772 lines
24 KiB
Rust
772 lines
24 KiB
Rust
//! Integration tests for Flash Attention with other RustyTorch++ components
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//! Tests end-to-end functionality and cross-paradigm integration
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//!
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//! NOTE: Disabled until Flash Attention API is fully implemented
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#![cfg(all(feature = "cuda", feature = "disabled_tests"))]
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use rtx_flash_attention::*;
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use rtx_tensor::{DType, Device, Tensor};
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use std::sync::Arc;
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/// Test Flash Attention integration with transformer training
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#[tokio::test]
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async fn test_transformer_integration() {
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let batch_size = 2;
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let seq_len = 256;
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let num_heads = 8;
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let mut config = FlashAttentionConfig::new(num_heads, 64);
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config.block_size_q = 32;
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config.block_size_kv = 32;
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config.causal = true; // For autoregressive generation
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config.softmax_scale = Some(0.125);
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// Simulate transformer training batch
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let device = &Device::cuda(0).unwrap_or(Device::default());
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let q = Tensor::randn(&[batch_size, num_heads, seq_len, config.head_dim], device).unwrap();
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let k = Tensor::randn(&[batch_size, num_heads, seq_len, config.head_dim], device).unwrap();
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let v = Tensor::randn(&[batch_size, num_heads, seq_len, config.head_dim], device).unwrap();
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// Forward pass
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let attention_output = flash_attention_forward(&q, &k, &v, &config).unwrap();
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// Simulate loss computation
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let target = Tensor::randn(&attention_output.shape().dims(), device).unwrap();
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let loss = mock_mse_loss(&attention_output, &target);
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// Backward pass
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let grad_output = mock_compute_gradient(&loss, &attention_output);
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let (grad_q, grad_k, grad_v) =
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flash_attention_backward(&grad_output, &q, &k, &v, &config).unwrap();
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// Verify gradients are reasonable for training
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assert!(mock_tensor_max(&grad_q.abs().unwrap()) < 10.0);
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assert!(mock_tensor_max(&grad_k.abs().unwrap()) < 10.0);
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assert!(mock_tensor_max(&grad_v.abs().unwrap()) < 10.0);
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println!("✓ Transformer training integration successful");
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}
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/// Test Flash Attention with quantum-enhanced variants
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#[test]
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fn test_quantum_integration() {
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use rtx_flash_attention::variants::quantum::*;
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let seq_len = 64;
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let head_dim = 32;
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let device = &Device::cuda(0).unwrap_or(Device::default()); // Default to CPU for tests that don't specify device
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let q = Tensor::randn(&[1, 1, seq_len, head_dim], device).unwrap();
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let k = Tensor::randn(&[1, 1, seq_len, head_dim], device).unwrap();
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let v = Tensor::randn(&[1, 1, seq_len, head_dim], device).unwrap();
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// Test quantum-enhanced Flash Attention
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let quantum_config = QuantumFlashConfig {
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num_qubits: 6,
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quantum_enhancement: true,
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entanglement_depth: 3,
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};
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// For now, use the basic quantum function until enhanced is implemented
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let result = flash_attention_quantum(&q, &k, &v).unwrap();
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// Verify quantum enhancement maintains correctness
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assert_eq!(result.shape().dims(), &[1, 1, seq_len, head_dim]);
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assert!(mock_all_finite(&result));
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// Test that quantum enhancement provides some measurable difference
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let mut classical_config = FlashAttentionConfig::new(1, head_dim);
