[Major Rewrite] Index/nd.size/nd.shape int→long#596
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Extended the keepdims fix to all remaining reduction operations: - ReduceAMax (np.amax, np.max) - ReduceAMin (np.amin, np.min) - ReduceProduct (np.prod) - ReduceStd (np.std) - ReduceVar (np.var) Also fixed np.amax/np.amin API layer which ignored keepdims when axis=null. Added comprehensive parameterized test covering all reductions with multiple dtypes (Int32, Int64, Single, Double, Int16, Byte) to prevent regression. All 7 reduction functions now correctly preserve dimensions with keepdims=true, matching NumPy 2.x behavior.
Apply .gitattributes normalization across all text files. No code changes - only CRLF → LF conversion. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
…N handling This commit adds comprehensive SIMD acceleration for reduction operations and fixes several NumPy compatibility issues. - AllSimdHelper<T>(): SIMD-accelerated boolean all() with early-exit on first zero - AnySimdHelper<T>(): SIMD-accelerated boolean any() with early-exit on first non-zero - ArgMaxSimdHelper<T>(): Two-pass SIMD: find max value, then find index - ArgMinSimdHelper<T>(): Two-pass SIMD: find min value, then find index - NonZeroSimdHelper<T>(): Collects indices where elements != 0 - CountTrueSimdHelper(): Counts true values in bool array - CopyMaskedElementsHelper<T>(): Copies elements where mask is true - ConvertFlatIndicesToCoordinates(): Converts flat indices to per-dimension arrays - **np.any axis-based reduction**: Fixed inverted logic in ComputeAnyPerAxis<T>. Was checking `Equals(default)` (returning true when zero found) instead of `!Equals(default)` (returning true when non-zero found). Also fixed return value to indicate computation success. - **ArgMax/ArgMin NaN handling**: Added NumPy-compatible NaN propagation where first NaN always wins. For both argmax and argmin, NaN takes precedence over any other value including Infinity. - **ArgMax/ArgMin empty array**: Now throws ArgumentException on empty arrays matching NumPy's ValueError behavior. - **ArgMax/ArgMin Boolean support**: Added Boolean type handling. For argmax, finds first True; for argmin, finds first False. - np.all(): Now uses AllSimdHelper for linear (axis=None) reduction - np.any(): Now uses AnySimdHelper for linear reduction - np.nonzero(): Added SIMD fast path for contiguous arrays - Boolean masking (arr[mask]): Added SIMD fast path using CountTrueSimdHelper and CopyMaskedElementsHelper Added comprehensive ownership/responsibility documentation to all ILKernelGenerator partial class files explaining the architecture: - ILKernelGenerator.cs: Core infrastructure and type mapping - ILKernelGenerator.Binary.cs: Same-type binary operations - ILKernelGenerator.MixedType.cs: Mixed-type with promotion - ILKernelGenerator.Unary.cs: Unary element-wise operations - ILKernelGenerator.Comparison.cs: Comparison operations - ILKernelGenerator.Reduction.cs: Reductions and SIMD helpers Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
…ions
Implements all missing kernel operations and routes SIMD helpers through
IKernelProvider interface for future backend abstraction.
- Power: IL kernel with Math.Pow scalar operation
- FloorDivide: np.floor_divide with NumPy floor-toward-negative-infinity semantics
- LeftShift/RightShift: np.left_shift, np.right_shift with SIMD Vector.ShiftLeft/Right
- Truncate: Vector.Truncate SIMD support
- Reciprocal: np.reciprocal (1/x) with SIMD
- Square: np.square optimized (x*x instead of power(x,2))
- Cbrt: np.cbrt cube root
- Deg2Rad/Rad2Deg: np.deg2rad, np.rad2deg (np.radians/np.degrees aliases)
- BitwiseNot: np.invert, np.bitwise_not with Vector.OnesComplement
- Var/Std: SIMD two-pass algorithm with interface integration
- NanSum/NanProd: np.nansum, np.nanprod (ignore NaN values)
- NanMin/NanMax: np.nanmin, np.nanmax (ignore NaN values)
- Route 6 SIMD helpers through IKernelProvider interface:
- All<T>, Any<T>, FindNonZero<T>, ConvertFlatToCoordinates
- CountTrue, CopyMasked<T>
- Clip kernel: SIMD Vector.Min/Max (~620→350 lines)
- Modf kernel: SIMD Vector.Truncate (.NET 9+)
- ATan2: Fixed wrong pointer type (byte*) for x operand in all non-byte cases
- ILKernelGenerator.Clip.cs, ILKernelGenerator.Modf.cs
- Default.{Cbrt,Deg2Rad,FloorDivide,Invert,Rad2Deg,Reciprocal,Shift,Square,Truncate}.cs
- np.{cbrt,deg2rad,floor_divide,invert,left_shift,nanprod,nansum,rad2deg,reciprocal,right_shift,trunc}.cs
- np.{nanmax,nanmin}.cs
- ShiftOpTests.cs, BinaryOpTests.cs (ATan2 tests)
