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[LANG] Fixed semantics of NaN in float comparisons (#281)

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This commit is contained in:
Philippe Tillet
2021-09-13 15:06:29 -07:00
committed by GitHub
parent cecca90bea
commit 3e395bc84e
8 changed files with 46 additions and 17 deletions
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89
python/test/unit/operators/test_matmul.py Normal file
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import pytest
import itertools
import triton
import torch
@pytest.mark.parametrize(
"BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K, NWARP, NSTAGE, M, N, K, AT, BT, DTYPE",
itertools.chain(
*[
[
# 1 warp
(16, 16, 16, 1, 1, 2, None, None, None, AT, BT, DTYPE),
(32, 16, 16, 1, 1, 2, None, None, None, AT, BT, DTYPE),
(16, 32, 16, 1, 1, 2, None, None, None, AT, BT, DTYPE),
(16, 16, 32, 1, 1, 2, None, None, None, AT, BT, DTYPE),
(32, 16, 32, 1, 1, 2, None, None, None, AT, BT, DTYPE),
(16, 32, 32, 1, 1, 2, None, None, None, AT, BT, DTYPE),
(16, 16, 64, 1, 1, 2, None, None, None, AT, BT, DTYPE),
(64, 16, 64, 1, 1, 2, None, None, None, AT, BT, DTYPE),
(16, 64, 64, 1, 1, 2, None, None, None, AT, BT, DTYPE),
# 2 warp
(64, 32, 64, 1, 2, 2, None, None, None, AT, BT, DTYPE),
(32, 64, 64, 1, 2, 2, None, None, None, AT, BT, DTYPE),
(64, 32, 16, 1, 2, 2, None, None, None, AT, BT, DTYPE),
(32, 64, 16, 1, 2, 2, None, None, None, AT, BT, DTYPE),
(128, 32, 32, 1, 2, 2, None, None, None, AT, BT, DTYPE),
(32, 128, 32, 1, 2, 2, None, None, None, AT, BT, DTYPE),
# 4 warp
(128, 64, 16, 1, 4, 2, None, None, None, AT, BT, DTYPE),
(64, 128, 16, 1, 4, 2, None, None, None, AT, BT, DTYPE),
(128, 32, 32, 1, 4, 2, None, None, None, AT, BT, DTYPE),
(32, 128, 32, 1, 4, 2, None, None, None, AT, BT, DTYPE),
(128, 32, 64, 1, 4, 2, None, None, None, AT, BT, DTYPE),
(32, 128, 64, 1, 4, 2, None, None, None, AT, BT, DTYPE),
# 8 warp
(128, 256, 16, 1, 8, 2, None, None, None, AT, BT, DTYPE),
(256, 128, 16, 1, 8, 2, None, None, None, AT, BT, DTYPE),
(256, 128, 32, 1, 8, 2, None, None, None, AT, BT, DTYPE),
# split-k
(64, 64, 16, 2, 4, 2, None, None, None, AT, BT, DTYPE),
(64, 64, 16, 4, 4, 2, None, None, None, AT, BT, DTYPE),
(64, 64, 16, 8, 4, 2, None, None, None, AT, BT, DTYPE),
# variable input
(128, 128, 32, 1, 4, 2, 1024, 1024, 1024, AT, BT, DTYPE),
(128, 128, 32, 1, 4, 2, 384, 128, 640, AT, BT, DTYPE),
(128, 128, 32, 1, 4, 2, 107, 233, 256, AT, BT, DTYPE),
(128, 128, 32, 1, 4, 2, 107, 233, 311, AT, BT, DTYPE),
] for DTYPE in ["float16", "float32"] for AT in [False, True] for BT in [False, True]
],
# n-stage
*[
[
(16, 16, 16, 1, 1, STAGES, 1024, 1024, 1024, AT, BT, DTYPE),
(64, 32, 64, 1, 2, STAGES, 1024, 1024, 1024, AT, BT, DTYPE),
(128, 64, 16, 1, 4, STAGES, 1024, 1024, 1024, AT, BT, DTYPE),
(256, 128, 32, 1, 8, STAGES, 1024, 1024, 1024, AT, BT, DTYPE),
(128, 128, 32, 1, 4, STAGES, 384, 128, 640, AT, BT, DTYPE),
# split-k
(64, 64, 16, 8, 4, STAGES, 1024, 1024, 1024, AT, BT, DTYPE),
(64, 64, 16, 8, 4, STAGES, 1024, 1024, 32, AT, BT, DTYPE),
] for DTYPE in ["float16", "float32"] for AT in [False, True] for BT in [False, True] for STAGES in [2, 3, 4]
]
),
)
def test_op(BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K, NWARP, NSTAGE, M, N, K, AT, BT, DTYPE):
torch.manual_seed(0)
# 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}
configs = [triton.Config(meta=META, num_warps=NWARP, num_stages=NSTAGE)]
kernel = triton.ops._matmul.kernel
decorators = kernel.kernel_decorators
kernel.kernel_decorators = []
triton.autotune(configs, [])(kernel)
kernel.kernel_decorators += decorators[1:]
# get matrix shape
M = BLOCK_M if M is None else M
N = BLOCK_N if N is None else N
K = BLOCK_K * SPLIT_K if K is None else K
# allocate/transpose inputs
DTYPE = {"float16": torch.float16, "float32": torch.float32}[DTYPE]
a = .1*torch.randn((K, M) if AT else (M, K), device="cuda", dtype=DTYPE)
b = .1*torch.randn((N, K) if BT else (K, N), device="cuda", dtype=DTYPE)
a = a.t() if AT else a
b = b.t() if BT else b
# run test
th_c = torch.matmul(a, b)
tt_c = triton.testing.catch_oor(lambda : triton.ops.matmul(a, b), pytest)
triton.testing.assert_almost_equal(th_c, tt_c)