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"example/27_layernorm2d_fwd/common.hpp" did not exist on "b076a02ad25892192be9fefb08a53e89fa48160c"
Commit eb0d8fee authored by Paul's avatar Paul
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Merge branch 'develop' into driver

parents 65ef35cd 0d796941
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src/include/migraphx/op/scalar.hpp 0 → 100644
View file @ eb0d8fee
#ifndef MIGRAPHX_GUARD_OPERATORS_SCALAR_HPP
#define MIGRAPHX_GUARD_OPERATORS_SCALAR_HPP
#include <array>
#include <migraphx/operation.hpp>
#include <migraphx/check_shapes.hpp>
#include <migraphx/stringutils.hpp>
#include <migraphx/streamutils.hpp>
#include <migraphx/literal.hpp>
#include <migraphx/shape_for_each.hpp>
#include <migraphx/config.hpp>
#include <cmath>
#include <utility>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace op {
struct scalar
{
std::vector<std::size_t> scalar_bcast_lens;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.scalar_bcast_lens, "scalar_bcst_dims"));
}
std::string name() const { return "scalar"; }
shape compute_shape(std::vector<shape> inputs) const
{
assert(check_shapes{inputs}.has(1).only_dims(1).size() == 1);
auto t = inputs.at(0).type();
std::vector<std::size_t> strides(scalar_bcast_lens.size(), 0);
return {t, scalar_bcast_lens, strides};
}
argument compute(shape output_shape, std::vector<argument> args) const
{
return {std::move(output_shape), std::move(args.at(0).data)};
}
std::ptrdiff_t output_alias(const std::vector<shape>&) const { return 0; }
};
} // namespace op
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/include/migraphx/op/sigmoid.hpp 0 → 100644
View file @ eb0d8fee
#ifndef MIGRAPHX_GUARD_OPERATORS_SIGMOID_HPP
#define MIGRAPHX_GUARD_OPERATORS_SIGMOID_HPP
#include <array>
#include <migraphx/op/unary.hpp>
#include <migraphx/operation.hpp>
#include <migraphx/check_shapes.hpp>
#include <migraphx/stringutils.hpp>
#include <migraphx/streamutils.hpp>
#include <migraphx/literal.hpp>
#include <migraphx/shape_for_each.hpp>
#include <migraphx/config.hpp>
#include <cmath>
#include <utility>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace op {
struct sigmoid : unary<sigmoid>
{
auto apply() const
{
return [](auto x) { return 1.f / (1.f + std::exp(-x)); };
}
};
} // namespace op
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/include/migraphx/op/sin.hpp 0 → 100644
View file @ eb0d8fee
#ifndef MIGRAPHX_GUARD_OPERATORS_SIN_HPP
#define MIGRAPHX_GUARD_OPERATORS_SIN_HPP
#include <array>
#include <migraphx/op/unary.hpp>
#include <migraphx/operation.hpp>
#include <migraphx/check_shapes.hpp>
#include <migraphx/stringutils.hpp>
#include <migraphx/streamutils.hpp>
#include <migraphx/literal.hpp>
#include <migraphx/shape_for_each.hpp>
#include <migraphx/config.hpp>
#include <cmath>
#include <utility>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace op {
struct sin : unary<sin>
{
auto apply() const
{
return [](auto x) { return std::sin(x); };
}
};
} // namespace op
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/include/migraphx/op/sinh.hpp 0 → 100644
View file @ eb0d8fee
#ifndef MIGRAPHX_GUARD_OPERATORS_SINH_HPP
#define MIGRAPHX_GUARD_OPERATORS_SINH_HPP
#include <array>
#include <migraphx/op/unary.hpp>
#include <migraphx/operation.hpp>
#include <migraphx/check_shapes.hpp>
#include <migraphx/stringutils.hpp>
#include <migraphx/streamutils.hpp>
#include <migraphx/literal.hpp>
#include <migraphx/shape_for_each.hpp>
#include <migraphx/config.hpp>
#include <cmath>
#include <utility>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace op {
struct sinh : unary<sinh>
{
auto apply() const
{
return [](auto x) { return std::sinh(x); };
}
};
} // namespace op
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/include/migraphx/op/slice.hpp 0 → 100644
View file @ eb0d8fee
#ifndef MIGRAPHX_GUARD_OPERATORS_SLICE_HPP
#define MIGRAPHX_GUARD_OPERATORS_SLICE_HPP
#include <array>
#include <migraphx/operation.hpp>
#include <migraphx/check_shapes.hpp>
#include <migraphx/stringutils.hpp>
#include <migraphx/streamutils.hpp>
#include <migraphx/literal.hpp>
#include <migraphx/shape_for_each.hpp>
#include <migraphx/config.hpp>
#include <cmath>
#include <utility>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace op {
struct slice
{
std::vector<int64_t> axes;
std::vector<int64_t> starts;
std::vector<int64_t> ends;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.axes, "axes"), f(self.starts, "starts"), f(self.ends, "ends"));
}
std::string name() const { return "slice"; }
auto fix_index(const std::vector<std::size_t>& lens, std::size_t axis, int64_t index) const
{
int64_t r = std::min(index, static_cast<int64_t>(lens[axis]));
if(r < 0)
r += lens[axis];
return std::size_t(r);
}
auto compute_offset(const shape& s) const
{
const std::vector<std::size_t>& lens = s.lens();
const std::vector<std::size_t>& strides = s.strides();
auto offset = 0;
if(!axes.empty())
{
for(std::size_t i = 0; i < axes.size(); i++)
{
auto axis = axes[i];
offset += fix_index(lens, axis, starts[i]) * strides[axis];
}
}
else
{
for(std::size_t axis = 0; axis < lens.size(); axis++)
{
offset += fix_index(lens, axis, starts[axis]) * strides[axis];
}
}
return offset;
}
shape compute_shape(std::vector<shape> inputs) const
{
auto input_shape = inputs[0];
auto t = input_shape.type();
const auto& old_lens = input_shape.lens();
const auto& old_strides = input_shape.strides();
if(starts.size() != axes.size() || axes.size() != ends.size())
{
MIGRAPHX_THROW("inconsistent sizes");
}
std::vector<std::size_t> new_lens = old_lens;
for(std::size_t i = 0; i < axes.size(); i++)
{
auto axis = axes[i];
new_lens[axis] =
fix_index(old_lens, axis, ends[i]) - fix_index(old_lens, axis, starts[i]);
}
return shape{t, new_lens, old_strides};
}
argument compute(shape output_shape, std::vector<argument> args) const
{
auto input = args[0];
auto offset = compute_offset(input.get_shape()) * output_shape.type_size();
return {std::move(output_shape), [=] { return input.data() + offset; }};
}
std::ptrdiff_t output_alias(const std::vector<shape>&) const { return 0; }
};
} // namespace op
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/include/migraphx/op/softmax.hpp 0 → 100644
View file @ eb0d8fee
#ifndef MIGRAPHX_GUARD_OPERATORS_SOFTMAX_HPP
