Questions about the amount of fine-tunable parameters

[kaicheng001]

Hello, author, first of all, thank you for your MoE-PEFT contact work. I encountered a problem when fine-tuning with your MoE-PEFT. The moe_peft.json configuration I used is as follows:

{
    "cutoff_len": 2048,
    "save_strategy": "epoch",
    "train_lora_candidate_num": 2,
    "train_lora_simultaneously_num": 2,
    "train_strategy": "optim",
    "lora": [
        {
            "name": "expert_0_name",
            "task_name": "expert_0",
            "optim": "adamw",
            "scheduler_type": "constant",
            "warmup_steps": 0,
            "lr": 0.0002,
            "batch_size": 1,
            "micro_batch_size": 1,
            "evaluate_batch_size": 1,
            "num_epochs": 3,
            "r": 4,
            "lora_alpha": 16,
            "lora_dropout": 0.05,
            "target_modules": {
                "q_proj": false,
                "k_proj": false,
                "v_proj": false,
                "o_proj": false,
                "gate_proj": true,
                "down_proj": false,
                "up_proj": false
            },
            "routing_strategy": "mixlora",
            "num_experts": 7,
            "top_k": 2,
            "group_by_length": false
        },
        {
            "name": "expert_1_name",
            "task_name": "expert_1",
            "optim": "adamw",
            "scheduler_type": "constant",
            "warmup_steps": 0,
            "lr": 0.0002,
            "batch_size": 1,
            "micro_batch_size": 1,
            "evaluate_batch_size": 1,
            "num_epochs": 3,
            "r": 4,
            "lora_alpha": 16,
            "lora_dropout": 0.05,
            "target_modules": {
                "q_proj": false,
                "k_proj": false,
                "v_proj": false,
                "o_proj": false,
                "gate_proj": true,
                "down_proj": false,
                "up_proj": false
            },
            "routing_strategy": "mixlora",
            "num_experts": 7,
            "top_k": 2,
            "group_by_length": false
        },
        {
            "name": "expert_2_name",
            "task_name": "expert_2",
            "optim": "adamw",
            "scheduler_type": "constant",
            "warmup_steps": 0,
            "lr": 0.0002,
            "batch_size": 1,
            "micro_batch_size": 1,
            "evaluate_batch_size": 1,
            "num_epochs": 3,
            "r": 4,
            "lora_alpha": 16,
            "lora_dropout": 0.05,
            "target_modules": {
                "q_proj": false,
                "k_proj": false,
                "v_proj": false,
                "o_proj": false,
                "gate_proj": true,
                "down_proj": false,
                "up_proj": false
            },
            "routing_strategy": "mixlora",
            "num_experts": 7,
            "top_k": 2,
            "group_by_length": false
        },
        {
            "name": "expert_3_name",
            "task_name": "expert_3",
            "optim": "adamw",
            "scheduler_type": "constant",
            "warmup_steps": 0,
            "lr": 0.0002,
            "batch_size": 1,
            "micro_batch_size": 1,
            "evaluate_batch_size": 1,
            "num_epochs": 3,
            "r": 4,
            "lora_alpha": 16,
            "lora_dropout": 0.05,
            "target_modules": {
                "q_proj": false,
                "k_proj": false,
                "v_proj": false,
                "o_proj": false,
                "gate_proj": true,
                "down_proj": false,
                "up_proj": false
            },
            "routing_strategy": "mixlora",
            "num_experts": 7,
            "top_k": 2,
            "group_by_length": false
        },
        {
            "name": "expert_4_name",
            "task_name": "expert_4",
            "optim": "adamw",
            "scheduler_type": "constant",
            "warmup_steps": 0,
            "lr": 0.0002,
            "batch_size": 1,
            "micro_batch_size": 1,
            "evaluate_batch_size": 1,
            "num_epochs": 3,
            "r": 4,
            "lora_alpha": 16,
            "lora_dropout": 0.05,
            "target_modules": {
                "q_proj": false,
                "k_proj": false,
                "v_proj": false,
                "o_proj": false,
                "gate_proj": true,
                "down_proj": false,
                "up_proj": false
            },
            "routing_strategy": "mixlora",
            "num_experts": 7,
            "top_k": 2,
            "group_by_length": false
        },
        {
            "name": "expert_5_name",
            "task_name": "expert_5",
            "optim": "adamw",
            "scheduler_type": "constant",
            "warmup_steps": 0,
            "lr": 0.0002,
            "batch_size": 1,
            "micro_batch_size": 1,
            "evaluate_batch_size": 1,
            "num_epochs": 3,
            "r": 4,
            "lora_alpha": 16,
            "lora_dropout": 0.05,
            "target_modules": {
                "q_proj": false,
                "k_proj": false,
                "v_proj": false,
                "o_proj": false,
                "gate_proj": true,
                "down_proj": false,
                "up_proj": false
            },
            "routing_strategy": "mixlora",
            "num_experts": 7,
            "top_k": 2,
            "group_by_length": false
        },
        {
            "name": "expert_6_name",
            "task_name": "expert_6",
            "optim": "adamw",
            "scheduler_type": "constant",
            "warmup_steps": 0,
            "lr": 0.0002,
            "batch_size": 1,
            "micro_batch_size": 1,
            "evaluate_batch_size": 1,
            "num_epochs": 3,
            "r": 4,
            "lora_alpha": 16,
            "lora_dropout": 0.05,
            "target_modules": {
                "q_proj": false,
                "k_proj": false,
                "v_proj": false,
                "o_proj": false,
                "gate_proj": true,
                "down_proj": false,
                "up_proj": false
            },
            "routing_strategy": "mixlora",
            "num_experts": 7,
            "top_k": 2,
            "group_by_length": false
        }
    ]
}

