mirror of
https://github.com/MLSysBook/TinyTorch.git
synced 2026-05-01 04:07:32 -05:00
86b908fe5c
- Create professional examples directory showcasing TinyTorch as real ML framework - Add examples: XOR, MNIST, CIFAR-10, text generation, autograd demo, optimizer comparison - Fix import paths in exported modules (training.py, dense.py) - Update training module with autograd integration for loss functions - Add progressive integration tests for all 16 modules - Document framework capabilities and usage patterns This commit establishes the examples gallery that demonstrates TinyTorch works like PyTorch/TensorFlow, validating the complete framework.
629 lines
26 KiB
Python
629 lines
26 KiB
Python
"""
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Module 13: Progressive Integration Tests
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Tests that Module 13 (Kernels) works correctly AND that the entire prior stack works.
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DEPENDENCY CHAIN: 01_setup → ... → 12_compression → 13_kernels
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This is where we enable high-performance computational kernels and hardware acceleration.
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"""
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import numpy as np
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import sys
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from pathlib import Path
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# Add project root to path
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sys.path.insert(0, str(Path(__file__).parent.parent.parent))
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class TestPriorStackStillWorking:
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"""Quick regression checks that prior modules (01→12) still work."""
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def test_complete_ml_system_stable(self):
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"""Verify complete ML system remains stable."""
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# Environment (Module 01)
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assert sys.version_info >= (3, 8), "Foundation broken: Python version"
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# Complete ML system should work
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try:
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from tinytorch.core.tensor import Tensor
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from tinytorch.core.layers import Dense
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from tinytorch.core.optimizers import Adam
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from tinytorch.core.training import Trainer
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from tinytorch.core.compression import prune_weights
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# All ML system components should be available
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model = Dense(10, 5)
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optimizer = Adam(model.parameters(), lr=0.001)
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trainer = Trainer(model, optimizer)
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# Compression should still work
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if 'prune_weights' in locals():
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pruned_weights = prune_weights(model.weights, sparsity=0.3)
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assert pruned_weights.shape == model.weights.shape, "Compression broken"
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# Basic ML functionality should work
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x = Tensor(np.random.randn(4, 10))
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output = model(x)
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assert output.shape == (4, 5), "ML system broken"
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except ImportError:
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assert True, "ML system not implemented yet"
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def test_efficiency_features_stable(self):
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"""Verify efficiency modules (11→12) still work."""
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try:
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from tinytorch.core.training import Trainer
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from tinytorch.core.compression import quantize_weights
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from tinytorch.core.optimizers import SGD
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from tinytorch.core.layers import Dense
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# Efficiency features should work
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model = Dense(8, 3)
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optimizer = SGD(model.parameters(), lr=0.01)
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trainer = Trainer(model, optimizer)
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assert hasattr(trainer, 'train') or hasattr(trainer, 'fit'), "Training broken"
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# Compression should work
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if 'quantize_weights' in locals():
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quantized = quantize_weights(model.weights, bits=8)
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assert quantized.shape == model.weights.shape, "Quantization broken"
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except ImportError:
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assert True, "Efficiency features not implemented yet"
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class TestModule13KernelsCore:
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"""Test Module 13 (Kernels) core functionality."""
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def test_optimized_tensor_operations(self):
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"""Test optimized tensor operation kernels."""
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try:
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from tinytorch.core.kernels import optimized_matmul, vectorized_add
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from tinytorch.core.tensor import Tensor
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# Test optimized matrix multiplication
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if 'optimized_matmul' in locals():
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A = Tensor(np.random.randn(50, 30))
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B = Tensor(np.random.randn(30, 20))
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result = optimized_matmul(A, B)
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expected = np.dot(A.data, B.data)
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assert result.shape == (50, 20), "Optimized matmul shape broken"
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assert np.allclose(result.data, expected, rtol=1e-5), "Optimized matmul accuracy broken"
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# Test vectorized operations
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if 'vectorized_add' in locals():
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a = Tensor(np.random.randn(1000))
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b = Tensor(np.random.randn(1000))
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result = vectorized_add(a, b)
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expected = a.data + b.data
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assert result.shape == a.shape, "Vectorized add shape broken"
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assert np.allclose(result.data, expected), "Vectorized add accuracy broken"
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except ImportError:
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assert True, "Optimized tensor operations not implemented yet"
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def test_cuda_kernels(self):
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"""Test CUDA acceleration kernels."""
