trustformers-core 0.1.1

// TensorRT export functionality (placeholder implementation)
#![allow(unused_variables)] // TensorRT export

use super::{ExportConfig, ExportFormat, ModelExporter};
use crate::traits::Model;
use anyhow::{anyhow, Result};

/// TensorRT engine configuration
#[derive(Debug, Clone)]
pub struct TensorRTConfig {
    pub max_batch_size: usize,
    pub max_sequence_length: usize,
    pub workspace_size: usize, // in MB
    pub fp16_enabled: bool,
    pub int8_enabled: bool,
    pub dynamic_shapes: bool,
    pub optimization_level: u8, // 0-5
}

impl Default for TensorRTConfig {
    fn default() -> Self {
        Self {
            max_batch_size: 32,
            max_sequence_length: 2048,
            workspace_size: 1024, // 1GB
            fp16_enabled: true,
            int8_enabled: false,
            dynamic_shapes: true,
            optimization_level: 3,
        }
    }
}

/// TensorRT network representation
#[derive(Debug)]
pub struct TensorRTNetwork {
    pub layers: Vec<TensorRTLayer>,
    pub inputs: Vec<TensorRTTensor>,
    pub outputs: Vec<TensorRTTensor>,
}

#[derive(Debug)]
pub struct TensorRTLayer {
    pub layer_type: TensorRTLayerType,
    pub name: String,
    pub inputs: Vec<String>,
    pub outputs: Vec<String>,
    pub parameters: Vec<u8>, // Serialized parameters
}

#[derive(Debug, Clone)]
pub enum TensorRTLayerType {
    Convolution,
    FullyConnected,
    Activation,
    Pooling,
    ElementWise,
    Softmax,
    Concatenation,
    MatrixMultiply,
    Gather,
    Scatter,
    LayerNorm,
    MultiHeadAttention,
    Embedding,
    PositionalEncoding,
    RNN,
    Plugin(String), // Custom plugin name
}

#[derive(Debug)]
pub struct TensorRTTensor {
    pub name: String,
    pub dimensions: Vec<i32>, // -1 for dynamic dimensions
    pub data_type: TensorRTDataType,
}

#[derive(Debug, Clone, Copy)]
pub enum TensorRTDataType {
    Float32,
    Float16,
    Int8,
    Int32,
    Bool,
}

/// TensorRT exporter implementation
#[derive(Clone)]
pub struct TensorRTExporter {
    config: TensorRTConfig,
}

impl Default for TensorRTExporter {
    fn default() -> Self {
        Self::new()
    }
}

impl TensorRTExporter {
    pub fn new() -> Self {
        Self {
            config: TensorRTConfig::default(),
        }
    }

    pub fn with_config(mut self, config: TensorRTConfig) -> Self {
        self.config = config;
        self
    }

    fn create_tensorrt_network<M: Model>(
        &self,
        model: &M,
        config: &ExportConfig,
    ) -> Result<TensorRTNetwork> {
        let mut layers = Vec::new();
        let mut inputs = Vec::new();
        let mut outputs = Vec::new();

        // Create input tensors
        let input_ids = TensorRTTensor {
            name: "input_ids".to_string(),
            dimensions: vec![-1, -1], // Dynamic batch and sequence length
            data_type: TensorRTDataType::Int32,
        };
        inputs.push(input_ids);

        let attention_mask = TensorRTTensor {
            name: "attention_mask".to_string(),
            dimensions: vec![-1, -1], // Dynamic batch and sequence length
            data_type: TensorRTDataType::Int32,
        };
        inputs.push(attention_mask);

        // Convert model to TensorRT layers
        self.convert_model_to_layers(model, &mut layers, config)?;

        // Create output tensor
        let logits = TensorRTTensor {
            name: "logits".to_string(),
            dimensions: vec![-1, -1, 50257], // Dynamic batch, sequence, vocab_size
            data_type: match config.precision {
                super::ExportPrecision::FP32 => TensorRTDataType::Float32,
                super::ExportPrecision::FP16 => TensorRTDataType::Float16,
                super::ExportPrecision::INT8 => TensorRTDataType::Int8,
                super::ExportPrecision::INT4 => TensorRTDataType::Int8, // TensorRT doesn't have INT4
            },
        };
        outputs.push(logits);

