Bitnet #205
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* feat(bitnet): Initial project setup for BitNet implementation * Added bitnet development configurations for Cursor * Some metadata added * docs: add Cursor rules for BitNet implementation and update tensor.go * test: add comprehensive unit tests for tensor implementation * test: add comprehensive benchmark tests for tensor implementation * test: fix type mismatches in tensor tests; chore: automate benchmarks script; chore: update .gitignore for profiles and generated files * docs: update PR update rules for BitNet branch * refactor: improve tensor implementation and memory management * docs: add benchmark testing rules for BitNet project * docs: add performance threshold rules for BitNet benchmarks * docs: add TDD and unit testing rules for BitNet project * docs: add environment and best practices documentation * docs: reorganize and enhance Cursor rules for better organization and clarity * test: add comprehensive test coverage and benchmarks --------- Co-authored-by: Jaakko Heusala <jhh@hg.fi>
* chore: update gitignore for generated files and profiles * chore: remove generated benchmark results and PR template files * Remove benchmark_results.txt and pr_description.md from git as per review. Add run_benchmarks.sh script. Resolve merge conflict in bitnet-development-process.mdc. --------- Co-authored-by: Jaakko Heusala <jhh@hg.fi>
…#197) * chore: add BitNet model files to .gitignore * feat: Add model loader for BitNet weights * fix: Improve model loader path handling and test robustness * feat: Fix model file embedding and add tests * Improved cursor rules * feat(bitnet/model): implement model weights and tokenizer integration with tests and benchmarks (issue #172) * Normalized MDC files to ANSI compatible format * Added a rule for go * Normalized as ansi * perf(bitnet/model): optimize model loading with memory pooling and benchmarks * test: add model loader streaming tests and integration tests * Added a script for task prompting * feat(bitnet/model): implement model weights and tokenizer embedding * feat(bitnet/model): add embedded model files * fix(bitnet/model): handle binary model file format * docs: improve task prompt formatting and clarity * Fixed prompt generator * feat(scripts): add PR number support to task prompt script * Update model file paths and download script for BitNet b1.58-2B-4T * refactor: update model file path to use GGUF format * Updated cursor configurations * Update model paths and download script to use GGUF format * Add tokenizer support and update model loader for GGUF format * Added pr review prompt * Fixed typo * refactor: improve error handling and model file paths - Remove duplicate embedded files - Update model paths to use correct location - Replace fmt.Errorf with static error variables - Simplify error handling in loader, tokenizer, and model * Improved prompt generator * Improved the script * refactor: improve error handling and remove duplicate model files * fix(bitnet): robust model loader path handling and chunk pool buffer management -- use absolute paths, fix chunk pool, improve loader tests and error handling * fix(bitnet): correct tokenizer file loading and improve test coverage -- loads from correct path, covers unknown words, decoding, special tokens, removes BPE fallback for unknown words * chore(bitnet): remove obsolete model and tokenizer files from pkg/bitnet/model -- all logic now lives in internal/model * Added a new prompt generator script * fix(bitnet): address all PR review comments for #172 (static errors, fs dependency, unified model/tokenizer loading, test/bench isolation) * Address all review comments for issue #172 * Address all review comments for issue #172 * Improved prompt script * Address all review comments for issue #172 * Update .cursor/rules/bitnet-benchmarks.mdc Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Address all review comments for issue #172 * Fix TestTokenize/unknown_word and add tests for math/ops.go and config/config.go --------- Co-authored-by: Jaakko Heusala <jhh@hg.fi> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* feat(bitnet): implement core model structures and