Added #

• BufferPool - Memory pooling API for buffer reuse (buffer_pool.dart):

  • Singleton BufferPool.instance for global buffer reuse
  • Power-of-2 size bucketing for efficient allocation
  • Per-dtype buffer pools (Float32, Float64, Int32, Uint8, etc.)
  • acquire(minSize, dtype) and release(buffer) methods
  • acquireFloat32(), acquireFloat64(), etc. convenience extensions
  • Max buffers per bucket limit (8) to prevent unbounded memory growth
  • pooledCount and pooledBytes for monitoring

• TypedData Views - Zero-copy tensor view utilities (typed_data_views.dart):

  • TypedDataViews.float32SublistView() - Zero-copy Float32List slicing
  • TypedDataViews.float64SublistView() - Zero-copy Float64List slicing
  • TypedDataViews.viewAs() - Create typed view from ByteBuffer at offset
  • TensorViewExtension on TensorBuffer:
    • sliceFirst(start, end) - Zero-copy slice along first dimension
    • isViewable - Check if tensor can be used as a view
    • toChannelsLast() - NCHW to NHWC without copying
    • toChannelsFirst() - NHWC to NCHW without copying
    • flatten() - 1D view of contiguous tensor
    • unbind(dim) - Split tensor into views along dimension
    • select(dim, index) - Select single index with reduced rank
    • narrow(dim, start, length) - Narrow dimension without copying

• Utility Libraries (lib/src/utils/):

  • dtype_dispatcher.dart - DTypeDispatcher for dtype-specialized dispatch
  • tensor_indexing.dart - TensorIndexer for index calculations (index2D, index3D, index4D, linearToCoords, coordsToLinear, computeStrides)

• TensorBuffer/TensorStorage Factory Methods:

  • TensorBuffer.fromFloat64List() - Create tensor from Float64List
  • TensorStorage.fromFloat64List() - Create storage from Float64List

Changed #

• SoftmaxOp Optimization: Now preserves input dtype (Float32/Float64) instead of always using Float64. Added dtype-specialized implementations for better performance.

• Double-copy elimination: Operations now use cloneForModification() pattern (input.isContiguous ? input.clone() : input.contiguous()) to avoid unnecessary copies:

  • ReLUOp, LeakyReLUOp, SigmoidOp, TanhOp, SoftmaxOp
  • AbsOp, NegOp, SqrtOp, ExpOp, LogOp (UnaryMathOp)
  • NormalizeOp, ScaleOp

Internal #

• Added cloneForModification() helper to RequiresContiguous mixin in transform_op.dart
• Integrated DTypeDispatcher into activation ops (ReLUOp, LeakyReLUOp, SigmoidOp, TanhOp) for dtype-specialized loops
• Integrated DTypeDispatcher into ScaleOp for consistent dtype handling
• Replaced stride computation with TensorIndexer.computeStrides() in SoftmaxOp (removed 3x code duplication)

Added #

• BatchNormOp - Batch normalization for CNN inference (batch_norm_op.dart):

  • Full PyTorch-compatible torch.nn.BatchNorm2d implementation
  • Pre-computed scale/shift coefficients for efficient inference: y = x * scale + shift
  • Supports 3D [C,H,W] and 4D [N,C,H,W] tensors
  • BatchNormOp.fromStateDict() factory for loading PyTorch weights
  • Dtype-specialized loops for Float32/Float64
  • In-place support via applyInPlace()

• LayerNormOp - Layer normalization for Transformer inference (layer_norm_op.dart):

  • Full PyTorch-compatible torch.nn.LayerNorm implementation
  • Normalizes over last N dimensions (e.g., [768] for BERT)
  • Welford's algorithm for numerically stable mean/variance computation
  • LayerNormOp.bert() and LayerNormOp.bertLarge() factory presets
  • LayerNormOp.fromStateDict() factory for loading PyTorch weights
  • Dtype-specialized loops for Float32/Float64
  • In-place support via applyInPlace()

PyTorch Compatibility #

Performance Optimizations #

• Dtype-specialized loops: Hot paths in transform operations now use dtype-specific code paths with direct Float32List/Float64List access, avoiding per-element switch overhead:

  • NormalizeOp._normalize3D(), NormalizeOp._normalize4D()
  • ScaleOp._scale()
  • ClipOp._clip()
  • GaussianBlurOp._applySeparableBlur()
  • ResizeOp._resizeNearest(), _resizeBilinear(), _resizeBicubic()
  • CenterCropOp._crop3D(), _crop4D()
  • concat() with optimized axis=0 bulk copy

• Clone-Before-Modify optimization: ClipOp.apply() now avoids double copy by checking isContiguous before deciding whether to clone() or contiguous()

• Isolate threshold: TensorPipeline.runAsync() now accepts optional isolateThreshold parameter (default: 100,000 elements). Small tensors skip isolate overhead and run synchronously

• Buffer reuse: GaussianBlurOp now pre-allocates and reuses temp buffer across channels, reducing allocations

• Concat linear copy: concat() now uses pre-computed strides for linear index calculation instead of recursive index computation. Axis=0 concatenation of contiguous tensors uses bulk setRange() copy

• Loop unrolling: ResizeOp._resizeBicubic() unrolls 4x4 kernel with pre-computed weights and indices

