mirror of
https://github.com/tinygrad/tinygrad.git
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c331798201
* move tests to test/backend * fix imports * fix CI * revert that one * Fix formatting in README for test command
317 lines
11 KiB
Python
317 lines
11 KiB
Python
import unittest
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from tinygrad import Tensor, TinyJit, Variable, dtypes, Device
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from tinygrad.helpers import Context
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import numpy as np
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class TestSetitem(unittest.TestCase):
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def test_simple_setitem(self):
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cases = (
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((6,6), (slice(2,4), slice(3,5)), Tensor.ones(2,2)),
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((6,6), (slice(2,4), slice(3,5)), Tensor([1.,2.])),
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((6,6), (slice(2,4), slice(3,5)), 1.0),
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((6,6), (3, 4), 1.0),
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((6,6), (3, None, 4, None), 1.0),
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((4,4,4,4), (Ellipsis, slice(1,3), slice(None)), Tensor(4.0)),
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((4,4,4,4), (Ellipsis, slice(1,3)), 4),
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((4,4,4,4), (2, slice(1,3), None, 1), 4),
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((4,4,4,4), (slice(1,3), slice(None), slice(0,4,2)), 4),
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((4,4,4,4), (slice(1,3), slice(None), slice(None), slice(0,3)), 4),
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((6,6), (slice(1,5,2), slice(0,5,3)), 1.0),
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((6,6), (slice(5,1,-2), slice(5,0,-3)), 1.0),
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)
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for shp, slc, val in cases:
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t = Tensor.zeros(shp).contiguous()
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t[slc] = val
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n = np.zeros(shp)
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n[slc] = val.numpy() if isinstance(val, Tensor) else val
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np.testing.assert_allclose(t.numpy(), n)
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def test_padded_setitem(self):
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t = Tensor.arange(10)
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t[4:1:-2] = 11
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self.assertListEqual(t.tolist(), [0, 1, 11, 3, 11, 5, 6, 7, 8, 9])
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def test_setitem_inplace_mul(self):
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t = Tensor.arange(10).realize()
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t[:3] *= 10
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self.assertListEqual(t.tolist(), [0, 10, 20, 3, 4, 5, 6, 7, 8, 9])
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def test_setitem_into_unrealized(self):
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t = Tensor.arange(4).reshape(2, 2)
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t[1] = 5
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np.testing.assert_allclose(t.numpy(), [[0, 1], [5, 5]])
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def test_setitem_into_unrealized_sliced_compute(self):
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# base computation contains SHRINK from prior slicing (like QR decomposition pattern)
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a = Tensor.arange(6, dtype=dtypes.float).reshape(2, 3)
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w = a[0] + a[1] # unrealized ADD with SHRINK in graph: [3, 5, 7]
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w[1] = 99
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np.testing.assert_allclose(w.numpy(), [3, 99, 7])
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def test_setitem_fancy_on_unrealized_view(self):
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# fancy indexing setitem on unrealized SHRINK view (triggered infinite loop in graph_rewrite)
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base = Tensor.arange(20, dtype=dtypes.float).reshape(4, 5)
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sub = base[1:3]
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flat = sub.reshape(sub.numel()).contiguous()
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idx = Tensor([0, 3, 7, 9])
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flat[idx] = Tensor([99, 98, 97, 96], dtype=dtypes.float)
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sub.assign(flat.reshape(2, 5))
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np.testing.assert_allclose(sub.numpy(), [[99, 6, 7, 98, 9], [10, 11, 97, 13, 96]])
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def test_setitem_dtype(self):
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for dt in (dtypes.int, dtypes.float, dtypes.bool):
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for v in (5., 5, True):
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t = Tensor.ones(6,6, dtype=dt).contiguous()
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t[1] = v
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self.assertEqual(t.dtype, dt)
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def test_setitem_dtype_mismatch(self):
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t = Tensor.zeros(6, dtype=dtypes.float).contiguous().realize()
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with self.assertRaises(RuntimeError): t[2:4] = Tensor([1, 2], dtype=dtypes.int)
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def test_setitem_into_noncontiguous(self):
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t = Tensor.ones(4)
