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Chennes
74d4f0ed66
T.fill of an int8 buffer with a negative scalar (other than -1) silently wrote only 1 of every 4 elements correctly and clobbered the other 3 to -1. The vectorized fill packs four int8 lanes into one 32-bit store via make_int(v,v,v,v); a negative signed char is integer-promoted to int and sign-extended before the shifts/OR, so its high 1-bits flood the neighbouring lanes (e.g. make_int(-7,-7,-7,-7) returned 0xFFFFFFF9 = [-7,-1,-1,-1]). Build the packed value from explicit unsigned bytes so the sign-extension bits cannot leak across lanes. Add an int8 negative-fill regression test. Fixes #2427
108 lines
3.5 KiB
Python
108 lines
3.5 KiB
Python
import tilelang
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import tilelang.testing
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import tilelang.language as T
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import pytest
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import torch
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from tilelang.contrib import nvcc
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# add decorator @tilelang.jit if you want to return a torch function
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# @tilelang.jit
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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 main(
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A: T.Tensor((M, K), dtype),
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B: T.Tensor((N, K), dtype),
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C: T.Tensor((M, N), dtype),
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):
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# Initialize Kernel Context
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with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) 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_N, block_K), 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 ko in T.Pipelined(T.ceildiv(K, block_K), num_stages=0):
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# Copy tile of A
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# This is a sugar syntax for parallelized copy
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T.copy(A[by * block_M, ko * block_K], A_shared)
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T.clear(A_shared)
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# Demonstrate parallelized copy from global to shared for B
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T.copy(B[bx * block_N, ko * block_K], B_shared)
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# Perform a tile-level GEMM on the shared buffers
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# Currently we dispatch to the cute/hip on Nvidia/AMD GPUs
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T.gemm(A_shared, B_shared, C_local, transpose_B=True)
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# Copy result back to global memory
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T.copy(C_local, C[by * block_M, bx * block_N])
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return main
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def run_matmul(M, N, K, block_M, block_N, block_K, dtype=T.float16, accum_dtype=T.float32):
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program = matmul(M, N, K, block_M, block_N, block_K, dtype, accum_dtype)
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kernel = tilelang.compile(program, out_idx=[2])
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import torch
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a = torch.randn((M, K), dtype=dtype.as_torch()).cuda()
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b = torch.randn((N, K), dtype=dtype.as_torch()).cuda()
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c = kernel(a, b)
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assert torch.allclose(c, torch.zeros_like(c))
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def test_matmul():
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run_matmul(1024, 1024, 1024, 128, 128, 32)
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@tilelang.testing.requires_cuda_compute_version(10)
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def test_shared_fill_cast_zero_uses_st_bulk():
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if nvcc.get_cuda_version() < (12, 8):
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pytest.skip("st.bulk shared lowering requires CUDA toolkit >= 12.8")
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@T.prim_func
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def main(out: T.Tensor((128,), T.float32)):
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with T.Kernel(1, threads=128):
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smem = T.alloc_shared((128,), T.float32)
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for i in T.Parallel(128):
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smem[i] = T.float32(1.0)
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T.fill(smem, T.cast(0, T.float32))
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T.copy(smem, out)
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kernel = tilelang.compile(main, target="cuda")
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assert "tl::st_bulk_shared<512, 0>" in kernel.get_kernel_source()
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out = torch.empty((128,), dtype=torch.float32, device="cuda")
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kernel(out)
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torch.cuda.synchronize()
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assert torch.allclose(out, torch.zeros_like(out))
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@tilelang.testing.requires_cuda
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def test_fill_int8_negative():
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M, N = 8, 128
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def program(value):
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@T.prim_func
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def main(out: T.Tensor((M, N), "int8")):
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with T.Kernel(1, threads=128):
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smem = T.alloc_shared((M, N), "int8")
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T.fill(smem, value)
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T.copy(smem, out)
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return main
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for value in (-7, -128, -1, 5):
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kernel = tilelang.compile(program(value), out_idx=[0])
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out = kernel()
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torch.cuda.synchronize()
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ref = torch.full((M, N), value, dtype=torch.int8, device="cuda")
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torch.testing.assert_close(out, ref)
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if __name__ == "__main__":
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test_matmul()
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