trustformers-models 0.1.1

//! Performance profiling for models

use anyhow::Result;
use std::collections::HashMap;
use std::time::{Duration, Instant};
use trustformers_core::tensor::Tensor;
use trustformers_core::traits::Model;

use super::config::{TestDataType, TestInputConfig, ValidationConfig};
use super::types::{
    LayerPerformance, MemoryAnalysis, OverallPerformance, PerformanceResults,
    ThroughputMeasurements,
};

/// Performance profiler for models
pub struct PerformanceProfiler {
    config: ValidationConfig,
}

impl PerformanceProfiler {
    /// Create a new performance profiler
    pub fn new() -> Self {
        Self {
            config: ValidationConfig::default(),
        }
    }

    /// Create profiler with custom configuration
    pub fn with_config(config: ValidationConfig) -> Self {
        Self { config }
    }

    /// Profile model performance
    pub fn profile_model<M: Model<Input = Tensor, Output = Tensor>>(
        &self,
        model: &M,
    ) -> Result<PerformanceResults> {
        let mut layer_performance = Vec::new();
        let mut total_time = Duration::ZERO;
        let total_memory = 0.0;

        // Profile each test input
        for test_input in &self.config.test_inputs {
            let input = self.create_test_input(test_input)?;

            let start_time = Instant::now();
            let _output = model.forward(input)?;
            let inference_time = start_time.elapsed();

            total_time += inference_time;

            // For now, create placeholder layer performance data
            // In a real implementation, this would hook into the model's layers
            layer_performance.push(LayerPerformance {
                layer_name: format!("layer_{}", layer_performance.len()),
                layer_type: "transformer".to_string(),
                forward_time: inference_time / 10, // Rough estimate
                memory_usage_mb: 100.0,            // Placeholder
                flops: None,
                utilization_percent: None,
            });
        }

        let overall_performance = OverallPerformance {
            total_inference_time: total_time,
            tokens_per_second: 1000.0, // Placeholder calculation
            total_flops: None,
            peak_memory_mb: total_memory,
            average_memory_mb: total_memory / self.config.test_inputs.len() as f64,
        };

        let memory_analysis = MemoryAnalysis {
            by_layer_type: HashMap::new(),
            by_tensor_type: HashMap::new(),
            efficiency_score: 75.0,     // Placeholder
            fragmentation_percent: 5.0, // Placeholder
        };

        let throughput = ThroughputMeasurements {
            batch_size: 1,
            sequence_length: 128,
            tokens_per_second: 1000.0,
            samples_per_second: 10.0,
            latency_per_token_ms: 1.0,
        };

        Ok(PerformanceResults {
            layer_performance,
            overall_performance,
            memory_analysis,
            throughput,
        })
    }

    /// Create test input (helper method)
    fn create_test_input(&self, config: &TestInputConfig) -> Result<Tensor> {
        match config.data_type {
            TestDataType::I32 => {
                // Create token IDs for language models
                let mut input_ids = Vec::new();
                for i in 0..config.dimensions.iter().product::<usize>() {
                    input_ids.push(((i % 1000 + 1) as i32) as f32); // Keep in reasonable token range
                }
                Ok(Tensor::from_vec(input_ids, &config.dimensions)?)
            },
            TestDataType::F32 => {
                // Create floating point input
                Ok(Tensor::randn(&config.dimensions)?)
            },
            TestDataType::F16 => {
                // Create half precision input (placeholder)
                Ok(Tensor::randn(&config.dimensions)?)
            },
            TestDataType::I64 => {
                // Create 64-bit integer input
                let mut input_ids = Vec::new();
                for i in 0..config.dimensions.iter().product::<usize>() {
                    input_ids.push(((i % 1000 + 1) as i64) as f32);
                }
                Ok(Tensor::from_vec(input_ids, &config.dimensions)?)
            },
        }
    }

    /// Get the model name being profiled
    pub fn get_model_name(&self) -> &str {
        "Unknown"
    }
}

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