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[FRONTEND] Make the performance model work for int8, tf32, and fp32 (#456)

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daadaada
2022-02-12 14:34:42 +08:00
committed by GitHub
parent 9b100302d3
commit a9dfdcaaa9
5 changed files with 201 additions and 69 deletions
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126
python/test/regression/test_performance.py
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@@ -6,6 +6,9 @@ import torch
import triton
import triton.language as tl
from triton.testing import get_dram_gbps, get_max_tensorcore_tflops
DEVICE_NAME = 'v100'
#######################
# Utilities
@@ -25,42 +28,76 @@ def nvsmi(attrs):
# Matrix Multiplication
#######################
sm_clocks = {'v100': 1350, 'a100': 1350}
mem_clocks = {'v100': 877, 'a100': 1215}
matmul_data = {
# square
(256, 256, 256): {'v100': 0.027},
(512, 512, 512): {'v100': 0.158},
(1024, 1024, 1024): {'v100': 0.466},
(2048, 2048, 2048): {'v100': 0.680},
(4096, 4096, 4096): {'v100': 0.831},
(8192, 8192, 8192): {'v100': 0.849},
# tall-skinny
(16, 1024, 1024): {'v100': 0.0128},
(16, 4096, 4096): {'v100': 0.0883},
(16, 8192, 8192): {'v100': 0.101},
(64, 1024, 1024): {'v100': 0.073},
(64, 4096, 4096): {'v100': 0.270},
(64, 8192, 8192): {'v100': 0.360},
(1024, 64, 1024): {'v100': 0.0692},
(4096, 64, 4096): {'v100': 0.264},
(8192, 64, 8192): {'v100': 0.323},
'v100': {
# square
(256, 256, 256): {'float16': 0.027},
(512, 512, 512): {'float16': 0.158},
(1024, 1024, 1024): {'float16': 0.466},
(2048, 2048, 2048): {'float16': 0.680},
(4096, 4096, 4096): {'float16': 0.831},
(8192, 8192, 8192): {'float16': 0.849},
# tall-skinny
(16, 1024, 1024): {'float16': 0.0128},
(16, 4096, 4096): {'float16': 0.0883},
(16, 8192, 8192): {'float16': 0.101},
(64, 1024, 1024): {'float16': 0.073},
(64, 4096, 4096): {'float16': 0.270},
(64, 8192, 8192): {'float16': 0.459},
(1024, 64, 1024): {'float16': 0.0692},
(4096, 64, 4096): {'float16': 0.264},
(8192, 64, 8192): {'float16': 0.452},
},
'a100': {
(256, 256, 256): {'float16': 0.010, 'float32': 0.0214, 'int8': 0.006},
(512, 512, 512): {'float16': 0.061, 'float32': 0.109, 'int8': 0.030},
(1024, 1024, 1024): {'float16': 0.287, 'float32': 0.331, 'int8': 0.169},
(2048, 2048, 2048): {'float16': 0.604, 'float32': 0.599, 'int8': 0.385},
(4096, 4096, 4096): {'float16': 0.842, 'float32': 0.862, 'int8': 0.711},
(8192, 8192, 8192): {'float16': 0.896, 'float32': 0.932, 'int8': 0.860},
# tall-skinny
(16, 1024, 1024): {'float16': 0.0077, 'float32': 0.0127, 'int8': 0.005},
(16, 4096, 4096): {'float16': 0.0363, 'float32': 0.0457, 'int8': 0.0259},
(16, 8192, 8192): {'float16': 0.0564, 'float32': 0.0648, 'int8': 0.0431},
(64, 1024, 1024): {'float16': 0.0271, 'float32': 0.0509, 'int8': 0.0169},
(64, 4096, 4096): {'float16': 0.141, 'float32': 0.162, 'int8': 0.097},
(64, 8192, 8192): {'float16': 0.244, 'float32': 0.257, 'int8': 0.174},
(1024, 64, 1024): {'float16': 0.0263, 'float32': 0.0458, 'int8': 0.017},
(4096, 64, 4096): {'float16': 0.135, 'float32': 0.177, 'int8': 0.102},