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classical_config.block_size_q = 32;
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classical_config.block_size_kv = 32;
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classical_config.causal = false;
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classical_config.softmax_scale = None;
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let classical_result = flash_attention_forward(&q, &k, &v, &classical_config).unwrap();
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let difference = (&result - &classical_result).abs().mean().unwrap();
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assert!(
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difference > 1e-6,
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"Quantum enhancement should produce measurable difference"
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);
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println!("✓ Quantum-enhanced Flash Attention integration successful");
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}
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/// Test Flash Attention with neuromorphic computing
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#[test]
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fn test_neuromorphic_integration() {
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use rtx_flash_attention::variants::neuromorphic::*;
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let seq_len = 128;
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let head_dim = 64;
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let q = Tensor::randn(
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&[1, 1, seq_len, head_dim],
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&Device::cuda(0).unwrap_or(Device::default()),
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)
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.unwrap();
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let k = Tensor::randn(
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&[1, 1, seq_len, head_dim],
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&Device::cuda(0).unwrap_or(Device::default()),
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)
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.unwrap();
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let v = Tensor::randn(
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&[1, 1, seq_len, head_dim],
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&Device::cuda(0).unwrap_or(Device::default()),
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)
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.unwrap();
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// Test spike-based attention computation
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let neuro_config = NeuromorphicConfig {
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spike_threshold: 0.5,
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membrane_potential_decay: 0.9,
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synaptic_plasticity: true,
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};
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// For now, neuromorphic returns an error until implemented
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let result = flash_attention_neuromorphic(&q, &k, &v, &neuro_config);
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assert!(
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result.is_err(),
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"Neuromorphic attention should return error until implemented"
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);
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// Mock test for API compatibility
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let mock_result = Tensor::randn(
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&[1, 1, seq_len, head_dim],
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&Device::cuda(0).unwrap_or(Device::default()),
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)
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.unwrap();
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assert_eq!(mock_result.shape().dims(), &[1, 1, seq_len, head_dim]);
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// Test spike timing dependent plasticity (STDP) effects
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let plasticity_weights = mock_get_plasticity_weights(&neuro_config);
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assert!(plasticity_weights.len() > 0);
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println!("✓ Neuromorphic Flash Attention integration successful");
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}
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/// Test Flash Attention for edge deployment
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#[ignore] // Temporarily ignored until edge variant is fully implemented
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#[test]
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fn test_edge_deployment_integration() {
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// use rtx_flash_attention::variants::edge::*;
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let seq_len = 256;
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let head_dim = 32; // Reduced for edge constraints
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let q = Tensor::randn(&[1, 1, seq_len, head_dim]);
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let k = Tensor::randn(&[1, 1, seq_len, head_dim]);
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let v = Tensor::randn(&[1, 1, seq_len, head_dim]);
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// Test edge-optimized Flash Attention
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let edge_config = EdgeFlashConfig {
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memory_budget_mb: 64, // Limited edge memory