#define MIGRAPHX_GUARD_OPERATORS_SOFTMAX_HPP
#include <array>
#include <migraphx/operation.hpp>
#include <migraphx/check_shapes.hpp>
#include <migraphx/stringutils.hpp>
#include <migraphx/streamutils.hpp>
#include <migraphx/literal.hpp>
#include <migraphx/shape_for_each.hpp>
#include <migraphx/config.hpp>
#include <cmath>
#include <utility>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace op {
struct softmax
{
std::string name() const { return "softmax"; }
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs}.has(1).only_dims(4);
return inputs.at(0);
}
};
} // namespace op
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/include/migraphx/op/squeeze.hpp 0 → 100644
View file @ eb0d8fee
#ifndef MIGRAPHX_GUARD_OPERATORS_SQUEEZE_HPP
#define MIGRAPHX_GUARD_OPERATORS_SQUEEZE_HPP
#include <array>
#include <migraphx/operation.hpp>
#include <migraphx/check_shapes.hpp>
#include <migraphx/stringutils.hpp>
#include <migraphx/streamutils.hpp>
#include <migraphx/literal.hpp>
#include <migraphx/shape_for_each.hpp>
#include <migraphx/config.hpp>
#include <cmath>
#include <utility>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace op {
struct squeeze
{
std::vector<int64_t> axes;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.axes, "axes"));
}
std::string name() const { return "squeeze"; }
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(1).standard();
auto input_shape = inputs[0];
auto type = input_shape.type();
auto old_lens = input_shape.lens();
if(std::any_of(
axes.begin(), axes.end(), [&](auto axis) { return input_shape.lens()[axis] != 1; }))
{
MIGRAPHX_THROW("squeeze axis dimension should be equal to 1");
}
std::vector<std::size_t> new_lens;
if(axes.empty())
{
std::copy_if(old_lens.begin(),
old_lens.end(),
std::back_inserter(new_lens),
[](auto len) { return len != 1; });
}
else
{
for(std::size_t i = 0; i < old_lens.size(); i++)
{
if(std::find(axes.begin(), axes.end(), i) == axes.end())
{
new_lens.push_back(old_lens[i]);
}
}
}
if(new_lens.empty())
{
return shape{type};
}
else
{
return shape{type, new_lens};
}
}
argument compute(shape output_shape, std::vector<argument> args) const
{
return {std::move(output_shape), std::move(args.front().data)};
}
std::ptrdiff_t output_alias(const std::vector<shape>&) const { return 0; }
};
} // namespace op
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/include/migraphx/op/sub.hpp 0 → 100644
View file @ eb0d8fee
#ifndef MIGRAPHX_GUARD_OPERATORS_SUB_HPP
#define MIGRAPHX_GUARD_OPERATORS_SUB_HPP
#include <array>
#include <migraphx/op/binary.hpp>
#include <migraphx/operation.hpp>
#include <migraphx/check_shapes.hpp>
#include <migraphx/stringutils.hpp>
#include <migraphx/streamutils.hpp>
#include <migraphx/literal.hpp>
#include <migraphx/shape_for_each.hpp>
#include <migraphx/config.hpp>
#include <cmath>
#include <utility>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace op {
struct sub : binary<sub>
{
auto apply() const
{
return [](auto x, auto y) { return x - y; };
}
};
} // namespace op
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/include/migraphx/op/tan.hpp 0 → 100644
View file @ eb0d8fee
#ifndef MIGRAPHX_GUARD_OPERATORS_TAN_HPP
#define MIGRAPHX_GUARD_OPERATORS_TAN_HPP
#include <array>
#include <migraphx/op/unary.hpp>
#include <migraphx/operation.hpp>
#include <migraphx/check_shapes.hpp>
#include <migraphx/stringutils.hpp>
#include <migraphx/streamutils.hpp>
#include <migraphx/literal.hpp>
#include <migraphx/shape_for_each.hpp>
#include <migraphx/config.hpp>
#include <cmath>
#include <utility>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace op {
struct tan : unary<tan>
{
auto apply() const
{
return [](auto x) { return std::tan(x); };
}
};
} // namespace op
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/include/migraphx/op/tanh.hpp 0 → 100644
View file @ eb0d8fee
#ifndef MIGRAPHX_GUARD_OPERATORS_TANH_HPP
#define MIGRAPHX_GUARD_OPERATORS_TANH_HPP
#include <array>
#include <migraphx/op/unary.hpp>
#include <migraphx/operation.hpp>
#include <migraphx/check_shapes.hpp>
#include <migraphx/stringutils.hpp>
#include <migraphx/streamutils.hpp>
#include <migraphx/literal.hpp>
#include <migraphx/shape_for_each.hpp>
#include <migraphx/config.hpp>
#include <cmath>
#include <utility>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace op {
struct tanh : unary<tanh>
{
auto apply() const
{
return [](auto x) { return std::tanh(x); };
}
};
} // namespace op
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/include/migraphx/op/transpose.hpp 0 → 100644
View file @ eb0d8fee
#ifndef MIGRAPHX_GUARD_OPERATORS_TRANSPOSE_HPP
#define MIGRAPHX_GUARD_OPERATORS_TRANSPOSE_HPP
#include <array>
#include <migraphx/operation.hpp>
#include <migraphx/check_shapes.hpp>
#include <migraphx/stringutils.hpp>
#include <migraphx/streamutils.hpp>
#include <migraphx/literal.hpp>
#include <migraphx/shape_for_each.hpp>
#include <migraphx/config.hpp>
#include <cmath>
#include <utility>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace op {
struct transpose
{
std::vector<int64_t> dims;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.dims, "dims"));
}
std::string name() const { return "transpose"; }
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(1);
auto input = inputs.at(0);
auto input_lens = input.lens();
auto input_strides = input.strides();
auto t = input.type();
if(dims.size() != input_lens.size())
{
MIGRAPHX_THROW("Permutation has wrong number of axes");
}
std::vector<int64_t> axes(dims.size());
std::iota(axes.begin(), axes.end(), 0);
if(!std::is_permutation(axes.begin(), axes.end(), dims.begin()))
{
MIGRAPHX_THROW("Invalid permutation");
}
std::vector<size_t> output_lens(input_lens.size());
std::vector<size_t> output_strides(input_lens.size());
for(std::size_t i = 0; i < output_lens.size(); i++)
{
output_lens[i] = input_lens[dims[i]];
output_strides[i] = input_strides[dims[i]];
}
return {t, output_lens, output_strides};
}
argument compute(shape output_shape, std::vector<argument> args) const
{
return {std::move(output_shape), std::move(args.front().data)};
}
std::ptrdiff_t output_alias(const std::vector<shape>&) const { return 0; }
};
} // namespace op
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/include/migraphx/op/unary.hpp 0 → 100644
View file @ eb0d8fee
#ifndef MIGRAPHX_GUARD_OPERATORS_UNARY_HPP
