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Min / max reductions (#2041)
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@@ -124,6 +124,22 @@ class TestMPIDistributed(mlx_distributed_tests.MLXDistributedCommonTestCase):
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x = mx.distributed.recv_like(x, neighbor, group=pairs)
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mx.eval(y, x)
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def test_min_max(self):
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world = mx.distributed.init()
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base = mx.arange(16).reshape(4, 4)
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x = base + world.rank() * 32
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def _test_reduction(reduction: str = "all_max"):
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target = base + ((world.size() - 1) * 16) * (reduction == "max")
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reducer = getattr(mx.distributed, reduction)
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y = reducer(x)
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self.assertTrue(mx.allclose(y, target))
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for reduction in ["all_max", "all_min"]:
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_test_reduction(reduction)
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if __name__ == "__main__":
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unittest.main()
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@@ -44,17 +44,23 @@ class TestRingDistributed(mlx_distributed_tests.MLXDistributedCommonTestCase):
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(1024, 1024),
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]
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key = mx.random.key(0)
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reductions = ["min", "max", "sum"]
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for dt, rtol in dtypes:
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for sh in sizes:
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x = (
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mx.random.uniform(shape=(world.size(),) + sh, key=key) * 10
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).astype(dt)
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y = mx.distributed.all_sum(x[world.rank()])
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z = sum(
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x[i] for i in range(world.size())
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) # to ensure that we don't sum to int32
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maxrelerror = ((y - z).abs() / z.abs()).max()
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self.assertLessEqual(maxrelerror, rtol)
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for reduction in reductions:
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reducer_distributed = getattr(mx.distributed, f"all_{reduction}")
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y = reducer_distributed(x[world.rank()])
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reducer = getattr(mx, reduction)
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z = reducer(x, axis=0)
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mx.eval(y, z)
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maxrelerror = ((y - z).abs() / z.abs()).max()
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self.assertLessEqual(maxrelerror, rtol)
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def test_all_gather(self):
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world = mx.distributed.init()
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