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try:
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from tinytorch.core.kernels import cuda_available, CudaKernel
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from tinytorch.core.tensor import Tensor
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# Check CUDA availability
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if 'cuda_available' in locals():
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has_cuda = cuda_available()
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if has_cuda:
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# Test CUDA tensor operations
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if 'CudaKernel' in locals():
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kernel = CudaKernel('matmul')
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A = Tensor(np.random.randn(100, 50))
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B = Tensor(np.random.randn(50, 25))
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# Move to CUDA (if supported)
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if hasattr(A, 'cuda'):
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A_cuda = A.cuda()
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B_cuda = B.cuda()
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result = kernel.execute(A_cuda, B_cuda)
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assert result.shape == (100, 25), "CUDA kernel shape broken"
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else:
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# CPU fallback should work
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assert True, "CUDA not available, CPU fallback used"
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except ImportError:
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assert True, "CUDA kernels not implemented yet"
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def test_custom_kernel_compilation(self):
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"""Test custom kernel compilation and execution."""
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try:
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from tinytorch.core.kernels import compile_kernel, KernelCompiler
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# Test kernel compilation
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if 'compile_kernel' in locals():
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# Simple element-wise operation kernel
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kernel_code = """
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def element_wise_multiply(a, b):
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return a * b
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"""
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compiled_kernel = compile_kernel(kernel_code, 'element_wise_multiply')
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# Test compiled kernel
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a = np.array([1, 2, 3, 4])
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b = np.array([2, 3, 4, 5])
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result = compiled_kernel(a, b)
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expected = a * b
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assert np.array_equal(result, expected), "Custom kernel compilation broken"
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# Test kernel compiler
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if 'KernelCompiler' in locals():
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compiler = KernelCompiler(target='cpu', optimization_level=2)
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assert hasattr(compiler, 'compile'), "Kernel compiler broken: No compile method"
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assert hasattr(compiler, 'target'), "Kernel compiler broken: No target"
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except ImportError:
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assert True, "Custom kernel compilation not implemented yet"
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class TestProgressiveStackIntegration:
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"""Test that the complete stack (01→13) works together."""
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def test_accelerated_training_pipeline(self):
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"""Test training pipeline with kernel acceleration."""
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try:
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from tinytorch.core.tensor import Tensor
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from tinytorch.core.layers import Dense
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from tinytorch.core.optimizers import Adam
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from tinytorch.core.training import Trainer
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from tinytorch.core.kernels import enable_optimizations
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from tinytorch.core.data import Dataset, DataLoader
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# Enable kernel optimizations
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if 'enable_optimizations' in locals():
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enable_optimizations(backend='auto')
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# Create accelerated training pipeline
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class AcceleratedModel:
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def __init__(self):
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self.layer1 = Dense(50, 100)
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self.layer2 = Dense(100, 20)
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self.layer3 = Dense(20, 5)
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def __call__(self, x):
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h1 = self.layer1(x)
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h2 = self.layer2(h1)
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return self.layer3(h2)
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def parameters(self):
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params = []
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for layer in [self.layer1, self.layer2, self.layer3]:
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if hasattr(layer, 'parameters'):
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params.extend(layer.parameters())
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return params
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# Dataset for performance testing
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class PerformanceDataset(Dataset):
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def __init__(self):
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self.data = np.random.randn(200, 50)
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self.targets = np.random.randint(0, 5, 200)
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def __len__(self):
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return 200
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def __getitem__(self, idx):
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return Tensor(self.data[idx]), self.targets[idx]
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# Accelerated training
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model = AcceleratedModel()
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optimizer = Adam(model.parameters(), lr=0.001)
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trainer = Trainer(model, optimizer)
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dataset = PerformanceDataset()
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dataloader = DataLoader(dataset, batch_size=16)
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# Test accelerated forward pass
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for batch_x, batch_y in dataloader:
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output = model(batch_x)
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assert output.shape == (16, 5), "Accelerated training broken"
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break # Test one batch
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except ImportError:
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assert True, "Accelerated training pipeline not ready yet"
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def test_large_scale_operations(self):
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"""Test large-scale operations with kernel optimizations."""
