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d39d78fa08
Adding new tests across the board for float32, bfloat16, non-powers-of-2 shapes (to test masks), and tests on sequence parallel for atomics. This also adds the sequence parallel features from https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/flash_attn_triton.py. I am not sure about the best way to grab the baseline benchmarking numbers. I have access to V100s and A100s, but I saw on the tests it mentions " # A100 in the CI server is slow-ish for some reason. # On some other servers, we are getting about 90% peak for 8kx8x8k float16". Current plan is to run CI here and use those numbers for baseline, then match against my GPUs as a sanity check. --------- Co-authored-by: Phil Tillet <phil@openai.com>
267 lines
12 KiB
Python
267 lines
12 KiB
Python
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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import triton.ops
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from triton.testing import get_dram_gbps, get_max_tensorcore_tflops
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DEVICE_NAME = {7: 'v100', 8: 'a100'}[torch.cuda.get_device_capability()[0]]
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#######################
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# Utilities
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#######################
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def print_perf(cur_ms, cur_util, ref_util):
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# print on the same line cur_ms, cur_util and ref_util with 3 decimal places
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print(f'{cur_ms:.3f} ms \t cur: {cur_util:.3f} \t ref: {ref_util:.3f} \t dif={cur_util - ref_util:.3f}', end='\t')
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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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(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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# Non pow 2 shapes
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(1000, 200, 100): {'float16': 0.084},
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(1000, 200, 700): {'float16': 0.084},
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(994, 136, 402): {'float16': 0.084},
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(995, 135, 409): {'float16': 0.084},
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(99, 1357, 409): {'float16': 0.084},
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},
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# NOTE:
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# A100 in the CI server is slow-ish for some reason.
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# On some other servers, we are getting about 90% peak for 8kx8x8k float16
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'a100': {
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# square
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(512, 512, 512): {'float16': 0.084, 'float32': 0.12, 'int8': 0.05},
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(1024, 1024, 1024): {'float16': 0.332, 'float32': 0.352, 'int8': 0.169},
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(2048, 2048, 2048): {'float16': 0.635, 'float32': 0.522, 'int8': 0.34},
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(4096, 4096, 4096): {'float16': 0.750, 'float32': 0.810, 'int8': 0.46},
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(8192, 8192, 8192): {'float16': 0.760, 'float32': 0.760, 'int8': 0.51},
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# tall-skinny
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(16, 1024, 1024): {'float16': 0.008, 'float32': 0.009, 'int8': 0.005},
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(16, 4096, 4096): {'float16': 0.036, 'float32': 0.038, 'int8': 0.026},
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(16, 8192, 8192): {'float16': 0.056, 'float32': 0.061, 'int8': 0.043},
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(64, 1024, 1024): {'float16': 0.020, 'float32': 0.030, 'int8': 0.017},
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(64, 4096, 4096): {'float16': 0.160, 'float32': 0.162, 'int8': 0.097},
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(64, 8192, 8192): {'float16': 0.280, 'float32': 0.257, 'int8': 0.174},
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(1024, 64, 1024): {'float16': 0.040, 'float32': 0.050, 'int8': 0.017},
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(4096, 64, 4096): {'float16': 0.160, 'float32': 0.200, 'int8': 0.102},
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(8192, 64, 8192): {'float16': 0.250, 'float32': 0.23, 'int8': 0.177},
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# Non pow 2 shapes
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(1000, 200, 100): {'float16': 0.011, 'float32': 0.017, 'int8': 0.05},
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(1000, 200, 700): {'float16': 0.027, 'float32': 0.047, 'int8': 0.05},
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(994, 136, 402): {'float16': 0.015, 'float32': 0.024, 'int8': 0.05},
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(995, 135, 409): {'float16': 0.015, 'float32': 0.025, 'int8': 0.05},
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(99, 1357, 409): {'float16': 0.011, 'float32': 0.036, 'int8': 0.05}
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}
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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', 'float32']])
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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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if (M, N, K) in [(64, 4096, 4096), (64, 8192, 8192), (8192, 64, 8192)] and dtype_str == 'float32':
