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Commit 6d2e8e92 authored by Khalique's avatar Khalique
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add support for asymmetric padding and auto_pad (SAME_UPPER)

parent 90a37082
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Showing with 76 additions and 22 deletions
src/include/migraphx/operators.hpp
View file @ 6d2e8e92
......@@ -16,6 +16,13 @@ namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace op {
enum padding_mode_t
{
default_, // NOLINT
same,
valid
};
struct not_computable
{
argument compute(const shape&, const std::vector<argument>&) const
......@@ -58,12 +65,7 @@ struct convolution
std::array<std::size_t, 2> padding = {{0, 0}};
std::array<std::size_t, 2> stride = {{1, 1}};
std::array<std::size_t, 2> dilation = {{1, 1}};
enum padding_mode_t
{
default_, // NOLINT
same,
valid
};
padding_mode_t padding_mode = default_;
int group = 1;
......@@ -189,12 +191,14 @@ struct pooling
std::array<std::size_t, 2> padding = {{0, 0}};
std::array<std::size_t, 2> stride = {{1, 1}};
std::array<std::size_t, 2> lengths = {{1, 1}};
padding_mode_t padding_mode = default_;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.mode, "mode"),
f(self.padding, "padding"),
f(self.padding, "padding_mode"),
f(self.stride, "stride"),
f(self.lengths, "lengths"));
}
......@@ -211,7 +215,10 @@ struct pooling
assert(lengths[0] <= (input.lens()[2] + 2 * padding[0]));
assert(lengths[1] <= (input.lens()[3] + 2 * padding[1]));
return {t,
if(padding_mode == default_)
{
return {t,
{
input.lens()[0],
input.lens()[1],
......@@ -226,6 +233,40 @@ struct pooling
static_cast<float>(stride[1]))) +
1)),
}};
}
else if(padding_mode == same)
{
return {t,
{input.lens()[0],
input.lens()[1],
static_cast<std::size_t>(
std::ceil(static_cast<double>(input.lens()[2]) / stride[0])),
static_cast<std::size_t>(
std::ceil(static_cast<double>(input.lens()[3]) / stride[1]))}};
}
else if(padding_mode == valid)
{
return {t,
{
input.lens()[0],
input.lens()[1],
std::size_t(std::max<std::ptrdiff_t>(
1,
std::ptrdiff_t(std::floor((input.lens()[2] - lengths[0]) /
static_cast<float>(stride[0]))) +
1)),
std::size_t(std::max<std::ptrdiff_t>(
1,
std::ptrdiff_t(std::floor((input.lens()[3] - lengths[1]) /
static_cast<float>(stride[1]))) +
1)),
}};
}
else
{
MIGRAPHX_THROW("Invalid padding mode");
}
}
};
......
src/onnx/onnx.cpp
View file @ 6d2e8e92
......@@ -215,24 +215,30 @@ struct onnx_parser
parse_conv(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
{
op::convolution op;
auto l0 = args[0];
if(contains(attributes, "pads"))
{
if(contains(attributes, "auto_pad"))
{
MIGRAPHX_THROW("auto_pad and padding cannot be specified simultaneously");
}
std::vector<std::size_t> padding(4);
copy(attributes["pads"].ints(), padding.begin());
std::vector<std::int64_t> padding;
copy(attributes["pads"].ints(), std::back_inserter(padding));
if(padding.size() != 4)
{
MIGRAPHX_THROW("padding should have 4 values");
}
if(padding[0] != padding[2] || padding[1] != padding[3])
{
MIGRAPHX_THROW("migraphx does not support asymetric padding");
// insert zeros for pad op (args[0] has 4 dims)
padding = {0, 0, padding[0], padding[1], 0, 0, padding[2], padding[3]};
l0 = prog.add_instruction(op::pad{padding}, l0);
}
op.padding[0] = padding[0];
op.padding[1] = padding[1];
else
{
op.padding[0] = padding[0];
op.padding[1] = padding[1];
}
}
if(contains(attributes, "strides"))
{
......@@ -252,7 +258,7 @@ struct onnx_parser
if(s.find("SAME") != std::string::npos)
{
op.padding_mode = op::convolution::same;
op.padding_mode = op::padding_mode_t::same;
}
}
if(contains(attributes, "group"))
......@@ -266,7 +272,7 @@ struct onnx_parser
auto l2 = prog.add_instruction(op::broadcast{axis, l1->get_shape()}, args[2]);
return prog.add_instruction(op::add{}, l1, l2);
}
return prog.add_instruction(op, args);
return prog.add_instruction(op, l0, args[1]);
}
instruction_ref parse_pooling(const std::string& name,
......@@ -274,6 +280,7 @@ struct onnx_parser
std::vector<instruction_ref> args)
{
op::pooling op{ends_with(name, "MaxPool") ? "max" : "average"};
auto l0 = args[0];
if(starts_with(name, "Global"))
{
auto lens = args.front()->get_shape().lens();
......@@ -281,18 +288,23 @@ struct onnx_parser
}
if(contains(attributes, "pads"))
{
std::vector<std::size_t> padding(4);
copy(attributes["pads"].ints(), padding.begin());
std::vector<std::int64_t> padding;
copy(attributes["pads"].ints(), std::back_inserter(padding));
if(padding.size() != 4)
{
MIGRAPHX_THROW("padding should have 4 values");
}
if(padding[0] != padding[2] || padding[1] != padding[3])
{
MIGRAPHX_THROW("migraphx does not support asymetric padding");
// insert zeros for pad op (args[0] has 4 dims)
padding = {0, 0, padding[0], padding[1], 0, 0, padding[2], padding[3]};
l0 = prog.add_instruction(op::pad{padding}, l0);
}
else
{
op.padding[0] = padding[0];
op.padding[1] = padding[1];
}
op.padding[0] = padding[0];
op.padding[1] = padding[1];
}
if(contains(attributes, "strides"))
{
......@@ -305,13 +317,14 @@ struct onnx_parser
if(contains(attributes, "auto_pad"))
{
auto s = attributes["auto_pad"].s();
if(to_upper(s) != "NOTSET")
if(s.find("SAME_UPPER") == std::string::npos)
{
MIGRAPHX_THROW("auto_pad is not supported for pooling");
MIGRAPHX_THROW("auto_pad only supports SAME_UPPER for pooling");
}
op.padding_mode = op::padding_mode_t::same;
}
return prog.add_instruction(op, std::move(args));
return prog.add_instruction(op, l0);
}
instruction_ref
......
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