686 lines
22 KiB
Rust
686 lines
22 KiB
Rust
//! Transformer model benchmarks
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//!
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//! Benchmarks for transformer architectures including:
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//! - BERT (encoder-only)
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//! - GPT (decoder-only)
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//! - T5 (encoder-decoder)
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//! - Different model sizes (base, large, XL)
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//! - Sequence length scaling
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//! - Batch size optimization
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//! - Attention mechanism performance
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use crate::{BenchmarkConfig, BenchmarkMeasurement, time_benchmark_async};
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use anyhow::{Context, Result};
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use std::collections::HashMap;
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use tracing::info;
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// Placeholder types for testing
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type TestModel = String;
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type TestAttentionLayer = String;
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type TestMultiheadAttention = String;
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/// Transformer model benchmarks
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pub struct TransformerBenchmarks;
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/// Model configuration for benchmarks
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#[derive(Debug, Clone)]
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pub struct ModelConfig {
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pub model_type: ModelType,
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pub hidden_size: usize,
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pub num_layers: usize,
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pub num_heads: usize,
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pub sequence_length: usize,
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pub vocab_size: usize,
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}
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#[derive(Debug, Clone)]
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pub enum ModelType {
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Bert,
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Gpt,
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T5,
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}
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impl TransformerBenchmarks {
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/// Create new transformer benchmarks
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pub fn new() -> Self {
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Self
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}
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/// Run all transformer benchmarks
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pub async fn run_all_benchmarks(
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&self,
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config: &BenchmarkConfig,
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) -> Result<HashMap<String, Vec<BenchmarkMeasurement>>> {
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let mut all_measurements = HashMap::new();
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// Standard model configurations
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let model_configs = self.get_standard_model_configs();
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for model_config in model_configs {
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let model_name = format!(
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"{:?}_{}L_{}H",
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model_config.model_type, model_config.num_layers, model_config.hidden_size
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);
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info!("Running benchmarks for {}", model_name);
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// Forward pass benchmarks
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all_measurements.extend(
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self.run_forward_pass_benchmarks(config, &model_config)
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.await
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.with_context(|| format!("Failed forward pass benchmarks for {model_name}"))?,
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);
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// Attention mechanism benchmarks
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all_measurements.extend(
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self.run_attention_benchmarks(config, &model_config)
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.await
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.with_context(|| format!("Failed attention benchmarks for {model_name}"))?,
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);
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// Batch size scaling
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all_measurements.extend(
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self.run_batch_scaling_benchmarks(config, &model_config)
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.await
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.with_context(|| format!("Failed batch scaling benchmarks for {model_name}"))?,
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);
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// Sequence length scaling
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all_measurements.extend(
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self.run_sequence_scaling_benchmarks(config, &model_config)
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.await
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.with_context(|| {
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format!("Failed sequence scaling benchmarks for {model_name}")
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})?,
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);
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// Training step benchmarks
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all_measurements.extend(
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self.run_training_step_benchmarks(config, &model_config)
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.await
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.with_context(|| format!("Failed training step benchmarks for {model_name}"))?,
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);
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}
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Ok(all_measurements)
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}
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/// Get standard model configurations for benchmarking
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fn get_standard_model_configs(&self) -> Vec<ModelConfig> {
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vec![
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// BERT configurations
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ModelConfig {
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model_type: ModelType::Bert,
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hidden_size: 768,
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num_layers: 12,
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num_heads: 12,
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sequence_length: 512,
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vocab_size: 30522,
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},
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ModelConfig {
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model_type: ModelType::Bert,