This commit concludes a comprehensive audit of all np.* and DefaultEngine operations against NumPy 2.x specifications. - **ATan2**: Fixed non-contiguous array handling by adding np.broadcast_arrays() and .copy() materialization before pointer-based processing - **NegateBoolean**: Removed buggy linear-indexing path, now routes through ExecuteUnaryOp with new UnaryOp.LogicalNot for proper stride handling - **np.square(int)**: Now preserves integer dtype instead of promoting to double - **np.invert(bool)**: Now uses logical NOT (!x) instead of bitwise NOT (~x) - **np.power(NDArray, NDArray)**: Added array-to-array power overloads - **np.logical_and/or/not/xor**: New functions in Logic/np.logical.cs - **np.equal/not_equal/less/greater/less_equal/greater_equal**: 18 new comparison functions in Logic/np.comparison.cs - **argmax/argmin keepdims**: Added keepdims parameter matching NumPy API - Renamed `outType` parameter to `dtype` in 19 np.*.cs files to match NumPy - Added UnaryOp.LogicalNot to KernelOp.cs for boolean array negation - Created docs/KERNEL_API_AUDIT.md tracking Definition of Done criteria - Updated .claude/CLAUDE.md with DOD section and current status - Added NonContiguousTests.cs with 35+ tests for strided/broadcast arrays - Added DtypeCoverageTests.cs with 26 parameterized tests for all 12 dtypes - Added np.comparison.Test.cs for new comparison functions - Updated KernelMisalignmentTests.cs to verify fixed behaviors Files: 43 changed, 5 new files added Tests: 3058 passed (93% of 3283 total) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Bug #126 - Empty array comparison returns scalar (FIXED): - All 6 comparison operators now return empty boolean arrays - Files: NDArray.Equals.cs, NotEquals.cs, Greater.cs, Lower.cs Bug #127 - Single-element axis reduction shares memory (FIXED): - Changed Storage.Alias() and squeeze_fast() to return copies - Fixed 8 files: Add, AMax, AMin, Product, Mean, Var, Std, CumAdd - Added 20 memory isolation tests Bug #128 - Empty array axis reduction returns scalar (FIXED): - Proper empty array handling for all 9 reduction operations - Sum→zeros, Prod→ones, Min/Max→ValueError, Mean/Std/Var→NaN - Added 22 tests matching NumPy behavior Bug #130 - np.unique NaN sorts to beginning (FIXED): - Added NaNAwareDoubleComparer and NaNAwareSingleComparer - NaN now sorts to end (NaN > any non-NaN value) - Matches NumPy: [-inf, 1, 2, inf, nan] Test summary: +54 new tests, all passing Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace 20K-line Regen template with clean 300-line implementation: - ILKernelGenerator.MatMul.cs: Cache-blocked SIMD kernels for float/double - 64x64 tile blocking for L1/L2 cache optimization - Vector256 with FMA (Fused Multiply-Add) when available - IKJ loop order for sequential memory access on B matrix - Parallel execution for matrices > 65K elements - Default.MatMul.2D2D.cs: Clean dispatcher with fallback - SIMD fast path for contiguous same-type float/double - Type-specific pointer loops for int/long - Generic double-accumulator fallback for mixed types | Size | Float32 | Float64 | |---------|---------|---------| | 32x32 | 34x | 18x | | 64x64 | 38x | 29x | | 128x128 | 15x | 58x | | 256x256 | 183x | 119x | - Before: 19,862 lines (Regen templates, 1728 type combinations) - After: 284 lines (clean, maintainable) Old Regen template preserved as .regen_disabled for reference. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
IL Kernel Infrastructure: - Add ILKernelGenerator.Scan.cs for CumSum scan kernels with SIMD V128/V256/V512 paths - Extend ILKernelGenerator.Reduction.cs with Var/Std/ArgMax/ArgMin axis reduction support - Extend ILKernelGenerator.Clip.cs with strided/broadcast array helpers - Extend ILKernelGenerator.Modf.cs with special value handling (NaN, Inf, -0) - Add IKernelProvider interface extensions for new kernel types DefaultEngine Migrations: - Default.Reduction.Var.cs: IL fast path for contiguous arrays, single-element fix - Default.Reduction.Std.cs: IL fast path for contiguous arrays, single-element fix - Default.Reduction.CumAdd.cs: IL scan kernel integration - Default.Reduction.ArgMax.cs: IL axis reduction with proper coordinate tracking - Default.Reduction.ArgMin.cs: IL axis reduction with proper coordinate tracking - Default.Power.cs: Scalar exponent path migrated to IL kernels - Default.Clip.cs: Unified IL path (76% code reduction, 914→240 lines) - Default.NonZero.cs: Strided IL fallback path - Default.Modf.cs: Unified IL with special float handling Bug Fixes: - np.var.cs / np.std.cs: ddof parameter now properly passed through - Var/Std single-element arrays now return double (matching NumPy) Tests (3,500+ lines added): - ArgMaxArgMinComprehensiveTests.cs: 480 lines covering all dtypes, shapes, axes - VarStdComprehensiveTests.cs: 462 lines covering ddof, empty arrays, edge cases - CumSumComprehensiveTests.cs: 381 lines covering accumulation, overflow, dtypes - np_nonzero_strided_tests.cs: 221 lines for strided/transposed array support - 7 NumPyPortedTests files: Edge cases from NumPy test suite Code Impact: - Net reduction: 543 lines removed (6,532 added - 2,172 removed from templates) - ReductionTests.cs removed (884 lines) - replaced by comprehensive per-operation tests - Eliminated ~1MB of switch/case template code via IL generation Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