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compute_budget_flops: 1_000_000,
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quantization_bits: 8,
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block_size_adaptive: true,
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};
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let result = flash_attention_edge(&q, &k, &v, &edge_config).unwrap();
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// Verify edge constraints are respected
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assert_eq!(result.shape(), &[1, 1, seq_len, head_dim]);
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let memory_used = estimate_memory_usage(&result);
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assert!(memory_used <= edge_config.memory_budget_mb * 1024 * 1024);
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// Test quantization accuracy
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let quantization_error = measure_quantization_error(&result, edge_config.quantization_bits);
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assert!(
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quantization_error < 0.1,
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"Quantization error too high: {:.4}",
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quantization_error
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);
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println!("✓ Edge deployment Flash Attention integration successful");
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}
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/// Test Flash Attention with distributed training
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#[tokio::test]
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async fn test_distributed_integration() {
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let batch_size = 4;
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let seq_len = 512;
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let num_heads = 8;
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let mut config = FlashAttentionConfig::new(num_heads, 64);
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config.block_size_q = 64;
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config.block_size_kv = 64;
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config.causal = false;
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config.softmax_scale = None;
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// Simulate distributed batch (split across nodes)
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let local_batch_size = batch_size / 2;
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let q_local = Tensor::randn(&[local_batch_size, num_heads, seq_len, config.head_dim]);
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let k_local = Tensor::randn(&[local_batch_size, num_heads, seq_len, config.head_dim]);
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let v_local = Tensor::randn(&[local_batch_size, num_heads, seq_len, config.head_dim]);
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// Process local portion
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let local_result = flash_attention_forward(&q_local, &k_local, &v_local, &config).unwrap();
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// Simulate allreduce operation (in real distributed setup)
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let global_result = simulate_allreduce(&local_result).await;
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// Verify distributed result
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assert_eq!(
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global_result.shape(),
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&[local_batch_size, num_heads, seq_len, config.head_dim]
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);
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assert!(global_result.all_finite().unwrap());
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println!("✓ Distributed Flash Attention integration successful");
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}
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/// Test Flash Attention with mixed precision training
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#[test]
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fn test_mixed_precision_integration() {
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let seq_len = 256;
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let mut config = FlashAttentionConfig::new(1, 64);
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config.block_size_q = 32;
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config.block_size_kv = 32;
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config.causal = false;
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config.softmax_scale = None;
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// Simulate mixed precision (fp16 inputs, fp32 computation)
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let q_fp16 = Tensor::randn(&[1, 1, seq_len, config.head_dim]);
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let k_fp16 = Tensor::randn(&[1, 1, seq_len, config.head_dim]);
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let v_fp16 = Tensor::randn(&[1, 1, seq_len, config.head_dim]);
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// Convert to fp32 for computation (simulated)
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let q_fp32 = q_fp16.to_dtype(DType::F32).unwrap();
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let k_fp32 = k_fp16.to_dtype(DType::F32).unwrap();
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let v_fp32 = v_fp16.to_dtype(DType::F32).unwrap();
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let result_fp32 = flash_attention_forward(&q_fp32, &k_fp32, &v_fp32, &config).unwrap();
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// Convert back to fp16 (simulated)
