LORA 모델에 대한 심층적인 이해
LORA 모델은 모델의 성능을 향상시키기 위해 학습을 통해 신경망의 계층 간 가중치를 자동으로 조정할 수 있는 신경망 모델입니다. 이 기사에서는 LORA 모델의 원리, 적용 시나리오, 장점 및 단점을 자세히 살펴봅니다.
1. LORA 모델의 원리
LORA 모델의 정식 명칭은 Learnable Re-Weighting이며, 학습 가능한 재가중화 모델입니다. 주로 신경망의 계층 간 가중치를 학습하여 모델의 성능을 향상시킵니다. 구체적으로 LORA 모델은 이전 레이어와 다음 레이어의 상관관계를 학습해 현재 레이어의 가중치를 자동으로 조정함으로써 모델의 성능을 향상시킨다.
LORA 모델의 기본 아이디어는 신경망의 각 레이어를 가중치가 있는 특징 추출기로 간주하고, 각 레이어의 가중치가 모델 출력에 미치는 영향을 결정하는 것입니다. LORA 모델을 사용하면 가중치를 조정하여 다양한 레이어가 다양한 작업에서 더 나은 역할을 수행할 수 있습니다.
LORA 모델에서 각 레이어의 가중치는 이전 레이어의 가중치와 다음 레이어의 가중치라는 두 부분으로 구성됩니다. 구체적으로 현재 레이어가 ii 번째 레이어라고 가정합니다.i 레이어, 이전 레이어는i − 1 i-1나-레벨 1 , 다음 레벨은i+1 i+1나+1개의 레이어가 있는 경우 현재 레이어의 가중치는 다음과 같이 표현될 수 있습니다.
wi = α i ⋅ W i − 1 ⋅ W i + 1 w_i = \alpha_i \cdot W_{i-1} \cdot W_{i+1}승나=ㅏ나⋅여나는 − 1⋅여나는 + 1
그 중 α i \alpha_iㅏ나학습된 학습 가능한 매개변수인 Wi − 1 W_{i-1}여나는 − 1합 W i + 1 W_{i+1}여나는 + 1는 각각 이전 레이어와 다음 레이어의 가중치입니다. α i \alpha_i 에 의해ㅏ나학습을 위해 LORA 모델은 현재 레이어의 가중치를 자동으로 조정하여 모델의 성능을 향상시킬 수 있습니다.
2. LORA 모델의 적용 시나리오
LORA 모델은 다양한 응용 시나리오를 갖고 있으며 자연어 처리, 컴퓨터 비전 등 복잡한 데이터 처리가 필요한 시나리오에 주로 사용됩니다. 자연어 처리 분야에서 LORA 모델은 상황별 맥락을 학습하여 텍스트 분류, 감정 분석 등의 작업 성능을 향상시킬 수 있습니다. 컴퓨터 비전 분야에서 LORA 모델은 서로 다른 계층 간의 상관관계를 학습하여 이미지 분류, 객체 감지 등의 작업 성능을 향상시킬 수 있습니다.
3. LORA 모델의 장점과 단점
LORA 모델의 가장 큰 장점은 레이어 간 상관관계를 자동으로 학습할 수 있어 모델의 성능이 향상된다는 점입니다. 기존의 수동 체중 조정과 달리 LORA 모델은 학습 데이터를 통해 자동으로 체중을 조정할 수 있어 수동 체중 조정으로 인한 제한을 피할 수 있습니다.
그러나 LORA 모델에도 몇 가지 단점이 있습니다. 우선, LORA 모델은 각 레이어가 이전 레이어와 후속 레이어의 영향을 받는다고 가정하므로 경우에 따라 문제가 발생할 수 있지만 일부 응용에서는 이러한 가정을 통해 모델의 설계 및 구현을 단순화할 수 있습니다.
LORA 모델에서는 각 계층에 대응하는 상위 계층 가중치와 하위 계층 가중치가 있으며, 이러한 가중치는 학습을 통해 얻을 수 있습니다. 훈련 과정에서 LORA 모델은 이러한 가중치를 자동으로 조정하여 모델이 데이터의 특징을 더 정확하게 학습할 수 있도록 합니다.
