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"cmake/DoxygenDoc.cmake" did not exist on "61487e0a00f1956d413fa015884e8e95a9636b13"
Commit 7d0847de authored by Khalique's avatar Khalique
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Merge branch 'develop' of https://github.com/ROCmSoftwarePlatform/AMDMIGraphX into group_conv

parents 8276bc83 436b459e
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View file @ 7d0847de
...@@ -113,6 +113,19 @@ rocmtest tidy: rocmnode('rocmtest') { cmake_build -> ...@@ -113,6 +113,19 @@ rocmtest tidy: rocmnode('rocmtest') { cmake_build ->
def cmake_linker_flags = "-DCMAKE_EXE_LINKER_FLAGS='${linker_flags}' -DCMAKE_SHARED_LINKER_FLAGS='${linker_flags}'" def cmake_linker_flags = "-DCMAKE_EXE_LINKER_FLAGS='${linker_flags}' -DCMAKE_SHARED_LINKER_FLAGS='${linker_flags}'"
// TODO: Add bounds-strict // TODO: Add bounds-strict
def sanitizers = "undefined,address" def sanitizers = "undefined,address"
cmake_build("g++-7", "-DCMAKE_BUILD_TYPE=debug ${cmake_linker_flags} -DCMAKE_CXX_FLAGS_DEBUG='-g -fno-omit-frame-pointer -fsanitize-address-use-after-scope -fsanitize=${sanitizers} -fno-sanitize-recover=${sanitizers}'") def debug_flags = "-g -fprofile-arcs -ftest-coverage -fno-omit-frame-pointer -fsanitize-address-use-after-scope -fsanitize=${sanitizers} -fno-sanitize-recover=${sanitizers}"
cmake_build("g++-7", "-DCMAKE_BUILD_TYPE=debug ${cmake_linker_flags} -DCMAKE_CXX_FLAGS_DEBUG='${debug_flags}'")
}
stage('Codecov') {
env.CODECOV_TOKEN="8545af1c-f90b-4345-92a5-0d075503ca56"
sh '''
cd build
lcov --directory . --capture --output-file coverage.info
lcov --remove coverage.info '/usr/*' --output-file coverage.info
lcov --list coverage.info
curl -s https://codecov.io/bash | bash
echo "Uploaded"
'''
} }
} }
src/include/migraphx/operators.hpp
View file @ 7d0847de
...@@ -235,10 +235,28 @@ struct leaky_relu ...@@ -235,10 +235,28 @@ struct leaky_relu
check_shapes{inputs, *this}.has(1); check_shapes{inputs, *this}.has(1);
return inputs.front(); 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 pack(f(self.alpha, "alpha"));
return os;
} }
}; };
...@@ -624,6 +642,11 @@ struct exp : unary ...@@ -624,6 +642,11 @@ struct exp : unary
std::string name() const { return "exp"; } std::string name() const { return "exp"; }
}; };
struct log : unary
{
std::string name() const { return "log"; }
};
struct sin : unary struct sin : unary
{ {
std::string name() const { return "sin"; } std::string name() const { return "sin"; }
...@@ -654,6 +677,16 @@ struct atan : unary ...@@ -654,6 +677,16 @@ struct atan : unary
std::string name() const { return "atan"; } 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 struct tanh : unary
{ {
std::string name() const { return "tanh"; } std::string name() const { return "tanh"; }
...@@ -864,6 +897,16 @@ struct div : binary ...@@ -864,6 +897,16 @@ struct div : binary
std::string name() const { return "div"; } 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 struct load
{ {
shape s; shape s;
......
