import subprocess import sys import pytest import torch import triton import triton.language as tl from triton.testing import get_dram_gbps, get_max_tensorcore_tflops DEVICE_NAME = 'v100' ####################### # Utilities ####################### def nvsmi(attrs): attrs = ','.join(attrs) cmd = ['nvidia-smi', '-i', '0', '--query-gpu=' + attrs, '--format=csv,noheader,nounits'] out = subprocess.check_output(cmd) ret = out.decode(sys.stdout.encoding).split(',') ret = [int(x) for x in ret] return ret ####################### # Matrix Multiplication ####################### sm_clocks = {'v100': 1350, 'a100': 1350} mem_clocks = {'v100': 877, 'a100': 1215} matmul_data = { 'v100': { # square (256, 256, 256): {'float16': 0.027}, (512, 512, 512): {'float16': 0.158}, (1024, 1024, 1024): {'float16': 0.466}, (2048, 2048, 2048): {'float16': 0.695}, (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) : {'a100': 0.}, # (64 , 64 , 65536) : {'a100': 0.}, # (256 , 256 , 8192 ) : {'a100': 0.}, # (256 , 256 , 32768) : {'a100': 0.}, } @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[DEVICE_NAME][(M, N, K)][dtype_str] cur_sm_clock = nvsmi(['clocks.current.sm'])[0] 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' 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 cur_gpu_util = cur_gpu_perf / max_gpu_perf triton.testing.assert_almost_equal(cur_gpu_util, ref_gpu_util, decimal=2) ####################### # Element-Wise ####################### @triton.jit def _add(x_ptr, y_ptr, output_ptr, n_elements, BLOCK_SIZE: tl.constexpr): pid = tl.program_id(axis=0) block_start = pid * BLOCK_SIZE offsets = block_start + tl.arange(0, BLOCK_SIZE) mask = offsets < n_elements x = tl.load(x_ptr + offsets, mask=mask) y = tl.load(y_ptr + offsets, mask=mask) output = x + y tl.store(output_ptr + offsets, output, mask=mask) elementwise_data = { 'v100': { 1024 * 16: 0.0219, 1024 * 64: 0.0791, 1024 * 256: 0.243, 1024 * 1024: 0.530, 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[DEVICE_NAME].keys()) def test_elementwise(N): torch.manual_seed(0) ref_gpu_util = elementwise_data[DEVICE_NAME][N] cur_mem_clock = nvsmi(['clocks.current.memory'])[0] ref_mem_clock = mem_clocks[DEVICE_NAME] max_gpu_perf = get_dram_gbps() assert abs(cur_mem_clock - ref_mem_clock) < 10, f'GPU memory must run at {ref_mem_clock} MHz' z = torch.empty((N, ), dtype=torch.float16, device='cuda') x = torch.randn_like(z) y = torch.randn_like(z) grid = lambda args: (triton.cdiv(N, args['BLOCK_SIZE']), ) fn = lambda: _add[grid](x, y, z, N, BLOCK_SIZE=1024) ms = triton.testing.do_bench(fn, percentiles=None, warmup=25, rep=250) cur_gpu_perf = 3. * N * z.element_size() / ms * 1e-6 cur_gpu_util = cur_gpu_perf / max_gpu_perf triton.testing.assert_almost_equal(cur_gpu_util, ref_gpu_util, decimal=2)