Comprensión profunda del modelo LORA.
El modelo LORA es un modelo de red neuronal que puede ajustar automáticamente los pesos entre capas de la red neuronal mediante el aprendizaje para mejorar el rendimiento del modelo. Este artículo explorará en profundidad los principios, escenarios de aplicación, ventajas y desventajas del modelo LORA.
1. Principio del modelo LORA
El nombre completo del modelo LORA es Learnable Re-Weighting, que es un modelo de reponderación que se puede aprender. Mejora principalmente el rendimiento del modelo al aprender los pesos entre las capas de la red neuronal. Específicamente, el modelo LORA ajusta automáticamente el peso de la capa actual al aprender la correlación entre la capa anterior y la siguiente, mejorando así el rendimiento del modelo.
La idea básica del modelo LORA es considerar cada capa de la red neuronal como un extractor de características ponderables, y el peso de cada capa determina su impacto en la salida del modelo. El modelo LORA puede permitir que diferentes capas desempeñen un mejor papel en diferentes tareas ajustando los pesos.
En el modelo LORA, el peso de cada capa consta de dos partes: el peso de la capa anterior y el peso de la siguiente capa. Específicamente, supongamos que la capa actual es la ii -ésimai capa, la capa anterior esi − 1 i-1i−Nivel 1 , el siguiente nivel esi+1 i+1i+1 capa, entonces el peso de la capa actual se puede expresar como:
wi = α yo ⋅ W yo − 1 ⋅ W yo + 1 w_i = \alpha_i \cdot W_{i-1} \cdot W_{i+1}wyo=ayo⋅W.yo − 1⋅W.yo + 1
Entre ellos, α i \alpha_iayoes el parámetro aprendible aprendido, W i − 1 W_{i-1}W.yo − 1Suma W i + 1 W_{i+1}W.yo + 1son los pesos de la capa anterior y la siguiente respectivamente. Por α i \alpha_iayoPara el aprendizaje, el modelo LORA puede ajustar automáticamente el peso de la capa actual, mejorando así el rendimiento del modelo.
2. Escenarios de aplicación del modelo LORA
El modelo LORA tiene una amplia gama de escenarios de aplicación y se utiliza principalmente en escenarios que requieren procesamiento de datos complejos, como procesamiento de lenguaje natural, visión por computadora, etc. En el campo del procesamiento del lenguaje natural, el modelo LORA puede mejorar el desempeño de tareas como la clasificación de texto y el análisis de sentimientos mediante el aprendizaje del contexto contextual. En el campo de la visión por computadora, el modelo LORA puede mejorar el desempeño de tareas como la clasificación de imágenes y la detección de objetos al aprender la correlación entre diferentes capas.
3. Ventajas y desventajas del modelo LORA
La principal ventaja del modelo LORA es que puede aprender automáticamente la correlación entre capas, mejorando así el rendimiento del modelo. A diferencia del ajuste de peso manual tradicional, el modelo LORA puede ajustar los pesos automáticamente mediante el aprendizaje de datos, evitando las limitaciones causadas por el ajuste de peso manual.
Sin embargo, el modelo LORA también tiene algunas deficiencias. En primer lugar, el modelo LORA supone que cada capa solo se ve afectada por las capas anterior y posterior, lo que puede causar algunos problemas en algunos casos, pero en algunas aplicaciones, esta suposición puede simplificar el diseño y la implementación del modelo.
En el modelo LORA, cada capa tiene un peso de capa superior y un peso de capa inferior correspondientes, y estos pesos se pueden obtener mediante el aprendizaje. Durante el proceso de entrenamiento, el modelo LORA ajustará automáticamente estos pesos, lo que le permitirá aprender características de los datos con mayor precisión.
El proceso de implementación del modelo LORA es relativamente simple: solo necesita realizar una operación de reponderación en cada capa del modelo, es decir, la multiplicación ponderada del peso de la capa superior y el peso de la capa inferior para obtener nuevos pesos, y luego usar estos Nuevos pesos para actualizar el modelo. Esta operación de reponderación se puede implementar utilizando la función torch.mm() en el marco PyTorch.
En general, el modelo LORA es un modelo de reponderación fácil de aprender, simple y eficaz, que puede mejorar significativamente el rendimiento del modelo en determinadas aplicaciones. Sin embargo, debido a las limitaciones de sus supuestos, el modelo LORA puede no ser adecuado para determinados conjuntos de datos y escenarios de aplicación.