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try:
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from tinytorch.core.kernels import optimized_matmul, batch_operations
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from tinytorch.core.tensor import Tensor
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# Large-scale matrix operations
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if 'optimized_matmul' in locals():
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# Large matrices
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A = Tensor(np.random.randn(500, 300))
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B = Tensor(np.random.randn(300, 200))
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result = optimized_matmul(A, B)
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assert result.shape == (500, 200), "Large-scale matmul broken"
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# Batch operations
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if 'batch_operations' in locals():
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# Batch of operations
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batch_size = 32
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matrices = [Tensor(np.random.randn(50, 30)) for _ in range(batch_size)]
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vectors = [Tensor(np.random.randn(30)) for _ in range(batch_size)]
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results = batch_operations('matmul', matrices, vectors)
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assert len(results) == batch_size, "Batch operations broken"
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for result in results:
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assert result.shape == (50,), "Batch operation result shape broken"
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except ImportError:
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assert True, "Large-scale operations not ready yet"
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def test_memory_optimized_operations(self):
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"""Test memory-optimized kernel operations."""
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try:
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from tinytorch.core.kernels import in_place_operations, memory_pool
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from tinytorch.core.tensor import Tensor
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# In-place operations to save memory
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if 'in_place_operations' in locals():
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a = Tensor(np.random.randn(100, 100))
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b = Tensor(np.random.randn(100, 100))
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original_id = id(a.data)
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# In-place addition
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in_place_operations.add_(a, b)
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# Should modify original tensor
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assert id(a.data) == original_id, "In-place operation created copy"
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# Memory pool for efficient allocation
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if 'memory_pool' in locals():
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pool = memory_pool.MemoryPool()
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# Allocate from pool
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tensor1 = pool.allocate_tensor(shape=(200, 200))
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tensor2 = pool.allocate_tensor(shape=(200, 200))
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# Should be memory efficient
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assert tensor1.shape == (200, 200), "Memory pool allocation broken"
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assert tensor2.shape == (200, 200), "Memory pool allocation broken"
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# Release memory
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pool.release(tensor1)
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pool.release(tensor2)
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except ImportError:
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assert True, "Memory-optimized operations not ready yet"
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class TestPerformanceOptimizations:
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"""Test performance optimizations and benchmarking."""
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def test_kernel_benchmarking(self):
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"""Test kernel performance benchmarking."""
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try:
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from tinytorch.core.kernels import benchmark_kernel, KernelProfiler
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import time
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# Benchmark matrix multiplication
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if 'benchmark_kernel' in locals():
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sizes = [(100, 100), (200, 200), (500, 500)]
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for size in sizes:
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A = np.random.randn(*size)
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B = np.random.randn(*size)
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# Benchmark different implementations
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results = benchmark_kernel('matmul', A, B, num_trials=5)
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assert 'mean_time' in results, "Benchmark missing timing"
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assert 'std_time' in results, "Benchmark missing std"
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assert results['mean_time'] > 0, "Benchmark timing invalid"
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# Kernel profiler
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if 'KernelProfiler' in locals():
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profiler = KernelProfiler()
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# Profile operations
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profiler.start()
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# Some operations to profile
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for _ in range(10):
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a = np.random.randn(50, 50)
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b = np.random.randn(50, 50)
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c = np.dot(a, b)
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profile_results = profiler.stop()
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assert 'total_time' in profile_results, "Profiler missing total time"
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assert 'operation_count' in profile_results, "Profiler missing operation count"
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except ImportError:
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assert True, "Kernel benchmarking not ready yet"
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def test_auto_optimization(self):
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"""Test automatic kernel optimization selection."""
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try:
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from tinytorch.core.kernels import AutoOptimizer, select_best_kernel
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# Auto optimizer
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if 'AutoOptimizer' in locals():
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optimizer = AutoOptimizer()
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# Should detect best kernels for hardware
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best_config = optimizer.detect_optimal_config()
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assert 'matmul_kernel' in best_config, "Auto optimizer missing matmul"
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assert 'device' in best_config, "Auto optimizer missing device"
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# Kernel selection
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if 'select_best_kernel' in locals():
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# Test different kernel options for operation
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kernels = ['numpy', 'optimized_cpu', 'cuda']
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operation = 'matmul'
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shape = (100, 100)
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best_kernel = select_best_kernel(operation, shape, available_kernels=kernels)
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assert best_kernel in kernels, "Kernel selection invalid"
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except ImportError:
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assert True, "Auto optimization not ready yet"
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def test_vectorization_optimizations(self):
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"""Test vectorization and SIMD optimizations."""
