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31 lines
1.0 KiB
Python
31 lines
1.0 KiB
Python
import os
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os.environ["NVIDIA_TF32_OVERRIDE"] = "0"
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os.environ["MKL_NUM_THREADS"] = "1"
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os.environ["NUMEXPR_NUM_THREADS"] = "1"
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os.environ["OMP_NUM_THREADS"] = "1"
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import time
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import torch
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torch.set_num_threads(1)
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from tinygrad.helpers import getenv
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CUDA = getenv("CUDA", 1)
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MPS = getenv("MPS", 0)
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if getenv("FP16_ACC"): torch.backends.cuda.matmul.allow_fp16_accumulation = True
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for dtype in [torch.float32, torch.float16, torch.bfloat16]:
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for N in [256, 512, 1024, 2048, 4096] + ([6144, 8192] if getenv("BIG") else []):
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FLOPS = N*N*N*2
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b = torch.rand((N,N), dtype=dtype)
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c = torch.rand((N,N), dtype=dtype)
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if CUDA: b,c = b.cuda(),c.cuda()
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if MPS: b,c = b.to('mps'),c.to('mps')
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def torch_prog(b, c):
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st = time.perf_counter()
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a = b@c
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if CUDA: torch.cuda.synchronize()
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if MPS: torch.mps.synchronize()
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return time.perf_counter() - st
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tm = min([torch_prog(b, c) for _ in range(20)])
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print(f"{N*N:10d} {tm*1e6:9.2f} us, would be {FLOPS*1e-9/tm:9.2f} GFLOPS {N:4d}x{N:4d}x{N:4d} matmul in {dtype}")
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