The model I chose is Meta-Llama-3-8B-Instruct, I printed the output of the model's trainable parameters as follows:

===== LoRA LAYER TYPE STATISTICS FOR expert_0_name =====
FFN: 17,432,576 parameters (100.00%)
Attention: 0 parameters (0.00%)
Other: 0 parameters (0.00%)
LoRA Total Parameters: 17,432,576
LoRA Rank: 4
LoRA Alpha: 16
Target Modules: ['gate_proj']

===== LoRA LAYER TYPE STATISTICS FOR expert_1_name =====
FFN: 17,432,576 parameters (100.00%)
Attention: 0 parameters (0.00%)
Other: 0 parameters (0.00%)
LoRA Total Parameters: 17,432,576
LoRA Rank: 4
LoRA Alpha: 16
Target Modules: ['gate_proj']

===== LoRA LAYER TYPE STATISTICS FOR expert_2_name =====
FFN: 17,432,576 parameters (100.00%)
Attention: 0 parameters (0.00%)
Other: 0 parameters (0.00%)
LoRA Total Parameters: 17,432,576
LoRA Rank: 4
LoRA Alpha: 16
Target Modules: ['gate_proj']

===== LoRA LAYER TYPE STATISTICS FOR expert_3_name =====
FFN: 17,432,576 parameters (100.00%)
Attention: 0 parameters (0.00%)
Other: 0 parameters (0.00%)
LoRA Total Parameters: 17,432,576
LoRA Rank: 4
LoRA Alpha: 16
Target Modules: ['gate_proj']

===== LoRA LAYER TYPE STATISTICS FOR expert_4_name =====
FFN: 17,432,576 parameters (100.00%)
Attention: 0 parameters (0.00%)
Other: 0 parameters (0.00%)
LoRA Total Parameters: 17,432,576
LoRA Rank: 4
LoRA Alpha: 16
Target Modules: ['gate_proj']


===== LoRA LAYER TYPE STATISTICS FOR expert_5_name =====
FFN: 17,432,576 parameters (100.00%)
Attention: 0 parameters (0.00%)
Other: 0 parameters (0.00%)
LoRA Total Parameters: 17,432,576
LoRA Rank: 4
LoRA Alpha: 16
Target Modules: ['gate_proj']

===== LoRA LAYER TYPE STATISTICS FOR expert_6_name =====
FFN: 17,432,576 parameters (100.00%)
Attention: 0 parameters (0.00%)
Other: 0 parameters (0.00%)
LoRA Total Parameters: 17,432,576
LoRA Rank: 4
LoRA Alpha: 16
Target Modules: ['gate_proj']
================================

Your code prints like this:

[2025-03-25 00:39:18,638] MoE-PEFT: expert_0_name total trainable params: 17432576
[2025-03-25 00:39:18,639] MoE-PEFT: expert_0_name total trainable params (except gates): 16515072
[2025-03-25 00:39:18,647] MoE-PEFT: expert_1_name total trainable params: 17432576
[2025-03-25 00:39:18,649] MoE-PEFT: expert_1_name total trainable params (except gates): 16515072
[2025-03-25 00:39:18,656] MoE-PEFT: expert_2_name total trainable params: 17432576
[2025-03-25 00:39:18,658] MoE-PEFT: expert_2_name total trainable params (except gates): 16515072
[2025-03-25 00:39:18,663] MoE-PEFT: expert_3_name total trainable params: 17432576
[2025-03-25 00:39:18,665] MoE-PEFT: expert_3_name total trainable params (except gates): 16515072
[2025-03-25 00:39:18,670] MoE-PEFT: expert_4_name total trainable params: 17432576
[2025-03-25 00:39:18,672] MoE-PEFT: expert_4_name total trainable params (except gates): 16515072
[2025-03-25 00:39:18,678] MoE-PEFT: expert_5_name total trainable params: 17432576
[2025-03-25 00:39:18,679] MoE-PEFT: expert_5_name total trainable params (except gates): 16515072
[2025-03-25 00:39:18,686] MoE-PEFT: expert_6_name total trainable params: 17432576
[2025-03-25 00:39:18,688] MoE-PEFT: expert_6_name total trainable params (except gates): 16515072

But the code printed in your trainer.py seems to print the total trainable parameters for each expert：

Under the above parameters, my model still exceeds the video memory on a single 24GB 4090, which seems a bit abnormal. Is there any good solution?

Looking forward to your answer and thank you！