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hidden_size: 1024,
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num_layers: 24,
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num_heads: 16,
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sequence_length: 512,
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vocab_size: 30522,
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},
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// GPT configurations
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ModelConfig {
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model_type: ModelType::Gpt,
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hidden_size: 768,
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num_layers: 12,
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num_heads: 12,
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sequence_length: 1024,
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vocab_size: 50257,
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},
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ModelConfig {
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model_type: ModelType::Gpt,
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hidden_size: 1536,
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num_layers: 48,
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num_heads: 25,
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sequence_length: 1024,
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vocab_size: 50257,
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},
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// T5 configurations
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ModelConfig {
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model_type: ModelType::T5,
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hidden_size: 768,
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num_layers: 12,
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num_heads: 12,
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sequence_length: 512,
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vocab_size: 32128,
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},
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]
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}
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/// Run forward pass benchmarks
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async fn run_forward_pass_benchmarks(
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&self,
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config: &BenchmarkConfig,
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model_config: &ModelConfig,
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) -> Result<HashMap<String, Vec<BenchmarkMeasurement>>> {
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let mut measurements = HashMap::new();
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let batch_sizes = vec![1, 8, 16, 32];
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for batch_size in batch_sizes {
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let benchmark_name = format!(
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"{:?}_forward_b{}_s{}",
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model_config.model_type, batch_size, model_config.sequence_length
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);
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measurements.insert(
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benchmark_name.clone(),
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self.benchmark_forward_pass(config, model_config, batch_size)
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.await
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.with_context(|| format!("Failed forward pass benchmark: {benchmark_name}"))?,
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);
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}
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Ok(measurements)
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}
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/// Run attention mechanism benchmarks
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async fn run_attention_benchmarks(
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&self,
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config: &BenchmarkConfig,
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model_config: &ModelConfig,
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) -> Result<HashMap<String, Vec<BenchmarkMeasurement>>> {
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let mut measurements = HashMap::new();
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// Single attention layer performance
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let benchmark_name = format!("{:?}_attention_single", model_config.model_type);
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measurements.insert(
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benchmark_name.clone(),
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self.benchmark_attention_layer(config, model_config)
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.await
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.with_context(|| format!("Failed attention benchmark: {benchmark_name}"))?,
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);
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// Multi-head attention scaling
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let benchmark_name = format!("{:?}_attention_multihead", model_config.model_type);
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measurements.insert(
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benchmark_name.clone(),
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self.benchmark_multihead_attention(config, model_config)
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.await
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.with_context(|| {
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format!("Failed multi-head attention benchmark: {benchmark_name}")
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})?,
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);
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Ok(measurements)
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}
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/// Run batch size scaling benchmarks
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async fn run_batch_scaling_benchmarks(
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&self,
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config: &BenchmarkConfig,
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model_config: &ModelConfig,
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) -> Result<HashMap<String, Vec<BenchmarkMeasurement>>> {
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let mut measurements = HashMap::new();
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let batch_sizes = vec![1, 2, 4, 8, 16, 32, 64];
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for batch_size in batch_sizes {
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let benchmark_name = format!(
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"{:?}_batch_scaling_b{}",
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model_config.model_type, batch_size
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);
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measurements.insert(
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benchmark_name.clone(),
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self.benchmark_batch_scaling(config, model_config, batch_size)
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.await
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.with_context(|| format!("Failed batch scaling benchmark: {benchmark_name}"))?,
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);
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}
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Ok(measurements)
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}
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/// Run sequence length scaling benchmarks
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async fn run_sequence_scaling_benchmarks(