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pytest.skip('Out of shared memory in float32')
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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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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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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, return_mode="min", warmup=100, rep=300)
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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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print_perf(ms, cur_gpu_util, ref_gpu_util)
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triton.testing.assert_close(cur_gpu_util, ref_gpu_util, atol=0.01, rtol=0.05)
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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: {'float16': 0.0219, 'float32': 0.010},
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1024 * 64: {'float16': 0.0791, 'float32': 0.010},
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1024 * 256: {'float16': 0.243, 'float32': 0.010},
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1024 * 1024: {'float16': 0.530, 'float32': 0.010},
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1024 * 4096: {'float16': 0.796, 'float32': 0.010},
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1024 * 16384: {'float16': 0.905, 'float32': 0.010},
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1024 * 65536: {'float16': 0.939, 'float32': 0.010},
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# Non pow 2
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1020 * 100: {'float16': 0.010, 'float32': 0.010},
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995 * 125: {'float16': 0.010, 'float32': 0.010},
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10003 * 7007: {'float16': 0.010, 'float32': 0.010},
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},
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'a100': {
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1024 * 16: {'float16': 0.010, 'bfloat16': 0.010, 'float32': 0.020},
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1024 * 64: {'float16': 0.040, 'bfloat16': 0.040, 'float32': 0.066},
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1024 * 256: {'float16': 0.132, 'bfloat16': 0.132, 'float32': 0.227},
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1024 * 1024: {'float16': 0.353, 'bfloat16': 0.353, 'float32': 0.488},
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1024 * 4096: {'float16': 0.605, 'bfloat16': 0.605, 'float32': 0.705},
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1024 * 16384: {'float16': 0.758, 'bfloat16': 0.750, 'float32': 0.819},
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1024 * 65536: {'float16': 0.850, 'bfloat16': 0.850, 'float32': 0.870},
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# Non pow 2
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1020 * 100: {'float16': 0.051, 'bfloat16': 0.051, 'float32': 0.103},
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995 * 125: {'float16': 0.063, 'bfloat16': 0.063, 'float32': 0.126},
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10003 * 7007: {'float16': 0.544, 'bfloat16': 0.541, 'float32': 0.861},
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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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@pytest.mark.parametrize("dtype_str", ['float16', 'bfloat16', 'float32'])
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def test_elementwise(N, dtype_str):
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torch.manual_seed(0)
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if dtype_str in ['bfloat16'] and DEVICE_NAME != 'a100':
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pytest.skip('Only test bfloat16 on a100')
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dtype = {'float16': torch.float16, 'bfloat16': torch.bfloat16, 'float32': torch.float32}[dtype_str]
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ref_gpu_util = elementwise_data[DEVICE_NAME][N][dtype_str]
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max_gpu_perf = get_dram_gbps()
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z = torch.empty((N, ), dtype=dtype, 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, return_mode="min", warmup=100, rep=500)
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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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print_perf(ms, cur_gpu_util, ref_gpu_util)
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triton.testing.assert_close(cur_gpu_util, ref_gpu_util, atol=0.01, rtol=0.05)
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#######################
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# Flash-Attention
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#######################
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flash_attention_data = {
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"a100": {
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(4, 48, 4096, 64, True, True, 'forward', 'float16'): 0.420,
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(4, 48, 4096, 64, True, True, 'backward', 'float16'): 0.202,
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(4, 48, 4096, 64, True, True, 'forward', 'bfloat16'): 0.355,
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(4, 48, 4096, 64, True, True, 'backward', 'bfloat16'): 0.201,
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(4, 48, 1024, 16, True, True, 'forward', 'float32'): 0.099,
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(4, 48, 1024, 16, True, True, 'backward', 'float32'): 0.087,
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(4, 48, 4096, 64, True, False, 'forward', 'float16'): 0.238,
