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6873e19da2b267d3cb7d0305561ba0bed63feae3
10
Commits
| Author | SHA1 | Message | Date | |
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 Liu YunuoandLeiWang1999
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b9a5907a20 |
[Example] Migrate eligible examples to eager style (#2010)
* [Example] Migrate eligible examples to eager style * fix wrong shape annotation in gemm_split_k examples * fix incorrect dtype in gemm_streamk example * fix * revert some changes * Refactor attention functions to use new parameter structures and improve readability. Updated `example_amd_flash_attn_bwd.py` and `example_amd_flash_attn_fwd.py` to define input parameters more clearly. Enhanced shared memory usage in `flashattn_fwd` and `flashattn_sink` functions across various examples, ensuring compatibility with new configurations. Adjusted kernel definitions and added necessary checks for GPU capabilities. * lint fix * FIX --------- Co-authored-by: LeiWang1999 <leiwang1999@outlook.com> |
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Lei Wang
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9c95a42dfb |
[Refactor] Phaseout legacy util map_torch_type with T.dtype.as_torch (#2075)
* Remove redundant `map_torch_type` in favor of `T.dtype.as_torch()` `map_torch_type` was a standalone function that converted dtype strings or TVM DataType objects to `torch.dtype`. This was redundant since `T.dtype.as_torch()` (and `T.dtype(x).as_torch()`) already provides the same functionality with better handling (HIP/CUDA awareness, proper dtype lookup tables, etc.). Changes: - Remove `map_torch_type` from `tilelang/utils/tensor.py` and its re-export - Make `T.dtype()` idempotent for existing dtype objects (pass-through) - Add backward-compat for `"e4m3fnuz_float8"` TVM internal name in `as_torch()` - Replace all usages across production code, tests, and examples - Rebuild Cython extension to reflect the import change * Refactor output and accumulation data types in GEMM FP8 example Updated the `out_dtype` and `accum_dtype` variables in the `example_tilelang_gemm_fp8_intrinsic.py` file to use `T.float32` instead of string literals. This change enhances type consistency and aligns with recent updates in type handling. |
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Lei WangandZhiwen Mo
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f8dc61c02c |
test: reduce CI runtime for slow Python suites (#1932)
* test: trim CI runtime for slow Python suites * remove useless test * Update TVM submodule to latest commit and remove manual device assert test from CI. Replace with a call to tilelang.testing.main(). * lint fix * fix * revert test for eager jit --------- Co-authored-by: Zhiwen Mo <zm125@ic.ac.uk> |
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Gongen-Ali
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4e53b50750 |
[Bugfix] Open Rocm ci test and fix some bugs. (#1443)
* Fix some bugs on ci and open rocm ci test * fix pre-commit * fix build issue on hip * refactor on hip * pre-commit fix * fix test with pdl * fix pdl test |
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9fd6ea7d3b |
[Feat] PDL Support (#1494)
* [Feat] PDL Support * remove unused comments/codes * fix mismatch funcname * Resolve duplicate config issue for AI review * fix typo error && clang-tidy error * chore: retrigger CI * change file comment * rename test_tilelang_jit_ctypes.py to test_tilelang_jit_cython.py * remove ctype backend * Move pdl attributes to header and remove redundant VisitStmt_ * chore: trigger CI * modify the cuda kernel with pdl due to nvcc's bug * throw an error when invoking __ldg with pdl due to nvcc's bug * Throw an error when pdl is not supported * fix checking about pdl * add comments about MarkCudaSyncCalls * remove pdl support with compute_capability<90 * ruff check * fix nvrtc about pdl * update tvm & extend pdl support for tvm_ffi & add test for tvm_ffi * ensure that kUseDynamicSharedMemoryTag is the last tag in launch_param_tags * fix bug introduced by branch merging * [Feature] Add get_host_source method to CythonKernelAdapter * Introduced a new method, get_host_source, in the CythonKernelAdapter class to return the source code of the host function. This addition enhances the functionality of the adapter by providing direct access to the host kernel source. --------- Co-authored-by: silentCoder-dev <silentcoder@foxmail.com> Co-authored-by: LeiWang1999 <leiwang1999@outlook.com> |
