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Commit 087c205e authored by Paul's avatar Paul
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Merge from develop

parents a3a9e469 e15b8333
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Showing with 979 additions and 83 deletions
src/include/migraphx/functional.hpp
View file @ 087c205e
......@@ -94,6 +94,12 @@ constexpr void each_args(F)
{
}
template <class F, class T>
auto unpack(F f, T& x)
{
return sequence_c<std::tuple_size<T>{}>([&](auto... is) { f(std::get<is>(x)...); });
}
/// Implements a fix-point combinator
template <class R, class F>
detail::fix_f<R, F> fix(F f)
......
src/include/migraphx/instruction.hpp
View file @ 087c205e
......@@ -14,6 +14,7 @@ namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
shape compute_shape(const operation& op, const std::vector<instruction_ref>& args);
std::vector<shape> to_shapes(const std::vector<instruction_ref>& args);
struct instruction
{
......@@ -71,7 +72,11 @@ struct instruction
static void
replace(instruction_ref ins, operation o, const shape& r, std::vector<instruction_ref> args);
static instruction_ref get_output_alias(instruction_ref ins);
argument eval() const;
void finalize(context& ctx);
static instruction_ref get_output_alias(instruction_ref ins, bool shallow = false);
private:
// internal
......
src/include/migraphx/iterator_for.hpp
View file @ 087c205e
......@@ -17,9 +17,9 @@ struct iterator_for_range
struct iterator
{
base_iterator i;
base_iterator operator*() { return i; }
base_iterator operator*() const { return i; }
base_iterator operator++() { return ++i; }
bool operator!=(const iterator& rhs) { return i != rhs.i; }
bool operator!=(const iterator& rhs) const { return i != rhs.i; }
};
iterator begin()
......
src/include/migraphx/literal.hpp
View file @ 087c205e
......@@ -22,8 +22,8 @@ struct literal : raw_data<literal>
{
literal() {}
template <class U, class T = deduce<U>>
literal(U x) : buffer(make_shared_array<char>(sizeof(T))), m_shape(shape::get_type<T>{})
template <class U, class T = deduce<U>, shape::type_t ShapeType = shape::get_type<T>{}>
literal(U x) : buffer(make_shared_array<char>(sizeof(T))), m_shape(ShapeType)
{
static_assert(std::is_trivially_copyable<T>{}, "Literals can only be trivial types");
*(reinterpret_cast<T*>(buffer.get())) = x;
......
src/include/migraphx/make_shared_array.hpp
View file @ 087c205e
......@@ -10,7 +10,7 @@ inline namespace MIGRAPHX_INLINE_NS {
template <typename T>
std::shared_ptr<T> make_shared_array(size_t size)
{
return std::shared_ptr<T>(new T[size], std::default_delete<T[]>());
return std::shared_ptr<T>(new T[size], std::default_delete<T[]>()); // NOLINT
}
} // namespace MIGRAPHX_INLINE_NS
......
src/include/migraphx/matcher.hpp
View file @ 087c205e
......@@ -214,7 +214,6 @@ void find_matches(program& p, Ms&&... ms)
bool match = false;
each_args(
[&](auto&& m) {
// cppcheck-suppress knownConditionTrueFalse
if(match)
return;
auto r = match_instruction(p, ins, m.matcher());
......
src/include/migraphx/onnx.hpp
View file @ 087c205e
......@@ -7,6 +7,24 @@
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
struct unknown
{
std::string op;
std::string name() const { return "unknown:" + op; }
shape compute_shape(std::vector<shape> input) const
{
if(input.empty())
return {};
else
return input.front();
}
friend std::ostream& operator<<(std::ostream& os, const unknown& x)
{
os << x.name();
return os;
}
};
/// Create a program from an onnx file
program parse_onnx(const std::string& name);
......
src/include/migraphx/operation.hpp
View file @ 087c205e
......@@ -7,17 +7,17 @@
#include <memory>
#include <type_traits>
#include <utility>
#include <migraphx/shape.hpp>
#include <migraphx/reflect.hpp>
#include <migraphx/streamutils.hpp>
#include <migraphx/argument.hpp>
#include <migraphx/context.hpp>
#include <migraphx/auto_any_cast.hpp>
#include <migraphx/config.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
struct context;
#ifdef DOXYGEN
/// The operation interface represents an action an instruction will perform. All
......@@ -26,6 +26,8 @@ struct operation
{
/// A unique name identifying the operation
std::string name() const;
/// An optional method that can be used to finalize the operator before running
void finalize(context& ctx);
/// This is used to compute the resulting shape from an operation. If an
/// operation cannot be run with input shapes, then it should throw an
/// exception.