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&self,
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config: &BenchmarkConfig,
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model_config: &ModelConfig,
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) -> Result<HashMap<String, Vec<BenchmarkMeasurement>>> {
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let mut measurements = HashMap::new();
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let sequence_lengths = vec![128, 256, 512, 1024, 2048];
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for seq_len in sequence_lengths {
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// Skip if sequence length is too large for the model
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if seq_len > model_config.sequence_length * 2 {
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continue;
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}
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let benchmark_name = format!(
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"{:?}_sequence_scaling_s{}",
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model_config.model_type, seq_len
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);
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measurements.insert(
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benchmark_name.clone(),
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self.benchmark_sequence_scaling(config, model_config, seq_len)
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.await
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.with_context(|| {
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format!("Failed sequence scaling benchmark: {benchmark_name}")
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})?,
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);
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}
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Ok(measurements)
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}
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/// Run training step benchmarks
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async fn run_training_step_benchmarks(
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&self,
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config: &BenchmarkConfig,
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model_config: &ModelConfig,
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) -> Result<HashMap<String, Vec<BenchmarkMeasurement>>> {
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let mut measurements = HashMap::new();
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let benchmark_name = format!("{:?}_training_step", model_config.model_type);
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measurements.insert(
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benchmark_name.clone(),
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self.benchmark_training_step(config, model_config)
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.await
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.with_context(|| format!("Failed training step benchmark: {benchmark_name}"))?,
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);
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Ok(measurements)
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}
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/// Individual benchmark implementations
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async fn benchmark_forward_pass(
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&self,
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config: &BenchmarkConfig,
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model_config: &ModelConfig,
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batch_size: usize,
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) -> Result<Vec<BenchmarkMeasurement>> {
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let mut measurements = Vec::with_capacity(config.measurement_iterations);
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// Create model (placeholder)
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let model = self.create_model(model_config).await?;
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// Warmup
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for _ in 0..config.warmup_iterations {
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let _output = self
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.forward_pass(&model, batch_size, model_config.sequence_length)
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.await?;
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}
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// Measurements
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for _ in 0..config.measurement_iterations {
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let measurement = time_benchmark_async(
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format!("{:?}_forward_b{}", model_config.model_type, batch_size),
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|| async {
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let _output = self
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.forward_pass(&model, batch_size, model_config.sequence_length)
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.await?;
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Ok(())
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},
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)
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.await?;
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// Estimate memory usage based on model size and batch size
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let estimated_memory = self.estimate_memory_usage(model_config, batch_size);
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let enhanced_measurement =
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measurement.with_memory_usage(estimated_memory, Some(estimated_memory));
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measurements.push(enhanced_measurement);
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}
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Ok(measurements)
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}
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async fn benchmark_attention_layer(
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&self,
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config: &BenchmarkConfig,
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model_config: &ModelConfig,
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) -> Result<Vec<BenchmarkMeasurement>> {
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let mut measurements = Vec::with_capacity(config.measurement_iterations);
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let attention_layer = self.create_attention_layer(model_config).await?;
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let batch_size = 8;
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for _ in 0..config.warmup_iterations {
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let _output = self
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.attention_forward(&attention_layer, batch_size, model_config.sequence_length)
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.await?;
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}
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for _ in 0..config.measurement_iterations {
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let measurement = time_benchmark_async(
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format!("{:?}_attention", model_config.model_type),
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|| async {
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let _output = self
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.attention_forward(
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&attention_layer,
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batch_size,
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model_config.sequence_length,
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)
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.await?;
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Ok(())
