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398 lines
13 KiB
398 lines
13 KiB
#!/usr/bin/env python
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import unittest
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import numpy as np
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from tinygrad import dtypes, Tensor, TinyJit, GlobalCounters, Variable
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from tinygrad.device import is_dtype_supported
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from tinygrad.helpers import temp
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N = 200 # has to be bigger than the cache to fail
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class TestAssign(unittest.TestCase):
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def test_simple_assignment(self):
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a = Tensor(np.arange(N*N, dtype=np.float32)).reshape(N,N)
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b = Tensor(np.arange(N*N, dtype=np.float32)).reshape(N,N)
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a.realize()
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b.realize()
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ba1 = a.lazydata.base.realized
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bb1 = b.lazydata.base.realized
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a += b
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a.realize()
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ba2 = a.lazydata.base.realized
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assert ba1 == ba2 and ba1 != bb1
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np.testing.assert_allclose(a.numpy(), (np.arange(N*N)*2).reshape((N,N)))
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def test_assign_zeros_good(self):
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a = Tensor.zeros(10,10).contiguous()
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a.assign(Tensor.ones(10,10))
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b = Tensor.zeros(10,10).contiguous()
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a.realize()
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np.testing.assert_allclose(b.numpy(), 0)
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def test_assign_zeros(self):
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a = Tensor.zeros(10,10).contiguous()
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b = Tensor.zeros(10,10).contiguous()
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a.assign(Tensor.ones(10,10))
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a.realize()
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np.testing.assert_allclose(b.numpy(), 0)
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def test_assign_add(self):
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def f(x):
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x += 1
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x.realize()
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x = Tensor([0])
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f(x)
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assert x.item() == 1
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def test_assign_add_twice(self):
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# NOTE: this has two kernels
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def f(x):
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x += 1
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x += 1
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x.realize()
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x = Tensor([0])
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f(x)
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assert x.item() == 2
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def test_assign_add_double(self):
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def f(x):
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x += 1
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x.realize()
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x = Tensor([0])
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f(x)
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out = x.item()
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assert out == 1, f"expected 1, got {out}"
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x = Tensor([0])
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f(x)
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out = x.item()
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assert out == 1, f"expected 1, got {out}"
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def test_assign_add_jit(self):
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@TinyJit
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def f(x):
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x += 1
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x.realize()
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x = Tensor([0])
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for _ in range(5): f(x)
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assert x.item() == 5
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def test_assign_add_jit_other(self):
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@TinyJit
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def f(x):
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x += 1
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x.realize()
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x = Tensor([0])
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for _ in range(5): f(x)
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assert x.item() == 5
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y = Tensor([0])
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for _ in range(4): f(y)
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assert y.item() == 4
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def test_assign_other_jit(self):
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@TinyJit
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def f(x, a):
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x.assign(a)
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x.realize()
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x = Tensor([0])
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for i in range(1, 6):
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f(x, x.full_like(i).contiguous()) # const would be implicitly folded without contiguous
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assert x.item() == i
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def test_assign_add_other_jit(self):
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@TinyJit
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def f(x, a):
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x += a
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x.realize()
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x = Tensor([0])
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a = 0
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for i in range(1, 6):
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a += i
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f(x, x.full_like(i).contiguous())
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assert x.item() == a
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def test_assign_changes(self):
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a = Tensor.ones(4).contiguous().realize()
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old_a = a
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a.assign(Tensor.full((4,), 2.).contiguous())
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# NOTE: old_a is now 2, and this would match the behavior of pytorch
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new = a + old_a
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np.testing.assert_allclose(new.numpy(), 4)
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def test_assign_diamond_cycle(self):
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# NOTE: should *not* raise AssertionError from numpy
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with self.assertRaisesRegex(RuntimeError, "cycle"):
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a = Tensor.ones(4).contiguous().realize()
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times_a = a*3
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a.assign(Tensor.full((4,), 2.).contiguous())
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new = a + (times_a-1)
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np.testing.assert_allclose(new.numpy(), 4)
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def test_assign_diamond_contiguous_cycle(self):
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with self.assertRaisesRegex(RuntimeError, "cycle"):
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a = Tensor.ones(4).contiguous().realize()
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times_a = a*3
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a.assign(Tensor.full((4,), 2.))
