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[Relay] Improve reduction op layout propagation for packed input #9253

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Oct 12, 2021
Merged

[Relay] Improve reduction op layout propagation for packed input #9253

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70685dd
wip
masahi Oct 11, 2021
30e0fdf
fixed packed dim size logic
masahi Oct 11, 2021
90d1c98
fixed test
masahi Oct 11, 2021
6b740ab
formatting
masahi Oct 11, 2021
6c9094d
fix compile warning
masahi Oct 11, 2021
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42 changes: 30 additions & 12 deletions src/relay/op/tensor/reduce.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -149,23 +149,41 @@ InferCorrectLayoutOutput ReduceInferCorrectLayout(const Attrs& attrs,
tvm::Array<tvm::Integer> new_r_axes;
std::string inferred_in_string = "";
std::string inferred_out_string = "";
int axis_index = 0;
for (auto iter_var : layout->axes) {
const auto& layout_axis = LayoutAxis::Get(iter_var);
auto push_new_axis = [&](const std::string& layout_dim, int axis) {
if ((old_r_dims.count(layout_dim) && !params->exclude) ||
(!old_r_dims.count(layout_dim) && params->exclude)) {
new_r_axes.push_back(tvm::Integer(axis));
return true;
}
return false;
};
for (size_t axis_index = 0; axis_index < layout->axes.size(); ++axis_index) {
const auto& layout_axis = LayoutAxis::Get(layout->axes[axis_index]);
const std::string& layout_dim = layout_axis.name();
// Collect only the primal axis.
if (layout_axis.IsPrimal()) {
if (old_r_dims.count(layout_dim) && !params->exclude) {
new_r_axes.push_back(tvm::Integer(axis_index));
}
if (!old_r_dims.count(layout_dim) && params->exclude) {
new_r_axes.push_back(tvm::Integer(axis_index));
}
push_new_axis(layout_dim, axis_index);
inferred_in_string += layout_dim;
if (!old_r_dims.count(layout_dim) || params->keepdims) {
inferred_out_string += layout_dim;
}
inferred_in_string += layout_dim;
axis_index++;
} else {
// For example, if the original layout is NCHW, the new layout is NCHW8c, and the original
// reduce axes is [1], the new reduce axes become [1, 4].
auto primal_dim = layout_axis.ToPrimal().name();
auto packed_dim = std::to_string(layout.FactorOf(layout_axis)) + layout_dim;
inferred_in_string += packed_dim;
if (push_new_axis(primal_dim, axis_index)) {
if (params->exclude) {
// The primal axis is not reduced, so keep the input packed dim.
inferred_out_string += packed_dim;
} else {
// If the primal axis is part of reduce axes in the original layout, the inner dim
// becomes 1 after reduction.
inferred_out_string += "1" + layout_dim;
}
} else {
inferred_out_string += packed_dim;
}
}
}

Expand Down
22 changes: 12 additions & 10 deletions tests/python/relay/test_pass_alter_op_layout.py
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Original file line number Diff line number Diff line change
Expand Up @@ -507,20 +507,23 @@ def expected():
bias = relay.layout_transform(bias, src_layout="NHWC", dst_layout="NCHW")
bias = relay.layout_transform(bias, src_layout="NCHW", dst_layout="NCHW16c")
add = relay.add(y, bias)
y = relay.layout_transform(add, src_layout="NCHW16c", dst_layout="NCHW")
mean = relay.mean(y, axis=1, exclude=True)
var = relay.variance(y, axis=1, exclude=True)
mean = relay.mean(add, axis=[1, 4], exclude=True)
var = relay.variance(add, axis=[1, 4], exclude=True)
denom = relay.const(1.0) / relay.sqrt(var + relay.const(1e-05))
gamma = relay.var("gamma", shape=(16,))
denom = denom * gamma
denom_c16c = denom * relay.layout_transform(gamma, src_layout="C", dst_layout="C16c")
denom = relay.layout_transform(denom_c16c, src_layout="C16c", dst_layout="C")
denom_expand1 = relay.expand_dims(denom, axis=1, num_newaxis=2)
denom_expand2 = relay.expand_dims(denom_expand1, axis=0)
denom_nchwc16 = relay.layout_transform(
denom_expand2, src_layout="NCHW", dst_layout="NCHW16c"
)
out = add * denom_nchwc16
beta = relay.var("beta", shape=(16,))
numerator = (-mean) * denom + beta
numerator_c16c = (-mean) * denom_c16c + relay.layout_transform(
beta, src_layout="C", dst_layout="C16c"
)
numerator = relay.layout_transform(numerator_c16c, src_layout="C16c", dst_layout="C")
numerator_expand1 = relay.expand_dims(numerator, axis=1, num_newaxis=2)
numerator_expand2 = relay.expand_dims(numerator_expand1, axis=0)
numerator_nchwc16 = relay.layout_transform(
Expand Down Expand Up @@ -1096,8 +1099,8 @@ def expected_nchw():
y = relay.nn.conv2d(
y, weight1, channels=32, kernel_size=(3, 3), padding=(1, 1), data_layout="NCHW16c"
)
ret = relay.layout_transform(y, "NCHW16c", "NCHW")
ret = relay.sum(ret, axis=[1], keepdims=True)
ret = relay.sum(y, axis=[1, 4], keepdims=True)
ret = relay.layout_transform(ret, "NCHW1c", "NCHW")
y = relay.Function(analysis.free_vars(ret), ret)
return y

Expand Down Expand Up @@ -1126,9 +1129,8 @@ def expected_nhwc():
y = relay.nn.conv2d(
y, weight1, channels=32, kernel_size=(3, 3), padding=(1, 1), data_layout="NCHW16c"
)
ret = relay.layout_transform(y, "NCHW16c", "NCHW")
ret = relay.sum(ret, axis=[1], keepdims=True)
ret = relay.layout_transform(ret, "NCHW", "NHWC")
ret = relay.sum(y, axis=[1, 4], keepdims=True)
ret = relay.layout_transform(ret, "NCHW1c", "NHWC")
y = relay.Function(analysis.free_vars(ret), ret)
return y

Expand Down