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},
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)
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.await?;
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measurements.push(measurement);
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}
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Ok(measurements)
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}
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async fn benchmark_multihead_attention(
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&self,
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config: &BenchmarkConfig,
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model_config: &ModelConfig,
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) -> Result<Vec<BenchmarkMeasurement>> {
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let mut measurements = Vec::with_capacity(config.measurement_iterations);
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let mha_layer = self.create_multihead_attention(model_config).await?;
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let batch_size = 8;
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for _ in 0..config.warmup_iterations {
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let _output = self
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.multihead_attention_forward(&mha_layer, batch_size, model_config.sequence_length)
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.await?;
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}
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for _ in 0..config.measurement_iterations {
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let measurement = time_benchmark_async(
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format!("{:?}_multihead_attention", model_config.model_type),
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|| async {
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let _output = self
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.multihead_attention_forward(
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&mha_layer,
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batch_size,
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model_config.sequence_length,
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)
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.await?;
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Ok(())
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},
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)
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.await?;
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measurements.push(measurement);
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}
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Ok(measurements)
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}
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async fn benchmark_batch_scaling(
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&self,
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config: &BenchmarkConfig,
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model_config: &ModelConfig,
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batch_size: usize,
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) -> Result<Vec<BenchmarkMeasurement>> {
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let mut measurements = Vec::with_capacity(config.measurement_iterations);
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let model = self.create_model(model_config).await?;
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for _ in 0..config.warmup_iterations {
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let _output = self
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.forward_pass(&model, batch_size, model_config.sequence_length)
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.await?;
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}
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for _ in 0..config.measurement_iterations {
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let measurement = time_benchmark_async(
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format!("{:?}_batch_{}", model_config.model_type, batch_size),
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|| async {
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let _output = self
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.forward_pass(&model, batch_size, model_config.sequence_length)
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.await?;
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Ok(())
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},
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)
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.await?;
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let memory_usage = self.estimate_memory_usage(model_config, batch_size);
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let enhanced_measurement =
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measurement.with_memory_usage(memory_usage, Some(memory_usage));
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measurements.push(enhanced_measurement);
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}
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Ok(measurements)
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}
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async fn benchmark_sequence_scaling(
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&self,
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config: &BenchmarkConfig,
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model_config: &ModelConfig,
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sequence_length: usize,
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) -> Result<Vec<BenchmarkMeasurement>> {
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let mut measurements = Vec::with_capacity(config.measurement_iterations);
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let model = self.create_model(model_config).await?;
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let batch_size = 8;
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for _ in 0..config.warmup_iterations {
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let _output = self
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.forward_pass(&model, batch_size, sequence_length)
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.await?;
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}
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for _ in 0..config.measurement_iterations {
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let measurement = time_benchmark_async(
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format!("{:?}_seq_{}", model_config.model_type, sequence_length),
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|| async {
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let _output = self
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.forward_pass(&model, batch_size, sequence_length)
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.await?;
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Ok(())
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},
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)
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.await?;
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measurements.push(measurement);
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}
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Ok(measurements)
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}
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async fn benchmark_training_step(
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&self,
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config: &BenchmarkConfig,
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model_config: &ModelConfig,
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) -> Result<Vec<BenchmarkMeasurement>> {
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let mut measurements = Vec::with_capacity(config.measurement_iterations);
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let model = self.create_model(model_config).await?;
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let batch_size = 16;
