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79 lines
3.2 KiB
Python
79 lines
3.2 KiB
Python
import unittest
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from tinygrad import Tensor, dtypes
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from tinygrad.helpers import Context
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from tinygrad.uop.ops import Ops
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class TestRingAllReduce(unittest.TestCase):
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def test_schedule_ring(self):
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with Context(RING=2):
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N = 4
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ds = tuple(f"CPU:{i}" for i in range(N))
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t = Tensor.empty(N, N*100).shard(ds, axis=0).realize()
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linear = t.sum(0).linear_with_vars()[0]
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copies = [si for si in linear.src if si.src[0].op is Ops.COPY]
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pairs = [(c.src[1].buffer.device, c.src[2].buffer.device) for c in copies]
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# N*(N-1) scatter reduce, and N*(N-1) allgather
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self.assertEqual(len(pairs), N*(N-1)*2)
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# copy topology forms a ring
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self.assertEqual(len(set(pairs)), N)
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def test_schedule_all2all(self):
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with Context(ALL2ALL=2):
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N = 4
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ds = tuple(f"CPU:{i}" for i in range(N))
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t = Tensor.empty(N, N*100).shard(ds, axis=0).realize()
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linear = t.sum(0).mul(2.0).contiguous().linear_with_vars()[0]
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copies = [si for si in linear.src if si.src[0].op is Ops.COPY]
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sinks = [si for si in linear.src if si.src[0].op is Ops.SINK]
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self.assertEqual(len(copies), 24)
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self.assertEqual(len(sinks), 26)
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@Context(RING=0, ALL2ALL=0)
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def test_schedule_naive(self):
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N = 4
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ds = tuple(f"NULL:{i}" for i in range(N))
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t = Tensor.empty(N, 4096).shard(ds, axis=0).realize()
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linear = t.sum(0).linear_with_vars()[0]
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copies = [si for si in linear.src if si.src[0].op is Ops.COPY]
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sinks = [si for si in linear.src if si.src[0].op is Ops.SINK]
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pairs = [(c.src[1].buffer.device, c.src[2].buffer.device) for c in copies]
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self.assertEqual(len(pairs), N*(N-1))
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self.assertEqual(len(sinks), 2)
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self.assertTrue(all(dst != src for dst, src in pairs))
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def test_correct_ring(self):
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with Context(RING=2):
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N = 4
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ds = tuple(f"CPU:{i}" for i in range(N))
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t = Tensor.ones(N, N*100).contiguous().shard(ds, axis=0).realize()
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out = t.sum(0)
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self.assertListEqual(out.tolist(), [4]*N*100)
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class TestAllreduceCast(unittest.TestCase):
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def _get_copy_dtypes(self, dtype, allreduce_cast):
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ds = tuple(f"CPU:{i}" for i in range(2))
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with Context(ALLREDUCE_CAST=allreduce_cast, RING=0, SCACHE=0):
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t = Tensor.empty(4, 4, dtype=dtype).shard(ds, axis=0)
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linear = t.sum(0).linear_with_vars()[0]
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return {si.src[1].buffer.dtype.scalar() for si in linear.src if si.src[0].op is Ops.COPY}
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def test_allreduce_cast_bf16(self):
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# with ALLREDUCE_CAST, allreduce copies stay in bfloat16 instead of promoting to float32
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self.assertNotIn(dtypes.float, self._get_copy_dtypes(dtypes.bfloat16, allreduce_cast=1))
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self.assertIn(dtypes.float, self._get_copy_dtypes(dtypes.bfloat16, allreduce_cast=0))
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def test_allreduce_cast_half(self):
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self.assertNotIn(dtypes.float, self._get_copy_dtypes(dtypes.half, allreduce_cast=1))
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self.assertIn(dtypes.float, self._get_copy_dtypes(dtypes.half, allreduce_cast=0))
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def test_allreduce_cast_float32_noop(self):
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# float32 should not be affected by ALLREDUCE_CAST (no promotion happens)
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dtypes_on = self._get_copy_dtypes(dtypes.float, allreduce_cast=1)
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dtypes_off = self._get_copy_dtypes(dtypes.float, allreduce_cast=0)
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self.assertEqual(dtypes_on, dtypes_off)
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if __name__ == '__main__':
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unittest.main()
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