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[Relay][CanonicalizeOps] Make Bias_add shape same as the other operand #4050

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[Relay][CanonicalizeOps] Make Bias_add shape same as the other operand #4050

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16 changes: 13 additions & 3 deletions src/relay/pass/pattern_util.h
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Original file line number Diff line number Diff line change
Expand Up @@ -148,13 +148,23 @@ inline Expr ExpandBiasToMatchAxis(Expr bias,
static const Op& expand_dims = Op::Get("expand_dims");
for (size_t i = axes.size(); i != 0; --i) {
if (i == axes.size()) {
int64_t num_pad_axis = target_ndim - axes[i - 1]->value - 1;
if (num_pad_axis > 0) {
// The idea is to make the shape of the operand same as target_ndim.
// Therefore, there are 2 expand dims, one before the axis and one after
// the axis.
int64_t num_pad_axis_inner = target_ndim - axes[i - 1]->value - 1;
int64_t num_pad_axis_outer = target_ndim - num_pad_axis_inner - 1;
if (num_pad_axis_inner > 0) {
auto attrs = make_node<ExpandDimsAttrs>();
attrs->axis = i;
attrs->num_newaxis = static_cast<int>(num_pad_axis);
attrs->num_newaxis = static_cast<int>(num_pad_axis_inner);
bias = CallNode::make(expand_dims, {bias}, Attrs(attrs), {});
}
if (num_pad_axis_outer > 0) {
auto new_attrs = make_node<ExpandDimsAttrs>();
new_attrs->axis = 0;
new_attrs->num_newaxis = static_cast<int>(num_pad_axis_outer);
bias = CallNode::make(expand_dims, {bias}, Attrs(new_attrs), {});
}
} else {
int64_t diff = axes[i]->value - axes[i - 1]->value;
CHECK_GE(diff, 0L);
Expand Down
33 changes: 33 additions & 0 deletions tests/python/relay/test_op_qnn_conv2d.py
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Expand Up @@ -607,6 +607,38 @@ def tflite_anistropic_strides():
golden_output = np.array((124, -92, 164, -132)).reshape(1, 1, 2, 2)
np.testing.assert_equal(qnn_output, golden_output)

def broadcast_layout_test():
# Test broadcast support for NHWC layout.
data_shape = (1, 229, 229, 3) # NHWC
data_dtype = 'uint8'
kernel_shape = (7, 7, 3, 64) # HWIO
kernel_dtype = 'int8'
_, qnn_func = get_funcs(data_shape=data_shape,
data_dtype=data_dtype,
kernel_shape=kernel_shape,
kernel_dtype=kernel_dtype,
input_zero_point=8,
kernel_zero_point=3,
kernel_size=(7, 7),
padding=(1, 1),
strides=(1, 1),
dilation=(1, 1),
data_layout="NHWC",
kernel_layout="HWIO",
out_dtype="int32")
func = qnn_func['main'].body
bias = relay.var("bias", shape=(64,), dtype="int32")

# Check broadcast support on both lhs and rhs
func = relay.nn.bias_add(func, bias, axis=3)
# func = relay.nn.bias_add(bias, func)

func = relay.Function(relay.analysis.free_vars(func), func)
mod = relay.Module.from_expr(func)
mod = transform.CanonicalizeOps()(mod)
with relay.build_config(opt_level=3):
graph, lib, params = relay.build(mod, "llvm -mcpu=skylake-avx512")

if __name__ == "__main__":
no_zero_point_test()
input_zero_point_test()
Expand All @@ -620,3 +652,4 @@ def tflite_anistropic_strides():
tflite_large_irregular_test()
tflite_output_multiplier_greater_than_one()
tflite_anistropic_strides()
broadcast_layout_test()
6 changes: 4 additions & 2 deletions tests/python/relay/test_pass_alter_op_layout.py
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Expand Up @@ -134,7 +134,8 @@ def expected():
kernel_layout="OIHW16i",
data_layout="NCHW16c")
b = relay.expand_dims(bias, axis=1, num_newaxis=2)
b = relay.layout_transform(b, "CHW", "CHW16c")
b = relay.expand_dims(b, axis=0, num_newaxis=1)
b = relay.layout_transform(b, "NCHW", "NCHW16c")
y = relay.add(y, b)

