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test_index_put_aten.py
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import torch
from parameterized import param, parameterized
from torch.testing._internal.common_utils import run_tests
from .harness import DispatchTestCase
class TestIndexPutConverter(DispatchTestCase):
@parameterized.expand(
[
param(
test_name="1d_indices_single",
source_tensor=torch.zeros([5], dtype=torch.int32),
indices_tensor=(torch.tensor([0], dtype=torch.int32),),
value_tensor=torch.tensor([1], dtype=torch.int32),
),
param(
test_name="1d_indices_multiple",
source_tensor=torch.zeros([5], dtype=torch.int32),
indices_tensor=(torch.tensor([0, 3], dtype=torch.int32),),
value_tensor=torch.tensor([1, 3], dtype=torch.int32),
),
param(
test_name="2d_indices_single",
source_tensor=torch.zeros([5, 5], dtype=torch.int32),
indices_tensor=(
torch.tensor([2], dtype=torch.int32),
torch.tensor([0], dtype=torch.int32),
),
value_tensor=torch.tensor([3], dtype=torch.int32),
),
param(
test_name="2d_indices_multiple",
source_tensor=torch.zeros([5, 5], dtype=torch.int32),
indices_tensor=(
torch.tensor([0, 2, 2], dtype=torch.int32),
torch.tensor([2, 0, 2], dtype=torch.int32),
),
value_tensor=torch.tensor([1, 3, 4], dtype=torch.int32),
),
param(
test_name="3d_indices_single",
source_tensor=torch.zeros([3, 3, 3], dtype=torch.int32),
indices_tensor=(
torch.tensor([1], dtype=torch.int32),
torch.tensor([2], dtype=torch.int32),
torch.tensor([2], dtype=torch.int32),
),
value_tensor=torch.tensor([7], dtype=torch.int32),
),
param(
test_name="3d_indices_multiple",
source_tensor=torch.zeros([3, 3, 3], dtype=torch.int32),
indices_tensor=(
torch.tensor([0, 1, 1], dtype=torch.int32),
torch.tensor([1, 2, 1], dtype=torch.int32),
torch.tensor([2, 0, 2], dtype=torch.int32),
),
value_tensor=torch.tensor([5, 7, 2], dtype=torch.int32),
),
param(
test_name="4d_indices_single",
source_tensor=torch.zeros([2, 2, 2, 2], dtype=torch.int32),
indices_tensor=(
torch.tensor([1], dtype=torch.int32),
torch.tensor([1], dtype=torch.int32),
torch.tensor([0], dtype=torch.int32),
torch.tensor([1], dtype=torch.int32),
),
value_tensor=torch.tensor([5], dtype=torch.int32),
),
param(
test_name="4d_indices_multiple",
source_tensor=torch.zeros([2, 2, 2, 2], dtype=torch.int32),
indices_tensor=(
torch.tensor([0, 1], dtype=torch.int32),
torch.tensor([1, 1], dtype=torch.int32),
torch.tensor([1, 0], dtype=torch.int32),
torch.tensor([1, 0], dtype=torch.int32),
),
value_tensor=torch.tensor([5, 7], dtype=torch.int32),
),
param(
test_name="negative_indices",
source_tensor=torch.zeros([5, 5], dtype=torch.int32),
indices_tensor=(
torch.tensor([-1, -2], dtype=torch.int32),
torch.tensor([2, 0], dtype=torch.int32),
),
value_tensor=torch.tensor([1, 3], dtype=torch.int32),
),
param(
test_name="mixed_indices",
source_tensor=torch.zeros([4, 4], dtype=torch.int32),
indices_tensor=(
torch.tensor([0, 1, -1, -2], dtype=torch.int32),
torch.tensor([0, -1, 2, 1], dtype=torch.int32),
),
value_tensor=torch.tensor([2, 4, 6, 8], dtype=torch.int32),
),
param(
test_name="1d_indices_float",
source_tensor=torch.zeros([5], dtype=torch.float32),
indices_tensor=(torch.tensor([0, 3], dtype=torch.int32),),
value_tensor=torch.tensor([1.5, 3.5], dtype=torch.float32),
),
param(
test_name="2d_indices_float",
source_tensor=torch.zeros([5, 5], dtype=torch.float32),
indices_tensor=(
torch.tensor([0, 2], dtype=torch.int32),
torch.tensor([2, 0], dtype=torch.int32),
),
value_tensor=torch.tensor([1.5, 3.5], dtype=torch.float32),
),
param(
test_name="3d_indices_float",
source_tensor=torch.zeros([3, 3, 3], dtype=torch.float32),
indices_tensor=(
torch.tensor([0, 1], dtype=torch.int32),
torch.tensor([1, 2], dtype=torch.int32),
torch.tensor([2, 0], dtype=torch.int32),
),
value_tensor=torch.tensor([5.5, 7.5], dtype=torch.float32),
),
param(
test_name="4d_indices_float",
source_tensor=torch.zeros([2, 2, 2, 2], dtype=torch.float32),
indices_tensor=(
torch.tensor([0, 1], dtype=torch.int32),
torch.tensor([1, 0], dtype=torch.int32),
torch.tensor([0, 1], dtype=torch.int32),
torch.tensor([1, 0], dtype=torch.int32),
),
value_tensor=torch.tensor([5.5, 7.5], dtype=torch.float32),
),
# param(
# test_name="3d_indices_float_broadcase_index",
# source_tensor=torch.zeros([3, 3, 3], dtype = torch.int32),
# indices_tensor=(
# torch.tensor([0,1], dtype=torch.int32),
# torch.tensor([0,1], dtype=torch.int32),
# ),
# value_tensor=torch.tensor([10], dtype = torch.int32),
# ),
# param(
# test_name="2d_indices_accumulate_True",
# source_tensor=torch.zeros([5, 5], dtype=torch.int32),
# indices_tensor=(torch.tensor([0, 0], dtype=torch.int32), torch.tensor([1, 1], dtype=torch.int32)),
# value_tensor=torch.tensor([1, 2], dtype=torch.int32),
# accumulate=True,
# ),
# param(
# test_name="3d_indices_accumulate_True",
# source_tensor=torch.zeros([3, 3, 3], dtype=torch.int32),
# indices_tensor=(torch.tensor([0, 0], dtype=torch.int32), torch.tensor([1, 1], dtype=torch.int32), torch.tensor([2, 2], dtype=torch.int32)),
# value_tensor=torch.tensor([1, 2], dtype=torch.int32),
# accumulate=True,
# ),
# param(
# test_name="4d_indices_accumulate_True",
# source_tensor=torch.zeros([2, 2, 2, 2], dtype=torch.int32),
# indices_tensor=(torch.tensor([0, 0], dtype=torch.int32), torch.tensor([1, 1], dtype=torch.int32), torch.tensor([0, 0], dtype=torch.int32), torch.tensor([1, 1], dtype=torch.int32)),
# value_tensor=torch.tensor([1, 2], dtype=torch.int32),
# accumulate=True,
# ),
]
)
def test_index_put(
self, test_name, source_tensor, indices_tensor, value_tensor, accumulate=False
):
@torch._dynamo.assume_constant_result
def get_indices_tensor():
return indices_tensor
class TestIndexPut(torch.nn.Module):
def forward(self, source_tensor, value_tensor):
indices_tensor_const = get_indices_tensor()
return torch.ops.aten.index_put.default(
source_tensor, indices_tensor_const, value_tensor, accumulate
)
self.run_test(
TestIndexPut(),
inputs=[source_tensor, value_tensor],
enable_passes=True,
use_dynamo_tracer=True,
)
if __name__ == "__main__":
run_tests()