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Jul 31, 2024
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feat: dynamic shape support for aten.select.int #2990

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e37812d
feat: dynamic shape support for aten.select.int
chohk88 Jul 9, 2024
7f86c63
chore: mark dynamic support for select.int
chohk88 Jul 10, 2024
20c547b
feat: fully dynamic support for aten.select.int
chohk88 Jul 20, 2024
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2 changes: 1 addition & 1 deletion 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 @@ -794,7 +794,7 @@ def aten_ops_scatter(
)


@dynamo_tensorrt_converter(torch.ops.aten.select.int)
@dynamo_tensorrt_converter(torch.ops.aten.select.int, supports_dynamic_shapes=True)
def aten_ops_select(
ctx: ConversionContext,
target: Target,
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16 changes: 3 additions & 13 deletions py/torch_tensorrt/dynamo/conversion/impl/select.py
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Expand Up @@ -47,26 +47,16 @@ def select(
if dynamic_shape:
# Check whether slice target dim is dynamic shape dim
assert input.shape[dim] != -1, "Can't select on negative shape dimension!"
index = index

if index >= input.shape[dim]:
raise RuntimeError(
f"cannot have index greater than the dimension length! {input.shape[dim]}"
)
output_shape = list(input.shape)
output_shape[dim] = 1
if dynamic_shape > 0:
output_shape = get_shape_with_dynamic_shape(
ctx, target, source_ir, name, output_shape, input
)

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If above asserts() are removed and full dynamic shape was used(e.g.(-1,-1,-1)), test worked.
I'm wondering if select on dynamic dim can be supported.

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I have tested the test you mentioned and confirmed that using -1, -1, -1 also passes the test cases successfully.

However, I didn't remove the asserts() because the test cases fail when the index is out of range, meaning the index is larger than the corresponding dimension in the dynamic input shape.

            (
                "success case",
                (1, 1, 1),
                (2, 2, 2),
                (3, 3, 3),
                torch.float,
                0,
                1,
            ),
            (
                "fail case",
                (1, 1, 1),
                (2, 2, 2),
                (3, 3, 3),
                torch.float,
                0,
                3,
            ),

It seems that modifying the index like dimension (dim = get_positive_dim(cast(int, dim), ranks)) when the index is greater than the size would solve the issue. Do you have an example that handles it this way?

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I think we cannot check invalid index for dynamic dim. Error will happen in runtime.
Maybe we can check index for only static dim.
if DYNAMIC_DIM != input.shape[dim] and index >= input.shape[dim]:

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I also tested by removing the assert() and raise RuntimeError() statements and tested with (-1, -1, -1).

The test case mentioned as a success case above passes, but the test case mentioned as a fail case fails. The reason for this is that in the fail case, the size of the 0-th dimension is 3, but the index selects positions larger than that (index=3, 4th position). The current converter does not handle this case where the index is larger than the size of the dimension specified by dim, as in the 'fail case'. (Note: It might be possible to handle this using the slice layer, lt (less than) and div functions, but currently, it is handled with an assert statement.)

Therefore, I left the assert() and raise RuntimeError() statement without removing it.

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@chohk88 chohk88 Jul 16, 2024 •
edited
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Oh! I misunderstood. If the index is larger than the size of the dimension specified by dim, it won't work in PyTorch either, so we don't need to handle this case. Therefore, we don't need to consider test cases like the 'fail case' mentioned above. This means that a dynamic shape is supported for all dimensions.

Pytorch example of correct usage of select.int

image

Pytorch example of IndexError occurring case of select.int

image

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@keehyuna Thanks for the suggestion!

Since PyTorch already raises an error when the index exceeds the input size, there’s no need for us to check this here. I removed those checks. Additionally, following your suggestion, I’ve added test cases to fully support dynamic shapes.

index_value = np.array(index, dtype=np.int32)
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Can index itself be dynamic (ITensor)?

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Based on the PyTorch docs and the schema, the index is always an integer and cannot be a list or tuple of integers. The size of the indices_tensor created with index and index_value will always be a scalar. Therefore, it cannot be dynamic.

indices_tensor = ctx.net.add_constant(
index_value.shape, to_numpy(index_value)
).get_output(0)
indices_tensor = ctx.net.add_constant(index_value.shape, index_value).get_output(0)
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can you use get_trt_tensor call here ?

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Thanks for the suggestion!

dim and index are now int (not Shape), and I've changed to using get_trt_tensor for indices_tensor.

layer = ctx.net.add_gather(input, indices_tensor, dim)
out = layer.get_output(0)
if len(out.shape) != 1:
layer = ctx.net.add_shuffle(out)

return layer.get_output(0)


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57 changes: 44 additions & 13 deletions tests/py/dynamo/conversion/test_select_aten.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,5 @@
import torch
import torch.nn as nn
from parameterized import parameterized
from torch.testing._internal.common_utils import run_tests
from torch_tensorrt import Input
Expand All @@ -13,7 +14,7 @@ class TestSelectConverterOne(DispatchTestCase):
]
)
def test_select(self, _, dim, index):
class TestModule(torch.nn.Module):
class select(nn.Module):
def __init__(self):
super().__init__()

Expand All @@ -22,7 +23,7 @@ def forward(self, input):

input = [torch.randn(1, 2)]
self.run_test(
TestModule(),
select(),
input,
)

Expand All @@ -34,7 +35,7 @@ class TestSelectConverterTwo(DispatchTestCase):
]
)
def test_select(self, _, dim, index):
class TestModule(torch.nn.Module):
class select(nn.Module):
def __init__(self):
super().__init__()

Expand All @@ -43,33 +44,63 @@ def forward(self, input):

input = [torch.randn(4, 4, 4, 4)]
self.run_test(
TestModule(),
select(),
input,
)


class TestSelectConverterWithDynamicShape(DispatchTestCase):
class TestSelectConverterDynamicShape(DispatchTestCase):
@parameterized.expand(
[
("select_dim_index", 1, 0),
(
"select_dim_index",
(1, 3, 3),
(2, 3, 3),
(3, 3, 3),
torch.int32,
1,
0,
),
(
"select_dim_index",
(1, 1, 3),
(2, 2, 3),
(3, 3, 3),
torch.float,
2,
0,
),
(
"select_dim_index",
(3, 1, 1),
(3, 2, 2),
(3, 3, 3),
torch.float,
0,
2,
),
]
)
def test_select_with_dynamic_shape(self, _, dim, index):
class TestModule(torch.nn.Module):
def test_dynamic_shape_select(
self, _, min_shape, opt_shape, max_shape, type, dim, index
):
class select(nn.Module):
def __init__(self):
super().__init__()

def forward(self, input):
return torch.ops.aten.select.int(input, dim, index)

input_spec = [
input_specs = [
Input(
shape=(-1, 3, 3),
dtype=torch.float32,
shape_ranges=[((1, 3, 3), (3, 3, 3), (3, 3, 3))],
min_shape=min_shape,
opt_shape=opt_shape,
max_shape=max_shape,
dtype=type,
),
]
self.run_test_with_dynamic_shape(TestModule(), input_spec)

self.run_test_with_dynamic_shape(select(), input_specs)


if __name__ == "__main__":
Expand Down
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