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Commit 3a4d36cf authored by charlie's avatar charlie
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Merge branch 'develop' of github.com:ROCmSoftwarePlatform/AMDMIGraphX into dyn_model_test

parents 6bec381f e19f78ae
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src/targets/gpu/kernels/include/migraphx/kernels/algorithm.hpp
View file @ 3a4d36cf
......@@ -163,7 +163,7 @@ constexpr Iterator1 search(Iterator1 first, Iterator1 last, Iterator2 s_first, I
{
return last;
}
if(!(*it == *s_it))
if(not(*it == *s_it))
{
break;
}
......
src/targets/gpu/kernels/include/migraphx/kernels/array.hpp
View file @ 3a4d36cf
......@@ -33,49 +33,95 @@
namespace migraphx {
// NOLINTNEXTLINE
#define MIGRAPHX_DEVICE_ARRAY_OP(op, binary_op) \
template <class U> \
constexpr array& operator op(const array<U, N>& x) \
{ \
for(index_int i = 0; i < N; i++) \
d[i] op x[i]; \
return *this; \
} \
template <class U, MIGRAPHX_REQUIRES(is_convertible<U, T>{})> \
constexpr array& operator op(const U& x) \
{ \
for(index_int i = 0; i < N; i++) \
d[i] op x; \
return *this; \
} \
template <class U> \
friend constexpr auto operator binary_op(const array& x, const array<U, N>& y) \
{ \
array<decltype(T {} binary_op U{}), N> z{}; \
for(index_int i = 0; i < N; i++) \
z[i] = x[i] binary_op y[i]; \
return z; \
} \
template <class U, MIGRAPHX_REQUIRES(is_convertible<U, T>{})> \
friend constexpr auto operator binary_op(const array& x, const U& y) \
{ \
array<decltype(T {} binary_op U{}), N> z{}; \
for(index_int i = 0; i < N; i++) \
z[i] = x[i] binary_op y; \
return z; \
} \
template <class U, MIGRAPHX_REQUIRES(is_convertible<U, T>{})> \
friend constexpr auto operator binary_op(const U& x, const array& y) \
{ \
array<decltype(T {} binary_op U{}), N> z{}; \
for(index_int i = 0; i < N; i++) \
z[i] = x binary_op y[i]; \
return z; \
#define MIGRAPHX_DEVICE_ARRAY_OP(op, binary_op) \
template <class U> \
constexpr array& operator op(const array<U, N>& x) \
{ \
array_detail::array_for_each(*this, x)([](auto& sy, auto sx) { sy op sx; }); \
return *this; \
} \
template <class U, MIGRAPHX_REQUIRES(is_convertible<U, T>{})> \
constexpr array& operator op(const U& x) \
{ \
array_detail::array_for_each (*this)([&](auto& sy) { sy op x; }); \
return *this; \
} \
template <class U> \
friend constexpr auto operator binary_op(const array& x, const array<U, N>& y) \
{ \
array<decltype(T {} binary_op U{}), N> z{}; \
array_detail::array_for_each(z, x, y)( \
[&](auto& sz, auto sx, auto sy) { sz = sx binary_op sy; }); \
return z; \
} \
template <class U, MIGRAPHX_REQUIRES(is_convertible<U, T>{})> \
friend constexpr auto operator binary_op(const array& x, const U& y) \
{ \
array<decltype(T {} binary_op U{}), N> z{}; \
array_detail::array_for_each(z, x)([&](auto& sz, auto sx) { sz = sx binary_op y; }); \
return z; \
} \
template <class U, MIGRAPHX_REQUIRES(is_convertible<U, T>{})> \
friend constexpr auto operator binary_op(const U& x, const array& y) \
{ \
array<decltype(T {} binary_op U{}), N> z{}; \
array_detail::array_for_each(z, y)([&](auto& sz, auto sy) { sz = x binary_op sy; }); \
return z; \
}
namespace array_detail {
template <class T>
constexpr auto is_vectorizable()
{
return not is_same<T, bool>{} and (is_fundamental<T>{} or is_same<T, half>{});
}
template <class T>
__device__ auto& array2vec(T& x)
{
using value_type = typename T::value_type;
constexpr auto size = decltype(x.size()){};
using type = vec<value_type, size>;
if constexpr(is_const<T>{})
return reinterpret_cast<const type&>(x);
else
return reinterpret_cast<type&>(x);
}
template <class T, class... Ts>
constexpr auto array_for_each(T& x, Ts&... xs)
{
MIGRAPHX_ASSERT(((x.size() == xs.size()) and ...));
return [&](auto f) {
constexpr auto size = decltype(x.size()){};
if constexpr((is_vectorizable<typename T::value_type>() or
(is_vectorizable<typename Ts::value_type>() or ...)) and
size <= 8 and size > 1 and (size % 2 == 0))
{
if(__builtin_is_constant_evaluated())
{
for(index_int i = 0; i < size; i++)
f(x[i], xs[i]...);
}
else
{
using vec_type = std::remove_reference_t<decltype(array2vec(x))>;
f(array2vec(x), __builtin_convertvector(array2vec(xs), vec_type)...);
}
}
else
{
for(index_int i = 0; i < size; i++)
f(x[i], xs[i]...);
}
};
}
} // namespace array_detail
template <class T, index_int N>
struct array
{
using value_type = T;
T d[N];
constexpr T& operator[](index_int i)
{
......@@ -108,18 +154,13 @@ struct array
