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feat: support aten.trunc dynamo converter #2543

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Merged
merged 2 commits into from
Dec 28, 2023
Merged

feat: support aten.trunc dynamo converter #2543

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ac25063
feat: support aten.trunc dynamo converter
zewenli98 Dec 14, 2023
1668d4e
fix type error when input is int
zewenli98 Dec 18, 2023
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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 @@ -2541,3 +2541,25 @@ def aten_ops_sort(
dim=args_bounds_check(args, 1, -1),
descending=args_bounds_check(args, 2, False),
)


@dynamo_tensorrt_converter(torch.ops.aten.trunc.default)
@enforce_tensor_types(
{
0: (TRTTensor,),
}
)
def aten_ops_trunc(
ctx: ConversionContext,
target: Target,
args: Tuple[Argument, ...],
kwargs: Dict[str, Argument],
name: str,
) -> Union[TRTTensor, Sequence[TRTTensor]]:
return impl.unary.trunc(
ctx,
target,
SourceIR.ATEN,
name,
args[0],
)
29 changes: 28 additions & 1 deletion py/torch_tensorrt/dynamo/conversion/impl/unary/ops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,10 @@
from torch.fx.node import Target
from torch_tensorrt.dynamo._SourceIR import SourceIR
from torch_tensorrt.dynamo.conversion._ConversionContext import ConversionContext
from torch_tensorrt.dynamo.conversion.converter_utils import cast_trt_tensor
from torch_tensorrt.dynamo.conversion.converter_utils import (
cast_trt_tensor,
get_trt_tensor,
)
from torch_tensorrt.dynamo.conversion.impl.unary.base import convert_unary
from torch_tensorrt.fx.types import TRTTensor

Expand Down Expand Up @@ -432,3 +435,27 @@ def erf(
return convert_unary(
ctx, target, source_ir, name, trt.UnaryOperation.ERF, input_val
)


def trunc(
ctx: ConversionContext,
target: Target,
source_ir: Optional[SourceIR],
name: str,
input_val: TRTTensor,
) -> TRTTensor:
if input_val.dtype not in (trt.float16, trt.float32):
return impl.cast.to_copy(
ctx,
target,
source_ir,
f"{name}_copy",
input_val,
input_val.dtype,
force_layer=True,
)

dividend = get_trt_tensor(ctx, 1, f"{name}_dividend")
return impl.elementwise.trunc_div(
ctx, target, source_ir, f"{name}_trunc", input_val, dividend
)
Comment on lines +459 to +461
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Based on the documentation of torch.trunc, it seems that the behavior differs based on the data type. Specifically, for float inputs, the output is also a float, and for int inputs, the output is also an int. Does this line up with the behavior of trunc_div as well?

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Additionally, could a line be added such as

if dtype not in (float16, float32):
   return input_val

This can help to avoid unnecessary layer insertion

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Thanks for pointing this out! I added the code below since TRT engine inputs cannot be TRT engine outputs:

if input_val.dtype not in (trt.float16, trt.float32):
        return impl.cast.to_copy(
            ctx,
            target,
            source_ir,
            f"{name}_copy",
            input_val,
            input_val.dtype,
            force_layer=True,
        )

52 changes: 52 additions & 0 deletions tests/py/dynamo/conversion/test_trunc_aten.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,52 @@
import torch
import torch.nn as nn
from parameterized import parameterized
from torch.testing._internal.common_utils import run_tests

from .harness import DispatchTestCase


class TestTruncConverter(DispatchTestCase):
@parameterized.expand(
[
((10,),),
((1, 20),),
((2, 3, 4),),
((2, 3, 4, 5),),
]
)
def test_trunc_float(self, shape):
class Trunc(nn.Module):
def forward(self, input):
return torch.ops.aten.trunc.default(input)

inputs = [torch.randn(shape)]
self.run_test(
Trunc(),
inputs,
enable_passes=True,
)

@parameterized.expand(
[
((10,),),
((1, 20),),
((2, 3, 4),),
((2, 3, 4, 5),),
]
)
def test_trunc_int(self, shape):
class Trunc(nn.Module):
def forward(self, input):
return torch.ops.aten.trunc.default(input)

inputs = [torch.randint(-10, 10, shape, dtype=torch.int32)]
self.run_test(
Trunc(),
inputs,
enable_passes=True,
)


if __name__ == "__main__":
run_tests()