[Model] Add huggingface skt/A.X-K1 model (#32407)
Signed-off-by: Sungwan(Alex) Kim <sw0726.kim@sktelecom.com> Signed-off-by: fort726 <38447663+fort726@users.noreply.github.com> Co-authored-by: Sungwan(Alex) Kim <sw0726.kim@sktelecom.com> Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> Co-authored-by: TJian <tunjian.tan@embeddedllm.com>
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fort726
@@ -369,6 +369,7 @@ th {
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| `AquilaForCausalLM` | Aquila, Aquila2 | `BAAI/Aquila-7B`, `BAAI/AquilaChat-7B`, etc. | ✅︎ | ✅︎ |
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| `ArceeForCausalLM` | Arcee (AFM) | `arcee-ai/AFM-4.5B-Base`, etc. | ✅︎ | ✅︎ |
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| `ArcticForCausalLM` | Arctic | `Snowflake/snowflake-arctic-base`, `Snowflake/snowflake-arctic-instruct`, etc. | | ✅︎ |
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| `AXK1ForCausalLM` | A.X-K1 | `skt/A.X-K1`, etc. | | ✅︎ |
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| `BaiChuanForCausalLM` | Baichuan2, Baichuan | `baichuan-inc/Baichuan2-13B-Chat`, `baichuan-inc/Baichuan-7B`, etc. | ✅︎ | ✅︎ |
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| `BailingMoeForCausalLM` | Ling | `inclusionAI/Ling-lite-1.5`, `inclusionAI/Ling-plus`, etc. | ✅︎ | ✅︎ |
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| `BailingMoeV2ForCausalLM` | Ling | `inclusionAI/Ling-mini-2.0`, etc. | ✅︎ | ✅︎ |
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@@ -194,6 +194,7 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
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"ArcticForCausalLM": _HfExamplesInfo(
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"Snowflake/snowflake-arctic-instruct", trust_remote_code=True
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),
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"AXK1ForCausalLM": _HfExamplesInfo("skt/A.X-K1", trust_remote_code=True),
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"BaiChuanForCausalLM": _HfExamplesInfo(
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"baichuan-inc/Baichuan-7B", trust_remote_code=True
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),
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1168
vllm/model_executor/models/AXK1.py
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1168
vllm/model_executor/models/AXK1.py
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@@ -75,6 +75,7 @@ _TEXT_GENERATION_MODELS = {
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"AquilaForCausalLM": ("llama", "LlamaForCausalLM"), # AquilaChat2
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"ArceeForCausalLM": ("arcee", "ArceeForCausalLM"),
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"ArcticForCausalLM": ("arctic", "ArcticForCausalLM"),
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"AXK1ForCausalLM": ("AXK1", "AXK1ForCausalLM"),
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# baichuan-7b, upper case 'C' in the class name
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"BaiChuanForCausalLM": ("baichuan", "BaiChuanForCausalLM"),
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# baichuan-13b, lower case 'c' in the class name
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215
vllm/transformers_utils/configs/AXK1.py
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215
vllm/transformers_utils/configs/AXK1.py
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@@ -0,0 +1,215 @@
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from typing import Any
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from transformers import PretrainedConfig
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class AXK1Config(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`AXK1Model`].
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It is used to instantiate an A.X model according to the specified arguments,
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defining the model architecture. Instantiating a configuration with the defaults
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will yield a similar configuration to that of the A.X K1.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control
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the model outputs. Read the documentation from [`PretrainedConfig`] for more
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information.
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Args:
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vocab_size (`int`, *optional*, defaults to 163840):
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Vocabulary size of the A.X K1 model. Defines the number of different
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tokens that can be represented by the `inputs_ids` passed when calling
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[`AXK1Model`]
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hidden_size (`int`, *optional*, defaults to 7168):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 18432):
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Dimension of the MLP representations.
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moe_intermediate_size (`int`, *optional*, defaults to 2048):
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Dimension of the MoE representations.
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num_hidden_layers (`int`, *optional*, defaults to 61):
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Number of hidden layers in the Transformer decoder.
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num_nextn_predict_layers (`int`, *optional*, defaults to 1):
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Number of nextn predict layers in the AXK1 Model.
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num_attention_heads (`int`, *optional*, defaults to 64):
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Number of attention heads for each attention layer in the Transformer
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decoder.
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n_shared_experts (`int`, *optional*, defaults to 1):
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Number of shared experts, None means dense model.
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n_routed_experts (`int`, *optional*, defaults to 192):
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Number of routed experts, None means dense model.
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routed_scaling_factor (`float`, *optional*, defaults to 2.5):
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Scaling factor or routed experts.
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topk_method (`str`, *optional*, defaults to `noaux_tc`):
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Topk method used in routed gate.
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n_group (`int`, *optional*, defaults to 8):
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Number of groups for routed experts.
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topk_group (`int`, *optional*, defaults to 4):
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Number of selected groups for each token(for each token, ensuring the
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selected experts is only within `topk_group` groups).
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num_experts_per_tok (`int`, *optional*, defaults to 8):
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Number of selected experts, None means dense model.
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moe_layer_freq (`int`, *optional*, defaults to 1):
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The frequency of the MoE layer: one expert layer for every
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`moe_layer_freq - 1` dense layers.
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first_k_dense_replace (`int`, *optional*, defaults to 1):
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Number of dense layers in shallow layers
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(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 weights of the routed experts.
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scoring_func (`str`, *optional*, defaults to 'sigmoid'):
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Method of computing expert weights.