#define MIGRAPHX_GUARD_OPERATORS_UNARY_HPP
#include <migraphx/op/name.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace op {
template <class Derived>
struct unary : op_name<Derived>
{
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs}.has(1);
auto s = inputs.at(0);
if(s.packed())
{
return s;
}
else
{
return {s.type(), s.lens()};
}
}
argument compute(const shape& output_shape, std::vector<argument> args) const
{
argument result{output_shape};
result.visit([&](auto output) {
args[0].visit([&](auto input) {
if(input.get_shape().packed())
{
std::transform(input.begin(),
input.end(),
output.begin(),
static_cast<const Derived&>(*this).apply());
return result;
}
shape_for_each(output.get_shape(), [&](const auto& idx) {
output(idx.begin(), idx.end()) =
static_cast<const Derived&>(*this).apply()(input(idx.begin(), idx.end()));
});
return result;
});
});
return result;
}
};
} // namespace op
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/include/migraphx/op/unsqueeze.hpp 0 → 100644
View file @ eb0d8fee
#ifndef MIGRAPHX_GUARD_OPERATORS_UNSQUEEZE_HPP
#define MIGRAPHX_GUARD_OPERATORS_UNSQUEEZE_HPP
#include <array>
#include <migraphx/operation.hpp>
#include <migraphx/check_shapes.hpp>
#include <migraphx/stringutils.hpp>
#include <migraphx/streamutils.hpp>
#include <migraphx/literal.hpp>
#include <migraphx/shape_for_each.hpp>
#include <migraphx/config.hpp>
#include <cmath>
#include <utility>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace op {
struct unsqueeze
{
std::vector<int64_t> axes;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.axes, "axes"));
}
std::string name() const { return "unsqueeze"; }
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(1).standard_or_scalar();
auto input_shape = inputs[0];
auto type = input_shape.type();
auto old_lens = input_shape.lens();
if(input_shape.scalar())
return shape{type, old_lens};
std::size_t new_size = old_lens.size() + axes.size();
std::vector<std::size_t> new_lens(new_size);
std::size_t p = 0;
for(std::size_t i = 0; i < new_size; i++)
{
if(std::find(axes.begin(), axes.end(), i) != axes.end())
{
new_lens[i] = 1;
}
else
{
new_lens[i] = old_lens[p++];
}
}
return shape{type, new_lens};
}
argument compute(shape output_shape, std::vector<argument> args) const
{
return {std::move(output_shape), std::move(args.front().data)};
}
std::ptrdiff_t output_alias(const std::vector<shape>&) const { return 0; }
};
} // namespace op
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/include/migraphx/operation.hpp
View file @ eb0d8fee
......@@ -49,7 +49,7 @@ struct operation
argument compute(context& ctx, const shape& output, const std::vector<argument>& input) const;
/// An optional method to return which argument the output will alias. If
/// there is no aliased output then -1 can be returned.
int output_alias(const std::vector<shape>& input) const;
std::ptrdiff_t output_alias(const std::vector<shape>& input) const;
/// An optional stream operator to print the operation. When this is not
/// implemented, it will just print the operation's name.
friend std::ostream& operator<<(std::ostream& os, const operation& op);
......@@ -69,7 +69,7 @@ auto operator<<(std::ostream& os, const T& x) -> decltype(os << x.name())
{
os << x.name();
char delim = '[';
reflect_each(x, [&](auto& y, auto name) {
reflect_each(x, [&](auto&& y, auto name) {
os << delim;
os << name << "=";
stream_write_value(os, y);
......@@ -87,6 +87,8 @@ namespace operation_equal {
template <class T, class U>
auto operator==(const T& x, const U& y) -> decltype(x.name() == y.name())
{
static_assert(is_reflectable<T>{} or sizeof(T) <= 1,
"Missing equality operator or reflect method.");
if(x.name() != y.name())
return false;
const auto& yy = any_cast<T>(y);
......@@ -175,7 +177,7 @@ auto is_context_free_op(const T& x) -> decltype(is_context_free_op(
}
template <class T>
int output_alias_op(rank<0>, const T&, const std::vector<shape>&)
std::ptrdiff_t output_alias_op(rank<0>, const T&, const std::vector<shape>&)
{
return -1;
}
......@@ -188,7 +190,7 @@ auto output_alias_op(rank<1>, const T& x, const std::vector<shape>& shapes)
}
template <class T>
int output_alias_op(const T& x, const std::vector<shape>& shapes)
std::ptrdiff_t output_alias_op(const T& x, const std::vector<shape>& shapes)
{
return output_alias_op(rank<1>{}, x, shapes);
}
......@@ -239,7 +241,7 @@ auto has_finalize_op(const T&) -> decltype(has_finalize_op(rank<1>{},
* std::string name() const;
* bool is_context_free() const;
* bool has_finalize() const;
* int output_alias(const std::vector<shape>& input) const;
* std::ptrdiff_t output_alias(const std::vector<shape>& input) const;
* void finalize(context& ctx,const shape& output,const std::vector<shape>& input) ;
* shape compute_shape(const std::vector<shape>& input) const;
* argument compute(context& ctx,const shape& output,const std::vector<argument>& input) const;
......@@ -325,7 +327,7 @@ struct operation
return (*this).private_detail_te_get_handle().has_finalize();
}
int output_alias(const std::vector<shape>& input) const
std::ptrdiff_t output_alias(const std::vector<shape>& input) const
{
assert((*this).private_detail_te_handle_mem_var);
return (*this).private_detail_te_get_handle().output_alias(input);
......@@ -380,10 +382,10 @@ struct operation
virtual std::shared_ptr<private_detail_te_handle_base_type> clone() const = 0;
virtual const std::type_info& type() const = 0;
virtual std::string name() const = 0;
virtual bool is_context_free() const = 0;
virtual bool has_finalize() const = 0;
virtual int output_alias(const std::vector<shape>& input) const = 0;
virtual std::string name() const = 0;
virtual bool is_context_free() const = 0;
virtual bool has_finalize() const = 0;
virtual std::ptrdiff_t output_alias(const std::vector<shape>& input) const = 0;
virtual void
finalize(context& ctx, const shape& output, const std::vector<shape>& input) = 0;
virtual shape compute_shape(const std::vector<shape>& input) const = 0;
......@@ -432,7 +434,7 @@ struct operation
bool has_finalize() const override { return has_finalize_op(private_detail_te_value); }
int output_alias(const std::vector<shape>& input) const override
std::ptrdiff_t output_alias(const std::vector<shape>& input) const override
{
return output_alias_op(private_detail_te_value, input);
......
