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dynamic input case for index_put with the test cases #3799

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dynamic input case for index_put with the test cases #3799

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4210eba
dynamic input case for index_put with the test cases
apbose Aug 28, 2025
b3f6f59
index_put validator for discontinuous cases
apbose Aug 29, 2025
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22 changes: 22 additions & 0 deletions py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py
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Original file line number Diff line number Diff line change
Expand Up @@ -881,8 +881,30 @@ def aten_ops_select(
)


def index_put_indices_continuity_validator(
node: Node, settings: Optional[CompilationSettings] = None
) -> bool:
idxs = node.args[1] # this is a list of indices
present_indices = [idx is not None for idx in idxs]
if len(present_indices) == 0:
return True
first_present_index = next((i for i, v in enumerate(present_indices) if v), None)
if first_present_index is None:
return False
rev_index = next((i for i, v in enumerate(reversed(present_indices)) if v), None)
if rev_index is None:
return False
last_present_index = len(present_indices) - 1 - rev_index
for i in range(first_present_index, last_present_index + 1):
if not present_indices[i]:
return False
return True


@dynamo_tensorrt_converter(
torch.ops.aten.index_put.default,
capability_validator=index_put_indices_continuity_validator,
supports_dynamic_shapes=True,
)
@enforce_tensor_types(
{
Expand Down
189 changes: 189 additions & 0 deletions tests/py/dynamo/conversion/test_index_put_aten.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
import torch
from parameterized import param, parameterized
from torch.testing._internal.common_utils import run_tests
from torch_tensorrt import Input

from .harness import DispatchTestCase

Expand Down Expand Up @@ -245,5 +246,193 @@ def forward(self, source_tensor, value_tensor):
)


class TestIndexIndexPutDynamicConverter(DispatchTestCase):
@parameterized.expand(
[
param(
test_name="1d_indices_single",
indices_tensor=(torch.tensor([0], dtype=torch.int32),),
value_tensor=torch.tensor([1], dtype=torch.float32),
input_min_shape=(1,),
input_opt_shape=(5,),
input_max_shape=(5,),
),
param(
test_name="1d_indices_multiple",
indices_tensor=(torch.tensor([0, 3], dtype=torch.int32),),
value_tensor=torch.tensor([1, 3], dtype=torch.float32),
input_min_shape=(1,),
input_opt_shape=(5,),
input_max_shape=(5,),
),
param(
test_name="2d_indices_single",
indices_tensor=(
torch.tensor([2], dtype=torch.int32),
torch.tensor([0], dtype=torch.int32),
),
value_tensor=torch.tensor([3], dtype=torch.float32),
input_min_shape=(2, 5),
input_opt_shape=(5, 5),
input_max_shape=(5, 5),
),
param(
test_name="2d_indices_multiple",
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.float32),
input_min_shape=(2, 5),
input_opt_shape=(5, 5),
input_max_shape=(5, 5),
),
param(
test_name="3d_indices_single",
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.float32),
input_min_shape=(2, 3, 3),
input_opt_shape=(3, 3, 3),
input_max_shape=(3, 3, 3),
),
param(
test_name="3d_indices_multiple",
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.float32),
input_min_shape=(2, 3, 3),
input_opt_shape=(3, 3, 3),
input_max_shape=(3, 3, 3),
),
param(
test_name="4d_indices_single",
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.float32),
input_min_shape=(1, 2, 2, 2),
input_opt_shape=(2, 2, 2, 2),
input_max_shape=(2, 2, 2, 2),
),
param(
test_name="4d_indices_multiple",
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.float32),
input_min_shape=(1, 2, 2, 2),
input_opt_shape=(2, 2, 2, 2),
input_max_shape=(2, 2, 2, 2),
),
param(
test_name="negative_indices",
indices_tensor=(
torch.tensor([-1, -2], dtype=torch.int32),
torch.tensor([2, 0], dtype=torch.int32),
),
value_tensor=torch.tensor([1, 3], dtype=torch.float32),
input_min_shape=(2, 5),
input_opt_shape=(5, 5),
input_max_shape=(5, 5),
),
param(
test_name="mixed_indices",
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.float32),
input_min_shape=(2, 4),
input_opt_shape=(4, 4),
input_max_shape=(4, 4),
),
param(
test_name="1d_indices_float",
indices_tensor=(torch.tensor([0, 3], dtype=torch.int32),),
value_tensor=torch.tensor([1.5, 3.5], dtype=torch.float32),
input_min_shape=(1,),
input_opt_shape=(5,),
input_max_shape=(5,),
),
param(
test_name="2d_indices_float",
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),
input_min_shape=(1, 5),
input_opt_shape=(5, 5),
input_max_shape=(5, 5),
),
param(
test_name="3d_indices_float",
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),
input_min_shape=(1, 3, 3),
input_opt_shape=(3, 3, 3),
input_max_shape=(3, 3, 3),
),
param(
test_name="4d_indices_float",
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),
input_min_shape=(1, 1, 2, 2),
input_opt_shape=(2, 2, 2, 2),
input_max_shape=(2, 2, 2, 2),
),
]
)
def test_index_constant_dynamic(
self,
test_name,
indices_tensor,
value_tensor,
input_min_shape,
input_opt_shape,
input_max_shape,
):
class TestModule(torch.nn.Module):
def __init__(self):
super().__init__()

def forward(self, input):
return torch.ops.aten.index_put.default(
input, indices_tensor, value_tensor, accumulate=False
)

input_specs = [
Input(
min_shape=input_min_shape,
opt_shape=input_opt_shape,
max_shape=input_max_shape,
dtype=torch.float32,
),
]
self.run_test_with_dynamic_shape(TestModule(), input_specs)


if __name__ == "__main__":
run_tests()
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