LORA 모델의 구현 과정은 비교적 간단하며 모델의 각 계층에 대해 재가중 작업, 즉 상위 계층 가중치와 하위 계층 가중치를 곱하여 새로운 가중치를 얻은 후 이를 사용하면 됩니다. 모델을 업데이트하기 위한 새로운 가중치. 이 가중치 재조정 작업은 PyTorch 프레임워크의 torch.mm() 함수를 사용하여 구현할 수 있습니다.
전반적으로 LORA 모델은 특정 애플리케이션에서 모델 성능을 크게 향상시킬 수 있는 간단하고 효과적인 학습 가능한 재가중 모델입니다. 그러나 가정의 한계로 인해 LORA 모델은 특정 데이터 세트 및 응용 시나리오에 적합하지 않을 수 있습니다.
4. LORA 모델 구성
LORA 모델은 , lora_down.weight및 lora_up.weight의 세 부분으로 구성됩니다 alpha.
여기서 lora_down.weight및 lora_up.weight는 LORA 모델의 상위 및 하위 계층 가중치이고 는 alpha가중치가 업데이트될 때의 스케일링 계수입니다.
5. 명명방법
이는 PyTorch 모델의 다양한 레이어의 가중치와 편향에 대한 이름입니다. PyTorch에서는 각 레이어의 가중치와 편향이 사전에 저장됩니다 state_dict. 이러한 명명 규칙은 일반적으로 레이어 유형과 모델의 레이어 위치에 따라 결정됩니다. 예를 들어, lora_te_text_model_encoder_layers_9_self_attn_k_proj.lora_up.weightLORA 모델에서 9번째 self-attention 레이어의 K투영 레이어의 상향 가중치를 표현합니다.
6. LORA 모델 키 예시
이 키는 PyTorch에서 LORA 모델의 가중치 매개변수를 로드하고 저장하는 데 사용됩니다. 각 키는 LORA 모델의 가중치 텐서와 연결됩니다.
# LORA模型中的权重参数的key
- lora_te_text_model_encoder_layers_0_mlp_fc1.alpha.
- lora_te_text_model_encoder_layers_0_mlp_fc1.lora_down.weight.
- lora_te_text_model_encoder_layers_0_mlp_fc1.lora_up.weight.
- lora_te_text_model_encoder_layers_0_mlp_fc2.alpha.
- lora_te_text_model_encoder_layers_0_mlp_fc2.lora_down.weight.
- lora_te_text_model_encoder_layers_0_mlp_fc2.lora_up.weight.
- lora_te_text_model_encoder_layers_0_self_attn_k_proj.alpha.
- lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_0_self_attn_out_proj.alpha.
- lora_te_text_model_encoder_layers_0_self_attn_out_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_0_self_attn_out_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_0_self_attn_q_proj.alpha.
- lora_te_text_model_encoder_layers_0_self_attn_q_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_0_self_attn_q_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_0_self_attn_v_proj.alpha.
- lora_te_text_model_encoder_layers_0_self_attn_v_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_0_self_attn_v_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_10_mlp_fc1.alpha.
- lora_te_text_model_encoder_layers_10_mlp_fc1.lora_down.weight.
- lora_te_text_model_encoder_layers_10_mlp_fc1.lora_up.weight.
- lora_te_text_model_encoder_layers_10_mlp_fc2.alpha.
- lora_te_text_model_encoder_layers_10_mlp_fc2.lora_down.weight.
- lora_te_text_model_encoder_layers_10_mlp_fc2.lora_up.weight.
- lora_te_text_model_encoder_layers_10_self_attn_k_proj.alpha.
- lora_te_text_model_encoder_layers_10_self_attn_k_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_10_self_attn_k_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_10_self_attn_out_proj.alpha.
- lora_te_text_model_encoder_layers_10_self_attn_out_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_10_self_attn_out_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_10_self_attn_q_proj.alpha.
