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Jokeren
164
python/tests/test_performance.py
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164
python/tests/test_performance.py
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import subprocess
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import sys
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import pytest
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import torch
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import triton
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import triton.language as tl
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from triton.testing import get_dram_gbps, get_max_tensorcore_tflops, set_gpu_clock
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DEVICE_NAME = 'v100'
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#######################
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# Utilities
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#######################
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def nvsmi(attrs):
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attrs = ','.join(attrs)
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cmd = ['nvidia-smi', '-i', '0', '--query-gpu=' + attrs, '--format=csv,noheader,nounits']
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out = subprocess.check_output(cmd)
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ret = out.decode(sys.stdout.encoding).split(',')
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ret = [int(x) for x in ret]
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return ret
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#######################
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# Matrix Multiplication
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#######################
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sm_clocks = {'v100': 1350, 'a100': 1350}
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mem_clocks = {'v100': 877, 'a100': 1215}
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matmul_data = {
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'v100': {
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# square
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(256, 256, 256): {'float16': 0.027},
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(512, 512, 512): {'float16': 0.158},
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(1024, 1024, 1024): {'float16': 0.466},
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(2048, 2048, 2048): {'float16': 0.695},
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(4096, 4096, 4096): {'float16': 0.831},
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(8192, 8192, 8192): {'float16': 0.849},
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# tall-skinny
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(16, 1024, 1024): {'float16': 0.0128},
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(16, 4096, 4096): {'float16': 0.0883},
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(16, 8192, 8192): {'float16': 0.101},
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(64, 1024, 1024): {'float16': 0.073},
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(64, 4096, 4096): {'float16': 0.270},
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(64, 8192, 8192): {'float16': 0.459},
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(1024, 64, 1024): {'float16': 0.0692},
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(4096, 64, 4096): {'float16': 0.264},
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(8192, 64, 8192): {'float16': 0.452},
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},
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'a100': {
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(256, 256, 256): {'float16': 0.010, 'float32': 0.0214, 'int8': 0.006},
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(512, 512, 512): {'float16': 0.061, 'float32': 0.109, 'int8': 0.030},
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(1024, 1024, 1024): {'float16': 0.287, 'float32': 0.331, 'int8': 0.169},
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(2048, 2048, 2048): {'float16': 0.604, 'float32': 0.599, 'int8': 0.385},
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(4096, 4096, 4096): {'float16': 0.842, 'float32': 0.862, 'int8': 0.711},
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(8192, 8192, 8192): {'float16': 0.896, 'float32': 0.932, 'int8': 0.860},
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# tall-skinny
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(16, 1024, 1024): {'float16': 0.0077, 'float32': 0.0127, 'int8': 0.005},
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(16, 4096, 4096): {'float16': 0.0363, 'float32': 0.0457, 'int8': 0.0259},
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(16, 8192, 8192): {'float16': 0.0564, 'float32': 0.0648, 'int8': 0.0431},
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(64, 1024, 1024): {'float16': 0.0271, 'float32': 0.0509, 'int8': 0.0169},
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(64, 4096, 4096): {'float16': 0.141, 'float32': 0.162, 'int8': 0.097},
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(64, 8192, 8192): {'float16': 0.244, 'float32': 0.257, 'int8': 0.174},
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(1024, 64, 1024): {'float16': 0.0263, 'float32': 0.0458, 'int8': 0.017},
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(4096, 64, 4096): {'float16': 0.135, 'float32': 0.177, 'int8': 0.102},
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(8192, 64, 8192): {'float16': 0.216, 'float32': 0.230, 'int8': 0.177},
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}
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# # deep reductions
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# (64 , 64 , 16384) : {'a100': 0.},
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# (64 , 64 , 65536) : {'a100': 0.},
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# (256 , 256 , 8192 ) : {'a100': 0.},
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# (256 , 256 , 32768) : {'a100': 0.},
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}
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@pytest.mark.parametrize('M, N, K, dtype_str',
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[(M, N, K, dtype_str)
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for M, N, K in matmul_data[DEVICE_NAME].keys()
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for dtype_str in ['float16']])