constexpr T dot(const array& x) const
{
T result = 0;
for(index_int i = 0; i < N; i++)
result += x[i] * d[i];
return result;
auto r = x * (*this);
return r.reduce([](auto a, auto b) { return a + b; }, 0);
}
constexpr T product() const
{
T result = 1;
for(index_int i = 0; i < N; i++)
result *= d[i];
return result;
return reduce([](auto x, auto y) { return x * y; }, 1);
}
constexpr T single(index_int width = 100) const
......@@ -134,6 +175,24 @@ struct array
return result;
}
template <class F>
constexpr auto apply(F f) const
{
array<decltype(f(d[0])), N> result;
for(index_int i = 0; i < N; i++)
result[i] = f(d[i]);
return result;
}
template <class F>
constexpr auto reduce(F f, T init) const
{
T result = init;
for(index_int i = 0; i < N; i++)
result = f(result, d[i]);
return result;
}
MIGRAPHX_DEVICE_ARRAY_OP(+=, +)
MIGRAPHX_DEVICE_ARRAY_OP(-=, -)
MIGRAPHX_DEVICE_ARRAY_OP(*=, *)
......@@ -153,7 +212,7 @@ struct array
return true;
}
friend constexpr bool operator!=(const array& x, const array& y) { return !(x == y); }
friend constexpr bool operator!=(const array& x, const array& y) { return not(x == y); }
// This uses the product order rather than lexical order
friend constexpr bool operator<(const array& x, const array& y)
{
......@@ -201,6 +260,11 @@ struct array
}
};
template <class T, class... Ts>
constexpr array<T, sizeof...(Ts) + 1> make_array(T x, Ts... xs)
{
return {x, static_cast<T>(xs)...};
}
template <class T, T... Xs>
struct integral_const_array : array<T, sizeof...(Xs)>
{
......
src/targets/gpu/include/migraphx/gpu/max.hpp → src/targets/gpu/kernels/include/migraphx/kernels/concat.hpp
View file @ 3a4d36cf
......@@ -21,22 +21,46 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
#ifndef MIGRAPHX_GUARD_RTGLIB_MAX_HPP
#define MIGRAPHX_GUARD_RTGLIB_MAX_HPP
#include <migraphx/gpu/oper.hpp>
#include <migraphx/gpu/device/max.hpp>
#include <migraphx/kernels/index.hpp>
#include <migraphx/kernels/functional.hpp>
#include <migraphx/kernels/tensor_view.hpp>
#ifndef MIGRAPHX_GUARD_KERNELS_CONCAT_HPP
#define MIGRAPHX_GUARD_KERNELS_CONCAT_HPP
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
struct hip_max : binary_device<hip_max, device::max>
template <index_int Axis, class Output, class Input, class Start>
constexpr auto concat_slice(Output out, Input, Start)
{
};
constexpr auto lens = get_shape_c<Input>{}.lens;
constexpr auto strides = get_shape_c<Output>{}.strides;
constexpr auto offset = return_c([] {
constexpr auto output_shape = get_shape_c<Output>{};
return Start{} * output_shape.strides[Axis];
});
constexpr auto s = make_shape(lens, strides);
return make_tensor_view(&out[offset], s);
}
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
template <index_int Axis, class Input>
constexpr auto concat_ends(Input)
{
constexpr auto lens = get_shape_c<Input>{}.lens;
return _c<lens[Axis]>;
}
#endif
template <index_int Axis, class Output, class... Inputs>
__device__ void concat(Output output, Inputs... inputs)
{
auto idx = make_index();
fold([&](auto start, auto input) {
auto y = concat_slice<Axis>(output, input, start);
idx.global_stride(input.get_shape().elements(), [&](auto i) { y[i] = input[i]; });
return start + concat_ends<Axis>(input);
})(_c<0>, inputs...);
}
} // namespace migraphx
#endif // MIGRAPHX_GUARD_KERNELS_CONCAT_HPP
src/targets/gpu/kernels/include/migraphx/kernels/functional.hpp
View file @ 3a4d36cf
......@@ -31,8 +31,9 @@
->decltype(__VA_ARGS__) { return __VA_ARGS__; }
// NOLINTNEXTLINE
#define MIGRAPHX_LIFT(...) \
[](auto&&... xs) MIGRAPHX_RETURNS((__VA_ARGS__)(static_cast<decltype(xs)>(xs)...))
#define MIGRAPHX_LIFT(...) \
[](auto&&... private_lisft_xs) MIGRAPHX_RETURNS( \
(__VA_ARGS__)(static_cast<decltype(private_lisft_xs)>(private_lisft_xs)...))
namespace migraphx {
......
src/targets/gpu/kernels/include/migraphx/kernels/index.hpp
View file @ 3a4d36cf
......@@ -28,9 +28,60 @@
#include <migraphx/kernels/types.hpp>
#include <migraphx/kernels/integral_constant.hpp>
#include <migraphx/kernels/type_traits.hpp>
#include <migraphx/kernels/debug.hpp>
namespace migraphx {
#if defined(MIGRAPHX_NGLOBAL) && defined(MIGRAPHX_NLOCAL)
#define MIGRAPHX_NGROUP ((MIGRAPHX_NGLOBAL + MIGRAPHX_NLOCAL - 1) / MIGRAPHX_NLOCAL)
#endif
inline __device__ __attribute__((const)) index_int compute_global_size()
{
#ifdef MIGRAPHX_NGLOBAL
return MIGRAPHX_NGLOBAL;
#else
// This actualy works even when global is not divisible by local size.
