⚡️ Speed up method UnoptimizedNeuralNet.forward by 37,896%

[codeflash-ai]

📄 37,896% (378.96x) speedup for UnoptimizedNeuralNet.forward in code_to_optimize/unoptimized_neural_net.py

⏱️ Runtime : 900 milliseconds → 2.37 milliseconds (best of 250 runs)

📝 Explanation and details

The optimized code achieves a 379x speedup (from 900ms to 2.37ms) by replacing nested Python loops with PyTorch's highly optimized vectorized operations.

Key Changes:

1. Matrix multiplication via F.linear(): The original code manually computes fully-connected layers using triple-nested loops (batch × hidden × input dimensions), performing ~23,552 individual scalar operations for the first layer alone. The line profiler shows this taking 71.3% of total runtime. The optimized version replaces this with a single F.linear() call that uses BLAS/LAPACK or CUDA kernels for matrix multiplication, reducing this to just 3.1% of runtime.

2. ReLU activation with torch.clamp(): The original code loops through every element to manually apply max(0, x), taking 2.2% of runtime. The optimized version uses torch.clamp(hidden, min=0.0), a vectorized C/CUDA operation that processes the entire tensor in parallel.

3. Softmax via torch.softmax(): The original implementation manually computes max, exponentials, sum, and division across nested loops (~5.2% of runtime combined). The optimized version uses PyTorch's numerically stable torch.softmax(), which is both faster and prevents numerical overflow/underflow issues.

4. Eliminated temporary tensor allocations: The original code creates many small tensors (torch.tensor(0.0), temp_values, etc.) inside loops, causing significant memory allocation overhead. The optimized version operates on entire tensors at once, drastically reducing memory churn.

Why This Matters:

• Python loop overhead: Each loop iteration in Python involves significant interpreter overhead. The original code had ~26,438 inner loop iterations per forward pass. Vectorized operations execute in compiled C/CUDA with minimal Python overhead.

• Hardware acceleration: F.linear() and other PyTorch ops leverage CPU SIMD instructions or GPU parallelism, processing thousands of elements simultaneously rather than sequentially.

• Memory efficiency: Vectorized operations have better cache locality and avoid the memory allocator being called thousands of times per forward pass.

Impact: This optimization is critical for any workload using neural networks, especially during training (thousands of forward passes) or real-time inference. The 379x speedup transforms this from impractical to production-ready code.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 14 Passed
🌀 Generated Regression Tests	🔘 None Found
⏪ Replay Tests	✅ 4 Passed
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

⚙️ Click to see Existing Unit Tests

Test File::Test Function	Original ⏱️	Optimized ⏱️	Speedup
test_compiled_neural_net.py::test_compiled_neural_net	100ms	307μs	32518%✅
test_unoptimized_neural_net.py::TestUnoptimizedNeuralNet.test_deterministic_output	114ms	354μs	32270%✅
test_unoptimized_neural_net.py::TestUnoptimizedNeuralNet.test_output_range	57.2ms	202μs	28184%✅
test_unoptimized_neural_net.py::TestUnoptimizedNeuralNet.test_output_requires_grad_false	57.3ms	200μs	28426%✅
test_unoptimized_neural_net.py::TestUnoptimizedNeuralNet.test_output_shape	57.6ms	203μs	28268%✅
test_unoptimized_neural_net.py::TestUnoptimizedNeuralNet.test_softmax_normalization	57.3ms	201μs	28336%✅
test_unoptimized_neural_net.py::TestUnoptimizedNeuralNet.test_zeros_input	56.7ms	201μs	28117%✅

⏪ Click to see Replay Tests

Test File::Test Function	Original ⏱️	Optimized ⏱️	Speedup
test_pytest_code_to_optimizetestspytesttest_compiled_neural_net_py__replay_test_0.py::test_code_to_optimize_unoptimized_neural_net_UnoptimizedNeuralNet_forward	398ms	698μs	57034%✅

To edit these changes git checkout codeflash/optimize-UnoptimizedNeuralNet.forward-mkqrenni and push.