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[Relay] Conv2D padding representation #4787

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merged 10 commits into from
Feb 5, 2020
Merged

[Relay] Conv2D padding representation #4787

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290c208
enforce 4-way padding
Jan 29, 2020
ff55f62
add util with get_pad_tuple
Jan 29, 2020
1faaad3
delete unnecessary arguments
Jan 29, 2020
87c35db
fix lint
Jan 29, 2020
eaee475
add container.Array case
Jan 30, 2020
4887886
fix cudnn conv2d asymmetric padding logic
Jan 30, 2020
427d796
rename get_pad_tuple to get_pad_tuple2d
Jan 31, 2020
efd2013
revert change for topi/python/topi/nn/conv2d.py
Jan 31, 2020
62b00d4
add get_pad_tuple2d for several contrib conv2d ops
Feb 1, 2020
00fd0c4
add get_pad_tuple2d for all conv2d ops
Feb 4, 2020
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3 changes: 3 additions & 0 deletions python/tvm/relay/op/nn/nn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,6 +19,7 @@
from __future__ import absolute_import as _abs
from ...expr import TupleWrapper
from . import _make
from .util import get_pad_tuple


def conv1d(data,
Expand Down Expand Up @@ -202,6 +203,8 @@ def conv2d(data,
dilation = (dilation, dilation)
if isinstance(padding, int):
padding = (padding, padding)
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# convert 2-way padding to 4-way padding
padding = get_pad_tuple(padding)
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return _make.conv2d(data, weight, strides, padding, dilation,
groups, channels, kernel_size, data_layout,
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53 changes: 53 additions & 0 deletions python/tvm/relay/op/nn/util.py
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@@ -0,0 +1,53 @@
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
# pylint: disable=invalid-name, unused-variable
"""NN operator common utilities"""
from __future__ import absolute_import

