[Example] Update GEMM FP8 Example #123
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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.
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
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
- 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
- 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
…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
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.
…arameter 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
- Implement `next_power_of_2()` to calculate the next power of 2 for an integer - Add `cdiv()` function for ceiling division of integers
- Implement `next_power_of_2()` to calculate the next power of 2 for an integer - Add `cdiv()` function for ceiling division of integers
…plementation - 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
- Modify the example link for Flash MLA Decoding to point to the correct directory - Ensure accurate navigation to the DeepSeek MLA decoding example
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This pull request introduces a new example script for performing GEMM (General Matrix Multiply) operations using the TileLang library in Python. The script includes functions for setting up swizzle layouts, defining matrix multiplication kernels, and testing the correctness of the implementation with different data types.
Key changes include:
examples/gemm_fp8/example_tilelang_gemm.py: Added a comprehensive example script that demonstrates how to perform GEMM operations using TileLang, including functions for setting up swizzle layouts, defining matrix multiplication kernels, and validating the results.