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265 lines (203 loc) · 8.16 KB
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import numpy as np
import pytest
import torch
from docarray import DocArray, DocArrayStacked
from docarray.documents import TextDoc
from docarray.typing import TorchTensor
@pytest.fixture()
def da():
texts = [f'hello {i}' for i in range(10)]
tensors = [torch.ones((4,)) * i for i in range(10)]
return DocArray[TextDoc](
[TextDoc(text=text, embedding=tens) for text, tens in zip(texts, tensors)],
)
@pytest.fixture()
def da_to_set():
texts = [f'hello {2*i}' for i in range(5)]
tensors = [torch.ones((4,)) * i * 2 for i in range(5)]
return DocArray[TextDoc](
[TextDoc(text=text, embedding=tens) for text, tens in zip(texts, tensors)],
)
###########
# getitem
###########
@pytest.mark.parametrize('stack', [True, False])
def test_simple_getitem(stack, da):
if stack:
da = da.stack(tensor_type=TorchTensor)
assert torch.all(da[0].embedding == torch.zeros((4,)))
assert da[0].text == 'hello 0'
@pytest.mark.parametrize('stack', [True, False])
def test_get_none(stack, da):
if stack:
da = da.stack(tensor_type=TorchTensor)
assert da[None] is da
@pytest.mark.parametrize('stack', [True, False])
@pytest.mark.parametrize('index', [(1, 2, 3, 4, 6), [1, 2, 3, 4, 6]])
def test_iterable_getitem(stack, da, index):
if stack:
da = da.stack(tensor_type=TorchTensor)
indexed_da = da[index]
for pos, d in zip(index, indexed_da):
assert d.text == f'hello {pos}'
assert torch.all(d.embedding == torch.ones((4,)) * pos)
@pytest.mark.parametrize('stack', [True, False])
@pytest.mark.parametrize('index_dtype', [torch.int64])
def test_torchtensor_getitem(stack, da, index_dtype):
if stack:
da = da.stack(tensor_type=TorchTensor)
index = torch.tensor([1, 2, 3, 4, 6], dtype=index_dtype)
indexed_da = da[index]
for pos, d in zip(index, indexed_da):
assert d.text == f'hello {pos}'
assert torch.all(d.embedding == torch.ones((4,)) * pos)
@pytest.mark.parametrize('stack', [True, False])
@pytest.mark.parametrize('index_dtype', [int, np.int_, np.int32, np.int64])
def test_nparray_getitem(stack, da, index_dtype):
if stack:
da = da.stack(tensor_type=TorchTensor)
index = np.array([1, 2, 3, 4, 6], dtype=index_dtype)
indexed_da = da[index]
for pos, d in zip(index, indexed_da):
assert d.text == f'hello {pos}'
assert torch.all(d.embedding == torch.ones((4,)) * pos)
@pytest.mark.parametrize('stack', [True, False])
@pytest.mark.parametrize(
'index',
[
[False, True, True, True, True, False, True, False, False, False],
(False, True, True, True, True, False, True, False, False, False),
torch.tensor([0, 1, 1, 1, 1, 0, 1, 0, 0, 0], dtype=torch.bool),
np.array([0, 1, 1, 1, 1, 0, 1, 0, 0, 0], dtype=bool),
],
)
def test_boolmask_getitem(stack, da, index):
if stack:
da = da.stack(tensor_type=TorchTensor)
indexed_da = da[index]
mask_true_idx = [1, 2, 3, 4, 6]
for pos, d in zip(mask_true_idx, indexed_da):
assert d.text == f'hello {pos}'
assert torch.all(d.embedding == torch.ones((4,)) * pos)
###########
# setitem
###########
@pytest.mark.parametrize('stack_left', [True, False])
def test_simple_setitem(stack_left, da, da_to_set):
if stack_left:
da = da.stack(tensor_type=TorchTensor)
da[0] = da_to_set[0]
assert torch.all(da[0].embedding == da_to_set[0].embedding)
assert da[0].text == da_to_set[0].text
@pytest.mark.parametrize('stack_left', [True, False])
@pytest.mark.parametrize('stack_right', [True, False])
@pytest.mark.parametrize('index', [(1, 2, 3, 4, 6), [1, 2, 3, 4, 6]])
def test_iterable_setitem(stack_left, stack_right, da, da_to_set, index):
if stack_left:
da = da.stack(tensor_type=TorchTensor)
if stack_right:
da_to_set = da_to_set.stack(tensor_type=TorchTensor)
da[index] = da_to_set
i_da_to_set = 0