// This doesnt actually do a multiplicatiosn. Instead it calls a device
// function to get the global size, which is why it works.
return blockDim.x * gridDim.x; // NOLINT
#endif
}
// We cant just use blockDim.x to get the local size since its broken on hip
// when global is not divisible by local size. In this case, we calulate the
// size for the last group.
inline __device__ __attribute__((const)) index_int compute_local_size()
{
#ifdef MIGRAPHX_NLOCAL
const auto nlocal = MIGRAPHX_NLOCAL;
#else
const auto nlocal = blockDim.x; // NOLINT
#endif
#ifdef MIGRAPHX_NGROUP
const auto ngroup = MIGRAPHX_NGROUP;
#else
const auto ngroup = gridDim.x; // NOLINT
#endif
const auto group_id = blockIdx.x; // NOLINT
const auto nglobal = compute_global_size();
if(group_id == ngroup - 1)
{
return 1 + (nglobal - 1) % nlocal;
}
else
{
return nlocal; // NOLINT
}
}
#ifdef MIGRAPHX_NGROUP
// If global is divisible by local then local can be a const
#if(MIGRAPHX_NGLOBAL % MIGRAPHX_NLOCAL == 0) || (MIGRAPHX_NGROUP == 1)
#define MIGRAPHX_HAS_CONST_LOCAL 1
#endif
#endif
struct index
{
index_int global = 0;
......@@ -38,20 +89,44 @@ struct index
index_int group = 0;
#ifdef MIGRAPHX_NGLOBAL
constexpr index_constant<MIGRAPHX_NGLOBAL> nglobal() const { return {}; }
constexpr index_constant<MIGRAPHX_NGLOBAL> nglobal() const
{
static_assert(MIGRAPHX_NGLOBAL > 0, "Global size must be greater than 0");
return {};
}
#else
__device__ index_int nglobal() const
{
return blockDim.x * gridDim.x; // NOLINT
MIGRAPHX_ASSERT(compute_global_size() > 0);
return compute_global_size(); // NOLINT
}
#endif
#ifdef MIGRAPHX_NLOCAL
constexpr index_constant<MIGRAPHX_NLOCAL> nlocal() const { return {}; }
#ifdef MIGRAPHX_HAS_CONST_LOCAL
constexpr index_constant<MIGRAPHX_NLOCAL> nlocal() const
{
static_assert(MIGRAPHX_NLOCAL > 0, "Local size must be greater than 0");
return {};
}
#else
__device__ index_int nlocal() const
{
return blockDim.x; // NOLINT
#ifdef MIGRAPHX_NGROUP
static_assert((MIGRAPHX_NGLOBAL % MIGRAPHX_NLOCAL != 0) and (MIGRAPHX_NGROUP > 1),
"Local size should be const");
#endif
MIGRAPHX_ASSERT(compute_local_size() > 0);
return compute_local_size(); // NOLINT
}
#endif
#ifdef MIGRAPHX_NLOCAL
constexpr index_constant<MIGRAPHX_NLOCAL> max_nlocal() const { return {}; }
#else
__device__ index_int max_nlocal() const
{
MIGRAPHX_ASSERT(blockDim.x > 0);
return blockDim.x;
}
#endif
template <class N, class Stride>
......@@ -63,6 +138,7 @@ struct index
template <class F, class N, class Stride>
static constexpr void for_stride(index_int start, N n, Stride stride, F f)
{
MIGRAPHX_ASSERT(start < stride);
if constexpr(not is_integral<N>{} and not is_integral<Stride>{} and
max_stride_iterations(n, stride) == 1)
{
......
src/targets/gpu/kernels/include/migraphx/kernels/integral_constant.hpp
View file @ 3a4d36cf
......@@ -73,10 +73,10 @@ MIGRAPHX_INTEGRAL_CONSTANT_BINARY_OP(!=)
MIGRAPHX_INTEGRAL_CONSTANT_BINARY_OP(&)
MIGRAPHX_INTEGRAL_CONSTANT_BINARY_OP(^)
MIGRAPHX_INTEGRAL_CONSTANT_BINARY_OP(|)
MIGRAPHX_INTEGRAL_CONSTANT_BINARY_OP(&&)
MIGRAPHX_INTEGRAL_CONSTANT_BINARY_OP(||)
MIGRAPHX_INTEGRAL_CONSTANT_BINARY_OP(and)
MIGRAPHX_INTEGRAL_CONSTANT_BINARY_OP(or)
MIGRAPHX_INTEGRAL_CONSTANT_UNARY_OP(!)
