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175 lines
7.1 KiB
Python
175 lines
7.1 KiB
Python
import unittest
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from tinygrad import Tensor, Device, dtypes, Variable
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from tinygrad.helpers import Context, GlobalCounters, getenv, DEBUG
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from tinygrad.uop.ops import graph_rewrite, PatternMatcher, UPat, Ops, UOp
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from tinygrad.codegen.opt import OptOps, Opt
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from tinygrad.renderer.ptx import PTXRenderer
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from tinygrad.renderer.nir import NIRRenderer
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@unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, (NIRRenderer, PTXRenderer)), "broken in LVP and PTX")
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class TestDoubleMatmul(unittest.TestCase):
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def setUp(self):
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with Context(DEBUG=0):
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self.a, self.b, self.c = [Tensor.randn(16, 16).contiguous().realize() for _ in range(3)]
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self.ref = (self.a @ self.b @ self.c).realize()
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def _test(self, opts):
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with Context(DEBUG=max(2, DEBUG.value)):
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out = (self.a @ self.b @ self.c).contiguous(arg=opts).realize()
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with Context(DEBUG=0):
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err = (out-self.ref).square()
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self.assertLess(err.max().item(), 1e-4)
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self.assertLess(err.mean().item(), 1e-6)
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def test_baseline(self): self._test(())
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def test_upcast_0(self): self._test((Opt(OptOps.UPCAST, 0, 4),))
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def test_upcast_1(self): self._test((Opt(OptOps.UPCAST, 1, 4),))
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def test_upcast_2(self): self._test((Opt(OptOps.UPCAST, 2, 4),))
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def test_upcast_01(self): self._test((Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 4)))
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def test_upcast_01_mismatch(self): self._test((Opt(OptOps.UPCAST, 0, 2), Opt(OptOps.UPCAST, 1, 4)))
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def test_upcast_02(self): self._test((Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 2, 4)))
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def test_upcast_12(self): self._test((Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UPCAST, 2, 4)))
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def test_unroll_0(self): self._test((Opt(OptOps.UNROLL, 0, 4),))
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def test_unroll_1(self): self._test((Opt(OptOps.UNROLL, 1, 4),))
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def test_unroll_01(self): self._test((Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.UNROLL, 1, 4)))
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def test_upcast_0_unroll_0(self): self._test((Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UNROLL, 0, 4)))
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def test_upcast_1_unroll_0(self): self._test((Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UNROLL, 0, 4)))
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def test_upcast_2_unroll_0(self): self._test((Opt(OptOps.UPCAST, 2, 4), Opt(OptOps.UNROLL, 0, 4)))
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def test_upcast_0_unroll_1(self): self._test((Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UNROLL, 1, 4)))
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def test_upcast_1_unroll_1(self): self._test((Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UNROLL, 1, 4)))
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def test_upcast_2_unroll_1(self): self._test((Opt(OptOps.UPCAST, 2, 4), Opt(OptOps.UNROLL, 1, 4)))
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def test_upcast_1_unroll_1_small(self): self._test((Opt(OptOps.UPCAST, 1, 2), Opt(OptOps.UNROLL, 1, 2)))
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def test_upcast_1_unroll_1_rev(self): self._test((Opt(OptOps.UNROLL, 1, 2), Opt(OptOps.UPCAST, 1, 2)))
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def test_upcast_01_unroll_01(self):
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self._test((Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.UNROLL, 1, 4)))
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def test_upcast_12_unroll_01(self):
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self._test((Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UPCAST, 2, 4), Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.UNROLL, 1, 4)))
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class TestRangeifyAssign(unittest.TestCase):
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def test_assign_permuted(self):
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A = Tensor.empty(4, 4, dtype='int')
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B = Tensor.arange(16).reshape(4,4)
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ret = A.permute(1,0).assign(B)
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lst = ret.tolist()
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lst2 = A.tolist()
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lst3 = B.tolist()
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print(lst)
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print(lst2)
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print(lst3)
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self.assertListEqual(lst, lst3)
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self.assertListEqual(lst2, B.permute(1, 0).tolist())
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class TestRangeifyEdgeCase(unittest.TestCase):
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def test_variable_stack_data(self):
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# a bound-Variable STACK used as data gets ranges from its graph position
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v = Variable("v", 0, 10).bind(3)
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t = Tensor(UOp.stack(v, v+1).cast(dtypes.int)) + Tensor.arange(2)
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self.assertListEqual(t.tolist(), [3, 5])
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def test_variable_data_and_shape(self):
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# the same Variable has a data edge through CAST and a structural edge through SHRINK
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v = Variable("shared_v", 1, 10).bind(3)
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t = Tensor.ones(10)[:v] * Tensor(v.cast(dtypes.float))
