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33 lines
1.3 KiB
33 lines
1.3 KiB
from tinygrad.helpers import getenv
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from tinygrad import dtypes, Tensor
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dtype_in = dtypes.half if getenv("HALF") else dtypes.bfloat16 if getenv("BFLOAT16") else dtypes.float
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acc_dtype = dtypes.half if getenv("ACC_HALF") else dtypes.bfloat16 if getenv("ACC_BFLOAT16") else None
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CNT = getenv("CNT", 8)
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BS = getenv("BS", 16)
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CIN = getenv("CIN", 128)
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COUT = getenv("COUT", 128)
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HW = getenv("HW", 128)
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K = getenv("K", 3)
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PADDING = getenv("PADDING", 1)
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COMP = getenv("COMP", 0)
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ATOL = getenv("ATOL", 1e-4)
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RTOL = getenv("RTOL", 3e-2)
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FLOPS = BS*K*K*CIN*HW*HW*COUT*2
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def rand_input(): return Tensor.rand(BS, CIN, HW, HW, dtype=dtype_in).realize(), Tensor.rand(COUT, CIN, K, K, dtype=dtype_in).realize()
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if __name__ == "__main__":
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a, b = rand_input()
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for i in range(CNT):
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if i > 0 and getenv("RAND", 0) != 0:
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a, b = rand_input()
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c = a.conv2d(b, padding=PADDING, dtype=acc_dtype).realize()
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if COMP:
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import numpy as np, time, torch
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torch_device = "cuda:0" if torch.cuda.is_available() else ("mps" if getenv("MPS", 0) else "cpu")
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ta, tb = torch.from_numpy(a.numpy()).to(torch_device), torch.from_numpy(b.numpy()).to(torch_device)
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tc = torch.nn.functional.conv2d(ta, tb, padding=PADDING)
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np.testing.assert_allclose(c.numpy(), tc.cpu(), atol=ATOL, rtol=RTOL)
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