# test cases are modified from pytorch test_indexing.py https://github.com/pytorch/pytorch/blob/597d3fb86a2f3b8d6d8ee067e769624dcca31cdb/test/test_indexing.py
import unittest, random, copy, warnings
import numpy as np
from tinygrad import Tensor, dtypes, Device, TinyJit
from tinygrad.device import is_dtype_supported
from tinygrad.shape.shapetracker import ShapeTracker
from tinygrad.shape.view import View
from tinygrad.helpers import CI, all_same, prod
random.seed(42)
def numpy_testing_assert_equal_helper(a, b):
if isinstance(a, Tensor): a = a.numpy()
if isinstance(b, Tensor): b = b.numpy()
np.testing.assert_equal(a, b)
def consec(shape, start=1):
return Tensor.arange(prod(shape)).reshape(shape)+start
# creates strided tensor with base set to reference tensor's base, equivalent to torch.set_()
def set_(reference: Tensor, shape, strides, offset):
if reference.lazydata.base.realized is None: reference.realize()
assert reference.lazydata.base.realized, "base has to be realized before setting it to strided's base"
strided = Tensor(reference.lazydata.view(ShapeTracker((View.create(shape=shape, strides=strides, offset=offset),))))
assert strided.lazydata.st.real_strides() == strides, "real_strides should equal strides for strided"
return strided
def clone(original:Tensor): return copy.copy(original)
def copy_(src:Tensor, other:Tensor) -> Tensor: return copy.copy(src)
# this is fine for tested usecases since as geohotstan understands,
# data_ptr is used to compare if operations needed between tensors is the same
def data_ptr(tensor:Tensor): return tensor.lazydata
# https://pytorch.org/docs/stable/generated/torch.Tensor.index_put_.html
def index_put_(tensor:Tensor, indices, values, accumulate) -> Tensor:
if accumulate: tensor[indices] += values
else: tensor[indices] = values
# https://pytorch.org/docs/stable/generated/torch.argsort.html
def argsort(tensor:Tensor) -> Tensor:
pass
# https://pytorch.org/docs/stable/generated/torch.all.html
def all_(tensor:Tensor) -> Tensor:
return tensor != 0
# https://pytorch.org/docs/stable/generated/torch.diagonal.html
def diagonal(tensor:Tensor) -> Tensor:
assert tensor.ndim == 2 and all_same(tensor.shape), 'only support 2 ndim square tensors'
return (Tensor.eye(tensor.shape[0]) * tensor).sum(0)
# https://numpy.org/doc/stable/reference/generated/numpy.unravel_index.html
def unravel_index(tensor, shape):
pass
# https://github.com/pytorch/pytorch/blob/79811e765c23242210ebdc623539d2103a166463/torch/testing/_creation.py#L38
def make_tensor(shape, dtype:dtypes, noncontiguous) -> Tensor:
r"""Creates a tensor with the given :attr:`shape`, :attr:`device`, and :attr:`dtype`, and filled with
values uniformly drawn from ``[low, high)``.
If :attr:`low` or :attr:`high` are specified and are outside the range of the :attr:`dtype`'s representable
finite values then they are clamped to the lowest or highest representable finite value, respectively.
If ``None``, then the following table describes the default values for :attr:`low` and :attr:`high`,
which depend on :attr:`dtype`.
+---------------------------+------------+----------+
| ``dtype`` | ``low`` | ``high`` |
+===========================+============+==========+
| boolean type | ``0`` | ``2`` |
+---------------------------+------------+----------+
| unsigned integral type | ``0`` | ``10`` |
+---------------------------+------------+----------+
| signed integral types | ``-9`` | ``10`` |
+---------------------------+------------+----------+
| floating types | ``-9`` | ``9`` |
+---------------------------+------------+----------+
| complex types | ``-9`` | ``9`` |
+---------------------------+------------+----------+
"""
contiguous = not noncontiguous
if dtype == dtypes.bool: return Tensor.randint(shape=shape, low=0, high=2, contiguous=contiguous).cast(dtypes.bool)
elif dtype.is_unsigned(): return Tensor.randint(shape=shape, low=0, high=10, contiguous=contiguous).cast(dtype)
elif dtype.is_int(): return Tensor.randint(shape=shape, low=-9, high=10, contiguous=contiguous).cast(dtype) # signed int
elif dtype.is_float(): return Tensor.rand(shape=shape, low=-9, high=9, dtype=dtype, contiguous=contiguous)
else: raise NotImplementedError(f"{dtype} not implemented")
class TestIndexing(unittest.TestCase):
def test_index(self):
reference = consec((3, 3, 3))
numpy_testing_assert_equal_helper(reference[0], consec((3, 3)))
numpy_testing_assert_equal_helper(reference[1], consec((3, 3), 10))
numpy_testing_assert_equal_helper(reference[2], consec((3, 3), 19))
numpy_testing_assert_equal_helper(reference[0, 1], consec((3,), 4))
numpy_testing_assert_equal_helper(reference[0:2], consec((2, 3, 3)))
numpy_testing_assert_equal_helper(reference[2, 2, 2], 27)
numpy_testing_assert_equal_helper(reference[:], consec((3, 3, 3)))
# indexing with Ellipsis
numpy_testing_assert_equal_helper(reference[..., 2], np.array([[3., 6., 9.],[12., 15., 18.],[21., 24., 27.]]))
numpy_testing_assert_equal_helper(reference[0, ..., 2], np.array([3., 6., 9.]))
numpy_testing_assert_equal_helper(reference[..., 2], reference[:, :, 2])
numpy_testing_assert_equal_helper(reference[0, ..., 2], reference[0, :, 2])
numpy_testing_assert_equal_helper(reference[0, 2, ...], reference[0, 2])
numpy_testing_assert_equal_helper(reference[..., 2, 2, 2], 27)
numpy_testing_assert_equal_helper(reference[2, ..., 2, 2], 27)
numpy_testing_assert_equal_helper(reference[2, 2, ..., 2], 27)
numpy_testing_assert_equal_helper(reference[2, 2, 2, ...], 27)
numpy_testing_assert_equal_helper(reference[...], reference)
reference_5d = consec((3, 3, 3, 3, 3))
numpy_testing_assert_equal_helper(reference_5d[..., 1, 0], reference_5d[:, :, :, 1, 0])
numpy_testing_assert_equal_helper(reference_5d[2, ..., 1, 0], reference_5d[2, :, :, 1, 0])
numpy_testing_assert_equal_helper(reference_5d[2, 1, 0, ..., 1], reference_5d[2, 1, 0, :, 1])
numpy_testing_assert_equal_helper(reference_5d[...], reference_5d)
# None indexing
numpy_testing_assert_equal_helper(reference[2, None], reference[2].unsqueeze(0))
numpy_testing_assert_equal_helper(reference[2, None, None], reference[2].unsqueeze(0).unsqueeze(0))
numpy_testing_assert_equal_helper(reference[2:4, None], reference[2:4].unsqueeze(1))
numpy_testing_assert_equal_helper(reference[None, 2, None, None], reference.unsqueeze(0)[:, 2].unsqueeze(0).unsqueeze(0))
numpy_testing_assert_equal_helper(reference[None, 2:5, None, None], reference.unsqueeze(0)[:, 2:5].unsqueeze(2).unsqueeze(2))
# indexing 0-length slice
numpy_testing_assert_equal_helper(np.empty((0, 3, 3)), reference[slice(0)])
numpy_testing_assert_equal_helper(np.empty((0, 3)), reference[slice(0), 2])
numpy_testing_assert_equal_helper(np.empty((0, 3)), reference[2, slice(0)])
numpy_testing_assert_equal_helper(np.empty([]), reference[2, 1:1, 2])
# indexing with step
reference = consec((10, 10, 10))
numpy_testing_assert_equal_helper(reference[1:5:2], Tensor.stack(reference[1], reference[3], dim=0))
numpy_testing_assert_equal_helper(reference[1:6:2], Tensor.stack(reference[1], reference[3], reference[5], dim=0))
numpy_testing_assert_equal_helper(reference[1:9:4], Tensor.stack(reference[1], reference[5], dim=0))
numpy_testing_assert_equal_helper(reference[2:4, 1:5:2], Tensor.stack(reference[2:4, 1], reference[2:4, 3], dim=1))
numpy_testing_assert_equal_helper(reference[3, 1:6:2], Tensor.stack(reference[3, 1], reference[3, 3], reference[3, 5], dim=0))
numpy_testing_assert_equal_helper(reference[None, 2, 1:9:4], Tensor.stack(reference[2, 1], reference[2, 5], dim=0).unsqueeze(0))
numpy_testing_assert_equal_helper(reference[:, 2, 1:6:2], Tensor.stack(reference[:, 2, 1], reference[:, 2, 3], reference[:, 2, 5], dim=1))
lst = [list(range(i, i+10)) for i in range(0, 100, 10)]
tensor = Tensor(lst)
for _ in range(100):
idx1_start = random.randrange(10)
idx1_end = idx1_start + random.randrange(1, 10 - idx1_start + 1)
idx1_step = random.randrange(1, 8)
idx1 = slice(idx1_start, idx1_end, idx1_step)
if random.randrange(2) == 0:
idx2_start = random.randrange(10)
idx2_end = idx2_start + random.randrange(1, 10 - idx2_start + 1)
idx2_step = random.randrange(1, 8)
idx2 = slice(idx2_start, idx2_end, idx2_step)
lst_indexed = [l[idx2] for l in lst[idx1]]
tensor_indexed = tensor[idx1, idx2]
else:
lst_indexed = lst[idx1]
tensor_indexed = tensor[idx1]
numpy_testing_assert_equal_helper(tensor_indexed, np.array(lst_indexed))
self.assertRaises(ValueError, lambda: reference[1:9:0])
# NOTE torch doesn't support this but numpy does so we should too. Torch raises ValueError
# see test_slice_negative_strides in test_ops.py
# self.assertRaises(ValueError, lambda: reference[1:9:-1])
self.assertRaises(IndexError, lambda: reference[1, 1, 1, 1])
self.assertRaises(IndexError, lambda: reference[1, 1, 1, 1:1])
self.assertRaises(IndexError, lambda: reference[3, 3, 3, 3, 3, 3, 3, 3])
self.assertRaises(IndexError, lambda: reference[0.0])
self.assertRaises(TypeError, lambda: reference[0.0:2.0])
self.assertRaises(IndexError, lambda: reference[0.0, 0.0:2.0])
self.assertRaises(IndexError, lambda: reference[0.0, :, 0.0:2.0])
self.assertRaises(IndexError, lambda: reference[0.0, ..., 0.0:2.0])
self.assertRaises(IndexError, lambda: reference[0.0, :, 0.0])