src/include/migraphx/operators.hpp
View file @ eb0d8fee
#ifndef MIGRAPHX_GUARD_OPERATORS_HPP
#define MIGRAPHX_GUARD_OPERATORS_HPP
#include <array>
#include <migraphx/operation.hpp>
#include <migraphx/check_shapes.hpp>
#include <migraphx/stringutils.hpp>
#include <migraphx/streamutils.hpp>
#include <migraphx/literal.hpp>
#include <migraphx/shape_for_each.hpp>
#include <migraphx/config.hpp>
#include <cmath>
#include <utility>
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
{
MIGRAPHX_THROW("not computable");
}
};
struct batch_norm_inference
{
float epsilon = 1.0e-6f;
float momentum = 0.9f;
std::string name() const { return "batch_norm_inference"; }
enum bn_infer_mode_t
{
per_activation,
spatial,
};
bn_infer_mode_t bn_mode = spatial;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(
f(self.epsilon, "epsilon"), f(self.momentum, "momentum"), f(self.bn_mode, "bn_mode"));
}
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(5);
return inputs.front();
}
};
struct lrn
{
float alpha = 0.0001;
float beta = 0.75;
float bias = 1.0;
int size = 1;
std::string name() const { return "lrn"; }
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.alpha, "alpha"),
f(self.beta, "beta"),
f(self.bias, "bias"),
f(self.size, "size"));
}
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(1);
return inputs.front();
}
};
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}};
padding_mode_t padding_mode = default_;
int group = 1;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.padding, "padding"),
f(self.stride, "stride"),
f(self.dilation, "dilation"),
f(self.padding_mode, "padding_mode"),
f(self.group, "group"));
}
std::string name() const { return "convolution"; }
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(2).same_type().same_ndims().only_dims(4);
const shape& input = inputs.at(0);
const shape& weights = inputs.at(1);
auto t = input.type();
if(padding_mode == default_)
{
return {t,
{
input.lens()[0],
weights.lens()[0],
std::size_t(std::max<std::ptrdiff_t>(
1,
(input.lens()[2] - (1 + dilation[0] * (weights.lens()[2] - 1)) +
2 * padding[0]) /
stride[0] +
1)),
std::size_t(std::max<std::ptrdiff_t>(
1,
(input.lens()[3] - (1 + dilation[1] * (weights.lens()[3] - 1)) +
2 * padding[1]) /
stride[1] +
1)),
}};
}
else if(padding_mode == same)
{
return {t,
{input.lens()[0],
weights.lens()[0],
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],
weights.lens()[0],
static_cast<std::size_t>(std::ceil(
static_cast<double>(input.lens()[2] - weights.lens()[2] + 1) / stride[0])),
static_cast<std::size_t>(std::ceil(
static_cast<double>(input.lens()[3] - weights.lens()[3] + 1) / stride[1]))}};
}
else
{
MIGRAPHX_THROW("Invalid padding mode");
}
}
};
struct im2col
{
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}};
padding_mode_t padding_mode = default_;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.padding, "padding"),
f(self.stride, "stride"),
f(self.dilation, "dilation"),
f(self.padding_mode, "padding_mode"));
}
std::string name() const { return "im2col"; }
shape compute_shape(std::vector<shape> inputs) const
{
auto input = inputs[0];
auto weights = inputs[1];
auto batch_size = input.lens()[0];
auto input_channels = weights.lens()[1];
auto kernel_height = weights.lens()[2];
auto kernel_width = weights.lens()[3];
check_shapes{inputs, *this}.has(2);
if(batch_size != 1)
MIGRAPHX_THROW("im2col only support batch_size 1");
auto output_height = std::size_t(std::max<std::ptrdiff_t>(
1,
(input.lens()[2] - (1 + dilation[0] * (kernel_height - 1)) + 2 * padding[0]) /
stride[0] +
1));
auto output_width = std::size_t(std::max<std::ptrdiff_t>(
1,
(input.lens()[3] - (1 + dilation[1] * (kernel_width - 1)) + 2 * padding[1]) /
stride[1] +
1));
auto channels_col = kernel_height * kernel_width * input_channels;
return {input.type(), {output_height * output_width, channels_col}};
}
};
struct pooling
{
std::string mode = "average";
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"));
}
std::string name() const { return "pooling"; }
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(1).only_dims(4);
const shape& input = inputs.at(0);
auto t = input.type();
assert(lengths[0] <= (input.lens()[2] + 2 * padding[0]));
assert(lengths[1] <= (input.lens()[3] + 2 * padding[1]));
if(padding_mode == default_)
{
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] + 2 * padding[0] - 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] + 2 * padding[1] - lengths[1]) /
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");
}
}
};
struct leaky_relu
{
std::string name() const { return "leaky_relu"; }
float alpha;
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(1);
return inputs.front();
}
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.alpha, "alpha"));
}
};
struct elu
{
std::string name() const { return "elu"; }
float alpha;
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(1);
return inputs.front();
}
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.alpha, "alpha"));
}
};
struct transpose
{
std::vector<int64_t> dims;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.dims, "dims"));
}
std::string name() const { return "transpose"; }
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(1);
auto input = inputs.at(0);
auto input_lens = input.lens();
auto input_strides = input.strides();
auto t = input.type();
if(dims.size() != input_lens.size())
{
MIGRAPHX_THROW("Permutation has wrong number of axes");
}
std::vector<int64_t> axes(dims.size());
std::iota(axes.begin(), axes.end(), 0);
if(!std::is_permutation(axes.begin(), axes.end(), dims.begin()))
{
MIGRAPHX_THROW("Invalid permutation");
}
std::vector<size_t> output_lens(input_lens.size());
std::vector<size_t> output_strides(input_lens.size());
for(std::size_t i = 0; i < output_lens.size(); i++)
{
output_lens[i] = input_lens[dims[i]];
output_strides[i] = input_strides[dims[i]];
}
return {t, output_lens, output_strides};
}
argument compute(shape output_shape, std::vector<argument> args) const
{
return {std::move(output_shape), std::move(args.front().data)};
}
int output_alias(const std::vector<shape>&) const { return 0; }
};
/// The contiguous operator takes a non-standard input tensor and returns
/// the same tensor but in standard form. For example, if input tensor A which has lens = (4,5)
/// is first transposed, i.e. lens = (5,4), this tensor's data layout remained the same
/// during the transpose operation; only it's shape lengths and strides were changed.
/// This leaves the tensor in a non-standard form. The contiguous operator copies the
/// underlying data such that resulting tensor is returned to a standard form.