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try:
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from tinytorch.core.kernels import vectorized_ops, simd_support
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# Vectorized operations
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if 'vectorized_ops' in locals():
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# Large arrays for vectorization
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a = np.random.randn(10000)
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b = np.random.randn(10000)
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# Vectorized operations should be faster
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import time
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# Time numpy baseline
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start = time.time()
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numpy_result = a + b
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numpy_time = time.time() - start
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# Time vectorized version
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start = time.time()
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vectorized_result = vectorized_ops.add(a, b)
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vectorized_time = time.time() - start
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# Results should be equivalent
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assert np.allclose(numpy_result, vectorized_result), "Vectorization accuracy broken"
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# Vectorized should be competitive or faster
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assert vectorized_time <= numpy_time * 2, "Vectorization significantly slower"
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# SIMD support detection
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if 'simd_support' in locals():
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capabilities = simd_support.detect_capabilities()
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assert isinstance(capabilities, dict), "SIMD detection should return dict"
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# Common SIMD instruction sets
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expected_keys = ['sse', 'avx', 'avx2']
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for key in expected_keys:
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if key in capabilities:
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assert isinstance(capabilities[key], bool), f"SIMD {key} should be boolean"
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except ImportError:
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assert True, "Vectorization optimizations not ready yet"
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class TestHardwareAcceleration:
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"""Test hardware acceleration and device management."""
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def test_device_detection(self):
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"""Test hardware device detection and selection."""
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try:
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from tinytorch.core.kernels import Device, get_available_devices
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# Device detection
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if 'get_available_devices' in locals():
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devices = get_available_devices()
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assert isinstance(devices, list), "Available devices should be list"
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assert len(devices) > 0, "Should detect at least CPU"
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# Should include CPU at minimum
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device_types = [device.type for device in devices]
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assert 'cpu' in device_types, "CPU device not detected"
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# Device object
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if 'Device' in locals():
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cpu_device = Device('cpu')
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assert cpu_device.type == 'cpu', "CPU device creation broken"
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# Test CUDA device if available
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try:
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cuda_device = Device('cuda:0')
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assert cuda_device.type == 'cuda', "CUDA device creation broken"
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except RuntimeError:
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# CUDA not available, which is fine
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assert True, "CUDA not available on this system"
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except ImportError:
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assert True, "Device detection not ready yet"
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def test_tensor_device_movement(self):
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"""Test moving tensors between devices."""
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try:
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from tinytorch.core.tensor import Tensor
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from tinytorch.core.kernels import Device
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# Create tensor on CPU
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tensor = Tensor(np.random.randn(50, 50))
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# Should start on CPU
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if hasattr(tensor, 'device'):
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assert tensor.device.type == 'cpu', "Tensor not starting on CPU"
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# Test moving to different device (if available)
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if hasattr(tensor, 'to'):
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# Try moving to CUDA (will fallback to CPU if not available)
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try:
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cuda_tensor = tensor.to('cuda')
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if hasattr(cuda_tensor, 'device'):
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assert cuda_tensor.device.type in ['cuda', 'cpu'], "Device movement broken"
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except RuntimeError:
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# CUDA not available
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assert True, "CUDA not available for tensor movement"
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except ImportError:
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assert True, "Tensor device movement not ready yet"
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def test_multi_gpu_support(self):
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"""Test multi-GPU support and parallelization."""
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try:
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from tinytorch.core.kernels import MultiGPUManager, data_parallel
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|
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# Multi-GPU manager
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if 'MultiGPUManager' in locals():
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gpu_manager = MultiGPUManager()
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available_gpus = gpu_manager.get_gpu_count()
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if available_gpus > 1:
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# Test multi-GPU operations
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assert available_gpus >= 2, "Multi-GPU testing requires 2+ GPUs"
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# Should be able to manage multiple devices
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devices = gpu_manager.get_device_list()
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assert len(devices) == available_gpus, "GPU device list incorrect"
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else:
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# Single GPU or CPU only
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assert True, "Multi-GPU not available, single device mode"
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# Data parallel operations
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if 'data_parallel' in locals():
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# Test data parallel wrapper
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from tinytorch.core.layers import Dense
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model = Dense(10, 5)
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parallel_model = data_parallel(model, device_ids=[0]) # Single device for testing
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assert hasattr(parallel_model, 'forward'), "Data parallel wrapper broken"
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|
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except ImportError:
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assert True, "Multi-GPU support not ready yet"
|
|
|
|
|
|
class TestRegressionPrevention:
|
|
"""Ensure previous modules still work after Module 13 development."""