… ClipEdgeCaseTests - Fix BeOfValues params array unpacking: Cast GetData<T>() to object[] for proper params expansion - Mark Power_Integer_LargeValues as Misaligned: Math.Pow precision loss for large integers is expected - Fix np.full argument order in Clip tests: NumSharp uses (fill_value, shapes) not NumPy's (shape, fill_value) - Mark Base_ReductionKeepdims_Size1Axis_ReturnsView as OpenBugs: view optimization not implemented Test results: 3,879 total, 3,868 passed, 11 skipped, 0 failed
Breaking change: Migrate from int32 to int64 for array indexing. Core type changes: - Shape: size, dimensions[], strides[], offset, bufferSize -> long - Slice: Start, Stop, Step -> long - SliceDef: Start, Step, Count -> long - NDArray: shape, size, strides properties -> long/long[] Helper methods: - Shape.ComputeLongShape() for int[] -> long[] conversion - Shape.Vector(long) overload Related to #584
- NDArray constructors: int size -> long size - NDArray.GetAtIndex/SetAtIndex: int index -> long index - UnmanagedStorage.GetAtIndex/SetAtIndex: int index -> long index - ValueCoordinatesIncrementor.Next(): int[] -> long[] - DefaultEngine.MoveAxis: int[] -> long[] Build still failing - cascading changes needed in: - All incrementors (NDCoordinatesIncrementor, NDOffsetIncrementor, etc.) - NDIterator and all cast files - UnmanagedStorage.Cloning - np.random.shuffle, np.random.choice Related to #584
- this[long index] indexer - GetIndex/SetIndex with long index - Slice(long start), Slice(long start, long length) - Explicit IArraySlice implementations Build has 439 cascading errors remaining across 50+ files. Most are straightforward loop index changes (int → long). Related to #584
…int[] convenience Pattern applied: - Get*(params long[] indices) - primary implementation calling Storage - Get*(params int[] indices) - delegates to long[] via Shape.ComputeLongShape() - Set*(value, params long[] indices) - primary implementation - Set*(value, params int[] indices) - delegates to long[] version Covers: GetData, GetBoolean, GetByte, GetChar, GetDecimal, GetDouble, GetInt16, GetInt32, GetInt64, GetSingle, GetUInt16, GetUInt32, GetUInt64, GetValue, GetValue<T>, SetData (3 overloads), SetValue (3 overloads), SetBoolean, SetByte, SetInt16, SetUInt16, SetInt32, SetUInt32, SetInt64, SetUInt64, SetChar, SetDouble, SetSingle, SetDecimal Related to #584
…check - Add overflow check when string length exceeds int.MaxValue - Explicitly cast Count to int with comment explaining .NET string limitation - Part of int32 to int64 indexing migration (#584)
- Add overflow check in AsString() instead of Debug.Assert - Implement empty SetString(string, int[]) wrapper to call long[] version - Change GetStringAt/SetStringAt offset parameter from int to long - Part of int32 to int64 indexing migration (#584)
…ndices - GetValue(int[]) -> GetValue(long[]) - GetValue<T>(int[]) -> GetValue<T>(long[]) - All direct getters (GetBoolean, GetByte, etc.) -> long[] indices - SetValue<T>(int[]) -> SetValue<T>(long[]) - SetValue(object, int[]) -> SetValue(object, long[]) - SetData(object/NDArray/IArraySlice, int[]) -> long[] indices - All typed setters (SetBoolean, SetByte, etc.) -> long[] indices - Fix int sliceSize -> long sliceSize in GetData Part of int32 to int64 indexing migration (#584)
- NDArray`1.cs: Add long[] indexer, int[] delegates to it - UnmanagedStorage.cs: Add Span overflow check (Span limited to int) - UnmanagedStorage.Cloning.cs: Add ArraySlice allocation overflow check - NDIterator.cs: Change size field from int to long Note: ~900 cascading errors remain from: - ArraySlice (needs long count) - Incrementors (need long coords) - Various Default.* operations - IKernelProvider interface Part of int32 to int64 indexing migration (#584)
- NDCoordinatesIncrementor: Next() returns long[], Index is long[] - NDCoordinatesIncrementorAutoResetting: all fields long - NDOffsetIncrementor: Next() returns long, index/offset are long - NDOffsetIncrementorAutoresetting: same changes - ValueOffsetIncrementor: Next() returns long - ValueOffsetIncrementorAutoresetting: same changes - NDCoordinatesAxisIncrementor: constructor takes long[] - NDCoordinatesLeftToAxisIncrementor: dimensions/Index are long[] - NDExtendedCoordinatesIncrementor: dimensions/Index are long[] Part of int64 indexing migration (#584)
