optimize memory usage in postprocess_adj

[groberts-flex]

In my test case, I am seeing about a 1.5x memory reduction which is helpful for very large geometry groups that have large monitors and frequency points.

Greptile Summary

Optimized memory usage in adjoint gradient postprocessing by replacing label-based array slicing (sel) with integer-based indexing (isel). The _slice_field_data function now detects contiguous frequency indices and creates zero-copy views instead of copies, achieving ~1.5x memory reduction for large geometry groups with extensive monitors and frequency points.

• Refactored _slice_field_data in backward.py:96-148 to use get_indexer and check for contiguous indices before slicing
• Added comprehensive performance test infrastructure with CPU and memory profiling capabilities
• Created pytest markers (postprocess_adj_generate, postprocess_adj_profile) to control execution of performance tests that require simulation runs
• Test dataset generator creates realistic workload (35×35 cylinder arrays) to measure optimization impact

Confidence Score: 4/5

• Safe to merge with minor style improvements suggested
• The core memory optimization is sound and well-implemented with proper error handling. The contiguity check ensures zero-copy views when possible while falling back to copies when necessary. Performance testing infrastructure is comprehensive. Minor style issues include error message formatting and redundant length check.
• No files require special attention - changes are focused and well-tested

Important Files Changed

Filename	Overview
tidy3d/web/api/autograd/backward.py	Optimized _slice_field_data to use integer indexing (isel) with contiguity checks for memory-efficient array slicing, replacing label-based sel which created copies
tests/test_components/autograd/performance/test_shape_performance.py	Added performance test suite with CPU/memory profiling capabilities for postprocess_adj, using pytest markers to control test execution
tests/test_components/autograd/performance/postprocess_adj_utils.py	Added utilities for generating, persisting, and loading large-scale test datasets (35x35 cylinder arrays) for postprocess_adj performance testing
CHANGELOG.md	Added changelog entry documenting the memory performance improvement in postprocess_adj
pyproject.toml	Added pytest markers for controlling performance test execution: postprocess_adj_generate and postprocess_adj_profile

Sequence Diagram

sequenceDiagram
    participant Caller as postprocess_adj caller
    participant PP as postprocess_adj
    participant Slice as _slice_field_data
    participant XR as xarray DataArray
    
    Caller->>PP: postprocess_adj(sim_data_adj, sim_data_orig, sim_data_fwd, sim_fields_keys)
    PP->>PP: Extract frequency chunks for processing
    loop For each frequency chunk
        PP->>Slice: _slice_field_data(field_components, freqs)
        Slice->>XR: get_index("f")
        XR-->>Slice: frequency index
        Slice->>Slice: get_indexer(freqs) → integer indices
        Slice->>Slice: Check if indices are contiguous (diff == 1)
        alt Contiguous indices
            Slice->>XR: isel(f=slice(start, stop))
            Note over XR: Zero-copy view (memory efficient)
            XR-->>Slice: View of data
        else Non-contiguous indices
            Slice->>XR: isel(f=array_of_indices)
            Note over XR: Copy required
            XR-->>Slice: Copy of data
        end
        Slice-->>PP: Sliced field data
        PP->>PP: Compute VJP for chunk
    end
    PP-->>Caller: AutogradFieldMap with gradients

Loading

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Note

Improves memory efficiency and robustness of adjoint postprocessing.

• Refactors postprocess_adj to use index-based frequency slicing: _slice_field_data now uses isel with validated slices; chunking switches from value-based sel to index slices; forward/adjoint datasets are sorted by f and frequency-aligned before processing
• Updates tests to reflect index-based slicing and error behavior; adds a numerical test validating gradient consistency under varied frequency selections
• Introduces performance profiling suite for postprocess_adj with dataset generation and CPU/memory profiling; adds pytest markers and config to control these runs
• Updates CHANGELOG.md to note memory improvement

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Diff Coverage

Diff: origin/develop...HEAD, staged and unstaged changes

• tidy3d/web/api/autograd/backward.py (100%)

Summary

• Total: 34 lines
• Missing: 0 lines
• Coverage: 100%

[marcorudolphflex]

Good catch and great to have that in.
Looks fine overall with some comments/questions left from my side.
Think would not be bad to have an unit test on _slice_field_data against expectation and/or the legacy code? Saw some "weak" tests in test_frequency_coordinate_alignment, but they don't test on len(freqs) > 1.

[yaugenst-flex]

LGTM! can merge when marco's comments are resolved

[groberts-flex]

thanks! @marcorudolphflex let me know how the unresolved conversations sound to you. I also added an additional numerical test that should test the frequency slicing more - let me know if that looks good to you or if you want some additional tests there. I ended up being able to simplify the frequency slicing by filtering the frequencies in the forward field.

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