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[TensorRT] Fixed access to pad_value #8131

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[TensorRT] Fixed access to pad_value #8131

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5fb7d13
Fixed access to pad_value
akmaru May 25, 2021
6e0bbc0
Added test cases
akmaru May 25, 2021
d7af28e
Added pad_value into PadOpConverter inputs
akmaru May 25, 2021
9cc90d8
Check pad_value is Relay.Constant by assertion
akmaru May 25, 2021
e50bc43
Fixed checking if constant pad_value
akmaru May 27, 2021
827797a
Formatted
akmaru Jun 8, 2021
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8 changes: 6 additions & 2 deletions python/tvm/relay/op/contrib/tensorrt.py
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Original file line number Diff line number Diff line change
Expand Up @@ -723,8 +723,12 @@ def pad_annotate_fn(expr): # pylint: disable=unused-variable
if attrs.pad_mode != "constant":
logger.info("nn.pad: pad mode is %s but must be constant.", attrs.pad_mode)
return False
if float(attrs.pad_value) != 0.0:
logger.info("nn.pad: pad value is %f but must be 0.0.", float(attrs.pad_value))
if (
not isinstance(args[1], relay.Constant)
or len(args[1].checked_type.shape) != 0
or args[1].data.numpy().item() != 0.0
):
logger.info("nn.pad: pad value is %s but must be 0.0.", args[1])
return False
if len(attrs.pad_width) not in [4, 5]:
logger.info("nn.pad: can only pad 4D or 5D inputs")
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2 changes: 1 addition & 1 deletion src/runtime/contrib/tensorrt/tensorrt_ops.cc
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Expand Up @@ -1057,7 +1057,7 @@ class ReshapeOpConverter : public TensorRTOpConverter {

class PadOpConverter : public TensorRTOpConverter {
public:
PadOpConverter() : TensorRTOpConverter({kTensor}) {}
PadOpConverter() : TensorRTOpConverter({kTensor, kWeight}) {}
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@akmaru akmaru May 25, 2021

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Added pad_value input into PadOpConverter.


void Convert(TensorRTOpConverterParams* params) const {
auto input = params->inputs.at(0).tensor;
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13 changes: 11 additions & 2 deletions tests/python/contrib/test_tensorrt.py
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Expand Up @@ -793,16 +793,25 @@ def get_graph(x_shape=(1, 16)):


def test_pad():
def get_graph(x_shape, pad_width):
def get_graph(x_shape, pad_width, pad_value=0.0):
x = relay.var("x", shape=(x_shape), dtype="float32")
out = relay.nn.pad(x, pad_width=pad_width)
out = relay.nn.pad(x, pad_width=pad_width, pad_value=pad_value)
f = relay.Function([x], out)
return f, {"x": x_shape}, []

run_and_verify_func(get_graph((1, 8, 16, 16), [[0, 0], [0, 0], [0, 0], [0, 0]]))
run_and_verify_func(get_graph((1, 8, 16, 16), [[0, 0], [0, 0], [1, 1], [1, 1]]))
run_and_verify_func(
get_graph((1, 8, 16, 16), [[0, 0], [0, 0], [1, 1], [1, 1]], pad_value=-1.0e30)
)
run_and_verify_func(get_graph((1, 8, 16, 16), [[0, 0], [0, 0], [0, 1], [2, 0]]))
run_and_verify_func(
get_graph((1, 8, 16, 16), [[0, 0], [0, 0], [0, 1], [2, 0]], pad_value=-1.0e30)
)
run_and_verify_func(get_graph((1, 8, 3, 16, 16), [[0, 0], [0, 0], [0, 0], [0, 0], [0, 0]]))
run_and_verify_func(
get_graph((1, 8, 3, 16, 16), [[0, 0], [0, 0], [0, 0], [0, 0], [0, 0]], pad_value=-1.0e30)
)


def test_softmax():
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