359 lines
16 KiB
Python
359 lines
16 KiB
Python
# coding=utf-8
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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 typing import Callable, Optional
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import torch
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from ...cache_utils import Cache
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from ...configuration_utils import PretrainedConfig, layer_type_validation
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from ...modeling_flash_attention_utils import FlashAttentionKwargs
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from ...modeling_rope_utils import rope_config_validation
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from ...modeling_utils import ALL_ATTENTION_FUNCTIONS
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from ...processing_utils import Unpack
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from ...utils import logging
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from ..llama.modeling_llama import (
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LlamaAttention,
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LlamaDecoderLayer,
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LlamaForCausalLM,
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LlamaForQuestionAnswering,
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LlamaForSequenceClassification,
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LlamaForTokenClassification,
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LlamaPreTrainedModel,
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apply_rotary_pos_emb,
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eager_attention_forward,
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)
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from ..qwen2.modeling_qwen2 import Qwen2Model
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logger = logging.get_logger(__name__)
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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://arxiv.org/pdf/2305.13245.pdf). 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_theta (`float`, *optional*, defaults to 2000000.0):
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The base period of the RoPE embeddings.
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rope_scaling (`Dict`, *optional*):
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Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
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and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
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accordingly.
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Expected contents:
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`rope_type` (`str`):
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The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
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'llama3'], with 'default' being the original RoPE implementation.
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`factor` (`float`, *optional*):
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Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
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most scaling types, a `factor` of x will enable the model to handle sequences of length x *
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original maximum pre-trained length.
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`original_max_position_embeddings` (`int`, *optional*):
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Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
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pretraining.
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`attention_factor` (`float`, *optional*):
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Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
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computation. If unspecified, it defaults to value recommended by the implementation, using the
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`factor` field to infer the suggested value.
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`beta_fast` (`float`, *optional*):
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Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
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ramp function. If unspecified, it defaults to 32.
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`beta_slow` (`float`, *optional*):
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Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
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ramp function. If unspecified, it defaults to 1.
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`short_factor` (`List[float]`, *optional*):
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Only used with 'longrope'. The scaling factor to be applied to short contexts (<
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`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
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size divided by the number of attention heads divided by 2
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`long_factor` (`List[float]`, *optional*):
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Only used with 'longrope'. The scaling factor to be applied to long contexts (<
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`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
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size divided by the number of attention heads divided by 2
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`low_freq_factor` (`float`, *optional*):
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Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
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`high_freq_factor` (`float`, *optional*):
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Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
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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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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=128256,
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hidden_size=2048,
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intermediate_size=11008,
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num_hidden_layers=36,
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num_attention_heads=16,
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num_key_value_heads=4,
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hidden_act="silu",
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max_position_embeddings=32768,
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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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pad_token_id=128004,
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bos_token_id=128000,
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eos_token_id=128001,
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rope_theta=2000000.0,
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rope_scaling=None,
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use_sliding_window=False,
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sliding_window=None,
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no_rope_layers=None,
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no_rope_layer_interval=4,
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layer_types=None,
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attention_bias=False,
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attention_dropout=0.0,
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mlp_bias=False,
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**kwargs,
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):
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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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**kwargs,
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)
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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.rope_theta = rope_theta
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self.rope_scaling = rope_scaling
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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)
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# Validate the correctness of rotary position embeddings parameters
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# BC: if there is a 'type' field, move it to 'rope_type'.
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if self.rope_scaling is not None and "type" in self.rope_scaling:
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self.rope_scaling["rope_type"] = self.rope_scaling["type"]
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rope_config_validation(self)
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class SmolLM3Attention(LlamaAttention):
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def __init__(self, config: SmolLM3Config, layer_idx: int):
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super().__init__(config, layer_idx)
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self.use_rope = config.no_rope_layers[layer_idx]
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self.sliding_window = (
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config.sliding_window
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if config.use_sliding_window and config.layer_types[layer_idx] == "sliding_attention"
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else None
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)
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_embeddings: tuple[torch.Tensor, torch.Tensor],
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attention_mask: Optional[torch.Tensor],
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past_key_value: Optional[Cache] = None,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs: Unpack[FlashAttentionKwargs],
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) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
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input_shape = hidden_states.shape[:-1]
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hidden_shape = (*input_shape, -1, self.head_dim)
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query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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if self.use_rope:
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cos, sin = position_embeddings
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
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if past_key_value is not None:
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cache_kwargs = {"cache_position": cache_position}
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key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
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attention_interface: Callable = eager_attention_forward
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if self.config._attn_implementation != "eager":
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attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
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attn_output, attn_weights = attention_interface(
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self,
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query_states,
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key_states,
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value_states,
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attention_mask,
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dropout=0.0 if not self.training else self.attention_dropout,
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scaling=self.scaling,
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sliding_window=self.sliding_window,
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**kwargs,
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)
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attn_output = attn_output.reshape(*input_shape, -1).contiguous()
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attn_output = self.o_proj(attn_output)
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return attn_output, attn_weights
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class SmolLM3DecoderLayer(LlamaDecoderLayer):
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def __init__(self, config: SmolLM3Config, layer_idx: int):
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super().__init__(config, layer_idx)
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self.attention_type = config.layer_types[layer_idx]
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class SmolLM3PreTrainedModel(LlamaPreTrainedModel):
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pass
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class SmolLM3Model(Qwen2Model):
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pass
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class SmolLM3ForCausalLM(LlamaForCausalLM):
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pass
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class SmolLM3ForSequenceClassification(LlamaForSequenceClassification):
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pass
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class SmolLM3ForTokenClassification(LlamaForTokenClassification):
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pass
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class SmolLM3ForQuestionAnswering(LlamaForQuestionAnswering):
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pass
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__all__ = [
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"SmolLM3Config",
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"SmolLM3PreTrainedModel",
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"SmolLM3Model",
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"SmolLM3ForCausalLM",
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"SmolLM3ForSequenceClassification",
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"SmolLM3ForTokenClassification",
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"SmolLM3ForQuestionAnswering",
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]
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