        Ok(TensorRTNetwork {
            layers,
            inputs,
            outputs,
        })
    }

    fn convert_model_to_layers<M: Model>(
        &self,
        model: &M,
        layers: &mut Vec<TensorRTLayer>,
        config: &ExportConfig,
    ) -> Result<()> {
        // Embedding layer
        layers.push(TensorRTLayer {
            layer_type: TensorRTLayerType::Embedding,
            name: "token_embedding".to_string(),
            inputs: vec!["input_ids".to_string()],
            outputs: vec!["embeddings".to_string()],
            parameters: Vec::new(), // Would contain actual embedding weights
        });

        // Positional encoding
        layers.push(TensorRTLayer {
            layer_type: TensorRTLayerType::PositionalEncoding,
            name: "positional_encoding".to_string(),
            inputs: vec!["embeddings".to_string()],
            outputs: vec!["positioned_embeddings".to_string()],
            parameters: Vec::new(),
        });

        // Transformer layers
        let mut current_input = "positioned_embeddings".to_string();
        for i in 0..12 {
            // Assuming 12 layers
            let layer_output = self.add_transformer_layer(layers, i, &current_input)?;
            current_input = layer_output;
        }

        // Final layer norm
        layers.push(TensorRTLayer {
            layer_type: TensorRTLayerType::LayerNorm,
            name: "final_layer_norm".to_string(),
            inputs: vec![current_input.clone()],
            outputs: vec!["normalized_output".to_string()],
            parameters: Vec::new(),
        });

        // Output projection
        layers.push(TensorRTLayer {
            layer_type: TensorRTLayerType::FullyConnected,
            name: "lm_head".to_string(),
            inputs: vec!["normalized_output".to_string()],
            outputs: vec!["logits".to_string()],
            parameters: Vec::new(),
        });

        Ok(())
    }

    fn add_transformer_layer(
        &self,
        layers: &mut Vec<TensorRTLayer>,
        layer_idx: usize,
        input_name: &str,
    ) -> Result<String> {
        let layer_prefix = format!("layer_{}", layer_idx);

        // Multi-head attention
        let attention_output = format!("{}_attention_output", layer_prefix);
        layers.push(TensorRTLayer {
            layer_type: TensorRTLayerType::MultiHeadAttention,
            name: format!("{}_attention", layer_prefix),
            inputs: vec![input_name.to_string()],
            outputs: vec![attention_output.clone()],
            parameters: Vec::new(),
        });

        // Residual connection after attention
        let attention_residual = format!("{}_attention_residual", layer_prefix);
        layers.push(TensorRTLayer {
            layer_type: TensorRTLayerType::ElementWise,
            name: format!("{}_attention_add", layer_prefix),
            inputs: vec![input_name.to_string(), attention_output],
            outputs: vec![attention_residual.clone()],
            parameters: Vec::new(),
        });

        // Layer norm after attention
        let norm_output = format!("{}_norm_output", layer_prefix);
        layers.push(TensorRTLayer {
            layer_type: TensorRTLayerType::LayerNorm,
            name: format!("{}_norm", layer_prefix),
            inputs: vec![attention_residual.clone()],
            outputs: vec![norm_output.clone()],
            parameters: Vec::new(),
        });

        // Feed-forward network (first linear layer)
        let ff_intermediate = format!("{}_ff_intermediate", layer_prefix);
        layers.push(TensorRTLayer {
            layer_type: TensorRTLayerType::FullyConnected,
            name: format!("{}_ff_up", layer_prefix),
            inputs: vec![norm_output.clone()],
            outputs: vec![ff_intermediate.clone()],
            parameters: Vec::new(),
        });

        // Activation function
        let ff_activated = format!("{}_ff_activated", layer_prefix);
        layers.push(TensorRTLayer {
            layer_type: TensorRTLayerType::Activation,
            name: format!("{}_activation", layer_prefix),
            inputs: vec![ff_intermediate],
            outputs: vec![ff_activated.clone()],
            parameters: Vec::new(),
        });

        // Feed-forward output projection
        let ff_output = format!("{}_ff_output", layer_prefix);
        layers.push(TensorRTLayer {
            layer_type: TensorRTLayerType::FullyConnected,
            name: format!("{}_ff_down", layer_prefix),
            inputs: vec![ff_activated],
            outputs: vec![ff_output.clone()],
            parameters: Vec::new(),
        });

        // Final residual connection
        let final_output = format!("{}_output", layer_prefix);
        layers.push(TensorRTLayer {
            layer_type: TensorRTLayerType::ElementWise,
            name: format!("{}_final_add", layer_prefix),
            inputs: vec![norm_output, ff_output],
            outputs: vec![final_output.clone()],
            parameters: Vec::new(),
        });