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new = a.contiguous() + times_a-1
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np.testing.assert_allclose(new.numpy(), 4)
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def test_assign_diamond_possible(self):
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a = Tensor.ones(4).contiguous().realize()
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times_a = a*3
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a.assign(Tensor.full((4,), 2.))
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new = a + (times_a-1).contiguous()
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np.testing.assert_allclose(new.numpy(), 4)
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def test_assign_diamond_possible_contiguous(self):
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a = Tensor.ones(4).contiguous().realize()
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times_a = a*3
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a.assign(Tensor.full((4,), 2.).contiguous())
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new = a + (times_a-1).contiguous()
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np.testing.assert_allclose(new.numpy(), 4)
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def test_assign_diamond_both_contiguous(self):
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a = Tensor.ones(4).contiguous().realize()
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times_a = a*3
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a.assign(Tensor.full((4,), 2.))
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new = a.contiguous() + (times_a-1).contiguous()
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np.testing.assert_allclose(new.numpy(), 4)
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def test_assign_diamond_alt(self):
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a = Tensor.ones(4).contiguous().realize()
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a.assign(Tensor.full((4,), 2.).contiguous())
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times_a = a*3
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new = a + times_a
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np.testing.assert_allclose(new.numpy(), 8)
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def test_double_assign(self):
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a = Tensor.ones(4).contiguous().realize()
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a += 1
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a += 1
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np.testing.assert_allclose(a.numpy(), 3)
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def test_crossover_assign(self):
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a = Tensor.full((4,), 2).contiguous().realize()
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b = Tensor.full((4,), 3).contiguous().realize()
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a += b
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b += a
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Tensor.realize(a,b)
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np.testing.assert_allclose(a.numpy(), 5)
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np.testing.assert_allclose(b.numpy(), 8)
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def test_assign_double_diamond(self):
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a = Tensor.full((4,), 2).contiguous().realize()
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b = Tensor.full((4,), 3).contiguous().realize()
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a_prev = a*4
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b_prev = b+3
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b += a_prev.contiguous()
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a += b_prev.contiguous()
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Tensor.realize(a, b)
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np.testing.assert_equal(b.numpy(), 11)
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np.testing.assert_equal(a.numpy(), 8)
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def test_assign_double_diamond_reduce(self):
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a0 = Tensor.full((16, 16), 10).contiguous().realize()
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a1 = Tensor.full((16, 16), 20).contiguous().realize()
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b0 = Tensor.full((16, ), 1).contiguous().realize()
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b1 = Tensor.full((16, ), 2).contiguous().realize()
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r0 = (a0 - b1.contiguous()).sum(1)
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r1 = (a1 - b0.contiguous()).sum(1)
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b0.assign(r0 * b0)
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b1.assign(r1 * b1)
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Tensor.realize(b0, b1)
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np.testing.assert_equal(b0.numpy(), 128)
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np.testing.assert_equal(b1.numpy(), 608)
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@unittest.skip("TODO: bring this assert back")
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def test_crossunder_assign(self):
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# NOTE: should *not* raise AssertionError from numpy
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with self.assertRaisesRegex(RuntimeError, "cycle"):
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a = Tensor.full((4,), 2).contiguous().realize()
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b = Tensor.full((4,), 3).contiguous().realize()
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c = a+9
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a += b
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b += c
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Tensor.realize(a,b)
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np.testing.assert_allclose(a.numpy(), 2+3)
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np.testing.assert_allclose(b.numpy(), 3+2+9)
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def test_assign_kv_cache(self):
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bsz, max_context = 2, 8
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class Attn:
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@TinyJit
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def __call__(self, xk:Tensor, start_pos:Variable):
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seqlen = xk.shape[1]
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if not hasattr(self, "cache_k"):
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self.cache_k = Tensor.zeros(bsz, max_context, 1, 1).contiguous()
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keys = self.cache_k.shrink((None, (0, start_pos), None, None)).cat(xk, dim=1).contiguous() if start_pos > 0 else xk
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self.cache_k.assign(keys.pad((None,(0,max_context-start_pos-seqlen),None,None)).contiguous()).realize()
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attn = Attn()
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xk = Tensor.ones(bsz, 3, 1, 1).contiguous()
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attn(xk, 0)
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for i in range(3,6):
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# copied from LLaMA
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start_pos = Variable("start_pos", 1, max_context).bind(i)
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xk = Tensor.ones(bsz, 1, 1, 1).contiguous()
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attn(xk, start_pos)
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out = attn.cache_k.flatten().numpy()
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np.testing.assert_allclose(out, [1.,1.,1.,1.,1.,1.,0.,0.,1.,1.,1.,1.,1.,1.,0.,0.])
