"driver/src/driver.cpp" did not exist on "a65ef9030880d51dd159e4d23f1dc6093b17651c"
- 07 Mar, 2025 2 commits
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Lei Wang authored
* [Refactor] Update BitBLAS Benchmark with TileLang Carver Imports and Roller Hints Generation - Replace BitBLAS imports with TileLang Carver imports in benchmark_matmul.py - Modify roller hints generation using new TileLang Carver template and utility functions - Update get_roller_hints_from_func to handle None cases and improve return logic - Adjust DefaultPolicy to handle different codegen dictionary formats * [Refactor] Update Thread Binding and Import Statements in TileLang Kernels - Replace T.thread_binding() with T.get_thread_binding() across multiple kernel test files - Update import statements for MMA layout and macro generator in dequantize GEMM and FP8 examples - Move map_torch_type utility function to tilelang.utils.tensor - Remove unnecessary imports and improve code organization * Refactor Native Sparse Attention Example with Enhanced Triton Kernel - Update parallel_nsa_fwd_kernel to support more flexible sparse attention computation - Add support for block counts and offsets in the Triton kernel - Modify kernel grid and computation logic for improved performance - Update example script to use naive_nsa_simple reference implementation - Improve type hints and kernel configuration * Add Native Sparse Attention Examples with Tilelang and Triton Implementations - Introduce new example scripts for native sparse attention: * example_tilelang_nsa_fwd.py: Forward pass implementation using TileLang * example_tilelang_nsa_decode.py: Decoding-specific sparse attention implementation * example_triton_nsa_fwd.py: Triton-based sparse attention forward pass - Update reference.py with naive implementations for sparse attention - Support different sparse attention scenarios including forward pass and inference - Add comprehensive testing and validation against reference implementations * lint fix
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Lei Wang authored
* [Refactor] Update BitBLAS Benchmark with TileLang Carver Imports and Roller Hints Generation - Replace BitBLAS imports with TileLang Carver imports in benchmark_matmul.py - Modify roller hints generation using new TileLang Carver template and utility functions - Update get_roller_hints_from_func to handle None cases and improve return logic - Adjust DefaultPolicy to handle different codegen dictionary formats * [Refactor] Update Thread Binding and Import Statements in TileLang Kernels - Replace T.thread_binding() with T.get_thread_binding() across multiple kernel test files - Update import statements for MMA layout and macro generator in dequantize GEMM and FP8 examples - Move map_torch_type utility function to tilelang.utils.tensor - Remove unnecessary imports and improve code organization * Refactor Native Sparse Attention Example with Enhanced Triton Kernel - Update parallel_nsa_fwd_kernel to support more flexible sparse attention computation - Add support for block counts and offsets in the Triton kernel - Modify kernel grid and computation logic for improved performance - Update example script to use naive_nsa_simple reference implementation - Improve type hints and kernel configuration
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- 28 Feb, 2025 1 commit
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Lei Wang authored
* Add DeepSeek MLA decode example with Flash Attention implementation * Add GEMM SplitK and StreamK example implementations This commit introduces two new example scripts demonstrating advanced GEMM (matrix multiplication) techniques: - `example_tilelang_gemm_splitk.py`: Implements a Split-K GEMM kernel using TileLang - `example_tilelang_gemm_streamk.py`: Implements a Stream-K GEMM kernel using TileLang Both examples showcase different parallel computation strategies for matrix multiplication, with comprehensive testing using PyTorch reference implementations. * Refactor GEMM SplitK and StreamK example implementations Clean up and improve code formatting for the SplitK and StreamK GEMM example scripts: - Remove unused import (Profiler) in splitk example - Simplify line breaks and improve code readability - Standardize indentation and remove unnecessary whitespace - Optimize atomic add and copy operations for better clarity * Add block sparse attention benchmarks for multiple libraries This commit introduces comprehensive block sparse attention benchmarks for different libraries: - TileLang block sparse FMHA implementation - Triton block sparse FMHA implementation - PyTorch reference block sparse FMHA implementation - FlashAttention dense FMHA reference implementation The benchmarks include: - Configurable benchmark parameters (batch size, heads, sequence length, etc.) - Sparse mask generation using top-k and threshold methods - Performance measurement for different sparse attention configurations - Utility functions for mask generation and benchmarking * Refactor block sparse attention benchmarks with code style improvements - Add Ruff linter ignore comments to benchmark files - Improve code formatting and line breaks - Remove unused imports - Standardize print statement formatting - Enhance code readability across multiple library benchmarks * lint fix * Add CUDA atomic operations for BFLOAT16 and update function naming - Implement AtomicAdd functions for BFLOAT16 and BFLOAT16x2 in CUDA common header - Rename existing atomic add functions to use PascalCase (atomicAdd -> AtomicAdd) - Add a new __pack_nv_bfloat162 function for packing BFLOAT16 values - Update kernel and language customization to use new function names - Add return type annotations in profiler module * lint fix * Add example for Group Query Attention (GQA) forward pass using Flash Attention in TileLang This commit introduces a new example script `example_gqa_fwd_bshd.py` that demonstrates: - Group Query Attention (GQA) implementation - Flash Attention forward pass - Performance benchmarking - Configurable parameters for batch, heads, sequence length, and dimension - Autotuning support - Reference implementation comparison * Refactor IR lowering pipeline into modular phases This commit introduces a new module `phase.py` to modularize the IR lowering process by splitting the complex lowering pipeline into two distinct phases: - `LowerAndLegalize`: Handles initial IR legalization and transformation - `OptimizeForTarget`: Applies target-specific optimizations The changes simplify the lowering logic in multiple files by extracting the transformation steps into