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Add dynamic shape support for leaky_relu/elu/hard_sigmoid/softplus #2927

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Merged
merged 1 commit into from
Jun 17, 2024
Merged

Add dynamic shape support for leaky_relu/elu/hard_sigmoid/softplus #2927

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14 changes: 10 additions & 4 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 @@ -459,7 +459,9 @@ def aten_ops_tanh(
)


@dynamo_tensorrt_converter(torch.ops.aten.leaky_relu.default)
@dynamo_tensorrt_converter(
torch.ops.aten.leaky_relu.default, supports_dynamic_shapes=True
)
def aten_ops_leaky_relu(
ctx: ConversionContext,
target: Target,
Expand All @@ -477,7 +479,7 @@ def aten_ops_leaky_relu(
)


@dynamo_tensorrt_converter(torch.ops.aten.elu.default)
@dynamo_tensorrt_converter(torch.ops.aten.elu.default, supports_dynamic_shapes=True)
def aten_ops_elu(
ctx: ConversionContext,
target: Target,
Expand All @@ -496,7 +498,9 @@ def aten_ops_elu(
)


@dynamo_tensorrt_converter(torch.ops.aten.softplus.default)
@dynamo_tensorrt_converter(
torch.ops.aten.softplus.default, supports_dynamic_shapes=True
)
def aten_ops_softplus(
ctx: ConversionContext,
target: Target,
Expand All @@ -514,7 +518,9 @@ def aten_ops_softplus(
)


@dynamo_tensorrt_converter(torch.ops.aten.hardsigmoid.default)
@dynamo_tensorrt_converter(
torch.ops.aten.hardsigmoid.default, supports_dynamic_shapes=True
)
def aten_ops_hard_sigmoid(
ctx: ConversionContext,
target: Target,
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10 changes: 6 additions & 4 deletions tests/py/dynamo/conversion/test_elu_aten.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,9 +22,10 @@ def forward(self, x):

input_specs = [
Input(
shape=(-1, -1, -1),
dtype=torch.float32,
shape_ranges=[((1, 1, 1), (1, 2, 3), (3, 3, 3))],
min_shape=(1, 1, 1),
opt_shape=(1, 2, 3),
max_shape=(3, 3, 3),
),
]
self.run_test_with_dynamic_shape(TestModule(), input_specs)
Expand All @@ -36,9 +37,10 @@ def forward(self, x):

input_specs = [
Input(
shape=(-1, -1, -1, -1),
dtype=torch.float32,
shape_ranges=[((1, 1, 1, 5), (1, 2, 3, 5), (3, 3, 3, 5))],
min_shape=(1, 1, 1, 5),
opt_shape=(1, 2, 3, 5),
max_shape=(3, 3, 3, 5),
),
]

Expand Down
10 changes: 6 additions & 4 deletions tests/py/dynamo/conversion/test_hard_sigmoid_aten.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,9 +22,10 @@ def forward(self, x):

input_specs = [
Input(
shape=(-1, -1, -1),
dtype=torch.float32,
shape_ranges=[((1, 1, 1), (1, 2, 3), (3, 3, 3))],
min_shape=(1, 1, 1),
opt_shape=(1, 2, 3),
max_shape=(3, 3, 3),
),
]
self.run_test_with_dynamic_shape(TestModule(), input_specs)
Expand All @@ -36,9 +37,10 @@ def forward(self, x):

input_specs = [
Input(
shape=(-1, -1, -1, -1),
dtype=torch.float32,
shape_ranges=[((1, 1, 1, 5), (1, 2, 3, 5), (3, 3, 3, 5))],
min_shape=(1, 1, 1, 5),
opt_shape=(1, 2, 3, 5),
max_shape=(3, 3, 3, 5),
),
]

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10 changes: 6 additions & 4 deletions tests/py/dynamo/conversion/test_leaky_relu_aten.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,9 +22,10 @@ def forward(self, x):

input_specs = [
Input(
shape=(-1, -1, -1),
dtype=torch.float32,
shape_ranges=[((1, 1, 1), (1, 2, 3), (3, 3, 3))],
min_shape=(1, 1, 1),
opt_shape=(1, 2, 3),
max_shape=(3, 3, 3),
),
]
self.run_test_with_dynamic_shape(TestModule(), input_specs)
Expand All @@ -36,9 +37,10 @@ def forward(self, x):

input_specs = [
Input(
shape=(-1, -1, -1, -1),
dtype=torch.float32,
shape_ranges=[((1, 1, 1, 5), (1, 2, 3, 5), (3, 3, 3, 5))],
min_shape=(1, 1, 1, 5),
opt_shape=(1, 2, 3, 5),
max_shape=(3, 3, 3, 5),
),
]

Expand Down
10 changes: 6 additions & 4 deletions tests/py/dynamo/conversion/test_softplus_aten.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -22,9 +22,10 @@ def forward(self, x):

input_specs = [
Input(
shape=(-1, -1, -1),
dtype=torch.float32,
shape_ranges=[((1, 1, 1), (1, 2, 3), (3, 3, 3))],
min_shape=(1, 1, 1),
opt_shape=(1, 2, 3),
max_shape=(3, 3, 3),
),
]
self.run_test_with_dynamic_shape(TestModule(), input_specs)
Expand All @@ -36,9 +37,10 @@ def forward(self, x):

input_specs = [
Input(
shape=(-1, -1, -1, -1),
dtype=torch.float32,
shape_ranges=[((1, 1, 1, 5), (1, 2, 3, 5), (3, 3, 3, 5))],
min_shape=(1, 1, 1, 5),
opt_shape=(1, 2, 3, 5),
max_shape=(3, 3, 3, 5),
),
]

Expand Down