[COMPAT] vLLM does not support quantized MoE except for Mixtral (i.e. 4bit DeepSeek-V2 not supported)

[Xu-Chen]

Using vllm to infer the deepseek model encountered an error

[rank0]:     self.mlp = DeepseekV2MoE(config=config, quant_config=quant_config)
[rank0]:   File "/home/root/.local/lib/python3.10/site-packages/vllm/model_executor/models/deepseek_v2.py", line 113, in __init__
[rank0]:     self.pack_params()
[rank0]:   File "/home/root/.local/lib/python3.10/site-packages/vllm/model_executor/models/deepseek_v2.py", line 135, in pack_params
[rank0]:     w1.append(expert.gate_up_proj.weight)
[rank0]:   File "/home/root/.local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1709, in __getattr__
[rank0]:     raise AttributeError(f"'{type(self).__name__}' object has no attribute '{name}'")
[rank0]: AttributeError: 'MergedColumnParallelLinear' object has no attribute 'weight'. Did you mean: 'qweight'?

[Qubitium]

@Xu-Chen Please share your model config.json and quantize_config.json so I can check the specs of your quantized model.

[Xu-Chen]

@Xu-Chen Please share your config.json and quantize_config.json so I can check the specs of your quantized model.

config.json

{
  "_name_or_path": "./DeepSeek-V2-Chat",
  "architectures": [
    "DeepseekV2ForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "attn_implementation": "flash_attention_2",
  "auto_map": {
    "AutoConfig": "configuration_deepseek.DeepseekV2Config",
    "AutoModel": "modeling_deepseek.DeepseekV2Model",
    "AutoModelForCausalLM": "modeling_deepseek.DeepseekV2ForCausalLM"
  },
  "aux_loss_alpha": 0.001,
  "bos_token_id": 100000,
  "eos_token_id": 100001,
  "ep_size": 1,
  "first_k_dense_replace": 1,
  "hidden_act": "silu",
  "hidden_size": 5120,
  "initializer_range": 0.02,
  "intermediate_size": 12288,
  "kv_lora_rank": 512,
  "max_position_embeddings": 163840,
  "model_type": "deepseek_v2",
  "moe_intermediate_size": 1536,
  "moe_layer_freq": 1,
  "n_group": 8,
  "n_routed_experts": 160,
  "n_shared_experts": 2,
  "norm_topk_prob": false,
  "num_attention_heads": 128,
  "num_experts_per_tok": 6,
  "num_hidden_layers": 60,
  "num_key_value_heads": 128,
  "pretraining_tp": 1,
  "q_lora_rank": 1536,
  "qk_nope_head_dim": 128,
  "qk_rope_head_dim": 64,
  "quantization_config": {
    "bits": 4,
    "checkpoint_format": "gptq",
    "damp_percent": 0.01,
    "desc_act": false,
    "group_size": 32,
    "lm_head": false,
    "meta": {
      "quantizer": "gptqmodel:0.9.1"
    },
    "model_file_base_name": null,
    "model_name_or_path": null,
    "quant_method": "gptq",
    "static_groups": false,
    "sym": true,
    "true_sequential": false
  },
  "rms_norm_eps": 1e-06,
  "rope_scaling": {
    "beta_fast": 32,
    "beta_slow": 1,
    "factor": 40,
    "mscale": 0.707,
    "mscale_all_dim": 0.707,
    "original_max_position_embeddings": 4096,
    "type": "yarn"
  },
  "rope_theta": 10000,
  "routed_scaling_factor": 16.0,
  "scoring_func": "softmax",
  "seq_aux": true,
  "tie_word_embeddings": false,
  "topk_group": 3,
  "topk_method": "group_limited_greedy",
  "torch_dtype": "bfloat16",
  "transformers_version": "4.41.2",
  "use_cache": true,
  "v_head_dim": 128,
  "vocab_size": 102400
}

quantize_config.json

{
  "bits": 4,
  "group_size": 32,
  "desc_act": false,
  "static_groups": false,
  "sym": true,
  "lm_head": false,
  "damp_percent": 0.01,
  "true_sequential": false,
  "model_name_or_path": "./DeepSeek-V2-Chat-gptq-int4",
  "model_file_base_name": "model",
  "quant_method": "gptq",
  "checkpoint_format": "gptq",
  "meta": {
    "quantizer": "gptqmodel:0.9.1"
  }
}

[Qubitium]

@Xu-Chen To confirm, did you have inference problem with GPTQMode.from_quantized() too or is the inference issue localized to vLLM?

[Xu-Chen]

@Xu-Chen To confirm, did you have inference problem with GPTQMode.from_quantized() too or is the inference issue localized to vLLM?

The inference issue localized to vLLM. I will try GPTQMode.from_quantized() .

[Qubitium]

Ref: https://github.com/vllm-project/vllm/issues/5343#issuecomment-2154882419

@Xu-Chen vllm currently does not support quantized MoE models except Mixtral.

[Xu-Chen]

Ref: vllm-project/vllm#5343 (comment)

@Xu-Chen vllm currently does not support quantized MoE models except Mixtral.

I try GPTQMode.from_quantized()，but the quantized model's output is error, eg "GPTQModel is !!!!!!!!!!!!!!!!!!", repeatedly outputs the character "!". Will re-quantify this model.

[Qubitium]

What calibration sataset and size of calib data did you use? Also please use something other than "GPTQModel is..." as prompt test since it would just hallucinate on unknown info.