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50 lines
1.5 KiB
50 lines
1.5 KiB
1 month ago
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import os
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#os.environ["METAL"] = "1"
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import numpy as np
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BS = 64
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CIN = 256
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COUT = 256
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HW = 32
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K = 3
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PADDING = 0
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# TODO: this is doing some trick, since with CIN=256 COUT=256 it's over 10.4 TFLOPS.
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# are winograd convs less flops? it appears so if they are batched
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# https://www.cse.ust.hk/~weiwa/papers/yan-ppopp20.pdf
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FLOPS = BS*K*K*CIN*HW*HW*COUT*2
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nb = np.random.default_rng().standard_normal(size=(BS,CIN,HW,HW), dtype=np.float32)
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nc = np.random.default_rng().standard_normal(size=(COUT,CIN,K,K), dtype=np.float32)
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try:
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import time, torch, torch.mps
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b = torch.from_numpy(nb).to('mps')
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c = torch.from_numpy(nc).to('mps')
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def torch_prog(b, c):
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st = time.perf_counter()
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a = torch.nn.functional.conv2d(b, c, padding=PADDING)
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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"{tm*1e6:9.2f} us, would be {FLOPS*1e-9/tm:9.2f} GFLOPS conv in torch")
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except RuntimeError:
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print("no torch metal conv")
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from tinygrad.tensor import Tensor
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from tinygrad.engine.jit import TinyJit
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from tinygrad import Device
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b = Tensor(nb)
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c = Tensor(nc)
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# TODO: slowness without the JIT I suspect comes from a lack of a caching allocator
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@TinyJit
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def tiny_jit(b, c):
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return b.conv2d(c, padding=PADDING).realize()
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def tiny_prog(b, c):
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st = time.perf_counter()
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a = tiny_jit(b, c)
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Device[a.device].synchronize()
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return time.perf_counter() - st
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tm = min([tiny_prog(b, c) for _ in range(5)])
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print(f"{tm*1e6:9.2f} us, would be {FLOPS*1e-9/tm:9.2f} GFLOPS conv in tinygrad")
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