# TODO: delitem
# def delitem(): del reference[0]
# self.assertRaises(TypeError, delitem)
# TODO: LLVM is quite fast, why are other compiled backends slow?
@unittest.skipIf(CI and Device.DEFAULT in ["CLANG", "GPU", "METAL", "NV", "AMD"], "slow")
def test_advancedindex(self):
# integer array indexing
# pick a random valid indexer type
def ri(indices):
choice = random.randint(0, 2)
if choice == 0: return Tensor(indices)
if choice == 1: return list(indices)
return tuple(indices)
def validate_indexing(x):
numpy_testing_assert_equal_helper(x[[0]], consec((1,)))
numpy_testing_assert_equal_helper(x[ri([0]),], consec((1,)))
numpy_testing_assert_equal_helper(x[ri([3]),], consec((1,), 4))
numpy_testing_assert_equal_helper(x[[2, 3, 4]], consec((3,), 3))
numpy_testing_assert_equal_helper(x[ri([2, 3, 4]),], consec((3,), 3))
numpy_testing_assert_equal_helper(x[ri([0, 2, 4]),], np.array([1, 3, 5]))
def validate_setting(x):
x[[0]] = -2
numpy_testing_assert_equal_helper(x[[0]], np.array([-2]))
x[[0]] = -1
numpy_testing_assert_equal_helper(x[ri([0]), ], np.array([-1]))
x[[2, 3, 4]] = 4
numpy_testing_assert_equal_helper(x[[2, 3, 4]], np.array([4, 4, 4]))
x[ri([2, 3, 4]), ] = 3
numpy_testing_assert_equal_helper(x[ri([2, 3, 4]), ], np.array([3, 3, 3]))
x[ri([0, 2, 4]), ] = Tensor([5, 4, 3])
numpy_testing_assert_equal_helper(x[ri([0, 2, 4]), ], np.array([5, 4, 3]))
# Case 1: Purely Integer Array Indexing
reference = consec((10,))
validate_indexing(reference)
# setting values
validate_setting(reference)
# Tensor with stride != 1
# strided is [1, 3, 5, 7]
reference = consec((10,))
strided = set_(reference, (4,), (2,), 0)
numpy_testing_assert_equal_helper(strided[[0]], np.array([1]))
numpy_testing_assert_equal_helper(strided[ri([0]), ], np.array([1]))
numpy_testing_assert_equal_helper(strided[ri([3]), ], np.array([7]))
numpy_testing_assert_equal_helper(strided[[1, 2]], np.array([3, 5]))
numpy_testing_assert_equal_helper(strided[ri([1, 2]), ], np.array([3, 5]))
numpy_testing_assert_equal_helper(strided[ri([[2, 1], [0, 3]]), ],
np.array([[5, 3], [1, 7]]))
# stride is [4, 8]
strided = set_(reference, (2,), (4,), offset=4)
numpy_testing_assert_equal_helper(strided[[0]], np.array([5]))
numpy_testing_assert_equal_helper(strided[ri([0]), ], np.array([5]))
numpy_testing_assert_equal_helper(strided[ri([1]), ], np.array([9]))
numpy_testing_assert_equal_helper(strided[[0, 1]], np.array([5, 9]))
numpy_testing_assert_equal_helper(strided[ri([0, 1]), ], np.array([5, 9]))
numpy_testing_assert_equal_helper(strided[ri([[0, 1], [1, 0]]), ],
np.array([[5, 9], [9, 5]]))
# reference is 1 2
# 3 4
# 5 6
reference = consec((3, 2))
numpy_testing_assert_equal_helper(reference[ri([0, 1, 2]), ri([0])], np.array([1, 3, 5]))
numpy_testing_assert_equal_helper(reference[ri([0, 1, 2]), ri([1])], np.array([2, 4, 6]))
numpy_testing_assert_equal_helper(reference[ri([0]), ri([0])], consec((1,)))
numpy_testing_assert_equal_helper(reference[ri([2]), ri([1])], consec((1,), 6))
numpy_testing_assert_equal_helper(reference[[ri([0, 0]), ri([0, 1])]], np.array([1, 2]))
numpy_testing_assert_equal_helper(reference[[ri([0, 1, 1, 0, 2]), ri([1])]], np.array([2, 4, 4, 2, 6]))
numpy_testing_assert_equal_helper(reference[[ri([0, 0, 1, 1]), ri([0, 1, 0, 0])]], np.array([1, 2, 3, 3]))
rows = ri([[0, 0],
[1, 2]])
columns = [0],
numpy_testing_assert_equal_helper(reference[rows, columns], np.array([[1, 1],
[3, 5]]))
rows = ri([[0, 0],
[1, 2]])
columns = ri([1, 0])
numpy_testing_assert_equal_helper(reference[rows, columns], np.array([[2, 1],
[4, 5]]))
rows = ri([[0, 0],
[1, 2]])
columns = ri([[0, 1],
[1, 0]])
numpy_testing_assert_equal_helper(reference[rows, columns], np.array([[1, 2],
[4, 5]]))
# setting values
reference[ri([0]), ri([1])] = -1
numpy_testing_assert_equal_helper(reference[ri([0]), ri([1])], np.array([-1]))
reference[ri([0, 1, 2]), ri([0])] = Tensor([-1, 2, -4])
numpy_testing_assert_equal_helper(reference[ri([0, 1, 2]), ri([0])],
np.array([-1, 2, -4]))
reference[rows, columns] = Tensor([[4, 6], [2, 3]])
numpy_testing_assert_equal_helper(reference[rows, columns],
np.array([[4, 6], [2, 3]]))
# Verify still works with Transposed (i.e. non-contiguous) Tensors
reference = Tensor([[0, 1, 2, 3],
[4, 5, 6, 7],
[8, 9, 10, 11]]).T
# Transposed: [[0, 4, 8],
# [1, 5, 9],
# [2, 6, 10],
# [3, 7, 11]]
numpy_testing_assert_equal_helper(reference[ri([0, 1, 2]), ri([0])], np.array([0, 1, 2]))
numpy_testing_assert_equal_helper(reference[ri([0, 1, 2]), ri([1])], np.array([4, 5, 6]))
numpy_testing_assert_equal_helper(reference[ri([0]), ri([0])], np.array([0]))
numpy_testing_assert_equal_helper(reference[ri([2]), ri([1])], np.array([6]))
numpy_testing_assert_equal_helper(reference[[ri([0, 0]), ri([0, 1])]], np.array([0, 4]))
numpy_testing_assert_equal_helper(reference[[ri([0, 1, 1, 0, 3]), ri([1])]], np.array([4, 5, 5, 4, 7]))
numpy_testing_assert_equal_helper(reference[[ri([0, 0, 1, 1]), ri([0, 1, 0, 0])]], np.array([0, 4, 1, 1]))
rows = ri([[0, 0],
[1, 2]])
columns = [0],
numpy_testing_assert_equal_helper(reference[rows, columns], np.array([[0, 0], [1, 2]]))
rows = ri([[0, 0],
[1, 2]])
columns = ri([1, 0])
numpy_testing_assert_equal_helper(reference[rows, columns], np.array([[4, 0], [5, 2]]))
rows = ri([[0, 0],
[1, 3]])
columns = ri([[0, 1],
[1, 2]])
numpy_testing_assert_equal_helper(reference[rows, columns], np.array([[0, 4], [5, 11]]))
# TODO: non contiguous setitem
'''
# setting values
reference[ri([0]), ri([1])] = -1
numpy_testing_assert_equal_helper(reference[ri([0]), ri([1])],
np.array([-1]))
reference[ri([0, 1, 2]), ri([0])] = np.array([-1, 2, -4])
numpy_testing_assert_equal_helper(reference[ri([0, 1, 2]), ri([0])],
np.array([-1, 2, -4]))
reference[rows, columns] = np.array([[4, 6], [2, 3]])
