Skip to content

GitLab

"test/vscode:/vscode.git/clone" did not exist on "b1100d7c5400cbe848f9a85219f6917c949c1560"
Unverified Commit eafd55de authored by Umang Yadav's avatar Umang Yadav Committed by GitHub
Browse files

FP8 GPU implementation (#2455)

parent 785ff7d7
Hide whitespace changes
Inline Side-by-side
Showing with 1099 additions and 51 deletions
src/include/migraphx/bit_cast.hpp
View file @ eafd55de
......@@ -21,10 +21,13 @@
* ************************************************************************ */
#ifndef MIGRAPHX_GUARD_RTGLIB_BITCAST_HPP
#define MIGRAPHX_GUARD_RTGLIB_BITCAST_HPP
#include <type_traits>
#if defined(__GNUC__) && !defined(__clang__)
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wstrict-aliasing"
#endif
#include <migraphx/requires.hpp>
#include <migraphx/config.hpp>
// NOLINTNEXTLINE(cppcoreguidelines-macro-usage)
......@@ -32,7 +35,10 @@
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
template <typename To, typename From>
template <typename To,
typename From,
MIGRAPHX_REQUIRES(std::is_trivially_copyable<To>{} and
std::is_trivially_copyable<From>{})>
inline constexpr To bit_cast(From fr) noexcept
{
static_assert(sizeof(To) == sizeof(From));
......
src/targets/cpu/dnnl.cpp
View file @ eafd55de
......@@ -68,6 +68,7 @@ dnnl::memory::data_type to_dnnl_memory_data_type(shape::type_t t)
case st::int32_type: return dt::s32;
case st::int8_type: return dt::s8;
case st::uint8_type: return dt::u8;
case st::fp8e4m3fnuz_type: MIGRAPHX_THROW("fp8e4m3fnuz unsupported in DNNL");
default: MIGRAPHX_THROW("Unsupported data type");
}
}
......
src/targets/cpu/lowering.cpp
View file @ eafd55de
......@@ -340,7 +340,6 @@ struct cpu_apply
{"reduce_min", "reduction_min"},
{"reduce_sum", "reduction_sum"},
});
extend_op("concat", "dnnl::concat");
extend_op("contiguous", "dnnl::reorder");
extend_op("convolution", "dnnl::convolution");
......@@ -376,6 +375,12 @@ struct cpu_apply
// Apply these operators first so the inputs can be const folded
for(auto it : iterator_for(*modl))
{
// skip lowering if input has fp8 as one of the inputs since oneDNN doesn't have fp8
// supported yet.
if(std::any_of(it->inputs().begin(), it->inputs().end(), [](const auto& i) {
return i->get_shape().type() == migraphx::shape::fp8e4m3fnuz_type;
}))
continue;
if(it->name() == "pow")
{
apply_pow(it);
......@@ -383,6 +388,12 @@ struct cpu_apply
}
for(auto it : iterator_for(*modl))
{
// skip lowering if input has fp8 as one of the inputs since oneDNN doesn't have fp8
// supported yet.
if(std::any_of(it->inputs().begin(), it->inputs().end(), [](const auto& i) {
return i->get_shape().type() == migraphx::shape::fp8e4m3fnuz_type;
}))
continue;
if(it->name() == "pooling")
{
apply_pooling(it);
......
src/targets/gpu/compile_gen.cpp
View file @ eafd55de
......@@ -54,6 +54,11 @@ vectorize vectorize::elements(std::size_t axis,
const std::vector<shape>& inputs,
const std::vector<std::size_t>& sizes)
{
// disable vectorization for fp8 types
if(std::any_of(inputs.begin(), inputs.end(), [&](auto ishape) {
return ishape.type() == migraphx::shape::fp8e4m3fnuz_type;
}))
return {1, axis};
if(std::all_of(
inputs.begin(), inputs.end(), [&](const auto& s) { return s.lens()[axis] == 1; }))
return {1, axis};
......@@ -86,6 +91,11 @@ vectorize vectorize::elements(std::size_t axis,
vectorize vectorize::elements(context& ctx, std::size_t axis, const std::vector<shape>& inputs)
{
// disable vectorization for fp8 types
if(std::any_of(inputs.begin(), inputs.end(), [&](auto ishape) {
return ishape.type() == migraphx::shape::fp8e4m3fnuz_type;
}))
return {1, axis};
if(inputs.empty())
return {1, axis};
std::size_t n = std::max_element(inputs.begin(),
......
src/targets/gpu/kernels/include/migraphx/kernels/bit_cast.hpp 0 → 100644
View file @ eafd55de
/* ************************************************************************
* Copyright (C) 2016-2023 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 cop-
* ies 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 IM-
* PLIED, 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 CONNE-
* CTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
*
* ************************************************************************ */
