mirror of
https://github.com/tile-ai/tilelang.git
synced 2026-10-24 07:04:00 +08:00
Lei Wang
f2e99180d5
* remove llvm build * [Refactor] Update kernel compilation and profiling in examples - Replaced `tilelang.lower` with `tilelang.compile` in multiple example scripts to streamline kernel compilation. - Updated profiling calls to utilize the new `get_profiler` method, enhancing performance measurement consistency. - Adjusted assertions and benchmarking methods to align with the new profiling structure across various examples, ensuring correctness and clarity in performance evaluations. * lint fix * License Update * [Refactor] Improve code formatting and documentation in CUDA header and HIP runtime files - Adjusted formatting in `cuda.h` for better readability, including alignment of comments and struct fields. - Cleaned up whitespace and improved comment clarity in `rt_mod_hip.cc` to enhance code maintainability. * [Refactor] Enhance formatting and clarity in CUDA header and HIP runtime files - Improved comment alignment and readability in `cuda.h`. - Cleaned up whitespace and formatting in `rt_mod_hip.cc` to enhance maintainability. * lint fix * lint fix * lint fix * lint fix * fix * License update * [Enhancement] Update JITKernel to use artifact for kernel source - Assigned the generated artifact to `self.artifact` for better management. - Updated kernel source references to use `artifact.kernel_source` for consistency in execution backend handling. * lint fix * Add @tilelang.testing.requires_llvm decorator to vectorization tests * Enhance setup.py and env.py for library management - Added functionality to remove original files after copying in CMakeBuild. - Updated TVM_LIBRARY_PATH in env.py to include the PyPI build library path for better integration. * Refactor TVM_LIBRARY_PATH assignment for improved readability in env.py * Refactor CMakeBuild file handling in setup.py - Added a check to ensure the target library directory exists before copying .so files. - Improved the logic for creating the target directory and copying files to enhance robustness. * bugfix * Rename BuildTLDebug to BuildTileLangCUDAWithoutCompile and update registration. Add @tilelang.testing.requires_llvm decorator to multiple tests for LLVM requirement. * lint fix * Enhance TileLang code generation by adding support for device code generation without compilation. Updated `host_codegen` and `device_codegen` functions to include new transformations and registration for `tilelang_hip_without_compile`. Refactored JIT kernel adapters to accommodate host and device modules, improving overall integration and flexibility. * lint fix * Add support for C target in device code generation - Updated `device_codegen_without_compile` to include handling for the C target by registering the `tilelang_cpp` function. * [Enhancement] Implement auto-clear cache feature based on environment variable * Added TILELANG_CLEAR_CACHE environment variable to control cache clearing. * Updated CI workflow to set TILELANG_CLEAR_CACHE during testing. * Modified cache initialization to clear cache if TILELANG_CLEAR_CACHE is set to true. * [Refactor] Update kernel invocation and import paths in tests and cache * Changed kernel invocation in `test_tilelang_kernel_dequantize_gemm.py` to return the result. * Updated import statements in `test_tilelang_kernel_int4_gemm_mma.py` to use `bitblas` instead of `tilelang`. * Refactored paths for artifact and parameters in `kernel_cache.py` for better maintainability. * [Refactor] Clean up whitespace and improve code formatting in kernel_cache.py * Removed unnecessary blank lines and adjusted spacing for better readability in the KernelCache class. * Enhanced overall code formatting to align with project standards. * [Enhancement] Add bfloat16 test case and improve kernel caching logic * Introduced a new test case for bfloat16 matrix multiplication in `test_tilelang_kernel_gemm_mma_intrinsic.py`. * Updated `KernelCache` to handle multiple kernel source files and improve error handling during saving and loading. * Refactored `JITKernel` to support instantiation from a database, enhancing flexibility in kernel management. * Adjusted `CtypesKernelAdapter` and `CythonKernelAdapter` to utilize the new kernel loading mechanism from the database. * Improved code formatting and readability across several files. * lint fix * Update bfloat16 matrix multiplication test case to use larger dimensions for improved coverage
66 lines
2.0 KiB
Python
66 lines
2.0 KiB
Python
import tilelang
|
|
import tilelang.testing
|
|
import tilelang.language as T
|
|
import torch
|
|
|
|
|
|
def matmul(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype="float"):
|
|
|
|
@T.prim_func
|
|
def main(
|
|
A: T.Buffer((M, K), dtype),
|
|
B: T.Buffer((N, K), dtype),
|
|
C: T.Buffer((M, N), dtype),
|
|
):
|
|
with T.Kernel(
|
|
T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (
|
|
bx,
|
|
by,
|
|
):
|
|
A_shared = T.alloc_shared((block_M, block_K), dtype)
|
|
B_shared = T.alloc_shared((block_N, block_K), dtype)
|
|
C_local = T.alloc_fragment((block_M, block_N), accum_dtype)
|
|
|
|
T.clear(C_local)
|
|
|
|
# changing num_stages to 0 gives correct results
|
|
for ko in T.Pipelined(T.ceildiv(K, block_K), num_stages=1):
|
|
T.copy(A[by * block_M, ko * block_K], A_shared)
|
|
|
|
for j, k in T.Parallel(block_N, block_K):
|
|
B_shared[j, k] = B[bx * block_N + j, ko * block_K + k]
|
|
|
|
T.gemm(A_shared, B_shared, C_local, transpose_B=True)
|
|
|
|
T.copy(C_local, C[by * block_M, bx * block_N])
|
|
|
|
return main
|
|
|
|
|
|
def run_gemm_pipeline_test(N, block_M=128, block_N=128, block_K=32):
|
|
func = matmul(N, N, N, block_M, block_N, block_K)
|
|
jit_kernel = tilelang.compile(func, out_idx=[2], target="cuda")
|
|
|
|
torch.manual_seed(0)
|
|
a = torch.randn(N, N, device="cuda", dtype=torch.float16)
|
|
b = torch.randn(N, N, device="cuda", dtype=torch.float16)
|
|
|
|
ref_c = a @ b.T
|
|
c = jit_kernel(a, b)
|
|
|
|
tilelang.testing.torch_assert_close(c, ref_c, rtol=1e-2, atol=0.2)
|
|
|
|
|
|
def test_pipeline_large_matrix():
|
|
"""Test pipeline stages with large matrix multiplication (8192x8192)"""
|
|
run_gemm_pipeline_test(8192)
|
|
|
|
|
|
def test_pipeline_small_matrix():
|
|
"""Test pipeline stages with smaller matrix multiplication"""
|
|
run_gemm_pipeline_test(1024)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
tilelang.testing.main()
|