reusable functions, improving code readability and maintainability. * lintfix * nas kernel * Enhance Native Sparse Attention Examples with Code Improvements and Parameter Updates - Updated example_tilelang_nsa.py and example_triton_nsa.py with code formatting and style improvements - Increased default number of heads and selected blocks in TileLang NSA example - Added Ruff linter ignore comments to reference.py - Standardized function signatures and improved code readability across NSA implementations * Add utility math functions for integer operations - Implement `next_power_of_2()` to calculate the next power of 2 for an integer - Add `cdiv()` function for ceiling division of integers * Add utility math functions for integer operations - Implement `next_power_of_2()` to calculate the next power of 2 for an integer - Add `cdiv()` function for ceiling division of integers * Refactor DeepSeek MLA Decode Example with Enhanced Flash Attention Implementation - Update flash attention kernel to support positional embeddings (PE) - Modify reference implementation to handle PE and group query attention - Increase default batch size and adjust benchmarking parameters - Improve kernel performance and readability - Add einops and torch operations for more flexible tensor manipulation * Update README.md with corrected Flash MLA Decoding example path - Modify the example link for Flash MLA Decoding to point to the correct directory - Ensure accurate navigation to the DeepSeek MLA decoding example * Refactor Native Sparse Attention Kernel and Improve Utility Functions This commit introduces several improvements: - Simplified native sparse attention kernel by inlining macro functions in example_tilelang_nsa.py - Enhanced error handling in loop_partition.cc with more informative error messages - Updated print.py to support multi-dimensional buffer printing - Improved torch_assert_close in testing/__init__.py with more detailed mismatch reporting - Reduced default absolute tolerance in torch comparison from 1e-3 to 1e-2 - Added shape validation and detailed mismatch information in tensor comparison * Refactor Code Formatting and Improve Utility Functions This commit introduces several code formatting and utility improvements: - Add Ruff linter ignore comment in example_tilelang_nsa.py - Enhance code readability in loop_partition.cc and lower_tile_op.cc with improved line breaks - Simplify print_flat_buffer_with_condition in print.py - Refactor torch_assert_close in testing/__init__.py with improved line formatting
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- 25 Feb, 2025 1 commit
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Lei Wang authored
* Add DeepSeek MLA decode example with Flash Attention implementation * Add GEMM SplitK and StreamK example implementations This commit introduces two new example scripts demonstrating advanced GEMM (matrix multiplication) techniques: - `example_tilelang_gemm_splitk.py`: Implements a Split-K GEMM kernel using TileLang - `example_tilelang_gemm_streamk.py`: Implements a Stream-K GEMM kernel using TileLang Both examples showcase different parallel computation strategies for matrix multiplication, with comprehensive testing using PyTorch reference implementations. * Refactor GEMM SplitK and StreamK example implementations Clean up and improve code formatting for the SplitK and StreamK GEMM example scripts: - Remove unused import (Profiler) in splitk example - Simplify line breaks and improve code readability - Standardize indentation and remove unnecessary whitespace - Optimize atomic add and copy operations for better clarity * Add block sparse attention benchmarks for multiple libraries This commit introduces comprehensive block sparse attention benchmarks for different libraries: - TileLang block sparse FMHA implementation - Triton block sparse FMHA implementation - PyTorch reference block sparse FMHA implementation - FlashAttention dense FMHA reference implementation The benchmarks include: - Configurable benchmark parameters (batch size, heads, sequence length, etc.) - Sparse mask generation using top-k and threshold methods - Performance measurement for different sparse attention configurations - Utility functions for mask generation and benchmarking * Refactor block sparse attention benchmarks with code style improvements - Add Ruff linter ignore comments to benchmark files - Improve code formatting and line breaks - Remove unused imports - Standardize print statement formatting - Enhance code readability across multiple library benchmarks * lint fix * Add CUDA atomic operations for BFLOAT16 and update function naming - Implement AtomicAdd functions for BFLOAT16 and BFLOAT16x2 in CUDA common header - Rename existing atomic add functions to use PascalCase (atomicAdd -> AtomicAdd) - Add a new __pack_nv_bfloat162 function for packing BFLOAT16 values - Update kernel and language customization to use new function names - Add return type annotations in profiler module * lint fix * Add example for Group Query Attention (GQA) forward pass using Flash Attention in TileLang This commit introduces a new example script `example_gqa_fwd_bshd.py` that demonstrates: - Group Query Attention (GQA) implementation - Flash Attention forward pass - Performance benchmarking - Configurable parameters for batch, heads, sequence length, and dimension - Autotuning support - Reference implementation comparison * Refactor IR lowering pipeline into modular phases This commit introduces a new module `phase.py` to modularize the IR lowering process by splitting the complex lowering pipeline into two distinct phases: - `LowerAndLegalize`: Handles initial IR legalization and transformation - `OptimizeForTarget`: Applies target-specific optimizations The changes simplify the lowering logic in multiple files by extracting the transformation steps into reusable functions, improving code readability and maintainability. * lintfix * nas kernel * Enhance Native Sparse Attention Examples with Code Improvements and Parameter Updates - Updated example_tilelang_nsa.py and example_triton_nsa.py with code formatting and style improvements - Increased default number of heads and selected blocks in TileLang NSA example - Added Ruff linter ignore comments to reference.py - Standardized function signatures and improved code readability across NSA implementations * Add utility math functions for integer operations - Implement `next_power_of_2()` to calculate the next power of 2 for an integer - Add `cdiv()` function for ceiling division of integers
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