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- import numpy as np
- from tinygrad.helpers import getenv
- from tinygrad import dtypes, Tensor, Device
- dtype_in = dtypes.half if getenv("HALF") else dtypes.bfloat16 if getenv("BFLOAT16") else dtypes.float
- acc_dtype = dtypes.half if getenv("ACC_HALF") else dtypes.bfloat16 if getenv("ACC_BFLOAT16") else None
- GPUS = getenv("GPUS", 0)
- M = getenv("M", 16384)
- N = getenv("N", 4096)
- CNT = getenv("CNT", 10)
- ATOL = getenv("ATOL", 1e-4)
- RTOL = getenv("RTOL", 3e-2)
- def _rand(device):
- a, b = Tensor.rand(M, N, dtype=dtype_in).realize(), Tensor.rand(N, dtype=dtype_in).realize()
- if isinstance(device, tuple):
- a.shard_(device, axis=1)
- b.shard_(device, axis=0)
- return a, b
- if __name__ == "__main__":
- device = tuple(f"{Device.DEFAULT}:{i}" for i in range(GPUS)) if GPUS > 1 else Device.DEFAULT
- a, b = _rand(device)
- for i in range(CNT):
- if i > 0 and getenv("RAND", 0) != 0:
- a, b = _rand(device)
- c = a.matmul(b, acc_dtype=acc_dtype).realize()
- nc = c.numpy()
- comp = a.numpy().astype(np.float32) @ b.numpy().astype(np.float32)
- np.testing.assert_allclose(nc, comp, atol=ATOL, rtol=RTOL)
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