mirror of
https://github.com/tile-ai/tilelang.git
synced 2026-10-21 06:40:23 +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.
156 lines
5.5 KiB
Python
156 lines
5.5 KiB
Python
import tilelang
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import tilelang.testing
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from tilelang import tvm as tvm
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import tilelang.language as T
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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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num_stages = 0
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@T.prim_func
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def matmul(
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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.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M)) as (bx, by):
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A_local = T.alloc_local((block_M, block_K), dtype)
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B_local = T.alloc_local((block_K, block_N), dtype)
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C_local = T.alloc_local((block_M, block_N), accum_dtype)
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T.clear(C_local)
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# Apply layout optimizations or define your own layout
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# (Optional).
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# T.annotate_layout(
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# {
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# A_local: make_swizzle_layout(A_local),
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# B_local: make_swizzle_layout(B_local),
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# }
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# )
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for ko in T.Pipelined(K // block_K, num_stages=num_stages):
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T.copy(A[by * block_M, ko * block_K], A_local)
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# Or Copy with Parallel
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for k, j in T.Parallel(block_K, block_N):
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B_local[k, j] = B[ko * block_K + k, by * block_N + j]
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for i, j, k in T.grid(block_M, block_N, block_K):
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C_local[i, j] += A_local[i, k] * B_local[k, j]
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T.copy(C_local, C[by * block_M, bx * block_N])
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return matmul
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def assert_matmul_codegen(M=1024, N=1024, K=1024, block_M=128, block_N=128, block_K=32):
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func = matmul(M, N, K, block_M, block_N, block_K)
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with tvm.target.Target("c"):
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artifact = tilelang.lower(func)
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code = artifact.kernel_source
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assert code is not None, "Code generation failed"
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def test_matmul_codegen():
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assert_matmul_codegen(M=1024, N=1024, K=1024, block_M=128, block_N=128, block_K=32)
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def test_matmul_compile():
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def matmul_jit_test(M, N, K, block_M, block_N, block_K, dtype=T.float16, accum_dtype=T.float32):
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# a simple kernel just for jit test
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@T.prim_func
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def matmul(
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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.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M)) as (bx, by):
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A_local = T.alloc_local((block_M, block_K), dtype)
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B_local = T.alloc_local((block_K, block_N), dtype)
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C_local = T.alloc_local((block_M, block_N), accum_dtype)
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for p in T.serial(block_M):
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for w in T.serial(block_N):
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C_local[p, w] = 0
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for ko in T.serial(K // block_K):
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for i in T.serial(block_M):
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for k in T.serial(block_K):
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A_local[i, k] = A[by * block_M + i, ko * block_K + k]
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for k in T.serial(block_K):
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for j in T.serial(block_N):
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B_local[k, j] = B[ko * block_K + k, bx * block_N + j]
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for i in T.serial(block_M):
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for j in T.serial(block_N):
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for k in T.serial(block_K):
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C_local[i, j] += A_local[i, k] * B_local[k, j]
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for i in T.serial(block_M):
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for j in T.serial(block_N):
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C[by * block_M + i, bx * block_N + j] = C_local[i, j]
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return matmul
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M, N, K = 1024, 512, 512
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block_M, block_N, block_K = M // 4, N // 4, K // 4
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cpu_func = matmul_jit_test(M, N, K, block_M, block_N, block_K)
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with tvm.target.Target("c"):
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complied_fun = tilelang.compile(cpu_func, -1, execution_backend="cython")
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in_dtype = T.float16
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A = torch.randn(M, K, dtype=torch.__getattribute__(in_dtype))
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B = torch.randn(K, N, dtype=torch.__getattribute__(in_dtype))
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C = complied_fun(A, B)
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C_torch = torch.matmul(A, B)
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tilelang.testing.torch_assert_close(C, C_torch, atol=1e-2, rtol=1e-2, max_mismatched_ratio=0.05)
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def test_matmul_with_copy_cython():
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"""CPU kernel using T.copy with cython backend.
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Verifies that T.copy works end-to-end on CPU: the vectorized copy
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uses vector types (e.g. float4) defined in common.h, and the
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wrapper correctly skips redundant re-lowering.
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"""
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M, N, K = 128, 128, 128
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block_M, block_N, block_K = 32, 32, 32
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@T.prim_func
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def matmul(
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A: T.Tensor((M, K), "float32"),
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B: T.Tensor((K, N), "float32"),
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C: T.Tensor((M, N), "float32"),
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):
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with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M)) as (bx, by):
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A_local = T.alloc_local((block_M, block_K), "float32")
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B_local = T.alloc_local((block_K, block_N), "float32")
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C_local = T.alloc_local((block_M, block_N), "float32")
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T.clear(C_local)
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for ko in T.serial(K // block_K):
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T.copy(A[by * block_M, ko * block_K], A_local)
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T.copy(B[ko * block_K, bx * block_N], B_local)
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for i, j, k in T.grid(block_M, block_N, block_K):
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C_local[i, j] += A_local[i, k] * B_local[k, j]
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T.copy(C_local, C[by * block_M, bx * block_N])
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compiled = tilelang.compile(matmul, target="c", out_idx=-1, execution_backend="cython")
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a = torch.randn(M, K, dtype=torch.float32)
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b = torch.randn(K, N, dtype=torch.float32)
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c = compiled(a, b)
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ref = a @ b
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torch.testing.assert_close(c, ref, rtol=1e-5, atol=1e-5)
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if __name__ == "__main__":
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tilelang.testing.main()
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