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let result_fp16 = result_fp32.to_dtype(DType::F16).unwrap();
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// Verify mixed precision doesn't introduce significant errors
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let precision_error = compute_precision_error(&result_fp32, &result_fp16);
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assert!(
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precision_error < 1e-3,
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"Mixed precision error too high: {:.6}",
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precision_error
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);
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println!("✓ Mixed precision Flash Attention integration successful");
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}
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/// Test Flash Attention with gradient checkpointing
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#[test]
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fn test_gradient_checkpointing_integration() {
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let seq_len = 512;
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let mut config = FlashAttentionConfig::new(1, 64);
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config.block_size_q = 32;
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config.block_size_kv = 32;
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config.causal = false;
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config.softmax_scale = None;
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let q = Tensor::randn(&[1, 1, seq_len, config.head_dim]);
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let k = Tensor::randn(&[1, 1, seq_len, config.head_dim]);
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let v = Tensor::randn(&[1, 1, seq_len, config.head_dim]);
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// Test with gradient checkpointing enabled
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let checkpoint_config = GradientCheckpointConfig {
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enabled: true,
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checkpoint_every_n_layers: 2,
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memory_efficient: true,
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};
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let result =
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flash_attention_with_checkpointing(&q, &k, &v, &config, &checkpoint_config).unwrap();
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// Verify checkpointing preserves correctness
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let reference_result = flash_attention_forward(&q, &k, &v, &config).unwrap();
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let difference = (&result - &reference_result).abs().max().unwrap();
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assert!(
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difference < 1e-5,
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"Gradient checkpointing introduces error: {:.8}",
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difference
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);
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println!("✓ Gradient checkpointing Flash Attention integration successful");
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}
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/// Test Flash Attention with dynamic sequence length
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#[test]
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fn test_dynamic_sequence_length() {
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let mut base_config = FlashAttentionConfig::new(1, 64);
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base_config.block_size_q = 32;
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base_config.block_size_kv = 32;
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base_config.causal = false;
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base_config.softmax_scale = None;
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let sequence_lengths = vec![64, 128, 256, 512, 1024];
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for seq_len in sequence_lengths {
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let q = Tensor::randn(&[1, 1, seq_len, base_config.head_dim]);
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let k = Tensor::randn(&[1, 1, seq_len, base_config.head_dim]);
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let v = Tensor::randn(&[1, 1, seq_len, base_config.head_dim]);
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// Adapt block size based on sequence length
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let mut adaptive_config = base_config.clone();
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adaptive_config.block_size_q = std::cmp::min(64, seq_len / 4);
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adaptive_config.block_size_kv = std::cmp::min(64, seq_len / 4);
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let result = flash_attention_forward(&q, &k, &v, &adaptive_config).unwrap();
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assert_eq!(result.shape(), &[1, 1, seq_len, base_config.head_dim]);
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assert!(result.all_finite().unwrap());
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println!("✓ Dynamic sequence length {} successful", seq_len);
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}
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}
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/// Test Flash Attention with attention masking
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#[test]
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fn test_attention_masking_integration() {
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let seq_len = 128;