- ArraySlice.cs: Change Allocate count parameter handling for long - UnmanagedMemoryBlock: Adjust for long count - np.random.choice.cs: Add explicit casts for int64 indices - np.random.shuffle.cs: Update index handling for long - ValueCoordinatesIncrementor.cs: Add long[] Index property - NDArray.cs: Remove duplicate/dead code (112 lines)
MatMul.2D2D.cs: - M, K, N parameters now long throughout - All method signatures updated (long M, long K, long N) - Loop counters changed to long - Coordinate arrays changed to long[] NDArray.unique.cs: - len variable changed to long - getOffset delegate now Func<long, long> - Loop counters changed to long NDArray.itemset.cs: - Parameters changed from int[] to long[] NdArray.Convolve.cs: - Explicit (int) casts for size - acceptable because convolution on huge arrays is computationally infeasible (O(n*m)) NDArray.matrix_power.cs: - Cast shape[0] to int for np.eye (pending np.eye long support) np.linalg.norm.cs: - Fixed bug: was casting int[] to long[] incorrectly Remaining work: - IL kernel interfaces still use int for count/size - SIMD helpers (SimdMatMul) expect int parameters - Default.Clip, Default.ATan2, Default.Transpose, Default.NonZero all need coordinated IL kernel + caller updates
….Unmanaged - IKernelProvider: Changed interface to use long for size/count parameters - Default.Transpose: Fixed int/long coordinate and stride handling - ILKernelGenerator.Clip: Updated to use long loop counters - TensorEngine: Updated method signatures for long indexing - UnmanagedStorage.Slicing: Fixed slice offset to use long - Shape.Unmanaged: Fixed unsafe pointer methods for long indices
- SimdMatMul.MatMulFloat accepts long M, N, K (validates <= int.MaxValue internally) - MatMul2DKernel delegate uses long M, N, K - np.nonzero returns NDArray<long>[] instead of NDArray<int>[] - NDArray pointer indexer changed from int* to long* - SwapAxes uses long[] for permutation
- AllSimdHelper<T> parameter: int totalSize → long totalSize - Loop counters and vectorEnd: int → long - Part of int64 indexing migration
ILKernelGenerator.Clip.cs: - All loop counters and vectorEnd variables changed from int to long - Scalar loops also changed to use long iterators Default.Dot.NDMD.cs: - contractDim, lshape, rshape, retShape → long/long[] - Method signatures updated for TryDotNDMDSimd, DotNDMDSimdFloat/Double - ComputeIterStrides, ComputeBaseOffset, ComputeRhsBaseOffset → long - DotProductFloat, DotProductDouble → long parameters - DotNDMDGeneric → long coordinates and iterators - DecomposeIndex, DecomposeRhsIndex → long parameters
… fixed statements ILKernelGenerator.Clip.cs: - Changed 'int offset = shape.TransformOffset' to 'long offset' Default.ATan2.cs: - Changed fixed (int* ...) to fixed (long* ...) for strides and dimensions - Updated ClassifyATan2Path signature to use long* - Updated ExecuteATan2Kernel fixed statements Note: StrideDetector and MixedTypeKernel delegate still need updating
- IsContiguous: int* strides/shape -> long* strides/shape - IsScalar: int* strides -> long* strides - CanSimdChunk: int* params -> long*, innerSize/lhsInner/rhsInner -> long - Classify: int* params -> long* - expectedStride local -> long
Comprehensive guide for developers continuing the migration: - Decision tree for when to use long vs int - 7 code patterns with before/after examples - Valid exceptions (Span, managed arrays, complexity limits) - What stays int (ndim, dimension indices, Slice) - Checklist for each file migration - Common error patterns and fixes - File priority categories - Quick reference table
Added patterns discovered from analyzing 38 commits on longindexing branch: - Pattern 8: LongRange helper replacing Enumerable.Range - Pattern 9: SIMD block loops (outer long, inner int for cache constants) - Pattern 10: Random sampling with NextLong and int->long delegation - Return type changes section (nonzero, argsort, argmax/argmin return int64) - NDArray accessor methods (GetAtIndex/SetAtIndex use long) - Parallel.For removal in axis reductions (single-threaded with long) - Files changed summary categorized by component
Comprehensive audit of codebase for int64 migration violations: HIGH Priority (3): - NDArray/UnmanagedStorage.Getters missing long[] overloads (14+ methods) - NDArray typed setters missing 9 long[] overloads - np.vstack uses int[] for shape MEDIUM Priority (5): - IL kernel comments reference int* but code uses long* - np.save/np.load internal processing uses int[] - Shape.InferNegativeCoordinates has int[] version LOW Priority (8): - Acceptable .NET boundary exceptions documented - Span, String, Array.CreateInstance limitations - Dimension iteration with Enumerable.Range (bounded by ndim) Includes verification commands and fix patterns.
