mirror of
https://github.com/tile-ai/tilelang.git
synced 2026-10-20 06:30:07 +08:00
Lei Wang
3471904f68
* [Feature] Add CTypes JIT kernel support for dynamic shapes and multi-stream execution - Enhance CtypesKernelAdapter to handle dynamic symbolic shapes - Add support for multi-stream kernel execution in CTypes backend - Implement dynamic shape handling in test_tilelang_jit_gemm_ctypes.py - Add symbolic shape utility function in tilelang.language - Update profiler to improve flexibility in benchmark selection * Remove redundant thread binding in GEMM kernel implementations - Remove unnecessary `thread_binding` line in GEMM kernel functions - Clean up code in `examples/gemm/README.md` and `testing/python/kernel/test_tilelang_kernel_int4_gemm_mma.py` - Enhance code readability by removing redundant thread binding annotation * Fix indentation in int4 GEMM kernel test file - Correct indentation for function calls in `test_tilelang_kernel_int4_gemm_mma.py` - Remove extra indentation in `mma_emitter.ldmatrix_a()` and `mma_emitter.ldmatrix_b()` calls - Improve code formatting for better readability * [Feature] Add Cython JIT kernel support for dynamic shapes and multi-stream execution - Implement CythonKernelAdapter to handle dynamic symbolic shapes - Add support for multi-stream kernel execution in Cython backend - Create comprehensive test suite for Cython GEMM kernel in test_tilelang_jit_gemm_cython.py - Update JITKernel to include "cython" as a valid execution backend - Add Cython-specific wrapper and library generation modules - Update .gitignore to exclude Cython cache directory - Modify setup.py to include Cython source files in package data * lint fix * [Refactor] Replace JITKernel with compile() function for kernel compilation - Add new `compile()` function in tilelang/jit/__init__.py as a wrapper for JITKernel - Update multiple test files and examples to use `tilelang.compile()` instead of `tilelang.JITKernel()` - Modify kernel adapters to support optional kernel-only source retrieval - Update `__init__.py` to import the new `compile()` function - Improve kernel source retrieval for different execution backends * lint fix * remove debug print * Add C/C++ compiler utility module and update Cython JIT kernel support - Introduce new `tilelang/contrib/cc.py` module with cross-platform C/C++ compiler utilities - Add functions to detect and retrieve system C/C++ compilers - Implement cross-compilation and shared library creation support - Update Cython JIT kernel to validate C++ compiler availability - Modify Cython adapter to use detected C++ compiler for library generation * Refactor float8 dtype mapping in tensor utility module - Move float8_dtype_map inside adapt_torch2tvm function - Simplify global scope by localizing the dtype mapping - Maintain existing functionality for converting torch float8 tensors to TVM ndarray * Refactor float8 dtype mapping in tensor utility module - Move float8_dtype_map inside adapt_torch2tvm function - Simplify global scope by localizing the dtype mapping - Maintain existing functionality for converting torch float8 tensors to TVM ndarray * revert * Enhance Cython JIT adapter with Cython compiler detection - Add `get_cython_compiler()` function to dynamically locate Cython executable - Update Cython adapter to use detected Cython compiler instead of hardcoded command - Raise an exception if no Cython compiler is found - Update requirements.txt to specify minimum PyTorch version (>=2.2.0) * Fix Cython kernel wrapper stream handling and type annotations - Update stream parameter type to int64_t for better compatibility - Directly use torch.cuda.current_stream().cuda_stream instead of casting - Improve type safety and precision in Cython kernel wrapper
105 lines
2.6 KiB
Python
105 lines
2.6 KiB
Python
# type: ignore
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import tilelang
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import tilelang.testing
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import tilelang.language as T
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def debug_print_buffer(M=16, N=16):
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dtype = "float16"
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@T.prim_func
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def program(Q: T.Buffer((M, N), dtype)):
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with T.Kernel(4, 4, 2, threads=128 * 2) as (bx, by, bz):
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shared_buf = T.alloc_shared([M, N], dtype)
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T.print(shared_buf)
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jit_kernel = tilelang.compile(program, target="cuda")
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profiler = jit_kernel.get_profiler()
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profiler.run_once()
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def test_debug_print_buffer():
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debug_print_buffer(16, 16)
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def debug_print_buffer_conditional(M=16, N=16):
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dtype = "float16"
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@T.prim_func
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def program(Q: T.Buffer((M, N), dtype)):
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with T.Kernel(4, 4, 2, threads=128 * 2) as (bx, by, bz):
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shared_buf = T.alloc_shared([M, N], dtype)
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if bx == 0 and by == 0 and bz == 0:
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T.print(shared_buf)
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jit_kernel = tilelang.compile(program, target="cuda")
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profiler = jit_kernel.get_profiler()
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profiler.run_once()
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def test_debug_print_buffer_conditional():
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debug_print_buffer_conditional(16, 16)
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def debug_print_value_conditional(M=16, N=16):
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dtype = "float16"
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@T.prim_func
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def program(Q: T.Buffer((M, N), dtype)):
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with T.Kernel(4, 4, 2, threads=128 * 2) as (bx, by, bz):
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tid = T.get_thread_binding()
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if tid == 0:
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T.print(bx + by + bz)
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jit_kernel = tilelang.compile(program, target="cuda")
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profiler = jit_kernel.get_profiler()
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profiler.run_once()
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def test_debug_print_value_conditional():
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debug_print_value_conditional(16, 16)
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def debug_print_register_files(M=16, N=16):
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dtype = "float16"
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@T.prim_func
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def program(Q: T.Buffer((M, N), dtype)):
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with T.Kernel(4, 4, 2, threads=128 * 2) as (bx, by, bz):
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register_buf = T.alloc_fragment([M, N], dtype)
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for i, j in T.Parallel(M, N):
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T.print(register_buf[i, j])
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jit_kernel = tilelang.compile(program, target="cuda")
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profiler = jit_kernel.get_profiler()
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profiler.run_once()
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def test_debug_print_register_files():
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debug_print_register_files(16, 16)
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def debug_print_msg(M=16, N=16):
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dtype = "float16"
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@T.prim_func
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def program(Q: T.Buffer((M, N), dtype)):
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with T.Kernel(4, 4, 2, threads=128 * 2) as (bx, by, bz):
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tid = T.get_thread_binding()
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if tid == 0:
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T.print(bx + by + bz, msg="hello world")
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jit_kernel = tilelang.compile(program, target="cuda")
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profiler = jit_kernel.get_profiler()
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profiler.run_once()
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def test_debug_print_msg():
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debug_print_msg(16, 16)
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if __name__ == "__main__":
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tilelang.testing.main()
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