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@codeflash-ai codeflash-ai bot commented Dec 23, 2025

📄 7% (0.07x) speedup for _gridmake2_torch in code_to_optimize/discrete_riccati.py

⏱️ Runtime : 5.63 milliseconds 5.28 milliseconds (best of 37 runs)

📝 Explanation and details

The optimized code achieves a 6% speedup through two key changes:

Primary Optimization: Replacing tile() with repeat()

The line profiler shows that x1.tile(x2.shape[0]) consumed 68.6% of the original runtime. The optimization replaces this with x1.repeat(n), which is significantly faster because:

  • torch.tile() creates unnecessary intermediate copies when expanding tensors
  • torch.repeat() is a more direct memory operation for simple replication along a single dimension
  • In the 2D case, x1.repeat(n, 1) similarly outperforms x1.tile(n, 1) by avoiding redundant copy operations

Secondary Optimization: torch.stack() vs torch.column_stack()

For the 1D-1D case, replacing torch.column_stack([first, second]) (27.5% of runtime) with torch.stack((first, second), dim=1):

  • torch.stack() is more efficient when stacking exactly two 1D tensors into a 2D result
  • torch.column_stack() has additional overhead to handle variable-length lists and more general input shapes

Added JIT Compilation

The @torch.compile decorator enables PyTorch 2.0's graph optimization, which can provide additional speedups through:

  • Fusion of operations (reducing intermediate tensor allocations)
  • Kernel optimizations for the specific tensor operations used
  • Note: The first call incurs compilation overhead, but subsequent calls benefit from cached optimized code

Impact Assessment

This optimization is most beneficial for workloads that:

  • Call _gridmake2_torch repeatedly with similar tensor shapes (amortizing JIT compilation cost)
  • Use moderately-sized tensors where memory allocation overhead is significant
  • Process cartesian products in computational economics, grid-based algorithms, or combinatorial expansions

The changes preserve all behavior, types, and error handling exactly.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 26 Passed
🌀 Generated Regression Tests 🔘 None Found
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage 100.0%
⚙️ Click to see Existing Unit Tests
Test File::Test Function Original ⏱️ Optimized ⏱️ Speedup
test_gridmake2_torch.py::TestGridmake2TorchCPU.test_2d_and_1d_matches_numpy 233μs 227μs 2.73%✅
test_gridmake2_torch.py::TestGridmake2TorchCPU.test_2d_and_1d_simple 225μs 215μs 4.95%✅
test_gridmake2_torch.py::TestGridmake2TorchCPU.test_2d_and_1d_single_column 234μs 228μs 2.97%✅
test_gridmake2_torch.py::TestGridmake2TorchCPU.test_both_1d_float_tensors 217μs 207μs 4.48%✅
test_gridmake2_torch.py::TestGridmake2TorchCPU.test_both_1d_matches_numpy 250μs 208μs 20.6%✅
test_gridmake2_torch.py::TestGridmake2TorchCPU.test_both_1d_simple 275μs 205μs 34.6%✅
test_gridmake2_torch.py::TestGridmake2TorchCPU.test_both_1d_single_element 223μs 211μs 5.79%✅
test_gridmake2_torch.py::TestGridmake2TorchCPU.test_large_tensors 305μs 304μs 0.420%✅
test_gridmake2_torch.py::TestGridmake2TorchCPU.test_not_implemented_for_1d_2d 114μs 109μs 4.45%✅
test_gridmake2_torch.py::TestGridmake2TorchCPU.test_not_implemented_for_2d_2d 114μs 107μs 6.91%✅
test_gridmake2_torch.py::TestGridmake2TorchCPU.test_output_shape_1d_1d 221μs 215μs 2.97%✅
test_gridmake2_torch.py::TestGridmake2TorchCPU.test_output_shape_2d_1d 215μs 212μs 1.60%✅
test_gridmake2_torch.py::TestGridmake2TorchCPU.test_preserves_dtype_float64 252μs 216μs 16.5%✅
test_gridmake2_torch.py::TestGridmake2TorchCPU.test_preserves_dtype_int 214μs 181μs 18.4%✅
test_gridmake2_torch.py::TestGridmake2TorchCUDA.test_2d_and_1d_cuda 230μs 219μs 5.16%✅
test_gridmake2_torch.py::TestGridmake2TorchCUDA.test_2d_and_1d_matches_cpu 405μs 395μs 2.57%✅
test_gridmake2_torch.py::TestGridmake2TorchCUDA.test_both_1d_matches_cpu 459μs 427μs 7.69%✅
test_gridmake2_torch.py::TestGridmake2TorchCUDA.test_both_1d_simple_cuda 251μs 215μs 17.1%✅
test_gridmake2_torch.py::TestGridmake2TorchCUDA.test_large_tensors_cuda 222μs 215μs 3.33%✅
test_gridmake2_torch.py::TestGridmake2TorchCUDA.test_matches_numpy_via_cpu_conversion 295μs 214μs 38.3%✅
test_gridmake2_torch.py::TestGridmake2TorchCUDA.test_output_stays_on_cuda 218μs 215μs 1.46%✅
test_gridmake2_torch.py::TestGridmake2TorchCUDA.test_preserves_dtype_float32_cuda 222μs 214μs 3.83%✅
test_gridmake2_torch.py::TestGridmake2TorchCUDA.test_preserves_dtype_float64_cuda 224μs 214μs 4.78%✅

To edit these changes git checkout codeflash/optimize-_gridmake2_torch-mjj3mowi and push.

