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[PYTHON] re-activated auto-tuner configurations for triton.ops.matmul (#212)

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This commit is contained in:
Philippe Tillet
2021-08-16 22:56:21 -07:00
committed by GitHub
parent bb1eebb4b4
commit a714b6b856
2 changed files with 28 additions and 14 deletions
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2
python/test/test_matmul.py
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@@ -66,7 +66,7 @@ import torch
def test_op(BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K, NWARP, NSTAGE, M, N, K, AT, BT, DTYPE): def test_op(BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K, NWARP, NSTAGE, M, N, K, AT, BT, DTYPE):
torch.manual_seed(0) torch.manual_seed(0)
# nuke kernel decorators -- will set meta-parameters manually # nuke kernel decorators -- will set meta-parameters manually
META = {'BLOCK_M': BLOCK_M, 'BLOCK_N': BLOCK_N, 'BLOCK_K': BLOCK_K, 'SPLIT_K': SPLIT_K, 'GROUP_M': 8} META = {'BLOCK_M': BLOCK_M, 'BLOCK_N': BLOCK_N, 'BLOCK_K': BLOCK_K, 'SPLIT_K': SPLIT_K}
configs = [triton.Config(meta=META, num_warps=NWARP, num_stages=NSTAGE)] configs = [triton.Config(meta=META, num_warps=NWARP, num_stages=NSTAGE)]
kernel = triton.ops._matmul.kernel kernel = triton.ops._matmul.kernel
decorators = kernel.kernel_decorators decorators = kernel.kernel_decorators

40
python/triton/ops/matmul.py
View File

@@ -8,19 +8,25 @@ import triton
}) })
@triton.autotune( @triton.autotune(
configs=[ configs=[
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1, 'GROUP_M': 8}, num_warps=4), triton.Config({'BLOCK_M': 128, 'BLOCK_N': 256, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
# triton.Config({'BLOCK_M': 64 , 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1, 'GROUP_M': 8}, num_warps=4),\ triton.Config({'BLOCK_M': 256, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
# triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64 , 'BLOCK_K': 32, 'SPLIT_K': 1, 'GROUP_M': 8}, num_warps=4),\ triton.Config({'BLOCK_M': 256, 'BLOCK_N': 64, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
# triton.Config({'BLOCK_M': 64 , 'BLOCK_N': 64 , 'BLOCK_K': 64, 'SPLIT_K': 1, 'GROUP_M': 8}, num_warps=4),\ triton.Config({'BLOCK_M': 64 , 'BLOCK_N': 256, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
# triton.Config({'BLOCK_M': 32 , 'BLOCK_N': 128, 'BLOCK_K': 64, 'SPLIT_K': 1, 'GROUP_M': 8}, num_warps=4), triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
# triton.Config({'BLOCK_M': 128, 'BLOCK_N': 32 , 'BLOCK_K': 64, 'SPLIT_K': 1, 'GROUP_M': 8}, num_warps=4),\ triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64 , 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
# triton.Config({'BLOCK_M': 64 , 'BLOCK_N': 32 , 'BLOCK_K': 64, 'SPLIT_K': 1, 'GROUP_M': 8}, num_warps=2),\ triton.Config({'BLOCK_M': 64 , 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
# triton.Config({'BLOCK_M': 32 , 'BLOCK_N': 64 , 'BLOCK_K': 64, 'SPLIT_K': 1, 'GROUP_M': 8}, num_warps=2), triton.Config({'BLOCK_M': 128, 'BLOCK_N': 32 , 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64 , 'BLOCK_N': 32 , 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=5, num_warps=2),
triton.Config({'BLOCK_M': 32 , 'BLOCK_N': 64 , 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=5, num_warps=2),
], ],
key=['M', 'N', 'K'] key=['M', 'N', 'K'],
) )
@triton.jit @triton.jit
def _kernel(A, B, C, M, N, K, stride_am, stride_ak, stride_bk, stride_bn, stride_cm, stride_cn, LOCKS, **META): def _kernel(A, B, C, M, N, K,
stride_am, stride_ak,
stride_bk, stride_bn,
stride_cm, stride_cn,
LOCKS, **META):
# extract meta-parameters # extract meta-parameters
BLOCK_M = META['BLOCK_M'] BLOCK_M = META['BLOCK_M']
BLOCK_N = META['BLOCK_N'] BLOCK_N = META['BLOCK_N']
@@ -40,12 +46,14 @@ def _kernel(A, B, C, M, N, K, stride_am, stride_ak, stride_bk, stride_bn, stride
pid_n = (pid % width) // (group_size) pid_n = (pid % width) // (group_size)
# do matrix multiplication # do matrix multiplication
rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
ram = tl.max_contiguous(tl.multiple_of(rm % M, BLOCK_M), BLOCK_M)
rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
rbn = tl.max_contiguous(tl.multiple_of(rn % N, BLOCK_N), BLOCK_N)
rk = tl.arange(0, BLOCK_K) rk = tl.arange(0, BLOCK_K)
# pointers # pointers
K = K // SPLIT_K K = K // SPLIT_K
A = A + (pid_z * K * stride_ak + rm[:, None] * stride_am + rk[None, :] * stride_ak) A = A + (pid_z * K * stride_ak + ram[:, None] * stride_am + rk[None, :] * stride_ak)
B = B + (pid_z * K * stride_bk + rk[:, None] * stride_bk + rn[None, :] * stride_bn) B = B + (pid_z * K * stride_bk + rk[:, None] * stride_bk + rbn[None, :] * stride_bn)
acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32) acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
for k in range(K, 0, -BLOCK_K): for k in range(K, 0, -BLOCK_K):
if META['EVEN_K']: if META['EVEN_K']:
@@ -106,7 +114,13 @@ class _matmul(torch.autograd.Function):
locks = _matmul._locks[device] locks = _matmul._locks[device]
# launch kernel # launch kernel
grid = lambda META: (triton.cdiv(M, META['BLOCK_M']) * triton.cdiv(N, META['BLOCK_N']), META['SPLIT_K']) grid = lambda META: (triton.cdiv(M, META['BLOCK_M']) * triton.cdiv(N, META['BLOCK_N']), META['SPLIT_K'])
_kernel[grid](a, b, c, M, N, K, a.stride(0), a.stride(1), b.stride(0), b.stride(1), c.stride(0), c.stride(1), locks) _kernel[grid](a, b, c,
M, N, K,
a.stride(0), a.stride(1),
b.stride(0), b.stride(1),
c.stride(0), c.stride(1),
locks,
GROUP_M=8)
# done # done
return c return c