You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
60 lines
1.9 KiB
60 lines
1.9 KiB
1 week ago
|
import numpy as np
|
||
|
|
from tinygrad import Tensor, Device, GlobalCounters
|
||
|
|
from tinygrad.helpers import Timing
|
||
|
|
|
||
|
|
d0, d1 = f"{Device.DEFAULT}:1", f"{Device.DEFAULT}:2"
|
||
|
|
N = 256
|
||
|
|
FLOPS = N*N*N*2
|
||
|
|
|
||
|
|
# LazyBuffer should make three fields lists: self.st (all must have the same shape), self.realized, and self.device
|
||
|
|
|
||
|
|
def explicit_shard_W_axis_1(X, W):
|
||
|
|
Xs = [X.to(d0), X.to(d1)]
|
||
|
|
Ws = [W[:, :N//2].to(d0), W[:, N//2:].to(d1)] # TODO: these shouldn't make copies on the original device
|
||
|
|
# pad them to form the correct size
|
||
|
|
Ws = [Ws[0].pad((None, (0,N//2))), Ws[1].pad((None, (N//2,0)))]
|
||
|
|
for x in Xs: assert x.shape == X.shape
|
||
|
|
for w in Ws: assert w.shape == W.shape
|
||
|
|
|
||
|
|
# TODO: it shouldn't be faster with these realize
|
||
|
|
for x in Xs+Ws: x.realize()
|
||
|
|
def lm(x:Tensor, w:Tensor):
|
||
|
|
# these are movement ops on the local device
|
||
|
|
x = x.reshape(N, 1, N).expand(N, N, N)
|
||
|
|
w = w.T.reshape(1, N, N).expand(N, N, N)
|
||
|
|
m = x*w
|
||
|
|
assert m.uop.st.views[0].mask is not None
|
||
|
|
ret = m.sum(2)
|
||
|
|
return ret
|
||
|
|
#Os = [lm(Xs[0], Ws[0]), lm(Xs[1], Ws[1])]
|
||
|
|
Os = [Xs[0] @ Ws[0], Xs[1] @ Ws[1]]
|
||
|
|
for x in Os: x.realize()
|
||
|
|
return Os[0].to(Device.DEFAULT) + Os[1].to(Device.DEFAULT)
|
||
|
|
|
||
|
|
#return Tensor.cat(*[x.to(Device.DEFAULT) for x in Os], dim=1) # TODO: someday we can remove this copy too
|
||
|
|
|
||
|
|
def matmul(X, W):
|
||
|
|
return explicit_shard_W_axis_1(X, W)
|
||
|
|
#return X@W
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
with Timing("init devices: "):
|
||
|
|
Device[d0], Device[d1]
|
||
|
|
|
||
|
|
with Timing("create tensors: "):
|
||
|
|
X = Tensor.kaiming_uniform(N, N).realize()
|
||
|
|
W = Tensor.kaiming_uniform(N, N).realize()
|
||
|
|
|
||
|
|
#with Timing("warmup: "):
|
||
|
|
# O = matmul(X, W).numpy()
|
||
|
|
|
||
|
|
GlobalCounters.reset()
|
||
|
|
print("******** multiply start")
|
||
|
|
with Timing("******** multiply done: ", lambda x: f" {FLOPS/x:.2f} GFLOPS"):
|
||
|
|
O = matmul(X, W).realize()
|
||
|
|
Device[Device.DEFAULT].synchronize()
|
||
|
|
|
||
|
|
with Timing("testing: "):
|
||
|
|
val = X.numpy() @ W.numpy()
|
||
|
|
np.testing.assert_allclose(val, O.numpy(), atol=1e-5)
|