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255 lines
10 KiB
255 lines
10 KiB
# Copyright 2026 the Tencent and HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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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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import torch
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from torch import nn
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from ... import initialization as init
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from ...modeling_rope_utils import RopeParameters
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from ...modeling_utils import PreTrainedModel
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from ...utils import logging
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from ..deepseek_v3.configuration_deepseek_v3 import DeepseekV3Config
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from ..deepseek_v3.modeling_deepseek_v3 import DeepseekV3Attention
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from ..llama.modeling_llama import (
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LlamaDecoderLayer,
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LlamaForCausalLM,
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LlamaModel,
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LlamaPreTrainedModel,
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LlamaRMSNorm,
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LlamaRotaryEmbedding,
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)
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from ..qwen3.modeling_qwen3 import Qwen3MLP
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logger = logging.get_logger(__name__)
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class YoutuConfig(DeepseekV3Config):
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r"""
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This is the configuration class to store the configuration of a [`YoutuModel`]. It is used to instantiate an Youtu
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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 Youtu-LLM-2B.
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e.g. [tencent/Youtu-LLM-2B](https://huggingface.co/tencent/Youtu-LLM-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 128256):
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Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`YoutuModel`]
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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 6144):
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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 16):
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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 16):
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In MLA, num_key_value_heads=num_attention_heads.
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kv_lora_rank (`int`, *optional*, defaults to 512):
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Rank of the LoRA matrices for key and value projections.
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q_lora_rank (`int`, *optional*, defaults to 1536):
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Rank of the LoRA matrices for query projections.
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qk_rope_head_dim (`int`, *optional*, defaults to 64):
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Dimension of the query/key heads that use rotary position embeddings.
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v_head_dim (`int`, *optional*, defaults to 128):
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Dimension of the value heads.
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qk_nope_head_dim (`int`, *optional*, defaults to 128):
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Dimension of the query/key heads that don't use rotary position embeddings.
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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 131072):
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The maximum sequence length that this model might ever be used with.
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initializer_range (`float`, *optional*):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices, except embedding matrices.
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embedding_initializer_range (`float`, *optional*):
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The standard deviation of the truncated_normal_initializer for initializing all embedding 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*):
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Padding token id.
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bos_token_id (`int`, *optional*, defaults to 128000):
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Beginning of stream token id.
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eos_token_id (`int`, *optional*, defaults to 128001):
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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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rope_interleave (`bool`, *optional*, defaults to `True`):
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Whether to interleave the rotary position embeddings.
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attention_bias (`bool`, defaults to `False`, *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 YoutuModel, YoutuConfig
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>>> # Initializing a Youtu-LLM-2B style configuration
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>>> configuration = YoutuConfig()
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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 = "youtu"
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base_model_tp_plan = {
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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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attribute_map = {}
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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 = 6144,
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num_hidden_layers: int | None = 32,
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num_attention_heads: int | None = 16,
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num_key_value_heads: int | None = 16,
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kv_lora_rank: int | None = 512,
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q_lora_rank: int | None = 1536,
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qk_rope_head_dim: int | None = 64,
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v_head_dim: int | None = 128,
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qk_nope_head_dim: int | None = 128,
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hidden_act: str | None = "silu",
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max_position_embeddings: int | None = 131072,
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initializer_range: float | None = None,
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embedding_initializer_range: float | None = None,
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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 = None,
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bos_token_id: int | None = 128000,
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eos_token_id: int | None = 128001,
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tie_word_embeddings: bool | None = True,
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rope_parameters: RopeParameters | dict[str, RopeParameters] = None,
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rope_interleave: bool | None = True,
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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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super().__init__(
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vocab_size=vocab_size,
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hidden_size=hidden_size,
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intermediate_size=intermediate_size,
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num_hidden_layers=num_hidden_layers,
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num_attention_heads=num_attention_heads,
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num_key_value_heads=num_key_value_heads,
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kv_lora_rank=kv_lora_rank,
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q_lora_rank=q_lora_rank,
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qk_rope_head_dim=qk_rope_head_dim,
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v_head_dim=v_head_dim,
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qk_nope_head_dim=qk_nope_head_dim,
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hidden_act=hidden_act,
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max_position_embeddings=max_position_embeddings,
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rms_norm_eps=rms_norm_eps,
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use_cache=use_cache,
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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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tie_word_embeddings=tie_word_embeddings,
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rope_parameters=rope_parameters,
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rope_interleave=rope_interleave,
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attention_bias=attention_bias,
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attention_dropout=attention_dropout,
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**kwargs,
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)
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# remove unused attribute
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del self.n_shared_experts
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del self.n_routed_experts
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del self.routed_scaling_factor
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del self.n_group
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del self.topk_group
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del self.num_experts_per_tok
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del self.first_k_dense_replace
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del self.norm_topk_prob
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del self.pretraining_tp
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del self.moe_intermediate_size
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# if initializer_range is None, set it to 2.0 / (5.0 * self.hidden_size) ** 0.5 (if hidden size is valid)
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if self.initializer_range is None:
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if self.hidden_size != 0:
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self.initializer_range = 2.0 / (5.0 * self.hidden_size) ** 0.5
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else:
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self.initializer_range = 0.02
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# if embedding_initializer_range is None, set it to 2.0 * self.initializer_range
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if embedding_initializer_range is None:
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self.embedding_initializer_range = 2.0 * self.initializer_range
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else:
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self.embedding_initializer_range = embedding_initializer_range
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def convert_rope_params_to_dict(self, ignore_keys_at_rope_validation: set | None = None, **kwargs):
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raise AttributeError("Not overwritten for the Youtu model!")
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class YoutuRMSNorm(LlamaRMSNorm):
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pass
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class YoutuRotaryEmbedding(LlamaRotaryEmbedding):
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pass
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class YoutuMLP(Qwen3MLP):
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pass
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class YoutuAttention(DeepseekV3Attention):
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pass
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class YoutuDecoderLayer(LlamaDecoderLayer):
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pass
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class YoutuPreTrainedModel(LlamaPreTrainedModel, PreTrainedModel):
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@torch.no_grad()
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def _init_weights(self, module):
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PreTrainedModel._init_weights(self, module)
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std = getattr(self.config, "initializer_range", 0.02)
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embed_std = getattr(self.config, "embedding_initializer_range", 2 * std)
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if isinstance(module, nn.Embedding):
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init.normal_(module.weight, mean=0.0, std=embed_std)
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if module.padding_idx is not None:
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init.zeros_(module.weight.data[module.padding_idx])
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class YoutuModel(LlamaModel):
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pass
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class YoutuForCausalLM(LlamaForCausalLM):
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pass
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__all__ = [
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"YoutuConfig",
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"YoutuPreTrainedModel",
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"YoutuModel",
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"YoutuForCausalLM",
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]
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