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242 lines
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13 KiB
Python
242 lines
No EOL
13 KiB
Python
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from src/transformers/models/glm4_moe/modular_glm4_moe.py.
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# Do NOT edit this file manually as any edits will be overwritten by the generation of
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# the file from the modular. If any change should be done, please apply the change to the
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# modular_glm4_moe.py file directly. One of our CI enforces this.
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# coding=utf-8
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# Copyright 2025 The ZhipuAI Inc. team and 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 transformers.configuration_utils import PretrainedConfig
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from transformers.modeling_rope_utils import rope_config_validation
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class Glm4MoeConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Glm4MoeModel`]. It is used to instantiate a
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Glm4Moe 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 [THUDM/GLM-4-100B-A10B](https://huggingface.co/THUDM/GLM-4-100B-A10B).
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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 151552):
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Vocabulary size of the Glm4Moe model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`Glm4MoeModel`]
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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 10944):
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Dimension of the MLP representations.
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num_hidden_layers (`int`, *optional*, defaults to 46):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 96):
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Number of attention heads for each attention layer in the Transformer encoder.
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partial_rotary_factor (`float`, *optional*, defaults to 0.5):
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The factor of the partial rotary position.
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num_key_value_heads (`int`, *optional*, defaults to 8):
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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 `32`.
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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*, 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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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether the model's input and output word embeddings should be tied.
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rope_theta (`float`, *optional*, defaults to 10000.0):
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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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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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moe_intermediate_size (`int`, *optional*, defaults to 1408):
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Intermediate size of the routed expert.
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num_experts_per_tok (`int`, *optional*, defaults to 8):
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number of experts per token.
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n_shared_experts (`int`, *optional*, defaults to 1):
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Number of shared experts.
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n_routed_experts (`int`, *optional*, defaults to 128):
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Number of routed experts.
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routed_scaling_factor (`float`, *optional*, defaults to 1.0):
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Scaling factor or routed experts.
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n_group (`int`, *optional*, defaults to 1):
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Number of groups for routed experts.
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topk_group (`int`, *optional*, defaults to 1):
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Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups).
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first_k_dense_replace (`int`, *optional*, defaults to 1):
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Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).
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\--k dense layers--/
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norm_topk_prob (`bool`, *optional*, defaults to `True`):
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Whether to normalize the topk probabilities.
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use_qk_norm (`bool`, *optional*, defaults to `False`):
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Whether to use query-key normalization in the attention
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```python
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>>> from transformers import Glm4MoeModel, Glm4MoeConfig
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>>> # Initializing a Glm4Moe style configuration
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>>> configuration = Glm4MoeConfig()
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>>> # Initializing a model from the GLM-4-MOE-100B-A10B style configuration
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>>> model = Glm4MoeModel(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 = "glm4_moe"
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keys_to_ignore_at_inference = ["past_key_values"]
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# Default tensor parallel plan for base model `Glm4Moe`
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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.experts.*.gate_proj": "colwise",
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"layers.*.mlp.experts.*.up_proj": "colwise",
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"layers.*.mlp.experts.*.down_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=151552,
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hidden_size=4096,
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intermediate_size=10944,
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num_hidden_layers=46,
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num_attention_heads=96,
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partial_rotary_factor=0.5,
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num_key_value_heads=8,
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hidden_act="silu",
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max_position_embeddings=131072,
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initializer_range=0.02,
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rms_norm_eps=1e-5,
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use_cache=True,
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tie_word_embeddings=False,
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rope_theta=10000.0,
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rope_scaling=None,
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attention_bias=False,
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attention_dropout=0.0,
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moe_intermediate_size=1408,
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num_experts_per_tok=8,
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n_shared_experts=1,
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n_routed_experts=128,
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routed_scaling_factor=1.0,
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n_group=1,
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topk_group=1,
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first_k_dense_replace=1,
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norm_topk_prob=True,
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use_qk_norm=False,
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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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self.partial_rotary_factor = partial_rotary_factor
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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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# 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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# MoE arguments
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self.moe_intermediate_size = moe_intermediate_size
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self.num_experts_per_tok = num_experts_per_tok
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self.n_group = n_group
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self.topk_group = topk_group
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self.n_shared_experts = n_shared_experts
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self.n_routed_experts = n_routed_experts
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self.routed_scaling_factor = routed_scaling_factor
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self.first_k_dense_replace = first_k_dense_replace
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self.norm_topk_prob = norm_topk_prob
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self.use_qk_norm = use_qk_norm
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super().__init__(
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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__all__ = ["Glm4MoeConfig"] |