def get_pad_tuple(padding):
"""Common code to get the pad option
Parameters
----------
padding : int or str
Padding size, or ['VALID', 'SAME']
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Returns
-------
pad_top : int
Padding size on top
pad_left : int
Padding size on left
pad_down : int
Padding size on down.
pad_right : int
Padding size on right.
"""
# compute the padding size
if isinstance(padding, (tuple, list)):
if len(padding) == 2:
pad_h = padding[0] * 2
pad_w = padding[1] * 2
elif len(padding) == 4:
return padding[0], padding[1], padding[2], padding[3]
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else:
raise ValueError("Size of padding can only be 2 or 4")
elif isinstance(padding, int):
pad_h = pad_w = padding * 2
else:
raise ValueError("Unknown padding option %s" % padding)
pad_top = (pad_h + 1) // 2
pad_left = (pad_w + 1) // 2
return pad_top, pad_left, pad_h - pad_top, pad_w - pad_left
22 changes: 11 additions & 11 deletions tests/python/unittest/test_graph_tuner_core.py
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Original file line number Diff line number Diff line change
Expand Up @@ -50,9 +50,9 @@ def _create_data(target, dshape, dtype, layout):
params=params,
ops=(relay.op.nn.conv2d,))
wkl_list = [
create_workload((1, 3, 8, 8), (16, 3, 3, 3), (1, 1), (1, 1), (1, 1), layout, layout, dtype, dtype),
create_workload((1, 16, 8, 8), (32, 16, 1, 1), (1, 1), (0, 0), (1, 1), layout, layout, dtype, dtype),
create_workload((1, 32, 8, 8), (32, 32, 3, 3), (1, 1), (1, 1), (1, 1), layout, layout, dtype, dtype),
create_workload((1, 3, 8, 8), (16, 3, 3, 3), (1, 1), (1, 1, 1, 1), (1, 1), layout, layout, dtype, dtype),
create_workload((1, 16, 8, 8), (32, 16, 1, 1), (1, 1), (0, 0, 0, 0), (1, 1), layout, layout, dtype, dtype),
create_workload((1, 32, 8, 8), (32, 32, 3, 3), (1, 1), (1, 1, 1, 1), (1, 1), layout, layout, dtype, dtype),
]
costs = [0.04, 0.012, 0.03]
config_list = []
Expand Down Expand Up @@ -279,9 +279,9 @@ def test_many_sub_graphs():
params=params,
ops=(relay.op.nn.conv2d,))
wkl_list = [
create_workload((1, 3, 8, 8), (16, 3, 3, 3), (1, 1), (1, 1), (1, 1), layout, layout, dtype, dtype),
create_workload((1, 16, 8, 8), (32, 16, 1, 1), (1, 1), (0, 0), (1, 1), layout, layout, dtype, dtype),
create_workload((1, 32, 8, 8), (32, 32, 3, 3), (1, 1), (1, 1), (1, 1), layout, layout, dtype, dtype),
create_workload((1, 3, 8, 8), (16, 3, 3, 3), (1, 1), (1, 1, 1, 1), (1, 1), layout, layout, dtype, dtype),
create_workload((1, 16, 8, 8), (32, 16, 1, 1), (1, 1), (0, 0, 0, 0), (1, 1), layout, layout, dtype, dtype),
create_workload((1, 32, 8, 8), (32, 32, 3, 3), (1, 1), (1, 1, 1, 1), (1, 1), layout, layout, dtype, dtype),
]
costs = [0.04, 0.012, 0.03, 0.02, 0.02, 0.045]
config_list = []
Expand Down Expand Up @@ -392,8 +392,8 @@ def test_tuple():
params=params,
ops=(relay.op.nn.conv2d,))
wkl_list = [
create_workload((1, 5, 32, 32), (2, 5, 3, 3), (1, 1), (1, 1), (1, 1), layout, layout, dtype, dtype),
create_workload((1, 5, 32, 32), (3, 5, 3, 3), (1, 1), (1, 1), (1, 1), layout, layout, dtype, dtype),
create_workload((1, 5, 32, 32), (2, 5, 3, 3), (1, 1), (1, 1, 1, 1), (1, 1), layout, layout, dtype, dtype),
create_workload((1, 5, 32, 32), (3, 5, 3, 3), (1, 1), (1, 1, 1, 1), (1, 1), layout, layout, dtype, dtype),
]
costs = [0.01, 0.012, 0.03, 0.04]
config_list = []
Expand Down Expand Up @@ -490,9 +490,9 @@ def test_triangle_block():
params=params,
ops=(relay.op.nn.conv2d,))
wkl_list = [
create_workload((1, 3, 8, 8), (16, 3, 3, 3), (1, 1), (1, 1), (1, 1), layout, layout, dtype, dtype),
create_workload((1, 16, 8, 8), (32, 16, 1, 1), (1, 1), (0, 0), (1, 1), layout, layout, dtype, dtype),
create_workload((1, 3, 8, 8), (32, 3, 3, 3), (1, 1), (1, 1), (1, 1), layout, layout, dtype, dtype),
create_workload((1, 3, 8, 8), (16, 3, 3, 3), (1, 1), (1, 1, 1, 1), (1, 1), layout, layout, dtype, dtype),
create_workload((1, 16, 8, 8), (32, 16, 1, 1), (1, 1), (0, 0, 0, 0), (1, 1), layout, layout, dtype, dtype),
create_workload((1, 3, 8, 8), (32, 3, 3, 3), (1, 1), (1, 1, 1, 1), (1, 1), layout, layout, dtype, dtype),
]
costs = [0.04, 0.012, 0.03, 0.02, 0.02, 0.045]
config_list = []
Expand Down
2 changes: 2 additions & 0 deletions topi/python/topi/nn/conv2d.py
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Expand Up @@ -62,6 +62,8 @@ def conv2d(input, filter, strides, padding, dilation, layout='NCHW', out_dtype=N
output : tvm.Tensor
4-D with shape [batch, out_channel, out_height, out_width]
"""
#only accepts 4-way padding
assert len(padding) == 4, "only accepts 4-way padding"
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I'm afraid that you have to add this assertion to all conv2d compute functions. Specifically, all conv2d functions with @autotvm.register_topi_compute(nn.conv2d, ...) decorator should have this assertion. @icemelon9 could you help confirm?

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Yes, @comaniac is correct. Probably directly contribute to #4644? 😄

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@zxy844288792 how about we revert this file first and add a note in python/tvm/relay/op/nn/nn.py to remind us to add it back when #4644 is merged?

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@comaniac Agree

# search platform specific declaration first
# default declaration
if layout == 'NCHW':
Expand Down