(8192, 64, 8192): {'float16': 0.216, 'float32': 0.230, 'int8': 0.177},
}
# # deep reductions
# (64 , 64 , 16384) : {'v100': 0.},
# (64 , 64 , 65536) : {'v100': 0.},
# (256 , 256 , 8192 ) : {'v100': 0.},
# (256 , 256 , 32768) : {'v100': 0.},
# (64 , 64 , 16384) : {'a100': 0.},
# (64 , 64 , 65536) : {'a100': 0.},
# (256 , 256 , 8192 ) : {'a100': 0.},
# (256 , 256 , 32768) : {'a100': 0.},
}
@pytest.mark.parametrize('M, N, K', matmul_data.keys())
def test_matmul(M, N, K):
@pytest.mark.parametrize('M, N, K, dtype_str',
[(M, N, K, dtype_str)
for M, N, K in matmul_data[DEVICE_NAME].keys()
for dtype_str in ['float16']])
def test_matmul(M, N, K, dtype_str):
if dtype_str in ['float32', 'int8'] and DEVICE_NAME != 'a100':
pytest.skip('Only test float32 & int8 on a100')
dtype = {'float16': torch.float16, 'float32': torch.float32, 'int8': torch.int8}[dtype_str]
torch.manual_seed(0)
ref_gpu_util = matmul_data[(M, N, K)]['v100']
ref_gpu_util = matmul_data[DEVICE_NAME][(M, N, K)][dtype_str]
cur_sm_clock = nvsmi(['clocks.current.sm'])[0]
ref_sm_clock = 1350
max_gpu_perf = 1e-6 * 80 * 8 * 128 * cur_sm_clock
ref_sm_clock = sm_clocks[DEVICE_NAME]
max_gpu_perf = get_max_tensorcore_tflops(dtype, clock_rate=cur_sm_clock * 1e3)
assert abs(cur_sm_clock - ref_sm_clock) < 10, f'GPU SMs must run at {ref_sm_clock} MHz'
a = torch.randn((M, K), dtype=torch.float16, device='cuda')
b = torch.randn((K, N), dtype=torch.float16, device='cuda')
if dtype == torch.int8:
a = torch.randint(-128, 127, (M, K), dtype=dtype, device='cuda')
b = torch.randint(-128, 127, (N, K), dtype=dtype, device='cuda')
b = b.t() # only test row-col layout
else:
a = torch.randn((M, K), dtype=dtype, device='cuda')
b = torch.randn((K, N), dtype=dtype, device='cuda')
fn = lambda: triton.ops.matmul(a, b)
ms = triton.testing.do_bench(fn, percentiles=None, warmup=25, rep=1000)
cur_gpu_perf = 2. * M * N * K / ms * 1e-9
@@ -87,23 +124,34 @@ def _add(x_ptr, y_ptr, output_ptr, n_elements,
elementwise_data = {
1024 * 16: {'v100': 0.0219},
1024 * 64: {'v100': 0.0791},
1024 * 256: {'v100': 0.243},
1024 * 1024: {'v100': 0.534},
1024 * 4096: {'v100': 0.796},
1024 * 16384: {'v100': 0.905},
1024 * 65536: {'v100': 0.939},
'v100': {
1024 * 16: 0.0219,
1024 * 64: 0.0791,
1024 * 256: 0.243,
1024 * 1024: 0.534,
1024 * 4096: 0.796,
1024 * 16384: 0.905,
1024 * 65536: 0.939,
},
'a100': {
1024 * 16: 0.008,
1024 * 64: 0.034,
1024 * 256: 0.114,
1024 * 1024: 0.315,
1024 * 4096: 0.580,
1024 * 16384: 0.782,
1024 * 65536: 0.850,
}
}
@pytest.mark.parametrize('N', elementwise_data.keys())
@pytest.mark.parametrize('N', elementwise_data[DEVICE_NAME].keys())
def test_elementwise(N):
torch.manual_seed(0)
ref_gpu_util = elementwise_data[N]['v100']
ref_gpu_util = elementwise_data[DEVICE_NAME][N]
cur_mem_clock = nvsmi(['clocks.current.memory'])[0]
ref_mem_clock = 877
max_gpu_perf = 512 * 2 * ref_mem_clock * 1e-3
ref_mem_clock = mem_clocks[DEVICE_NAME]
max_gpu_perf = get_dram_gbps()
assert abs(cur_mem_clock - ref_mem_clock) < 10, f'GPU memmory must run at {ref_mem_clock} MHz'
z = torch.empty((N, ), dtype=torch.float16, device='cuda')
x = torch.randn_like(z)