Codeflash Static Badge

The optimized code achieves a **6% speedup** through two key changes:

## Primary Optimization: Replacing `tile()` with `repeat()`

The line profiler shows that `x1.tile(x2.shape[0])` consumed **68.6% of the original runtime**. The optimization replaces this with `x1.repeat(n)`, which is significantly faster because:
- `torch.tile()` creates unnecessary intermediate copies when expanding tensors
- `torch.repeat()` is a more direct memory operation for simple replication along a single dimension
- In the 2D case, `x1.repeat(n, 1)` similarly outperforms `x1.tile(n, 1)` by avoiding redundant copy operations

## Secondary Optimization: `torch.stack()` vs `torch.column_stack()`

For the 1D-1D case, replacing `torch.column_stack([first, second])` (27.5% of runtime) with `torch.stack((first, second), dim=1)`:
- `torch.stack()` is more efficient when stacking exactly two 1D tensors into a 2D result
- `torch.column_stack()` has additional overhead to handle variable-length lists and more general input shapes

## Added JIT Compilation

The `@torch.compile` decorator enables PyTorch 2.0's graph optimization, which can provide additional speedups through:
- Fusion of operations (reducing intermediate tensor allocations)
- Kernel optimizations for the specific tensor operations used
- Note: The first call incurs compilation overhead, but subsequent calls benefit from cached optimized code

## Impact Assessment

This optimization is most beneficial for workloads that:
- Call `_gridmake2_torch` repeatedly with similar tensor shapes (amortizing JIT compilation cost)
- Use moderately-sized tensors where memory allocation overhead is significant
- Process cartesian products in computational economics, grid-based algorithms, or combinatorial expansions

The changes preserve all behavior, types, and error handling exactly.
@codeflash-ai codeflash-ai bot requested a review from aseembits93 December 23, 2025 21:30
@codeflash-ai codeflash-ai bot added ⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: High Optimization Quality according to Codeflash labels Dec 23, 2025
@claude
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claude bot commented Dec 23, 2025

Code Review for PR #989: ⚡️ Speed up function _gridmake2_torch by 7%

Summary

This PR optimizes the _gridmake2_torch function by replacing tile() operations with repeat(), switching from column_stack() to stack(), and adding @torch.compile JIT compilation. The optimization achieves a 7% speedup with excellent test coverage (26 passing tests).


Strengths

  1. Performance improvements are validated: The PR includes comprehensive benchmarking data showing 7% speedup (5.63ms → 5.28ms)
  2. Excellent test coverage: All 26 existing unit tests pass, with 100% test coverage
  3. Correctness preserved: Tests verify behavior matches NumPy implementation and handles both CPU and CUDA tensors
  4. Clear documentation: The PR description explains the optimization rationale well

🔍 Code Quality & Best Practices

Minor Issues:

  1. Docstring format (code_to_optimize/discrete_riccati.py:123)

    • The one-line docstring format is now consistent with the rest of the file
    • Impact: Very minor - improves consistency
  2. Intermediate variables (lines 156-157, 163-164)

    m = x1.shape[0]
    n = x2.shape[0]
    • These add some verbosity but improve readability by giving semantic names
    • Impact: Negligible - acceptable trade-off
  3. Comment quality (lines 155, 165)

    • Comments explain "why" (avoiding slow .tile()) rather than just "what" ✅
    • Good practice followed

🐛 Potential Bugs/Issues

No bugs identified. The optimization preserves all semantics:

  • ✅ Correct cartesian product logic
  • ✅ Dtype preservation verified by tests
  • ✅ Device placement (CPU/CUDA) maintained
  • ✅ Shape handling correct for all test cases
  • ✅ Edge cases (single elements, large tensors) covered

Performance Considerations

Positive:

  1. repeat() vs tile(): Excellent choice - avoids unnecessary intermediate copies
  2. stack() vs column_stack(): Appropriate for the fixed two-tensor case
  3. @torch.compile decorator: Enables graph optimization and kernel fusion

Important consideration:

JIT compilation overhead (code_to_optimize/discrete_riccati.py:122: @torch.compile)

  • ⚠️ First-call penalty: The first invocation will be significantly slower due to compilation
  • Amortization: Beneficial if called multiple times with similar tensor shapes
  • 💡 Recommendation: Document this behavior in the docstring

Suggested docstring addition:

@torch.compile
def _gridmake2_torch(x1: torch.Tensor, x2: torch.Tensor) -> torch.Tensor:
    """PyTorch version of _gridmake2.

    Note: This function uses @torch.compile. The first call incurs compilation
    overhead but subsequent calls with similar shapes benefit from optimized code.

🔒 Security Concerns

No security issues identified.

  • Function operates on tensor shapes without external input
  • No file I/O, network operations, or command execution
  • Type annotations present ✅

🧪 Test Coverage

Excellent coverage:

  • ✅ 26 tests passing (CPU and CUDA)
  • ✅ 100% code coverage
  • ✅ Matches NumPy implementation
  • ✅ Edge cases: single elements, large tensors, different dtypes
  • ✅ Error handling for unsupported cases
  • ✅ Device placement verified

No additional tests needed - coverage is comprehensive.


📝 Additional Observations

  1. PyTorch compatibility: @torch.compile requires PyTorch 2.0+. Project specifies torch>=2.8.0
  2. Code style: Changes follow Python best practices and improve file consistency
  3. Formatting: if-elif-else to if-if-raise pattern improves clarity

🎯 Recommendations

  1. Medium Priority: Document JIT compilation behavior and first-call overhead in docstring
  2. Optional: Benchmark @torch.compile benefit for your typical use patterns (especially important if function is often called once or with varying shapes)

Verdict

LGTM with minor suggestion

This is a solid optimization PR with excellent testing and clear performance benefits. The code is correct, well-tested, and follows good practices. The main recommendation is to document the JIT compilation behavior for users who might encounter first-call latency.

Approval recommended


Review generated by Claude Code

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