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triton/python/tests/test_vecadd_no_scf.py

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import torch
from torch.testing import assert_allclose
import triton
import triton.language as tl
import triton.runtime as runtime
NUM_WARPS = 4
BLOCK_SIZE = 256
# triton kernel
def test_vecadd_no_scf():
@triton.jit
def kernel(x_ptr, stride_xn,
y_ptr, stride_yn,
z_ptr, stride_zn,
BLOCK_SIZE_N: tl.constexpr):
pid = tl.program_id(axis=0)
offset = pid * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
x_ptrs = x_ptr + offset
y_ptrs = y_ptr + offset
x = tl.load(x_ptrs)
y = tl.load(y_ptrs)
z = x + y
z_ptrs = z_ptr + offset
tl.store(z_ptrs, z)
ptx, shem_size, kernel_name = triton.compile(kernel, "*fp32,i32,*fp32,i32,*fp32,i32", constants={"BLOCK_SIZE_N": 256}, num_warps=NUM_WARPS, device=0, output="ptx")
torch.zeros([10], device=torch.device('cuda'))
device = torch.cuda.current_device()
binary = runtime.build_kernel(kernel, "*fp32,i32,*fp32,i32,*fp32,i32",
device=device,
constants={"BLOCK_SIZE_N": BLOCK_SIZE},
num_warps=NUM_WARPS,
num_stages=3)
grid = lambda META: (1, )
x = torch.randn((256,), device='cuda', dtype=torch.float32)
y = torch.randn((256,), device='cuda', dtype=torch.float32)
z = torch.empty((256,), device=x.device, dtype=x.dtype)
runtime.launch_kernel(fn=kernel,
binary=binary,
grid=grid,
num_warps=NUM_WARPS,
num_stages=3,
x_ptr=x,
stride_xn=x.stride(0),
y_ptr=y,
stride_yn=y.stride(0),
z_ptr=z,
stride_zn=z.stride(0),
BLOCK_SIZE_N=tl.constexpr(BLOCK_SIZE))
golden_z = x + y
assert_allclose(z, golden_z, rtol=1e-7, atol=1e-7)