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346 lines
14 KiB
346 lines
14 KiB
# Copyright 2025 the HuggingFace Inc. team and the Swiss AI Initiative. All rights reserved.
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#
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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 collections.abc import Callable
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import torch
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from torch import nn
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from ...activations import ACT2CLS
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from ...cache_utils import Cache
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from ...configuration_utils import PreTrainedConfig
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from ...modeling_rope_utils import RopeParameters
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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 TransformersKwargs, 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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LlamaForTokenClassification,
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LlamaModel,
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LlamaPreTrainedModel,
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LlamaRMSNorm,
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LlamaRotaryEmbedding,
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apply_rotary_pos_emb,
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eager_attention_forward,
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)
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from ..nemotron.modeling_nemotron import NemotronMLP
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logger = logging.get_logger(__name__)
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class ApertusConfig(PreTrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`ApertusModel`]. It is used to instantiate a Apertus
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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 Apertus-8B.
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e.g. [swiss-ai/Apertus-8B](https://huggingface.co/swiss-ai/Apertus-8B)
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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 131072):
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Vocabulary size of the Apertus model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`ApertusModel`]
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hidden_size (`int`, *optional*, defaults to 4096):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 14336):
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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*):
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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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hidden_act (`str` or `function`, *optional*, defaults to `"xielu"`):
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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 65536):
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The maximum sequence length that this model might ever be used with. Apertus supports up to 65536 tokens.
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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-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 3):
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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 `False`):
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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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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 ApertusModel, ApertusConfig
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>>> # Initializing a Apertus-8B style configuration
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>>> configuration = ApertusConfig()
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>>> # Initializing a model from the Apertus-8B style configuration
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>>> model = ApertusModel(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 = "apertus"
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keys_to_ignore_at_inference = ["past_key_values"]
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default_theta = 12000000.0
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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.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 = 131072,
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hidden_size: int | None = 4096,
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intermediate_size: int | None = 14336,
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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 = None,
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hidden_act: str | None = "xielu",
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max_position_embeddings: int | None = 65536,
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initializer_range: float | None = 0.02,
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rms_norm_eps: float | None = 1e-5,
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use_cache: bool | None = True,
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pad_token_id: int | None = 3,
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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 = False,
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rope_parameters: RopeParameters | None = {
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"rope_type": "llama3",
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"rope_theta": 12000000.0,
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"factor": 8.0,
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"original_max_position_embeddings": 8192,
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"low_freq_factor": 1.0,
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"high_freq_factor": 4.0,
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},
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attention_bias: bool | None = False,
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attention_dropout: float | None = 0.0,
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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.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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# 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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self.rope_parameters = rope_parameters
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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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class ApertusMLP(NemotronMLP):
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def __init__(self, config):
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super().__init__(config)
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self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
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self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
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if config.hidden_act == "xielu":
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self.act_fn = ACT2CLS["xielu"](dtype=config.dtype)
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class ApertusRMSNorm(LlamaRMSNorm):
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pass
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class ApertusRotaryEmbedding(LlamaRotaryEmbedding):
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pass
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class ApertusAttention(LlamaAttention):
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def __init__(self, config: ApertusConfig, layer_idx: int | None = None):
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super().__init__(config, layer_idx)
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self.q_norm = ApertusRMSNorm(self.head_dim, config.rms_norm_eps)
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self.k_norm = ApertusRMSNorm(self.head_dim, config.rms_norm_eps)
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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: torch.Tensor | None,
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past_key_values: Cache | None = None,
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cache_position: torch.LongTensor | None = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> tuple[torch.Tensor, 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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query_states = self.q_norm(query_states)
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key_states = self.k_norm(key_states)
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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_values is not None:
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cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
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key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
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attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
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self.config._attn_implementation, eager_attention_forward
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)
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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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**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 ApertusDecoderLayer(LlamaDecoderLayer):
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def __init__(self, config: ApertusConfig, layer_idx: int):
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super().__init__(config, layer_idx)
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self.attention_layernorm = ApertusRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.feedforward_layernorm = ApertusRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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del self.input_layernorm
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del self.post_attention_layernorm
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: torch.Tensor | None = None,
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position_ids: torch.LongTensor | None = None,
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past_key_values: Cache | None = None,
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use_cache: bool | None = False,
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cache_position: torch.LongTensor | None = None,
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position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> tuple[torch.Tensor]:
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residual = hidden_states
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hidden_states = self.attention_layernorm(hidden_states)
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hidden_states, _ = self.self_attn(
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hidden_states=hidden_states,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_values=past_key_values,
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use_cache=use_cache,
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cache_position=cache_position,
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position_embeddings=position_embeddings,
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**kwargs,
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)
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hidden_states = residual + hidden_states
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# Fully Connected
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residual = hidden_states
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hidden_states = self.feedforward_layernorm(hidden_states)
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hidden_states = self.mlp(hidden_states)
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hidden_states = residual + hidden_states
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return hidden_states
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class ApertusPreTrainedModel(LlamaPreTrainedModel):
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pass
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class ApertusModel(LlamaModel):
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pass
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class ApertusForCausalLM(LlamaForCausalLM):
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def forward(self, **super_kwargs):
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r"""
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labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
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Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
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config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
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(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
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Example:
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```python
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>>> from transformers import AutoTokenizer, ApertusForCausalLM
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>>> model = ApertusForCausalLM.from_pretrained("swiss-ai/Apertus-8B")
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>>> tokenizer = AutoTokenizer.from_pretrained("swiss-ai/Apertus-8B")
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>>> prompt = "Hey, are you conscious? Can you talk to me?"
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>>> inputs = tokenizer(prompt, return_tensors="pt")
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>>> # Generate
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>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
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>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
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"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
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```"""
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return super().forward(**super_kwargs)
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class ApertusForTokenClassification(LlamaForTokenClassification):
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pass
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__all__ = [
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"ApertusConfig",
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"ApertusModel",
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"ApertusForCausalLM",
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"ApertusForTokenClassification",
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"ApertusPreTrainedModel",
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]
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