Added 4 more HIGH priority issues: - H4: np.repeat uses int count for per-element repeats - H5: NDArray.unique SortUniqueSpan uses int count - H6: np.searchsorted empty array returns int instead of long - H7: nanmean/nanstd/nanvar allocate managed arrays with (int) cast Added 1 more MEDIUM priority issue: - M6: np.load internal int total accumulator Added 2 more LOW priority issues: - L9: Hashset<T>.Count is int (acceptable) - L10: IMemoryBlock.ItemLength is int (acceptable) Updated checklist with actionable items.
Additional issues found via deep codebase analysis: HIGH Priority (new): - H8: np.linspace uses int num parameter + int loop counters - H9: np.roll uses int shift parameter - H10: UnmanagedHelper.CopyTo uses int countOffsetDestination - H11: np.array<T>(IEnumerable, int size) needs long overload MEDIUM Priority (new): - M7: np.save internal int total accumulator - M8: NdArrayToJaggedArray loops use int for large arrays Search techniques used: - Grep for int parameters in public API signatures - Buffer.MemoryCopy offset parameter analysis - Loop counter variable type analysis - Creation function parameter audit
Added 14 new issues found via grep/code search: - H12: SimdMatMul.MatMulFloatSimple int M,N,K parameters - H13: ArgMax/ArgMin SIMD helpers return int, take int totalSize - H14: Default.Dot ExpandStartDim/ExpandEndDim return int[] - H15: NDArray.Normalize int loop counters - H16: Slice.Index uses ToInt32 cast in selection code - H17: Shape dimension parsing uses List<int> - H18: NdArrayFromJaggedArr uses List<int> - H19: Arrays.GetDimensions uses List<int> - H20: np.asarray uses new int[0] for scalar shape - H21: ArrayConvert uses int[] for dimensions - H22: UnmanagedStorage FromMultiDimArray uses int[] dim - M9: NDArray<T> generic only has int size constructors - M10: np.arange(int) returns int32 (NumPy 2.x returns int64) - M11: Default.Transpose uses int[] for permutation
- ILKernelGenerator.Reduction.Axis.Simd: bridge long interface to int helper with bounds check - np.random.choice: add System namespace for NotSupportedException - np.random.shuffle: add int.MaxValue check for n (shape[0] is now long)
- H23: NumSharp.Bitmap shape casts without overflow check - L11: SetData uses int[0] instead of long[] (acceptable) - L12: NdArrayToMultiDimArray int[] for .NET boundary (acceptable) Updated totals: 23 HIGH, 11 MEDIUM, 13 LOW
Fixed HIGH priority issues: - H4: np.repeat - GetInt32→GetInt64, int count/j→long for per-element repeats - H6: np.searchsorted - empty array returns typeof(long) for consistency - H10: UnmanagedHelper.CopyTo - offset parameter int→long - H12: SimdMatMul.MatMulFloatSimple - int M,N,K→long, all loop counters→long - H14: Default.Dot.ExpandStartDim/ExpandEndDim - returns long[] instead of int[] - H15: NDArray.Normalize - loop counters int col/row→long col/row - H16: Slice.Index in Selection Getter/Setter - ToInt32→ToInt64 - H20: np.asarray - new int[0]→Array.Empty<long>() for scalar shapes Reclassified as LOW (not bugs): - H3: np.vstack - dead code (commented out) - H17,H18,H19,H21,H22: .NET boundary (Array.Length/GetLength return int) Updated docs/LONG_INDEXING_ISSUES.md with fix status and reclassifications.
Fixed HIGH priority issues: - H8: np.linspace - added long num overloads, changed all loop counters int→long - H9: np.roll/NDArray.roll - added long shift primary overloads - H11: np.array<T>(IEnumerable, size) - added long size overload - H23: NumSharp.Bitmap - added overflow checks before casting shape to int All int overloads now delegate to long overloads for backward compatibility. Total fixed this session: 12 issues (8 in batch 1, 4 in batch 2).
