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 = {7: 'v100', 8: 'a100'}[torch.cuda.get_device_capability()[0]] ####################### # 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 (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}, }, # NOTE: # A100 in the CI server is slow-ish for some reason. # On some other servers, we are getting about 90% peak for 8kx8x8k float16 'a100': { (512, 512, 512): {'float16': 0.08, 'float32': 0.13, 'int8': 0.05}, (1024, 1024, 1024): {'float16': 0.33, 'float32': 0.35, 'int8': 0.169}, (2048, 2048, 2048): {'float16': 0.64, 'float32': 0.57, 'int8': 0.34}, (4096, 4096, 4096): {'float16': 0.81, 'float32': 0.75, 'int8': 0.46}, (8192, 8192, 8192): {'float16': 0.77, 'float32': 0.85, 'int8': 0.51}, # 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': 0.07, 'float32': 0.0648, 'int8': 0.0431}, (64, 1024, 1024): {'float16': 0.0271, 'float32': 0.0509, 'int8': 0.0169}, (64, 4096, 4096): {'float16': 0.16, 'float32': 0.162, 'int8': 0.097}, (64, 8192, 8192): {'float16': 0.30, 'float32': 0.257, 'int8': 0.174}, (1024, 64, 1024): {'float16': 0.0263, 'float32': 0.0458, 'int8': 0.017}, (4096, 64, 4096): {'float16': 0.16, 'float32': 0.177, 'int8': 0.102}, (8192, 64, 8192): {'float16': 0.25, 'float32': 0.230, 'int8': 0.177}, } } @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] max_gpu_perf = get_max_tensorcore_tflops(dtype, clock_rate=cur_sm_clock * 1e3) 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=100, rep=300) cur_gpu_perf = 2. * M * N * K / ms * 1e-9 cur_gpu_util = cur_gpu_perf / max_gpu_perf assert triton.testing.allclose(cur_gpu_util, ref_gpu_util, atol=0.01, rtol=0.05) ####################### # 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] max_gpu_perf = get_dram_gbps() 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=100, rep=500) cur_gpu_perf = 3. * N * z.element_size() / ms * 1e-6 cur_gpu_util = cur_gpu_perf / max_gpu_perf assert triton.testing.allclose(cur_gpu_util, ref_gpu_util, atol=0.01, rtol=0.05) ####################### # Flash-Attention ####################### flash_attention_data = { "a100": { (4, 48, 4096, 64, 'forward', 'float16'): 0.37, (4, 48, 4096, 64, 'backward', 'float16'): 0.25, } } @pytest.mark.parametrize("Z, H, N_CTX, D_HEAD", [[4, 48, 4096, 64]]) @pytest.mark.parametrize("mode", ['forward', 'backward']) @pytest.mark.parametrize("dtype_str", ['float16']) def test_flash_attention(Z, H, N_CTX, D_HEAD, mode, dtype_str): is_backward = mode == 'backward' capability = torch.cuda.get_device_capability() if capability[0] < 8: pytest.skip("Flash attention only supported for compute capability < 80") torch.manual_seed(20) dtype = {'float16': torch.float16, 'float32': torch.float32, 'int8': torch.int8}[dtype_str] # init data q = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0.1, std=0.2).requires_grad_() k = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0.4, std=0.2).requires_grad_() v = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0.3, std=0.2).requires_grad_() sm_scale = 0.2 # benchmark fn = lambda: triton.ops.attention(q, k, v, sm_scale) if is_backward: o = fn() do = torch.randn_like(o) fn = lambda: o.backward(do, retain_graph=True) ms = triton.testing.do_bench(fn, percentiles=None, warmup=100, rep=500) # compute flops flops_per_matmul = 2. * Z * H * N_CTX * N_CTX * D_HEAD * 0.5 total_flops = 2 * flops_per_matmul if is_backward: total_flops *= 2.5 # 2.0(bwd) + 0.5(recompute) cur_gpu_perf = total_flops / ms * 1e-9 # maximum flops cur_sm_clock = nvsmi(['clocks.current.sm'])[0] max_gpu_perf = get_max_tensorcore_tflops(dtype, clock_rate=cur_sm_clock * 1e3) cur_gpu_util = cur_gpu_perf / max_gpu_perf ref_gpu_util = flash_attention_data[DEVICE_NAME][(Z, H, N_CTX, D_HEAD, mode, dtype_str)] assert triton.testing.allclose(cur_gpu_util, ref_gpu_util, atol=0.01, rtol=0.05)