numpy_testing_assert_equal_helper(reference[rows, columns],
np.array([[4, 6], [2, 3]]))
'''
# stride != 1
# strided is [[1 3 5 7],
# [9 11 13 15]]
reference = Tensor.arange(0., 24).reshape(3, 8)
strided = set_(reference, (2,4), (8,2), 1)
numpy_testing_assert_equal_helper(strided[ri([0, 1]), ri([0])],
np.array([1, 9]))
numpy_testing_assert_equal_helper(strided[ri([0, 1]), ri([1])],
np.array([3, 11]))
numpy_testing_assert_equal_helper(strided[ri([0]), ri([0])],
np.array([1]))
numpy_testing_assert_equal_helper(strided[ri([1]), ri([3])],
np.array([15]))
numpy_testing_assert_equal_helper(strided[[ri([0, 0]), ri([0, 3])]],
np.array([1, 7]))
numpy_testing_assert_equal_helper(strided[[ri([1]), ri([0, 1, 1, 0, 3])]],
np.array([9, 11, 11, 9, 15]))
numpy_testing_assert_equal_helper(strided[[ri([0, 0, 1, 1]), ri([0, 1, 0, 0])]],
np.array([1, 3, 9, 9]))
rows = ri([[0, 0],
[1, 1]])
columns = [0],
numpy_testing_assert_equal_helper(strided[rows, columns],
np.array([[1, 1], [9, 9]]))
rows = ri([[0, 1],
[1, 0]])
columns = ri([1, 2])
numpy_testing_assert_equal_helper(strided[rows, columns],
np.array([[3, 13], [11, 5]]))
rows = ri([[0, 0],
[1, 1]])
columns = ri([[0, 1],
[1, 2]])
numpy_testing_assert_equal_helper(strided[rows, columns],
np.array([[1, 3], [11, 13]]))
# setting values
# strided is [[10, 11],
# [17, 18]]
reference = Tensor.arange(0., 24).reshape(3, 8)
strided = set_(reference, (2,2), (7,1), 10)
numpy_testing_assert_equal_helper(strided[ri([0]), ri([1])],
np.array([11]))
# TODO non contiguous setitem
'''
strided[ri([0]), ri([1])] = -1
numpy_testing_assert_equal_helper(strided[ri([0]), ri([1])],
Tensor([-1]))
'''
reference = Tensor.arange(0., 24).reshape(3, 8)
strided = set_(reference, (2,2), (7,1), 10)
numpy_testing_assert_equal_helper(strided[ri([0, 1]), ri([1, 0])],
np.array([11, 17]))
# TODO non contiguous setitem
'''
strided[ri([0, 1]), ri([1, 0])] = Tensor([-1, 2])
numpy_testing_assert_equal_helper(strided[ri([0, 1]), ri([1, 0])],
Tensor([-1, 2]))
'''
reference = Tensor.arange(0., 24).realize().reshape(3, 8)
strided = set_(reference, (2,2), (7,1), 10)
rows = ri([[0],
[1]])
columns = ri([[0, 1],
[0, 1]])
numpy_testing_assert_equal_helper(strided[rows, columns],
np.array([[10, 11], [17, 18]]))
# TODO non contiguous setitem
'''
strided[rows, columns] = Tensor([[4, 6], [2, 3]])
numpy_testing_assert_equal_helper(strided[rows, columns],
Tensor([[4, 6], [2, 3]]))
'''
# Tests using less than the number of dims, and ellipsis
# reference is 1 2
# 3 4
# 5 6
reference = consec((3, 2))
numpy_testing_assert_equal_helper(reference[ri([0, 2]),], np.array([[1, 2], [5, 6]]))
numpy_testing_assert_equal_helper(reference[ri([1]), ...], np.array([[3, 4]]))
numpy_testing_assert_equal_helper(reference[..., ri([1])], np.array([[2], [4], [6]]))
# verify too many indices fails
with self.assertRaises(IndexError): reference[ri([1]), ri([0, 2]), ri([3])]
# test invalid index fails
reference = Tensor.empty(10)
for err_idx in (10, -11):
with self.assertRaises(IndexError):
reference[err_idx]
# NOTE cannot check for out of bounds with Tensor indexing
# see tensor.py: __getitem__ (Tiny Things)
'''
with self.assertRaises(IndexError):
reference[Tensor([err_idx], dtype=dtypes.int64)]
with self.assertRaises(IndexError):
reference[[err_idx]]
'''
def tensor_indices_to_np(tensor: Tensor, indices):
npt = tensor.numpy()
idxs = tuple(i.numpy().tolist() if isinstance(i, Tensor) and i.dtype == dtypes.int64 else
i for i in indices)
return npt, idxs
def get_numpy(tensor, indices):
npt, idxs = tensor_indices_to_np(tensor, indices)
return Tensor(npt[idxs])
def set_numpy(tensor:Tensor, indices, value):
if not isinstance(value, int):
value = value.numpy()
npt, idxs = tensor_indices_to_np(tensor, indices)
npt[idxs] = value
return npt
def assert_get_eq(tensor, indexer):
numpy_testing_assert_equal_helper(tensor[indexer], get_numpy(tensor, indexer))
def assert_set_eq(tensor: Tensor, indexer, val):
pyt = clone(tensor)
numt = clone(tensor)
pyt[indexer] = val
numt = set_numpy(numt, indexer, val)
numpy_testing_assert_equal_helper(pyt, numt)
# NOTE: torch initiates the gradients using g0cpu (rand as gradients)
def assert_backward_eq(tensor: Tensor, indexer):
cpu = clone(tensor.float())
cpu.requires_grad = True
outcpu = cpu[indexer].sum()
outcpu.backward()
dev = cpu.detach()
dev.requires_grad = True
outdev = dev[indexer].sum()
outdev.backward()
numpy_testing_assert_equal_helper(cpu.grad, dev.grad)
def get_set_tensor(indexed: Tensor, indexer):
set_size = indexed[indexer].shape
set_count = indexed[indexer].numel()
set_tensor = Tensor.randint(set_count, high=set_count).reshape(set_size) #.cast(dtypes.float64)
return set_tensor
# Tensor is 0 1 2 3 4
# 5 6 7 8 9
# 10 11 12 13 14
# 15 16 17 18 19
reference = Tensor.arange(0., 20).reshape(4, 5)
indices_to_test = [
# grab the second, fourth columns
[slice(None), [1, 3]],
# first, third rows,
[[0, 2], slice(None)],
# weird shape
[slice(None), [[0, 1],
[2, 3]]],
# negatives
[[-1], [0]],
[[0, 2], [-1]],
[slice(None), [-1]],
]
# only test dupes on gets
get_indices_to_test = indices_to_test + [[slice(None), [0, 1, 1, 2, 2]]]
for indexer in get_indices_to_test:
assert_get_eq(reference, indexer)
assert_backward_eq(reference, indexer)
for indexer in indices_to_test:
assert_set_eq(reference, indexer, 44)
assert_set_eq(reference, indexer, get_set_tensor(reference, indexer))
reference = Tensor.arange(0., 160).reshape(4, 8, 5)
indices_to_test = [
[slice(None), slice(None), [0, 3, 4]],
[slice(None), [2, 4, 5, 7], slice(None)],
[[2, 3], slice(None), slice(None)],
[slice(None), [0, 2, 3], [1, 3, 4]],
[slice(None), [0], [1, 2, 4]],
[slice(None), [0, 1, 3], [4]],
[slice(None), [[0, 1], [1, 0]], [[2, 3]]],
[slice(None), [[0, 1], [2, 3]], [[0]]],
[slice(None), [[5, 6]], [[0, 3], [4, 4]]],