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with self.assertRaises(RuntimeError): t[1] = 5
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def test_setitem_chained_indexing(self):
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# N[i][j] must work the same as N[i, j]
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N1 = Tensor.zeros((3, 3)).contiguous().realize()
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N1[1, 2] = 5
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N2 = Tensor.zeros((3, 3)).contiguous().realize()
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N2[1][2] = 5
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np.testing.assert_equal(N1.numpy(), N2.numpy())
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def test_setitem_detach(self):
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# setitem on detached tensor should work
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t = Tensor.zeros((3, 3)).contiguous().realize()
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t.detach()[1, 2] = 5
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self.assertEqual(t[1, 2].item(), 5.0)
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def test_setitem_permute(self):
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# setitem on permuted tensor should modify original
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t = Tensor.zeros((2, 3)).contiguous().realize()
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t.T[1, 0] = 5 # t.T is (3, 2), so [1, 0] maps to t[0, 1]
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self.assertEqual(t[0, 1].item(), 5.0)
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def test_setitem_flip(self):
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# setitem on flipped tensor should modify original
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t = Tensor.zeros((3,)).contiguous().realize()
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t[::-1][0] = 5 # flip, then set first element (which is last in original)
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self.assertEqual(t[2].item(), 5.0)
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def test_setitem_inplace_operator(self):
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t = Tensor.arange(4).reshape(2, 2).contiguous()
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t[1] += 2
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np.testing.assert_allclose(t.numpy(), [[0, 1], [4, 5]])
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t = Tensor.arange(4).reshape(2, 2).contiguous()
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t[1] -= 1
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np.testing.assert_allclose(t.numpy(), [[0, 1], [1, 2]])
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t = Tensor.arange(4).reshape(2, 2).contiguous()
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t[1] *= 2
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np.testing.assert_allclose(t.numpy(), [[0, 1], [4, 6]])
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# NOTE: have to manually cast setitem target to least_upper_float for div
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t = Tensor.arange(4, dtype=dtypes.float).reshape(2, 2).contiguous()
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t[1] /= 2
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np.testing.assert_allclose(t.numpy(), [[0, 1], [1, 1.5]])
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t = Tensor.arange(4).reshape(2, 2).contiguous()
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t[1] **= 2
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np.testing.assert_allclose(t.numpy(), [[0, 1], [4, 9]])
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t = Tensor.arange(4).reshape(2, 2).contiguous()
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t[1] ^= 5
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np.testing.assert_allclose(t.numpy(), [[0, 1], [7, 6]])
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def test_setitem_consecutive_inplace_operator(self):
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t = Tensor.arange(4).reshape(2, 2).contiguous()
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t[1] += 2
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t[1] -= 1
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np.testing.assert_allclose(t.numpy(), [[0, 1], [3, 4]])
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def test_setitem_overlapping_indices(self):
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t = Tensor([1,2,3,4])
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# regular overlapping indices
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t[[1,1]] = Tensor([5,6])
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np.testing.assert_allclose(t.numpy(), [1,6,3,4])
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# overlapping indices with zero value overlapped
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t[[1,1]] = Tensor([0,1])
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np.testing.assert_allclose(t.numpy(), [1,1,3,4])
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def test_setitem_overlapping_indices_with_0(self):
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t = Tensor([1,2,3,4])
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t[[1,1]] = Tensor([1,0])
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np.testing.assert_allclose(t.numpy(), [1,0,3,4])
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def test_setitem_with_1_in_shape(self):
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t = Tensor([[1],[2],[3]])
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t[[0,0]] = Tensor([[1],[2]])
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np.testing.assert_allclose(t.numpy(), [[2],[2],[3]])
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def test_fancy_setitem(self):
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t = Tensor.zeros(6,6).contiguous()
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t[[1,2], [3,2]] = 3
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n = np.zeros((6,6))