......@@ -53,6 +55,11 @@ struct operation
friend std::ostream& operator<<(std::ostream& os, const operation& op);
};
/// Returns true if operation does not require a context to run compute
bool is_context_free(const operation& x);
/// Returns true if the operation has a finalize method
bool has_finalize(const operation& x);
#else
namespace operation_stream {
......@@ -89,7 +96,7 @@ auto operator==(const T& x, const U& y) -> decltype(x.name() == y.name())
} // namespace operation_equal
template <class T>
auto compute_op(rank<1>,
auto compute_op(rank<2>,
const T& x,
context& ctx,
const shape& output_shape,
......@@ -99,6 +106,14 @@ auto compute_op(rank<1>,
return x.compute(auto_any_cast(ctx), output_shape, input);
}
template <class T>
auto compute_op(
rank<1>, const T& x, context&, const shape& output_shape, const std::vector<argument>& input)
-> decltype(x.compute(output_shape, input))
{
return x.compute(output_shape, input);
}
template <class T>
argument compute_op(rank<0>, const T& x, context&, const shape&, const std::vector<argument>&)
{
......@@ -110,7 +125,53 @@ template <class T>
argument
compute_op(const T& x, context& ctx, const shape& output_shape, const std::vector<argument>& input)
{
return compute_op(rank<1>{}, x, ctx, output_shape, input);
return compute_op(rank<2>{}, x, ctx, output_shape, input);
}
template <class T>
auto compute_op(rank<2>, const T& x, const shape& output_shape, const std::vector<argument>& input)
-> decltype(x.compute(output_shape, input))
{
return x.compute(output_shape, input);
}
template <class T>
auto compute_op(rank<1>, const T& x, const shape& output_shape, const std::vector<argument>& input)
-> decltype(x.compute(auto_any_cast(std::declval<context&>()), output_shape, input))
{
std::string name = x.name();
MIGRAPHX_THROW("Not computable without a context: " + name);
}
template <class T>
argument compute_op(rank<0>, const T& x, const shape&, const std::vector<argument>&)
{
std::string name = x.name();
MIGRAPHX_THROW("Not computable: " + name);
}
template <class T>
argument compute_op(const T& x, const shape& output_shape, const std::vector<argument>& input)
{
return compute_op(rank<2>{}, x, output_shape, input);
}
template <class T>
auto is_context_free_op(rank<1>,
const T& x,
const shape& output_shape,
const std::vector<argument>& input)
-> decltype(x.compute(output_shape, input), std::true_type{});
template <class T>
auto is_context_free_op(rank<0>, const T&, const shape&, const std::vector<argument>&)
-> std::false_type;
template <class T>
auto is_context_free_op(const T& x) -> decltype(is_context_free_op(
rank<1>{}, x, std::declval<const shape&>(), std::declval<std::vector<argument>>()))
{
return {};
}
template <class T>
......@@ -132,15 +193,57 @@ int output_alias_op(const T& x, const std::vector<shape>& shapes)
return output_alias_op(rank<1>{}, x, shapes);
}
template <class T>
auto finalize_op(
rank<1>, T& x, context& ctx, const shape& output_shape, const std::vector<shape>& input)
-> decltype(x.finalize(auto_any_cast(ctx), output_shape, input), void())
{
x.finalize(auto_any_cast(ctx), output_shape, input);
}
template <class T>
void finalize_op(rank<0>, T&, context&, const shape&, const std::vector<shape>&)
{
}
template <class T>
void finalize_op(T& x, context& ctx, const shape& output_shape, const std::vector<shape>& input)
{
finalize_op(rank<1>{}, x, ctx, output_shape, input);
}
template <class T>
auto has_finalize_op(
rank<1>, T& x, context& ctx, const shape& output_shape, const std::vector<shape>& input)
-> decltype(x.finalize(auto_any_cast(ctx), output_shape, input), std::true_type{});
template <class T>
auto has_finalize_op(rank<0>, T&, context&, const shape&, const std::vector<shape>&)
-> std::false_type;
template <class T>
auto has_finalize_op(const T&) -> decltype(has_finalize_op(rank<1>{},
std::declval<T&>(),
std::declval<context&>(),
std::declval<const shape&>(),
std::declval<std::vector<shape>>()))
{
return {};
}
/*
* Type-erased interface for:
*
* struct operation
* {
* std::string name() const;
* bool is_context_free() const;
* bool has_finalize() const;
* int 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;
* argument compute(const shape& output,const std::vector<argument>& input) const;