[[0, 2, 3], [1, 3, 4], slice(None)],
[[0], [1, 2, 4], slice(None)],
[[0, 1, 3], [4], slice(None)],
[[[0, 1], [1, 0]], [[2, 1], [3, 5]], slice(None)],
[[[0, 1], [1, 0]], [[2, 3]], slice(None)],
[[[0, 1], [2, 3]], [[0]], slice(None)],
[[[2, 1]], [[0, 3], [4, 4]], slice(None)],
[[[2]], [[0, 3], [4, 1]], slice(None)],
# non-contiguous indexing subspace
[[0, 2, 3], slice(None), [1, 3, 4]],
# less dim, ellipsis
[[0, 2], ],
[[0, 2], slice(None)],
[[0, 2], Ellipsis],
[[0, 2], slice(None), Ellipsis],
[[0, 2], Ellipsis, slice(None)],
[[0, 2], [1, 3]],
[[0, 2], [1, 3], Ellipsis],
[Ellipsis, [1, 3], [2, 3]],
[Ellipsis, [2, 3, 4]],
[Ellipsis, slice(None), [2, 3, 4]],
[slice(None), Ellipsis, [2, 3, 4]],
# ellipsis counts for nothing
[Ellipsis, slice(None), slice(None), [0, 3, 4]],
[slice(None), Ellipsis, slice(None), [0, 3, 4]],
[slice(None), slice(None), Ellipsis, [0, 3, 4]],
[slice(None), slice(None), [0, 3, 4], Ellipsis],
[Ellipsis, [[0, 1], [1, 0]], [[2, 1], [3, 5]], slice(None)],
[[[0, 1], [1, 0]], [[2, 1], [3, 5]], Ellipsis, slice(None)],
[[[0, 1], [1, 0]], [[2, 1], [3, 5]], slice(None), Ellipsis],
]
for indexer in indices_to_test:
assert_get_eq(reference, indexer)
assert_set_eq(reference, indexer, 212)
assert_set_eq(reference, indexer, get_set_tensor(reference, indexer))
assert_backward_eq(reference, indexer)
reference = Tensor.arange(0., 1296).reshape(3, 9, 8, 6)
indices_to_test = [
[slice(None), slice(None), slice(None), [0, 3, 4]],
[slice(None), slice(None), [2, 4, 5, 7], slice(None)],
[slice(None), [2, 3], slice(None), slice(None)],
[[1, 2], slice(None), slice(None), slice(None)],
[slice(None), slice(None), [0, 2, 3], [1, 3, 4]],
[slice(None), slice(None), [0], [1, 2, 4]],
[slice(None), slice(None), [0, 1, 3], [4]],
[slice(None), slice(None), [[0, 1], [1, 0]], [[2, 3]]],
[slice(None), slice(None), [[0, 1], [2, 3]], [[0]]],
[slice(None), slice(None), [[5, 6]], [[0, 3], [4, 4]]],
[slice(None), [0, 2, 3], [1, 3, 4], slice(None)],
[slice(None), [0], [1, 2, 4], slice(None)],
[slice(None), [0, 1, 3], [4], slice(None)],
[slice(None), [[0, 1], [3, 4]], [[2, 3], [0, 1]], slice(None)],
[slice(None), [[0, 1], [3, 4]], [[2, 3]], slice(None)],
[slice(None), [[0, 1], [3, 2]], [[0]], slice(None)],
[slice(None), [[2, 1]], [[0, 3], [6, 4]], slice(None)],
[slice(None), [[2]], [[0, 3], [4, 2]], slice(None)],
[[0, 1, 2], [1, 3, 4], slice(None), slice(None)],
[[0], [1, 2, 4], slice(None), slice(None)],
[[0, 1, 2], [4], slice(None), slice(None)],
[[[0, 1], [0, 2]], [[2, 4], [1, 5]], slice(None), slice(None)],
[[[0, 1], [1, 2]], [[2, 0]], slice(None), slice(None)],
[[[2, 2]], [[0, 3], [4, 5]], slice(None), slice(None)],
[[[2]], [[0, 3], [4, 5]], slice(None), slice(None)],
[slice(None), [3, 4, 6], [0, 2, 3], [1, 3, 4]],
[slice(None), [2, 3, 4], [1, 3, 4], [4]],
[slice(None), [0, 1, 3], [4], [1, 3, 4]],
[slice(None), [6], [0, 2, 3], [1, 3, 4]],
[slice(None), [2, 3, 5], [3], [4]],
[slice(None), [0], [4], [1, 3, 4]],
[slice(None), [6], [0, 2, 3], [1]],
[slice(None), [[0, 3], [3, 6]], [[0, 1], [1, 3]], [[5, 3], [1, 2]]],
[[2, 2, 1], [0, 2, 3], [1, 3, 4], slice(None)],
[[2, 0, 1], [1, 2, 3], [4], slice(None)],
[[0, 1, 2], [4], [1, 3, 4], slice(None)],
[[0], [0, 2, 3], [1, 3, 4], slice(None)],
[[0, 2, 1], [3], [4], slice(None)],
[[0], [4], [1, 3, 4], slice(None)],
[[1], [0, 2, 3], [1], slice(None)],
[[[1, 2], [1, 2]], [[0, 1], [2, 3]], [[2, 3], [3, 5]], slice(None)],
# less dim, ellipsis
[Ellipsis, [0, 3, 4]],
[Ellipsis, slice(None), [0, 3, 4]],
[Ellipsis, slice(None), slice(None), [0, 3, 4]],
[slice(None), Ellipsis, [0, 3, 4]],
[slice(None), slice(None), Ellipsis, [0, 3, 4]],
[slice(None), [0, 2, 3], [1, 3, 4]],
[slice(None), [0, 2, 3], [1, 3, 4], Ellipsis],
[Ellipsis, [0, 2, 3], [1, 3, 4], slice(None)],
[[0], [1, 2, 4]],
[[0], [1, 2, 4], slice(None)],
[[0], [1, 2, 4], Ellipsis],
[[0], [1, 2, 4], Ellipsis, slice(None)],
[[1], ],
[[0, 2, 1], [3], [4]],
[[0, 2, 1], [3], [4], slice(None)],
[[0, 2, 1], [3], [4], Ellipsis],
[Ellipsis, [0, 2, 1], [3], [4]],
]
for indexer in indices_to_test:
assert_get_eq(reference, indexer)
assert_set_eq(reference, indexer, 1333)
assert_set_eq(reference, indexer, get_set_tensor(reference, indexer))
indices_to_test += [
[slice(None), slice(None), [[0, 1], [1, 0]], [[2, 3], [3, 0]]],
[slice(None), slice(None), [[2]], [[0, 3], [4, 4]]],
]
for indexer in indices_to_test:
assert_get_eq(reference, indexer)
assert_set_eq(reference, indexer, 1333)
assert_backward_eq(reference, indexer)
# TODO setitem backward
'''
def test_set_item_to_scalar_tensor(self):
m = random.randint(1, 10)
n = random.randint(1, 10)
z = Tensor.randn([m, n])
a = 1.0
w = Tensor(a, requires_grad=True)
z[:, 0] = w
z.sum().backward()
numpy_testing_assert_equal_helper(w.grad, m * a)
'''
def test_single_int(self):
v = Tensor.randn(5, 7, 3)
numpy_testing_assert_equal_helper(v[4].shape, (7, 3))
def test_multiple_int(self):
v = Tensor.randn(5, 7, 3)
numpy_testing_assert_equal_helper(v[4].shape, (7, 3))
numpy_testing_assert_equal_helper(v[4, :, 1].shape, (7,))
def test_none(self):
v = Tensor.randn(5, 7, 3)
numpy_testing_assert_equal_helper(v[None].shape, (1, 5, 7, 3))
numpy_testing_assert_equal_helper(v[:, None].shape, (5, 1, 7, 3))
numpy_testing_assert_equal_helper(v[:, None, None].shape, (5, 1, 1, 7, 3))
numpy_testing_assert_equal_helper(v[..., None].shape, (5, 7, 3, 1))
def test_step(self):
v = Tensor.arange(10)
numpy_testing_assert_equal_helper(v[::1], v)
numpy_testing_assert_equal_helper(v[::2], [0, 2, 4, 6, 8])
numpy_testing_assert_equal_helper(v[::3], [0, 3, 6, 9])
numpy_testing_assert_equal_helper(v[::11], [0])
numpy_testing_assert_equal_helper(v[1:6:2], [1, 3, 5])
def test_step_assignment(self):
v = Tensor.zeros(4, 4).contiguous()
v[0, 1::2] = Tensor([3., 4.])