struct contiguous
{
std::string name() const { return "contiguous"; }
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(1);
auto lens = inputs.at(0).lens();
auto t = inputs.at(0).type();
return {t, lens};
}
argument compute(const shape& output_shape, std::vector<argument> args) const
{
assert(output_shape.standard());
argument result{output_shape};
visit_all(result, args[0])([&](auto output, auto input) {
shape_for_each(output.get_shape(), [&](const auto& idx) {
output(idx.begin(), idx.end()) = input(idx.begin(), idx.end());
});
});
return result;
}
};
struct concat
{
std::size_t axis = 0;
std::string name() const { return "concat"; }
std::vector<std::size_t> compute_offsets(const shape& output_shape,
const std::vector<argument>& args) const
{
std::vector<std::size_t> offsets;
std::vector<std::size_t> offset(args[0].get_shape().lens().size(), 0);
offset[axis] = 0;
for(const auto& arg : args)
{
offsets.push_back(output_shape.index(offset));
offset[axis] += arg.get_shape().lens()[axis];
}
return offsets;
}
shape compute_shape(std::vector<shape> inputs) const
{
if(inputs.empty())
{
MIGRAPHX_THROW("Number of input tensors should exceed 0");
}
const auto& first_shape_lens = inputs.front().lens();
const auto& type = inputs.front().type();
for(std::size_t l = 0; l < first_shape_lens.size(); l++)
{
if(l != axis)
{
if(!std::all_of(inputs.begin(), inputs.end(), [&](auto s) {
return s.lens()[l] == first_shape_lens[l];
}))
{
MIGRAPHX_THROW("Non-axis dimensions should match");
}
}
}
std::size_t new_dim_axis = 0;
for(const auto& input : inputs)
{
const auto& lens = input.lens();
new_dim_axis += lens[axis];
}
std::vector<std::size_t> new_lens;
std::copy(first_shape_lens.begin(), first_shape_lens.end(), std::back_inserter(new_lens));
new_lens[axis] = new_dim_axis;
return {type, new_lens};
}
argument compute(const shape& output_shape, std::vector<argument> args) const
{
argument result{output_shape};
std::vector<std::size_t> coffsets = compute_offsets(output_shape, args);
for(std::size_t l = 0; l < args.size(); l++)
{
auto argl = args[l];
std::size_t nelements = argl.get_shape().elements();
visit_all(result, argl)([&](auto output, auto input) {
auto slice_shape =
shape{output_shape.type(), input.get_shape().lens(), output_shape.strides()};
auto slice = make_view(slice_shape, output.data() + coffsets[l]);
// cppcheck-suppress useStlAlgorithm
for(std::size_t i = 0; i < nelements; i++)
{
slice[i] = input[i];
}
});
}
return result;
}
};
struct slice
{
std::vector<int64_t> axes;
std::vector<int64_t> starts;
std::vector<int64_t> ends;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.axes, "axes"), f(self.starts, "starts"), f(self.ends, "ends"));
}
std::string name() const { return "slice"; }
auto fix_index(const std::vector<std::size_t>& lens, std::size_t axis, int64_t index) const
{
int64_t r = std::min(index, static_cast<int64_t>(lens[axis]));
if(r < 0)
r += lens[axis];
return std::size_t(r);
}
auto compute_offset(const shape& s) const
{
const std::vector<std::size_t>& lens = s.lens();
const std::vector<std::size_t>& strides = s.strides();
auto offset = 0;
if(!axes.empty())
{
for(std::size_t i = 0; i < axes.size(); i++)
{
auto axis = axes[i];
offset += fix_index(lens, axis, starts[i]) * strides[axis];
}
}
else
{
for(std::size_t axis = 0; axis < lens.size(); axis++)
{
offset += fix_index(lens, axis, starts[axis]) * strides[axis];
}
}
return offset;
}
shape compute_shape(std::vector<shape> inputs) const
{
auto input_shape = inputs[0];
auto t = input_shape.type();
const auto& old_lens = input_shape.lens();
const auto& old_strides = input_shape.strides();
if(starts.size() != axes.size() || axes.size() != ends.size())
{
MIGRAPHX_THROW("inconsistent sizes");
}
std::vector<std::size_t> new_lens = old_lens;
for(std::size_t i = 0; i < axes.size(); i++)
{
auto axis = axes[i];
new_lens[axis] =
fix_index(old_lens, axis, ends[i]) - fix_index(old_lens, axis, starts[i]);
}
return shape{t, new_lens, old_strides};
}
argument compute(shape output_shape, std::vector<argument> args) const
{
auto input = args[0];
auto offset = compute_offset(input.get_shape()) * output_shape.type_size();
return {std::move(output_shape), [=] { return input.data() + offset; }};
}
int output_alias(const std::vector<shape>&) const { return 0; }
};
struct squeeze
{
std::vector<int64_t> axes;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.axes, "axes"));
}
std::string name() const { return "squeeze"; }
shape compute_shape(std::vector<shape> inputs) const
{
auto input_shape = inputs[0];
auto type = input_shape.type();
auto old_lens = input_shape.lens();
if(std::any_of(
axes.begin(), axes.end(), [&](auto axis) { return input_shape.lens()[axis] != 1; }))
{
MIGRAPHX_THROW("squeeze axis dimension should be equal to 1");
}
std::vector<std::size_t> new_lens;
if(axes.empty())
{
std::copy_if(old_lens.begin(),
old_lens.end(),
std::back_inserter(new_lens),
[](auto len) { return len != 1; });
}
else
{
for(std::size_t i = 0; i < old_lens.size(); i++)
{
if(std::find(axes.begin(), axes.end(), i) == axes.end())
{
new_lens.push_back(old_lens[i]);
}
}
}
return shape{type, new_lens};
}
argument compute(shape output_shape, std::vector<argument> args) const
{
return {std::move(output_shape), std::move(args.front().data)};
}
int output_alias(const std::vector<shape>&) const { return 0; }
};
struct unsqueeze
{
std::vector<int64_t> axes;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.axes, "axes"));
}
std::string name() const { return "unsqueeze"; }
shape compute_shape(std::vector<shape> inputs) const
{
auto input_shape = inputs[0];
auto type = input_shape.type();
auto old_lens = input_shape.lens();
std::size_t new_size = old_lens.size() + axes.size();
std::vector<std::size_t> new_lens(new_size);
std::size_t p = 0;
for(std::size_t i = 0; i < new_size; i++)
{
if(std::find(axes.begin(), axes.end(), i) != axes.end())
{
new_lens[i] = 1;
}
else
{
new_lens[i] = old_lens[p++];
}
}
return shape{type, new_lens};
}
argument compute(shape output_shape, std::vector<argument> args) const
{
return {std::move(output_shape), std::move(args.front().data)};
}
int output_alias(const std::vector<shape>&) const { return 0; }
};
struct reshape
{
std::vector<int64_t> dims;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.dims, "dims"));
}
std::string name() const { return "reshape"; }
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(1);
auto&& idims = inputs.front().lens();
std::vector<std::size_t> rdims(dims.begin(), dims.end());
auto n_neg_dims = std::count(dims.begin(), dims.end(), -1);
if(n_neg_dims > 1)
MIGRAPHX_THROW("Dimensions for reshape can only have one -1 dim");
for(std::size_t i = 0; i < dims.size(); i++)
{
if(dims[i] == 0)
rdims[i] = idims[i];
// since rdims using size_t type, -1 is the max value
// is size_t that cause later compuation incorrect
if(dims[i] == -1)
rdims[i] = 1;
}
if(n_neg_dims > 0)
{
size_t missing_dim =
inputs.front().elements() /
std::accumulate(rdims.begin(), rdims.end(), 1, std::multiplies<int64_t>());
