[PYTHON] Added automated benchmark script (#63)
This adds a bench functionality to the setup.py that can be used to run the benchmark suite and generates a bunch of csv files (and optionally plots) python setup.py bench python setup.py bench --with-plots python setup.py bench --filter=cross_entropy
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Philippe Tillet
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Philippe Tillet
Philippe Tillet
parent
66c94f21d7
commit
5e3c7f5a60
@@ -1,151 +0,0 @@
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import pytest
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import itertools
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import triton as tt
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import torch as th
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@pytest.mark.parametrize("TM, TN, TK, TZ, NWARP, M, N, K, AT, BT, DTYPE", itertools.chain(*[
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[
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# 1 warp
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(16, 16, 16, 1, 1, None, None, None, AT, BT, DTYPE),
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(32, 16, 16, 1, 1, None, None, None, AT, BT, DTYPE),
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(16, 32, 16, 1, 1, None, None, None, AT, BT, DTYPE),
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(16, 16, 32, 1, 1, None, None, None, AT, BT, DTYPE),
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(32, 16, 32, 1, 1, None, None, None, AT, BT, DTYPE),
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(16, 32, 32, 1, 1, None, None, None, AT, BT, DTYPE),
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(16, 16, 64, 1, 1, None, None, None, AT, BT, DTYPE),
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(64, 16, 64, 1, 1, None, None, None, AT, BT, DTYPE),
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(16, 64, 64, 1, 1, None, None, None, AT, BT, DTYPE),
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# 2 warp
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(64, 32, 64, 1, 2, None, None, None, AT, BT, DTYPE),
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(32, 64, 64, 1, 2, None, None, None, AT, BT, DTYPE),
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(64, 32, 16, 1, 2, None, None, None, AT, BT, DTYPE),
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(32, 64, 16, 1, 2, None, None, None, AT, BT, DTYPE),
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(128, 32, 32, 1, 2, None, None, None, AT, BT, DTYPE),
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(32, 128, 32, 1, 2, None, None, None, AT, BT, DTYPE),
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# 4 warp
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(128, 64, 16, 1, 4, None, None, None, AT, BT, DTYPE),
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(64, 128, 16, 1, 4, None, None, None, AT, BT, DTYPE),
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(128, 32, 32, 1, 4, None, None, None, AT, BT, DTYPE),
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(32, 128, 32, 1, 4, None, None, None, AT, BT, DTYPE),
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(128, 32, 64, 1, 4, None, None, None, AT, BT, DTYPE),
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(32, 128, 64, 1, 4, None, None, None, AT, BT, DTYPE),
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# 8 warp
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(128, 256, 16, 1, 8, None, None, None, AT, BT, DTYPE),
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(256, 128, 16, 1, 8, None, None, None, AT, BT, DTYPE),
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(256, 128, 32, 1, 8, None, None, None, AT, BT, DTYPE),
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# split-k
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(64, 64, 16, 2, 4, None, None, None, AT, BT, DTYPE),
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(64, 64, 16, 4, 4, None, None, None, AT, BT, DTYPE),
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(64, 64, 16, 8, 4, None, None, None, AT, BT, DTYPE),
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# variable input
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(128, 128, 32, 1, 4, 256, 256, 256 , AT, BT, DTYPE),
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(128, 128, 32, 1, 4, 384, 128, 640 , AT, BT, DTYPE),
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(128, 128, 32, 1, 4, 107, 233, 256 , AT, BT, DTYPE),
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(128, 128, 32, 1, 4, 107, 233, 311 , AT, BT, DTYPE)
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]
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for DTYPE in ['float16']
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for AT in [False, True]
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for BT in [False, True]
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]))
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def test_op(TM, TN, TK, TZ, NWARP, M, N, K, AT, BT, DTYPE):
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DTYPE = {'float16': th.float16, 'float32': th.float32}[DTYPE]
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th.manual_seed(0)
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tt.ops._matmul._kernels = dict()
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tt.ops._matmul._CONFIGS = [({'TM': str(TM) , 'TN': str(TN) , 'TK': str(TK), 'TZ': str(TZ)}, NWARP)]
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if M is None: M = TM
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if N is None: N = TN
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if K is None: K = TK*TZ
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a = th.randn((K, M) if AT else (M, K), device='cuda', dtype=DTYPE) / K**.5
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b = th.randn((N, K) if BT else (K, N), device='cuda', dtype=DTYPE) / K**.5
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a = a.t() if AT else a
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b = b.t() if BT else b
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th_c = th.matmul(a, b)
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tt_c = tt.ops.matmul(a, b)
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rtol, atol = {th.float32: (1e-4, 1e-5),
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th.float16: (1e-2, 1e-3)}[DTYPE]
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assert th.allclose(tt_c, th_c, atol=atol, rtol=rtol)
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def do_bench(fn, flops = 0, warmup = 10, rep = 50):
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start_event = th.cuda.Event(enable_timing=True)
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end_event = th.cuda.Event(enable_timing=True)
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ret = fn()
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for i in range(warmup):
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fn()
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th.cuda.synchronize()
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start_event.record()
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for i in range(rep):
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fn()
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end_event.record()
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th.cuda.synchronize()
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time_ms = start_event.elapsed_time(end_event) / rep
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return time_ms
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def time_all(fn, x_names, x_vals, y_name, y_vals, y_lines, ylabel, loglog=True, plot_name='', **kwargs):
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import matplotlib.pyplot as plt
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import pandas as pd
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df = pd.DataFrame(columns = [x_names[0]] + y_lines)
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for x in x_vals:
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x_args = {x_name: x for x_name in x_names}
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row = [fn(**x_args, **{y_name: y}, **kwargs) for y in y_vals]
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df.loc[len(df)] = [x] + row
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print(df)
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if plot_name:
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df.plot(x=x_names[0], y=y_lines, ylabel=ylabel, xlabel=' = '.join(x_names), title=f'{plot_name}', loglog=loglog)
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plt.savefig(f'{plot_name}.pdf')
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def perf_op(M, N, K, AT, BT, dtype, provider, warmup=10, rep=50):
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import os
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a = th.randn((K, M) if AT else (M, K), device='cuda', dtype=dtype) / K**.5
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b = th.randn((N, K) if BT else (K, N), device='cuda', dtype=dtype) / K**.5
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if AT: a = a.t()
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if BT: b = b.t()
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num_flops = 2*M*N*K
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if provider == 'torch':
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torch_ms = do_bench(lambda: th.matmul(a, b), warmup = warmup, rep = rep)
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torch_tflops = num_flops / torch_ms * 1e-9
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return torch_tflops
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if provider == 'triton':
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triton_ms = do_bench(lambda: tt.ops.matmul(a, b), warmup = warmup, rep = rep)
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triton_tflops = num_flops / triton_ms * 1e-9
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return triton_tflops
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if provider == 'cutlass' and 'CUTLASS_PROFILER' in os.environ:
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import subprocess
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import tempfile
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import pandas as pd
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# run program specified by CUTLASS_PROFILER env variable
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layout_a = 'column' if AT else 'row'
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layout_b = 'column' if BT else 'row'
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# create temporary file name
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fd, fname = tempfile.mkstemp()
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# run program and gets its output
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cmd = [os.environ['CUTLASS_PROFILER'], f'--m={M}', f'--n={N}', f'--k={K}', f'--A=f16:{layout_a}', f'--B=f16:{layout_b}', \
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'--C=f16:column', '--accum=f32', '--operation=gemm', '--verification-enabled=false', '--warmup-iterations=10', \
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'--profiling-iterations=50', f'--output={fname}', '--verbose=false']
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# run cmd
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subprocess.run(cmd, stdout=subprocess.PIPE)
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# read CSV output
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df_c = pd.read_csv(f'{fname}.gemm.csv')
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cutlass_tflops = max(df_c['GFLOPs'])/1e3
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return cutlass_tflops
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return None
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if __name__ == '__main__':
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# # square
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x_square = [128, 256, 512, 1024, 2048, 3072, 4096, 6144]
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time_all(perf_op, x_names = ['M', 'N', 'K'], x_vals = x_square, y_name = 'provider' , y_vals = ['torch', 'triton', 'cutlass'],
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ylabel = 'TFLOPS', y_lines = ['Torch', 'Triton', 'CUTLASS'], AT = False, BT = False, dtype = th.float16, loglog=False, plot_name = 'matmul-square-nn')
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time_all(perf_op, x_names = ['M', 'N', 'K'], x_vals = x_square, y_name = 'provider' , y_vals = ['torch', 'triton', 'cutlass'],
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ylabel = 'TFLOPS', y_lines = ['Torch', 'Triton', 'CUTLASS'], AT = False, BT = True, dtype = th.float16, loglog=False, plot_name = 'matmul-square-nt')
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time_all(perf_op, x_names = ['M', 'N', 'K'], x_vals = x_square, y_name = 'provider' , y_vals = ['torch', 'triton', 'cutlass'],
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ylabel = 'TFLOPS', y_lines = ['Torch', 'Triton', 'CUTLASS'], AT = True, BT = False, dtype = th.float16, loglog=False, plot_name = 'matmul-square-tn')
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time_all(perf_op, x_names = ['M', 'N', 'K'], x_vals = x_square, y_name = 'provider' , y_vals = ['torch', 'triton', 'cutlass'],
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ylabel = 'TFLOPS', y_lines = ['Torch', 'Triton', 'CUTLASS'], AT = True, BT = True, dtype = th.float16, loglog=False, plot_name = 'matmul-square-tt')
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# tall-skinny
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x_tall_skinny = [64, 96, 128, 160, 192, 256, 320, 384, 512, 768, 1024, 1536]
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time_all(perf_op, x_names = ['M'], x_vals = x_tall_skinny, y_name = 'provider', y_vals = ['torch', 'triton', 'cutlass'],
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ylabel = 'TFLOPS', y_lines = ['Torch', 'Triton', 'CUTLASS'], AT = False, BT = False, N=2048, K=2048, dtype = th.float16, loglog=False, plot_name = 'matmul-tall-skinny-2k-2k')
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time_all(perf_op, x_names = ['M'], x_vals = x_tall_skinny, y_name = 'provider', y_vals = ['torch', 'triton', 'cutlass'],
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ylabel = 'TFLOPS', y_lines = ['Torch', 'Triton', 'CUTLASS'], AT = False, BT = False, N=4096, K=4096, dtype = th.float16, loglog=False, plot_name = 'matmul-tall-skinny-4k-4k')
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time_all(perf_op, x_names = ['M'], x_vals = x_tall_skinny, y_name = 'provider', y_vals = ['torch', 'triton', 'cutlass'],
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ylabel = 'TFLOPS', y_lines = ['Torch', 'Triton', 'CUTLASS'], AT = False, BT = False, N=6144, K=6144, dtype = th.float16, loglog=False, plot_name = 'matmul-tall-skinny-6k-6k')
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