src/onnx/onnx.cpp
View file @ 7d0847de
...@@ -52,18 +52,35 @@ struct onnx_parser ...@@ -52,18 +52,35 @@ struct onnx_parser
{ {
add_generic_op("MatMul", op::dot{}); add_generic_op("MatMul", op::dot{});
add_generic_op("Relu", op::relu{}); add_generic_op("Relu", op::relu{});
add_generic_op("Sigmoid", op::sigmoid{});
add_generic_op("Abs", op::abs{});
add_generic_op("Exp", op::exp{});
add_generic_op("Log", op::log{});
// disable dropout for inference // disable dropout for inference
add_generic_op("Dropout", op::identity{}); add_generic_op("Dropout", op::identity{});
add_generic_op("Identity", op::identity{}); add_generic_op("Identity", op::identity{});
add_generic_op("Sin", op::sin{});
add_broadcastable_binary_op("Add", op::add{}); add_generic_op("Cos", op::cos{});
add_broadcastable_binary_op("Div", op::div{}); add_generic_op("Tan", op::tan{});
add_broadcastable_binary_op("Mul", op::mul{}); add_generic_op("Sinh", op::sinh{});
add_broadcastable_binary_op("Sub", op::sub{}); add_generic_op("Cosh", op::cosh{});
add_broadcastable_binary_op("Sum", op::add{}); add_generic_op("Tanh", op::tanh{});
add_generic_op("Asin", op::asin{});
add_generic_op("Acos", op::acos{});
add_generic_op("Atan", op::atan{});
add_binary_op("Add", op::add{});
add_binary_op("Div", op::div{});
add_binary_op("Mul", op::mul{});
add_binary_op("Sub", op::sub{});
add_variadic_op("Sum", op::add{});
add_variadic_op("Max", op::max{});
add_variadic_op("Min", op::min{});
add_mem_op("ImageScaler", &onnx_parser::parse_imagescaler); add_mem_op("ImageScaler", &onnx_parser::parse_imagescaler);
add_mem_op("LeakyRelu", &onnx_parser::parse_leaky_relu); add_mem_op("LeakyRelu", &onnx_parser::parse_leaky_relu);
add_mem_op("Elu", &onnx_parser::parse_elu);
add_mem_op("Constant", &onnx_parser::parse_constant); add_mem_op("Constant", &onnx_parser::parse_constant);
add_mem_op("Conv", &onnx_parser::parse_conv); add_mem_op("Conv", &onnx_parser::parse_conv);
add_mem_op("MaxPool", &onnx_parser::parse_pooling); add_mem_op("MaxPool", &onnx_parser::parse_pooling);
...@@ -95,8 +112,9 @@ struct onnx_parser ...@@ -95,8 +112,9 @@ struct onnx_parser
return std::mem_fn(f)(*this, name, std::forward<decltype(xs)>(xs)...); return std::mem_fn(f)(*this, name, std::forward<decltype(xs)>(xs)...);
}); });
} }
template <class T> template <class T>
void add_broadcastable_binary_op(std::string name, T x) void add_binary_op(std::string name, T x)
{ {
ops.emplace(name, [this, x](attribute_map attributes, std::vector<instruction_ref> args) { ops.emplace(name, [this, x](attribute_map attributes, std::vector<instruction_ref> args) {
if(args.size() != 2) if(args.size() != 2)
...@@ -115,49 +133,57 @@ struct onnx_parser ...@@ -115,49 +133,57 @@ struct onnx_parser
} }
return prog.add_instruction(x, args); return prog.add_instruction(x, args);
} }
else if(args[0]->get_shape() != args[1]->get_shape())
{
// Example:
// s0 = (3,2,4,5) and s1 = (2,1,1)
//
// In this case we need to broadcast (:,1,1) portion of
// s1 plus broadcast the 1st dimension of s1
// giving output_lens = (3,2,4,5)
//
// Another example:
// s0 = (3,2,1,5) and s1 = (2,7,5)
// In this case we need to broadcast the (:,:,1:,:) axis
// of s0 plus the 1st dimension of s1 giving
// output_lens = (3,2,7,5)
//
// Get lengths for both arguments
const std::vector<std::size_t>* s0 = &args[0]->get_shape().lens();
const std::vector<std::size_t>* s1 = &args[1]->get_shape().lens();
// Make sure s0 is the smaller size
if(s0->size() > s1->size())
std::swap(s0, s1);
// Copy the larger vector to output_lens
std::vector<std::size_t> output_lens = *s1;
auto offset = s1->size() - s0->size();
std::transform(s0->begin(),
s0->end(),
s1->begin() + offset,