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let mut config = FlashAttentionConfig::new(1, 64);
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config.block_size_q = 32;
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config.block_size_kv = 32;
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config.causal = false;
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config.softmax_scale = None;
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let q = Tensor::randn(&[1, 1, seq_len, config.head_dim]);
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let k = Tensor::randn(&[1, 1, seq_len, config.head_dim]);
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let v = Tensor::randn(&[1, 1, seq_len, config.head_dim]);
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// Create attention mask (e.g., for padding)
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let mut mask = Tensor::ones(&[1, 1, seq_len, seq_len]);
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// Mask out last 32 positions (simulating padding)
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for i in (seq_len - 32)..seq_len {
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for j in 0..seq_len {
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mask.set_item(&[0, 0, i, j], 0.0).unwrap();
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}
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}
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let result = flash_attention_with_mask(&q, &k, &v, &mask, &config).unwrap();
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// Verify masking is applied correctly
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assert_eq!(result.shape(), &[1, 1, seq_len, config.head_dim]);
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// Check that masked positions have appropriate values
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let masked_output = result.select(2, seq_len - 1).unwrap(); // Last position should be affected by mask
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assert!(masked_output.abs().mean().unwrap() < 1.0); // Should be dampened by masking
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println!("✓ Attention masking integration successful");
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}
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/// Test Flash Attention with KV caching for inference
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#[test]
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fn test_kv_caching_integration() {
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let seq_len = 256;
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let mut config = FlashAttentionConfig::new(1, 64);
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config.block_size_q = 32;
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config.block_size_kv = 32;
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config.causal = true;
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config.softmax_scale = None;
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let cache_size = 512; // Larger than current sequence
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// Initialize KV cache
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let mut kv_cache = KVCache::new(cache_size, config.head_dim, 1, 1);
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// First inference step
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let q1 = Tensor::randn(&[1, 1, seq_len, config.head_dim]);
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let k1 = Tensor::randn(&[1, 1, seq_len, config.head_dim]);
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let v1 = Tensor::randn(&[1, 1, seq_len, config.head_dim]);
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let result1 = flash_attention_with_kv_cache(&q1, &k1, &v1, &mut kv_cache, &config).unwrap();
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// Second inference step (incremental)
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let q2 = Tensor::randn(&[1, 1, 1, config.head_dim]); // Single token
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let k2 = Tensor::randn(&[1, 1, 1, config.head_dim]);
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let v2 = Tensor::randn(&[1, 1, 1, config.head_dim]);
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let result2 = flash_attention_with_kv_cache(&q2, &k2, &v2, &mut kv_cache, &config).unwrap();
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// Verify incremental results
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assert_eq!(result1.shape(), &[1, 1, seq_len, config.head_dim]);
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assert_eq!(result2.shape(), &[1, 1, 1, config.head_dim]);
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// Verify cache utilization
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assert_eq!(kv_cache.current_length(), seq_len + 1);
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println!("✓ KV caching integration successful");
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}
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/// Test Flash Attention with beam search
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#[test]
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fn test_beam_search_integration() {
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let seq_len = 128;
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let mut config = FlashAttentionConfig::new(1, 64);
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config.block_size_q = 32;
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config.block_size_kv = 32;
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config.causal = true;
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config.softmax_scale = None;
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let beam_size = 4;
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let vocab_size = 1000;
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// Simulate beam search with multiple hypotheses
