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https://github.com/firestar5683/StarPilot.git
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firestar5683
22a8575199
Keep official openpilot ancestry and dependency licenses. Store resources directly in Git, remove LFS setup requirements, and leave the downloadable big model outside the source tree.
60 lines
1.9 KiB
Python
60 lines
1.9 KiB
Python
import numpy as np
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from tinygrad import Tensor, Device, GlobalCounters
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from tinygrad.helpers import Timing
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d0, d1 = f"{Device.DEFAULT}:1", f"{Device.DEFAULT}:2"
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N = 256
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FLOPS = N*N*N*2
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# LazyBuffer should make three fields lists: self.st (all must have the same shape), self.realized, and self.device
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def explicit_shard_W_axis_1(X, W):
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Xs = [X.to(d0), X.to(d1)]
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Ws = [W[:, :N//2].to(d0), W[:, N//2:].to(d1)] # TODO: these shouldn't make copies on the original device
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# pad them to form the correct size
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Ws = [Ws[0].pad((None, (0,N//2))), Ws[1].pad((None, (N//2,0)))]
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for x in Xs: assert x.shape == X.shape
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for w in Ws: assert w.shape == W.shape
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# TODO: it shouldn't be faster with these realize
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for x in Xs+Ws: x.realize()
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def lm(x:Tensor, w:Tensor):
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# these are movement ops on the local device
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x = x.reshape(N, 1, N).expand(N, N, N)
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w = w.T.reshape(1, N, N).expand(N, N, N)
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m = x*w
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assert m.uop.st.views[0].mask is not None
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ret = m.sum(2)
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return ret
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#Os = [lm(Xs[0], Ws[0]), lm(Xs[1], Ws[1])]
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Os = [Xs[0] @ Ws[0], Xs[1] @ Ws[1]]
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for x in Os: x.realize()
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return Os[0].to(Device.DEFAULT) + Os[1].to(Device.DEFAULT)
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#return Tensor.cat(*[x.to(Device.DEFAULT) for x in Os], dim=1) # TODO: someday we can remove this copy too
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def matmul(X, W):
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return explicit_shard_W_axis_1(X, W)
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#return X@W
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if __name__ == "__main__":
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with Timing("init devices: "):
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Device[d0], Device[d1]
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with Timing("create tensors: "):
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X = Tensor.kaiming_uniform(N, N).realize()
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W = Tensor.kaiming_uniform(N, N).realize()
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#with Timing("warmup: "):
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# O = matmul(X, W).numpy()
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GlobalCounters.reset()
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print("******** multiply start")
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with Timing("******** multiply done: ", lambda x: f" {FLOPS/x:.2f} GFLOPS"):
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O = matmul(X, W).realize()
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Device[Device.DEFAULT].synchronize()
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with Timing("testing: "):
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val = X.numpy() @ W.numpy()
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np.testing.assert_allclose(val, O.numpy(), atol=1e-5)
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