output_lens.begin() + offset,
[](auto a, auto b) { return std::max(a, b); });
auto l0 = prog.add_instruction(op::multibroadcast{output_lens}, args[0]);
auto l1 = prog.add_instruction(op::multibroadcast{output_lens}, args[1]);
return prog.add_instruction(x, l0, l1);
}
else else
{ {
return prog.add_instruction(x, args); return add_broadcastable_binary_op(args[0], args[1], x);
} }
}); });
} }
template <class T>
instruction_ref add_broadcastable_binary_op(instruction_ref arg0, instruction_ref arg1, T x)
{
if(arg0->get_shape() != arg1->get_shape())
{
// Example:
// s0 = (3,2,4,5) and s1 = (2,1,1)
//
// In this case we need to broadcast (:,1,1) portion of
// s1 plus broadcast the 1st dimension of s1
// giving output_lens = (3,2,4,5)
//
// Another example:
// s0 = (3,2,1,5) and s1 = (2,7,5)
// In this case we need to broadcast the (:,:,1:,:) axis
// of s0 plus the 1st dimension of s1 giving
// output_lens = (3,2,7,5)
//
// Get lengths for both arguments
const std::vector<std::size_t>* s0 = &arg0->get_shape().lens();
const std::vector<std::size_t>* s1 = &arg1->get_shape().lens();
// Make sure s0 is the smaller size
if(s0->size() > s1->size())
std::swap(s0, s1);
std::vector<std::size_t> output_lens(s1->size());
auto offset = s1->size() - s0->size();
std::transform(s0->begin(),
s0->end(),
s1->begin() + offset,
output_lens.begin() + offset,
[](auto a, auto b) { return std::max(a, b); });
auto l0 = prog.add_instruction(op::multibroadcast{output_lens}, arg0);
auto l1 = prog.add_instruction(op::multibroadcast{output_lens}, arg1);
return prog.add_instruction(x, l0, l1);
}
else
{
return prog.add_instruction(x, {arg0, arg1});
}
}
template <class T> template <class T>
void add_generic_op(std::string name, T x) void add_generic_op(std::string name, T x)
{ {
...@@ -166,6 +192,19 @@ struct onnx_parser ...@@ -166,6 +192,19 @@ struct onnx_parser
}); });
} }
template <class T>
void add_variadic_op(std::string name, T x)
{
ops.emplace(name, [this, x](attribute_map, std::vector<instruction_ref> args) {
return std::accumulate(std::next(args.begin()),
args.end(),
args.front(),
[this, x](instruction_ref a, instruction_ref b) {
return add_broadcastable_binary_op(a, b, x);
});
});
}
instruction_ref instruction_ref
parse_softmax(const std::string&, const attribute_map&, std::vector<instruction_ref> args) parse_softmax(const std::string&, const attribute_map&, std::vector<instruction_ref> args)
{ {
...@@ -440,6 +479,18 @@ struct onnx_parser ...@@ -440,6 +479,18 @@ struct onnx_parser
return prog.add_instruction(op, args.front()); return prog.add_instruction(op, args.front());
} }
instruction_ref
parse_elu(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
{
float alpha = 1.0; // default alpha val for elu
if(contains(attributes, "alpha"))
{
alpha = parse_value(attributes.at("alpha")).at<float>();
}
op::elu op{alpha};
return prog.add_instruction(op, args.front());
}
instruction_ref parse_imagescaler(const std::string&, instruction_ref parse_imagescaler(const std::string&,
attribute_map attributes, attribute_map attributes,
std::vector<instruction_ref> args) std::vector<instruction_ref> args)
...@@ -625,6 +676,11 @@ struct onnx_parser ...@@ -625,6 +676,11 @@ struct onnx_parser
static literal parse_tensor(const onnx::TensorProto& t) static literal parse_tensor(const onnx::TensorProto& t)
{ {
std::vector<std::size_t> dims(t.dims().begin(), t.dims().end()); std::vector<std::size_t> dims(t.dims().begin(), t.dims().end());
// in case of scalar constants in onnx file, use dims=1 to fill initializer data
if(dims.empty())
{
dims = {1};
}
if(t.has_raw_data()) if(t.has_raw_data())
{ {
const std::string& s = t.raw_data(); const std::string& s = t.raw_data();
......