numpy_testing_assert_equal_helper(v[0].numpy().tolist(), [0, 3, 0, 4])
numpy_testing_assert_equal_helper(v[1:].sum(), 0)
@unittest.skip("bool indexing not supported")
def test_bool_indices(self):
v = Tensor.randn(5, 7, 3)
boolIndices = Tensor([True, False, True, True, False], dtype=dtypes.bool)
numpy_testing_assert_equal_helper(v[boolIndices].shape, (3, 7, 3))
numpy_testing_assert_equal_helper(v[boolIndices], Tensor.stack([v[0], v[2], v[3]]))
v = Tensor([True, False, True], dtype=dtypes.bool)
boolIndices = Tensor([True, False, False], dtype=dtypes.bool)
uint8Indices = Tensor([1, 0, 0], dtype=dtypes.uint8)
with warnings.catch_warnings(record=True) as w:
numpy_testing_assert_equal_helper(v[boolIndices].shape, v[uint8Indices].shape)
numpy_testing_assert_equal_helper(v[boolIndices], v[uint8Indices])
numpy_testing_assert_equal_helper(v[boolIndices], Tensor([True]))
numpy_testing_assert_equal_helper(len(w), 2)
@unittest.skip("bool indexing not supported")
def test_bool_indices_accumulate(self):
mask = Tensor.zeros(size=(10, ), dtype=dtypes.bool)
y = Tensor.ones(size=(10, 10))
index_put_(y, (mask, ), y[mask], accumulate=True)
numpy_testing_assert_equal_helper(y, Tensor.ones(size=(10, 10)))
@unittest.skip("bool indexing not supported")
def test_multiple_bool_indices(self):
v = Tensor.randn(5, 7, 3)
# note: these broadcast together and are transposed to the first dim
mask1 = Tensor([1, 0, 1, 1, 0], dtype=dtypes.bool)
mask2 = Tensor([1, 1, 1], dtype=dtypes.bool)
numpy_testing_assert_equal_helper(v[mask1, :, mask2].shape, (3, 7))
@unittest.skip("bool indexing not supported")
def test_byte_mask(self):
v = Tensor.randn(5, 7, 3)
mask = Tensor([1, 0, 1, 1, 0], dtype=dtypes.uint8)
with warnings.catch_warnings(record=True) as w:
numpy_testing_assert_equal_helper(v[mask].shape, (3, 7, 3))
numpy_testing_assert_equal_helper(v[mask], Tensor.stack([v[0], v[2], v[3]]))
numpy_testing_assert_equal_helper(len(w), 2)
v = Tensor([1.])
numpy_testing_assert_equal_helper(v[v == 0], Tensor([]))
@unittest.skip("bool indexing not supported")
def test_byte_mask_accumulate(self):
mask = Tensor.zeros(size=(10, ), dtype=dtypes.uint8)
y = Tensor.ones(size=(10, 10))
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
index_put_(y, (mask, ), y[mask], accumulate=True)
numpy_testing_assert_equal_helper(y, Tensor.ones(size=(10, 10)))
numpy_testing_assert_equal_helper(len(w), 2)
# TODO setitem
# NOTE: tinygrad doesn't support idx.max that big
'''
def test_index_put_accumulate_large_tensor(self):
# This test is for tensors with number of elements >= INT_MAX (2^31 - 1).
N = (1 << 31) + 5
dt = dtypes.int8
a = Tensor.ones(N, dtype=dt).contiguous()
indices = Tensor([-2, 0, -2, -1, 0, -1, 1], dtype=dtypes.int64)
values = Tensor([6, 5, 6, 6, 5, 7, 11], dtype=dt)
index_put_(a, (indices, ), values, accumulate=True)
numpy_testing_assert_equal_helper(a[0], 11)
numpy_testing_assert_equal_helper(a[1], 12)
numpy_testing_assert_equal_helper(a[2], 1)
numpy_testing_assert_equal_helper(a[-3], 1)
numpy_testing_assert_equal_helper(a[-2], 13)
numpy_testing_assert_equal_helper(a[-1], 14)
a = Tensor.ones((2, N), dtype=dt).contiguous()
indices0 = np.array([0, -1, 0, 1], dtype=dtypes.int64)
indices1 = np.array([-2, -1, 0, 1], dtype=dtypes.int64)
values = np.array([12, 13, 10, 11], dtype=dt)
index_put_(a, (indices0, indices1), values, accumulate=True)
numpy_testing_assert_equal_helper(a[0, 0], 11)
numpy_testing_assert_equal_helper(a[0, 1], 1)
numpy_testing_assert_equal_helper(a[1, 0], 1)
numpy_testing_assert_equal_helper(a[1, 1], 12)
numpy_testing_assert_equal_helper(a[:, 2], Tensor.ones(2, dtype=dtypes.int8))
numpy_testing_assert_equal_helper(a[:, -3], Tensor.ones(2, dtype=dtypes.int8))
numpy_testing_assert_equal_helper(a[0, -2], 13)
numpy_testing_assert_equal_helper(a[1, -2], 1)
numpy_testing_assert_equal_helper(a[-1, -1], 14)
numpy_testing_assert_equal_helper(a[0, -1], 1)
'''
# TODO fancy setitem
'''
def test_index_put_accumulate_duplicate_indices(self):
for i in range(1, 512):
# generate indices by random walk, this will create indices with
# lots of duplicates interleaved with each other
delta = Tensor.uniform(low=-1, high=1, dtype=dtypes.double)
indices = delta.cumsum(0).cast(dtypes.int64)
# input = torch.randn(indices.abs().max() + 1)
input = Tensor.randn(indices.abs().max().item() + 1)
# values = torch.randn(indices.size(0))
values = Tensor.randn(indices.shape(0))
output = index_put_(input, (indices,), values, accumulate=True)
input_list = input.numpy().tolist()
indices_list = indices.numpy().tolist()
values_list = values.numpy().tolist()
for i, v in zip(indices_list, values_list):
input_list[i] += v
numpy_testing_assert_equal_helper(output, input_list)
'''
@unittest.skipUnless(is_dtype_supported(dtypes.long), f"long dtype not supported on {Device.DEFAULT}")
def test_index_ind_dtype(self):
x = Tensor.randn(4, 4)
# ind_long = torch.randint(4, (4,), dtype=torch.long)
# TODO should we spend an extra line to allow for randint other dtypes?
# copied from randint
ind_long = (Tensor.rand((4,),)*(4-0)+0).cast(dtypes.int64)
# ind_int = ind_long.int()
ind_int = (ind_long).cast(dtypes.int32)
ref = x[ind_long, ind_long]
res = x[ind_int, ind_int]
numpy_testing_assert_equal_helper(ref, res)
ref = x[ind_long, :]
res = x[ind_int, :]
numpy_testing_assert_equal_helper(ref, res)
ref = x[:, ind_long]
res = x[:, ind_int]
numpy_testing_assert_equal_helper(ref, res)
# no repeating indices for index_put
# TODO fancy setitem
'''
src = Tensor.randn(4)
ind_long = Tensor.arange(4, dtype=dtypes.int64)
ind_int = ind_long.cast(dtypes.int32)
for accum in (True, False):
inp_ref = clone(x)
inp_res = clone(x)
index_put_(inp_ref, (ind_long, ind_long), src, accum)
index_put_(inp_res, (ind_int, ind_int), src, accum)
numpy_testing_assert_equal_helper(inp_ref, inp_res)
'''
# TODO empty setitem
'''
def test_index_put_accumulate_empty(self):
# Regression test for https://github.com/pytorch/pytorch/issues/94667
input = Tensor.rand([], dtype=dtypes.float32)
with self.assertRaises(RuntimeError):
index_put_(input, [], np.array([1.0]), True)
'''
@unittest.skip("bool indexing not supported")
def test_multiple_byte_mask(self):
v = Tensor.randn(5, 7, 3)
# note: these broadcast together and are transposed to the first dim
mask1 = Tensor([1, 0, 1, 1, 0], dtype=dtypes.uint8)
mask2 = Tensor([1, 1, 1], dtype=dtypes.uint8)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
numpy_testing_assert_equal_helper(v[mask1, :, mask2].shape, (3, 7))
numpy_testing_assert_equal_helper(len(w), 2)
@unittest.skip("bool indexing not supported")
def test_byte_mask2d(self):
v = Tensor.randn(5, 7, 3)
c = Tensor.randn(5, 7)
num_ones = (c > 0).sum()
r = v[c > 0]
numpy_testing_assert_equal_helper(r.shape, (num_ones, 3))
@unittest.skip("bool indexing not supported")