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def test_assign_contiguous(self):
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b = Tensor.rand(4,4).realize()
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a = (Tensor.rand(4,4).realize() + 1)
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kc = GlobalCounters.kernel_count
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b.assign(a.contiguous()).realize()
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assert GlobalCounters.kernel_count - kc == 2
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def test_assign_contiguous_permute(self):
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b = Tensor.rand(4,4).realize()
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a = (Tensor.rand(4,4).realize() + 1).permute((1,0))
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kc = GlobalCounters.kernel_count
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b.assign(a.contiguous()).realize()
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assert GlobalCounters.kernel_count - kc == 2
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def test_permuted_assignment(self):
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a = Tensor(np.arange(N*N, dtype=np.float32)).reshape(N,N)
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b = Tensor(np.arange(N*N, dtype=np.float32)).reshape(N,N)
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a.realize()
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b.realize()
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ba1 = a.lazydata.base.realized
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bb1 = b.lazydata.base.realized
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with self.assertRaises((RuntimeError, AssertionError)):
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a = a.permute(1,0)
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a += b
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a.realize()
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ba2 = a.lazydata.base.realized
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assert ba1 != ba2 and ba1 != bb1
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np.testing.assert_allclose(a.numpy(), np.arange(N*N).reshape((N,N)) + np.arange(N*N).reshape((N,N)).transpose(1,0))
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def test_post_permuted_assignment(self):
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a = Tensor(np.arange(N*N, dtype=np.float32)).reshape(N,N)
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b = Tensor(np.arange(N*N, dtype=np.float32)).reshape(N,N)
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a.realize()
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b.realize()
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#GlobalCounters.cache = []
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ba1 = a.lazydata.base.realized # noqa: F841
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bb1 = b.lazydata.base.realized # noqa: F841
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with self.assertRaisesRegex(RuntimeError, "contiguous"):
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a.assign(a.permute(1,0) + b) # this should not work!
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a.realize()
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ba2 = a.lazydata.base.realized # noqa: F841
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# NOTE: don't test that it's assigned
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#assert ba1 == ba2 and ba1 != bb1
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np.testing.assert_allclose(a.numpy(), np.arange(N*N).reshape((N,N)) + np.arange(N*N).reshape((N,N)).transpose(1,0))
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@unittest.skip("multi output not supported anymore")
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def test_simple_assignment_multioutput(self):
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a = Tensor.randn(32, 32).realize()
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b = Tensor.full((32, ), 1.).contiguous().realize()
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c = Tensor.full((32, ), 2.).contiguous().realize()
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d = Tensor.full((32, ), 3.).contiguous().realize()
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r = a.sum(axis=1)
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b.assign(r + b)
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c.assign(r + c)
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d.assign(r + d)
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kc = GlobalCounters.kernel_count
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Tensor.realize(b, c, d)
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assert GlobalCounters.kernel_count - kc == 1
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np.testing.assert_allclose(b.numpy(), a.sum(1).numpy()+1)
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np.testing.assert_allclose(c.numpy(), a.sum(1).numpy()+2)
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np.testing.assert_allclose(d.numpy(), a.sum(1).numpy()+3)
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# NOTE: if the assign target is read/write in a single kernel, it should be contiguous
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def test_permuted_assignment_correct(self):
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a = Tensor.arange(4 * 4).reshape(4, 4).contiguous().realize()
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b = Tensor.arange(4 * 4).reshape(4, 4).contiguous().realize()
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# TODO: scheduler limitation, should NOT raise AssertionError from numpy.