        Ok(final_output)
    }

    fn serialize_tensorrt_plan(&self, network: &TensorRTNetwork, output_path: &str) -> Result<()> {
        // In a real implementation, this would use the TensorRT C++ API
        // to build and serialize the engine

        let plan_content = self.generate_plan_description(network)?;
        std::fs::write(format!("{}.plan", output_path), plan_content)?;

        // Also generate a JSON description for debugging
        let json_content = self.generate_json_description(network)?;
        std::fs::write(format!("{}_tensorrt.json", output_path), json_content)?;

        Ok(())
    }

    fn generate_plan_description(&self, network: &TensorRTNetwork) -> Result<String> {
        let mut content = String::new();

        content.push_str("TensorRT Engine Plan\n");
        content.push_str("==================\n\n");

        content.push_str("Configuration:\n");
        content.push_str(&format!(
            "  Max Batch Size: {}\n",
            self.config.max_batch_size
        ));
        content.push_str(&format!(
            "  Max Sequence Length: {}\n",
            self.config.max_sequence_length
        ));
        content.push_str(&format!(
            "  Workspace Size: {} MB\n",
            self.config.workspace_size
        ));
        content.push_str(&format!("  FP16 Enabled: {}\n", self.config.fp16_enabled));
        content.push_str(&format!("  INT8 Enabled: {}\n", self.config.int8_enabled));
        content.push_str(&format!(
            "  Dynamic Shapes: {}\n",
            self.config.dynamic_shapes
        ));
        content.push_str(&format!(
            "  Optimization Level: {}\n",
            self.config.optimization_level
        ));
        content.push('\n');

        content.push_str("Inputs:\n");
        for input in &network.inputs {
            content.push_str(&format!(
                "  {}: {:?} {:?}\n",
                input.name, input.dimensions, input.data_type
            ));
        }
        content.push('\n');

        content.push_str("Outputs:\n");
        for output in &network.outputs {
            content.push_str(&format!(
                "  {}: {:?} {:?}\n",
                output.name, output.dimensions, output.data_type
            ));
        }
        content.push('\n');

        content.push_str("Layers:\n");
        for layer in &network.layers {
            content.push_str(&format!(
                "  {} ({:?}): {} -> {}\n",
                layer.name,
                layer.layer_type,
                layer.inputs.join(", "),
                layer.outputs.join(", ")
            ));
        }

        Ok(content)
    }

    fn generate_json_description(&self, network: &TensorRTNetwork) -> Result<String> {
        // Simple JSON serialization (in practice, you'd use serde)
        let mut json = String::new();

        json.push_str("{\n");
        json.push_str("  \"config\": {\n");
        json.push_str(&format!(
            "    \"max_batch_size\": {},\n",
            self.config.max_batch_size
        ));
        json.push_str(&format!(
            "    \"max_sequence_length\": {},\n",
            self.config.max_sequence_length
        ));
        json.push_str(&format!(
            "    \"workspace_size\": {},\n",
            self.config.workspace_size
        ));
        json.push_str(&format!(
            "    \"fp16_enabled\": {},\n",
            self.config.fp16_enabled
        ));
        json.push_str(&format!(
            "    \"int8_enabled\": {},\n",
            self.config.int8_enabled
        ));
        json.push_str(&format!(
            "    \"dynamic_shapes\": {},\n",
            self.config.dynamic_shapes
        ));
        json.push_str(&format!(
            "    \"optimization_level\": {}\n",
            self.config.optimization_level
        ));
        json.push_str("  },\n");

        json.push_str("  \"inputs\": [\n");
        for (i, input) in network.inputs.iter().enumerate() {
            json.push_str(&format!(
                "    {{ \"name\": \"{}\", \"dimensions\": {:?}, \"data_type\": \"{:?}\" }}",
                input.name, input.dimensions, input.data_type
            ));
            if i < network.inputs.len() - 1 {
                json.push(',');
            }
            json.push('\n');
        }
        json.push_str("  ],\n");

        json.push_str("  \"outputs\": [\n");
        for (i, output) in network.outputs.iter().enumerate() {
            json.push_str(&format!(
                "    {{ \"name\": \"{}\", \"dimensions\": {:?}, \"data_type\": \"{:?}\" }}",
                output.name, output.dimensions, output.data_type
            ));
            if i < network.outputs.len() - 1 {
                json.push(',');
            }
            json.push('\n');
        }
        json.push_str("  ],\n");

        json.push_str("  \"layers\": [\n");
        for (i, layer) in network.layers.iter().enumerate() {
            json.push_str(&format!("    {{ \"name\": \"{}\", \"type\": \"{:?}\", \"inputs\": {:?}, \"outputs\": {:?} }}",
                layer.name, layer.layer_type, layer.inputs, layer.outputs));
            if i < network.layers.len() - 1 {
                json.push(',');
            }
            json.push('\n');
        }
        json.push_str("  ]\n");

        json.push_str("}\n");