def test_jit_indexing(self):
def fn1(x):
x[x < 50] = 1.0
return x
def fn2(x):
x[0:50] = 1.0
return x
scripted_fn1 = TinyJit(fn1)
scripted_fn2 = TinyJit(fn2)
data = Tensor.arange(100, dtype=dtypes.float)
out = scripted_fn1(clone(data))
ref = Tensor(np.concatenate((np.ones(50), np.arange(50, 100))), dtype=dtypes.float)
numpy_testing_assert_equal_helper(out, ref)
out = scripted_fn2(clone(data))
numpy_testing_assert_equal_helper(out, ref)
def test_int_indices(self):
v = Tensor.randn(5, 7, 3)
numpy_testing_assert_equal_helper(v[[0, 4, 2]].shape, (3, 7, 3))
numpy_testing_assert_equal_helper(v[:, [0, 4, 2]].shape, (5, 3, 3))
numpy_testing_assert_equal_helper(v[:, [[0, 1], [4, 3]]].shape, (5, 2, 2, 3))
# TODO fancy setitem
'''
def test_index_put_src_datatype(self, dtype):
src = Tensor.ones(3, 2, 4, dtype=dtype)
vals = Tensor.ones(3, 2, 4, dtype=dtype)
indices = (np.array([0, 2, 1]),)
res = index_put_(src, indices, vals, accumulate=True)
numpy_testing_assert_equal_helper(res.shape, src.shape)
'''
def test_index_src_datatype(self):
src = Tensor.ones(3, 2, 4)
# test index
res = src[[0, 2, 1], :, :]
numpy_testing_assert_equal_helper(res.shape, src.shape)
# test index_put, no accum
# TODO fancy setitem
'''
src[[0, 2, 1], :, :] = res
numpy_testing_assert_equal_helper(res.shape, src.shape)
'''
def test_int_indices2d(self):
# From the NumPy indexing example
x = Tensor.arange(0, 12).reshape(4, 3)
rows = Tensor([[0, 0], [3, 3]])
columns = Tensor([[0, 2], [0, 2]])
numpy_testing_assert_equal_helper(x[rows, columns].numpy().tolist(), [[0, 2], [9, 11]])
def test_int_indices_broadcast(self):
# From the NumPy indexing example
x = Tensor.arange(0, 12).reshape(4, 3)
rows = Tensor([0, 3])
columns = Tensor([0, 2])
result = x[rows[:, None], columns]
numpy_testing_assert_equal_helper(result.numpy().tolist(), [[0, 2], [9, 11]])
# TODO jax supports empty tensor indexing
@unittest.skip("empty tensor indexing not supported")
def test_empty_index(self):
x = Tensor.arange(0, 12).reshape(4, 3)
idx = Tensor([], dtype=dtypes.int64)
numpy_testing_assert_equal_helper(x[idx].numel(), 0)
# TODO empty setitem
'''
# empty assignment should have no effect but not throw an exception
y = clone(x)
y[idx] = -1
numpy_testing_assert_equal_helper(x, y)
mask = Tensor.zeros(4, 3).cast(dtypes.bool)
y[mask] = -1
numpy_testing_assert_equal_helper(x, y)
'''
# TODO jax supports empty tensor indexing
@unittest.skip("empty tensor indexing not supported")
def test_empty_ndim_index(self):
x = Tensor.randn(5)
numpy_testing_assert_equal_helper(Tensor.empty(0, 2), x[Tensor.empty(0, 2, dtype=dtypes.int64)])
x = Tensor.randn(2, 3, 4, 5)
numpy_testing_assert_equal_helper(Tensor.empty(2, 0, 6, 4, 5),
x[:, Tensor.empty(0, 6, dtype=dtypes.int64)])
x = Tensor.empty(10, 0)
numpy_testing_assert_equal_helper(x[[1, 2]].shape, (2, 0))
numpy_testing_assert_equal_helper(x[[], []].shape, (0,))
with self.assertRaises(IndexError):
x[:, [0, 1]]
def test_empty_slice(self):
x = Tensor.randn(2, 3, 4, 5)
y = x[:, :, :, 1]
z = y[:, 1:1, :]
numpy_testing_assert_equal_helper((2, 0, 4), z.shape)
# this isn't technically necessary, but matches NumPy stride calculations.
# NOTE: this is empty and shouldn't have strides
#numpy_testing_assert_equal_helper((60, 20, 5), z.lazydata.st.real_strides())
# NOTE tinygrad's int slicing implementation makes this not contiguous
# self.assertTrue(z.lazydata.st.contiguous)
@unittest.skip("bool indexing not supported")
def test_index_getitem_copy_bools_slices(self):
true = Tensor(1, dtype=dtypes.uint8)
false = Tensor(0, dtype=dtypes.uint8)
tensors = [Tensor.randn(2, 3), Tensor(3.)]
for a in tensors:
self.assertNotEqual(data_ptr(a), data_ptr(a[True]))
numpy_testing_assert_equal_helper(Tensor.empty(0, *a.shape), a[False])
self.assertNotEqual(data_ptr(a), data_ptr(a[true]))
numpy_testing_assert_equal_helper(Tensor.empty(0, *a.shape), a[false])
self.assertEqual(data_ptr(a), data_ptr(a[None]))
self.assertEqual(data_ptr(a), data_ptr(a[...]))
@unittest.skip("bool indexing not supported")
def test_index_setitem_bools_slices(self):
true = Tensor(1, dtype=dtypes.uint8)
false = Tensor(0, dtype=dtypes.uint8)
tensors = [Tensor.randn(2, 3), Tensor(3)]
for a in tensors:
# prefix with a 1,1, to ensure we are compatible with numpy which cuts off prefix 1s
# (some of these ops already prefix a 1 to the size)
neg_ones = Tensor.ones_like(a) * -1
neg_ones_expanded = neg_ones.unsqueeze(0).unsqueeze(0)
a[True] = neg_ones_expanded
numpy_testing_assert_equal_helper(a, neg_ones)
a[False] = 5
numpy_testing_assert_equal_helper(a, neg_ones)
a[true] = neg_ones_expanded * 2
numpy_testing_assert_equal_helper(a, neg_ones * 2)
a[false] = 5
numpy_testing_assert_equal_helper(a, neg_ones * 2)
a[None] = neg_ones_expanded * 3
numpy_testing_assert_equal_helper(a, neg_ones * 3)
a[...] = neg_ones_expanded * 4
numpy_testing_assert_equal_helper(a, neg_ones * 4)
if a.dim() == 0:
with self.assertRaises(IndexError):
a[:] = neg_ones_expanded * 5
@unittest.skip("bool indexing not supported")
def test_index_scalar_with_bool_mask(self):
a = Tensor(1)
uintMask = Tensor(True, dtype=dtypes.uint8)
boolMask = Tensor(True, dtype=dtypes.bool)
numpy_testing_assert_equal_helper(a[uintMask], a[boolMask])
numpy_testing_assert_equal_helper(a[uintMask].dtype, a[boolMask].dtype)
a = Tensor(True, dtype=dtypes.bool)
numpy_testing_assert_equal_helper(a[uintMask], a[boolMask])
numpy_testing_assert_equal_helper(a[uintMask].dtype, a[boolMask].dtype)
@unittest.skip("bool indexing not supported")
def test_setitem_expansion_error(self):
true = Tensor(True)
a = Tensor.randn(2, 3)
# check prefix with non-1s doesn't work
# a_expanded = a.expand(torch.Size([5, 1]) + a.size())
a_expanded = a.expand((5, 1) + a.shape)
# NumPy: ValueError
with self.assertRaises(RuntimeError):
a[True] = a_expanded
with self.assertRaises(RuntimeError):
a[true] = a_expanded
def test_getitem_scalars_simple(self):
src = Tensor([[[1.,2.],[3.,4.]], [[1,1],[1,1]]])
a = src[0].mul(src[1])
self.assertEqual(a[0,1].item(), 2)
def test_getitem_scalars(self):
zero = Tensor(0, dtype=dtypes.int64)
one = Tensor(1, dtype=dtypes.int64)
# non-scalar indexed with scalars
a = Tensor.randn(2, 3)
numpy_testing_assert_equal_helper(a[0], a[zero])
numpy_testing_assert_equal_helper(a[0][1], a[zero][one])
numpy_testing_assert_equal_helper(a[0, 1], a[zero, one])
numpy_testing_assert_equal_helper(a[0, one], a[zero, 1])
# indexing by a scalar should slice (not copy)
numpy_testing_assert_equal_helper(a[0, 1], a[zero, one])
numpy_testing_assert_equal_helper(a[1], a[one.cast(dtypes.int32)])
numpy_testing_assert_equal_helper(a[1], a[one.cast(dtypes.int16)])
# scalar indexed with scalar
r = Tensor.randn()
with self.assertRaises(IndexError):
r[:]
with self.assertRaises(IndexError):
r[zero]
numpy_testing_assert_equal_helper(r, r[...])