src/targets/cpu/lowering.cpp
View file @ 7d0847de
...@@ -19,6 +19,14 @@ T zero(const T&) ...@@ -19,6 +19,14 @@ T zero(const T&)
return T(0); return T(0);
} }
template <class T>
typename std::conditional_t<std::is_integral<T>{}, std::make_signed<T>, std::enable_if<true, T>>::
type
make_signed(T x)
{
return x;
}
// //
// cpu implemenataion of batch norm for inference // cpu implemenataion of batch norm for inference
// //
...@@ -339,7 +347,7 @@ struct abs_op ...@@ -339,7 +347,7 @@ struct abs_op
std::string name() const { return "cpu::abs"; } std::string name() const { return "cpu::abs"; }
auto fcn() const auto fcn() const
{ {
return [](auto x) { return std::abs(x); }; return [](auto x) { return std::abs(make_signed(x)); };
} }
}; };
...@@ -352,6 +360,15 @@ struct exp_op ...@@ -352,6 +360,15 @@ struct exp_op
} }
}; };
struct log_op
{
std::string name() const { return "cpu::log"; }
auto fcn() const
{
return [](auto x) { return std::log(x); };
}
};
struct sin_op struct sin_op
{ {
std::string name() const { return "cpu::sin"; } std::string name() const { return "cpu::sin"; }
...@@ -406,6 +423,24 @@ struct atan_op ...@@ -406,6 +423,24 @@ struct atan_op
} }
}; };
struct sinh_op
{
std::string name() const { return "cpu::sinh"; }
auto fcn() const
{
return [](auto x) { return std::sinh(x); };
}
};
struct cosh_op
{
std::string name() const { return "cpu::cosh"; }
auto fcn() const
{
return [](auto x) { return std::cosh(x); };
}
};
struct tanh_op struct tanh_op
{ {
std::string name() const { return "cpu::tanh"; } std::string name() const { return "cpu::tanh"; }
...@@ -453,6 +488,17 @@ struct leaky_relu_op ...@@ -453,6 +488,17 @@ struct leaky_relu_op
} }
}; };
struct elu_op
{
op::elu op;
std::string name() const { return "cpu::elu"; }
auto fcn() const
{
auto& a = op.alpha;
return [a](auto x) { return x > 0 ? x : a * std::expm1(x); };
}
};
template <typename Op> template <typename Op>
struct cpu_unary struct cpu_unary
{ {
...@@ -545,6 +591,24 @@ struct div_op ...@@ -545,6 +591,24 @@ struct div_op
} }
}; };
struct max_op
{
std::string name() const { return "max"; }
auto fcn() const
{
return [](auto x, auto y) { return std::max(x, y); };
}
};
struct min_op
{
std::string name() const { return "min"; }
auto fcn() const
{
return [](auto x, auto y) { return std::min(x, y); };
}
};
template <typename Op> template <typename Op>
struct cpu_binary struct cpu_binary
{ {
...@@ -599,19 +663,29 @@ struct cpu_apply ...@@ -599,19 +663,29 @@ struct cpu_apply
apply_map["contiguous"] = extend_op<cpu_contiguous, op::contiguous>(); apply_map["contiguous"] = extend_op<cpu_contiguous, op::contiguous>();
apply_map["concat"] = extend_op<cpu_concat, op::concat>(); apply_map["concat"] = extend_op<cpu_concat, op::concat>();
apply_map["leaky_relu"] = extend_op<cpu_unary<leaky_relu_op>, op::leaky_relu>(); apply_map["leaky_relu"] = extend_op<cpu_unary<leaky_relu_op>, op::leaky_relu>();
apply_map["elu"] = extend_op<cpu_unary<elu_op>, op::elu>();