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with self.assertRaisesRegex(RuntimeError, "contiguous"):
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a = a.permute(1, 0)
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new_val = a + b
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a.assign(new_val)
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np.testing.assert_equal(a.numpy(), np.arange(4 * 4).reshape(4, 4).transpose(1, 0) + np.arange(4 * 4).reshape(4, 4))
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def test_permuted_reduceop_child_dual_use(self):
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a = Tensor.randn(32, 32, 32).realize()
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b = Tensor.full((32, 32), 1.).contiguous().realize()
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with self.assertRaisesRegex(RuntimeError, "contiguous"):
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r = a.sum(axis=1)
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b.assign(r + b.permute(1, 0))
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b.realize()
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@unittest.skip("multi output not supported anymore")
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def test_permuted_reduceop_multioutput_dual_use(self):
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a = Tensor.randn(32, 32, 32).realize()
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b = Tensor.full((32, 32), 1.).contiguous().realize()
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c = Tensor.full((32, 32), 2.).contiguous().realize()
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with self.assertRaisesRegex(RuntimeError, "contiguous"):
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r = a.sum(axis=1)
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b_perm = b.permute(1, 0)
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b.assign(r + b)
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c.assign(r + b_perm)
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Tensor.realize(b, c)
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@unittest.skip("multi output not supported anymore")
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def test_permuted_reduceop_multioutput_dual_use_possible(self):
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a = Tensor.randn(32, 32, 32, dtype=dtypes.int).realize()
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b = Tensor.arange(32 * 32).reshape(32, 32).realize()
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c = Tensor.arange(32 * 32).reshape(32, 32).realize()
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kc = GlobalCounters.kernel_count
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r = a.sum(axis=1)
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b_perm = b.permute(1, 0)
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b.assign(r + b)
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c.assign(r + b_perm.contiguous())
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Tensor.realize(b, c)
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assert GlobalCounters.kernel_count - kc == 2
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np.testing.assert_equal(b.numpy(), a.numpy().sum(1) + np.arange(32 * 32).reshape(32, 32))
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np.testing.assert_equal(c.numpy(), a.numpy().sum(1) + np.arange(32 * 32).reshape(32, 32).transpose(1, 0))
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def test_permuted_assignment_masked_view_possible(self):
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a = Tensor.ones(4, 4).contiguous().realize()
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b = a.shrink((None, (0, 2))).pad((None, (0, 2)), value=2)
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a.assign(a + b)
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kc = GlobalCounters.kernel_count
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a.realize()
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assert GlobalCounters.kernel_count - kc == 1
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np.testing.assert_equal(a.numpy(), np.ones((4, 4))+np.pad(np.ones((4, 4))[:, 0:2], ((0, 0), (0, 2)), constant_values=2))
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def test_permuted_assignment_masked_view_not_contiguous(self):
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a = Tensor.ones(4, 4).contiguous().realize()
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with self.assertRaisesRegex(RuntimeError, "contiguous"):
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b = a.shrink((None, (0, 2))).pad((None, (0, 2)), value=2).permute(1, 0)
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a.assign(a + b)
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a.realize()
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# TODO: is there a way to sneak in a permute such that it returns the wrong answer?
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@unittest.skipUnless(is_dtype_supported(dtypes.half), "need half")
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def test_setitem_half(self):
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a = Tensor.full((8,), 1.0, dtype=dtypes.half).contiguous().realize()
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b = Tensor.full((4,), 2.0, dtype=dtypes.half).contiguous().realize()
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assign = a[:4].assign(b)
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assign.realize()
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np.testing.assert_allclose(a.numpy(), [2., 2., 2., 2., 1., 1., 1., 1.])
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@unittest.skip("don't use output buffer, and mismatch dtype no longer supported")
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def test_cast_assignment(self):
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a = Tensor(np.arange(N*N, dtype=np.float32)).reshape(N,N)
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a.realize()
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oba1 = a.lazydata.base.output_buffer
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a.assign(a.cast(dtypes.int32).realize())
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a.realize()
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oba2 = a.lazydata.base.output_buffer
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assert oba1 is None and oba2 is None
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np.testing.assert_allclose(a.numpy(), np.arange(N*N,dtype=np.int32).reshape((N,N)))
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def test_disk_assignment(self):
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a = Tensor.empty(5, device=f"disk:{temp('disk_assignment')}").assign(Tensor.ones(5)).numpy()
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np.testing.assert_equal(a, np.ones(5))
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if __name__ == "__main__":
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unittest.main()
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