# TODO fancy setitem
'''
def test_setitem_scalars(self):
zero = Tensor(0, dtype=dtypes.int64)
# non-scalar indexed with scalars
a = Tensor.randn(2, 3).contiguous()
a_set_with_number = clone(a).contiguous()
a_set_with_scalar = clone(a).contiguous()
b = Tensor.randn(3)
a_set_with_number[0] = b
a_set_with_scalar[zero] = b
numpy_testing_assert_equal_helper(a_set_with_number, a_set_with_scalar)
a[1, zero] = 7.7
# TODO: weird inaccuracy Max relative difference: 2.47707621e-08
# numpy_testing_assert_equal_helper(7.7, a[1, 0])
np.testing.assert_allclose(7.7, a[1, 0].numpy(), rtol=1e-7)
# scalar indexed with scalars
r = Tensor.randn().contiguous()
with self.assertRaises(IndexError):
r[:] = 8.8
with self.assertRaises(IndexError):
r[zero] = 8.8
r[...] = 9.9
# TODO: weird inaccuracy Max relative difference: 3.85322971e-08
# numpy_testing_assert_equal_helper(9.9, r)
np.testing.assert_allclose(9.9, r, rtol=1e-7)
'''
@unittest.skip("getitem expects elementwise ops folding to be instant, but it happens late in the scheduler")
def test_getitem_casted_scalars_folding(self):
Tensor.manual_seed(0)
# cast of const is just another const, don't need extra kernels for this
a = Tensor.randn(2, 3)
one = Tensor(1, dtype=dtypes.int64)
self.assertEqual(data_ptr(a[1]), data_ptr(a[one.cast(dtypes.int32)]))
self.assertEqual(data_ptr(a[1]), data_ptr(a[one.cast(dtypes.int16)]))
def test_getitem_scalars_simple_folding(self):
a = Tensor.randn(2, 3)
zero = Tensor(0, dtype=dtypes.int64)
one = Tensor(1, dtype=dtypes.int64)
self.assertEqual(data_ptr(a[0, 1]), data_ptr(a[zero, one]))
def test_basic_advanced_combined(self):
# From the NumPy indexing example
x = Tensor.arange(0, 12).reshape(4, 3)
numpy_testing_assert_equal_helper(x[1:2, 1:3], x[1:2, [1, 2]])
numpy_testing_assert_equal_helper(x[1:2, 1:3].numpy().tolist(), [[4, 5]])
# Check that it is a copy
unmodified = clone(x)
x[1:2, [1, 2]].zeros_like()
numpy_testing_assert_equal_helper(x, unmodified)
# But assignment should modify the original
# TODO fancy setitem
'''
unmodified = clone(x)
x[1:2, [1, 2]] = 0
self.assertNotEqual(x, unmodified)
'''
def test_int_assignment(self):
x = Tensor.arange(0, 4).reshape(2, 2)
x[1] = 5
numpy_testing_assert_equal_helper(x.numpy().tolist(), [[0, 1], [5, 5]])
x = Tensor.arange(0, 4).reshape(2, 2)
x[1] = Tensor.arange(5, 7)
numpy_testing_assert_equal_helper(x.numpy().tolist(), [[0, 1], [5, 6]])
# TODO fancy setitem
'''
def test_byte_tensor_assignment(self):
x = Tensor.arange(0., 16).reshape(4, 4)
b = Tensor([True, False, True, False], dtype=dtypes.uint8)
value = Tensor([3., 4., 5., 6.])
with warnings.catch_warnings(record=True) as w:
x[b] = value
numpy_testing_assert_equal_helper(len(w), 1)
numpy_testing_assert_equal_helper(x[0], value)
numpy_testing_assert_equal_helper(x[1], Tensor.arange(4., 8))
numpy_testing_assert_equal_helper(x[2], value)
numpy_testing_assert_equal_helper(x[3], Tensor.arange(12., 16))
'''
@unittest.skip("Tensor unpacking not supported")
def test_variable_slicing(self):
x = Tensor.arange(0, 16).reshape(4, 4)
indices = Tensor([0, 1], dtype=dtypes.int32)
i, j = indices
numpy_testing_assert_equal_helper(x[i:j], x[0:1])
def test_ellipsis_tensor(self):
x = Tensor.arange(0, 9).reshape(3, 3)
idx = Tensor([0, 2])
numpy_testing_assert_equal_helper(x[..., idx].numpy().tolist(), [[0, 2],
[3, 5],
[6, 8]])
numpy_testing_assert_equal_helper(x[idx, ...].numpy().tolist(), [[0, 1, 2],
[6, 7, 8]])
# TODO unravel_index
'''
def test_unravel_index_errors(self):
with self.assertRaises(TypeError):
unravel_index(
Tensor(0.5),
(2, 2))
with self.assertRaises(TypeError):
unravel_index(
Tensor([]),
(10, 3, 5))
with self.assertRaises(TypeError):
unravel_index(
Tensor([1], dtype=dtypes.int64),
Tensor([1, 2, 3]))
with self.assertRaises(TypeError):
unravel_index(
Tensor([1], dtype=dtypes.int64),
(1, 2, 2.0))
with self.assertRaises(ValueError):
unravel_index(
Tensor(0),
(2, -3))
'''
def test_invalid_index(self):
x = Tensor.arange(0, 16).reshape(4, 4)
self.assertRaises(TypeError, lambda: x["0":"1"])
def test_out_of_bound_index(self):
x = Tensor.arange(0, 100).reshape(2, 5, 10)
self.assertRaises(IndexError, lambda: x[0, 5])
self.assertRaises(IndexError, lambda: x[4, 5])
self.assertRaises(IndexError, lambda: x[0, 1, 15])
self.assertRaises(IndexError, lambda: x[:, :, 12])
def test_zero_dim_index(self):
x = Tensor(10)
numpy_testing_assert_equal_helper(x, x.item())
def runner():
print(x[0])
return x[0]
self.assertRaises(IndexError, runner)
# TODO fancy setitem
'''
def test_cpu_indices(self):
idx = Tensor([0, 1])
b = Tensor.zeros(2)
x = Tensor.ones(10).contiguous()
x[idx] = b # index_put_
ref = Tensor.ones(10).contiguous()
ref[:2] = 0
numpy_testing_assert_equal_helper(x, ref)
out = x[idx] # index
numpy_testing_assert_equal_helper(out, Tensor.zeros(2))
'''
def test_take_along_dim(self):
def _test_against_numpy(t: Tensor, indices: Tensor, dim):
actual = t.gather(dim, indices)
t_np = t.numpy()
indices_np = indices.numpy()
expected = np.take_along_axis(t_np, indices_np, axis=dim)
numpy_testing_assert_equal_helper(actual, expected)
# TODO argsort
'''
for shape in [(3, 2), (2, 3, 5), (2, 4, 0), (2, 3, 1, 4)]:
for noncontiguous in [True, False]:
for dtype in (dtypes.float32, dtypes.int64):
t = make_tensor(shape, dtype=dtype, noncontiguous=noncontiguous)
for dim in list(range(t.ndim)) + [None]:
if dim is None:
indices = argsort(t.reshape(-1))
else:
indices = argsort(t, dim=dim)
_test_against_numpy(t, indices, dim)
'''
# test broadcasting
t = Tensor.ones((3, 4, 1))
indices = Tensor.ones((1, 2, 5), dtype=dtypes.int64)
_test_against_numpy(t, indices, 1)
# test empty indices
t = Tensor.ones((3, 4, 5))
indices = Tensor.ones((3, 0, 5), dtype=dtypes.int64)
_test_against_numpy(t, indices, 1)
# TODO argsort
'''
def test_take_along_dim_invalid(self):
for dtype in (dtypes.int64, dtypes.float32):
shape = (2, 3, 1, 4)
dim = 0
t = make_tensor(shape, dtype=dtype)
indices = argsort(t, dim=dim)
# dim of `t` and `indices` does not match
with self.assertRaises(RuntimeError, "input and indices should have the same number of dimensions"):
t.gather(0, indices[0])
# invalid `indices` dtype
with self.assertRaises(RuntimeError):
t.gather(0, indices.cast(dtypes.bool))
with self.assertRaises(RuntimeError):
t.gather(0, indices.cast(dtypes.float32))
with self.assertRaises(RuntimeError):
t.gather(0, indices.cast(dtypes.int32))
# invalid axis
with self.assertRaises(IndexError):
t.gather(-7, indices)
with self.assertRaises(IndexError):
t.gather(7, indices)
'''
class TestNumpy(unittest.TestCase):
def test_index_no_floats(self):
a = Tensor([[[5.]]])
self.assertRaises(IndexError, lambda: a[0.0])
self.assertRaises(IndexError, lambda: a[0, 0.0])
self.assertRaises(IndexError, lambda: a[0.0, 0])
self.assertRaises(IndexError, lambda: a[0.0, :])
self.assertRaises(IndexError, lambda: a[:, 0.0])
self.assertRaises(IndexError, lambda: a[:, 0.0, :])
self.assertRaises(IndexError, lambda: a[0.0, :, :])
self.assertRaises(IndexError, lambda: a[0, 0, 0.0])
self.assertRaises(IndexError, lambda: a[0.0, 0, 0])
self.assertRaises(IndexError, lambda: a[0, 0.0, 0])
self.assertRaises(IndexError, lambda: a[-1.4])
self.assertRaises(IndexError, lambda: a[0, -1.4])
self.assertRaises(IndexError, lambda: a[-1.4, 0])
self.assertRaises(IndexError, lambda: a[-1.4, :])
self.assertRaises(IndexError, lambda: a[:, -1.4])
self.assertRaises(IndexError, lambda: a[:, -1.4, :])
self.assertRaises(IndexError, lambda: a[-1.4, :, :])
self.assertRaises(IndexError, lambda: a[0, 0, -1.4])
self.assertRaises(IndexError, lambda: a[-1.4, 0, 0])
self.assertRaises(IndexError, lambda: a[0, -1.4, 0])
# these two trigger slice internal type verification first
self.assertRaises(TypeError, lambda: a[0.0:, 0.0])
self.assertRaises(TypeError, lambda: a[0.0:, 0.0,:])
def test_none_index(self):
# `None` index adds newaxis
a = Tensor([1, 2, 3])
numpy_testing_assert_equal_helper(a[None].ndim, a.ndim+1)
def test_empty_tuple_index(self):
# Empty tuple index creates a view
a = Tensor([1, 2, 3])
numpy_testing_assert_equal_helper(a[()], a)
#self.assertEqual(data_ptr(a[()]), data_ptr(a))
# TODO jax supports empty tensor indexing
@unittest.skip("empty tensor indexing not supported")
def test_empty_fancy_index(self):
# Empty list index creates an empty array
a = Tensor([1, 2, 3])
numpy_testing_assert_equal_helper(a[[]], np.array([]))
b = Tensor([]).cast(dtypes.int64)
numpy_testing_assert_equal_helper(a[[]], np.array([]))
b = Tensor([]).float()
self.assertRaises(IndexError, lambda: a[b])
def test_ellipsis_index(self):
a = Tensor([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
self.assertIsNot(a[...], a)
numpy_testing_assert_equal_helper(a[...], a)