weight loading * Added a rule to avoid global states * Fixed broken cursor rules * Address all review comments for issue #173 * Address all review comments for issue #173 * Removed static content from PR description * Address all review comments for issue #173 * feat(bitnet): implement model structures and weight loading for issue #173 * feat(bitnet): implement model structures and weight loading for issue #173 * Improved normalize script * Added a cursor rule for performance optimizations * fix(bitnet): address PR review feedback and align ternary weights test for issue #173 * Address all review comments for issue #173 * refactor: remove locks and use goroutines in BitNet model * Added rule about TODO comments * docs: add issue numbers to TODO comments in model.go * Normalized rules * feat: update PR description script with BitNet model benchmarks * Added new character * fix: correct dimension mismatch in feedForward function * Improved the benchmark script * Updated task script --------- Co-authored-by: Jaakko Heusala <jhh@hg.fi>
## Test Coverage - Current coverage: 82.9% - Coverage changes: <previous> → 82.9% ## Performance Metrics ### Memory Usage #### Tensor Operations - Allocations per operation: - New tensor creation: 120 allocs/op - Get/Set operations: 0 allocs/op - Parallel operations: 160749 allocs/op #### BitNet Model Operations - Allocations per operation: - Model weights loading: N/A allocs/op (TODO #178) - Model inference: N/A allocs/op (TODO #190) - Ternary weights reading: N/A allocs/op (TODO #178) ### CPU Performance #### Tensor Operations - Operation timing: - Basic operations: 11.84 ns/op - Parallel operations: 94679 ns/op - Large tensor operations: 1041 ns/op #### BitNet Model Operations - Operation timing: - Model weights loading: N/A ns/op (TODO #178) - Model inference: N/A ns/op (TODO #190) - Ternary weights reading: N/A ns/op (TODO #178) ## Areas for Improvement ### High Priority - [ ] Optimize memory allocations in model operations (TODO #191) - [ ] Implement proper self-attention (TODO #186) ### Medium Priority - [ ] Improve error handling in model operations (TODO #192) - [ ] Add more comprehensive benchmarks (TODO #192) - [ ] Enhance documentation - [ ] Implement proper feed-forward network (TODO #187) ### Low Priority - [ ] Consider SIMD optimizations (TODO #191) - [ ] Add more model operations (TODO #190) - [ ] Improve test organization (TODO #192) - [ ] Implement proper output generation (TODO #189) --------- Co-authored-by: Jaakko Heusala <jhh@hg.fi>
## Changes - refactor(scripts): improve PR description generation robustness - feat(scripts): add issue closing reference to PR template - feat(scripts): add git history coverage tracking - chore(scripts): update PR template to close ## Test Coverage - Current coverage: 82.9% - Coverage changes: 82.9% → 82.9% ## Performance Metrics ### Memory Usage #### Tensor Operations - Allocations per operation: - New tensor creation: 120 allocs/op - Get/Set operations: 0 allocs/op - Parallel operations: 160749 allocs/op #### BitNet Model Operations - Allocations per operation: - Model weights loading: N/A allocs/op (TODO #178) - Model inference: N/A allocs/op (TODO #190) - Ternary weights reading: N/A allocs/op (TODO #178) ### CPU Performance #### Tensor Operations - Operation timing: - Basic operations: 11.93 ns/op - Parallel operations: 94913 ns/op - Large tensor operations: 1075 ns/op #### BitNet Model Operations - Operation timing: - Model weights loading: N/A ns/op (TODO #178) - Model inference: N/A ns/op (TODO #190) - Ternary weights reading: N/A ns/op (TODO #178) ## Areas for Improvement ### High Priority - [ ] Optimize memory allocations in model operations (TODO #191) - [ ] Implement proper self-attention (TODO #186) ### Medium Priority - [ ] Improve error handling in model operations (TODO #192) - [ ] Add more comprehensive benchmarks (TODO #192) - [ ] Enhance documentation - [ ] Implement proper feed-forward network (TODO #187) ### Low Priority - [ ] Consider SIMD optimizations (TODO #191) - [ ] Add more model operations (TODO #190) - [ ] Improve test organization (TODO #192) - [ ] Implement proper output generation (TODO #189) Closes #201 --------- Co-authored-by: Jaakko Heusala <jhh@hg.fi>
## Changes - [x] Implement token embedding layer with ternary weights - [x] Add comprehensive test coverage for embedding layer - [x] Fix ternary weight unpacking test cases - [x] Add memory usage tests for embedding layer - [x] Add performance benchmarks for embedding layer File changes: - `pkg/bitnet/model/model.go`: Added embedding layer implementation - `pkg/bitnet/model/model_test.go`: Added comprehensive tests and benchmarks ## Test Coverage - Current coverage: 83.0% - Coverage changes: 82.9% → 83.0% ## Performance Metrics ### Memory Usage #### Tensor Operations - Allocations per operation: - New tensor creation: 120 allocs/op - Get/Set operations: 0 allocs/op - Parallel operations: 160750 allocs/op #### BitNet Model Operations - Allocations per operation: - Model weights loading: N/A allocs/op (TODO #178) - Model inference: N/A allocs/op (TODO #190) - Ternary weights reading: N/A allocs/op (TODO #178) ### CPU Performance #### Tensor Operations - Operation timing: - Basic operations: 11.98 ns/op - Parallel operations: 94327 ns/op - Large tensor operations: 1065 ns/op #### BitNet Model Operations - Operation timing: - Model weights loading: N/A ns/op (TODO #178) - Model inference: N/A ns/op (TODO #190) - Ternary weights reading: N/A ns/op (TODO #178) ## Areas for Improvement ### High Priority - [ ] Optimize memory allocations in model operations (TODO #191) - [ ] Implement proper self-attention (TODO #186) ### Medium Priority - [ ] Improve error handling in model operations (TODO #192) - [ ] Add more comprehensive benchmarks (TODO #192) - [ ] Enhance documentation - [ ] Implement proper feed-forward network (TODO #187) ### Low Priority - [ ] Consider SIMD optimizations (TODO #191) - [ ] Add more model operations (TODO #190) - [ ] Improve test organization (TODO #192) - [ ] Implement proper output generation (TODO #189) Closes #175 --------- Co-authored-by: Jaakko Heusala <jhh@hg.fi>
## Changes - Updated model architecture constants in `pkg/bitnet/internal/config/config.go` to match BitNet b1.58-2B specification: - Set `HiddenSize` to 2560 - Set `IntermediateSize` to 6912 - Set `NumHiddenLayers` to 30 - Set `NumAttentionHeads` to 20 - Set `NumKeyValueHeads` to 5 - Set `MaxPositionEmbeddings` to 4096 - Added `HiddenAct` as "relu2" for squared ReLU activation - Added `NormType` as "rms" for RMS normalization - Added `RMSNormEps` as 1e-6 for RMS normalization epsilon ## Test Coverage - Current coverage: 83.0% - Coverage changes: 83.0% → 83.0% ## Performance Metrics ### Memory Usage #### Tensor Operations - Allocations per operation: - New tensor creation: 120 allocs/op - Get/Set operations: 0 allocs/op - Parallel operations: 160749 allocs/op #### BitNet Model Operations - Allocations per operation: - Model weights loading: N/A allocs/op (TODO #178) - Model inference: N/A allocs/op (TODO #190) - Ternary weights reading: N/A allocs/op (TODO #178) ### CPU Performance #### Tensor Operations - Operation timing: - Basic operations: 11.87 ns/op - Parallel operations: 97795 ns/op - Large tensor operations: 1068 ns/op #### BitNet Model Operations - Operation timing: - Model weights loading: N/A ns/op (TODO #178) - Model inference: N/A ns/op (TODO #190) - Ternary weights reading: N/A ns/op (TODO #178) ## Areas for Improvement ### High Priority - [ ] Optimize memory allocations in model operations (TODO #191) - [ ] Implement proper self-attention (TODO #186) ### Medium Priority - [ ] Improve error handling in model operations (TODO #192) - [ ] Add more comprehensive benchmarks (TODO #192) - [ ] Enhance documentation - [ ] Implement proper feed-forward network (TODO #187) ### Low Priority - [ ] Consider SIMD optimizations (TODO #191) - [ ] Add more model operations (TODO #190) - [ ] Improve test organization (TODO #192) - [ ] Implement proper output generation (TODO #189) Closes #176 Co-authored-by: Jaakko Heusala <jhh@hg.fi>
Co-authored-by: Jaakko Heusala <jhh@hg.fi>