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aux_loss_alpha (`float`, *optional*, defaults to 0.0001):
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Auxiliary loss weight coefficient.
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seq_aux = (`bool`, *optional*, defaults to True):
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Whether to compute the auxiliary loss for each individual sample.
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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
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Grouped Query Attention. If `num_key_value_heads=num_attention_heads`,
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the model will use Multi Head Attention (MHA), if `num_key_value_heads=1
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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
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value head should be constructed by meanpooling all the original heads
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within that group. For more details checkout
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[this paper](https://arxiv.org/pdf/2305.13245.pdf).
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If it is not specified, will default to `num_attention_heads`.
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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
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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
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(not used by all models). Only 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 163691):
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Beginning of stream token id.
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eos_token_id (`int`, *optional*, defaults to 163691):
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End of stream token id.
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pretraining_tp (`int`, *optional*, defaults to 1):
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Experimental feature. Tensor parallelism rank used during pretraining.
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Please refer to
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[this document](https://huggingface.co/docs/transformers/parallelism)
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to understand more about it. This value is necessary to ensure exact
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reproducibility of the pretraining results. Please refer to
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[this issue](https://github.com/pytorch/pytorch/issues/76232).
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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_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.
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Currently supports two scaling strategies: linear and dynamic.
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Their scaling factor must be a float greater than 1. The expected format
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is `{"type": strategy name, "factor": scaling factor}`. When using this
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flag, don't update `max_position_embeddings` to the expected new maximum.
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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
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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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"""
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model_type = "AXK1"
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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vocab_size: int = 163840,
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hidden_size: int = 7168,
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intermediate_size: int = 18432,
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moe_intermediate_size: int = 2048,
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num_hidden_layers: int = 61,
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num_nextn_predict_layers: int | None = 1,
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num_attention_heads: int = 64,
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num_key_value_heads: int = 64,
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n_shared_experts: int | None = 1,
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n_routed_experts: int | None = 192,
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ep_size: int | None = 8, ## Ignored - Expert parallel size
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routed_scaling_factor: float | None = 2.5,
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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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topk_method: str | None = "noaux_tc",
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n_group: int | None = 8,
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topk_group: int | None = 4,
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num_experts_per_tok: int | None = 8,
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moe_layer_freq: int | None = 1,
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first_k_dense_replace: int = 1,
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norm_topk_prob: bool = True,
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scoring_func: str | None = "sigmoid",
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aux_loss_alpha: float | None = 0.0001,
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seq_aux: float | None = True,
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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 = 0.02,
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rms_norm_eps: float = 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 = 163691,
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eos_token_id: int | None = 163691,
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pretraining_tp: int | None = 1,
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tie_word_embeddings: bool | None = False,
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rope_theta: float | None = 10000.0,
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rope_scaling: dict[str, Any] | None = None,
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rope_parameters: dict[str, Any] | None = None,
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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.moe_intermediate_size = moe_intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_nextn_predict_layers = num_nextn_predict_layers
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self.num_attention_heads = num_attention_heads
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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.ep_size = ep_size
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self.routed_scaling_factor = routed_scaling_factor
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self.kv_lora_rank = kv_lora_rank
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self.q_lora_rank = q_lora_rank
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self.qk_rope_head_dim = qk_rope_head_dim
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self.v_head_dim = v_head_dim
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self.qk_nope_head_dim = qk_nope_head_dim
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self.topk_method = topk_method
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self.n_group = n_group
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self.topk_group = topk_group
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self.num_experts_per_tok = num_experts_per_tok
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self.moe_layer_freq = moe_layer_freq
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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.scoring_func = scoring_func
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self.aux_loss_alpha = aux_loss_alpha
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self.seq_aux = seq_aux
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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.pretraining_tp = pretraining_tp
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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.rope_parameters = rope_parameters
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self.attention_bias = attention_bias
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self.attention_dropout = attention_dropout
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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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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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@@ -16,6 +16,7 @@ import importlib
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_CLASS_TO_MODULE: dict[str, str] = {
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"AfmoeConfig": "vllm.transformers_utils.configs.afmoe",
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"AXK1Config": "vllm.transformers_utils.configs.AXK1",
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"BagelConfig": "vllm.transformers_utils.configs.bagel",
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"ChatGLMConfig": "vllm.transformers_utils.configs.chatglm",
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"ColModernVBertConfig": "vllm.transformers_utils.configs.colmodernvbert",
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@@ -70,6 +71,7 @@ _CLASS_TO_MODULE: dict[str, str] = {
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__all__ = [
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"AfmoeConfig",
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"AXK1Config",
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"BagelConfig",
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"ChatGLMConfig",
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"ColModernVBertConfig",
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@@ -233,6 +233,7 @@ class ModelArchConfigConvertorBase:
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if not hasattr(self.hf_text_config, "model_type"):
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return False
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elif self.hf_text_config.model_type in (
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"AXK1",
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"deepseek_v2",
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"deepseek_v3",
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"deepseek_v32",
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@@ -253,7 +254,13 @@ class ModelArchConfigConvertorBase:
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# underlying architecture
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return (
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self.hf_text_config.model.model_type
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in ("deepseek_v2", "deepseek_v3", "deepseek_v32", "deepseek_mtp")
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in (
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"AXK1",
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"deepseek_v2",
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"deepseek_v3",
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"deepseek_v32",
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"deepseek_mtp",
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)
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and self.hf_text_config.kv_lora_rank is not None
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)
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return False
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