self.rope_theta = rope_theta self.rope_scaling = rope_scaling self.attention_bias = attention_bias self.attention_dropout = attention_dropout if no_rope_layers is None: self.no_rope_layers = [ int((layer_idx + 1) % no_rope_layer_interval != 0) for layer_idx in range(num_hidden_layers) ] else: self.no_rope_layers = no_rope_layers self.no_rope_layer_interval = no_rope_layer_interval # Update layer_types based on sliding window and NoPE pattern if layer_types is None: layer_types = [] for layer_idx in range(num_hidden_layers): has_rope = self.no_rope_layers[layer_idx] if use_sliding_window and sliding_window is not None and not has_rope: layer_types.append("sliding_attention") else: layer_types.append("full_attention") self.layer_types = layer_types layer_type_validation(self.layer_types) # Validate the correctness of rotary position embeddings parameters # BC: if there is a 'type' field, move it to 'rope_type'. if self.rope_scaling is not None and "type" in self.rope_scaling: self.rope_scaling["rope_type"] = self.rope_scaling["type"] rope_config_validation(self) class SmolLM3Attention(LlamaAttention): def __init__(self, config: SmolLM3Config, layer_idx: int): super().__init__(config, layer_idx) self.use_rope = config.no_rope_layers[layer_idx] self.sliding_window = ( config.sliding_window if config.use_sliding_window and config.layer_types[layer_idx] == "sliding_attention" else None ) def forward( self, hidden_states: torch.Tensor, position_embeddings: tuple[torch.Tensor, torch.Tensor], attention_mask: Optional[torch.Tensor], past_key_value: Optional[Cache] = None, cache_position: Optional[torch.LongTensor] = None, **kwargs: Unpack[FlashAttentionKwargs], ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]: input_shape = hidden_states.shape[:-1] hidden_shape = (*input_shape, -1, self.head_dim) query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) if self.use_rope: cos, sin = position_embeddings query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) if past_key_value is not None: cache_kwargs = {"cache_position": cache_position} key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) attention_interface: Callable = eager_attention_forward if self.config._attn_implementation != "eager": attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation] attn_output, attn_weights = attention_interface( self, query_states, key_states, value_states, attention_mask, dropout=0.0 if not self.training else self.attention_dropout, scaling=self.scaling, sliding_window=self.sliding_window, **kwargs, ) attn_output = attn_output.reshape(*input_shape, -1).contiguous() attn_output = self.o_proj(attn_output) return attn_output, attn_weights class SmolLM3DecoderLayer(LlamaDecoderLayer): def __init__(self, config: SmolLM3Config, layer_idx: int): super().__init__(config, layer_idx) self.attention_type = config.layer_types[layer_idx] class SmolLM3PreTrainedModel(LlamaPreTrainedModel): pass class SmolLM3Model(Qwen2Model): pass class SmolLM3ForCausalLM(LlamaForCausalLM): pass class SmolLM3ForSequenceClassification(LlamaForSequenceClassification): pass class SmolLM3ForTokenClassification(LlamaForTokenClassification): pass class SmolLM3ForQuestionAnswering(LlamaForQuestionAnswering): pass __all__ = [ "SmolLM3Config", "SmolLM3PreTrainedModel", "SmolLM3Model", "SmolLM3ForCausalLM", "SmolLM3ForSequenceClassification", "SmolLM3ForTokenClassification", "SmolLM3ForQuestionAnswering", ]