## Test Coverage - Current coverage: 84.1% - Coverage changes: 83.0% → 84.1% ## Performance Metrics ### Memory Usage #### Tensor Operations - Allocations per operation: - New tensor creation: 2 allocs/op (100, 100x100, 50x50x50, 20x20x20x20) - Get/Set operations: 0 allocs/op (2D access) - Parallel operations: 10,022 allocs/op (100x100), 1,000,023 allocs/op (1000x1000) #### BitNet Model Operations - Allocations per operation: - Model weights loading: 22 allocs/op (small), 22 allocs/op (medium), 22 allocs/op (large) - Model inference: N/A allocs/op (TODO #190) - Ternary weights reading: 1 allocs/op (small), 1 allocs/op (medium), 1 allocs/op (large) ### CPU Performance #### Tensor Operations - Operation timing: - Basic operations: 11.8 ns/op (Get), 10.7 ns/op (Set) - Parallel operations: 93,694 ns/op (100x100), 6,507,018 ns/op (1000x1000) - Large tensor operations: 1,238 ns/op (NewTensor 100x100), 13,093 ns/op (NewTensor 50x50x50), 16,230 ns/op (NewTensor 20x20x20x20) #### BitNet Model Operations - Operation timing: - Model weights loading: 417,682 ns/op (small), 1,578,995 ns/op (medium), 5,973,249 ns/op (large) - Model inference: N/A ns/op (TODO #190) - Ternary weights reading: 30,791 ns/op (small), 124,614 ns/op (medium), 216,101 ns/op (large) ## Areas for Improvement ### High Priority - [ ] Optimize memory allocations in model operations (TODO #191) - [ ] Implement proper self-attention (TODO #186) ### Medium Priority - [ ] Improve error handling in model operations (TODO #192) - [ ] Add more comprehensive benchmarks (TODO #192) - [ ] Enhance documentation - [ ] Implement proper feed-forward network (TODO #187) ### Low Priority - [ ] Consider SIMD optimizations (TODO #191) - [ ] Add more model operations (TODO #190) - [ ] Improve test organization (TODO #192) - [ ] Implement proper output generation (TODO #189) Closes #178 --------- Co-authored-by: Jaakko Heusala <jhh@hg.fi>
## Test Coverage - Current coverage: 85.0% - Coverage changes: 84.1% → 85.0% ## Performance Metrics ### Memory Usage #### Tensor Operations - Allocations per operation: - New tensor creation: 120 allocs/op - Get/Set operations: 0 allocs/op - Parallel operations: 160749 allocs/op - BitLinear operations: 2101765 allocs/op #### BitNet Model Operations - Allocations per operation: - Model weights loading: 1289985000 allocs/op - Model inference: N/A (TODO #190) allocs/op (TODO #190) - Ternary weights reading: 48 allocs/op ### CPU Performance #### Tensor Operations - Operation timing: - Basic operations: 11.92 ns/op - Parallel operations: 95819 ns/op - Large tensor operations: 1282 ns/op - BitLinear operations: 1574935 ns/op #### BitNet Model Operations - Operation timing: - Model weights loading: 1349778167 ns/op - Model inference: BenchmarkModel_Infer ns/op (TODO #190) - Ternary weights reading: 3865 ns/op ## Areas for Improvement ### High Priority - [ ] Optimize memory allocations in model operations (TODO #191) - [ ] Implement proper self-attention (TODO #186) ### Medium Priority - [ ] Improve error handling in model operations (TODO #192) - [ ] Add more comprehensive benchmarks (TODO #192) - [ ] Enhance documentation - [ ] Implement proper feed-forward network (TODO #187) ### Low Priority - [ ] Consider SIMD optimizations (TODO #191) - [ ] Add more model operations (TODO #190) - [ ] Improve test organization (TODO #192) - [ ] Implement proper output generation (TODO #189) Closes #179 --------- Co-authored-by: Jaakko Heusala <jhh@hg.fi>
## Changes - Implemented squared ReLU activation (ReLU²) in `pkg/bitnet/internal/math/relu2.go` - Added comprehensive tests in `pkg/bitnet/internal/math/relu2_test.go` - Fixed RoPE benchmark test to use valid sequence positions - Added parallel processing for both single vector and batch operations ## Test Coverage - Current coverage: 85.8% - Coverage changes: 85.0% → 85.8% ## Performance Metrics ### Memory Usage #### Activation Functions - Allocations per operation: - ReLU² (single vector): 25 allocs/op - ReLU² (batch): 857 allocs/op ### CPU Performance #### Activation Functions - Operation timing: - ReLU² (single vector): 6.05 µs/op - ReLU² (batch): 87.77 µs/op ## Areas for Improvement ### High Priority - [ ] Optimize memory allocations in model operations (TODO #191) - [ ] Implement proper self-attention (TODO #186) ### Medium Priority - [ ] Improve error handling in model operations (TODO #192) - [ ] Add more comprehensive benchmarks (TODO #192) - [ ] Enhance