mirror of
https://github.com/tile-ai/tilelang.git
synced 2026-10-24 07:04:00 +08:00
Lei Wang
d238837022
* Refactor kernel launch materialization * [Refactor] Improve git commit ID retrieval and version labeling logic * Enhance the get_git_commit_id function to check for the existence of the .git directory before attempting to retrieve the commit hash, ensuring more robust behavior. * Update the dynamic_metadata function to conditionally set the NO_VERSION_LABEL environment variable based on the presence of the .git directory. * Modify the GitHub Actions workflow to unset the NO_VERSION_LABEL environment variable during the installation and version check of the package, allowing for accurate version labeling in SDist builds. * [Refactor] Update WebGPU backend registration * Import the WebGPU package to automatically register its pass pipeline. * Remove the explicit registration of the WebGPU pass pipeline, streamlining the backend setup process.
133 lines
4.2 KiB
Python
133 lines
4.2 KiB
Python
import tilelang
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import tilelang.language as T
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import tilelang.testing
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import torch
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def matmul(M, N, K, block_M, block_N, block_K, dtype=T.float16, accum_dtype=T.float32):
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@T.prim_func
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def gemm(
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A: T.Tensor((M, K), dtype),
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B: T.Tensor((K, N), dtype),
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C: T.Tensor((M, N), dtype),
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):
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with T.ClusterKernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128, cluster_dims=(2, 1, 1)) as (bx, by):
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A_shared = T.alloc_shared((block_M, block_K), dtype)
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B_shared = T.alloc_shared((block_K, block_N), dtype)
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C_local = T.alloc_fragment((block_M, block_N), accum_dtype)
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T.clear(C_local)
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for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=3):
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T.copy(A[by * block_M, k * block_K], A_shared)
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T.copy(B[k * block_K, bx * block_N], B_shared)
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T.gemm(A_shared, B_shared, C_local)
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T.copy(C_local, C[by * block_M, bx * block_N])
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return gemm
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@tilelang.jit(out_idx=-1)
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def get_cta_rank_in_cluster(cluster_size=4):
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assert 128 % cluster_size == 0
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@T.prim_func
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def main(A: T.Tensor((128), T.int32)):
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with T.ClusterKernel(128, cluster_dims=(cluster_size, 1, 1)) as bx:
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T.cluster_arrive()
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T.cluster_wait()
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cta_rank_in_cluster = T.block_rank_in_cluster()
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if T.get_thread_binding() == 0:
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A[bx] = cta_rank_in_cluster
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T.cluster_sync()
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return main
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@tilelang.jit(out_idx=-1)
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def barrier_kernel():
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@T.prim_func
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def main(A: T.Tensor((128), T.int32)):
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with T.ClusterKernel(128, threads=128, cluster_dims=(4, 1, 1)):
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mbar = T.alloc_cluster_barrier([256])
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T.cluster_sync()
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T.mbarrier_arrive(mbar, 0)
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if T.block_rank_in_cluster() == 0:
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T.mbarrier_wait_parity(mbar, 0)
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T.cluster_sync()
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return main
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def _get_clc_query_codegen_source() -> str:
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@T.prim_func
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def main(A: T.Tensor((2,), T.int32)):
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with T.Kernel(1, threads=1):
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result = T.alloc_shared((4,), T.uint32)
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A[0] = T.clc_is_canceled(result)
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A[1] = T.Cast("int32", T.clc_get_first_ctaid_x(result))
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artifact = tilelang.lower(main, target="cuda")
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return artifact.kernel_source
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def run_cython_cluster_launch():
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kernel = matmul(1024, 1024, 1024, 128, 128, 32)
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mod = tilelang.compile(kernel, execution_backend="cython")
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assert "clusterDim = {2, 1, 1}" in mod.get_host_source()
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def run_tvm_ffi_cluster_launch():
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kernel = matmul(1024, 1024, 1024, 128, 128, 32)
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mod = tilelang.compile(kernel, execution_backend="tvm_ffi")
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check_str = r"""
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(((TVMFFIAny*)stack_ffi_any)[3].type_index) = 1;
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(((TVMFFIAny*)stack_ffi_any)[3].zero_padding) = 0;
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(((TVMFFIAny*)stack_ffi_any)[3].v_int64) = ((int64_t)2);
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(((TVMFFIAny*)stack_ffi_any)[4].type_index) = 1;
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(((TVMFFIAny*)stack_ffi_any)[4].zero_padding) = 0;
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(((TVMFFIAny*)stack_ffi_any)[4].v_int64) = ((int64_t)1);
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(((TVMFFIAny*)stack_ffi_any)[5].type_index) = 1;
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(((TVMFFIAny*)stack_ffi_any)[5].zero_padding) = 0;
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(((TVMFFIAny*)stack_ffi_any)[5].v_int64) = ((int64_t)1);
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"""
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assert check_str in mod.get_host_source()
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@tilelang.testing.requires_cuda
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@tilelang.testing.requires_cuda_compute_version_ge(9, 0)
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def test_cluster_launch():
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run_cython_cluster_launch()
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run_tvm_ffi_cluster_launch()
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@tilelang.testing.requires_cuda
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@tilelang.testing.requires_cuda_compute_version_ge(9, 0)
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def test_cluster_launch_intrinsics(cluster_size=4):
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kernel = get_cta_rank_in_cluster(cluster_size)
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result = kernel()
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ref = torch.arange(128, dtype=torch.int32, device="cuda") % cluster_size
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assert torch.all(result == ref)
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@tilelang.testing.requires_cuda
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@tilelang.testing.requires_cuda_compute_version_ge(9, 0)
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def test_cluster_barrier():
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kernel = barrier_kernel()
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kernel()
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@tilelang.testing.requires_cuda
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def test_clc_query_codegen_includes_cluster_header():
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src = _get_clc_query_codegen_source()
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print("=== clc query codegen ===")
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print(src)
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assert "#include <tl_templates/cuda/cluster.h>" in src
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assert "tl::clc_is_canceled(" in src
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assert "tl::clc_get_first_ctaid_x(" in src
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if __name__ == "__main__":
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tilelang.testing.main()
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