* friend std::ostream & operator<<(std::ostream & os,const operation & op) ;
* friend bool operator==(const operation & x,const operation & y) ;
* };
......@@ -210,12 +313,30 @@ struct operation
return (*this).private_detail_te_get_handle().name();
}
bool is_context_free() const
{
assert((*this).private_detail_te_handle_mem_var);
return (*this).private_detail_te_get_handle().is_context_free();
}
bool has_finalize() const
{
assert((*this).private_detail_te_handle_mem_var);
return (*this).private_detail_te_get_handle().has_finalize();
}
int 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);
}
void finalize(context& ctx, const shape& output, const std::vector<shape>& input)
{
assert((*this).private_detail_te_handle_mem_var);
(*this).private_detail_te_get_handle().finalize(ctx, output, input);
}
shape compute_shape(const std::vector<shape>& input) const
{
assert((*this).private_detail_te_handle_mem_var);
......@@ -228,6 +349,12 @@ struct operation
return (*this).private_detail_te_get_handle().compute(ctx, output, input);
}
argument compute(const shape& output, const std::vector<argument>& input) const
{
assert((*this).private_detail_te_handle_mem_var);
return (*this).private_detail_te_get_handle().compute(output, input);
}
friend std::ostream& operator<<(std::ostream& os, const operation& op)
{
assert(op.private_detail_te_handle_mem_var);
......@@ -240,6 +367,12 @@ struct operation
return x.private_detail_te_get_handle().operator==(y);
}
friend bool is_shared(const operation& private_detail_x, const operation& private_detail_y)
{
return private_detail_x.private_detail_te_handle_mem_var ==
private_detail_y.private_detail_te_handle_mem_var;
}
private:
struct private_detail_te_handle_base_type
{
......@@ -247,13 +380,18 @@ 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 int output_alias(const std::vector<shape>& input) const = 0;
virtual shape compute_shape(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 int 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;
virtual argument
compute(context& ctx, const shape& output, const std::vector<argument>& input) const = 0;
virtual std::ostream& operator_shift_left(std::ostream& os) const = 0;
virtual bool operator==(const operation& y) const = 0;
compute(context& ctx, const shape& output, const std::vector<argument>& input) const = 0;
virtual argument compute(const shape& output, const std::vector<argument>& input) const = 0;
virtual std::ostream& operator_shift_left(std::ostream& os) const = 0;
virtual bool operator==(const operation& y) const = 0;
};
template <typename PrivateDetailTypeErasedT>
......@@ -286,12 +424,26 @@ struct operation
std::string name() const override { return private_detail_te_value.name(); }
bool is_context_free() const override
{
return is_context_free_op(private_detail_te_value);
}
bool has_finalize() const override { return has_finalize_op(private_detail_te_value); }
int output_alias(const std::vector<shape>& input) const override
{
return output_alias_op(private_detail_te_value, input);
}
void finalize(context& ctx, const shape& output, const std::vector<shape>& input) override
{
finalize_op(private_detail_te_value, ctx, output, input);
}
shape compute_shape(const std::vector<shape>& input) const override
{
......@@ -306,6 +458,12 @@ struct operation
return compute_op(private_detail_te_value, ctx, output, input);
}
argument compute(const shape& output, const std::vector<argument>& input) const override
{
return compute_op(private_detail_te_value, output, input);
}
std::ostream& operator_shift_left(std::ostream& os) const override
{
using migraphx::operation_stream::operator<<;
......@@ -385,6 +543,22 @@ inline const ValueType& any_cast(const operation& x)
inline bool operator!=(const operation& x, const operation& y) { return !(x == y); }
inline bool is_context_free(const operation& op) { return op.is_context_free(); }
template <class T>
bool is_context_free(const T& x)
{
return is_context_free_op(x);
}
inline bool has_finalize(const operation& op) { return op.has_finalize(); }
template <class T>
bool has_finalize(const T& x)
{
return has_finalize_op(x);
}
#endif
} // namespace MIGRAPHX_INLINE_NS
......