apply_map["identity"] = simple_op<cpu_unary<identity_op>>(); apply_map["identity"] = simple_op<cpu_unary<identity_op>>();
apply_map["abs"] = simple_op<cpu_unary<abs_op>>();
apply_map["sinh"] = simple_op<cpu_unary<sinh_op>>();
apply_map["cosh"] = simple_op<cpu_unary<cosh_op>>();
apply_map["tanh"] = simple_op<cpu_unary<tanh_op>>(); apply_map["tanh"] = simple_op<cpu_unary<tanh_op>>();
apply_map["sigmoid"] = simple_op<cpu_unary<sigmoid_op>>(); apply_map["sigmoid"] = simple_op<cpu_unary<sigmoid_op>>();
apply_map["exp"] = simple_op<cpu_unary<exp_op>>(); apply_map["exp"] = simple_op<cpu_unary<exp_op>>();
apply_map["log"] = simple_op<cpu_unary<log_op>>();
apply_map["neg"] = simple_op<cpu_unary<neg_op>>(); apply_map["neg"] = simple_op<cpu_unary<neg_op>>();
apply_map["sin"] = simple_op<cpu_unary<sin_op>>(); apply_map["sin"] = simple_op<cpu_unary<sin_op>>();
apply_map["cos"] = simple_op<cpu_unary<cos_op>>(); apply_map["cos"] = simple_op<cpu_unary<cos_op>>();
apply_map["tan"] = simple_op<cpu_unary<tan_op>>(); apply_map["tan"] = simple_op<cpu_unary<tan_op>>();
apply_map["asin"] = simple_op<cpu_unary<asin_op>>();
apply_map["acos"] = simple_op<cpu_unary<acos_op>>();
apply_map["atan"] = simple_op<cpu_unary<atan_op>>();
apply_map["relu"] = simple_op<cpu_unary<relu_op>>(); apply_map["relu"] = simple_op<cpu_unary<relu_op>>();
apply_map["add"] = simple_op<cpu_binary<add_op>>(); apply_map["add"] = simple_op<cpu_binary<add_op>>();
apply_map["sub"] = simple_op<cpu_binary<sub_op>>(); apply_map["sub"] = simple_op<cpu_binary<sub_op>>();
apply_map["mul"] = simple_op<cpu_binary<mul_op>>(); apply_map["mul"] = simple_op<cpu_binary<mul_op>>();
apply_map["div"] = simple_op<cpu_binary<div_op>>(); apply_map["div"] = simple_op<cpu_binary<div_op>>();
apply_map["max"] = simple_op<cpu_binary<max_op>>();
apply_map["min"] = simple_op<cpu_binary<min_op>>();
apply_map["softmax"] = simple_op<softmax2d>(); apply_map["softmax"] = simple_op<softmax2d>();
} }
......
src/targets/gpu/CMakeLists.txt
View file @ 7d0847de
...@@ -12,7 +12,18 @@ endif() ...@@ -12,7 +12,18 @@ endif()
add_library(migraphx_device add_library(migraphx_device
device/add.cpp device/add.cpp
device/max.cpp
device/min.cpp
device/exp.cpp
device/log.cpp
device/sin.cpp device/sin.cpp
device/cos.cpp
device/tan.cpp
device/sinh.cpp
device/cosh.cpp
device/asin.cpp
device/acos.cpp
device/atan.cpp
device/add_relu.cpp device/add_relu.cpp
device/contiguous.cpp device/contiguous.cpp
device/mul.cpp device/mul.cpp
...@@ -38,12 +49,13 @@ add_library(migraphx_gpu ...@@ -38,12 +49,13 @@ add_library(migraphx_gpu
concat.cpp concat.cpp
relu.cpp relu.cpp
leaky_relu.cpp leaky_relu.cpp
add.cpp tanh.cpp
sin.cpp
mul.cpp
batchnorm.cpp batchnorm.cpp
write_literals.cpp write_literals.cpp
rocblas.cpp rocblas.cpp
sigmoid.cpp
abs.cpp
elu.cpp
) )
set_target_properties(migraphx_gpu PROPERTIES EXPORT_NAME gpu) set_target_properties(migraphx_gpu PROPERTIES EXPORT_NAME gpu)
rocm_clang_tidy_check(migraphx_gpu) rocm_clang_tidy_check(migraphx_gpu)
......
