107 lines
4.3 KiB
Python
107 lines
4.3 KiB
Python
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, 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, None, None, None, AT, BT, DTYPE),
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(32, 16, 16, 1, None, None, None, AT, BT, DTYPE),
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(16, 32, 16, 1, None, None, None, AT, BT, DTYPE),
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(16, 16, 32, 1, None, None, None, AT, BT, DTYPE),
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(32, 16, 32, 1, None, None, None, AT, BT, DTYPE),
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(16, 32, 32, 1, None, None, None, AT, BT, DTYPE),
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(16, 16, 64, 1, None, None, None, AT, BT, DTYPE),
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(64, 16, 64, 1, None, None, None, AT, BT, DTYPE),
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(16, 64, 64, 1, None, None, None, AT, BT, DTYPE),
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# 2 warp
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(64, 32, 64, 2, None, None, None, AT, BT, DTYPE),
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(32, 64, 64, 2, None, None, None, AT, BT, DTYPE),
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(64, 32, 16, 2, None, None, None, AT, BT, DTYPE),
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(32, 64, 16, 2, None, None, None, AT, BT, DTYPE),
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(128, 32, 32, 2, None, None, None, AT, BT, DTYPE),
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(32, 128, 32, 2, None, None, None, AT, BT, DTYPE),
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# 4 warp
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(128, 64, 16, 4, None, None, None, AT, BT, DTYPE),
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(64, 128, 16, 4, None, None, None, AT, BT, DTYPE),
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(128, 32, 32, 4, None, None, None, AT, BT, DTYPE),
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(32, 128, 32, 4, None, None, None, AT, BT, DTYPE),
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(128, 32, 64, 4, None, None, None, AT, BT, DTYPE),
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(32, 128, 64, 4, None, None, None, AT, BT, DTYPE),
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# 8 warp
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(128, 256, 16, 8, None, None, None, AT, BT, DTYPE),
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(256, 128, 16, 8, None, None, None, AT, BT, DTYPE),
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(256, 128, 32, 8, None, None, None, AT, BT, DTYPE),
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# variable input
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(128, 128, 32, 4, 256, 256, 256 , AT, BT, DTYPE),
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(128, 128, 32, 4, 384, 128, 640 , AT, BT, DTYPE),
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(128, 128, 32, 4, 107, 233, 256 , AT, BT, DTYPE),
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(128, 128, 32, 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, 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.kernel = dict()
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tt.ops._matmul.TM = [TM]
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tt.ops._matmul.TN = [TN]
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tt.ops._matmul.TK = [TK]
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tt.ops._matmul.num_warps = [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
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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 perf_op(dtype=th.float16, warmup=10, rep=50):
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import pandas as pd
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AT, BT = False, False
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df = pd.DataFrame(columns=['AT', 'BT', 'N', 'TRITON', 'TORCH'])
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# Ns = [128, 256, 512, 1024, 2048, 3072, 4096, 6144, 8192]
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Ns = [8192]
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configs = [(AT, BT, N, N, N) for AT in [False, True] for BT in [False, True] for N in Ns]
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for AT, BT, M, N, K in configs:
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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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# benchmarks
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torch_ms = do_bench(lambda: th.matmul(a, b), warmup = warmup, rep = rep)
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triton_ms = do_bench(lambda: tt.ops.matmul(a, b), warmup = warmup, rep = rep)
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# store result
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num_flops = 2*M*N*K
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torch_tflops = num_flops / torch_ms * 1e-9
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triton_tflops = num_flops / triton_ms * 1e-9
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#print(min(alpha*bandwidth*1e-12, max_tflops), triton_tflops)
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#./tools/profiler/cutlass_profiler --m=8192 --n=8192 --k=8192 --A=f16:column --B=f16:column --C=f16:column --accum=f32 --operation=gemm
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df = df.append({'AT': AT, 'BT': BT, 'N': N, 'TRITON': triton_tflops, 'TORCH': torch_tflops}, ignore_index=True)
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pd.options.display.float_format = lambda x: '{:.2f}'.format(x)
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print(df) |