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n[[1,2], [3,2]] = 3
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np.testing.assert_allclose(t.numpy(), n)
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def test_simple_jit_setitem(self):
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@TinyJit
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def f(t:Tensor, a:Tensor):
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t[2:4, 3:5] = a
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for i in range(1, 6):
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t = Tensor.zeros(6, 6).contiguous().realize()
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a = Tensor.full((2, 2), fill_value=i, dtype=dtypes.float).contiguous()
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f(t, a)
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n = np.zeros((6, 6))
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n[2:4, 3:5] = np.full((2, 2), i)
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np.testing.assert_allclose(t.numpy(), n)
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def test_jit_setitem_variable_offset(self):
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with Context(CHECK_OOB=0):
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@TinyJit
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def f(t:Tensor, a:Tensor, v:Variable):
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t.shrink(((v,v+1), None)).assign(a).realize()
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t = Tensor.zeros(6, 6).contiguous().realize()
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n = np.zeros((6, 6))
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for i in range(6):
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v = Variable("v", 0, 6).bind(i)
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a = Tensor.full((1, 6), fill_value=i+1, dtype=dtypes.float).contiguous()
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n[i, :] = i+1
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f(t, a, v)
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np.testing.assert_allclose(t.numpy(), n)
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np.testing.assert_allclose(t.numpy(), [[1,1,1,1,1,1],[2,2,2,2,2,2],[3,3,3,3,3,3],[4,4,4,4,4,4],[5,5,5,5,5,5],[6,6,6,6,6,6]])
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def test_setitem_overlapping_inplace1(self):
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t = Tensor([[3.0], [2.0], [1.0]]).contiguous()
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t[1:] = t[:-1]
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self.assertEqual(t.tolist(), [[3.0], [3.0], [2.0]])
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def test_setitem_overlapping_inplace2(self):
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t = Tensor([[3.0], [2.0], [1.0]]).contiguous()
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t[:-1] = t[1:]
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self.assertEqual(t.tolist(), [[2.0], [1.0], [1.0]])
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# TODO: WEBGPU pipeline validation error. this generates (1==gidx0)|(2==gidx0)|(3==gidx0)|(4==gidx0)|(5==gidx0) ...
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@unittest.skipIf(Device.DEFAULT == "WEBGPU", "WEBGPU pipeline validation error")
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def test_setitem_big(self):
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idx_size, val = 256, 4
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t = Tensor.arange(0, idx_size+1)
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idx = Tensor.arange(0, idx_size)
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t[idx] = val
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self.assertEqual(t.tolist(), [val]*idx_size+[idx_size])
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def test_setitem_advanced_indexing(self):
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# Example from https://numpy.org/doc/stable/user/basics.indexing.html#combining-advanced-and-basic-indexing
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t = Tensor.zeros(10,20,30,40,50, dtype=dtypes.int).contiguous()
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ind_1 = Tensor([5,3,7,8])
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ind_2 = Tensor([[[0],[1],[2]],[[3],[4],[5]]])
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v = Tensor.arange(2*3*4*10*30*50).reshape(2,3,4,10,30,50)
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t[:, ind_1, :, ind_2, :] = v
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n = np.zeros((10,20,30,40,50), dtype=np.int32)
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n[:, ind_1.numpy(), :, ind_2.numpy(), :] = v.numpy()
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np.testing.assert_equal(t.numpy(), n)
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def test_setitem_2d_tensor_indexing(self):
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t = Tensor.zeros(2, dtype=dtypes.int).contiguous()
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index = Tensor([[0, 1], [1,0]])
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v = Tensor.arange(2*2).reshape(2, 2).contiguous()
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t[index] = v
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n = np.zeros((2,), dtype=np.int32)
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n[index.numpy()] = v.numpy()
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np.testing.assert_equal(t.numpy(), n)
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def test_setitem_swap_rows(self):
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t = Tensor.arange(6, dtype=dtypes.float).reshape(3, 2).contiguous().realize()
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tmp = t[0]
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t[0] = t[1]
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t[2] = tmp
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# NOTE: not [[2, 3], [2, 3], [0, 1]], same with eager