src/targets/gpu/abs.cpp 0 → 100644
View file @ 7d0847de
#include <migraphx/gpu/abs.hpp>
#include <migraphx/operators.hpp>
#include <migraphx/manage_ptr.hpp>
#include <migraphx/gpu/miopen.hpp>
#include <utility>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
shape miopen_abs::compute_shape(const std::vector<shape>& inputs) const
{
check_shapes{inputs, *this}.has(2).not_broadcasted();
return inputs.at(1);
}
argument miopen_abs::compute(context& ctx,
const shape& output_shape,
const std::vector<argument>& args) const
{
float alpha = 1, beta = 0;
auto x_desc = make_tensor(args[0].get_shape());
auto y_desc = make_tensor(output_shape);
miopenActivationForward(ctx.get_stream().get_miopen(),
ad.get(),
&alpha,
x_desc.get(),
args[0].implicit(),
&beta,
y_desc.get(),
args[1].implicit());
return args[1];
}
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
src/targets/gpu/add.cpp deleted 100644 → 0
View file @ 8276bc83
#include <migraphx/gpu/add.hpp>
#include <migraphx/operators.hpp>
#include <migraphx/manage_ptr.hpp>
#include <migraphx/config.hpp>
#include <migraphx/gpu/miopen.hpp>
#include <utility>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
shape hip_add::compute_shape(const std::vector<shape>& inputs) const
{
// check_shapes{inputs, *this}.has(3).standard();
check_shapes{inputs, *this}.has(3);
return inputs.at(0);
}
argument hip_add::compute(context& ctx, const shape&, const std::vector<argument>& args) const
{
device::add(ctx.get_stream().get(), args[2], args[0], args[1]);
return args[2];
}
shape miopen_add::compute_shape(const std::vector<shape>& inputs) const
{
check_shapes{inputs, *this}.has(3).not_broadcasted();
return inputs.at(0);
}
argument miopen_add::compute(context& ctx,
const shape& output_shape,
const std::vector<argument>& args) const
{
float alpha = 1, beta = 0;
auto a_desc = make_tensor(args[0].get_shape());
auto b_desc = make_tensor(args[1].get_shape());
auto c_desc = make_tensor(output_shape);
miopenOpTensor(ctx.get_stream().get_miopen(),
miopenTensorOpAdd,
&alpha,
a_desc.get(),
args[0].implicit(),
&alpha,
b_desc.get(),
args[1].implicit(),
&beta,
c_desc.get(),
args[2].implicit());
return args[2];
}
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
src/targets/gpu/device/acos.cpp 0 → 100644
View file @ 7d0847de
#include <migraphx/gpu/device/acos.hpp>
#include <migraphx/gpu/device/nary.hpp>
#include <migraphx/gpu/device/types.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
namespace device {
void acos(hipStream_t stream, const argument& result, const argument& arg)
{
nary(stream, result, arg)([](auto x) { return ::acos(to_hip_type(x)); });
}
} // namespace device
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
src/targets/gpu/device/asin.cpp 0 → 100644
View file @ 7d0847de
#include <migraphx/gpu/device/asin.hpp>
#include <migraphx/gpu/device/nary.hpp>
#include <migraphx/gpu/device/types.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
namespace device {
void asin(hipStream_t stream, const argument& result, const argument& arg)
{
nary(stream, result, arg)([](auto x) { return ::asin(to_hip_type(x)); });
}
} // namespace device
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
src/targets/gpu/device/atan.cpp 0 → 100644
View file @ 7d0847de
#include <migraphx/gpu/device/atan.hpp>
#include <migraphx/gpu/device/nary.hpp>
#include <migraphx/gpu/device/types.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
namespace device {
void atan(hipStream_t stream, const argument& result, const argument& arg)
{
nary(stream, result, arg)([](auto x) { return ::atan(to_hip_type(x)); });
}
} // namespace device
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
src/targets/gpu/device/cos.cpp 0 → 100644
View file @ 7d0847de
#include <migraphx/gpu/device/cos.hpp>
#include <migraphx/gpu/device/nary.hpp>
#include <migraphx/gpu/device/types.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
namespace device {
void cos(hipStream_t stream, const argument& result, const argument& arg)
{
nary(stream, result, arg)([](auto x) { return ::cos(to_hip_type(x)); });
}
} // namespace device
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
src/targets/gpu/device/cosh.cpp 0 → 100644
View file @ 7d0847de
#include <migraphx/gpu/device/cosh.hpp>
#include <migraphx/gpu/device/nary.hpp>
#include <migraphx/gpu/device/types.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
namespace device {
void cosh(hipStream_t stream, const argument& result, const argument& arg)
{
nary(stream, result, arg)([](auto x) { return ::cosh(to_hip_type(x)); });
}
} // namespace device
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
src/targets/gpu/device/exp.cpp 0 → 100644
View file @ 7d0847de
#include <migraphx/gpu/device/exp.hpp>
#include <migraphx/gpu/device/nary.hpp>
#include <migraphx/gpu/device/types.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