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for _ in 0..config.warmup_iterations {
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self.training_step(&model, batch_size, model_config.sequence_length)
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.await?;
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}
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for _ in 0..config.measurement_iterations {
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let measurement = time_benchmark_async(
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format!("{:?}_training_step", model_config.model_type),
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|| async {
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self.training_step(&model, batch_size, model_config.sequence_length)
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.await?;
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Ok(())
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},
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)
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.await?;
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// Training uses more memory due to gradients
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let memory_usage = self.estimate_memory_usage(model_config, batch_size) * 3; // Rough estimate
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let enhanced_measurement =
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measurement.with_memory_usage(memory_usage, Some(memory_usage));
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measurements.push(enhanced_measurement);
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}
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Ok(measurements)
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}
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/// Helper functions - these would be implemented with actual model operations
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async fn create_model(&self, config: &ModelConfig) -> Result<TestModel> {
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// Simulate model creation time based on model size
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let creation_time = config.num_layers * config.hidden_size / 10000;
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tokio::time::sleep(std::time::Duration::from_millis(creation_time as u64)).await;
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Ok(format!("{:?}_model", config.model_type))
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}
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async fn create_attention_layer(&self, config: &ModelConfig) -> Result<TestAttentionLayer> {
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tokio::time::sleep(std::time::Duration::from_millis(10)).await;
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Ok(format!("{:?}_attention", config.model_type))
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}
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async fn create_multihead_attention(
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&self,
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config: &ModelConfig,
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) -> Result<TestMultiheadAttention> {
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tokio::time::sleep(std::time::Duration::from_millis(20)).await;
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Ok(format!("{:?}_mha", config.model_type))
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}
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async fn forward_pass(
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&self,
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_model: &TestModel,
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batch_size: usize,
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sequence_length: usize,
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) -> Result<Vec<f32>> {
|
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// Simulate forward pass time - scales with batch size and sequence length
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let computation_time = (batch_size * sequence_length) / 1000;
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tokio::time::sleep(std::time::Duration::from_millis(computation_time as u64)).await;
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Ok(vec![0.0; batch_size * sequence_length])
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}
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|
|
|
async fn attention_forward(
|
|
&self,
|
|
_layer: &TestAttentionLayer,
|
|
batch_size: usize,
|
|
sequence_length: usize,
|
|
) -> Result<Vec<f32>> {
|
|
// Attention is O(n^2) in sequence length
|
|
let computation_time = (batch_size * sequence_length * sequence_length) / 100000;
|
|
tokio::time::sleep(std::time::Duration::from_millis(computation_time as u64)).await;
|
|
Ok(vec![0.0; batch_size * sequence_length])
|
|
}
|
|
|
|
async fn multihead_attention_forward(
|
|
&self,
|
|
_layer: &TestMultiheadAttention,
|
|
batch_size: usize,
|
|
sequence_length: usize,
|
|
) -> Result<Vec<f32>> {
|
|
// Multi-head attention with multiple heads
|
|
let computation_time = (batch_size * sequence_length * sequence_length) / 50000;
|
|
tokio::time::sleep(std::time::Duration::from_millis(computation_time as u64)).await;
|
|
Ok(vec![0.0; batch_size * sequence_length])
|
|
}
|
|
|
|
async fn training_step(
|
|
&self,
|
|
_model: &TestModel,
|
|
batch_size: usize,
|
|
sequence_length: usize,
|
|
) -> Result<f32> {
|
|
// Training includes forward + backward pass
|
|
let computation_time = (batch_size * sequence_length) / 500;
|
|
tokio::time::sleep(std::time::Duration::from_millis(computation_time as u64)).await;
|
|
Ok(0.1) // Mock loss value
|
|
}
|
|
|
|
fn estimate_memory_usage(&self, config: &ModelConfig, batch_size: usize) -> u64 {
|
|
// Rough estimation of memory usage in bytes
|
|
let params = config.num_layers * config.hidden_size * config.hidden_size * 4; // 4 bytes per float
|
|
let activations = batch_size * config.sequence_length * config.hidden_size * 4;
|
|
(params + activations) as u64
|
|
}
|
|
}
|
|
|
|
impl Default for TransformerBenchmarks {
|
|
fn default() -> Self {
|
|
Self::new()
|
|
}
|
|
}
|
|
|
|
#[cfg(test)]
|
|
mod tests {
|
|
use super::*;
|
|
|
|
#[test]
|
|
fn test_transformer_benchmarks_creation() {
|
|
let benchmarks = TransformerBenchmarks::new();
|
|
// Compilation test
|
|
}
|
|
|
|
#[test]
|
|
fn test_model_configs() {
|
|
let benchmarks = TransformerBenchmarks::new();
|
|
let configs = benchmarks.get_standard_model_configs();
|
|
assert!(!configs.is_empty());
|
|
|
|
// Check that we have different model types
|
|
let has_bert = configs
|
|
.iter()
|
|
.any(|c| matches!(c.model_type, ModelType::Bert));
|
|
let has_gpt = configs
|
|
.iter()
|
|
.any(|c| matches!(c.model_type, ModelType::Gpt));
|
|
let has_t5 = configs
|
|
.iter()
|
|
.any(|c| matches!(c.model_type, ModelType::T5));
|
|
|
|
assert!(has_bert);
|
|
assert!(has_gpt);
|
|
assert!(has_t5);
|
|
}
|
|
|
|
#[tokio::test]
|
|
async fn test_forward_pass_benchmark() {
|
|
let benchmarks = TransformerBenchmarks::new();
|
|
let config = BenchmarkConfig::default()
|
|
.with_measurement_iterations(2)
|
|
.with_warmup_iterations(1);
|
|
|
|
let model_config = ModelConfig {
|
|
model_type: ModelType::Bert,
|
|
hidden_size: 128,
|
|
num_layers: 2,
|
|
num_heads: 4,
|
|
sequence_length: 64,
|
|
vocab_size: 1000,
|
|
};
|
|
|
|
let result = benchmarks
|
|
.benchmark_forward_pass(&config, &model_config, 4)
|
|
.await;
|
|
assert!(result.is_ok());
|
|
|
|
let measurements = result.unwrap();
|
|
assert_eq!(measurements.len(), 2);
|
|
assert!(measurements[0].memory_bytes.is_some());
|
|
}
|
|
|
|
#[tokio::test]
|
|
async fn test_memory_estimation() {
|
|
let benchmarks = TransformerBenchmarks::new();
|
|
let model_config = ModelConfig {
|
|
model_type: ModelType::Bert,
|
|
hidden_size: 768,
|
|
num_layers: 12,
|
|
num_heads: 12,
|
|
sequence_length: 512,
|
|
vocab_size: 30522,
|
|
};
|
|
|
|
let memory_b1 = benchmarks.estimate_memory_usage(&model_config, 1);
|
|
let memory_b8 = benchmarks.estimate_memory_usage(&model_config, 8);
|
|
|
|
// Memory usage should scale with batch size
|
|
assert!(memory_b8 > memory_b1);
|
|
}
|
|
}
|