- lora_te_text_model_encoder_layers_10_self_attn_q_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_10_self_attn_q_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_10_self_attn_v_proj.alpha.
- lora_te_text_model_encoder_layers_10_self_attn_v_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_10_self_attn_v_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_11_mlp_fc1.alpha.
- lora_te_text_model_encoder_layers_11_mlp_fc1.lora_down.weight.
- lora_te_text_model_encoder_layers_11_mlp_fc1.lora_up.weight.
- lora_te_text_model_encoder_layers_11_mlp_fc2.alpha.
- lora_te_text_model_encoder_layers_11_mlp_fc2.lora_down.weight.
- lora_te_text_model_encoder_layers_11_mlp_fc2.lora_up.weight.
- lora_te_text_model_encoder_layers_11_self_attn_k_proj.alpha.
- lora_te_text_model_encoder_layers_11_self_attn_k_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_11_self_attn_k_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_11_self_attn_out_proj.alpha.
- lora_te_text_model_encoder_layers_11_self_attn_out_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_11_self_attn_out_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_11_self_attn_q_proj.alpha.
- lora_te_text_model_encoder_layers_11_self_attn_q_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_11_self_attn_q_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_11_self_attn_v_proj.alpha.
- lora_te_text_model_encoder_layers_11_self_attn_v_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_11_self_attn_v_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_1_mlp_fc1.alpha.
- lora_te_text_model_encoder_layers_1_mlp_fc1.lora_down.weight.
- lora_te_text_model_encoder_layers_1_mlp_fc1.lora_up.weight.
- lora_te_text_model_encoder_layers_1_mlp_fc2.alpha.
- lora_te_text_model_encoder_layers_1_mlp_fc2.lora_down.weight.
- lora_te_text_model_encoder_layers_1_mlp_fc2.lora_up.weight.
- lora_te_text_model_encoder_layers_1_self_attn_k_proj.alpha.
- lora_te_text_model_encoder_layers_1_self_attn_k_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_1_self_attn_k_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_1_self_attn_out_proj.alpha.
- lora_te_text_model_encoder_layers_1_self_attn_out_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_1_self_attn_out_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_1_self_attn_q_proj.alpha.
- lora_te_text_model_encoder_layers_1_self_attn_q_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_1_self_attn_q_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_1_self_attn_v_proj.alpha.
- lora_te_text_model_encoder_layers_1_self_attn_v_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_1_self_attn_v_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_2_mlp_fc1.alpha.
- lora_te_text_model_encoder_layers_2_mlp_fc1.lora_down.weight.
- lora_te_text_model_encoder_layers_2_mlp_fc1.lora_up.weight.
- lora_te_text_model_encoder_layers_2_mlp_fc2.alpha.
- lora_te_text_model_encoder_layers_2_mlp_fc2.lora_down.weight.
- lora_te_text_model_encoder_layers_2_mlp_fc2.lora_up.weight.
- lora_te_text_model_encoder_layers_2_self_attn_k_proj.alpha.
- lora_te_text_model_encoder_layers_2_self_attn_k_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_2_self_attn_k_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_2_self_attn_out_proj.alpha.
- lora_te_text_model_encoder_layers_2_self_attn_out_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_2_self_attn_out_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_2_self_attn_q_proj.alpha.
- lora_te_text_model_encoder_layers_2_self_attn_q_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_2_self_attn_q_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_2_self_attn_v_proj.alpha.
- lora_te_text_model_encoder_layers_2_self_attn_v_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_2_self_attn_v_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_3_mlp_fc1.alpha.
- lora_te_text_model_encoder_layers_3_mlp_fc1.lora_down.weight.
- lora_te_text_model_encoder_layers_3_mlp_fc1.lora_up.weight.
- lora_te_text_model_encoder_layers_3_mlp_fc2.alpha.
- lora_te_text_model_encoder_layers_3_mlp_fc2.lora_down.weight.
- lora_te_text_model_encoder_layers_3_mlp_fc2.lora_up.weight.