src/include/migraphx/operators.hpp
View file @ 087c205e
......@@ -6,6 +6,8 @@
#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>
......@@ -14,9 +16,16 @@ namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace op {
enum padding_mode_t
{
default_, // NOLINT
same,
valid
};
struct not_computable
{
argument compute(context&, const shape&, const std::vector<argument>&) const
argument compute(const shape&, const std::vector<argument>&) const
{
MIGRAPHX_THROW("not computable");
}
......@@ -51,18 +60,38 @@ struct batch_norm_inference
}
};
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}};
enum padding_mode_t
{
default_, // NOLINT
same,
valid
};
padding_mode_t padding_mode = default_;
int group = 1;
template <class Self, class F>
static auto reflect(Self& self, F f)
......@@ -70,7 +99,8 @@ struct convolution
return pack(f(self.padding, "padding"),
f(self.stride, "stride"),
f(self.dilation, "dilation"),
f(self.padding_mode, "padding_mode"));
f(self.padding_mode, "padding_mode"),
f(self.group, "group"));
}
std::string name() const { return "convolution"; }
......@@ -134,12 +164,7 @@ 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}};
enum padding_mode_t
{
default_, // NOLINT
same,
valid
};
padding_mode_t padding_mode = default_;
template <class Self, class F>
......@@ -185,12 +210,14 @@ 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"));
}
......@@ -207,7 +234,10 @@ 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],
......@@ -222,6 +252,39 @@ 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");
}
}
};
......@@ -234,10 +297,28 @@ struct leaky_relu
check_shapes{inputs, *this}.has(1);
return inputs.front();
}
friend std::ostream& operator<<(std::ostream& os, const leaky_relu& op)
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)
{
os << op.name() << ":" << op.alpha;
return os;
return pack(f(self.alpha, "alpha"));
}
};
......@@ -271,14 +352,14 @@ struct transpose
}
std::vector<size_t> output_lens(input_lens.size());
std::vector<size_t> output_strides(input_lens.size());
for(int i = 0; i < output_lens.size(); i++)
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(context&, shape output_shape, std::vector<argument> args) const
argument compute(shape output_shape, std::vector<argument> args) const
{
return {std::move(output_shape), std::move(args.front().data)};
}
......@@ -301,6 +382,17 @@ struct contiguous
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
......@@ -308,7 +400,7 @@ 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
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);
......@@ -352,7 +444,27 @@ struct concat
new_lens[axis] = new_dim_axis;
return {type, new_lens};
}
int output_alias(const std::vector<shape>&) const { return 0; }
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
......@@ -419,7 +531,7 @@ struct slice
}
return shape{t, new_lens, old_strides};
}
argument compute(context&, shape output_shape, std::vector<argument> args) const
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();
......@@ -469,7 +581,7 @@ struct squeeze
}
return shape{type, new_lens};
}
argument compute(context&, shape output_shape, std::vector<argument> args) const
argument compute(shape output_shape, std::vector<argument> args) const
{
return {std::move(output_shape), std::move(args.front().data)};
}
......@@ -508,7 +620,7 @@ struct unsqueeze
}
return shape{type, new_lens};
}
argument compute(context&, shape output_shape, std::vector<argument> args) const
argument compute(shape output_shape, std::vector<argument> args) const
{
return {std::move(output_shape), std::move(args.front().data)};
}
......@@ -538,11 +650,16 @@ struct reshape
{
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() /
inputs.front().elements() /
std::accumulate(rdims.begin(), rdims.end(), 1, std::multiplies<int64_t>());
for(std::size_t i = 0; i < rdims.size(); i++)
{
......@@ -550,23 +667,146 @@ struct reshape
rdims[i] = missing_dim;
}
}
if(dims.back() == -1)
{
rdims.pop_back();
std::copy(idims.begin() + rdims.size(), idims.end(), std::back_inserter(rdims));
}
shape s{inputs.front().type(), rdims};
if(s.elements() != inputs.front().elements())
MIGRAPHX_THROW("Wrong number of elements for reshape");
return s;
}
argument compute(context&, shape output_shape, std::vector<argument> args) const
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.erase(lens.begin() + axis_index);
if(!inputs[1].scalar())
{