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Jinjie LiuandZihua Wu
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9ff7c523a9 |
[Enhancement] Refactor KernelCache to use inheritance-based design (#1483)
* refactor kernel_cache Signed-off-by: Jinjie Liu <jjliu@baai.ac.cn> * set key to auto when execution_backend is None Signed-off-by: Jinjie Liu <jjliu@baai.ac.cn> * fix type error on _load_kernel_source override Signed-off-by: Jinjie Liu <jjliu@baai.ac.cn> * put kernel cache subclasses in adapter Signed-off-by: Jinjie Liu <jjliu@baai.ac.cn> * remove auto in execution_backend Signed-off-by: Jinjie Liu <jjliu@baai.ac.cn> * remove _save_kernel_source_code_to_disk for NVRTCKernelCache Signed-off-by: Jinjie Liu <jjliu@baai.ac.cn> * refactor all cutedsl if statements Signed-off-by: Jinjie Liu <jjliu@baai.ac.cn> * remove unused kernel_cubin_path Signed-off-by: Jinjie Liu <jjliu@baai.ac.cn> * add func arguments Signed-off-by: Jinjie Liu <jjliu@baai.ac.cn> * remove ctypes Signed-off-by: Jinjie Liu <jjliu@baai.ac.cn> * update ci.yaml to test kernel cache Signed-off-by: Jinjie Liu <jjliu@baai.ac.cn> * fix bugs after #1512 merged Signed-off-by: Jinjie Liu <jjliu@baai.ac.cn> * test: fix kernel cache test * fix: ruff * fix: ruff * fix: coderabbit * fix: coderabbit * fix: ruff * fix: MMA * fix: coderabbit * fix: codex * fix: coderabbit * fix: coderabbit --------- Signed-off-by: Jinjie Liu <jjliu@baai.ac.cn> Co-authored-by: Zihua Wu <13583761+lucifer1004@users.noreply.github.com> |
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Lei Wang
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09385e7deb |
[Refactor] Support auto swizzling for tma store and phaseout related layout annotations (#1509)
* Remove unnecessary swizzled layout annotations from various attention sink examples and kernel analysis script for improved clarity and performance. * Remove unnecessary swizzled layout annotations from various examples to enhance code clarity and performance. * lint fix * Uncomment main test execution for vectorized cast * Add swizzled layout annotation for B_shared in dequantize GEMM example This change introduces a layout annotation for the B_shared tensor in the example_dequant_gemm_bf16_mxfp4_hopper.py file, enhancing the memory layout optimization for better performance during matrix multiplication. * Remove unnecessary cache disabling in GQA sink example for improved clarity and performance. * lint fix * Refactor layout functions to support row-major linear layout for any dimension - Renamed `makeGemmLayoutLinear` to `makeLinearLayout` and updated its implementation to handle arbitrary dimensions. - Updated related function calls in `gemm_layouts.cc` and `layout.cc` to use the new layout function. - Enhanced layout inference in `CumSumOpNode` to enforce linear layout for shared buffers in strict mode. * Refactor `make_linear_layout` to accept a single argument and support arbitrary dimensions - Updated the function signature to take a `Buffer`, `BufferLoad`, or `BufferRegion` directly. - Simplified the implementation by removing argument checks and directly obtaining the shape from the buffer info. - Enhanced the documentation to clarify the function's purpose and usage. * skip callback test * Remove example_dequant_gemm_bf16_mxfp4_hopper_tma.py file, eliminating unused code related to dequantization GEMM example. * typo fix |
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Lei Wang
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c750fb8a98 |
[Enhancement] Update examples and tests for improved type handling functionality (#1448)
* [Enhancement] Update examples and tests for improved type handling and functionality
- Enhanced various example scripts to support new data types and improve compatibility with PyTorch.