Fixed HIGH priority issues: - H1: Confirmed already fixed (all typed getters have long[] overloads) - H2: Added missing long[] overloads for 9 typed setters in NDArray: SetBoolean, SetByte, SetInt16, SetUInt16, SetUInt32, SetUInt64, SetChar, SetSingle, SetDecimal - H13: ArgMax/ArgMin now return Int64 indices (supports >2B arrays): - ReductionKernel.cs: ResultType returns NPTypeCode.Int64 for ArgMax/ArgMin - ArgMaxSimdHelper/ArgMinSimdHelper: return long, take long totalSize - All loop counters/indices changed from int to long Total fixed this session: 15 issues Remaining HIGH: H5, H7 (both acceptable - protected by .NET limits)
M1: IL kernel comments updated from int* to long* (5 files) - ILKernelGenerator.Comparison.cs (2 locations) - ILKernelGenerator.MixedType.cs - ILKernelGenerator.Reduction.cs - ILKernelGenerator.Unary.cs M4: Confirmed complete - long* version exists in Shape.Unmanaged.cs - Added cross-reference comment to int* version in Shape.cs M6: np.load int total accumulator changed to long M7: np.save int total accumulator changed to long M9: Confirmed already fixed - NDArray<T> has long size constructors New issues found and fixed: - M12: np.random.randn loop counter int -> long - M13: ILKernelGenerator.Scan.cs loop counters int -> long (42 replacements: outer, inner, i over outerSize/innerSize/axisSize) Updated LONG_INDEXING_ISSUES.md with batch 4 status
Implement Vector256/Vector128 SIMD for all NaN-aware statistics: - nanmean, nanvar, nanstd: Two-pass algorithm with sum+count tracking - nansum, nanprod: Identity masking (NaN → 0 for sum, NaN → 1 for prod) - nanmin, nanmax: Sentinel masking (NaN → ±∞) with all-NaN detection SIMD Algorithm (NaN masking via self-comparison): nanMask = Equals(vec, vec) // True for non-NaN, false for NaN cleaned = BitwiseAnd(vec, nanMask) // Zero out NaN values countMask = BitwiseAnd(oneVec, nanMask) // Count non-NaN elements Performance (1M elements, ~10% NaN): - nanmean: ~3ms/call (Vector256<double> = 4 elements/vector) - nanvar: ~4.5ms/call (two-pass: mean, then squared differences) - nanstd: ~4ms/call Files changed: - ILKernelGenerator.Masking.NaN.cs: SIMD helpers for float/double - ILKernelGenerator.Reduction.NaN.cs: NEW - IL generation infrastructure - ILKernelGenerator.Reduction.Axis.NaN.cs: Axis reduction SIMD - np.nanmean/var/std/sum/prod/min/max.cs: Simplified to use SIMD helpers Tested: 70 tests covering edge cases, boundary sizes, large arrays, sliced/strided arrays, axis reductions, and float32/float64 dtypes. All results match NumPy 2.4.2 exactly.
…arge collections Hashset<T> now supports collections exceeding int.MaxValue elements with long-based indexing throughout. Key changes: Core implementation (Hashset`1.cs): - All index fields converted to long: m_count, m_lastIndex, m_freeList, Slot.next - Buckets array changed to long[] for large collection support - New LongCount property returns count as long - Count property throws OverflowException if count > int.MaxValue - CopyTo methods support long arrayIndex and count parameters HashHelpersLong (new helper class): - Extended primes table with values up to ~38 billion - IsPrime(long), GetPrime(long), ExpandPrime(long) for large capacities - 33% growth (1.33x) for collections >= 1 billion elements - Standard 2x growth for smaller collections - LargeGrowthThreshold = 1_000_000_000L constant BitHelperLong (new helper class): - Uses long[] instead of int[] for bit marking - Supports marking/checking bits beyond int.MaxValue positions - ToLongArrayLength() calculates required array size for n bits ConcurrentHashset<T> updates: - Added LongCount property for thread-safe long count access - Updated CopyTo to use long parameters Tests (HashsetLongIndexingTests.cs - 24 tests): - Basic functionality: Add, Remove, Clear, Enumeration - Long indexing: LongCount, long capacity constructor - HashHelpersLong: IsPrime, GetPrime, 33% expansion verification - BitHelperLong: MarkBit, IsMarked, ToLongArrayLength - Set operations: Union, Intersect, Except, SymmetricExcept - Stress test: 1 million elements - Edge cases: TrimExcess, TryGetValue, SetEquals, Overlaps
- Remove duplicate zeros/zeros<T> overloads in np.zeros.cs - Remove duplicate SetIndex method in ArraySlice<T> - Remove duplicate constructor in ValueCoordinatesIncrementor - Fix int/long type handling: axis/ndim stay as int (dimension indices), element indices (size, offset, strides) use long - Fix np.moveaxis to convert long[] axes to int[] internally - Fix np.linalg.norm axis parameter handling - Simplify ILKernelGenerator axis reduction to use long* directly
np.arange(10) now returns int64 instead of int32, matching NumPy 2.x behavior. Integer overloads delegate to long overloads to ensure consistent dtype. Verified against NumPy 2.4.2: - np.arange(10).dtype = int64 - np.arange(0, 10, 1).dtype = int64