MIGRAPHX_INTEGRAL_CONSTANT_UNARY_OP(not )
MIGRAPHX_INTEGRAL_CONSTANT_UNARY_OP(~)
MIGRAPHX_INTEGRAL_CONSTANT_UNARY_OP(+)
MIGRAPHX_INTEGRAL_CONSTANT_UNARY_OP(-)
......
src/targets/gpu/device/add_sigmoid.cpp → src/targets/gpu/kernels/include/migraphx/kernels/layernorm.hpp
View file @ 3a4d36cf
......@@ -21,35 +21,69 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
#include <migraphx/gpu/device/add_sigmoid.hpp>
#include <migraphx/gpu/device/nary.hpp>
#ifndef MIGRAPHX_GUARD_KERNELS_LAYERNORM_HPP
#define MIGRAPHX_GUARD_KERNELS_LAYERNORM_HPP
#include <migraphx/kernels/reduce.hpp>
#include <migraphx/kernels/ops.hpp>
#include <migraphx/kernels/print.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
namespace device {
void add_sigmoid(hipStream_t stream,
const argument& result,
const argument& arg1,
const argument& arg2)
template <class T, index_int N, class Op>
constexpr auto vec_reduce(const array<T, N>& a, Op op)
{
nary(stream, result, arg1, arg2)(
[](auto x, auto y) __device__ { return 1.f / (1.f + ::exp(to_hip_type(-(x + y)))); });
return a.apply([&](auto x) { return vec_reduce(x, op); });
}
void add_sigmoid(hipStream_t stream,
const argument& result,
const argument& arg1,
const argument& arg2,
const argument& arg3)
template <index_int Axis,
class F,
class BinOp,
class Output,
class Input1,
class Input2,
class... Inputs>
__device__ void generic_binary_layernorm(
F compute, BinOp op, float eps, Output output, Input1 input1, Input2 input2, Inputs... inputs)
{
nary(stream, result, arg1, arg2, arg3)([](auto x, auto y, auto z) __device__ {
return 1.f / (1.f + ::exp(to_hip_type(-(x + y + z))));
using reduce_output = reduce::with_axis<Input1, Axis>;
reduce::block::run<reduce_output>([&](auto, auto r) {
using value_type = typename Input1::type;
constexpr auto relements = r.template elements<Input1>();
auto means =
r.reduce(op::sum{}, make_array<vec_type<value_type>>(0, 0), [&](auto x1, auto x2) {
auto x = op(x1, x2);
return make_array(x, x * x) * vec_type<value_type>{1.0 / relements};
})(input1, input2);
auto mean_x = means[0];
auto mean_x2 = means[1];
auto variance = mean_x2 - (mean_x * mean_x);
value_type eps_val = eps; // implicit conversion for eps
r.inner([&](auto& y, auto x1, auto x2, auto... xs) {
auto x = op(x1, x2);
auto m = x - mean_x;
// m * rsqrt(mean(m ^ 2) + epsilon)
y = compute(m * rsqrt(variance + eps_val), xs...);
})(output, input1, input2, inputs...);
});
}
} // namespace device
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
template <index_int Axis, class F, class Output, class Input, class... Inputs>
__device__ void layernorm(F compute, float eps, Output output, Input input, Inputs... inputs)
{
generic_binary_layernorm<Axis>(
compute, [](auto x, auto) { return x; }, eps, output, input, input, inputs...);
}
template <index_int Axis, class F, class Output, class Input1, class Input2, class... Inputs>
__device__ void
add_layernorm(F compute, float eps, Output output, Input1 input1, Input2 input2, Inputs... inputs)
{
generic_binary_layernorm<Axis>(
compute, [](auto x1, auto x2) { return x1 + x2; }, eps, output, input1, input2, inputs...);
}
} // namespace migraphx
#endif // MIGRAPHX_GUARD_KERNELS_LAYERNORM_HPP
src/targets/gpu/kernels/include/migraphx/kernels/math.hpp
View file @ 3a4d36cf
......@@ -104,6 +104,7 @@ MIGRAPHX_DEVICE_MATH(floor, ::floor)
MIGRAPHX_DEVICE_MATH(isnan, ::isnan)
MIGRAPHX_DEVICE_MATH(log, ::log)
MIGRAPHX_DEVICE_MATH(pow, ::pow)
MIGRAPHX_DEVICE_MATH(remainder, ::remainder)
MIGRAPHX_DEVICE_MATH(round, ::round)
MIGRAPHX_DEVICE_MATH(rsqrt, ::rsqrt)
MIGRAPHX_DEVICE_MATH(sin, ::sin)
......@@ -111,6 +112,7 @@ MIGRAPHX_DEVICE_MATH(sinh, ::sinh)
MIGRAPHX_DEVICE_MATH(sqrt, ::sqrt)
MIGRAPHX_DEVICE_MATH(tan, ::tan)
MIGRAPHX_DEVICE_MATH(tanh, ::tanh)
MIGRAPHX_DEVICE_MATH(fmod, ::fmod)
// Float overloads
MIGRAPHX_DEVICE_MATH_FOR(float, acos, ::acosf)
......@@ -126,6 +128,7 @@ MIGRAPHX_DEVICE_MATH_FOR(float, sin, ::sinf)
MIGRAPHX_DEVICE_MATH_FOR(float, sinh, ::sinhf)
MIGRAPHX_DEVICE_MATH_FOR(float, tan, ::tanf)
MIGRAPHX_DEVICE_MATH_FOR(float, tanh, ::tanhf)
MIGRAPHX_DEVICE_MATH_FOR(float, fmod, ::fmodf)
// Builtin half functions