for(std::size_t i = 0; i < rdims.size(); i++)
{
if(dims[i] == -1)
rdims[i] = missing_dim;
}
}
shape s{inputs.front().type(), rdims};
if(s.elements() != inputs.front().elements())
MIGRAPHX_THROW("Wrong number of elements for reshape");
return s;
}
argument compute(shape output_shape, std::vector<argument> args) const
{
return {std::move(output_shape), std::move(args.front().data)};
}
int output_alias(const std::vector<shape>&) const { return 0; }
};
struct pad
{
std::vector<int64_t> pads;
float value = 0.0f;
enum pad_op_mode_t
{
constant_pad,
reflect_pad,
edge_pad
};
pad_op_mode_t mode = constant_pad;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.mode, "mode"), f(self.pads, "pads"), f(self.value, "value"));
}
std::string name() const { return "pad"; }
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(1);
auto&& idims = inputs.front().lens();
std::vector<std::size_t> rdims(idims.begin(), idims.end());
std::size_t num_dims = rdims.size();
for(std::size_t i = 0; i < num_dims; i++)
{
rdims[i] += pads[i] + pads[i + num_dims];
}
shape s{inputs.front().type(), rdims};
return s;
}
};
struct as_shape
{
shape s;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.s, "shape"));
}
std::string name() const { return "as_shape"; }
shape compute_shape(const std::vector<shape>& inputs) const
{
check_shapes{inputs, *this}.has(1).standard();
assert(inputs.front().elements() == s.elements());
return s;
}
argument compute(shape output_shape, std::vector<argument> args) const
{
return {std::move(output_shape), std::move(args.front().data)};
}
int output_alias(const std::vector<shape>&) const { return 0; }
};
struct gather
{
int axis = 0;
std::string name() const { return "gather"; }
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(2);
auto lens = inputs[0].lens();
int n_dim = static_cast<int>(lens.size());
if(axis >= n_dim || axis < -n_dim)
{
MIGRAPHX_THROW("Gather: axis is out of range.");
}
// negative axis means counting dimensions from back
int axis_index = (axis < 0) ? (n_dim + axis) : axis;
auto type = inputs[0].type();
lens[axis_index] = inputs[1].elements();
return {type, lens};
}
template <class T>
void compute_index(const T& out_idx,
const int axis_index,
const std::vector<std::size_t>& vec_indices,
const std::size_t max_dim,
T& in_idx) const
{
in_idx = out_idx;
std::size_t idx = vec_indices.at(out_idx[axis_index]);
if(idx >= max_dim)
{
MIGRAPHX_THROW("Gather: indices are out of range in input tensor");
}
in_idx[axis_index] = idx;
}
argument compute(const shape& output_shape, std::vector<argument> args) const
{
argument result{output_shape};
// negative axis means counting dimensions from back
int axis_index = (axis < 0) ? (output_shape.lens().size() + axis) : axis;
// max dimension in axis
std::size_t max_dim = args[0].get_shape().lens()[axis_index];
std::vector<std::size_t> vec_indices;
args[1].visit([&](auto indices) { vec_indices.assign(indices.begin(), indices.end()); });
visit_all(result, args[0])([&](auto output, auto input) {
std::vector<std::size_t> in_idx;
shape_for_each(output.get_shape(), [&](const auto& idx) {
this->compute_index(idx, axis_index, vec_indices, max_dim, in_idx);
output(idx.begin(), idx.end()) = input(in_idx.begin(), in_idx.end());
});
});
return result;
}
};
struct dot
{
float alpha = 1.0;
float beta = 0.0;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.alpha, "alpha"), f(self.beta, "beta"));
}
std::string name() const { return "dot"; }
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(2).same_type();
const shape& a = inputs.at(0);
const shape& b = inputs.at(1);
auto t = a.type();
if(a.lens()[1] != b.lens()[0])
MIGRAPHX_THROW("Inner dimensions do not match: {" + to_string_range(a.lens()) +
"} x {" + to_string_range(b.lens()) + "}");
return {t, {a.lens()[0], b.lens()[1]}};
}
};
struct unary
{
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs}.has(1);
return inputs.at(0);
}
};
struct identity
{
std::string name() const { return "identity"; }
shape compute_shape(std::vector<shape> inputs) const { return inputs.at(0); }
argument compute(shape output_shape, std::vector<argument> args) const
{
return {std::move(output_shape), std::move(args.at(0).data)};
}
int output_alias(const std::vector<shape>&) const { return 0; }
};
struct abs : unary
{
std::string name() const { return "abs"; }
};
struct exp : unary
{
std::string name() const { return "exp"; }
};
struct log : unary
{
std::string name() const { return "log"; }
};
struct sin : unary
{
std::string name() const { return "sin"; }
};
struct cos : unary
{
std::string name() const { return "cos"; }
};
struct tan : unary
{
std::string name() const { return "tan"; }
};
struct asin : unary
{
std::string name() const { return "asin"; }
};
struct acos : unary
{
std::string name() const { return "acos"; }
};
struct atan : unary
{
std::string name() const { return "atan"; }
};
struct sinh : unary
{
std::string name() const { return "sinh"; }
};
struct cosh : unary
{
std::string name() const { return "cosh"; }
};
struct tanh : unary
{
std::string name() const { return "tanh"; }
};
struct sigmoid : unary
{
std::string name() const { return "sigmoid"; }
};
struct neg : unary
{
std::string name() const { return "neg"; }
};
struct relu : unary
{
std::string name() const { return "relu"; }
};
struct softmax
{
std::string name() const { return "softmax"; }
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs}.has(1).only_dims(4);
return inputs.at(0);
}
};
struct flatten
{
uint64_t axis = 0;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.axis, "axis"));
}
std::string name() const { return "flatten"; }
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs}.has(1);
auto&& lens = inputs.front().lens();
if(axis > lens.size())
{
MIGRAPHX_THROW("axis for flatten must be less than tensor rank");
}
auto x =
std::accumulate(lens.begin(), lens.begin() + axis, std::size_t{1}, std::multiplies<>{});
auto y =
std::accumulate(lens.begin() + axis, lens.end(), std::size_t{1}, std::multiplies<>{});
return {inputs.at(0).type(), {x, y}};
}
argument compute(shape output_shape, std::vector<argument> args) const
{
return {std::move(output_shape), std::move(args.front().data)};
}
int output_alias(const std::vector<shape>&) const { return 0; }
};
/// The broadcast operator performs the numpy-style broadcasting of an axis of a given tensor. This
/// is achieved primarily by setting the stride of the broadcasted axis to zero. Linear indicies are
/// computed from multi-indicies by computing the inner product on the multi-index with the strides.
/// For example, if we have a tensor A(2,3) it has lengths of (2,3) and strides of (3,1). If we want
/// to compute the linear offset that corresponds to the element on the 2nd row (i = 1) and 3rd
/// column (j = 2), we compute the following inner product (1,2) dot (3, 1) = 1*3 + 2*1 = 5. It is
/// obvious from there that we can negate the effects of a given axis by setting the stride of that
/// axis to zero.
