GitLab

* Add TMA lowering configuration option and update copyright notices

This commit introduces a new configuration option to disable TMA (Tensor Memory Access) lowering and updates copyright notices across multiple files. Key changes include:

- Add `kDisableTMALower` configuration option in builtin.h and builtin.cc
- Update copyright notices from Microsoft Corporation to Tile-AI Corporation
- Modify `LowerArgs` struct to include `disable_tma_lower` flag
- Update JIT compilation interfaces to support pass configuration
- Enhance error reporting in bulk copy lowering
- Propagate pass configuration through various adapter layers

* lint fix

* 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

* README.md fixed

* update test ci

* Lint and Typo Fix

* Clang Format Lint Fix

* Add format.sh script for code formatting and linting

* docs update

* center align the title

* lint fix

* add ignore

* Add .gitignore for 3rdparty directory

* Add requirements-dev.txt, requirements-test.txt, and requirements.txt

* 3rdparty

* Add gemm.h, CMakeLists.txt, _ffi_api.py, __init__.py, runtime.h, reduce.h, loop_partition.h, utils.h, and loop_vectorize.h

* Refactor CMakeLists.txt and include statements

- Update CMakeLists.txt to use a newer version of CMake and add project name
- Remove unnecessary include directories

Fix include paths in layout.cc, codegen.cc, codegen.h, rt_mod.cc, frontend_legalize.cc, inject_pipeline.cc, layout_inference.cc, loop_vectorize.cc, and lower_tile_op.cc

- Update include paths to use relative paths instead of absolute paths

* Update submodule for 3rdparty/tvm

* update

* load dll first

* Refactor CMakeLists.txt and include statements

* Refactor CMakeLists.txt and include statements

* git keep update

* Refactor CMakeLists.txt and include statements

* Refactor CMakeLists.txt and include statements

* refactor code structure

* Update Readme

* CMakeLists Customized

* update readme

* update README

* update readme

* update usage

* with TVM_IMPORT_PYTHON_PATH to handle own tvm build python import

* annotate lower transform global func with `transform` prefix

* Migrate Simplify Pass from tilelang tvm branch

* enhance system environment handling with __init__ and CMake

* Initial commit

* CODE_OF_CONDUCT.md committed

* LICENSE committed

* README.md committed

* SECURITY.md committed

* SUPPORT.md committed

* CODE_OF_CONDUCT Commit

* LICENSE Commit

* SECURITY Commit

* SUPPORT Commit

* Modify Support

* Update README.md

* security ci update

* remove examples

* Update and implement clang-format

* add composable kernel components

* Migrate from latest update

* submodule update

* Test update

* Update License

* Spell check

* lint fix

* add clang-tidy to apply static analysis for c source

* update tilelang examples

* Update Install Docs

* Refactor filetree

* Enhance Install

* conflict resloved

* annotate_version

* Initial Update

* test fix

* install

* Implement setup.py

* lint fix

* Separate Init

* Separate test

* docker file commit

* add logo

* Update Readme and Examples

* update readme

* update logo

* Implement AMD Installation

* Add License

* Update AMD MI300x Benchmark

* update README

* update mi300 benchmark scripts

* update ignore

* enhance build scirpt

* update image

* enhance setup.py to remove duplicated libraries

* remove debug files

* update readme

* update image

* update gemm examples

* update flashattention README

* readme update

* add cmake into requirements

* libinfo fix

* auto update submodule

* lint fix

* Fix AMD Build and Test

* Update check for transpose attribute for CDNA Arch

* typo fix for amd

* Implement Matmul Benchmark

* Refactor Code

* [TypoFix] Fix GEMM Example

* [Docs] Init Linear Attention README

* [TYPO] Typo fix

* [Lint] Lint Fix

* enhance example with intrinsics

* [Enhancement] Improve Buffer Collection during IR Parser

* [Dev] Introduce Current classmethod to get current frame

* submodule update

* fake test pass update

* support thread_extent_api

* code optimize

* Add GEMM function implementation for matrix multiplication

* Update logging format to reflect TileLang in logger messages

* Refactor CMakeLists.txt for improved readability and set default build type to Release

* Support Gemm SS Primitives Implementation

* [README] Upload Tile Language Logo (#5)

* update logo

* Update README.md to enhance formatting and center the title

---------
Co-authored-by: microsoft-github-operations[bot] <55726097+microsoft-github-operations[bot]@users.noreply.github.com>
Co-authored-by: Microsoft Open Source <microsoftopensource@users.noreply.github.com>
Co-authored-by: Yu Cheng <yu.cheng@pku.edu.cn>