Audited 67 locations with (int) casts that could silently overflow for arrays > 2 billion elements. Fixed 9 critical issues: Storage/Allocation: - UnmanagedStorage.cs: Remove (int)shape.size casts - long overload exists - IndexCollector.cs: Add MaxCapacity check to prevent growth beyond Array.MaxLength Shape/Reshape: - np.meshgrid.cs: Use reshape(long, long) overload directly - np.nanvar.cs, np.nanstd.cs, np.nanmean.cs: Remove (int) cast in List<long>.Add Array Conversion: - NdArrayToMultiDimArray.cs: Add overflow check before converting to int[] Verified 30+ locations already have proper guards (Span creations, string ops, SIMD gather fallbacks, Shape operators with checked(), etc.) Documented 7 known .NET limitations (Array.IndexOf, GetLength return int). Added docs/INT32_CAST_LANDMINES.md tracking all findings. Build: 0 errors | Tests: 3887 passed
INT64 Developer Guide compliance for recent rebase commits: ILKernelGenerator fixes: - Reduction.Arg.cs: All ArgMax/ArgMin helpers return long, take long totalSize - Reduction.Axis.Simd.cs: Size/stride params, loop counters, arrays -> long - Reduction.Axis.cs: Loop counters -> long - Reduction.NaN.cs: Fix sealed -> static (rebase conflict) - Masking.cs: Shape parameters -> long[] DefaultEngine fixes: - Default.Reduction.Nan.cs: All size/offset/stride/arrays -> long - Default.NonZero.cs: CountNonZero returns long, arrays -> long[] - Default.BooleanMask.cs: Size/count variables -> long API fixes: - TensorEngine.cs: CountNonZero returns long, remove duplicates - np.count_nonzero.cs: Returns long - np.any.cs, np.all.cs: Shape arrays -> long[], loop vars -> long Rebase conflict fixes: - np.random.rand.cs: Remove duplicate random() methods - Delete duplicate Default.Op.Boolean.template.cs Remaining: ~96 errors in Shape.Broadcasting.cs, NDArray`1.cs See docs/INT64_MIGRATION_PROGRESS.md for full details
Complete migration of int32 to int64 for indices, sizes, strides, offsets across 16 files. Build now compiles successfully. Core changes: - Shape.Broadcasting.cs: All dimension/stride arrays now long[] - NDArray.cs: Added long size constructor overloads - TensorEngine/np.nonzero: Return NDArray<long>[] for index arrays - ILKernelGenerator.Reduction.Axis.Simd: AxisReductionKernel delegate now uses long* for strides/shapes and long for sizes - np.size: Return type changed to long - np.array: Stride variables changed to long for pointer arithmetic - NDArray.Indexing.Masking: Shape arrays and counts now long Random functions: - np.random.choice/shuffle: Added overflow checks for int.MaxValue limit (Random.Next only supports int; full long support deferred) Build infrastructure: - NumSharp.Core.csproj: Exclude *.template.cs and *.regen_disabled files Test status: 193 failures due to memory corruption - needs investigation in stride/offset calculations.
…4 default Root cause identified: "memory corruption" errors were NOT actual corruption. Tests were calling GetInt32() on Int64 arrays (np.arange now returns int64). Fixes: - np.random.shuffle.cs: NextInt64 → NextLong (correct method name) - np.random.shuffle.cs: SwapSlicesAxis0 int → long parameters - BattleProofTests.cs: GetInt32 → GetInt64 for arange-based tests - np.transpose.Test.cs: long[] → int[] for axis array (axes stay int) - ReadmeExample.cs: cast n_samples to int for np.ones() calls - NpApiOverloadTests: int → long for count_nonzero return, NDArray<int>[] → NDArray<long>[] for nonzero - BooleanIndexing.BattleTests.cs: shape.SequenceEqual(new[]) → shape.SequenceEqual(new long[]) - Updated INT64_MIGRATION_PROGRESS.md with root cause analysis
- Default.All.cs: int i -> long i for size iteration - Default.Any.cs: int i -> long i for size iteration - StackedMemoryPool.cs: int i -> long i (count param is long) - np.random.poisson.cs: int i -> long i for size iteration - np.random.bernoulli.cs: int i -> long i for size iteration - np.random.randn.cs: int i -> long i for size iteration - NDArray.Indexing.Masking.cs: - int idx -> long idx for trueCount iteration - GetInt32 -> GetInt64 (nonzero returns NDArray<long>[]) - int valueIdx -> long valueIdx for mask.size iteration All changes follow INT64_DEVELOPER_GUIDE.md patterns.
…ters - NdArrayToJaggedArray.cs: Add overflow checks for managed array limits, explicit (int) casts with validation before allocation, loop comparisons against .Length instead of shape[x] - NDArray.matrix_power.cs: Add overflow check for np.eye dimension - NDArray.Indexing.Masking.cs: Fix loop counter int→long for mask.size iteration - Update INT64_MIGRATION_PROGRESS.md with session 5 fixes and audit results (np.load.cs, np.save.cs confirmed as valid exceptions)
- IndexingEdgeCaseTests.cs: GetInt32 → GetInt64 for arange-based arrays - LinearAlgebraTests.cs: GetInt32 → GetInt64 for dot product tests - NDArray.Base.Test.cs: GetInt32 → GetInt64 for base memory tests np.arange now returns Int64 (NumPy 2.x alignment), so tests must use GetInt64() instead of GetInt32() to access values correctly.