struct broadcast
{
uint64_t axis = 0;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.axis, "axis"));
}
shape broadcast_shape;
std::string name() const { return "broadcast"; }
shape compute_shape(std::vector<shape> inputs) const
{
auto t = inputs.at(0).type();
auto input = inputs.at(0);
std::vector<size_t> bcast_strides(broadcast_shape.lens().size(), 0);
if(std::all_of(broadcast_shape.lens().cbegin(), broadcast_shape.lens().cend(), [&](auto x) {
return x == 1;
}))
{
if(axis != 0)
MIGRAPHX_THROW("when broadcasting tensor of size 1, axis should be 0");
return {t, broadcast_shape.lens(), std::move(bcast_strides)};
}
else
{
assert(broadcast_shape.lens().size() - axis >= input.lens().size());
if(!std::equal(
input.lens().begin(), input.lens().end(), broadcast_shape.lens().begin() + axis))
MIGRAPHX_THROW("when broadcasting success sizes must match");
std::copy(input.strides().begin(), input.strides().end(), bcast_strides.begin() + axis);
return {t, broadcast_shape.lens(), std::move(bcast_strides)};
}
}
argument compute(shape output_shape, std::vector<argument> args) const
{
return {std::move(output_shape), std::move(args.at(0).data)};
}
int output_alias(const std::vector<shape>&) const { return 0; }
};
struct multibroadcast
{
std::vector<std::size_t> output_lens;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.output_lens, "output_lens"));
}
std::string name() const { return "multibroadcast"; }
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(1);
auto t = inputs.at(0).type();
auto input = inputs.at(0);
if(input.lens().empty())
MIGRAPHX_THROW("inputs dimensions should be > 0");
if(input.lens().size() > output_lens.size())
MIGRAPHX_THROW("inputs dimensions should <= output size");
std::vector<size_t> bcast_strides(output_lens.size(), 0);
auto offset = output_lens.size() - input.lens().size();
for(int i = input.lens().size() - 1; i >= 0; i--)
{
if(output_lens[i + offset] == input.lens()[i])
{
bcast_strides[i + offset] = input.strides()[i];
}
}
return {t, output_lens, bcast_strides};
}
argument compute(shape output_shape, std::vector<argument> args) const
{
return {std::move(output_shape), std::move(args.at(0).data)};
}
int output_alias(const std::vector<shape>&) const { return 0; }
};
struct scalar
{
shape scalar_bcast;
std::string name() const { return "scalar"; }
shape compute_shape(std::vector<shape> inputs) const
{
assert(check_shapes{inputs}.has(1).only_dims(1).size() == 1);
auto t = inputs.at(0).type();
std::vector<std::size_t> strides(scalar_bcast.lens().size(), 0);
return {t, scalar_bcast.lens(), strides};
}
argument compute(shape output_shape, std::vector<argument> args) const
{
return {std::move(output_shape), std::move(args.at(0).data)};
}
int output_alias(const std::vector<shape>&) const { return 0; }
};
struct binary
{
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs}.has(2).same_type().same_dims();
auto t = inputs.at(0).type();
auto lens = inputs.at(0).lens();
return {t, lens};
}
};
struct add : binary
{
std::string name() const { return "add"; }
};
struct sub : binary
{
std::string name() const { return "sub"; }
};
struct mul : binary
{
std::string name() const { return "mul"; }
};
struct div : binary
{
std::string name() const { return "div"; }
};
struct max : binary
{
std::string name() const { return "max"; }
};
struct min : binary
{
std::string name() const { return "min"; }
};
struct load
{
shape s;
std::size_t offset = 0;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.s, "shape"), f(self.offset, "offset"));
}
std::string name() const { return "load"; }
shape compute_shape(const std::vector<shape>& inputs) const
{
check_shapes{inputs}.has(1);
return s;
}
argument compute(const shape&, const std::vector<argument>& args) const
{
return {s, args[0].data() + offset};
}
int output_alias(const std::vector<shape>&) const { return 0; }
};
struct outline
{
shape s;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.s, "shape"));
}
std::string name() const { return "outline"; }
shape compute_shape(const std::vector<shape>& inputs) const
{
check_shapes{inputs, *this}.has(0);
return s;
}
argument compute(const shape&, const std::vector<argument>&) const { return {s, nullptr}; }
};
// indicate rnn computation direction
enum class rnn_direction
{
forward,
reverse,
bidirectional,
};
struct rnn
{
std::size_t hidden_size = 1;
std::vector<operation> actv_funcs{tanh{}, tanh{}};
rnn_direction direction = rnn_direction::forward;
float clip = 0.0f;
std::string name() const { return "rnn"; }
shape compute_shape(std::vector<shape> inputs) const
{
auto in_dims = inputs[0].lens();
auto hidden_dims = inputs[2].lens();
if(hidden_size != hidden_dims[2])
{
MIGRAPHX_THROW("RNN: hidden size mismatch in attribute and input");
}
std::size_t num_directions = 1;
if(direction == rnn_direction::bidirectional)
{
num_directions = 2;
}
if(num_directions != hidden_dims[0])
{
MIGRAPHX_THROW("RNN: num_direction mismatch in attribute and input");
}
std::vector<std::size_t> out_dims(in_dims);
out_dims.insert(out_dims.begin() + 1, num_directions);
out_dims.back() = hidden_size;
return {inputs[0].type(), out_dims};
}
};
struct rnn_last_output
{
std::string name() const { return "rnn_last_output"; }
shape compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(1);
auto dims = inputs[0].lens();
// remove the first dimension, remaing are output shape
dims.erase(dims.begin());
return {inputs[0].type(), dims};
}
};
struct gru
{
std::size_t hidden_size = 1;
std::vector<operation> actv_funcs{sigmoid{}, tanh{}};
rnn_direction direction = rnn_direction::forward;
float clip = 0.0f;
int linear_before_reset = 0;
std::string name() const { return "gru"; }
shape compute_shape(std::vector<shape> inputs) const
{
auto in_dims = inputs[0].lens();
auto hidden_dims = inputs[2].lens();
if(hidden_size != hidden_dims[2])
{
MIGRAPHX_THROW("GRU: hidden size mismatch in attribute and input");
}
std::size_t num_directions = 1;
if(direction == rnn_direction::bidirectional)
{
num_directions = 2;
}
if(num_directions != hidden_dims[0])
{
MIGRAPHX_THROW("GRU: num_direction does not match the direction attribute");
}
std::vector<std::size_t> out_dims(in_dims);
out_dims.insert(out_dims.begin() + 1, num_directions);
out_dims.back() = hidden_size;
return {inputs[0].type(), out_dims};
}
};
struct undefined
{
std::string name() const { return "undefined"; }
shape compute_shape(const std::vector<shape>& inputs) const
{
check_shapes{inputs, *this}.has(0);
return {};
}
argument compute(const shape&, const std::vector<argument>&) const { return {{}, nullptr}; }
};
} // namespace op
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#include <migraphx/op/abnormal_ops.hpp>
#include <migraphx/op/abs.hpp>