namespace device {
void exp(hipStream_t stream, const argument& result, const argument& arg)
{
nary(stream, result, arg)([](auto x) { return ::exp(to_hip_type(x)); });
}
} // namespace device
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
src/targets/gpu/device/log.cpp 0 → 100644
View file @ 7d0847de
#include <migraphx/gpu/device/log.hpp>
#include <migraphx/gpu/device/nary.hpp>
#include <migraphx/gpu/device/types.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
namespace device {
void log(hipStream_t stream, const argument& result, const argument& arg)
{
nary(stream, result, arg)([](auto x) { return ::log(to_hip_type(x)); });
}
} // namespace device
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
src/targets/gpu/device/max.cpp 0 → 100644
View file @ 7d0847de
#include <migraphx/gpu/device/max.hpp>
#include <migraphx/gpu/device/nary.hpp>
#include <migraphx/gpu/device/types.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
namespace device {
void max(hipStream_t stream, const argument& result, const argument& arg1, const argument& arg2)
{
nary(stream, result, arg1, arg2)(
[](auto x, auto y) { return std::max(to_hip_type(x), to_hip_type(y)); });
}
} // namespace device
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
src/targets/gpu/device/min.cpp 0 → 100644
View file @ 7d0847de
#include <migraphx/gpu/device/min.hpp>
#include <migraphx/gpu/device/nary.hpp>
#include <migraphx/gpu/device/types.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
namespace device {
void min(hipStream_t stream, const argument& result, const argument& arg1, const argument& arg2)
{
nary(stream, result, arg1, arg2)(
[](auto x, auto y) { return std::min(to_hip_type(x), to_hip_type(y)); });
}
} // namespace device
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
src/targets/gpu/device/sinh.cpp 0 → 100644
View file @ 7d0847de
#include <migraphx/gpu/device/sinh.hpp>
#include <migraphx/gpu/device/nary.hpp>
#include <migraphx/gpu/device/types.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
namespace device {
void sinh(hipStream_t stream, const argument& result, const argument& arg)
{
nary(stream, result, arg)([](auto x) { return ::sinh(to_hip_type(x)); });
}
} // namespace device
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
src/targets/gpu/device/tan.cpp 0 → 100644
View file @ 7d0847de
#include <migraphx/gpu/device/tan.hpp>
#include <migraphx/gpu/device/nary.hpp>
#include <migraphx/gpu/device/types.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
namespace device {
void tan(hipStream_t stream, const argument& result, const argument& arg)
{
nary(stream, result, arg)([](auto x) { return ::tan(to_hip_type(x)); });
}
} // namespace device
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
src/targets/gpu/elu.cpp 0 → 100644
View file @ 7d0847de
#include <migraphx/gpu/elu.hpp>
#include <migraphx/operators.hpp>
#include <migraphx/manage_ptr.hpp>
#include <migraphx/gpu/miopen.hpp>
#include <utility>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
shape miopen_elu::compute_shape(const std::vector<shape>& inputs) const
{
check_shapes{inputs, *this}.has(2).not_broadcasted();
return inputs.at(1);
}
argument miopen_elu::compute(context& ctx,
const shape& output_shape,
const std::vector<argument>& args) const
{
float alpha = 1, beta = 0;
auto x_desc = make_tensor(args[0].get_shape());
auto y_desc = make_tensor(output_shape);
miopenActivationForward(ctx.get_stream().get_miopen(),
ad.get(),
&alpha,
x_desc.get(),
args[0].implicit(),
&beta,
y_desc.get(),
args[1].implicit());
return args[1];
}
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
src/targets/gpu/include/migraphx/gpu/abs.hpp 0 → 100644
View file @ 7d0847de
#ifndef MIGRAPHX_GUARD_RTGLIB_ABS_HPP
#define MIGRAPHX_GUARD_RTGLIB_ABS_HPP
#include <migraphx/gpu/lowering.hpp>
#include <migraphx/manage_ptr.hpp>
#include <migraphx/instruction.hpp>
#include <migraphx/operators.hpp>
#include <migraphx/generate.hpp>
#include <migraphx/shape_for_each.hpp>
#include <migraphx/config.hpp>
#include <migraphx/gpu/miopen.hpp>
#include <migraphx/gpu/hip.hpp>
#include <migraphx/dfor.hpp>
#include <migraphx/gpu/device/contiguous.hpp>
#include <migraphx/gpu/device/add.hpp>
#include <migraphx/iterator_for.hpp>
#include <migraphx/gpu/rocblas.hpp>
#include <migraphx/gpu/context.hpp>
#include <utility>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
struct miopen_abs
{
shared<activation_descriptor> ad;
std::string name() const { return "gpu::abs"; }
shape compute_shape(const std::vector<shape>& inputs) const;
argument
compute(context& ctx, const shape& output_shape, const std::vector<argument>& args) const;
int output_alias(const std::vector<shape>& shapes) const { return shapes.size() - 1; }
};
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
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