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def test_matmul(M, N, K, dtype_str):
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if dtype_str in ['float32', 'int8'] and DEVICE_NAME != 'a100':
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pytest.skip('Only test float32 & int8 on a100')
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dtype = {'float16': torch.float16, 'float32': torch.float32, 'int8': torch.int8}[dtype_str]
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torch.manual_seed(0)
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ref_gpu_util = matmul_data[DEVICE_NAME][(M, N, K)][dtype_str]
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ref_sm_clock = sm_clocks[DEVICE_NAME]
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cur_sm_clock = nvsmi(['clocks.current.sm'])[0]
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max_gpu_perf = get_max_tensorcore_tflops(dtype, clock_rate=cur_sm_clock * 1e3)
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assert abs(cur_sm_clock - ref_sm_clock) < 10, f'GPU SMs must run at {ref_sm_clock} MHz'
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if dtype == torch.int8:
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a = torch.randint(-128, 127, (M, K), dtype=dtype, device='cuda')
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b = torch.randint(-128, 127, (N, K), dtype=dtype, device='cuda')
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b = b.t() # only test row-col layout
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else:
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a = torch.randn((M, K), dtype=dtype, device='cuda')
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b = torch.randn((K, N), dtype=dtype, device='cuda')
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fn = lambda: triton.ops.matmul(a, b)
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ms = triton.testing.do_bench(fn, percentiles=None, warmup=25, rep=1000)
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cur_gpu_perf = 2. * M * N * K / ms * 1e-9
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cur_gpu_util = cur_gpu_perf / max_gpu_perf
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triton.testing.assert_almost_equal(cur_gpu_util, ref_gpu_util, decimal=2)
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#######################
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# Element-Wise
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#######################
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@triton.jit
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def _add(x_ptr, y_ptr, output_ptr, n_elements,
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BLOCK_SIZE: tl.constexpr):
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pid = tl.program_id(axis=0)
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block_start = pid * BLOCK_SIZE
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offsets = block_start + tl.arange(0, BLOCK_SIZE)
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mask = offsets < n_elements
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x = tl.load(x_ptr + offsets, mask=mask)
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y = tl.load(y_ptr + offsets, mask=mask)
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output = x + y
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tl.store(output_ptr + offsets, output, mask=mask)
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elementwise_data = {
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'v100': {
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1024 * 16: 0.0219,
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1024 * 64: 0.0791,
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1024 * 256: 0.243,
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1024 * 1024: 0.530,
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1024 * 4096: 0.796,
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1024 * 16384: 0.905,
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1024 * 65536: 0.939,
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},
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'a100': {
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1024 * 16: 0.008,
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1024 * 64: 0.034,
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1024 * 256: 0.114,
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1024 * 1024: 0.315,
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1024 * 4096: 0.580,
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1024 * 16384: 0.782,
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1024 * 65536: 0.850,
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}
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}
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@pytest.mark.parametrize('N', elementwise_data[DEVICE_NAME].keys())
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def test_elementwise(N):
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torch.manual_seed(0)
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ref_gpu_util = elementwise_data[DEVICE_NAME][N]
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cur_mem_clock = nvsmi(['clocks.current.memory'])[0]
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ref_mem_clock = mem_clocks[DEVICE_NAME]
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max_gpu_perf = get_dram_gbps()
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assert abs(cur_mem_clock - ref_mem_clock) < 10, f'GPU memory must run at {ref_mem_clock} MHz'
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z = torch.empty((N, ), dtype=torch.float16, device='cuda')
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x = torch.randn_like(z)
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y = torch.randn_like(z)
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grid = lambda args: (triton.cdiv(N, args['BLOCK_SIZE']), )
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fn = lambda: _add[grid](x, y, z, N, BLOCK_SIZE=1024)
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ms = triton.testing.do_bench(fn, percentiles=None, warmup=25, rep=250)
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cur_gpu_perf = 3. * N * z.element_size() / ms * 1e-6
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cur_gpu_util = cur_gpu_perf / max_gpu_perf
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triton.testing.assert_almost_equal(cur_gpu_util, ref_gpu_util, decimal=2)
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