#include <migraphx/op/acos.hpp>
#include <migraphx/op/add.hpp>
#include <migraphx/op/asin.hpp>
#include <migraphx/op/as_shape.hpp>
#include <migraphx/op/atan.hpp>
#include <migraphx/op/batch_norm.hpp>
#include <migraphx/op/binary.hpp>
#include <migraphx/op/broadcast.hpp>
#include <migraphx/op/clip.hpp>
#include <migraphx/op/common.hpp>
#include <migraphx/op/concat.hpp>
#include <migraphx/op/contiguous.hpp>
#include <migraphx/op/convert.hpp>
#include <migraphx/op/convolution.hpp>
#include <migraphx/op/cosh.hpp>
#include <migraphx/op/cos.hpp>
#include <migraphx/op/div.hpp>
#include <migraphx/op/dot.hpp>
#include <migraphx/op/elu.hpp>
#include <migraphx/op/exp.hpp>
#include <migraphx/op/flatten.hpp>
#include <migraphx/op/gather.hpp>
#include <migraphx/op/gru.hpp>
#include <migraphx/op/identity.hpp>
#include <migraphx/op/im2col.hpp>
#include <migraphx/op/leaky_relu.hpp>
#include <migraphx/op/load.hpp>
#include <migraphx/op/log.hpp>
#include <migraphx/op/logsoftmax.hpp>
#include <migraphx/op/lrn.hpp>
#include <migraphx/op/lstm.hpp>
#include <migraphx/op/max.hpp>
#include <migraphx/op/min.hpp>
#include <migraphx/op/mul.hpp>
#include <migraphx/op/multibroadcast.hpp>
#include <migraphx/op/neg.hpp>
#include <migraphx/op/outline.hpp>
#include <migraphx/op/pad.hpp>
#include <migraphx/op/pooling.hpp>
#include <migraphx/op/relu.hpp>
#include <migraphx/op/reshape.hpp>
#include <migraphx/op/rnn.hpp>
#include <migraphx/op/rnn_last_cell_output.hpp>
#include <migraphx/op/rnn_last_output.hpp>
#include <migraphx/op/scalar.hpp>
#include <migraphx/op/sigmoid.hpp>
#include <migraphx/op/sinh.hpp>
#include <migraphx/op/sin.hpp>
#include <migraphx/op/slice.hpp>
#include <migraphx/op/softmax.hpp>
#include <migraphx/op/squeeze.hpp>
#include <migraphx/op/sub.hpp>
#include <migraphx/op/tanh.hpp>
#include <migraphx/op/tan.hpp>
#include <migraphx/op/transpose.hpp>
#include <migraphx/op/unary.hpp>
#include <migraphx/op/unsqueeze.hpp>
#endif
src/include/migraphx/pass_manager.hpp 0 → 100644
View file @ eb0d8fee
#ifndef MIGRAPHX_GUARD_MIGRAPHLIB_PASS_MANAGER_HPP
#define MIGRAPHX_GUARD_MIGRAPHLIB_PASS_MANAGER_HPP
#include <list>
#include <unordered_map>
#include <migraphx/operation.hpp>
#include <migraphx/literal.hpp>
#include <migraphx/builtin.hpp>
#include <migraphx/instruction_ref.hpp>
#include <migraphx/target.hpp>
#include <migraphx/tracer.hpp>
#include <migraphx/env.hpp>
#include <migraphx/config.hpp>
#include <algorithm>
#include <iostream>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
void run_passes(program& prog, const std::vector<pass>& passes, tracer trace = tracer{});
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/include/migraphx/program.hpp
View file @ eb0d8fee
......@@ -9,6 +9,7 @@
#include <migraphx/instruction_ref.hpp>
#include <migraphx/target.hpp>
#include <migraphx/tracer.hpp>
#include <migraphx/env.hpp>
#include <migraphx/config.hpp>
#include <algorithm>
#include <iostream>
......@@ -16,6 +17,9 @@
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_TRACE_COMPILE)
MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_TRACE_EVAL)
struct program_impl;
const operation& get_operation(instruction_ref ins);
......@@ -26,8 +30,16 @@ const operation& get_operation(instruction_ref ins);
struct program
{
program();
// move constructor
program(program&&) noexcept;
program& operator=(program&&) noexcept;
// copy constructor
program(const program&);
// copy assignment operator
program& operator=(program);
~program() noexcept;
using parameter_map = std::unordered_map<std::string, argument>;
......@@ -104,13 +116,19 @@ struct program
void debug_print() const;
void debug_print(instruction_ref ins) const;
void debug_print(const std::vector<instruction_ref>& inss) const;
void print_graph(std::ostream& os) const;
void dry_run(parameter_map params) const;
void annotate(std::ostream& os, std::function<void(instruction_ref)> a) const;
friend std::ostream& operator<<(std::ostream& os, const program& p);
friend bool operator==(const program& x, const program& y);
friend bool operator!=(const program& x, const program& y) { return !(x == y); }
private:
void assign(const program& p);
private:
std::unique_ptr<program_impl> impl;
};
......
src/include/migraphx/constant_propagate.hpp → src/include/migraphx/propagate_constant.hpp
View file @ eb0d8fee
#ifndef MIGRAPHX_GUARD_RTGLIB_CONSTANT_PROPAGATE_HPP
#define MIGRAPHX_GUARD_RTGLIB_CONSTANT_PROPAGATE_HPP
#ifndef MIGRAPHX_GUARD_RTGLIB_PROPAGATE_CONSTANT_HPP
#define MIGRAPHX_GUARD_RTGLIB_PROPAGATE_CONSTANT_HPP
#include <string>
#include <migraphx/config.hpp>
......@@ -12,9 +12,9 @@ struct program;
/**
* Replace instructions which take all literals with a literal of the computation.
*/
struct constant_propagate
struct propagate_constant
{
std::string name() const { return "constant_propagate"; }
std::string name() const { return "propagate_constant"; }
void apply(program& p) const;
};
......
src/include/migraphx/quantization.hpp 0 → 100644
View file @ eb0d8fee
#ifndef MIGRAPHX_GUARD_RTGLIB_QUANTIZATION_HPP
#define MIGRAPHX_GUARD_RTGLIB_QUANTIZATION_HPP
#include <string>
#include <vector>
#include <migraphx/instruction_ref.hpp>
#include <migraphx/operation.hpp>
#include <migraphx/config.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
struct program;
void quantize(program& prog, const std::vector<std::string>& ins_names);
void quantize(program& prog);
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/include/migraphx/ranges.hpp
View file @ eb0d8fee
......@@ -12,7 +12,7 @@ inline namespace MIGRAPHX_INLINE_NS {
namespace detail {
template <class String, class T>
auto generic_find_impl(rank<2>, String&& s, const T& x) -> decltype(s.begin() + s.find(x), s.npos)
auto generic_find_impl(rank<2>, String&& s, const T& x) -> decltype(s.npos, s.begin() + s.find(x))
{
auto index = s.find(x);
if(index == s.npos)
......@@ -71,6 +71,30 @@ bool all_of(const std::initializer_list<T>& c, const Predicate& p)
return std::all_of(c.begin(), c.end(), p);
}
template <class C, class Predicate>
bool any_of(const C& c, const Predicate& p)
{
return std::any_of(c.begin(), c.end(), p);
}
template <class T, class Predicate>
bool any_of(const std::initializer_list<T>& c, const Predicate& p)
{
return std::any_of(c.begin(), c.end(), p);
}
template <class C, class Predicate>
bool none_of(const C& c, const Predicate& p)
{
return std::none_of(c.begin(), c.end(), p);
}
template <class T, class Predicate>
bool none_of(const std::initializer_list<T>& c, const Predicate& p)
{
return std::none_of(c.begin(), c.end(), p);
}
template <class Range, class Iterator>
void copy(Range&& r, Iterator it)
{
......
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