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np.testing.assert_allclose(t.numpy(), [[2, 3], [2, 3], [2, 3]])
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# eager version
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t = Tensor.arange(6, dtype=dtypes.float).reshape(3, 2).contiguous().realize()
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tmp = t[0].realize()
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t[0] = t[1].realize()
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t[2] = tmp.realize()
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np.testing.assert_allclose(t.numpy(), [[2, 3], [2, 3], [2, 3]])
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def test_lazy_sum_between_writes(self):
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# lazy sums should capture buffer state at the time they were created
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t = Tensor.zeros(6).contiguous().realize()
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s0 = t.sum()
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t[:3].assign(1.0)
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s1 = t.sum()
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t[3:].assign(2.0)
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s2 = t.sum()
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# TODO: s0 and s1 see final buffer state, should be [0.0, 3.0, 9.0]
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np.testing.assert_allclose([s0.item(), s1.item(), s2.item()], [9.0, 9.0, 9.0])
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# eager version
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t = Tensor.zeros(6).contiguous().realize()
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s0 = t.sum().realize()
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t[:3].assign(1.0).realize()
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s1 = t.sum().realize()
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t[3:].assign(2.0).realize()
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s2 = t.sum().realize()
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np.testing.assert_allclose([s0.item(), s1.item(), s2.item()], [0.0, 3.0, 9.0])
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def test_cross_assign_independence(self):
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# when assigning to two tensors using computations from both,
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# both assigns should see the OLD values of both tensors
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a = Tensor.arange(4, dtype=dtypes.float).contiguous().realize()
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b = Tensor.arange(4, 8, dtype=dtypes.float).contiguous().realize()
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new_a = a + b # [4, 6, 8, 10]
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new_b = a * 2 # [0, 2, 4, 6] -- should use OLD a
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a.assign(new_a)
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b.assign(new_b)
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np.testing.assert_allclose(a.numpy(), [4, 6, 8, 10])
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# TODO: new_b sees mutated a, should be [0, 2, 4, 6]
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np.testing.assert_allclose(b.numpy(), [8, 12, 16, 20])
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# eager version
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a = Tensor.arange(4, dtype=dtypes.float).contiguous().realize()
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b = Tensor.arange(4, 8, dtype=dtypes.float).contiguous().realize()
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new_a = (a + b).realize()
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new_b = (a * 2).realize()
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a.assign(new_a).realize()
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b.assign(new_b).realize()
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np.testing.assert_allclose(a.numpy(), [4, 6, 8, 10])
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np.testing.assert_allclose(b.numpy(), [0, 2, 4, 6])
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class TestWithGrad(unittest.TestCase):
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def test_no_requires_grad_works(self):
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z = Tensor.rand(8, 8)
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x = Tensor.rand(8)
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z[:3] = x
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def test_set_into_requires_grad(self):
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z = Tensor.rand(8, 8, requires_grad=True)
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x = Tensor.rand(8)
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with self.assertRaises(NotImplementedError):
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z[:3] = x
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def test_set_with_requires_grad(self):
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z = Tensor.rand(8, 8)
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x = Tensor.rand(8, requires_grad=True)
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with self.assertRaises(NotImplementedError):
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z[:3] = x
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class TestSetitemLoop(unittest.TestCase):
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def test_arange(self):
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N = 10
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cmp = Tensor.empty(N)
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for i in range(N): cmp[i] = i
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self.assertListEqual(Tensor.arange(N).tolist(), cmp.tolist())
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if __name__ == '__main__':
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unittest.main()
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