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[API Compatibility] enhance __getitem__ tests -part #884

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272 changes: 272 additions & 0 deletions tests/test_Tensor___getitem__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -51,3 +51,275 @@ def test_case_3():
"""
)
obj.run(pytorch_code, ["result"])


def test_case_4():
"""Multi-dimensional int tensor indexing"""
pytorch_code = textwrap.dedent(
"""
import torch
x = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
idx0 = torch.tensor([0, 1, 2])
idx1 = torch.tensor([2, 1, 0])
result = x[idx0, idx1]
"""
)
obj.run(pytorch_code, ["result"])


def test_case_5():
"""2D broadcast int tensor indexing on 3D tensor"""
pytorch_code = textwrap.dedent(
"""
import torch
x = torch.arange(24).reshape(2, 3, 4).float()
idx0 = torch.tensor([[0, 1], [1, 1]])
idx1 = torch.tensor([[0, 2], [1, 0]])
result = x[idx0, idx1]
"""
)
obj.run(pytorch_code, ["result"])


def test_case_6():
"""Bool tensor indexing (1D bool on 2D tensor)"""
pytorch_code = textwrap.dedent(
"""
import torch
x = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
mask = torch.tensor([True, False, True])
result = x[mask]
"""
)
obj.run(pytorch_code, ["result"])


def test_case_7():
"""Bool tensor indexing with 2D mask matching input shape"""
pytorch_code = textwrap.dedent(
"""
import torch
x = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
mask = torch.tensor([[True, False, True], [False, True, False], [True, False, True]])
result = x[mask]
"""
)
obj.run(pytorch_code, ["result"])


def test_case_8():
"""Negative indices in tensor"""
pytorch_code = textwrap.dedent(
"""
import torch
x = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
idx0 = torch.tensor([0, -1, 2])
idx1 = torch.tensor([-1, 0, -2])
result = x[idx0, idx1]
"""
)
obj.run(pytorch_code, ["result"])


def test_case_9():
"""Trailing dims: fewer indices than dimensions"""
pytorch_code = textwrap.dedent(
"""
import torch
x = torch.arange(24).reshape(2, 3, 4).float()
idx = torch.tensor([0, 1])
result = x[idx]
"""
)
obj.run(pytorch_code, ["result"])


def test_case_10():
"""Trailing dims: 1D index on 3D tensor"""
pytorch_code = textwrap.dedent(
"""
import torch
x = torch.arange(120).reshape(4, 5, 6).float()
idx0 = torch.tensor([0, 2])
idx1 = torch.tensor([1, 3])
result = x[idx0, idx1]
"""
)
obj.run(pytorch_code, ["result"])


def test_case_11():
"""Gradient computation with tensor indexing"""
pytorch_code = textwrap.dedent(
"""
import torch
x = torch.tensor([[1., 2., 3.], [4., 5., 6.]], requires_grad=True)
idx0 = torch.tensor([0, 1])
idx1 = torch.tensor([2, 0])
y = x[idx0, idx1]
y.sum().backward()
x_grad = x.grad
"""
)
obj.run(pytorch_code, ["y", "x_grad"], check_stop_gradient=False)


def test_case_12():
"""Mixed indexing: tensor for first dim, slice for second"""
pytorch_code = textwrap.dedent(
"""
import torch
x = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
idx = torch.tensor([0, 2])
result = x[idx, 0:2]
"""
)
obj.run(pytorch_code, ["result"])


def test_case_13():
"""Float32 dtype indexing"""
pytorch_code = textwrap.dedent(
"""
import torch
x = torch.arange(12).reshape(3, 4).float()
idx0 = torch.tensor([0, 2])
idx1 = torch.tensor([1, 3])
result = x[idx0, idx1]
"""
)
obj.run(pytorch_code, ["result"])


def test_case_14():
"""Float64 dtype indexing"""
pytorch_code = textwrap.dedent(
"""
import torch
x = torch.arange(12).reshape(3, 4).double()
idx0 = torch.tensor([0, 1])
idx1 = torch.tensor([2, 3])
result = x[idx0, idx1]
"""
)
obj.run(pytorch_code, ["result"])


def test_case_15():
"""Int64 index dtype (torch.long)"""
pytorch_code = textwrap.dedent(
"""
import torch
x = torch.tensor([[10, 20, 30], [40, 50, 60]])
idx0 = torch.tensor([0, 1], dtype=torch.long)
idx1 = torch.tensor([2, 0], dtype=torch.long)
result = x[idx0, idx1]
"""
)
obj.run(pytorch_code, ["result"])


def test_case_16():
"""Single element result"""
pytorch_code = textwrap.dedent(
"""
import torch
x = torch.tensor([[1, 2, 3], [4, 5, 6]])
result = x[1, 2]
"""
)
obj.run(pytorch_code, ["result"])


def test_case_17():
"""1D tensor with 1D tensor indexing"""
pytorch_code = textwrap.dedent(
"""
import torch
x = torch.arange(10).float()
idx = torch.tensor([9, 0, 5, 3])
result = x[idx]
"""
)
obj.run(pytorch_code, ["result"])


def test_case_18():
"""202 broadcast int tensor indexing (2D indices)"""
pytorch_code = textwrap.dedent(
"""
import torch
x = torch.arange(60).reshape(5, 4, 3).float()
idx0 = torch.tensor([[0, 1], [2, 3]])
idx1 = torch.tensor([[0, 2], [1, 1]])
result = x[idx0, idx1]
"""
)
obj.run(pytorch_code, ["result"])


def test_case_19():
"""All-false bool mask (empty result)"""
pytorch_code = textwrap.dedent(
"""
import torch
x = torch.tensor([[1, 2], [3, 4]])
mask = torch.tensor([False, False])
result = x[mask]
"""
)
obj.run(pytorch_code, ["result"])


def test_case_20():
"""Bool tensor indexing on 3D tensor"""
pytorch_code = textwrap.dedent(
"""
import torch
x = torch.arange(24).reshape(2, 3, 4).float()
mask = torch.tensor([True, False])
result = x[mask]
"""
)
obj.run(pytorch_code, ["result"])


def test_case_21():
"""Broadcast indices with 1-rank difference"""
pytorch_code = textwrap.dedent(
"""
import torch
x = torch.arange(100).reshape(10, 10).float()
idx0 = torch.tensor([1, 2, 3]).reshape(3, 1)
idx1 = torch.tensor([1, 2, 3, 4]).reshape(1, 4)
result = x[idx0, idx1]
"""
)
obj.run(pytorch_code, ["result"])


def test_case_22():
"""Large tensor indexing"""
pytorch_code = textwrap.dedent(
"""
import torch
x = torch.arange(1000).reshape(10, 100).float()
idx = torch.arange(0, 100, 10)
result = x[:, idx]
"""
)
obj.run(pytorch_code, ["result"])


def test_case_23():
"""Int32 index dtype"""
pytorch_code = textwrap.dedent(
"""
import torch
x = torch.tensor([[1., 2.], [3., 4.], [5., 6.]])
idx0 = torch.tensor([0, 2], dtype=torch.int32)
idx1 = torch.tensor([1, 0], dtype=torch.int32)
result = x[idx0, idx1]
"""
)
obj.run(pytorch_code, ["result"])