- Updated tests across multiple modules to ensure correct functionality with the latest changes in type handling.
- Refactored code in examples to streamline operations and improve clarity, particularly in tensor operations and memory management.
- Added comprehensive tests for new features and fixed existing issues related to type conversions and buffer handling.
* [Refactor] Update accumulation data type to float32 across examples
- Changed accumulation data type from "float" to T.float32 in multiple example scripts to ensure consistency and improve numerical stability.
- This update affects various modules including flash attention, GEMM analysis, convolution, and deepseek MLA examples, enhancing type handling across the board.
* [Refactor] Standardize data type usage across benchmark scripts
- Updated data type definitions in benchmark scripts to use T.float16 and T.float32 consistently, enhancing clarity and type handling.
- Adjusted dtype assignments in matmul functions and configuration setups to align with the new standard.
- Improved overall code consistency and maintainability by ensuring uniform data type usage across various modules.
* [Refactor] Standardize data type usage in templates and scripts
- Updated data type definitions in various templates and scripts to use string representations (e.g., "float16", "int32") instead of T.float16 and T.int32 for improved consistency and clarity.
- Enhanced overall code maintainability by ensuring uniform data type usage across multiple modules, including convolution, elementwise operations, and matrix multiplication templates.
- This change aims to streamline type handling and improve compatibility with existing workflows.
* [Refactor] Standardize data type usage in examples and benchmarks
- Updated data type definitions in various example and benchmark scripts to use T.float16 and T.int32 consistently, enhancing clarity and maintainability.
- Adjusted dtype assignments in kernel functions and configuration setups to align with the new standard.
- Improved overall code consistency by ensuring uniform data type usage across multiple modules, including attention mechanisms, matrix multiplication, and GEMM examples.
* [Refactor] Import dtypes from language.v2 module
- Added import statement for dtypes from the language.v2 module to enhance type handling and maintain consistency across the codebase.
- This change aims to streamline data type management and improve overall code clarity.
* fix
* [Refactor] Standardize data type usage across scripts
- Updated data type definitions in various scripts to use string representations (e.g., "float16", "int8") instead of T.float16 and T.int8 for improved consistency and clarity.
- Adjusted dtype assignments in functions and configuration setups to align with the new standard, enhancing overall code maintainability.
- This change affects multiple modules, including benchmark and attention mechanisms, ensuring uniform data type usage throughout the codebase.
* [Refactor] Update data type handling for consistency and clarity
- Changed string representations of data types in the Hint class to use T.float32 and T.int32 for improved consistency.
- Added new data types "int4" and "int16" to the dtypes module, enhancing type support across the codebase.
- Updated function signatures and assertions in the lop3 and mxfp modules to utilize the new data types, ensuring uniformity in type handling.
- This refactor aims to streamline data type management and improve overall code clarity and maintainability.
* [Enhancement] Improve data type handling and error messaging
- Introduced a mapping for canonical data types to their display strings, enhancing clarity in type representation.
- Updated the dtype creation logic to utilize the new mapping, ensuring more intuitive handling of string inputs.
- Refined error messages in the lop3 module to provide clearer feedback on invalid source formats, improving debugging and user experience.
* [Fix] Correct boolean flag in GEMM SP test case
- Updated the boolean flag in the test_gemm_sp_sm90 function to ensure proper functionality in the test case.
- This change enhances the accuracy of the test and aligns it with expected behavior for the GEMM SP implementation.
* [Refactor] Standardize data type usage across scripts
- Updated data type definitions in various scripts to use T.float16 and T.bfloat16 consistently, enhancing clarity and maintainability.
- Adjusted dtype assignments in function signatures and argument parsing to align with the new standard, ensuring uniform data type usage throughout the codebase.
- This change affects multiple modules, including benchmarks and examples, improving overall code consistency and readability.
