mirror of
https://github.com/tile-ai/tilelang.git
synced 2026-10-20 06:30:07 +08:00
Lei Wang
74da369695
* [Refactor] Update FFI type handling and simplify argument management * Refactored FFI type definitions in runtime and code generation files to use `TVMFFIAny` instead of `TVMValue`, enhancing type clarity. * Updated function registration in `runtime.cc` to utilize canonical names for better consistency. * Simplified argument handling in the `simplify` transformation, ensuring unused buffer parameters are removed only when simplification is enabled. * Adjusted autotuner and profiler parameters to standardize the execution backend to `tvm_ffi`, improving clarity in backend selection. * Removed obsolete `adapt_torch2tvm` function from tensor utilities to streamline the codebase and reduce complexity. * [Update] Sync TVM submodule and enhance kernel source handling * Updated the TVM submodule to commit cdc2aced, ensuring compatibility with recent changes. * Added functionality to print kernel source in `example_blocksparse_gemm.py` for better debugging. * Commented out the main execution call in test files to prevent unintended execution during testing. * Introduced `tilelang.disable_cache()` in various test files to streamline testing and avoid cache-related issues. * Refactored kernel source retrieval methods to improve clarity and consistency across different execution backends. * [Refactor] Clean up imports and improve code formatting * Removed unused import of `tilelang.testing` in `test_example_blocksparse_gemm.py` to streamline the code. * Reformatted several lines in `arg_binder.cc`, `make_packed_api.cc`, `tvm_ffi.py`, and `adapter.py` for improved readability and consistency. * Updated comments and spacing in `tvm_ffi.py` to enhance clarity without altering functionality. * Update execution backend options and improve resolution logic - Changed default execution backend from "cython" to "auto" in multiple locations to allow automatic selection based on the target. - Expanded the list of supported execution backends to include "torch" and "nvrtc" across various classes and functions. - Enhanced backend resolution logic in `KernelCache` and `AutoTuner` to ensure appropriate backend selection based on the target. - Updated documentation to reflect changes in execution backend options and their defaults. * lint fix * fix * Enhance argument handling in CUDA and HIP runtime modules - Updated `ExtractFuncInfo` in `rt_mod_cuda.cc` and `rt_mod_hip.cc` to map boolean argument types to int32, ensuring compatibility with device runtime. - Refactored `BindDLTensor` in `arg_binder.cc` to improve null handling and validation checks for DLTensor parameters, utilizing expression-level guards to prevent dereferencing null pointers. - Enhanced error checking for buffer shape, strides, and data fields, ensuring robust handling of optional inputs and maintaining consistency across various checks. * lint fix * lint fix * lint fix * lint fix * minor fix * fix * recover check * Refactor argument binding and validation in `arg_binder.cc` - Improved null handling and validation checks in `BindDLTensor`, ensuring safe dereferencing of pointers. - Enhanced consistency checks for buffer shape, strides, and data fields, utilizing expression-level guards. - Updated `MakePackedAPI` to maintain code clarity and consistency in argument handling. - Minor adjustments in test files to streamline kernel execution and improve readability. * lint fix * stride fix * minor fix * fix * lint fix * lint fix * Add CUDA stream access policy window helpers and integrate with L2 persistent cache management - Introduced functions to set and reset the CUDA stream access policy window, allowing for better control over L2 cache usage. - Updated runtime files to include new FFI packed functions for managing stream attributes. - Modified lower_hopper_intrin to incorporate prologue and epilogue statements for L2 cache setup and teardown. - Enhanced tests to verify the inclusion of new FFI calls in the generated kernel source. * check with symbolic * support null ptr * Update CMakeLists and lower.py for code generation and subproject status - Added `codegen_c_host.cc` to the list of source files in CMakeLists.txt for improved code generation support. - Updated the function call in `lower.py` to use `target.build.tilelang_c` for C target host code generation, enhancing compatibility. - Marked the TVM subproject as dirty to indicate local modifications. * lint fix * Update comments for clarity in quickstart.py
106 lines
2.8 KiB
Python
106 lines
2.8 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, dtype="float16"):
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@T.prim_func
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def program(Q: T.Tensor((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", execution_backend="tvm_ffi")
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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, dtype="float")
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debug_print_buffer(16, 16, dtype="float16")
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debug_print_buffer(16, 16, dtype="uint8")
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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.Tensor((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.Tensor((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.Tensor((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.Tensor((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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