MIGRAPHX_DEVICE_MATH_FOR(migraphx::half, abs, ::__habs)
......@@ -148,11 +151,13 @@ MIGRAPHX_DEVICE_MATH_HALF(erf, ::erf)
MIGRAPHX_DEVICE_MATH_HALF(floor, ::floor)
MIGRAPHX_DEVICE_MATH_HALF(isnan, ::isnan)
MIGRAPHX_DEVICE_MATH_HALF(pow, ::pow)
MIGRAPHX_DEVICE_MATH_HALF(remainder, ::remainder)
MIGRAPHX_DEVICE_MATH_HALF(round, ::round)
MIGRAPHX_DEVICE_MATH_HALF(sin, ::sin)
MIGRAPHX_DEVICE_MATH_HALF(sinh, ::sinh)
MIGRAPHX_DEVICE_MATH_HALF(tan, ::tan)
MIGRAPHX_DEVICE_MATH_HALF(tanh, ::tanh)
MIGRAPHX_DEVICE_MATH_HALF(fmod, ::fmod)
// Map math functions to hip half2 functions
// The half2 type is defined in include/hip/amd_detail/hip_fp16_gcc.h and is 2 16-bit floats
......@@ -226,11 +231,13 @@ MIGRAPHX_DEVICE_MATH_VEC(cosh)
MIGRAPHX_DEVICE_MATH_VEC(erf)
MIGRAPHX_DEVICE_MATH_VEC(exp)
MIGRAPHX_DEVICE_MATH_VEC(floor)
MIGRAPHX_DEVICE_MATH_VEC(fmod)
MIGRAPHX_DEVICE_MATH_VEC(isnan)
MIGRAPHX_DEVICE_MATH_VEC(log)
MIGRAPHX_DEVICE_MATH_VEC(max)
MIGRAPHX_DEVICE_MATH_VEC(min)
MIGRAPHX_DEVICE_MATH_VEC(pow)
MIGRAPHX_DEVICE_MATH_VEC(remainder)
MIGRAPHX_DEVICE_MATH_VEC(round)
MIGRAPHX_DEVICE_MATH_VEC(rsqrt)
MIGRAPHX_DEVICE_MATH_VEC(sin)
......
src/targets/gpu/kernels/include/migraphx/kernels/ops.hpp
View file @ 3a4d36cf
......@@ -90,7 +90,7 @@ struct lowest
template <class T>
constexpr operator T() const
{
return numeric_lowest<T>();
return numeric_lowest<vec_type<T>>();
}
};
......@@ -99,7 +99,7 @@ struct highest
template <class T>
constexpr operator T() const
{
return numeric_max<T>();
return numeric_max<vec_type<T>>();
}
};
} // namespace migraphx
......
src/targets/gpu/kernels/include/migraphx/kernels/reduce.hpp
View file @ 3a4d36cf
......@@ -94,16 +94,17 @@ MIGRAPHX_DPP_REDUCE(op::max, v_max)
MIGRAPHX_DPP_REDUCE(op::min, v_min)
MIGRAPHX_DPP_REDUCE(op::product, v_mul)
template <class Op, class T, class F>
__device__ auto block_reduce(index idx, Op op, T init, index_int n, F f)
template <class Op, class T, class Index, class F>
__device__ auto block_reduce(index idx, Op op, T init, Index n, F f)
{
MIGRAPHX_ASSERT(idx.max_nlocal() == idx.nlocal());
#if __AMDGCN_WAVEFRONT_SIZE == 32
constexpr index_int lanes_per_thread = 16;
#else
constexpr index_int lanes_per_thread = 64;
#endif
using type = decltype(f(0));
__shared__ type buffer[idx.nlocal() / lanes_per_thread];
__shared__ type buffer[idx.max_nlocal() / lanes_per_thread];
type x = init;
idx.local_stride(n, [&](auto i) { x = op(x, f(i)); });
dpp_reduce(x, op);
......@@ -123,12 +124,12 @@ __device__ auto block_reduce(index idx, Op op, T init, index_int n, F f)
return y;
}
#else
template <class Op, class T, class F>
__device__ auto block_reduce(index idx, Op op, T init, index_int n, F f)
template <class Op, class T, class Index, class F>
__device__ auto block_reduce(index idx, Op op, T init, Index n, F f)
{
MIGRAPHX_ASSERT(idx.max_nlocal() == idx.nlocal());
using type = decltype(f(0));
__shared__ type buffer[idx.nlocal()];
__shared__ type buffer[idx.max_nlocal()];
type x = init;
idx.local_stride(n, [&](auto i) { x = op(x, f(i)); });
buffer[idx.local] = x;
......@@ -201,12 +202,9 @@ struct block
__device__ auto reduce(Op op, T init, Read read) const
{
return sliced(slicer, [=](auto x, auto... xs) {
return vec_reduce(block_reduce(idx,
op,
init,
x.get_shape().elements(),
[&](auto j) { return read(x[j], xs[j]...); }),
op);
return block_reduce(idx, op, init, x.get_shape().elements(), [&](auto j) {
return vec_reduce(read(x[j], xs[j]...), op);
});
});
}
......@@ -224,6 +222,18 @@ struct block
idx.local_stride(x.get_shape().elements(), [&](auto j) { f(x[j], xs[j]...); });
});
}
template <class Input>
constexpr auto elements() const
{
using reduce_type = decltype(slicer(Input{}));
using value_type = typename Input::type;
constexpr auto relements = get_shape_c<reduce_type>{}.elements();
if constexpr(vec_size<value_type>() > 1)
return relements * vec_size<value_type>();
else
return relements;
}
};
template <class Slicer>
......@@ -281,6 +291,13 @@ struct lane
}
});
}
template <class Input>
constexpr auto elements() const
{
using reduce_type = decltype(slicer(Input{}));
return get_shape_c<reduce_type>{}.elements();
}
};
template <class Slicer>
......