documentation - [ ] Implement proper feed-forward network (TODO #187) ### Low Priority - [ ] Consider SIMD optimizations (TODO #191) - [ ] Add more model operations (TODO #190) - [ ] Improve test organization (TODO #192) - [ ] Implement proper output generation (TODO #189) Closes #180 Co-authored-by: Jaakko Heusala <jhh@hg.fi>
## Test Coverage - Current coverage: 86.3% - Coverage changes: 85.8% → 86.3% ## Performance Metrics ### Memory Usage #### Tensor Operations - Allocations per operation: - New tensor creation: 120 allocs/op - Get/Set operations: 0 allocs/op - Parallel operations: 160749 allocs/op - BitLinear operations: 3584 allocs/op #### BitNet Model Operations - Allocations per operation: - Model weights loading: 1289985000 allocs/op - Model inference: N/A (TODO #190) allocs/op (TODO #190) - Ternary weights reading: 48 allocs/op ### CPU Performance #### Tensor Operations - Operation timing: - Basic operations: 11.67 ns/op - Parallel operations: 96403 ns/op - Large tensor operations: 1316 ns/op - BitLinear operations: 24172494 ns/op #### BitNet Model Operations - Operation timing: - Model weights loading: 1469523708 ns/op - Model inference: BenchmarkModel_Infer ns/op (TODO #190) - Ternary weights reading: 3837 ns/op ## Areas for Improvement ### High Priority - [ ] Optimize memory allocations in model operations (TODO #191) - [ ] Implement proper self-attention (TODO #186) ### Medium Priority - [ ] Improve error handling in model operations (TODO #192) - [ ] Add more comprehensive benchmarks (TODO #192) - [ ] Enhance documentation - [ ] Implement proper feed-forward network (TODO #187) ### Low Priority - [ ] Consider SIMD optimizations (TODO #191) - [ ] Add more model operations (TODO #190) - [ ] Improve test organization (TODO #192) - [ ] Implement proper output generation (TODO #189) Closes #181 --------- Co-authored-by: Jaakko Heusala <jhh@hg.fi>
## Test Coverage - Current coverage: 87.1% - Coverage changes: 86.3% → 87.1% ## Performance Metrics ### Memory Usage #### Tensor Operations - Allocations per operation: - New tensor creation: 120 allocs/op - Get/Set operations: 0 allocs/op - Parallel operations: 160748 allocs/op - BitLinear operations: 3548 allocs/op #### BitNet Model Operations - Allocations per operation: - Model weights loading: 1289985000 allocs/op - Model inference: N/A (TODO #190) allocs/op (TODO #190) - Ternary weights reading: 48 allocs/op ### CPU Performance #### Tensor Operations - Operation timing: - Basic operations: 12.86 ns/op - Parallel operations: 97222 ns/op - Large tensor operations: 1287 ns/op - BitLinear operations: 24284088 ns/op #### BitNet Model Operations - Operation timing: - Model weights loading: 1228637000 ns/op - Model inference: BenchmarkModel_Infer ns/op (TODO #190) - Ternary weights reading: 3840 ns/op ## Areas for Improvement ### High Priority - [ ] Optimize memory allocations in model operations (TODO #191) - [ ] Implement proper self-attention (TODO #186) ### Medium Priority - [ ] Improve error handling in model operations (TODO #192) - [ ] Add more comprehensive benchmarks (TODO #192) - [ ] Enhance documentation - [ ] Implement proper feed-forward network (TODO #187) ### Low Priority - [ ] Consider SIMD optimizations (TODO #191) - [ ] Add more model operations (TODO #190) - [ ] Improve test organization (TODO #192) - [ ] Implement proper output generation (TODO #189) Closes #182 --------- Co-authored-by: Jaakko Heusala <jhh@hg.fi>
## Changes - Optimized attention weights multiplication with values in `pkg/bitnet/internal/math/attention.go` - Added SIMD-like optimizations by processing 4 elements at a time for better cache utilization - Improved memory efficiency by reducing allocations in the attention computation - Added helper functions for branchless clamping to int8 range - Maintained higher precision (float32) for accumulation to avoid precision loss ## Test Coverage - Current coverage: 87.3% - Coverage changes: 87.1% → 87.3% ## Performance Metrics ### Memory Usage #### Tensor Operations - Allocations per operation: - New tensor creation: 120 allocs/op - Get/Set operations: 0 allocs/op - Parallel operations: 160749 allocs/op - BitLinear operations: 3255 allocs/op #### BitNet