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[CODEGEN] Major performance improvements on A100 (#70)

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Improved handling of asynchronous copy, scheduling and synchronization for A100. Now achieving CUTLASS-like performance on large square dense matrix multiplication tasks
This commit is contained in:
Philippe Tillet
2021-02-21 15:19:39 -08:00
committed by Philippe Tillet
parent 045ab5d62a
commit 5b83259592
31 changed files with 1331 additions and 1115 deletions
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72
python/bench/bench_matmul.py
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@@ -2,58 +2,74 @@ import triton
import torch
# square benchmarks
nt = {False: 'n', True: 't'}
nt = {False: "n", True: "t"}
square_confs = [
triton.testing.Benchmark(
x_names = ['M', 'N', 'K'],
x_vals = [128, 256, 512, 1024, 2048, 3072, 4096, 6144],
y_name = 'provider',
y_vals = ['torch', 'triton', 'cutlass'],
y_lines = ['Torch', 'Triton', 'CUTLASS'],
ylabel = 'TFLOPS',
loglog = False,
plot_name = f'matmul-square-{nt[AT]}{nt[BT]}',
args = {'AT': False, 'BT': False, 'dtype': torch.float16}
)\
for AT in [False, True] for BT in [False, True]
x_names=["M", "N", "K"],
x_vals=[512 * i for i in range(1, 16)],
y_name="provider",
y_vals=["torch", "triton", "cutlass"],
y_lines=["Torch", "Triton", "CUTLASS"],
ylabel="TFLOPS",
loglog=False,
plot_name=f"matmul-square-{nt[AT]}{nt[BT]}",
args={"AT": AT, "BT": BT, "dtype": torch.float16},
) for AT in [False, True] for BT in [False, True]
]
@triton.testing.perf_report(square_confs)
def bench_op(M, N, K, AT, BT, dtype, provider, warmup=5, rep=5):
def bench_op(M, N, K, AT, BT, dtype, provider, warmup=5, rep=20):
import os
a = torch.randn((K, M) if AT else (M, K), device='cuda', dtype=dtype) / K**.5
b = torch.randn((N, K) if BT else (K, N), device='cuda', dtype=dtype) / K**.5
if AT: a = a.t()
if BT: b = b.t()
a = torch.rand((K, M) if AT else (M, K), device="cuda", dtype=dtype)
b = torch.rand((N, K) if BT else (K, N), device="cuda", dtype=dtype)
if AT:
a = a.t()
if BT:
b = b.t()
num_flops = 2 * M * N * K
if provider == 'torch':
if provider == "torch":
torch_ms = triton.testing.do_bench(lambda: torch.matmul(a, b), warmup=warmup, rep=rep)
torch_tflops = num_flops / torch_ms * 1e-9
return torch_tflops
if provider == 'triton':
if provider == "triton":
triton_ms = triton.testing.do_bench(lambda: triton.ops.matmul(a, b), warmup=warmup, rep=rep)
triton_tflops = num_flops / triton_ms * 1e-9
return triton_tflops
if provider == 'cutlass' and 'CUTLASS_PROFILER' in os.environ:
if provider == "cutlass" and "CUTLASS_PROFILER" in os.environ:
import subprocess
import tempfile
import pandas as pd
# run program specified by CUTLASS_PROFILER env variable
layout_a = 'column' if AT else 'row'
layout_b = 'column' if BT else 'row'
layout_a = "column" if AT else "row"
layout_b = "column" if BT else "row"
# create temporary file name
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', f'--warmup-iterations={warmup}', \
f'--profiling-iterations={rep}', f'--output={fname}', '--verbose=false']
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",
f"--warmup-iterations={warmup}",
f"--profiling-iterations={rep}",
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
df_c = pd.read_csv(f"{fname}.gemm.csv")
cutlass_tflops = max(df_c["GFLOPs"]) / 1e3
return cutlass_tflops
return None
if __name__ == '__main__':
if __name__ == "__main__":
bench_op.run()