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[Frontend][Paddle] [PaddlePaddle Hackathon 4]add attribute support for dropout/hard_sigmoid/pixel_shuffle #14575

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Apr 12, 2023
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[Frontend][Paddle] [PaddlePaddle Hackathon 4]add attribute support for dropout/hard_sigmoid/pixel_shuffle #14575

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e4844fe
add attr support for paddle ops
MayYouBeProsperous Apr 11, 2023
9348600
Update paddlepaddle.py
MayYouBeProsperous Apr 11, 2023
571c8cc
Update test_forward.py
MayYouBeProsperous Apr 11, 2023
052ddf5
codestyle
MayYouBeProsperous Apr 11, 2023
a42162a
remove approximate strategy in gelu
MayYouBeProsperous Apr 12, 2023
f47e7e9
remove gelu test
MayYouBeProsperous Apr 12, 2023
bfc3a9c
codestyle
MayYouBeProsperous Apr 12, 2023
6a38cc8
add dropout_implementation attr for dropout
MayYouBeProsperous Apr 12, 2023
b99dead
add tests for dropout
MayYouBeProsperous Apr 12, 2023
abc3746
codestyle
MayYouBeProsperous Apr 12, 2023
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14 changes: 11 additions & 3 deletions python/tvm/relay/frontend/paddlepaddle.py
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Original file line number Diff line number Diff line change
Expand Up @@ -502,7 +502,13 @@ def convert_dropout(g, op, block):
"""Operator converter for dropout."""

x = g.get_node(op.input("X")[0])
g.add_node(op.output("Out")[0], x)
dropout_prob = op.attr("dropout_prob")
dropout_implementation = op.attr("dropout_implementation")
if dropout_implementation == "downgrade_in_infer":
out = _op.nn.dropout(x, dropout_prob) * _expr.const(1 - dropout_prob, dtype="float32")
else:
out = _op.nn.dropout(x, dropout_prob)
g.add_node(op.output("Out")[0], out)
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@jiangjiajun jiangjiajun Apr 12, 2023 •
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There're two mode in dropout, refer https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Dropout_cn.html#dropout for more details

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Done.



def convert_dot(g, op, block):
Expand Down Expand Up @@ -897,8 +903,9 @@ def convert_hard_sigmoid(g, op, block):
"""Operator converter for hard_sigmoid."""

slope = op.attr("slope")
offset = op.attr("offset")
x = g.get_node(op.input("X")[0])
out = x * _expr.const(slope) + _expr.const(0.5)
out = x * _expr.const(slope) + _expr.const(offset)
out = _op.clip(out, 0, 1)
g.add_node(op.output("Out")[0], out)

Expand Down Expand Up @@ -1425,7 +1432,8 @@ def convert_pixel_shuffle(g, op, block):

x = g.get_node(op.input("X")[0])
upscale_factor = op.attr("upscale_factor")
out = _op.nn.depth_to_space(x, upscale_factor, mode="CRD")
data_format = op.attr("data_format")
out = _op.nn.depth_to_space(x, block_size=upscale_factor, layout=data_format, mode="CRD")
g.add_node(op.output("Out")[0], out)


Expand Down
25 changes: 23 additions & 2 deletions tests/python/frontend/paddlepaddle/test_forward.py
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Expand Up @@ -617,10 +617,22 @@ def test_forward_dropout():
def dropout(inputs):
return nn.functional.dropout(inputs)

@paddle.jit.to_static
def dropout1(inputs):
return nn.functional.dropout(inputs, 0.1)

@paddle.jit.to_static
def dropout2(inputs):
return nn.functional.dropout(inputs, 0.1, mode="downscale_in_infer")

input_shape = [1, 3, 10, 10]
input_data = paddle.rand(input_shape, dtype="float32")
verify_model(dropout, input_data=input_data[0, 0])
verify_model(dropout, input_data=input_data)
verify_model(dropout1, input_data=input_data[0, 0])
verify_model(dropout1, input_data=input_data)
verify_model(dropout2, input_data=input_data[0, 0])
verify_model(dropout2, input_data=input_data)


def test_forward_elemwise():
Expand Down Expand Up @@ -1008,9 +1020,13 @@ def test_forward_hard_sigmoid():
def hard_sigmoid(inputs):
return nn.functional.hardsigmoid(inputs)

def hard_sigmoid1(inputs):
return nn.functional.hardsigmoid(inputs, offset=0.6)

input_shape = [1, 3, 10, 10]
input_data = paddle.rand(input_shape, dtype="float32")
verify_model(hard_sigmoid, input_data=input_data)
verify_model(hard_sigmoid1, input_data=input_data)


@tvm.testing.uses_gpu
Expand Down Expand Up @@ -1781,9 +1797,9 @@ def forward(self, input1, input2):
@tvm.testing.uses_gpu
def test_forward_pixel_shuffle():
class PixelShuffle(nn.Layer):
def __init__(self, upscale_factor):
def __init__(self, upscale_factor, data_format="NCHW"):
super(PixelShuffle, self).__init__()
self.pixel_shuffle = paddle.nn.PixelShuffle(upscale_factor)
self.pixel_shuffle = paddle.nn.PixelShuffle(upscale_factor, data_format)

@paddle.jit.to_static
def forward(self, x):
Expand All @@ -1794,6 +1810,11 @@ def forward(self, x):
x = paddle.rand(input_shape, dtype="float32")
verify_model(PixelShuffle(2), x)

input_shapes = [[1, 3, 3, 4], [2, 2, 5, 8]]
for input_shape in input_shapes:
x = paddle.rand(input_shape, dtype="float32")
verify_model(PixelShuffle(2, data_format="NHWC"), x)


@tvm.testing.uses_gpu
def test_forward_prelu():
Expand Down