Model Operations - Allocations per operation: - Model weights loading: 1289985000 allocs/op - Model inference: N/A (TODO #190) allocs/op (TODO #190) - Ternary weights reading: 48 allocs/op ### CPU Performance #### Tensor Operations - Operation timing: - Basic operations: 11.70 ns/op - Parallel operations: 93464 ns/op - Large tensor operations: 1217 ns/op - BitLinear operations: 24815662 ns/op #### BitNet Model Operations - Operation timing: - Model weights loading: 1168159333 ns/op - Model inference: BenchmarkModel_Infer ns/op (TODO #190) - Ternary weights reading: 3828 ns/op ## Areas for Improvement ### High Priority - [ ] Optimize memory allocations in model operations (TODO #191) - [ ] Implement proper self-attention (TODO #186) ### Medium Priority - [ ] Improve error handling in model operations (TODO #192) - [ ] Add more comprehensive benchmarks (TODO #192) - [ ] Enhance documentation - [ ] Implement proper feed-forward network (TODO #187) ### Low Priority - [ ] Consider SIMD optimizations (TODO #191) - [ ] Add more model operations (TODO #190) - [ ] Improve test organization (TODO #192) - [ ] Implement proper output generation (TODO #189) Closes #183 --------- Co-authored-by: Jaakko Heusala <jhh@hg.fi>
## Test Coverage - Current coverage: 87.5% - Coverage changes: 87.3% → 87.5% ## Performance Metrics ### Memory Usage #### Tensor Operations - Allocations per operation: - New tensor creation: 120 allocs/op - Get/Set operations: 0 allocs/op - Parallel operations: 160750 allocs/op - BitLinear operations: 3290 allocs/op #### BitNet Model Operations - Allocations per operation: - Model weights loading: 1289985000 allocs/op - Model inference: N/A (TODO #190) allocs/op (TODO #190) - Ternary weights reading: 48 allocs/op ### CPU Performance #### Tensor Operations - Operation timing: - Basic operations: 11.80 ns/op - Parallel operations: 95489 ns/op - Large tensor operations: 1265 ns/op - BitLinear operations: 24563123 ns/op #### BitNet Model Operations - Operation timing: - Model weights loading: 1739052667 ns/op - Model inference: BenchmarkModel_Infer ns/op (TODO #190) - Ternary weights reading: 3814 ns/op ## Areas for Improvement ### High Priority - [ ] Optimize memory allocations in model operations (TODO #191) - [ ] Implement proper self-attention (TODO #186) ### Medium Priority - [ ] Improve error handling in model operations (TODO #192) - [ ] Add more comprehensive benchmarks (TODO #192) - [ ] Enhance documentation - [ ] Implement proper feed-forward network (TODO #187) ### Low Priority - [ ] Consider SIMD optimizations (TODO #191) - [ ] Add more model operations (TODO #190) - [ ] Improve test organization (TODO #192) - [ ] Implement proper output generation (TODO #189) Closes #184 --------- Co-authored-by: Jaakko Heusala <jhh@hg.fi>
## Test Coverage - Current coverage: 87.8% - Coverage changes: 87.5% → 87.8% ## Areas for Improvement ### High Priority - [ ] Optimize memory allocations in model operations (TODO #191) - [ ] Implement proper self-attention (TODO #186) ### Medium Priority - [ ] Improve error handling in model operations (TODO #192) - [ ] Add more comprehensive benchmarks (TODO #192) - [ ] Enhance documentation - [ ] Implement proper feed-forward network (TODO #187) ### Low Priority - [ ] Consider SIMD optimizations (TODO #191) - [ ] Add more model operations (TODO #190) - [ ] Improve test organization (TODO #192) - [ ] Implement proper output generation (TODO #189) Closes #185 --------- Co-authored-by: Jaakko Heusala <jhh@hg.fi>
## Changes - Implemented attention sublayer with pre-norm and residual connections in `pkg/bitnet/internal/math/attention_sublayer.go` - Added comprehensive test suite in `pkg/bitnet/internal/math/attention_sublayer_test.go` - Implemented parallel processing using goroutines for efficient computation - Added proper quantization handling with int8 clamping - Added benchmarks for different tensor sizes and configurations ## Test Coverage - Current coverage: 88.4% - Coverage changes: 87.8% → 88.4% ## Areas for Improvement ### High Priority - [ ] Optimize memory allocations in model operations (TODO #191) - [ ] Implement proper feed-forward network (TODO #187) ### Medium Priority - [ ] Improve error handling in model operations (TODO #192) - [ ] Add more comprehensive benchmarks (TODO #192) - [ ] Enhance documentation ### Low Priority - [ ] Consider SIMD optimizations (TODO #191) - [ ] Add more model operations (TODO #190) - [ ] Improve test organization (TODO #192) - [ ] Implement proper output generation (TODO #189) Closes #186 --------- Co-authored-by: Jaakko Heusala <jhh@hg.fi>
…ual connections (#215) ## Changes - [ ] List of specific changes made - [ ] Include file paths and line numbers for major changes - [ ] Reference related issues/tickets ## Test Coverage - Current coverage: 88.6% - Coverage changes: 88.4% → 88.6% ## Areas for Improvement ### High Priority - [ ] Optimize memory allocations in model operations (TODO #191) ### Medium Priority - [ ] Improve error handling in model operations (TODO #192) - [ ] Add more comprehensive benchmarks (TODO #192) - [ ] Enhance documentation ### Low Priority - [ ] Consider SIMD optimizations (TODO #191) - [ ] Add more model operations (TODO #190) - [ ] Improve test organization (TODO #192) - [ ] Implement proper output generation (TODO #189) Closes #187 --------- Co-authored-by: Jaakko Heusala <jhh@hg.fi>
## Changes - Implemented transformer block stacking functionality in the BitNet model - Added final normalization layer with proper weight handling - Enhanced tensor operations with improved thread safety and memory management - Added comprehensive documentation for tensor package - Improved error handling and validation in tensor operations - Added new tensor operations: Transpose, Repeat, and Add - Optimized memory allocations and parallel processing in BitLinear operation - Added debug logging for better observability ## Test Coverage - Current coverage: 88.9% - Coverage changes: 88.6% → 88.9% ## Areas for Improvement ### High Priority - [ ] Optimize memory allocations in model operations (TODO #191) - [ ] Implement proper output projection and token prediction (TODO #189) ### Medium Priority - [ ] Improve error handling in model operations (TODO #192) - [ ] Add more comprehensive benchmarks (TODO #192) - [ ] Enhance documentation for model operations ### Low Priority - [ ] Consider SIMD optimizations (TODO #191) - [ ] Add more model operations (TODO #190) - [ ] Improve test organization (TODO #192) ## Implementation Details - Added final normalization layer with proper weight conversion from int8 to float32 - Enhanced tensor operations with improved thread safety using mutex locks - Implemented efficient parallel processing in BitLinear operation - Added comprehensive documentation for tensor package and its operations - Improved error handling with proper validation and panic messages - Added new tensor operations for better flexibility in model operations Closes #188 --------- Co-authored-by: Jaakko Heusala <jhh@hg.fi>
## Changes - Implemented final output layer (LM Head) in `pkg/bitnet/internal/math/lm_head.go` - Enhanced error handling in `BitLinear` operations with proper error types - Added thread-safe tensor operations with atomic flags and proper locking - Improved memory management with proper cleanup in tensor operations - Added new error types in `pkg/bitnet/tensor/errors.go` for better error handling Key implementation details: - Uses transposed embedding weights for the output layer (weight tying) - Produces logits for each token in the vocabulary (128k tokens) - Handles 8-bit input activations and ternary weights - No bias used in the output layer as per BitNet specification - Memory-efficient implementation with proper cleanup ## Test Coverage - Current coverage: 88.4% - Coverage changes: 88.9% → 88.4% ## Areas for Improvement ### High Priority - [ ] Optimize memory allocations in model operations (TODO #191) ### Medium Priority - [ ] Improve error handling in model operations (TODO #192) - [ ] Add more comprehensive benchmarks (TODO #192) - [ ] Enhance documentation ### Low Priority - [ ] Consider SIMD optimizations (TODO #191) - [ ] Add more model operations (TODO #190) - [ ] Improve test organization (TODO #192) Closes #189 --------- Co-authored-by: Jaakko Heusala <jhh@hg.fi>
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See full roadmap at #170