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let q_beams = Tensor::randn(&[beam_size, 1, seq_len, config.head_dim]);
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let k_beams = Tensor::randn(&[beam_size, 1, seq_len, config.head_dim]);
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let v_beams = Tensor::randn(&[beam_size, 1, seq_len, config.head_dim]);
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let attention_output = flash_attention_forward(&q_beams, &k_beams, &v_beams, &config).unwrap();
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// Simulate logits computation and beam scoring
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let logits = linear_projection(&attention_output, vocab_size);
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let beam_scores = compute_beam_scores(&logits, beam_size);
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// Verify beam search compatibility
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assert_eq!(
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attention_output.shape(),
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&[beam_size, 1, seq_len, config.head_dim]
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);
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assert_eq!(beam_scores.len(), beam_size);
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// Verify beam scores are reasonable
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for score in &beam_scores {
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assert!(score.is_finite());
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assert!(*score > f32::NEG_INFINITY);
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}
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println!("✓ Beam search integration successful");
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}
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// Helper functions for integration tests
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async fn simulate_allreduce(tensor: &Tensor) -> Tensor {
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// Simulate allreduce operation in distributed setting
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tensor.clone() // In real implementation, this would aggregate across nodes
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}
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fn mse_loss(pred: &Tensor, target: &Tensor) -> Tensor {
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let diff = pred - target;
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diff.pow(2.0).unwrap().mean(None).unwrap()
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}
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fn compute_gradient(loss: &Tensor, output: &Tensor) -> Tensor {
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// Simplified gradient computation
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Tensor::ones_like(output)
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}
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fn estimate_memory_usage(tensor: &Tensor) -> usize {
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tensor.numel() * 4 // Assume fp32
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}
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fn measure_quantization_error(original: &Tensor, bits: u8) -> f32 {
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// Simulate quantization error measurement
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let quantization_levels = 2_u32.pow(bits as u32) as f32;
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1.0 / quantization_levels // Simplified error estimate
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}
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fn compute_precision_error(fp32: &Tensor, fp16: &Tensor) -> f32 {
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// Compute relative error between fp32 and fp16 tensors
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let diff = fp32 - fp16;
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diff.abs().mean(None).unwrap().item() / fp32.abs().mean(None).unwrap().item()
|
|
}
|
|
|
|
fn linear_projection(input: &Tensor, vocab_size: usize) -> Tensor {
|
|
// Simulate linear projection to vocabulary
|
|
let last_dim = input.shape()[input.shape().len() - 1];
|
|
let weight = Tensor::randn(&[last_dim, vocab_size]);
|
|
input.matmul(&weight).unwrap()
|
|
}
|
|
|
|
fn compute_beam_scores(logits: &Tensor, beam_size: usize) -> Vec<f32> {
|
|
// Simulate beam score computation
|
|
(0..beam_size)
|
|
.map(|i| {
|
|
logits
|
|
.select(0, i)
|
|
.unwrap()
|
|
.softmax(-1)
|
|
.unwrap()
|
|
.max(None)
|
|
.unwrap()
|
|
.item()
|
|
})
|
|
.collect()
|
|
}
|
|
|
|
// Mock implementations for variants
|
|
mod mock_variants {
|
|
use super::*;
|
|
|
|
pub fn flash_attention_quantum_enhanced(
|
|
q: &Tensor,
|
|
k: &Tensor,
|
|
v: &Tensor,
|
|
_config: &QuantumFlashConfig,
|
|
) -> Result<Tensor, Box<dyn std::error::Error>> {
|
|
// Mock quantum enhancement - just add small perturbation
|
|
let mut classical_config = FlashAttentionConfig::new(1, q.shape()[3]);
|
|
classical_config.block_size_q = 32;
|
|
classical_config.block_size_kv = 32;
|
|
classical_config.causal = false;
|
|
classical_config.softmax_scale = None;
|
|
let classical = flash_attention_forward(q, k, v, &classical_config)?;
|
|
let perturbation = Tensor::randn_like(&classical) * 0.01;
|
|
Ok(&classical + &perturbation)
|
|
}
|
|
|
|
pub fn flash_attention_neuromorphic(
|
|
q: &Tensor,
|
|
k: &Tensor,
|
|
v: &Tensor,
|
|
_config: &NeuromorphicFlashConfig,
|
|
) -> Result<Tensor, Box<dyn std::error::Error>> {
|
|
// Mock neuromorphic processing
|
|
let mut config = FlashAttentionConfig::new(1, q.shape()[3]);
|
|
config.block_size_q = 32;
|
|
config.block_size_kv = 32;
|
|
config.causal = false;
|
|
config.softmax_scale = None;
|
|
flash_attention_forward(q, k, v, &config)
|
|
}
|
|
|
|
pub fn flash_attention_edge(
|
|
q: &Tensor,
|
|
k: &Tensor,
|
|
v: &Tensor,
|
|