y = relay.nn.relu(y)
Expand Down Expand Up @@ -304,7 +305,8 @@ def expected():
weight = relay.var("weight")
x = relay.layout_transform(x, "NCHW", "NCHW16c")
bias = relay.expand_dims(bias, 1, 2)
bias = relay.layout_transform(bias, "CHW", "CHW16c")
bias = relay.expand_dims(bias, 0, 1)
bias = relay.layout_transform(bias, "NCHW", "NCHW16c")
scale = relay.layout_transform(scale, "CHW", "CHW16c")
y = relay.nn.conv2d(x, weight, channels=64, kernel_size=(3, 3), padding=(1, 1),
data_layout="NCHW16c")
Expand Down
28 changes: 17 additions & 11 deletions tests/python/relay/test_pass_fold_scale_axis.py
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Expand Up @@ -51,11 +51,12 @@ def expected(x, conv_weight, in_bias, in_scale, channels):
args = [x, conv_weight, in_bias]
in_bias = relay.expand_dims(in_bias, axis=1, num_newaxis=2)
squeezed_scale = relay.squeeze(in_scale, axis=[1,2])
squeezed_scale = relay.expand_dims(squeezed_scale, axis=1, num_newaxis=2)
squeezed_scale = relay.expand_dims(squeezed_scale, axis=0, num_newaxis=1)
x = relay.nn.relu(x)
in_bias = relay.divide(in_bias, relay.expand_dims(squeezed_scale, axis=1, num_newaxis=2))
in_bias = relay.divide(in_bias, squeezed_scale)
x = relay.add(x, in_bias)
conv_weight = relay.multiply(
conv_weight , relay.expand_dims(squeezed_scale, axis=1, num_newaxis=2))
conv_weight = relay.multiply(conv_weight, squeezed_scale)
y = relay.nn.conv2d(x, conv_weight,
channels=channels,
kernel_size=(3, 3),
Expand Down Expand Up @@ -109,18 +110,21 @@ def before(x, conv_weight, in_bias, in_scale, channels):
def expected(x, conv_weight, in_bias, in_scale, channels):
args = [x, conv_weight, in_bias]
x = relay.nn.relu(x)
in_bias = relay.divide(in_bias, in_scale)
in_scale1 = relay.expand_dims(in_scale, 0, 3)
in_scale2 = relay.expand_dims(in_scale, 0, 3)
in_scale3 = relay.expand_dims(in_scale, 0, 3)
in_bias = relay.divide(in_bias, in_scale1)
x = relay.subtract(x, in_bias)
y1 = relay.nn.conv2d(x,
relay.multiply(conv_weight, in_scale),
relay.multiply(conv_weight, in_scale2),
channels=channels,
kernel_size=(3, 3),
data_layout="NHWC",
kernel_layout="HWIO",
groups=channels,
padding=(1, 1))
y2 = relay.nn.conv2d(x,
relay.multiply(conv_weight, in_scale),
relay.multiply(conv_weight, in_scale3),
channels=channels,
kernel_size=(3, 3),
data_layout="NHWC",
Expand Down Expand Up @@ -225,8 +229,9 @@ def expected(x, conv_weight, in_scale, channels):
# use a fixed order of args so alpha equal check can pass
args = [x, conv_weight]
squeezed_scale = relay.squeeze(in_scale, axis=[1,2])
conv_weight = relay.multiply(
conv_weight , relay.expand_dims(squeezed_scale, axis=1, num_newaxis=2))
squeezed_scale = relay.expand_dims(squeezed_scale, axis=1, num_newaxis=2)
squeezed_scale = relay.expand_dims(squeezed_scale, axis=0, num_newaxis=1)
conv_weight = relay.multiply(conv_weight, squeezed_scale)
y = relay.nn.conv2d(x,
conv_weight,
channels=channels,
Expand Down Expand Up @@ -271,14 +276,15 @@ def expected(x, conv_weight, out_bias, out_scale, channels):
out_bias = relay.expand_dims(out_bias, axis=1, num_newaxis=2)
squeezed_scale = relay.squeeze(out_scale, axis=[1,2])
conv_weight = relay.multiply(
conv_weight , relay.expand_dims(squeezed_scale, axis=1, num_newaxis=3))
conv_weight, relay.expand_dims(squeezed_scale, axis=1, num_newaxis=3))

y = relay.nn.conv2d(x, conv_weight,
channels=channels,
kernel_size=(3, 3),
padding=(1, 1))
out_bias = relay.multiply(out_bias,
relay.expand_dims(squeezed_scale, axis=1, num_newaxis=2))
squeezed_scale = relay.expand_dims(squeezed_scale, axis=1, num_newaxis=2)
squeezed_scale = relay.expand_dims(squeezed_scale, axis=0, num_newaxis=1)
out_bias = relay.multiply(out_bias, squeezed_scale)
y = relay.add(y, out_bias)
y = relay.nn.relu(y)
return relay.Function(args, y)
Expand Down
3 changes: 3 additions & 0 deletions tests/python/relay/test_pass_simplify_inference.py
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Expand Up @@ -29,6 +29,9 @@ def simple_bn(x, gamma, beta, moving_mean, moving_var,
if num_newaxis:
scale = rly.expand_dims(scale, axis=1, num_newaxis=num_newaxis)
shift = rly.expand_dims(shift, axis=1, num_newaxis=num_newaxis)
if num_newaxis != 3:
shift = rly.expand_dims(shift, axis=0, num_newaxis=1)
scale = rly.expand_dims(scale, axis=0, num_newaxis=1)
return x * scale + shift

def check(dim, axis, nstep):
Expand Down