src/targets/gpu/kernels/include/migraphx/kernels/type_traits.hpp
View file @ 3a4d36cf
......@@ -192,9 +192,13 @@ struct common_type<T, U, Us...>
template <class... Ts>
using common_type_t = typename common_type<Ts...>::type;
#define MIGRAPHX_REQUIRES(...) class = enable_if_t<__VA_ARGS__>
constexpr unsigned long int_max(unsigned long n) { return (1u << (n * 8)) - 1; }
template <class T>
template <class T,
MIGRAPHX_REQUIRES(is_integral<T>{} or is_floating_point<T>{} or
is_same<T, migraphx::half>{})>
constexpr T numeric_max()
{
if constexpr(is_integral<T>{})
......@@ -230,8 +234,6 @@ constexpr T numeric_lowest()
}
}
#define MIGRAPHX_REQUIRES(...) class = enable_if_t<__VA_ARGS__>
} // namespace migraphx
#endif
src/targets/gpu/kernels/include/migraphx/kernels/vec.hpp
View file @ 3a4d36cf
......@@ -175,7 +175,7 @@ template <class T, class Op>
constexpr auto vec_reduce(T x, Op op)
{
if constexpr(vec_size<T>() < 2)
return x;
return vec_type<T>{x};
else
{
vec_type<T> result = x[0];
......
src/targets/gpu/lowering.cpp
View file @ 3a4d36cf
......@@ -27,42 +27,24 @@
#include <migraphx/instruction.hpp>
#include <migraphx/make_op.hpp>
#include <migraphx/op/abs.hpp>
#include <migraphx/op/batch_norm_inference.hpp>
#include <migraphx/op/convolution.hpp>
#include <migraphx/op/deconvolution.hpp>
#include <migraphx/op/dot.hpp>
#include <migraphx/op/elu.hpp>
#include <migraphx/op/if_op.hpp>
#include <migraphx/op/leaky_relu.hpp>
#include <migraphx/op/lrn.hpp>
#include <migraphx/op/pooling.hpp>
#include <migraphx/op/reshape.hpp>
#include <migraphx/op/quant_convolution.hpp>
#include <migraphx/op/quant_dot.hpp>
#include <migraphx/gpu/abs.hpp>
#include <migraphx/gpu/batch_norm_inference.hpp>
#include <migraphx/gpu/context.hpp>
#include <migraphx/gpu/convolution.hpp>
#include <migraphx/gpu/deconvolution.hpp>
#include <migraphx/gpu/device_name.hpp>
#include <migraphx/gpu/elu.hpp>
#include <migraphx/gpu/equal.hpp>
#include <migraphx/gpu/gemm.hpp>
#include <migraphx/gpu/greater.hpp>
#include <migraphx/gpu/int8_conv_pack.hpp>
#include <migraphx/gpu/leaky_relu.hpp>
#include <migraphx/gpu/less.hpp>
#include <migraphx/gpu/logical_and.hpp>
#include <migraphx/gpu/logical_or.hpp>
#include <migraphx/gpu/logical_xor.hpp>
#include <migraphx/gpu/lrn.hpp>
#include <migraphx/gpu/miopen.hpp>
#include <migraphx/gpu/quant_convolution.hpp>
#include <migraphx/gpu/rocblas.hpp>
#include <migraphx/gpu/unary_not.hpp>
#include <migraphx/gpu/where.hpp>
#include <migraphx/gpu/compiler.hpp>
#include <migraphx/iterator_for.hpp>
#include <migraphx/program.hpp>
......@@ -99,78 +81,21 @@ struct miopen_apply
(void)i;
}
const std::unordered_set<std::string>& get_rocblas_fp32_archs()
{
static std::unordered_set<std::string> supported_archs{"gfx908", "gfx90a"};
return supported_archs;
}
void init()
{
assert(mod != nullptr);
assert(pass != nullptr);
#if ROCBLAS_VERSION_MAJOR >= 2 && ROCBLAS_VERSION_MINOR >= 38
auto& ctx = get_context();
const auto device_name = trim(split_string(get_device_name(), ':').front());
if(contains(get_rocblas_fp32_archs(), device_name))
compute_fp32 = true;
rocblas_gemm_flags flag;
rocblas_query_int8_layout_flag(ctx.get_stream().get_rocblas(), &flag);
int8_x4_format = (flag == rocblas_gemm_flags_pack_int8x4);
#endif
auto& ctx = get_context();
int8_x4_format = get_int8_x4_format(ctx);
compute_fp32 = get_compute_fp32_flag();
offload_copy = (mod->name() == "main") ? pass->offload_copy : false;
add_generic_op("acos");
add_generic_op("acosh");
add_generic_op("add");
add_generic_op("asin");
add_generic_op("asinh");
add_generic_op("atan");
add_generic_op("atanh");
add_generic_op("ceil");
add_generic_op("contiguous");
add_generic_op("cos");
add_generic_op("cosh");
add_generic_op("div");
add_generic_op("equal");
add_generic_op("erf");
add_generic_op("exp");
add_generic_op("floor");
add_generic_op("greater");
add_generic_op("less");
add_generic_op("log");
add_generic_op("logical_and");
add_generic_op("logical_or");
add_generic_op("logical_xor");
add_generic_op("max");
add_generic_op("min");
add_generic_op("mul");
add_generic_op("not");
add_generic_op("pow");
add_generic_op("prelu");
add_generic_op("recip");
add_generic_op("relu");
add_generic_op("round");
add_generic_op("rsqrt");
add_generic_op("sigmoid");
add_generic_op("sign");
add_generic_op("sin");
add_generic_op("sinh");
add_generic_op("sqdiff");
add_generic_op("sqrt");
add_generic_op("sub");
add_generic_op("tan");
add_generic_op("tanh");
add_generic_op("where");
add_extend_op("abs");
add_extend_op("argmax");
add_extend_op("argmin");
add_extend_op("clip");
add_extend_op("concat");
add_extend_op("convert");
add_extend_op("elu");
add_extend_op("gather");
add_extend_op("leaky_relu");
......@@ -341,7 +266,7 @@ struct miopen_apply
catch(migraphx::exception&)
{
// In case no solver supports the default format, retry using the other format.