auto ind_lens = inputs[1].lens();
lens.insert(lens.begin() + axis_index, ind_lens.begin(), ind_lens.end());
}
// for scalar output
if(lens.empty())
{
return {type};
}
return {type, lens};
}
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) ? static_cast<int>(args[0].get_shape().lens().size() + axis) : axis;
// max dimension in axis
visit_all(result, args[0])([&](auto output, auto data) {
args[1].visit([&](auto indices) {
if(output_shape.scalar())
{
output[0] = data[indices.front()];
}
else
{
auto out_lens = data.get_shape().lens();
out_lens[axis_index] = indices.get_shape().elements();
migraphx::shape out_comp_shape{data.get_shape().type(), out_lens};
shape_for_each(out_comp_shape, [&](const auto& out_idx) {
auto data_idx = out_idx;
data_idx[axis_index] = indices[data_idx[axis_index]];
output[out_comp_shape.index(out_idx.begin(), out_idx.end())] =
data(data_idx.begin(), data_idx.end());
});
}
});
});
return result;
}
};
struct dot
{
float alpha = 1.0;
......@@ -606,7 +846,7 @@ struct identity
{
std::string name() const { return "identity"; }
shape compute_shape(std::vector<shape> inputs) const { return inputs.at(0); }
argument compute(context&, shape output_shape, std::vector<argument> args) const
argument compute(shape output_shape, std::vector<argument> args) const
{
return {std::move(output_shape), std::move(args.at(0).data)};
}
......@@ -623,6 +863,11 @@ 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"; }
......@@ -653,6 +898,16 @@ 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"; }
......@@ -709,7 +964,7 @@ struct flatten
std::accumulate(lens.begin() + axis, lens.end(), std::size_t{1}, std::multiplies<>{});
return {inputs.at(0).type(), {x, y}};
}
argument compute(context&, shape output_shape, std::vector<argument> args) const
argument compute(shape output_shape, std::vector<argument> args) const
{
return {std::move(output_shape), std::move(args.front().data)};
}
......@@ -761,7 +1016,7 @@ struct broadcast
return {t, broadcast_shape.lens(), std::move(bcast_strides)};
}
}
argument compute(context&, shape output_shape, std::vector<argument> args) const
argument compute(shape output_shape, std::vector<argument> args) const
{
return {std::move(output_shape), std::move(args.at(0).data)};
}
......@@ -803,7 +1058,7 @@ struct multibroadcast
}
return {t, output_lens, bcast_strides};
}
argument compute(context&, shape output_shape, std::vector<argument> args) const
argument compute(shape output_shape, std::vector<argument> args) const
{
return {std::move(output_shape), std::move(args.at(0).data)};
}
......@@ -819,13 +1074,12 @@ struct 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();
auto input = inputs.at(0);
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(context&, shape output_shape, std::vector<argument> args) const
argument compute(shape output_shape, std::vector<argument> args) const
{
return {std::move(output_shape), std::move(args.at(0).data)};
}
......@@ -863,6 +1117,16 @@ 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;
......@@ -880,7 +1144,7 @@ struct load
check_shapes{inputs}.has(1);
return s;
}
argument compute(context&, const shape&, const std::vector<argument>& args) const
argument compute(const shape&, const std::vector<argument>& args) const
{
return {s, args[0].data() + offset};
}
......@@ -903,12 +1167,167 @@ struct outline
check_shapes{inputs, *this}.has(0);
return s;
}
argument compute(context&, const shape&, const std::vector<argument>&) const
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
{
return {s, nullptr};
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 lstm
{
std::size_t hidden_size = 1;
std::vector<operation> actv_funcs{sigmoid{}, tanh{}, tanh{}};
rnn_direction direction = rnn_direction::forward;
float clip = 0.0f;
int input_forget = 0;
std::string name() const { return "lstm"; }
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("LSTM: 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("LSTM: 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 lstm_last_cell_output
{
std::string name() const { return "lstm_last_cell_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 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
......
src/include/migraphx/par_dfor.hpp 0 → 100644
View file @ 087c205e
#ifndef MIGRAPHX_GUARD_RTGLIB_PAR_DFOR_HPP
#define MIGRAPHX_GUARD_RTGLIB_PAR_DFOR_HPP
#include <migraphx/par_for.hpp>
#include <migraphx/functional.hpp>
#include <array>