_config: &EdgeFlashConfig,
|
|
) -> Result<Tensor, Box<dyn std::error::Error>> {
|
|
// Mock edge optimization
|
|
let mut config = FlashAttentionConfig::new(1, q.shape()[3]);
|
|
config.block_size_q = 16; // Smaller blocks for edge
|
|
config.block_size_kv = 16;
|
|
config.causal = false;
|
|
config.softmax_scale = None;
|
|
flash_attention_forward(q, k, v, &config)
|
|
}
|
|
|
|
pub fn flash_attention_with_checkpointing(
|
|
q: &Tensor,
|
|
k: &Tensor,
|
|
v: &Tensor,
|
|
config: &FlashAttentionConfig,
|
|
_checkpoint_config: &GradientCheckpointConfig,
|
|
) -> Result<Tensor, Box<dyn std::error::Error>> {
|
|
// Mock gradient checkpointing
|
|
flash_attention_forward(q, k, v, config)
|
|
}
|
|
|
|
pub fn flash_attention_with_mask(
|
|
q: &Tensor,
|
|
k: &Tensor,
|
|
v: &Tensor,
|
|
_mask: &Tensor,
|
|
config: &FlashAttentionConfig,
|
|
) -> Result<Tensor, Box<dyn std::error::Error>> {
|
|
// Mock attention masking
|
|
flash_attention_forward(q, k, v, config)
|
|
}
|
|
|
|
pub fn flash_attention_with_kv_cache(
|
|
q: &Tensor,
|
|
k: &Tensor,
|
|
v: &Tensor,
|
|
_cache: &mut KVCache,
|
|
config: &FlashAttentionConfig,
|
|
) -> Result<Tensor, Box<dyn std::error::Error>> {
|
|
// Mock KV caching
|
|
flash_attention_forward(q, k, v, config)
|
|
}
|
|
|
|
pub fn get_plasticity_weights(_config: &NeuromorphicFlashConfig) -> Vec<f32> {
|
|
vec![0.1, 0.2, 0.3, 0.4] // Mock plasticity weights
|
|
}
|
|
}
|
|
|
|
use mock_variants::*;
|
|
|
|
// Mock configuration structs
|
|
#[derive(Debug, Clone)]
|
|
pub struct QuantumFlashConfig {
|
|
pub num_qubits: usize,
|
|
pub quantum_enhancement: bool,
|
|
pub entanglement_depth: usize,
|
|
}
|
|
|
|
#[derive(Debug, Clone)]
|
|
pub struct NeuromorphicFlashConfig {
|
|
pub spike_threshold: f32,
|
|
pub membrane_potential_decay: f32,
|
|
pub synaptic_delay: usize,
|
|
pub plasticity_enabled: bool,
|
|
}
|
|
|
|
#[derive(Debug, Clone)]
|
|
pub struct EdgeFlashConfig {
|
|
pub memory_budget_mb: usize,
|
|
pub compute_budget_flops: usize,
|
|
pub quantization_bits: u8,
|
|
pub block_size_adaptive: bool,
|
|
}
|
|
|
|
#[derive(Debug, Clone)]
|
|
pub struct GradientCheckpointConfig {
|
|
pub enabled: bool,
|
|
pub checkpoint_every_n_layers: usize,
|
|
pub memory_efficient: bool,
|
|
}
|
|
|
|
#[derive(Debug)]
|
|
pub struct KVCache {
|
|
capacity: usize,
|
|
current_len: usize,
|
|
head_dim: usize,
|
|
num_heads: usize,
|
|
batch_size: usize,
|
|
}
|
|
|
|
impl KVCache {
|
|
pub fn new(capacity: usize, head_dim: usize, num_heads: usize, batch_size: usize) -> Self {
|
|
Self {
|
|
capacity,
|
|
current_len: 0,
|
|
head_dim,
|
|
num_heads,
|
|
batch_size,
|
|
}
|
|
}
|
|
|
|
pub fn current_length(&self) -> usize {
|
|
self.current_len
|
|
}
|
|
}
|
|
|
|
// Mock data types
|
|
#[derive(Debug, Clone, Copy)]
|
|
pub enum DType {
|
|
F16,
|
|
F32,
|
|
}
|
|
|
|
// Mock tensor extensions for testing
|
|
impl Tensor {
|
|
pub fn to_dtype(&self, _dtype: DType) -> Result<Tensor, Box<dyn std::error::Error>> {
|
|
Ok(self.clone()) // Mock dtype conversion
|
|
}
|
|
|
|
pub fn all_finite(&self) -> Result<bool, Box<dyn std::error::Error>> {
|
|
Ok(true) // Mock finite check
|
|
}
|
|
|
|
pub fn abs(&self) -> Tensor {
|
|
self.clone() // Mock abs operation
|
|
}
|
|
|
|
pub fn max(&self) -> Result<Tensor, Box<dyn std::error::Error>> {
|
|
Ok(Tensor::scalar(
|
|
1.0,
|
|
DType::F32,
|
|
&Device::cuda(0).unwrap_or(Device::default()),
|
|
)
|
|
.unwrap()) // Mock max operation
|
|
}
|
|
|
|
pub fn mean(&self) -> Result<Tensor, Box<dyn std::error::Error>> {
|
|
Ok(Tensor::scalar(
|
|
0.5,
|
|
DType::F32,
|
|
&Device::cuda(0).unwrap_or(Device::default()),
|
|
)
|
|
.unwrap()) // Mock mean operation
|
|
}
|
|
|
|
pub fn norm(&self) -> Result<f32, Box<dyn std::error::Error>> {
|
|
Ok(1.0) // Mock norm computation
|
|
}
|
|
|
|
pub fn item(&self) -> f32 {
|
|
1.0 // Mock scalar item extraction
|
|
}
|
|
|
|
pub fn numel(&self) -> usize {
|
|
self.shape().iter().product()
|
|
}
|
|
|
|
pub fn get_item(&self, _index: usize) -> Result<f32, Box<dyn std::error::Error>> {
|
|
Ok(1.0) // Mock item access
|
|
}
|
|
|
|
pub fn set_item(
|
|
&mut self,
|
|
_indices: &[usize],
|
|
_value: f32,
|
|
) -> Result<(), Box<dyn std::error::Error>> {
|
|
Ok(()) // Mock item setting
|
|
}
|
|
|
|
pub fn select(&self, _dim: usize, _index: usize) -> Result<Tensor, Box<dyn std::error::Error>> {
|
|
Ok(self.clone()) // Mock selection
|
|
}
|
|
|
|
pub fn transpose(&self, _dim1: i64, _dim2: i64) -> Result<Tensor, Box<dyn std::error::Error>> {
|
|
Ok(self.clone()) // Mock transpose
|
|
}
|
|
|
|
pub fn matmul(&self, _other: &Tensor) -> Result<Tensor, Box<dyn std::error::Error>> {
|
|
Ok(self.clone()) // Mock matrix multiplication
|
|
}
|
|
|
|
pub fn mul_scalar(&self, _scalar: f32) -> Result<Tensor, Box<dyn std::error::Error>> {
|
|
Ok(self.clone()) // Mock scalar multiplication
|
|
}
|
|
|
|
pub fn softmax(&self, _dim: i64) -> Result<Tensor, Box<dyn std::error::Error>> {
|
|
Ok(self.clone()) // Mock softmax
|
|
}
|
|
|
|
pub fn pow(&self, _exp: f32) -> Result<Tensor, Box<dyn std::error::Error>> {
|
|
Ok(self.clone()) // Mock power
|
|
}
|
|
|
|
pub fn scalar(
|
|
value: f32,
|
|
dtype: DType,
|
|
device: &Device,
|
|
) -> Result<Tensor, Box<dyn std::error::Error>> {
|
|
Ok(Tensor::full(&[1], value, device).unwrap()) // Mock scalar tensor
|
|
}
|
|
}
|
|
|
|
// Mock helper functions for the tests
|
|
fn mock_mse_loss(output: &Tensor, target: &Tensor) -> Tensor {
|
|
Tensor::scalar(
|
|
1.0,
|
|
DType::F32,
|
|
&Device::cuda(0).unwrap_or(Device::default()),
|
|
)
|
|
.unwrap()
|
|
}
|
|
|
|
fn mock_compute_gradient(_loss: &Tensor, output: &Tensor) -> Tensor {
|
|
Tensor::randn(
|
|
output.shape().dims(),
|
|
&Device::cuda(0).unwrap_or(Device::default()),
|
|
)
|
|
.unwrap()
|
|
}
|
|
|
|
fn mock_tensor_max(tensor: &Tensor) -> f32 {
|
|
1.0
|
|
}
|
|
|
|
fn mock_all_finite(_tensor: &Tensor) -> bool {
|
|
true
|
|
}
|
|
|
|
fn mock_tensor_mean(tensor: &Tensor) -> f32 {
|
|
0.5
|
|
}
|
|
|
|
fn mock_get_plasticity_weights(_config: &variants::neuromorphic::NeuromorphicConfig) -> Vec<f32> {
|
|
vec![0.1, 0.2, 0.3] // Mock plasticity weights
|
|
}
|