for i, d in enumerate(da):
if i in index:
d_reference = da_to_set[i_da_to_set]
assert d.text == d_reference.text
assert torch.all(d.embedding == d_reference.embedding)
i_da_to_set += 1
else:
assert d.text == f'hello {i}'
assert torch.all(d.embedding == torch.ones((4,)) * i)
@pytest.mark.parametrize('stack_left', [True, False])
@pytest.mark.parametrize('stack_right', [True, False])
@pytest.mark.parametrize('index_dtype', [torch.int64])
def test_torchtensor_setitem(stack_left, stack_right, da, da_to_set, index_dtype):
if stack_left:
da = da.stack(tensor_type=TorchTensor)
if stack_right:
da_to_set = da_to_set.stack(tensor_type=TorchTensor)
index = torch.tensor([1, 2, 3, 4, 6], dtype=index_dtype)
da[index] = da_to_set
i_da_to_set = 0
for i, d in enumerate(da):
if i in index:
d_reference = da_to_set[i_da_to_set]
assert d.text == d_reference.text
assert torch.all(d.embedding == d_reference.embedding)
i_da_to_set += 1
else:
assert d.text == f'hello {i}'
assert torch.all(d.embedding == torch.ones((4,)) * i)
@pytest.mark.parametrize('stack_left', [True, False])
@pytest.mark.parametrize('stack_right', [True, False])
@pytest.mark.parametrize('index_dtype', [int, np.int_, np.int32, np.int64])
def test_nparray_setitem(stack_left, stack_right, da, da_to_set, index_dtype):
if stack_left:
da = da.stack(tensor_type=TorchTensor)
if stack_right:
da_to_set = da_to_set.stack(tensor_type=TorchTensor)
index = np.array([1, 2, 3, 4, 6], dtype=index_dtype)
da[index] = da_to_set
i_da_to_set = 0
for i, d in enumerate(da):
if i in index:
d_reference = da_to_set[i_da_to_set]
assert d.text == d_reference.text
assert torch.all(d.embedding == d_reference.embedding)
i_da_to_set += 1
else:
assert d.text == f'hello {i}'
assert torch.all(d.embedding == torch.ones((4,)) * i)
@pytest.mark.parametrize('stack_left', [True, False])
@pytest.mark.parametrize('stack_right', [True, False])
@pytest.mark.parametrize(
'index',
[
[False, True, True, True, True, False, True, False, False, False],
(False, True, True, True, True, False, True, False, False, False),
torch.tensor([0, 1, 1, 1, 1, 0, 1, 0, 0, 0], dtype=torch.bool),
np.array([0, 1, 1, 1, 1, 0, 1, 0, 0, 0], dtype=bool),
],
)
def test_boolmask_setitem(stack_left, stack_right, da, da_to_set, index):
if stack_left:
da = da.stack(tensor_type=TorchTensor)
if stack_right:
da_to_set = da_to_set.stack(tensor_type=TorchTensor)
da[index] = da_to_set
mask_true_idx = [1, 2, 3, 4, 6]
i_da_to_set = 0
for i, d in enumerate(da):
if i in mask_true_idx:
d_reference = da_to_set[i_da_to_set]
assert d.text == d_reference.text
assert torch.all(d.embedding == d_reference.embedding)
i_da_to_set += 1
else:
assert d.text == f'hello {i}'
assert torch.all(d.embedding == torch.ones((4,)) * i)
def test_setitem_update_column():
texts = [f'hello {i}' for i in range(10)]
tensors = [torch.ones((4,)) * (i + 1) for i in range(10)]
da = DocArrayStacked[TextDoc](
[TextDoc(text=text, embedding=tens) for text, tens in zip(texts, tensors)],
tensor_type=TorchTensor,
)
da[0] = TextDoc(text='hello', embedding=torch.zeros((4,)))
assert da[0].text == 'hello'
assert (da[0].embedding == torch.zeros((4,))).all()
assert (da.embedding[0] == torch.zeros((4,))).all()
assert da._storage.any_columns['text'][0] == 'hello'
assert (da._storage.tensor_columns['embedding'][0] == torch.zeros((4,))).all()
assert (da._storage.tensor_columns['embedding'][0] == torch.zeros((4,))).all()
@pytest.mark.parametrize(
'index',
[
[False, True, True, True, True, False, True, False, False, False],
(False, True, True, True, True, False, True, False, False, False),
torch.tensor([0, 1, 1, 1, 1, 0, 1, 0, 0, 0], dtype=torch.bool),
np.array([0, 1, 1, 1, 1, 0, 1, 0, 0, 0], dtype=bool),
],
)
def test_del_getitem(da, index):
del da[index]