compile_quant_conv_with_format(!int8_x4_format);
compile_quant_conv_with_format(not int8_x4_format);
}
auto args = ins->inputs();
......
src/targets/gpu/mlir.cpp
View file @ 3a4d36cf
......@@ -78,7 +78,7 @@ struct mlir_handle
friend bool operator==(ptr x, ptr y) { return x.get_value() == y.get_value(); }
friend bool operator!=(ptr x, ptr y) { return !(x == y); }
friend bool operator!=(ptr x, ptr y) { return not(x == y); }
T obj{};
};
......@@ -503,7 +503,7 @@ struct mlir_program
pp =
problem_params{ins->get_operator(), to_shapes(ins->inputs()), ins->get_shape()};
std::string tuned = get_tune_params();
if(!tuned.empty())
if(not tuned.empty())
ops.add_attributes({{"perf_config", tuned}});
// check if HW supports xdlops
if(contains(get_xdlops_archs(), target_name))
......
src/targets/gpu/pack_int8_args.cpp
View file @ 3a4d36cf
......@@ -154,7 +154,7 @@ void pack_int8_args::apply(module& m) const
bool transa = inputs[0]->get_shape().transposed();
bool transb = inputs[1]->get_shape().transposed();
if(!transb)
if(not transb)
{
auto packed_b = m.insert_instruction(
ins, make_op("hip::allocate", {{"shape", to_value(inputs[1]->get_shape())}}));
......
src/targets/gpu/prefuse_ops.cpp
View file @ 3a4d36cf
......@@ -23,13 +23,62 @@
*/
#include <migraphx/gpu/prefuse_ops.hpp>
#include <migraphx/match/layernorm.hpp>
#include <migraphx/check_shapes.hpp>
#include <migraphx/make_op.hpp>
#include <migraphx/register_op.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
namespace {
template <class Derived, std::size_t N>
struct layernorm_base
{
float epsilon = 1e-12f;
template <class Self, class F>
static auto reflect(Self& self, F f)
{
return pack(f(self.epsilon, "epsilon"));
}
shape compute_shape(std::vector<shape> inputs, std::vector<module_ref> mods) const
{
std::size_t nargs = 1;
if(not mods.empty())
{
auto* pm = mods.front();
nargs = pm->get_parameter_names().size();
}
check_shapes{inputs, static_cast<const Derived&>(*this)}.has(nargs + N);
auto s = inputs.at(0);
if(s.scalar())
{
return s;
}
else if(s.broadcasted())
{
return {s.type(), s.lens()};
}
else
{
return s.with_lens(s.lens());
}
}
};
struct layernorm : layernorm_base<layernorm, 0>
{
std::string name() const { return "gpu::prelayernorm"; }
};
MIGRAPHX_REGISTER_OP(layernorm);
struct add_layernorm : layernorm_base<add_layernorm, 1>
{
std::string name() const { return "gpu::preadd_layernorm"; }
};
MIGRAPHX_REGISTER_OP(add_layernorm);
struct find_layernorm
{
auto matcher() const { return match::layernorm(); }
......@@ -38,60 +87,33 @@ struct find_layernorm
{
auto ins = r.result;
auto x_ins = r.instructions["x"];
auto eps = r.instructions["eps"]->eval().at<float>();
if(not x_ins->get_shape().standard())
x_ins = m.insert_instruction(ins, make_op("contiguous"), x_ins);
auto relements = x_ins->get_shape().lens().back();
if(relements > 1024 or (relements % 4 != 0 and relements > 256))
return;
auto a = m.insert_instruction(
ins, make_op("hip::allocate", {{"shape", to_value(x_ins->get_shape())}}));
m.replace_instruction(ins, make_op("gpu::layernorm"), x_ins, a);
m.replace_instruction(ins, layernorm{eps}, x_ins);
}
};
struct find_triaddlayernorm
struct find_add_layernorm
{
auto matcher() const
{
auto add1 =
match::name("add")(match::none_of(match::is_constant()),
match::args(match::any().bind("z1"), match::any().bind("z2")));
auto add2 = match::name("add")(match::either_arg(0, 1)(add1, match::any().bind("z3")));
return match::layernorm()(match::var("x")(add2));
return match::layernorm()(match::var("x")(match::name("add").bind("add")));
}
void apply(module& m, const match::matcher_result& r) const
{
auto ins = r.result;
auto x_ins = r.instructions["z1"];
auto y_ins = r.instructions["z2"];
auto z_ins = r.instructions["z3"];
for(auto* pins : {&x_ins, &y_ins, &z_ins})
{
if(not(*pins)->get_shape().standard())
*pins = m.insert_instruction(ins, make_op("contiguous"), *pins);
}
auto relements = x_ins->get_shape().lens().back();
if(relements > 1024 or (relements % 4 != 0 and relements > 256))
return;
auto ins = r.result;
auto add_ins = r.instructions["add"];
auto eps = r.instructions["eps"]->eval().at<float>();
auto a = m.insert_instruction(
ins, make_op("hip::allocate", {{"shape", to_value(x_ins->get_shape())}}));
m.replace_instruction(ins, make_op("gpu::triadd_layernorm"), x_ins, y_ins, z_ins, a);
m.replace_instruction(ins, add_layernorm{eps}, add_ins->inputs());
}
};
} // namespace
void prefuse_ops::apply(module& m) const
{
match::find_matches(m, find_triaddlayernorm{}, find_layernorm{});
match::find_matches(m, find_add_layernorm{}, find_layernorm{});
}
} // namespace gpu
......
