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Conv3D ONNX support and conv3D_ncdhw x86 schedules #4949

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merged 3 commits into from
Mar 11, 2020
Merged

Conv3D ONNX support and conv3D_ncdhw x86 schedules #4949

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ad0cfa3
Support 3d Convolution with the ONNX frontend
Feb 20, 2020
93efdfd
first attempt at conv3d autotuning
Feb 25, 2020
11d4879
add unit tests for conv3d in onnx frontend
Feb 24, 2020
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16 changes: 9 additions & 7 deletions python/tvm/relay/frontend/onnx.py
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Original file line number Diff line number Diff line change
Expand Up @@ -91,16 +91,18 @@ def get_numpy(tensor_proto):
return to_array(tensor_proto)


def dimension_picker(prefix, surfix=''):
def dimension_picker(prefix, suffix=''):
"""Check that dimensions are supported."""
def _impl(attr):
kernel = attr['kernel_shape']
if len(kernel) == 1:
return prefix + '1d' + surfix
return prefix + '1d' + suffix
if len(kernel) == 2:
return prefix + '2d' + surfix
msg = 'Only 1D and 2D kernels are supported for operator {}.'
op_name = prefix + '1d/2d'
return prefix + '2d' + suffix
if len(kernel) == 3:
return prefix + '3d' + suffix
msg = 'Only 1D, 2D, and 3D kernels are supported for operator {}.'
op_name = prefix + '1d/2d/3d'
raise tvm.error.OpAttributeInvalid(msg.format(op_name))

return _impl
Expand Down Expand Up @@ -155,11 +157,11 @@ def onnx_storage_order2layout(storage_order, dims=2):

def dimension_constraint():
def _dim_check(attrs):
if len(attrs['kernel_shape']) == 2 or len(attrs['kernel_shape']) == 1:
if len(attrs['kernel_shape']) in [1, 2, 3]:
return True
return False

return _dim_check, "Only 1d and 2d kernel supported."
return _dim_check, "Only 1d, 2d and 3d kernel supported."


class OnnxOpConverter(object):
Expand Down
7 changes: 3 additions & 4 deletions python/tvm/relay/op/strategy/x86.py
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Expand Up @@ -188,10 +188,9 @@ def conv3d_strategy_cpu(attrs, inputs, out_type, target):
strategy = _op.OpStrategy()
layout = attrs.data_layout
if layout == "NCDHW":
logger.warning("conv3d with layout NCDHW is not optimized for x86.")
strategy.add_implementation(wrap_compute_conv3d(topi.nn.conv3d_ncdhw),
wrap_topi_schedule(topi.generic.schedule_conv3d_ncdhw),
name="conv3d_ncdhw.generic")
strategy.add_implementation(wrap_compute_conv3d(topi.x86.conv3d_ncdhw),
wrap_topi_schedule(topi.x86.schedule_conv3d_ncdhw),
name="conv3d_ncdhw.x86")
elif layout == "NDHWC":
strategy.add_implementation(wrap_compute_conv3d(topi.x86.conv3d_ndhwc),
wrap_topi_schedule(topi.x86.schedule_conv3d_ndhwc),
Expand Down
76 changes: 45 additions & 31 deletions tests/python/frontend/onnx/test_forward.py
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Expand Up @@ -1794,37 +1794,51 @@ def verify_conv(x_shape, w_shape, y_shape, padding, kernel_shape, strides, dilat


def test_conv():
# Convolution with padding
# Conv2D
verify_conv((1, 1, 5, 5), (1, 1, 3, 3), (1, 1, 5, 5), [1, 1, 1, 1], [3, 3], [1, 1], [1, 1])
# Conv1D
verify_conv((1, 1, 5), (1, 1, 3), (1, 1, 5), [1, 1], [3], [1], [1])

# Convolution without padding
# Conv2D
verify_conv((1, 1, 5, 5), (1, 1, 3, 3), (1, 1, 3, 3), [0, 0, 0, 0], [3, 3], [1, 1], [1, 1])
# Conv1D
verify_conv((1, 1, 5), (1, 1, 3), (1, 1, 3), [0, 0], [3], [1], [1])

# Convolution with autopadding
verify_conv((1, 1, 5, 5), (1, 1, 3, 3), (1, 1, 5, 5),
None, [3, 3], [1, 1], [1, 1],
auto_pad="SAME_UPPER")
# Conv1D
verify_conv((1, 1, 5), (1, 1, 3), (1, 1, 5), None, [3], [1], [1], auto_pad="SAME_UPPER")

# Convolution with non uniform stride
verify_conv((1, 1, 5, 5), (1, 1, 3, 3), (1, 1, 3, 3),
None, [3, 3], [2, 2], [1, 1],
auto_pad="SAME_UPPER")
# Conv1D
verify_conv((1, 1, 5), (1, 1, 3), (1, 1, 3), None, [3], [2], [1], auto_pad="SAME_UPPER")

# Convolution with dilation
verify_conv((1, 1, 5, 5), (1, 1, 3, 3), (1, 1, 5, 5), [2, 2, 2, 2], [3, 3], [1, 1], [2, 2])
# Conv1D
verify_conv((1, 1, 5), (1, 1, 3), (1, 1, 5), [2, 2], [3], [1], [2])

def repeat(N, D):
return tuple([N for _ in range(D)])
for D in [1, 2, 3]:
# Convolution with padding
verify_conv((1, 1) + repeat(5, D),
(1, 1) + repeat(3, D),
(1, 1) + repeat(5, D),
2 * repeat(1, D),
repeat(3, D),
repeat(1, D),
repeat(1, D))
# Convolution without padding
verify_conv((1, 1) + repeat(5, D),
(1, 1) + repeat(3, D),
(1, 1) + repeat(3, D),
2 * repeat(0, D),
repeat(3, D),
repeat(1, D),
repeat(1, D))
# Convolution with autopadding
verify_conv((1, 1) + repeat(5, D),
(1, 1) + repeat(3, D),
(1, 1) + repeat(5, D),
None,
repeat(3, D),
repeat(1, D),
repeat(1, D),
auto_pad="SAME_UPPER")
# Convolution with non uniform stride
verify_conv((1, 1) + repeat(5, D),
(1, 1) + repeat(3, D),
(1, 1) + repeat(3, D),
None,
repeat(3, D),
repeat(2, D),
repeat(1, D),
auto_pad="SAME_UPPER")
# Convolution with dilation
verify_conv((1, 1) + repeat(5, D),
(1, 1) + repeat(3, D),
(1, 1) + repeat(5, D),
2 * repeat(2, D),
repeat(3, D),
repeat(1, D),
repeat(2, D))

def verify_convtranspose(x_shape, w_shape, y_shape, p):
node = onnx.helper.make_node("ConvTranspose",
Expand Down
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