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self.assertEqual(t.sum().item(), 9)
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def test_matmul_relu_cat(self):
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a = Tensor.ones(100, 512).contiguous().realize()
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c = Tensor.ones(1, 512).contiguous().realize()
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cm = Tensor.ones(512, 512)
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c = c @ cm
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c = c.relu()
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res = Tensor.cat(a, c, dim=0)
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self.assertEqual(res.numpy()[-1, :16].tolist(), [512] * 16)
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def test_multi_gather(self):
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# regression test: local bufferize must have device set for const_like to work
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# NOTE: with uint type, this will become a long and fail on WEBGPU
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forest = Tensor(list(range(8)), dtype='int')
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idx = Tensor([0, 0], dtype='int')
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node_val = forest.gather(0, idx)
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idx2 = idx * 2 + 1
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node_val2 = forest.gather(0, idx2)
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result = (node_val + node_val2).numpy()
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self.assertEqual(result.tolist(), [1, 1])
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if getenv("BIG") > 2:
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# llama 8B (8192)
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BS, HEADS, SEQLEN, EMB = 4, 32, 8192, 128
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elif getenv("BIG") > 1:
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# llama 8B
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BS, HEADS, SEQLEN, EMB = 4, 32, 2048, 128
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elif getenv("BIG") > 0:
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# bigger
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BS, HEADS, SEQLEN, EMB = 4, 32, 128, 128
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else:
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BS, HEADS, SEQLEN, EMB = 4, 2, 16, 8
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def fa():
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Tensor.manual_seed(1337)
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with Context(DEBUG=0): q,k,v = [Tensor.rand(BS, HEADS, SEQLEN, EMB).contiguous().realize() for _ in range(3)]
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GlobalCounters.reset()
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return q.scaled_dot_product_attention(k, v)
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# contiguous + reduce can support ranges?
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@unittest.skip("pm_rangeify no longer exists. test this in a different way")
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class TestRangeifyPM(unittest.TestCase):
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def setUp(self): self.base = Tensor.empty(10*10).reshape(10, 10).contiguous()
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def assert_same(self, a, b):
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def run_pm_rangeify(t:Tensor):
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from tinygrad.schedule.rangeify import pm_rangeify, RangeifyContext
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sink = t.uop.sink()
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pm_realize = PatternMatcher([(UPat(Ops.CONTIGUOUS, name="x"), lambda x: x.replace(op=Ops.REALIZE))])
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sink = graph_rewrite(sink, pm_realize)
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return graph_rewrite(sink, pm_rangeify, ctx=RangeifyContext())
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self.assertIs(run_pm_rangeify(a.contiguous()), run_pm_rangeify(b.contiguous()))
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def test_nothing_match(self):
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a = self.base.pad(((0,0),(0,1)))
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b = self.base.pad(((0,0),(0,1)))
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self.assert_same(a, b)
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def test_reshape_match(self):
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a = self.base
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b = self.base.reshape(100).reshape(10, 10)
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self.assert_same(a, b)
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def test_permute_reshape_match(self):
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a = self.base
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b = self.base.permute(1,0).reshape(100).reshape(10, 10).permute(1,0)
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self.assert_same(a, b)
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def test_padded_permute_match(self):
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a = self.base.pad(((0,0),(0,1)))
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b = self.base.permute(1,0).pad(((0,1),(0,0))).permute(1,0)
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self.assert_same(a, b)
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@unittest.expectedFailure
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def test_padded_reshape_match(self):
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a = self.base.pad(((0,0),(0,1)))
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b = self.base.reshape(100).reshape(10, 10).pad(((0,0),(0,1)))
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self.assert_same(a, b)
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@unittest.expectedFailure
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def test_padded_permute_reshape_match(self):
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a = self.base.pad(((0,0),(0,1)))
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b = self.base.permute(1,0).reshape(100).reshape(10, 10).pad(((0,1),(0,0))).permute(1,0)
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self.assert_same(a, b)
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# why is this failing?
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@unittest.expectedFailure
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def test_cross_pad_match(self):
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a = self.base.pad(((0,0),(0,1))).pad(((0,1),(0,0)))
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b = self.base.pad(((0,1),(0,0))).pad(((0,0),(0,1)))
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self.assert_same(a, b)
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if __name__ == '__main__':
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unittest.main()
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