… int64 default NumPy 2.x returns int64 from arange() by default. This batch updates tests that used GetInt32/MakeGeneric<int> on arange-sourced arrays to use the correct int64 accessors. Test updates: - Change GetInt32 -> GetInt64 for arange-sourced arrays - Change MakeGeneric<int>() -> MakeGeneric<long>() for arange results - Change np.array(new int[]) -> np.array(new long[]) where comparing to arange - Fix NDIterator<int> -> NDIterator<long> for arange iteration - Restore GetInt32 for explicit int32 arrays (np.array(42), np.array(new int[])) Bug fix (Default.ClipNDArray.cs): - Fixed mixed-dtype clip bug where int32 min/max arrays were read as int64 - Now casts min/max arrays to output dtype before calling SIMD kernel - This prevented garbage values like 34359738376 (8 * 2^32+1) Dtype preservation tests: - Clip_Int32_PreservesDtype: use explicit int32 array - Ravel_PreservesDtype_Int32: use explicit int32 array - Reshape_Int32: use explicit int32 array - Roll_PreservesDtype_Int32: use explicit int32 array Test results: 103 failures -> 29 failures (74 tests fixed)
…type handling Test fixes: - Change shape comparisons from int[] to long[] (shape now returns long[]) - Fix Array.Empty<int>() to Array.Empty<long>() for scalar shape comparisons - Fix GetInt32 -> GetInt64 for arange-sourced arrays in NDArray.Base.Test.cs - Fix ToArray<int> -> ToArray<long> for arange-sourced data - Fix GetInt64 -> GetInt32 for explicit int32 scalars (NDArray.Scalar(42)) Bug fix (np.repeat.cs): - Fixed GetInt64() calls on repeats array that could be int32 - Now uses Convert.ToInt64(GetAtIndex()) to handle any integer dtype - This fixes "index < Count" errors when repeat counts are int32 Test results: 29 failures -> 19 failures (10 tests fixed)
Tests were using wrong getter methods for array dtypes:
- np.arange() returns Int64 (NumPy 2.x) → use GetInt64()
- np.array(new[] { int }) returns Int32 → use GetInt32()
- NDArray.Scalar(int) returns Int32 → use GetInt32()
Fixes:
- NdArray.Roll.Test.cs: int[,] → long[,] for arange result cast
- np.concatenate.Test.cs: GetInt32 → GetInt64 for arange-based tests
- np.empty_like.Test.cs: GetInt32 → GetInt64 for arange-based tests
- NDArray.Base.Test.cs: GetInt64 → GetInt32 for int[] literal array
- NpBroadcastFromNumPyTests.cs: GetInt64 → GetInt32 for int scalar
Reduces test failures from 6 to 0 (excluding unrelated stack overflow).
Nucs
added
architecture
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Summary
Migrates all index, stride, offset, and size operations from
int(int32) tolong(int64), aligning NumSharp with NumPy'snpy_intptype. This enables support for arrays exceeding 2GB (int32 max = 2.1B elements) and ensures compatibility with NumPy 2.x behavior.Motivation
NumPy uses
npy_intp(equivalent toPy_ssize_t) for all indexing operations, which is 64-bit on x64 platforms. NumSharp's previous int32 limitation prevented working with large arrays and caused silent overflow bugs when array sizes approached int32 limits.Key drivers:
npy_intpsemanticsWhat Changed
size,dimensions,strides,offset,bufferSize→longGetOffset(),GetCoordinates(),TransformOffset()→longparameters and return typeslong[],int[]overloads delegate tolong[]int*→long*for strides/shapeslongCountproperty →longCountproperty and all index parameters →longCountproperty →longlong, addedlong[]overloadslong, addedlong[]overloadslongsize,lenproperties →longshape,stridesproperties →long[]long[]coordinate overloads,int[]delegates tolong[]long[]overloadsFunc<int[], int>→Func<long[], long>long[]long[]long[]long[]long[]longlonglongLdc_I4→Ldc_I8,Conv_I4→Conv_I8where appropriatelonglonglonglonglongnp.arange(int)andnp.arange(int, int, int)now returnint64arrays (NumPy 2.x alignment)np.argmax/np.argmin: return type →longnp.nonzero: return type →long[][]int*→long*, local stride calculations →longBreaking Changes
NDArray.sizereturnslongintif needed, or use directlyNDArray.shapereturnslong[]int[]NDArray.stridesreturnslong[]int[]np.arange(int)returnsint64dtype.astype(NPTypeCode.Int32)if int32 needednp.argmax/np.argminreturnlongintif needednp.nonzeroreturnslong[][]int[][]Shape[dim]returnslongintif neededlong[]Performance Impact
Benchmarked at 1-3% overhead for scalar loops, <1% overhead for SIMD-optimized paths. This is acceptable given the benefits of large array support.
longoffsets (zero overhead)What Stays
intNDArray.ndim/Shape.NDimSlice.Start/Stop/Stepfor (int d = 0; d < ndim; d++))NPTypeCodeenum valuesRelated
npy_intpdefined innumpy/_core/include/numpy/npy_common.h:217docs/INT64_DEVELOPER_GUIDE.md