replace_layer_fields and AutoLoraConvert not working as expected

[edwin0cheng]

The proc-macro does not work as expected:

From running :

cd candle-lora-macro/examples
cargo expand --test linear

Output: (Only the `Model` part)

impl Model {
    /// Be sure to provide a configuration for each type!
    pub fn get_lora_model<'a>(
        &'a mut self,
        lora_config: candle_lora::LoraConfig,
        vb: &candle_nn::VarBuilder,
        linear_config: Option<candle_lora::LoraLinearConfig>,
        conv1d_config: Option<candle_lora::LoraConv1dConfig>,
        conv2d_config: Option<candle_lora::LoraConv2dConfig>,
        embed_config: Option<candle_lora::LoraEmbeddingConfig>,
    ) {
        let mut linear: ::std::collections::HashMap<
            String,
            &dyn candle_lora::LinearLayerLike,
        > = ::std::collections::HashMap::new();
        let mut conv1d: ::std::collections::HashMap<
            String,
            &dyn candle_lora::Conv1dLayerLike,
        > = ::std::collections::HashMap::new();
        let mut conv2d: ::std::collections::HashMap<
            String,
            &dyn candle_lora::Conv2dLayerLike,
        > = ::std::collections::HashMap::new();
        let mut embed: ::std::collections::HashMap<
            String,
            &dyn candle_lora::EmbeddingLayerLike,
        > = ::std::collections::HashMap::new();
        if !linear.is_empty() && linear_config.is_none() {
            {
                ::core::panicking::panic_fmt(
                    format_args!("Config not speified for linear layers."),
                );
            };
        }
        if !conv1d.is_empty() && conv1d_config.is_none() {
            {
                ::core::panicking::panic_fmt(
                    format_args!("Config not speified for conv1d layers."),
                );
            };
        }
        if !conv2d.is_empty() && conv2d_config.is_none() {
            {
                ::core::panicking::panic_fmt(
                    format_args!("Config not speified for conv2d layers."),
                );
            };
        }
        if !embed.is_empty() && embed_config.is_none() {
            {
                ::core::panicking::panic_fmt(
                    format_args!("Config not speified for embedding layers."),
                );
            };
        }
        let mut builder = candle_lora::SelectedLayersBuilder::new();
        if linear_config.is_some() {
            builder = builder.add_linear_layers(linear, linear_config.unwrap());
        }
        if conv1d_config.is_some() {
            builder = builder.add_conv1d_layers(conv1d, conv1d_config.unwrap());
        }
        if conv2d_config.is_some() {
            builder = builder.add_conv2d_layers(conv2d, conv2d_config.unwrap());
        }
        if embed_config.is_some() {
            builder = builder.add_embed_layers(embed, embed_config.unwrap());
        }
        let selection = builder.build();
        let new_layers = candle_lora::Lora::convert_model(selection, lora_config, &vb);
        let _ = "Start";
        let _ = "Done!";
    }
    /// Be sure to provide a configuration for each type!
    pub fn get_merged_lora_model<'a>(
        &'a mut self,
        lora_config: candle_lora::LoraConfig,
        vb: &candle_nn::VarBuilder,
        linear_config: Option<candle_lora::LoraLinearConfig>,
        conv1d_config: Option<candle_lora::LoraConv1dConfig>,
        conv2d_config: Option<candle_lora::LoraConv2dConfig>,
        embed_config: Option<candle_lora::LoraEmbeddingConfig>,
    ) {
        use candle_lora::Merge;
        let mut linear: ::std::collections::HashMap<
            String,
            &dyn candle_lora::LinearLayerLike,
        > = ::std::collections::HashMap::new();
        let mut conv1d: ::std::collections::HashMap<
            String,
            &dyn candle_lora::Conv1dLayerLike,
        > = ::std::collections::HashMap::new();
        let mut conv2d: ::std::collections::HashMap<
            String,
            &dyn candle_lora::Conv2dLayerLike,
        > = ::std::collections::HashMap::new();
        let mut embed: ::std::collections::HashMap<
            String,
            &dyn candle_lora::EmbeddingLayerLike,
        > = ::std::collections::HashMap::new();
        if !linear.is_empty() && linear_config.is_none() {
            {
                ::core::panicking::panic_fmt(
                    format_args!("Config not speified for linear layers."),
                );
            };
        }
        if !conv1d.is_empty() && conv1d_config.is_none() {
            {
                ::core::panicking::panic_fmt(
                    format_args!("Config not speified for conv1d layers."),
                );
            };
        }
        if !conv2d.is_empty() && conv2d_config.is_none() {
            {
                ::core::panicking::panic_fmt(
                    format_args!("Config not speified for conv2d layers."),
                );
            };
        }
        if !embed.is_empty() && embed_config.is_none() {
            {
                ::core::panicking::panic_fmt(
                    format_args!("Config not speified for embedding layers."),
                );
            };
        }
        let mut builder = candle_lora::SelectedLayersBuilder::new();
        if linear_config.is_some() {
            builder = builder.add_linear_layers(linear, linear_config.unwrap());
        }
        if conv1d_config.is_some() {
            builder = builder.add_conv1d_layers(conv1d, conv1d_config.unwrap());
        }
        if conv2d_config.is_some() {
            builder = builder.add_conv2d_layers(conv2d, conv2d_config.unwrap());
        }
        if embed_config.is_some() {
            builder = builder.add_embed_layers(embed, embed_config.unwrap());
        }
        let selection = builder.build();
        let mut new_layers = candle_lora::Lora::convert_model(
            selection,
            lora_config,
            &vb,
        );
    }
}

Note that these lines:

candle-lora/candle-lora-macro/src/lib.rs

Lines 547 to 555 in 273eef4

	#linear_stream
	#conv1d_stream
	#conv2d_stream
	#embed_stream
	#linear_option1_stream
	#conv1d_option1_stream
	#conv2d_option1_stream
	#embed_option1_stream

are not expanded.

[EricLBuehler]

Thanks for noticing that! I just identified a bug relating to the use of Box instead of Arc during layer swapping, and here is the result after the fix:

pub fn get_lora_model<'a>(
        &'a mut self,
        lora_config: candle_lora::LoraConfig,
        vb: &candle_nn::VarBuilder,
        linear_config: Option<candle_lora::LoraLinearConfig>,
        conv1d_config: Option<candle_lora::LoraConv1dConfig>,
        conv2d_config: Option<candle_lora::LoraConv2dConfig>,
        embed_config: Option<candle_lora::LoraEmbeddingConfig>,
    ) {
        let mut linear: ::std::collections::HashMap<
            String,
            &dyn candle_lora::LinearLayerLike,
        > = ::std::collections::HashMap::new();
        let mut conv1d: ::std::collections::HashMap<
            String,
            &dyn candle_lora::Conv1dLayerLike,
        > = ::std::collections::HashMap::new();
        let mut conv2d: ::std::collections::HashMap<
            String,
            &dyn candle_lora::Conv2dLayerLike,
        > = ::std::collections::HashMap::new();
        let mut embed: ::std::collections::HashMap<
            String,
            &dyn candle_lora::EmbeddingLayerLike,
        > = ::std::collections::HashMap::new();
        [(linear.insert("a".to_string(), &*self.a))];