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[Relay][TOPI]Fix meaning of conv2d_transpose output_padding parameter (…
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…apache#4318)

* Add output_padding to generic

* Add output_padding to the reference impl

* Add output_padding to arm_cpu

* Add output_padding to the test

* Add output_padding for cuda

* Add output_padding for x86

* Make use of the new output_padding argument in Relay

* Adjust conv2d_transpose Relay test

* Fix lint errors

* Fix the VTA declaration of conv2d_transpose

* support for output padding in conv2d transpose

* some output padding will break IR pass

* Fix new conv2d_transpose test

* Update tophub

* Fix conv1d output_padding too.

* Fix the conv1d_transpose reference function.

* Fix the cuda impl

* fix the topi test for conv1d

* Update the versions in tophub.py

Co-authored-by: Thierry Moreau <tmoreau@octoml.ai>
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@abergeron @tmoreau89 @vinx13
2 people authored and vinx13 committed Jan 11, 2020
1 parent 7ffd5c5 commit 3667499
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Showing 3 changed files with 31 additions and 20 deletions.
16 changes: 10 additions & 6 deletions python/vta/top/vta_conv2d_transpose.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,24 +27,28 @@
from ..environment import get_env

@autotvm.register_topi_compute(topi.nn.conv2d_transpose_nchw, 'vta', 'direct')
def _declatation_conv2d_transpose(cfg,
def _declaration_conv2d_transpose(cfg,
data,
kernel,
strides,
padding,
out_dtype):
out_dtype,
output_padding=(0, 0)):
ishape = get_const_tuple(data.shape)
kshape = get_const_tuple(kernel.shape)
b, c_i, i_h, i_w, t_b, t_ci = ishape
c_o, _, k_h, k_w, t_co, t_ci = kshape
stride_h, stride_w = strides
opad_h, opad_w = output_padding
# FIXME(tmoreau89): currently IR pass breaks when output padding != (0,0)
assert opad_h == 0 and opad_w == 0, "VTA does not support output padding for now"

# derive padding parameters
fpad_top, fpad_left, fpad_bottom, fpad_right = get_pad_tuple(padding, (k_h, k_w))
bpad_top = k_h - 1 - fpad_top
bpad_bottom = k_h - 1 - fpad_bottom
bpad_bottom = k_h - 1 - fpad_bottom + opad_h
bpad_left = k_w - 1 - fpad_left
bpad_right = k_w - 1 - fpad_right
bpad_right = k_w - 1 - fpad_right + opad_w

# padding stage
dilated_input = topi.nn.dilate(data, [1, 1, stride_h, stride_w, 1, 1])
Expand All @@ -53,8 +57,8 @@ def _declatation_conv2d_transpose(cfg,
[0, 0, bpad_bottom, bpad_right, 0, 0])

# convolution transpose stage
out_h = (i_h - 1) * stride_h - fpad_top - fpad_bottom + k_h
out_w = (i_w - 1) * stride_w - fpad_left - fpad_right + k_w
out_h = (i_h - 1) * stride_h - fpad_top - fpad_bottom + k_h + opad_h
out_w = (i_w - 1) * stride_w - fpad_left - fpad_right + k_w + opad_w
oshape = (b, c_o, out_h, out_w, t_b, t_co)
d_c = tvm.reduce_axis((0, c_i), name='d_c')
d_h = tvm.reduce_axis((0, k_h), name='d_h')
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19 changes: 12 additions & 7 deletions scripts/tune_conv2d_transpose.py
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Expand Up @@ -33,13 +33,15 @@

Workload = namedtuple("Conv2DTransposeWorkload",
['batch', 'height', 'width', 'in_filter', 'out_filter',
'hkernel', 'wkernel', 'hpad', 'wpad', 'hstride', 'wstride'])
'hkernel', 'wkernel', 'hpad', 'wpad', 'hstride', 'wstride',
'o_hpad', 'o_wpad'])

# DCGAN workloads
dcgan_wkls = [
# dcgan
('DCGAN.CT1', Workload(env.BATCH, 4, 4, 1024, 512, 4, 4, 1, 1, 2, 2)),
('DCGAN.CT2', Workload(env.BATCH, 8, 8, 512, 256, 4, 4, 1, 1, 2, 2)),
('DCGAN.CT3', Workload(env.BATCH, 16, 16, 256, 128, 4, 4, 1, 1, 2, 2)),
('DCGAN.CT1', Workload(env.BATCH, 4, 4, 1024, 512, 4, 4, 1, 1, 2, 2, 0, 0)),
('DCGAN.CT2', Workload(env.BATCH, 8, 8, 512, 256, 4, 4, 1, 1, 2, 2, 0, 0)),
('DCGAN.CT3', Workload(env.BATCH, 16, 16, 256, 128, 4, 4, 1, 1, 2, 2, 0, 0)),
]

