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[PYTHON] Added automated benchmark script (#63)

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This adds a bench functionality to the setup.py that can be used to run the benchmark suite and generates a bunch of csv files (and optionally plots)

python setup.py bench
python setup.py bench --with-plots
python setup.py bench --filter=cross_entropy
This commit is contained in:
Philippe Tillet
2021-02-08 12:16:41 -08:00
committed by Philippe Tillet
parent 66c94f21d7
commit 5e3c7f5a60
12 changed files with 472 additions and 339 deletions
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63
python/test/test_matmul.py Normal file
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import pytest
import itertools
import triton
import torch
@pytest.mark.parametrize(
"TM, TN, TK, TZ, NWARP, M, N, K, AT, BT, DTYPE",
itertools.chain(*[
[
# 1 warp
(16, 16, 16, 1, 1, None, None, None, AT, BT, DTYPE),
(32, 16, 16, 1, 1, None, None, None, AT, BT, DTYPE),
(16, 32, 16, 1, 1, None, None, None, AT, BT, DTYPE),
(16, 16, 32, 1, 1, None, None, None, AT, BT, DTYPE),
(32, 16, 32, 1, 1, None, None, None, AT, BT, DTYPE),
(16, 32, 32, 1, 1, None, None, None, AT, BT, DTYPE),
(16, 16, 64, 1, 1, None, None, None, AT, BT, DTYPE),
(64, 16, 64, 1, 1, None, None, None, AT, BT, DTYPE),
(16, 64, 64, 1, 1, None, None, None, AT, BT, DTYPE),
# 2 warp
(64, 32, 64, 1, 2, None, None, None, AT, BT, DTYPE),
(32, 64, 64, 1, 2, None, None, None, AT, BT, DTYPE),
(64, 32, 16, 1, 2, None, None, None, AT, BT, DTYPE),
(32, 64, 16, 1, 2, None, None, None, AT, BT, DTYPE),
(128, 32, 32, 1, 2, None, None, None, AT, BT, DTYPE),
(32, 128, 32, 1, 2, None, None, None, AT, BT, DTYPE),
# 4 warp
(128, 64, 16, 1, 4, None, None, None, AT, BT, DTYPE),
(64, 128, 16, 1, 4, None, None, None, AT, BT, DTYPE),
(128, 32, 32, 1, 4, None, None, None, AT, BT, DTYPE),
(32, 128, 32, 1, 4, None, None, None, AT, BT, DTYPE),
(128, 32, 64, 1, 4, None, None, None, AT, BT, DTYPE),
(32, 128, 64, 1, 4, None, None, None, AT, BT, DTYPE),
# 8 warp
(128, 256, 16, 1, 8, None, None, None, AT, BT, DTYPE),
(256, 128, 16, 1, 8, None, None, None, AT, BT, DTYPE),
(256, 128, 32, 1, 8, None, None, None, AT, BT, DTYPE),
# split-k
(64, 64, 16, 2, 4, None, None, None, AT, BT, DTYPE),
(64, 64, 16, 4, 4, None, None, None, AT, BT, DTYPE),
(64, 64, 16, 8, 4, None, None, None, AT, BT, DTYPE),
# variable input
(128, 128, 32, 1, 4, 256, 256, 256, AT, BT, DTYPE),
(128, 128, 32, 1, 4, 384, 128, 640, AT, BT, DTYPE),
(128, 128, 32, 1, 4, 107, 233, 256, AT, BT, DTYPE),
(128, 128, 32, 1, 4, 107, 233, 311, AT, BT, DTYPE)
] for DTYPE in ['float16'] for AT in [False, True] for BT in [False, True]
]))
def test_op(TM, TN, TK, TZ, NWARP, M, N, K, AT, BT, DTYPE):
DTYPE = {'float16': torch.float16, 'float32': torch.float32}[DTYPE]
torch.manual_seed(0)
triton.ops._matmul._kernels = dict()
triton.ops._matmul._CONFIGS = [({'TM': str(TM), 'TN': str(TN), 'TK': str(TK), 'TZ': str(TZ)}, NWARP)]
if M is None: M = TM
if N is None: N = TN
if K is None: K = TK * TZ
a = torch.randn((K, M) if AT else (M, K), device='cuda', dtype=DTYPE) / K**.5
b = torch.randn((N, K) if BT else (K, N), device='cuda', dtype=DTYPE) / K**.5
a = a.t() if AT else a
b = b.t() if BT else b
th_c = torch.matmul(a, b)
tt_c = triton.ops.matmul(a, b)
assert triton.testing.allclose(th_c, tt_c)