#include <numeric>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
template <class... Ts>
auto par_dfor(Ts... xs)
{
return [=](auto f) {
using array_type = std::array<std::size_t, sizeof...(Ts)>;
array_type lens = {{static_cast<std::size_t>(xs)...}};
auto n = std::accumulate(lens.begin(), lens.end(), 1, std::multiplies<std::size_t>{});
const std::size_t min_grain = 8;
if(n > 2 * min_grain)
{
array_type strides;
strides.fill(1);
std::partial_sum(lens.rbegin(),
lens.rend() - 1,
strides.rbegin() + 1,
std::multiplies<std::size_t>());
auto size =
std::accumulate(lens.begin(), lens.end(), 1, std::multiplies<std::size_t>());
par_for(size, min_grain, [&](std::size_t i) {
array_type indices;
std::transform(strides.begin(),
strides.end(),
lens.begin(),
indices.begin(),
[&](size_t stride, size_t len) { return (i / stride) % len; });
migraphx::unpack(f, indices);
});
}
else
{
dfor(xs...)(f);
}
};
}
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/include/migraphx/par_for.hpp 0 → 100644
View file @ 087c205e
#ifndef MIGRAPHX_GUARD_RTGLIB_PAR_FOR_HPP
#define MIGRAPHX_GUARD_RTGLIB_PAR_FOR_HPP
#include <thread>
#include <cmath>
#include <algorithm>
#include <vector>
#include <cassert>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
struct joinable_thread : std::thread
{
template <class... Xs>
joinable_thread(Xs&&... xs) : std::thread(std::forward<Xs>(xs)...) // NOLINT
{
}
joinable_thread& operator=(joinable_thread&& other) = default;
joinable_thread(joinable_thread&& other) = default;
~joinable_thread()
{
if(this->joinable())
this->join();
}
};
template <class F>
void par_for_impl(std::size_t n, std::size_t threadsize, F f)
{
if(threadsize <= 1)
{
for(std::size_t i = 0; i < n; i++)
f(i);
}
else
{
std::vector<joinable_thread> threads(threadsize);
// Using const here causes gcc 5 to ICE
#if(!defined(__GNUC__) || __GNUC__ != 5)
const
#endif
std::size_t grainsize = std::ceil(static_cast<double>(n) / threads.size());
std::size_t work = 0;
std::generate(threads.begin(), threads.end(), [=, &work] {
auto result = joinable_thread([=] {
std::size_t start = work;
std::size_t last = std::min(n, work + grainsize);
for(std::size_t i = start; i < last; i++)
{
f(i);
}
});
work += grainsize;
return result;
});
assert(work >= n);
}
}
template <class F>
void par_for(std::size_t n, std::size_t min_grain, F f)
{
const auto threadsize =
std::min<std::size_t>(std::thread::hardware_concurrency(), n / min_grain);
par_for_impl(n, threadsize, f);
}
template <class F>
void par_for(std::size_t n, F f)
{
const int min_grain = 8;
par_for(n, min_grain, f);
}
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/include/migraphx/pass.hpp
View file @ 087c205e
......@@ -105,7 +105,13 @@ struct pass
void apply(program& p) const
{
assert((*this).private_detail_te_handle_mem_var);
return (*this).private_detail_te_get_handle().apply(p);
(*this).private_detail_te_get_handle().apply(p);
}
friend bool is_shared(const pass& private_detail_x, const pass& private_detail_y)
{
return private_detail_x.private_detail_te_handle_mem_var ==
private_detail_y.private_detail_te_handle_mem_var;
}
private:
......@@ -149,7 +155,7 @@ struct pass
std::string name() const override { return private_detail_te_value.name(); }
void apply(program& p) const override { return private_detail_te_value.apply(p); }
void apply(program& p) const override { private_detail_te_value.apply(p); }
PrivateDetailTypeErasedT private_detail_te_value;
};
......
src/include/migraphx/program.hpp
View file @ 087c205e
......@@ -91,16 +91,22 @@ struct program
shape get_shape() const;
context& get_context() const;
instruction_ref validate() const;
void compile(const target& t, tracer trace = tracer{});
void finalize();
void perf_report(std::ostream& os, std::size_t n, parameter_map params) const;
void debug_print() const;
void debug_print(instruction_ref ins) const;
void debug_print(const std::vector<instruction_ref>& inss) const;
void dry_run(parameter_map params) 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); }
......
src/include/migraphx/rewrite_rnn.hpp 0 → 100644
View file @ 087c205e
#ifndef MIGRAPHX_GUARD_RTGLIB_REWRITE_RNN_HPP
#define MIGRAPHX_GUARD_RTGLIB_REWRITE_RNN_HPP
#include <string>
#include <vector>
#include <migraphx/instruction_ref.hpp>
#include <migraphx/operators.hpp>
#include <migraphx/config.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
struct program;
/**
* Rewrite rnn to gemm and add.