* [Refactor] Standardize data type usage in various modules
- Updated data type assignments in multiple scripts to utilize T.float32, T.int8, and T.int32 consistently, enhancing clarity and maintainability.
- Adjusted function signatures and parameter types across benchmarks, examples, and tests to align with the new standard, ensuring uniform data type usage throughout the codebase.
- This change improves overall code consistency and readability, impacting modules related to matrix multiplication, GEMM, and tensor operations.
* [Refactor] Update argument parsing for data types in benchmarks
- Changed argument parsing for data types in benchmark_matmul_intrinsic.py and benchmark_matmul_sp.py to use string representations ("float16", "int8", "float") instead of T.float16 and T.float.
- This update enhances consistency in data type handling across benchmark scripts, improving clarity and maintainability.
* [Refactor] Update data type handling in benchmark and example scripts
- Changed data type arguments in benchmark and example scripts to use string representations ("float16") instead of T.float16 for improved consistency.
- Updated function signatures and argument parsing to align with the new standard, enhancing clarity and maintainability across the codebase.
- This change affects multiple modules related to attention mechanisms and tensor operations, ensuring uniform data type usage throughout the examples.
* [Refactor] Fix data type conversion in multiple scripts
- Corrected the usage of the data type conversion method from dtype..as_torch() to dtype.as_torch() across various benchmark and example scripts.
- This change enhances consistency in data type handling and improves code readability, impacting modules related to attention mechanisms and tensor operations.
* [Refactor] Update float8 data type usage across multiple scripts
- Changed instances of T.float8_e4m3 to T.float8_e4m3fn in various benchmark, example, and test scripts to ensure consistency in data type handling.
- This update enhances clarity and maintainability across the codebase, particularly in modules related to matrix multiplication and tensor operations.
* [Refactor] Enhance float8 data type handling in CUDA code generation
- Updated the handling of float8 data types in the CUDA code generation to include additional float8 variants, improving type conversion logic.
- Adjusted conditions to ensure proper type checks for float8 conversions, enhancing clarity and maintainability in the codebase.
- Modified layout inference to streamline float8 type checks, ensuring consistency across the implementation.
- This change impacts modules related to matrix operations and CUDA code generation, improving overall type handling and conversion accuracy.
* [Refactor] Streamline float8 data type handling in CUDA and related modules
- Enhanced float8 data type handling in CUDA code generation by refining type conversion logic and ensuring consistent type checks.
- Updated layout inference for float8 types to improve clarity and maintainability across the implementation.
- This change impacts modules related to matrix operations and CUDA code generation, improving overall type handling and conversion accuracy.
* [Refactor] Remove unnecessary cache disabling in float8 example script
- Eliminated the call to tilelang.disable_cache() in example_group_per_split_token_cast_to_fp8.py to streamline the code.
- This change enhances clarity and maintainability of the example script without affecting its functionality.
* [Refactor] Update data type usage in debug print tests
- Changed the argument for dtype in the test_debug_print_buffer function from a string representation to the corresponding T.bool type.
- This update enhances consistency in data type handling within the test suite, improving clarity and maintainability.
* lint fix
* Update function parameter types from `str` to `T.dtype` for improved type safety in attention sink and related examples
* Refactor `gemv_alloc_reducer` function signature for improved readability by formatting parameters across multiple lines.
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Lei Wang
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29051439db | [Lint] Phaseout Yapf format and embrace ruff format (#1417) | ||
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Gabriel Wu
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eb41574431 |
[fix] NVRTC execution backend (#1256)
* [fix] NVRTC execution backend
* [fmt] run pre-commit
* [fix] coderabbit reviews
* [test] add cuda-python to test dep
* [fix] coderabbit reviews
* [fix] CUDA 13 compatibility
* [fix] sm90
* [fix] CUDA 13 compatibility
* [fix] pre-commit
* [fix] always use cuda::std::__atomic_ref_impl
* [fix] restore to external API
* Revert "[fix] restore to external API"
This reverts commit
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