add initial support for torch.ops.aten.neg.default converter #2147
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added
component: api [Python]
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There are some changes that do not conform to Python style guidelines:
--- py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py 2023-07-30 01:37:11.581293 +0000
+++ py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py 2023-07-30 01:37:33.758338 +0000
@@ -256,17 +256,17 @@
network: TRTNetwork,
target: Target,
args: Tuple[Argument, ...],
kwargs: Dict[str, Argument],
name: str,
-) -> Union[TRTTensor, Sequence[TRTTensor]]:
-
+) -> Union[TRTTensor, Sequence[TRTTensor]]:
+
return impl.unary.neg(
network,
target,
SourceIR.ATEN,
- name,
+ name,
args[0],
)
@dynamo_tensorrt_converter(torch.ops.aten.squeeze.dim)
--- py/torch_tensorrt/dynamo/conversion/impl/unary/ops.py 2023-07-30 01:37:11.585293 +0000
+++ py/torch_tensorrt/dynamo/conversion/impl/unary/ops.py 2023-07-30 01:37:33.831570 +0000
@@ -95,20 +95,16 @@
trt.ElementWiseOperation.SUB,
double_floor_div_output,
1,
)
+
def neg(
network: TRTNetwork,
target: Target,
source_ir: Optional[SourceIR],
name: str,
input_val: TRTTensor,
) -> TRTTensor:
return convert_unary(
- network,
- target,
- source_ir,
- name,
- trt.UnaryOperation.EXP,
- input_val
- )
\ No newline at end of file
+ network, target, source_ir, name, trt.UnaryOperation.EXP, input_val
+ )
narendasan
reviewed
Jul 31, 2023
There was a problem hiding this comment.
There are some changes that do not conform to Python style guidelines:
--- py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py 2023-07-31 16:31:00.909463 +0000
+++ py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py 2023-07-31 16:31:24.852864 +0000
@@ -256,17 +256,17 @@
network: TRTNetwork,
target: Target,
args: Tuple[Argument, ...],
kwargs: Dict[str, Argument],
name: str,
-) -> Union[TRTTensor, Sequence[TRTTensor]]:
-
+) -> Union[TRTTensor, Sequence[TRTTensor]]:
+
return impl.unary.neg(
network,
target,
SourceIR.ATEN,
- name,
+ name,
args[0],
)
@dynamo_tensorrt_converter(torch.ops.aten.squeeze.dim)
--- py/torch_tensorrt/dynamo/conversion/impl/unary/ops.py 2023-07-31 16:31:00.909463 +0000
+++ py/torch_tensorrt/dynamo/conversion/impl/unary/ops.py 2023-07-31 16:31:25.012634 +0000
@@ -95,20 +95,16 @@
trt.ElementWiseOperation.SUB,
double_floor_div_output,
1,
)
+
def neg(
network: TRTNetwork,
target: Target,
source_ir: Optional[SourceIR],
name: str,
input_val: TRTTensor,
) -> TRTTensor:
return convert_unary(
- network,
- target,
- source_ir,
- name,
- trt.UnaryOperation.NEG,
- input_val
- )
\ No newline at end of file
+ network, target, source_ir, name, trt.UnaryOperation.NEG, input_val
+ )
github-actions
bot
added
component: conversion
gs-olive
reviewed
Aug 2, 2023
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Consider renaming this file test_unary_aten.py, so any subsequent unary op tests can go in the same file
| def forward(self, input): | ||
| return torch.neg(input) | ||
|
|
||
| inputs = [torch.randn(1, 10)] |
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I believe randn defaults to torch.int64, so consider switching to rand for float or adding .int() for int, to the instantiation of the tensor
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Hey @gs-olive I went through the pytorch documentation here: https://pytorch.org/docs/stable/generated/torch.randn.html
I don't think it's torch.int64 because here it mentions in default it's float type.
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I believe randn should cover the different data types. Also you could try including the parameterized test cases to check for different shapes of the input tensort.
|
|
||
|
|
||
| class TestNegConverter(DispatchTestCase): | ||
| def test_neg(self): |
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Consider adding cases to check both integer and float tensor negation. It could just be two functions in the same class.