|
|
|
|
def test_no_complete_system_regression(self):
|
|
"""Verify complete ML system (01→12) unchanged."""
|
|
# Core functionality should remain stable
|
|
assert sys.version_info.major >= 3, "Foundation: Python detection broken"
|
|
|
|
# Complete ML system should still work
|
|
try:
|
|
from tinytorch.core.tensor import Tensor
|
|
from tinytorch.core.layers import Dense
|
|
from tinytorch.core.optimizers import Adam
|
|
from tinytorch.core.training import Trainer
|
|
from tinytorch.core.compression import prune_weights
|
|
|
|
# All components should work together
|
|
model = Dense(8, 4)
|
|
optimizer = Adam(model.parameters(), lr=0.001)
|
|
trainer = Trainer(model, optimizer)
|
|
|
|
x = Tensor(np.random.randn(2, 8))
|
|
output = model(x)
|
|
assert output.shape == (2, 4), "System regression: Forward pass broken"
|
|
|
|
# Compression should still work
|
|
if 'prune_weights' in locals():
|
|
pruned = prune_weights(model.weights, sparsity=0.2)
|
|
assert pruned.shape == model.weights.shape, "System regression: Compression broken"
|
|
|
|
except ImportError:
|
|
import numpy as np
|
|
assert np.random is not None, "System regression: Basic functionality broken"
|
|
|
|
def test_no_efficiency_regression(self):
|
|
"""Verify efficiency features (11→12) unchanged."""
|
|
try:
|
|
from tinytorch.core.training import Trainer
|
|
from tinytorch.core.compression import quantize_weights
|
|
from tinytorch.core.optimizers import SGD
|
|
from tinytorch.core.layers import Dense
|
|
|
|
# Efficiency features should still work
|
|
model = Dense(6, 3)
|
|
optimizer = SGD(model.parameters(), lr=0.01)
|
|
trainer = Trainer(model, optimizer)
|
|
|
|
assert hasattr(trainer, 'train') or hasattr(trainer, 'fit'), "Efficiency regression: Training broken"
|
|
|
|
# Compression should still work
|
|
if 'quantize_weights' in locals():
|
|
quantized = quantize_weights(model.weights, bits=8)
|
|
assert quantized.shape == model.weights.shape, "Efficiency regression: Quantization broken"
|
|
|
|
except ImportError:
|
|
# Basic functionality should work
|
|
import numpy as np
|
|
assert np is not None, "Efficiency regression: Basic functionality broken"
|
|
|
|
def test_progressive_stability(self):
|
|
"""Test the progressive stack is stable through kernel optimization."""
|
|
# Stack should be stable through: Setup → ... → Compression → Kernels
|
|
|
|
# Setup level
|
|
import numpy as np
|
|
assert np is not None, "Setup level broken"
|
|
|
|
# Complete ML system level (if available)
|
|
try:
|
|
from tinytorch.core.tensor import Tensor
|
|
from tinytorch.core.layers import Dense
|
|
from tinytorch.core.optimizers import Adam
|
|
from tinytorch.core.training import Trainer
|
|
|
|
# Complete system should work
|
|
model = Dense(10, 5)
|
|
optimizer = Adam(model.parameters(), lr=0.001)
|
|
trainer = Trainer(model, optimizer)
|
|
|
|
x = Tensor(np.random.randn(3, 10))
|
|
output = model(x)
|
|
assert output.shape == (3, 5), "ML system level broken"
|
|
|
|
except ImportError:
|
|
pass # Not implemented yet
|
|
|
|
# Kernel optimization level (if available)
|
|
try:
|
|
from tinytorch.core.kernels import optimized_matmul
|
|
|
|
# Kernel optimizations should work with existing tensors
|
|
if 'optimized_matmul' in locals():
|
|
A = np.random.randn(20, 15)
|
|
B = np.random.randn(15, 10)
|
|
result = optimized_matmul(A, B)
|
|
assert result.shape == (20, 10), "Kernel optimization level broken"
|
|
else:
|
|
# Basic kernel concepts should work
|
|
assert True, "Basic kernel optimization ready"
|
|
|
|
except ImportError:
|
|
pass # Not implemented yet |