- lora_te_text_model_encoder_layers_3_self_attn_k_proj.alpha.
- lora_te_text_model_encoder_layers_3_self_attn_k_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_3_self_attn_k_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_3_self_attn_out_proj.alpha.
- lora_te_text_model_encoder_layers_3_self_attn_out_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_3_self_attn_out_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_3_self_attn_q_proj.alpha.
- lora_te_text_model_encoder_layers_3_self_attn_q_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_3_self_attn_q_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_3_self_attn_v_proj.alpha.
- lora_te_text_model_encoder_layers_3_self_attn_v_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_3_self_attn_v_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_4_mlp_fc1.alpha.
- lora_te_text_model_encoder_layers_4_mlp_fc1.lora_down.weight.
- lora_te_text_model_encoder_layers_4_mlp_fc1.lora_up.weight.
- lora_te_text_model_encoder_layers_4_mlp_fc2.alpha.
- lora_te_text_model_encoder_layers_4_mlp_fc2.lora_down.weight.
- lora_te_text_model_encoder_layers_4_mlp_fc2.lora_up.weight.
- lora_te_text_model_encoder_layers_4_self_attn_k_proj.alpha.
- lora_te_text_model_encoder_layers_4_self_attn_k_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_4_self_attn_k_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_4_self_attn_out_proj.alpha.
- lora_te_text_model_encoder_layers_4_self_attn_out_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_4_self_attn_out_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_4_self_attn_q_proj.alpha.
- lora_te_text_model_encoder_layers_4_self_attn_q_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_4_self_attn_q_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_4_self_attn_v_proj.alpha.
- lora_te_text_model_encoder_layers_4_self_attn_v_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_4_self_attn_v_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_5_mlp_fc1.alpha.
- lora_te_text_model_encoder_layers_5_mlp_fc1.lora_down.weight.
- lora_te_text_model_encoder_layers_5_mlp_fc1.lora_up.weight.
- lora_te_text_model_encoder_layers_5_mlp_fc2.alpha.
- lora_te_text_model_encoder_layers_5_mlp_fc2.lora_down.weight.
- lora_te_text_model_encoder_layers_5_mlp_fc2.lora_up.weight.
- lora_te_text_model_encoder_layers_5_self_attn_k_proj.alpha.
- lora_te_text_model_encoder_layers_5_self_attn_k_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_5_self_attn_k_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_5_self_attn_out_proj.alpha.
- lora_te_text_model_encoder_layers_5_self_attn_out_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_5_self_attn_out_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_5_self_attn_q_proj.alpha.
- lora_te_text_model_encoder_layers_5_self_attn_q_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_5_self_attn_q_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_5_self_attn_v_proj.alpha.
- lora_te_text_model_encoder_layers_5_self_attn_v_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_5_self_attn_v_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_6_mlp_fc1.alpha.
- lora_te_text_model_encoder_layers_6_mlp_fc1.lora_down.weight.
- lora_te_text_model_encoder_layers_6_mlp_fc1.lora_up.weight.
- lora_te_text_model_encoder_layers_6_mlp_fc2.alpha.
- lora_te_text_model_encoder_layers_6_mlp_fc2.lora_down.weight.
- lora_te_text_model_encoder_layers_6_mlp_fc2.lora_up.weight.
- lora_te_text_model_encoder_layers_6_self_attn_k_proj.alpha.
- lora_te_text_model_encoder_layers_6_self_attn_k_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_6_self_attn_k_proj.lora_up.weight.
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- lora_unet_up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_q.lora_down.weight.
- lora_unet_up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_q.lora_up.weight.
- lora_unet_up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_v.alpha.
- lora_unet_up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_v.lora_down.weight.
- lora_unet_up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_v.lora_up.weight.
- lora_unet_up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj.alpha.
- lora_unet_up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj.lora_down.weight.
- lora_unet_up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj.lora_up.weight.
- lora_unet_up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2.alpha.
- lora_unet_up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2.lora_down.weight.
- lora_unet_up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2.lora_up.weight.