Added #

• Arithmetic Operations (arithmetic_op.dart):
  • AddOp - Element-wise addition (scalar or tensor)
  • SubOp - Element-wise subtraction (scalar or tensor)
  • MulOp - Element-wise multiplication (scalar or tensor)
  • DivOp - Element-wise division (scalar or tensor)
  • PowOp - Element-wise power operation
• Math Operations (math_op.dart):
  • AbsOp - Element-wise absolute value
  • NegOp - Element-wise negation
  • SqrtOp - Element-wise square root
  • ExpOp - Element-wise exponential (e^x)
  • LogOp - Element-wise natural logarithm
• Activation Functions (activation_op.dart):
  • ReLUOp - Rectified Linear Unit
  • LeakyReLUOp - Leaky ReLU with configurable negative slope
  • SigmoidOp - Sigmoid activation
  • TanhOp - Hyperbolic tangent activation
  • SoftmaxOp - Softmax along specified axis
• TensorBuffer Factory Methods:
  • TensorBuffer.full() - Create tensor filled with specified value
  • TensorBuffer.random() - Create tensor with uniform random values [0, 1)
  • TensorBuffer.randn() - Create tensor with standard normal distribution
  • TensorBuffer.eye() - Create identity matrix (supports rectangular)
  • TensorBuffer.linspace() - Create tensor with evenly spaced values
  • TensorBuffer.arange() - Create tensor with sequence values
• Utility Libraries (lib/src/utils/):
  • index_utils.dart - Index manipulation utilities (reflectIndex, replicateIndex, circularIndex)
  • validation_utils.dart - Common tensor validation patterns

Changed #

• Exception Consistency: TensorStorage._checkBounds() now throws IndexOutOfBoundsException instead of RangeError for consistent exception handling across the library

Internal #

• Extracted duplicate _reflectIndex code from pad_op.dart and augmentation_op.dart into shared utility
• Added TensorValidation extension with requireRank3Or4(), requireExactRank(), requireMinRank() methods

Added #

• Performance benchmark suite (benchmark/ directory):
  • tensor_creation_benchmark.dart - Tensor creation performance
  • tensor_ops_benchmark.dart - Zero-copy and copy operations
  • pipeline_benchmark.dart - Pipeline sync/async comparison
  • memory_benchmark.dart - Memory usage measurement
  • run_all.dart - Unified benchmark runner
  • utils/benchmark_utils.dart - Benchmark utilities

Fixed #

• Removed unused variables in benchmark files
• Fixed lint issues in benchmark files

Added #

• ClipOp - Element-wise value clamping with factory presets (unit, symmetric, uint8)
• PadOp - Padding with multiple modes (constant, reflect, replicate, circular)
• SliceOp - Python-like tensor slicing with support for negative indices and steps
• RandomCropOp - Random cropping for data augmentation with deterministic seed support
• GaussianBlurOp - Gaussian blur using separable convolution with factory presets
• concat() - Utility function for tensor concatenation along specified axis

Fixed #

• concat() axis-based copy logic now correctly handles multi-axis concatenation

Changed #

• BREAKING: Unified exception handling across the library
  • All exceptions now extend TensorException sealed class
  • ArgumentError → ShapeMismatchException, InvalidParameterException
  • RangeError → IndexOutOfBoundsException

Added #

• IndexOutOfBoundsException - Thrown when an index or axis is out of valid range
• DTypeMismatchException - Thrown when tensor data types do not match

Changed #

• BREAKING: Unified exception handling across the library
  • All exceptions now extend TensorException sealed class
  • ArgumentError → ShapeMismatchException, InvalidParameterException
  • RangeError → IndexOutOfBoundsException
  • StateError → NonContiguousException, DTypeMismatchException
• Shape validation now happens before buffer creation in zeros() and ones()

Migration Guide #

If you were catching standard Dart exceptions, update your code:

Added #

• Reduction operations for TensorBuffer:
  • sum() - Returns the sum of all elements
  • mean() - Returns the arithmetic mean of all elements
  • min() - Returns the minimum value
  • max() - Returns the maximum value
• Axis-wise reduction operations:
  • sumAxis(int axis, {bool keepDims}) - Sum along a specific axis
  • meanAxis(int axis, {bool keepDims}) - Mean along a specific axis
  • minAxis(int axis, {bool keepDims}) - Min along a specific axis
  • maxAxis(int axis, {bool keepDims}) - Max along a specific axis
• Support for negative axis indexing in axis-wise operations
• Comprehensive test coverage for all reduction operations (49 tests)

Added #

• TensorBuffer.toList() method for extracting tensor data as List<double>

Fixed #

• Unused import in test file (dart:math)
• Unused variable in test file
• prefer_final_locals lint warnings in test files
• dangling_library_doc_comments lint warnings in test files

Added #

• .gitignore file for Git
• .pubignore file for pub.dev publishing

Added #

• Comprehensive dartdoc comments for all public API elements
• Library-level documentation with usage examples

Added #

• Core tensor operations

  • TensorBuffer with shape, strides, and view/storage separation
  • TensorStorage for immutable typed data wrapper
  • DType enum with ONNX-compatible data types

• Transform operations

  • ResizeOp with nearest, bilinear, bicubic interpolation
  • ResizeShortestOp for aspect-ratio preserving resize
  • CenterCropOp for center cropping
  • NormalizeOp with ImageNet, CIFAR-10, symmetric presets
  • ScaleOp for value scaling
  • PermuteOp for axis reordering
  • ToTensorOp for HWC uint8 to CHW float32 conversion
  • ToImageOp for CHW float32 to HWC uint8 conversion
  • UnsqueezeOp, SqueezeOp, ReshapeOp, FlattenOp for shape manipulation
  • TypeCastOp for dtype conversion

• Pipeline system

  • TensorPipeline for chaining operations
  • PipelinePresets with ImageNet, ResNet, YOLO, CLIP, ViT, MobileNet presets
  • Async execution via Isolate.run

• Zero-copy operations

  • transpose() via stride manipulation
  • squeeze(), unsqueeze() as shape-only changes