# `a[...]` was `a` in numpy <1.9.
#numpy_testing_assert_equal_helper(data_ptr(a[...]), data_ptr(a))
# Slicing with ellipsis can skip an
# arbitrary number of dimensions
numpy_testing_assert_equal_helper(a[0, ...], a[0])
numpy_testing_assert_equal_helper(a[0, ...], a[0, :])
numpy_testing_assert_equal_helper(a[..., 0], a[:, 0])
# In NumPy, slicing with ellipsis results in a 0-dim array. In PyTorch
# we don't have separate 0-dim arrays and scalars.
numpy_testing_assert_equal_helper(a[0, ..., 1], np.array(2))
# Assignment with `(Ellipsis,)` on 0-d arrays
b = np.array(1)
b[(Ellipsis,)] = 2
numpy_testing_assert_equal_helper(b, 2)
def test_single_int_index(self):
# Single integer index selects one row
a = Tensor([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
numpy_testing_assert_equal_helper(a[0], [1, 2, 3])
numpy_testing_assert_equal_helper(a[-1], [7, 8, 9])
self.assertRaises(IndexError, a.__getitem__, 1 << 30)
self.assertRaises(IndexError, a.__getitem__, 1 << 64)
@unittest.skip("bool indexing not supported")
def test_single_bool_index(self):
# Single boolean index
a = Tensor([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
numpy_testing_assert_equal_helper(a[True], a[None])
numpy_testing_assert_equal_helper(a[False], a[None][0:0])
@unittest.skip("bool indexing not supported")
def test_boolean_shape_mismatch(self):
arr = Tensor.ones((5, 4, 3))
index = Tensor([True])
self.assertRaises(IndexError, lambda: arr[index])
index = Tensor([False] * 6)
self.assertRaises(IndexError, lambda: arr[index])
index = Tensor.zeros(4, 4, dtype=dtypes.uint8)
self.assertRaises(IndexError, lambda: arr[index])
self.assertRaises(IndexError, lambda: arr[(slice(None), index)])
@unittest.skip("bool indexing not supported")
def test_boolean_indexing_onedim(self):
# Indexing a 2-dimensional array with
# boolean array of length one
a = Tensor([[0., 0., 0.]])
b = Tensor([True])
numpy_testing_assert_equal_helper(a[b], a)
# boolean assignment
a[b] = 1.
numpy_testing_assert_equal_helper(a, Tensor([[1., 1., 1.]]))
@unittest.skip("bool indexing not supported")
def test_boolean_assignment_value_mismatch(self):
# A boolean assignment should fail when the shape of the values
# cannot be broadcast to the subscription. (see also gh-3458)
a = Tensor.arange(0, 4)
def f(a, v):
a[a > -1] = Tensor(v)
self.assertRaises(Exception, f, a, [])
self.assertRaises(Exception, f, a, [1, 2, 3])
self.assertRaises(Exception, f, a[:1], [1, 2, 3])
@unittest.skip("bool indexing not supported")
def test_boolean_indexing_twodim(self):
# Indexing a 2-dimensional array with
# 2-dimensional boolean array
a = Tensor([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
b = Tensor([[True, False, True],
[False, True, False],
[True, False, True]])
numpy_testing_assert_equal_helper(a[b], Tensor([1, 3, 5, 7, 9]))
numpy_testing_assert_equal_helper(a[b[1]], Tensor([[4, 5, 6]]))
numpy_testing_assert_equal_helper(a[b[0]], a[b[2]])
# boolean assignment
a[b] = 0
numpy_testing_assert_equal_helper(a, Tensor([[0, 2, 0],
[4, 0, 6],
[0, 8, 0]]))
@unittest.skip("bool indexing not supported")
def test_boolean_indexing_weirdness(self):
# Weird boolean indexing things
a = Tensor.ones((2, 3, 4))
numpy_testing_assert_equal_helper((0, 2, 3, 4), a[False, True, ...].shape)
numpy_testing_assert_equal_helper(Tensor.ones(1, 2), a[True, [0, 1], True, True, [1], [[2]]])
self.assertRaises(IndexError, lambda: a[False, [0, 1], ...])
@unittest.skip("bool indexing not supported")
def test_boolean_indexing_weirdness_tensors(self):
# Weird boolean indexing things
false = Tensor(False)
true = Tensor(True)
a = Tensor.ones((2, 3, 4))
numpy_testing_assert_equal_helper((0, 2, 3, 4), a[False, True, ...].shape)
numpy_testing_assert_equal_helper(Tensor.ones(1, 2), a[true, [0, 1], true, true, [1], [[2]]])
self.assertRaises(IndexError, lambda: a[false, [0, 1], ...])
@unittest.skip("bool indexing not supported")
def test_boolean_indexing_alldims(self):
true = Tensor(True)
a = Tensor.ones((2, 3))
numpy_testing_assert_equal_helper((1, 2, 3), a[True, True].shape)
numpy_testing_assert_equal_helper((1, 2, 3), a[true, true].shape)
@unittest.skip("bool indexing not supported")
def test_boolean_list_indexing(self):
# Indexing a 2-dimensional array with
# boolean lists
a = Tensor([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
b = [True, False, False]
c = [True, True, False]
numpy_testing_assert_equal_helper(a[b], Tensor([[1, 2, 3]]))
numpy_testing_assert_equal_helper(a[b, b], Tensor([1]))
numpy_testing_assert_equal_helper(a[c], Tensor([[1, 2, 3], [4, 5, 6]]))
numpy_testing_assert_equal_helper(a[c, c], Tensor([1, 5]))
def test_everything_returns_views(self):
# Before `...` would return a itself.
a = Tensor([5])
self.assertIsNot(a, a[()])
self.assertIsNot(a, a[...])
self.assertIsNot(a, a[:])
def test_broaderrors_indexing(self):
a = Tensor.zeros(5, 5)
self.assertRaises(IndexError, a.__getitem__, ([0, 1], [0, 1, 2]))
self.assertRaises(IndexError, a.contiguous().__setitem__, ([0, 1], [0, 1, 2]), 0)
# TODO out of bound getitem does not raise error
'''
def test_trivial_fancy_out_of_bounds(self):
a = Tensor.zeros(5)
ind = Tensor.ones(20, dtype=dtypes.int64)
ind[-1] = 10
self.assertRaises(IndexError, a.__getitem__, ind)
self.assertRaises(IndexError, a.__setitem__, ind, 0)
ind = Tensor.ones(20, dtype=dtypes.int64)
ind[0] = 11
self.assertRaises(IndexError, a.__getitem__, ind)
self.assertRaises(IndexError, a.__setitem__, ind, 0)
'''
# TODO fancy setitem
'''
def test_index_is_larger(self):
# Simple case of fancy index broadcasting of the index.
a = Tensor.zeros((5, 5))
a[[[0], [1], [2]], [0, 1, 2]] = Tensor([2., 3., 4.])
self.assertTrue((a[:3, :3] == all_(Tensor([2., 3., 4.]))))
'''
# TODO fancy setitem
'''
def test_broadcast_subspace(self):
a = Tensor.zeros((100, 100))
v = Tensor.arange(0., 100)[:, None]
b = Tensor.arange(99, -1, -1).cast(dtypes.int64)
a[b] = v
expected = b.float().unsqueeze(1).expand(100, 100)
numpy_testing_assert_equal_helper(a, expected)
'''
# TODO fancy setitem
'''
def test_truncate_leading_1s(self):
col_max = Tensor.randn(1, 4)
kernel = col_max.T * col_max # [4, 4] tensor
kernel2 = clone(kernel)
# Set the diagonal
# len(torch.tensor) is just tensor.shape[0]
kernel[range(kernel.shape[0]), range(kernel.shape[0])] = col_max.square()
kernel2 = diagonal(kernel2)
# torch.diagonal(kernel2).copy_(torch.square(col_max.view(4)))
kernel2 = copy_(kernel2, col_max.reshape(4).square())
numpy_testing_assert_equal_helper(kernel, kernel2)
'''
if __name__ == '__main__':
unittest.main()