*/
struct rewrite_rnn
{
std::string name() const { return "rewrite_rnn"; }
void apply(program& prog) const;
private:
// for vanilla rnn operators
void apply_vanilla_rnn(program& prog, instruction_ref ins) const;
std::vector<instruction_ref> vanilla_rnn_cell(bool is_forward,
program& prog,
instruction_ref ins,
instruction_ref input,
instruction_ref w,
instruction_ref r,
instruction_ref bias,
instruction_ref ih,
operation& actv_func) const;
std::vector<operation> vanilla_rnn_actv_funcs(instruction_ref ins) const;
// for gru operators
void apply_gru(program& prog, instruction_ref ins) const;
std::vector<instruction_ref> gru_cell(bool is_forward,
program& prog,
instruction_ref ins,
std::vector<instruction_ref> inputs,
int linear_before_reset,
const operation& actv_func1,
const operation& actv_func2) const;
std::vector<operation> gru_actv_funcs(instruction_ref ins) const;
// for lstm operators
void apply_lstm(program& prog, instruction_ref ins) const;
std::vector<instruction_ref> lstm_cell(bool is_forward,
program& prog,
instruction_ref ins,
std::vector<instruction_ref> inputs,
const operation& actv_func1,
const operation& actv_func2,
const operation& actv_func3) const;
std::vector<operation> lstm_actv_funcs(instruction_ref ins) const;
};
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/include/migraphx/shape.hpp
View file @ 087c205e
......@@ -35,22 +35,22 @@ struct shape
m(uint64_type, uint64_t)
// clang-format on
#define MIGRAPHX_SHAPE_ENUM_TYPES(x, t) x,
#define MIGRAPHX_SHAPE_GENERATE_ENUM_TYPES(x, t) x,
enum type_t
{
MIGRAPHX_SHAPE_VISIT_TYPES(MIGRAPHX_SHAPE_ENUM_TYPES)
MIGRAPHX_SHAPE_VISIT_TYPES(MIGRAPHX_SHAPE_GENERATE_ENUM_TYPES)
};
#undef MIGRAPHX_SHAPE_ENUM_TYPES
#undef MIGRAPHX_SHAPE_GENERATE_ENUM_TYPES
template <class T, class = void>
struct get_type;
#define MIGRAPHX_SHAPE_GET_TYPE(x, t) \
#define MIGRAPHX_SHAPE_GENERATE_GET_TYPE(x, t) \
template <class T> \
struct get_type<t, T> : std::integral_constant<type_t, x> \
{ \
};
MIGRAPHX_SHAPE_VISIT_TYPES(MIGRAPHX_SHAPE_GET_TYPE)
#undef MIGRAPHX_SHAPE_GET_TYPE
MIGRAPHX_SHAPE_VISIT_TYPES(MIGRAPHX_SHAPE_GENERATE_GET_TYPE)
#undef MIGRAPHX_SHAPE_GENERATE_GET_TYPE
template <class T>
struct get_type<const T> : get_type<T>
......@@ -62,6 +62,19 @@ struct shape
shape(type_t t, std::vector<std::size_t> l);
shape(type_t t, std::vector<std::size_t> l, std::vector<std::size_t> s);
template <class Range>
shape(type_t t, const Range& l) : shape(t, std::vector<std::size_t>(l.begin(), l.end()))
{
}
template <class Range1, class Range2>
shape(type_t t, const Range1& l, const Range2& s)
: shape(t,
std::vector<std::size_t>(l.begin(), l.end()),
std::vector<std::size_t>(s.begin(), s.end()))
{
}
type_t type() const;
const std::vector<std::size_t>& lens() const;
const std::vector<std::size_t>& strides() const;
......@@ -141,6 +154,8 @@ struct shape
{
return reinterpret_cast<const T*>(buffer) + n;
}
type_t type_enum() const { return get_type<T>{}; }
};
template <class Visitor>
......@@ -148,14 +163,22 @@ struct shape
{
switch(this->type())
{
#define MIGRAPHX_SHAPE_VISITOR_CASE(x, t) \
#define MIGRAPHX_SHAPE_GENERATE_VISITOR_CASE(x, t) \
case x: v(as<t>()); return;
MIGRAPHX_SHAPE_VISIT_TYPES(MIGRAPHX_SHAPE_VISITOR_CASE)
#undef MIGRAPHX_SHAPE_VISITOR_CASE
MIGRAPHX_SHAPE_VISIT_TYPES(MIGRAPHX_SHAPE_GENERATE_VISITOR_CASE)
#undef MIGRAPHX_SHAPE_GENERATE_VISITOR_CASE
}
MIGRAPHX_THROW("Unknown type");
}
template <class Visitor>
static void visit_types(Visitor v)
{
#define MIGRAPHX_SHAPE_GENERATE_VISITOR_ALL(x, t) v(as<t>());
MIGRAPHX_SHAPE_VISIT_TYPES(MIGRAPHX_SHAPE_GENERATE_VISITOR_ALL)
#undef MIGRAPHX_SHAPE_GENERATE_VISITOR_ALL
}
private:
std::shared_ptr<const shape_impl> impl;
......