4. Composición del modelo LORA
Un modelo LORA consta de tres partes lora_down.weight: lora_up.weighty alpha.
donde lora_down.weighty lora_up.weightson los pesos de las capas superior e inferior en el modelo LORA, y alphason los coeficientes de escala cuando se actualizan los pesos.
5. Método de denominación
Estos son los nombres de los pesos y sesgos de las distintas capas del modelo PyTorch. En PyTorch, los pesos y sesgos de cada capa se almacenan en un state_dictdiccionario llamado. Estas reglas de nomenclatura suelen estar determinadas por el tipo de capa y la ubicación de la capa en el modelo. Por ejemplo, represente el peso ascendente de la capa de proyección lora_te_text_model_encoder_layers_9_self_attn_k_proj.lora_up.weightde la novena capa de autoatención en el modelo LORA .K
6. Ejemplo clave del modelo LORA
Estas claves se utilizan para cargar y guardar los parámetros de peso del modelo LORA en PyTorch. Cada tecla está asociada a un tensor de peso en el modelo LORA.
# 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.
- lora_te_text_model_encoder_layers_6_self_attn_out_proj.alpha.
- lora_te_text_model_encoder_layers_6_self_attn_out_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_6_self_attn_out_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_6_self_attn_q_proj.alpha.
- lora_te_text_model_encoder_layers_6_self_attn_q_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_6_self_attn_q_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_6_self_attn_v_proj.alpha.
- lora_te_text_model_encoder_layers_6_self_attn_v_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_6_self_attn_v_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_7_mlp_fc1.alpha.
- lora_te_text_model_encoder_layers_7_mlp_fc1.lora_down.weight.
- lora_te_text_model_encoder_layers_7_mlp_fc1.lora_up.weight.
- lora_te_text_model_encoder_layers_7_mlp_fc2.alpha.
- lora_te_text_model_encoder_layers_7_mlp_fc2.lora_down.weight.
- lora_te_text_model_encoder_layers_7_mlp_fc2.lora_up.weight.
- lora_te_text_model_encoder_layers_7_self_attn_k_proj.alpha.
- lora_te_text_model_encoder_layers_7_self_attn_k_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_7_self_attn_k_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_7_self_attn_out_proj.alpha.
- lora_te_text_model_encoder_layers_7_self_attn_out_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_7_self_attn_out_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_7_self_attn_q_proj.alpha.
- lora_te_text_model_encoder_layers_7_self_attn_q_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_7_self_attn_q_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_7_self_attn_v_proj.alpha.
- lora_te_text_model_encoder_layers_7_self_attn_v_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_7_self_attn_v_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_8_mlp_fc1.alpha.
- lora_te_text_model_encoder_layers_8_mlp_fc1.lora_down.weight.
- lora_te_text_model_encoder_layers_8_mlp_fc1.lora_up.weight.
- lora_te_text_model_encoder_layers_8_mlp_fc2.alpha.
- lora_te_text_model_encoder_layers_8_mlp_fc2.lora_down.weight.
- lora_te_text_model_encoder_layers_8_mlp_fc2.lora_up.weight.
- lora_te_text_model_encoder_layers_8_self_attn_k_proj.alpha.
- lora_te_text_model_encoder_layers_8_self_attn_k_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_8_self_attn_k_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_8_self_attn_out_proj.alpha.
- lora_te_text_model_encoder_layers_8_self_attn_out_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_8_self_attn_out_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_8_self_attn_q_proj.alpha.
- lora_te_text_model_encoder_layers_8_self_attn_q_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_8_self_attn_q_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_8_self_attn_v_proj.alpha.
- lora_te_text_model_encoder_layers_8_self_attn_v_proj.lora_down.weight.
- lora_te_text_model_encoder_layers_8_self_attn_v_proj.lora_up.weight.
- lora_te_text_model_encoder_layers_9_mlp_fc1.alpha.
- lora_te_text_model_encoder_layers_9_mlp_fc1.lora_down.weight.
- lora_te_text_model_encoder_layers_9_mlp_fc1.lora_up.weight.
- lora_te_text_model_encoder_layers_9_mlp_fc2.alpha.
- lora_te_text_model_encoder_layers_9_mlp_fc2.lora_down.weight.
- lora_te_text_model_encoder_layers_9_mlp_fc2.lora_up.weight.
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