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features/110-split_output_randn: Adding the new code for random #114

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features/110-split_output_randn: Adding the new code for random #114

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9ce86d0
reset banch to current master (13.02.19), adding the new code for random
Feb 13, 2019
1879b06
changes to pass tests for argmin, min, max
Feb 13, 2019
ef98f85
removed failing tests, added get rank function to comm, commit prior …
Feb 20, 2019
4dc5f66
Merge branch 'master' into features/110-split_output_randn
Feb 20, 2019
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Feb 20, 2019
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21 changes: 14 additions & 7 deletions heat/core/random.py
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Original file line number Diff line number Diff line change
Expand Up @@ -30,7 +30,7 @@ def randn(*args, split=None, comm=MPI_WORLD):

Parameters
----------
d0, d1, …, dn : int, optional
d0, d1, …, dn : ints, optional
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Implementation of the function is closely related to issue #54. What is the intended behaviour of this function? Output a number of random numbers where each of the split parts is random and not identical to the other split chunks of the other nodes? What will happen if I use the very same seed but a different node count? Will the random tensor differ?

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It looks like torch's randn will not generate the same values for arrays of different sizes. This means that even though the same seed is used, the split tensor will not be the same as a tensor generated only on one process. i.e.

torch.randn((3, 3, 3))[0] != torch.randn((1, 3, 3))

One solution to this would be to generate the whole dataset then split it. But this will not scale. I do not see another way to do this.

It must be noted that the matrix generated by the ht.randn with a fixed seed (torch.manual_seed) and a fixed size produces a reproducible result

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In my opinion, we can leave it as proposed for now, however, for the future, I would want to actually have a different behaviour. Consider the following example:

ht.set_seed(1)
ht.randn(100, 5, 3, split=0)

Should always produce the same set of random numbers independent of the utilized nodes (for reproducibility reasons as you mentioned). This is exactly what is requested in issue #54. Obviously, this means that we would have to come up with a pseudo random generator allowing to skip to arbitrary/some fixed positions into the random sequence.

In the proposed fix, we would have a simplified randn() call. It will only provide reproducible results for the exact same node count.

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100% agree. i am trying to figure a way to do this. i will move this discussion to the issue and close this request.

The dimensions of the returned array, should be all positive.

Returns
Expand All @@ -55,22 +55,29 @@ def randn(*args, split=None, comm=MPI_WORLD):
[-1.8548, -1.2574, 0.2391, -0.3302],
[ 1.3365, -1.5212, 1.4159, -0.1671],
[ 0.1260, 1.2126, -0.0804, 0.0907]])

>>> ht.randn(4, 4, split=0)
(two processes)
tensor([[-1.1261, 0.5971, 0.2851, 0.9998],
[-1.8548, -1.2574, 0.2391, -0.3302]])
tensor([[ 1.3365, -1.5212, 1.4159, -0.1671],
[ 0.1260, 1.2126, -0.0804, 0.0907]])
"""
# check if all positional arguments are integers
if not all(isinstance(_, int) for _ in args):
raise TypeError('dimensions have to be integers')
if not all(_ > 0 for _ in args):
raise ValueError('negative dimension are not allowed')

gshape = tuple(args) if args else(1,)
gshape = tuple(args) if args else (1,)
split = stride_tricks.sanitize_axis(gshape, split)
_, lshape, _ = comm.chunk(gshape, split)

try:
torch.randn(gshape)
data = torch.randn(lshape)
except RuntimeError as exception:
# re-raise the exception to be consistent with numpy's exception interface
raise ValueError(str(exception))

# compose the local tensor
data = torch.randn(args)

return tensor(data, gshape, types.canonical_heat_type(data.dtype), split, MPI_WORLD)
# compose the local tensor/s
return tensor(data, gshape, types.canonical_heat_type(data.dtype), split, comm)
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