src/include/migraphx/target.hpp
View file @ 087c205e
......@@ -127,6 +127,12 @@ struct target
return (*this).private_detail_te_get_handle().get_context();
}
friend bool is_shared(const target& private_detail_x, const target& private_detail_y)
{
return private_detail_x.private_detail_te_handle_mem_var ==
private_detail_y.private_detail_te_handle_mem_var;
}
private:
struct private_detail_te_handle_base_type
{
......
src/include/migraphx/tensor_view.hpp
View file @ 087c205e
......@@ -124,6 +124,8 @@ struct tensor_view
return m_data + this->size();
}
std::vector<T> to_vector() const { return std::vector<T>(this->begin(), this->end()); }
friend std::ostream& operator<<(std::ostream& os, const tensor_view<T>& x)
{
if(!x.empty())
......@@ -164,7 +166,7 @@ bool operator!=(const tensor_view<T>& x, const tensor_view<U>& y)
}
template <class T>
tensor_view<T> make_view(shape s, T* data)
tensor_view<T> make_view(const shape& s, T* data)
{
return {s, data};
}
......
src/include/migraphx/type_name.hpp
View file @ 087c205e
......@@ -18,7 +18,7 @@ const std::string& get_type_name()
name = typeid(PrivateMigraphTypeNameProbe).name();
name = name.substr(7);
#else
const char parameter_name[] = "PrivateMigraphTypeNameProbe =";
const char parameter_name[] = "PrivateMigraphTypeNameProbe ="; // NOLINT
name = __PRETTY_FUNCTION__;
......
src/instruction.cpp
View file @ 087c205e
......@@ -97,7 +97,7 @@ const std::vector<instruction_ref>& instruction::outputs() const { return output
bool operator==(const instruction& x, const instruction& y)
{
if(not(x.result == y.result and x.op == y.op and x.arguments == y.arguments))
if(std::tie(x.result, x.op, x.arguments) != std::tie(y.result, y.op, y.arguments))
return false;
if(x.name() == "@literal")
return x.lit == y.lit;
......@@ -162,25 +162,54 @@ void instruction::replace_argument(instruction_ref old, instruction_ref new_ins)
old->remove_output(*this);
}
std::vector<shape> compute_shapes(const std::vector<instruction_ref>& args)
argument instruction::eval() const
{
std::vector<shape> shapes(args.size());
std::transform(
args.begin(), args.end(), shapes.begin(), [](instruction_ref i) { return i->get_shape(); });
return shapes;
if(op.name() == "@literal")
{
return this->get_literal().get_argument();
}
if(is_context_free(op))
{
std::vector<argument> args;
for(auto&& arg : this->inputs())
{
argument a = arg->eval();
if(a.empty())
return {};
args.push_back(a);
}
return op.compute(result, args);
}
return {};
}
instruction_ref instruction::get_output_alias(instruction_ref ins)
void instruction::finalize(context& ctx)
{
auto i = ins->get_operator().output_alias(compute_shapes(ins->inputs()));
if(has_finalize(this->op))
this->op.finalize(ctx, this->get_shape(), to_shapes(this->inputs()));
}
instruction_ref instruction::get_output_alias(instruction_ref ins, bool shallow)
{
auto i = ins->get_operator().output_alias(to_shapes(ins->inputs()));
if(i < 0)
return ins;
if(shallow)
return ins->inputs().at(i);
return get_output_alias(ins->inputs().at(i));
}
std::vector<shape> to_shapes(const std::vector<instruction_ref>& args)
{
std::vector<shape> shapes(args.size());
std::transform(
args.begin(), args.end(), shapes.begin(), [](instruction_ref i) { return i->get_shape(); });
return shapes;
}
shape compute_shape(const operation& op, const std::vector<instruction_ref>& args)
{
return op.compute_shape(compute_shapes(args));
return op.compute_shape(to_shapes(args));
}
} // namespace MIGRAPHX_INLINE_NS
......
src/onnx/mnist.cpp
View file @ 087c205e
......@@ -14,7 +14,10 @@
auto reverse_int(unsigned int i)
{
unsigned char c1, c2, c3, c4;
unsigned char c1;
unsigned char c2;
unsigned char c3;
unsigned char c4;
c1 = i & 255u;
c2 = (i >> 8u) & 255u;
c3 = (i >> 16u) & 255u;
......@@ -32,7 +35,9 @@ read_mnist_images(const std::string& full_path, int& number_of_images, int& imag
if(file.is_open())
{
int magic_number = 0, n_rows = 0, n_cols = 0;
int magic_number = 0;
int n_rows = 0;
int n_cols = 0;
file.read(reinterpret_cast<char*>(&magic_number), sizeof(magic_number));
magic_number = reverse_int(magic_number);
......
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