        Ok(json)
    }
}

impl ModelExporter for TensorRTExporter {
    fn export<M: Model>(&self, model: &M, config: &ExportConfig) -> Result<()> {
        if config.format != ExportFormat::TensorRT {
            return Err(anyhow!("TensorRTExporter only supports TensorRT format"));
        }

        let network = self.create_tensorrt_network(model, config)?;
        self.serialize_tensorrt_plan(&network, &config.output_path)?;

        println!("TensorRT plan exported to {}.plan", config.output_path);
        println!(
            "Network description saved to {}_tensorrt.json",
            config.output_path
        );

        Ok(())
    }

    fn supported_formats(&self) -> Vec<ExportFormat> {
        vec![ExportFormat::TensorRT]
    }

    fn validate_model<M: Model>(&self, _model: &M, format: ExportFormat) -> Result<()> {
        if format != ExportFormat::TensorRT {
            return Err(anyhow!("TensorRTExporter only supports TensorRT format"));
        }

        // Additional validation could check for TensorRT compatibility
        // - Supported layer types
        // - Dynamic shape constraints
        // - Memory requirements

        Ok(())
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_tensorrt_exporter_creation() {
        let exporter = TensorRTExporter::new();
        assert_eq!(exporter.config.max_batch_size, 32);
        assert_eq!(exporter.config.max_sequence_length, 2048);
        assert!(exporter.config.fp16_enabled);
        assert!(!exporter.config.int8_enabled);
    }

    #[test]
    fn test_tensorrt_config_custom() {
        let config = TensorRTConfig {
            max_batch_size: 64,
            max_sequence_length: 4096,
            workspace_size: 2048,
            fp16_enabled: false,
            int8_enabled: true,
            dynamic_shapes: false,
            optimization_level: 5,
        };

        let exporter = TensorRTExporter::new().with_config(config);
        assert_eq!(exporter.config.max_batch_size, 64);
        assert_eq!(exporter.config.max_sequence_length, 4096);
        assert_eq!(exporter.config.workspace_size, 2048);
        assert!(!exporter.config.fp16_enabled);
        assert!(exporter.config.int8_enabled);
        assert!(!exporter.config.dynamic_shapes);
        assert_eq!(exporter.config.optimization_level, 5);
    }

    #[test]
    fn test_tensorrt_data_types() {
        let float32 = TensorRTDataType::Float32;
        let float16 = TensorRTDataType::Float16;
        let int8 = TensorRTDataType::Int8;
        let int32 = TensorRTDataType::Int32;
        let bool_type = TensorRTDataType::Bool;

        // Just test that all types exist and can be created
        assert!(matches!(float32, TensorRTDataType::Float32));
        assert!(matches!(float16, TensorRTDataType::Float16));
        assert!(matches!(int8, TensorRTDataType::Int8));
        assert!(matches!(int32, TensorRTDataType::Int32));
        assert!(matches!(bool_type, TensorRTDataType::Bool));
    }

    #[test]
    fn test_tensorrt_layer_types() {
        let layer_types = [
            TensorRTLayerType::Convolution,
            TensorRTLayerType::FullyConnected,
            TensorRTLayerType::Activation,
            TensorRTLayerType::MultiHeadAttention,
            TensorRTLayerType::LayerNorm,
            TensorRTLayerType::Plugin("custom_plugin".to_string()),
        ];

        assert_eq!(layer_types.len(), 6);

        match &layer_types[5] {
            TensorRTLayerType::Plugin(name) => assert_eq!(name, "custom_plugin"),
            _ => panic!("Expected Plugin layer type but got {:?}", &layer_types[5]),
        }
    }

    #[test]
    fn test_supported_formats() {
        let exporter = TensorRTExporter::new();
        let formats = exporter.supported_formats();
        assert_eq!(formats.len(), 1);
        assert_eq!(formats[0], ExportFormat::TensorRT);
    }

    #[test]
    fn test_tensorrt_tensor_creation() {
        let tensor = TensorRTTensor {
            name: "test_tensor".to_string(),
            dimensions: vec![-1, 512, 768],
            data_type: TensorRTDataType::Float32,
        };

        assert_eq!(tensor.name, "test_tensor");
        assert_eq!(tensor.dimensions, vec![-1, 512, 768]);
        assert!(matches!(tensor.data_type, TensorRTDataType::Float32));
    }
}