src/targets/gpu/quant_convolution.cpp
View file @ 3a4d36cf
......@@ -22,7 +22,6 @@
* THE SOFTWARE.
*/
#include <migraphx/gpu/quant_convolution.hpp>
#include <migraphx/gpu/device/convert.hpp>
#include <migraphx/gpu/context.hpp>
#include <migraphx/generate.hpp>
......
src/targets/gpu/rocblas.cpp
View file @ 3a4d36cf
......@@ -21,7 +21,13 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
#include <unordered_set>
#include <migraphx/ranges.hpp>
#include <migraphx/stringutils.hpp>
#include <migraphx/gpu/device_name.hpp>
#include <migraphx/gpu/rocblas.hpp>
#include <migraphx/gpu/context.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
......@@ -41,6 +47,33 @@ rocblas_handle_ptr create_rocblas_handle_ptr(hipStream_t s)
return rb;
}
const std::unordered_set<std::string>& get_rocblas_fp32_archs()
{
static std::unordered_set<std::string> supported_archs{"gfx908", "gfx90a"};
return supported_archs;
}
bool get_compute_fp32_flag()
{
bool compute_fp32 = false;
#if ROCBLAS_VERSION_MAJOR >= 2 && ROCBLAS_VERSION_MINOR >= 38
const auto device_name = trim(split_string(get_device_name(), ':').front());
if(contains(get_rocblas_fp32_archs(), device_name))
compute_fp32 = true;
#endif
return compute_fp32;
}
bool get_int8_x4_format(context& ctx)
{
bool int8_x4_format = true;
#if ROCBLAS_VERSION_MAJOR >= 2 && ROCBLAS_VERSION_MINOR >= 38
rocblas_gemm_flags flag;
rocblas_query_int8_layout_flag(ctx.get_stream().get_rocblas(), &flag);
int8_x4_format = (flag == rocblas_gemm_flags_pack_int8x4);
#endif
return int8_x4_format;
}
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
src/targets/gpu/softmax.cpp deleted 100644 → 0
View file @ 6bec381f
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
#include <migraphx/gpu/softmax.hpp>
#include <migraphx/gpu/device/softmax.hpp>
#include <migraphx/gpu/context.hpp>
#include <migraphx/tune_axis.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
shape hip_softmax::compute_shape(const std::vector<shape>& inputs) const
{
check_shapes{inputs, *this}.has(2).standard();
return op.normalize_compute_shape({inputs.at(0)});
}
argument hip_softmax::compute(context& ctx, const shape&, const std::vector<argument>& args) const
{
auto n_dim = args.front().get_shape().lens().size();
auto tuned_axis = tune_axis(n_dim, op.axis, op.name());
device::softmax(ctx.get_stream().get(), args.back(), args.front(), tuned_axis);
return args.back();
}
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
src/targets/gpu/target.cpp
View file @ 3a4d36cf
......@@ -42,6 +42,7 @@
#include <migraphx/register_target.hpp>
#include <migraphx/replace_allocate.hpp>
#include <migraphx/rewrite_batchnorm.hpp>
#include <migraphx/rewrite_gelu.hpp>
#include <migraphx/rewrite_pooling.hpp>
#include <migraphx/rewrite_quantization.hpp>
#include <migraphx/rewrite_rnn.hpp>
......@@ -116,6 +117,8 @@ std::vector<pass> target::get_passes(migraphx::context& gctx, const compile_opti
inline_module{},
rewrite_pooling{},
dead_code_elimination{},
rewrite_gelu{},
dead_code_elimination{},
eliminate_common_subexpression{},
dead_code_elimination{},
simplify_algebra{},
......@@ -134,8 +137,6 @@ std::vector<pass> target::get_passes(migraphx::context& gctx, const compile_opti
lowering{&ctx, options.offload_copy},
eliminate_contiguous{"gpu::contiguous"},
dead_code_elimination{},
replace_allocate{gpu_allocation_model{}, options.offload_copy},
dead_code_elimination{},
eliminate_concat{concat_gpu_optimization{}},
dead_code_elimination{},
pack_int8_args{},
......@@ -144,6 +145,8 @@ std::vector<pass> target::get_passes(migraphx::context& gctx, const compile_opti
dead_code_elimination{},
fuse_ops{&ctx, options.fast_math},
dead_code_elimination{},
replace_allocate{gpu_allocation_model{}, options.offload_copy},
dead_code_elimination{},
compile_ops{&ctx},
dead_code_elimination{},
write_literals{&ctx},
......
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