@tvm.tag_scope(tag=topi.tag.ELEMWISE)
Expand All @@ -51,7 +53,7 @@ def my_clip(x, a_min, a_max):
x = tvm.compute(x.shape, lambda *i: tvm.max(x(*i), const_min), name="clipB")
return x

def conv2d_transpose(N, CI, H, W, CO, KH, KW, strides, padding):
def conv2d_transpose(N, CI, H, W, CO, KH, KW, strides, padding, opadding):
data_shape = (N//env.BATCH, CI//env.BLOCK_IN, H, W, env.BATCH, env.BLOCK_IN)
kernel_shape = (CO//env.BLOCK_OUT, CI//env.BLOCK_IN, KH, KW, env.BLOCK_OUT, env.BLOCK_IN)

Expand All @@ -64,7 +66,9 @@ def conv2d_transpose(N, CI, H, W, CO, KH, KW, strides, padding):
Filter=kernel,
strides=strides,
padding=padding,
out_dtype=env.acc_dtype)
out_dtype=env.acc_dtype,
output_padding=opadding
)
res = topi.right_shift(res, env.WGT_WIDTH)
res = my_clip(res, 0, (1 << env.OUT_WIDTH - 1) - 1)
res = topi.cast(res, env.out_dtype)
Expand Down Expand Up @@ -109,11 +113,12 @@ def conv2d_transpose(N, CI, H, W, CO, KH, KW, strides, padding):
KW = wl.wkernel
strides = (wl.hstride, wl.wstride)
padding = (wl.hpad, wl.wpad)
opadding = (wl.o_hpad, wl.o_wpad)

# Create task
task = autotvm.task.create(
conv2d_transpose,
args=(N, CI, H, W, CO, KH, KW, strides, padding),
args=(N, CI, H, W, CO, KH, KW, strides, padding, opadding),
target=tvm.target.vta(),
target_host=env.target_host,
template_key='direct')
Expand Down
16 changes: 9 additions & 7 deletions tests/python/integration/test_benchmark_topi_conv2d_transpose.py
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Original file line number Diff line number Diff line change
Expand Up @@ -37,17 +37,18 @@

Workload = namedtuple("Conv2DTransposeWorkload",
['batch', 'height', 'width', 'in_filter', 'out_filter',
'hkernel', 'wkernel', 'hpad', 'wpad', 'hstride', 'wstride'])
'hkernel', 'wkernel', 'hpad', 'wpad', 'hstride', 'wstride',
'o_hpad', 'o_wpad'])

# Get batch info from env
env = vta.get_env()

# DCGAN workloads
dcgan_wklds = [
# dcgan
('DCGAN.CT1', Workload(env.BATCH, 4, 4, 1024, 512, 4, 4, 1, 1, 2, 2)),
('DCGAN.CT2', Workload(env.BATCH, 8, 8, 512, 256, 4, 4, 1, 1, 2, 2)),
('DCGAN.CT3', Workload(env.BATCH, 16, 16, 256, 128, 4, 4, 1, 1, 2, 2)),
('DCGAN.CT1', Workload(env.BATCH, 4, 4, 1024, 512, 4, 4, 1, 1, 2, 2, 0, 0)),
('DCGAN.CT2', Workload(env.BATCH, 8, 8, 512, 256, 4, 4, 1, 1, 2, 2, 0, 0)),
('DCGAN.CT3', Workload(env.BATCH, 16, 16, 256, 128, 4, 4, 1, 1, 2, 2, 0, 0)),
]

# FIXME: we need a custom clip operator to circumvent a pattern detection limitation
Expand Down Expand Up @@ -102,7 +103,8 @@ def run_conv2d_transpose(env, remote, wl, target,
# Define base computation schedule
with target:
res = topi.nn.conv2d_transpose_nchw(
data, kernel, (wl.hstride, wl.wstride), (wl.hpad, wl.wpad), env.acc_dtype)
data, kernel, (wl.hstride, wl.wstride),
(wl.hpad, wl.wpad), env.acc_dtype, (wl.o_hpad, wl.o_wpad))
res = topi.right_shift(res, env.WGT_WIDTH)
res = my_clip(res, 0, (1 << env.OUT_WIDTH - 1) - 1)
res = topi.cast(res, env.out_dtype)
Expand All @@ -112,8 +114,8 @@ def run_conv2d_transpose(env, remote, wl, target,
print(vta.lower(s, [data, kernel, res], simple_mode=True))

# Derive number of ops
fout_height = (wl.height - 1) * wl.hstride - 2 * wl.hpad + wl.hkernel
fout_width = (wl.width - 1) * wl.wstride - 2 * wl.wpad + wl.wkernel
fout_height = (wl.height - 1) * wl.hstride - 2 * wl.hpad + wl.hkernel + wl.o_hpad
fout_width = (wl.width - 1) * wl.wstride - 2 * wl.wpad + wl.wkernel + wl.o_wpad
num_ops = 2 * wl.batch * fout_height * fout_width * wl.hkernel * wl.wkernel * wl.out_filter * wl.in_filter

# @memoize("vta.tests.test_benchmark_topi.conv2d.verify_nhwc")
Expand Down

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