⚡️ Speed up method UnoptimizedNeuralNet.forward by 36,860%

[codeflash-ai]

📄 36,860% (368.60x) speedup for UnoptimizedNeuralNet.forward in code_to_optimize/unoptimized_neural_net.py

⏱️ Runtime : 134 milliseconds → 363 microseconds (best of 250 runs)

📝 Explanation and details

The optimized code achieves a ~369x speedup (from 134ms to 363μs) by replacing inefficient nested Python loops with vectorized PyTorch operations that leverage highly optimized BLAS/LAPACK libraries and potential GPU acceleration.

Key Optimizations

1. Matrix Multiplication via F.linear()

• Original: Triple-nested loops manually computing dot products element-by-element (lines consuming 75.8% + 7.2% = 83% of runtime)
• Optimized: Single F.linear() calls that use optimized BLAS routines (GEMM operations)
• Why faster: BLAS implementations use CPU vectorization (SIMD), cache-friendly memory access patterns, and multi-threading. A single batched matrix multiplication (batch_size, input_size) × (hidden_size, input_size)ᵀ replaces ~46,000 individual multiply-add operations scattered across Python loops.

2. ReLU Activation with .clamp()

• Original: Nested loops with branching (if val > 0) for each element
• Optimized: hidden.clamp(min=0.0) applies ReLU in a single vectorized operation
• Why faster: Eliminates Python interpreter overhead, avoids branch misprediction penalties, and uses contiguous memory operations

3. Softmax via torch.softmax()

• Original: Manual computation with loops for max finding, exp calculation, and normalization (consuming ~2.6% of runtime)
• Optimized: torch.softmax(output, dim=1) uses numerically stable, vectorized implementation
• Why faster: Single kernel call handles max-shifting, exponentials, and normalization in optimized C++ code with proper memory layout

4. Eliminated Redundant Overhead

• Original: Creating temporary tensors like neuron_sum, temp_values, exp_values inside loops (~1.9% overhead)
• Optimized: Direct in-place or single-allocation operations
• Why faster: Reduces memory allocations, garbage collection pressure, and tensor indexing overhead

Performance Characteristics

The line profiler shows the original code spent 83% of time in innermost loop tensor arithmetic operations that required Python interpreter involvement for each element. The optimized version completes the entire forward pass in less time than the original spent on a single matrix multiplication loop iteration.

This optimization is particularly effective for:

• Batch processing: Speedup scales with batch size as matrix operations amortize setup costs
• Larger networks: Bigger hidden layers benefit more from optimized GEMM
• GPU execution: PyTorch operations can leverage CUDA kernels (original loops cannot)
• Production inference: Where the function is called repeatedly on similar-sized inputs

The transformation maintains identical mathematical semantics while leveraging PyTorch's optimized computational graph, making it suitable for any workload using this neural network forward pass.

✅ 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	15.0ms	58.1μs	25716%✅
test_unoptimized_neural_net.py::TestUnoptimizedNeuralNet.test_deterministic_output	16.9ms	54.5μs	30904%✅
test_unoptimized_neural_net.py::TestUnoptimizedNeuralNet.test_output_range	8.47ms	27.9μs	30277%✅
test_unoptimized_neural_net.py::TestUnoptimizedNeuralNet.test_output_requires_grad_false	8.42ms	27.5μs	30519%✅
test_unoptimized_neural_net.py::TestUnoptimizedNeuralNet.test_output_shape	8.49ms	28.8μs	29381%✅
test_unoptimized_neural_net.py::TestUnoptimizedNeuralNet.test_softmax_normalization	8.42ms	28.2μs	29783%✅
test_unoptimized_neural_net.py::TestUnoptimizedNeuralNet.test_zeros_input	8.40ms	27.0μs	31015%✅

⏪ 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	60.1ms	111μs	53973%✅

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