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cool will do! thanks!
There was a problem hiding this comment.
There are some changes that do not conform to Python style guidelines:
--- tests/py/dynamo/converters/test_neg_aten.py 2023-08-04 21:30:31.103961 +0000
+++ tests/py/dynamo/converters/test_neg_aten.py 2023-08-04 21:30:50.795785 +0000
@@ -9,18 +9,17 @@
class TestNegConverter(DispatchTestCase):
@parameterized.expand(
[
("2d_dim_dtype_float", (2, 2), torch.float),
("3d_dim_dtype_float", (2, 2, 2), torch.float),
-
]
)
def test_neg_float(self, _, x, type):
class neg(nn.Module):
def forward(self, input):
return torch.neg(input)
-
+
inputs = [torch.randn(x, dtype=type)]
self.run_test(
neg(),
inputs,
expected_ops={torch.ops.aten.neg.default},
@@ -30,21 +29,21 @@
[
("2d_dim_dtype_int", (2, 2), torch.int32, 0, 5),
("3d_dim_dtype_int", (2, 2, 2), torch.int32, 0, 5),
]
)
-
def test_neg_int(self, _, x, type, min, max):
class neg(nn.Module):
def forward(self, input):
return torch.neg(input)
-
+
inputs = [torch.randint(min, max, (x), dtype=type)]
self.run_test(
neg(),
inputs,
expected_ops={torch.ops.aten.neg.default},
)
+
if __name__ == "__main__":
run_tests()
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There are some changes that do not conform to Python style guidelines:
--- tests/py/dynamo/converters/test_neg_aten.py 2023-08-04 21:33:26.363439 +0000
+++ tests/py/dynamo/converters/test_neg_aten.py 2023-08-04 21:33:50.370156 +0000
@@ -9,18 +9,17 @@
class TestNegConverter(DispatchTestCase):
@parameterized.expand(
[
("2d_dim_dtype_float", (2, 2), torch.float),
("3d_dim_dtype_float", (2, 2, 2), torch.float),
-
]
)
def test_neg_float(self, _, x, type):
class neg(nn.Module):
def forward(self, input):
return torch.neg(input)
-
+
inputs = [torch.randn(x, dtype=type)]
self.run_test(
neg(),
inputs,
expected_ops={torch.ops.aten.neg.default},
@@ -30,21 +29,21 @@
[
("2d_dim_dtype_int", (2, 2), torch.int32, 0, 5),
("3d_dim_dtype_int", (2, 2, 2), torch.int32, 0, 5),
]
)
-
def test_neg_int(self, _, x, type, min, max):
class neg(nn.Module):
def forward(self, input):
return torch.neg(input)
-
+
inputs = [torch.randint(min, max, (x), dtype=type)]
self.run_test(
neg(),
inputs,
expected_ops={torch.ops.aten.neg.default},
)
+
if __name__ == "__main__":
run_tests()
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gs-olive
reviewed
Aug 31, 2023
There was a problem hiding this comment.
The changes here can be removed since it was added in #2246
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There are some changes that do not conform to Python style guidelines:
--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py 2023-09-05 23:01:00.766154+00:00
+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py 2023-09-05 23:03:49.447087+00:00
@@ -351,10 +351,11 @@
SourceIR.ATEN,
name,
args[0],
)
+
@dynamo_tensorrt_converter(torch.ops.aten.neg.default)
def aten_ops_neg(
network: TRTNetwork,
target: Target,
args: Tuple[Argument, ...],
@@ -372,10 +373,11 @@
target,
SourceIR.ATEN,
name,
input_val,
)
+
@dynamo_tensorrt_converter(torch.ops.aten.squeeze.dim) # type: ignore[misc]
@dynamo_tensorrt_converter(torch.ops.aten.squeeze.dims) # type: ignore[misc]
def aten_ops_squeeze(
network: TRTNetwork,
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gs-olive
approved these changes
Sep 7, 2023
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Description
Initial support for a dynamo converter.
Checklist: