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[PYTHON] Added benchmark code for CUTLASS

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This commit is contained in:
Philippe Tillet
2021-01-31 21:23:01 -05:00
parent ad5a30bae1
commit ad005d49ac
1 changed files with 24 additions and 6 deletions
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30
python/tests/test_matmul.py
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@@ -83,10 +83,11 @@ def do_bench(fn, flops = 0, warmup = 10, rep = 50):
def perf_op(dtype=th.float16, warmup=10, rep=50): def perf_op(dtype=th.float16, warmup=10, rep=50):
import pandas as pd import pandas as pd
import os
AT, BT = False, False AT, BT = False, False
df = pd.DataFrame(columns=['AT', 'BT', 'N', 'TRITON', 'TORCH']) has_cutlass = 'CUTLASS_PROFILER' in os.environ
# Ns = [128, 256, 512, 1024, 2048, 3072, 4096, 6144, 8192] df = pd.DataFrame(columns=['AT', 'BT', 'N', 'TRITON', 'TORCH', 'CUTLASS'])
Ns = [8192] Ns = [128, 256, 512, 1024, 2048, 3072, 4096, 6144]
configs = [(AT, BT, N, N, N) for AT in [False, True] for BT in [False, True] for N in Ns] configs = [(AT, BT, N, N, N) for AT in [False, True] for BT in [False, True] for N in Ns]
for AT, BT, M, N, K in configs: for AT, BT, M, N, K in configs:
a = th.randn((K, M) if AT else (M, K), device='cuda', dtype=dtype) / K**.5 a = th.randn((K, M) if AT else (M, K), device='cuda', dtype=dtype) / K**.5
@@ -100,8 +101,25 @@ def perf_op(dtype=th.float16, warmup=10, rep=50):
num_flops = 2*M*N*K num_flops = 2*M*N*K
torch_tflops = num_flops / torch_ms * 1e-9 torch_tflops = num_flops / torch_ms * 1e-9
triton_tflops = num_flops / triton_ms * 1e-9 triton_tflops = num_flops / triton_ms * 1e-9
#print(min(alpha*bandwidth*1e-12, max_tflops), triton_tflops) if 'CUTLASS_PROFILER' in os.environ:
#./tools/profiler/cutlass_profiler --m=8192 --n=8192 --k=8192 --A=f16:column --B=f16:column --C=f16:column --accum=f32 --operation=gemm import subprocess
df = df.append({'AT': AT, 'BT': BT, 'N': N, 'TRITON': triton_tflops, 'TORCH': torch_tflops}, ignore_index=True) # run program specified by CUTLASS_PROFILER env variable
layout_a = 'column' if AT else 'row'
layout_b = 'column' if BT else 'row'
# create temporary file name
import tempfile
fd, fname = tempfile.mkstemp()
# run program and gets its output
cmd = [os.environ['CUTLASS_PROFILER'], f'--m={M}', f'--n={N}', f'--k={K}', f'--A=f16:{layout_a}', f'--B=f16:{layout_b}', \
'--C=f16:column', '--accum=f32', '--operation=gemm', '--verification-enabled=false', '--warmup-iterations=10', \
'--profiling-iterations=50', f'--output={fname}', '--verbose=false']
# run cmd
subprocess.run(cmd, stdout=subprocess.PIPE)
# read CSV output
df_c = pd.read_csv(f'{fname}.gemm.csv')
cutlass_tflops = max(df_c['GFLOPs'])/1e3
else:
cutlass_tflops = None
df = df.append({'AT': AT, 'BT': BT, 'N': N, 'TRITON': triton_tflops, 'TORCH': torch_tflops, 'CUTLASS': cutlass_tflops}, ignore_index=True)
pd.options.display.float_format = lambda x: '{:.2f}'.format(x) pd.options.display.float_format = lambda x: '{:.2f}'.format(x)
print(df) print(df)