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(4, 48, 4096, 64, True, False, 'backward', 'float16'): 0.135,
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(4, 48, 4096, 64, True, False, 'forward', 'bfloat16'): 0.211,
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(4, 48, 4096, 64, True, False, 'backward', 'bfloat16'): 0.135,
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(4, 48, 1024, 16, True, False, 'forward', 'float32'): 0.062,
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(4, 48, 1024, 16, True, False, 'backward', 'float32'): 0.052,
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(4, 48, 4096, 64, False, True, 'forward', 'float16'): 0.424,
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(4, 48, 4096, 64, False, True, 'backward', 'float16'): 0.262,
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(4, 48, 4096, 64, False, True, 'forward', 'bfloat16'): 0.370,
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(4, 48, 4096, 64, False, True, 'backward', 'bfloat16'): 0.254,
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(4, 48, 1024, 16, False, True, 'forward', 'float32'): 0.099,
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(4, 48, 1024, 16, False, True, 'backward', 'float32'): 0.125,
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(4, 48, 4096, 64, False, False, 'forward', 'float16'): 0.238,
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(4, 48, 4096, 64, False, False, 'backward', 'float16'): 0.158,
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(4, 48, 4096, 64, False, False, 'forward', 'bfloat16'): 0.211,
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(4, 48, 4096, 64, False, False, 'backward', 'bfloat16'): 0.134,
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(4, 48, 1024, 16, False, False, 'forward', 'float32'): 0.062,
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(4, 48, 1024, 16, False, False, 'backward', 'float32'): 0.075,
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}
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}
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@pytest.mark.parametrize("dtype_str", ['float16', 'bfloat16', 'float32'])
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@pytest.mark.parametrize("mode", ['forward', 'backward'])
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@pytest.mark.parametrize("causal", [True, False])
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@pytest.mark.parametrize("seq_par", [True, False])
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@pytest.mark.parametrize("Z, H, N_CTX, D_HEAD", [[4, 48, 4096, 64]])
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def test_flash_attention(Z, H, N_CTX, D_HEAD, seq_par, causal, mode, dtype_str):
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is_backward = mode == 'backward'
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capability = torch.cuda.get_device_capability()
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if capability[0] < 8:
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pytest.skip("Flash attention only supported for compute capability < 80")
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torch.manual_seed(20)
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dtype = {'float16': torch.float16, 'bfloat16': torch.bfloat16, 'float32': torch.float32}[dtype_str]
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# init data
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if dtype_str == 'float32':
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N_CTX = 1024
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D_HEAD = 16
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q = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0.1, std=0.2).requires_grad_()
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k = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0.4, std=0.2).requires_grad_()
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v = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0.3, std=0.2).requires_grad_()
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sm_scale = 0.2
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# benchmark
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fn = lambda: triton.ops.attention(q, k, v, causal, sm_scale, seq_par)
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if is_backward:
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o = fn()
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do = torch.randn_like(o)
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fn = lambda: o.backward(do, retain_graph=True)
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ms = triton.testing.do_bench(fn, return_mode="min", warmup=100, rep=500)
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# compute flops
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flops_per_matmul = 2. * Z * H * N_CTX * N_CTX * D_HEAD * 0.5
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total_flops = 2 * flops_per_matmul
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if is_backward:
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total_flops *= 2.5 # 2.0(bwd) + 0.5(recompute)
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cur_gpu_perf = total_flops / ms * 1e-9
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# maximum flops
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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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cur_gpu_util = cur_gpu_perf / max_gpu_perf
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ref_gpu_util = flash_attention_data[DEVICE_NAME][(Z, H, N_CTX, D_HEAD, seq_par, causal, mode, dtype_str)]
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print_perf(ms, cur_gpu_util, ref_gpu_util)
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triton.testing.assert_close(cur_gpu_util, ref_gpu_util, atol=0.01, rtol=0.05)
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