#ifndef MIGRAPHX_GUARD_KERNELS_BITCAST_HPP
#define MIGRAPHX_GUARD_KERNELS_BITCAST_HPP
#include <migraphx/kernels/type_traits.hpp>
namespace migraphx {
template <typename To,
typename From,
MIGRAPHX_REQUIRES(is_trivially_copyable<To>{} and is_trivially_copyable<From>{})>
inline constexpr To bit_cast(From fr) noexcept
{
static_assert(sizeof(To) == sizeof(From));
return __builtin_bit_cast(To, fr);
}
} // namespace migraphx
#endif // MIGRAPHX_GUARD_KERNELS_BITCAST_HPP
src/targets/gpu/kernels/include/migraphx/kernels/float8.hpp 0 → 100644
View file @ eafd55de
/* ************************************************************************
* Copyright (C) 2016-2023 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 cop-
* ies 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 IM-
* PLIED, 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 CONNE-
* CTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
*
* ************************************************************************ */
#ifndef MIGRAPHX_GUARD_KERNELS_FLOAT8_HPP
#define MIGRAPHX_GUARD_KERNELS_FLOAT8_HPP
#if defined(__clang__)
#pragma clang diagnostic push
#pragma clang diagnostic ignored "-Wfloat-equal"
#pragma clang diagnostic ignored "-Wc++20-extensions" // required for "asm" inside constexpr
#endif // __clang__
// We are clipping in down conversion by default
#define MIGRAPHX_F8_DOWNCAST_CLIPPING 1 // NOLINT
#include <migraphx/kernels/types.hpp>
#include <migraphx/kernels/type_traits.hpp>
#include <migraphx/kernels/float8_impl.hpp>
namespace migraphx {
namespace fp8 {
enum class rounding_mode
{
standard, // standard rounding is doing RNE -- round to nearest even
stochastic
};
enum class f8_type
{
bf8 = 0, // s1e5m2
fp8 = 1 // s1e4m3
};
template <typename T>
class numeric_limits;
template <migraphx::fp8::f8_type T = migraphx::fp8::f8_type::fp8, bool FNUZ = true>
struct float8
{
uint8_t data;
// default constructor
__device__ constexpr float8() = default;
// default copy constructor
__device__ constexpr float8(const float8& y) = default;
struct from_bits_t
{
};
static constexpr __device__ from_bits_t from_bits() { return from_bits_t(); }
__device__ explicit constexpr float8(uint8_t bits, from_bits_t) : data(bits) {}
#if defined(__gfx940__) || defined(__gfx941__) || defined(__gfx942__)
// device specific optimized F8 down-conversion code
template <bool stochastic_rounding = false>
static __device__ uint8_t cast_to_f8_from_f32(float v, uint32_t rng = 0)
{
uint8_t i8data = 0x00;
union
{
float fval;
uint32_t i32val;
uint8_t i8val[4]; // NOTE: not endian independent
} val;
uint32_t ival = 0;
val.fval = v;
#ifdef MIGRAPHX_F8_DOWNCAST_CLIPPING
if constexpr(T == migraphx::fp8::f8_type::fp8)
{
if((val.i32val & 0x7F800000) != 0x7F800000) /// propagate NAN/INF, no clipping
val.fval = __builtin_amdgcn_fmed3f(val.fval, 240.0, -240.0);
}
else
{
if((val.i32val & 0x7F800000) != 0x7F800000) // propagate NAN/INF, no clipping
val.fval = __builtin_amdgcn_fmed3f(val.fval, 57344.0, -57344.0);
}
#endif
if(stochastic_rounding)
{
if constexpr(T == migraphx::fp8::f8_type::fp8)
{
ival = __builtin_amdgcn_cvt_sr_fp8_f32(val.fval, rng, ival, 0); // 0 pos
}
else
{
ival = __builtin_amdgcn_cvt_sr_bf8_f32(val.fval, rng, ival, 0); // 0 pos
}
}
else // RNE CVT
{
if constexpr(T == migraphx::fp8::f8_type::fp8)
{
ival = __builtin_amdgcn_cvt_pk_fp8_f32(
val.fval, val.fval, ival, false); // false -> WORD0
}
else
{
ival = __builtin_amdgcn_cvt_pk_bf8_f32(
val.fval, val.fval, ival, false); // false -> WORD0}
}
}
val.i32val = ival;
i8data = val.i8val[0]; // little endian
return i8data;
}
#endif // __gfx940__
// constructor from float
#if defined(__gfx940__) || defined(__gfx941__) || defined(__gfx942__)
// NOTE: ON-DEVICE... always optimal bias
explicit constexpr __device__
float8(const float v,
migraphx::fp8::rounding_mode rm = migraphx::fp8::rounding_mode::standard,
uint32_t rng = 0)
{
if(__builtin_is_constant_evaluated())
{
if constexpr(T == migraphx::fp8::f8_type::fp8)
{
#ifdef MIGRAPHX_F8_DOWNCAST_CLIPPING
data = migraphx::fp8::impl::
cast_to_f8<3, 4, float, FNUZ /*negative_zero_nan*/, true /*clip*/>(
v, (rm == migraphx::fp8::rounding_mode::stochastic), rng);
#else // MIGRAPHX_F8_DOWNCAST_CLIPPING
data = migraphx::fp8::impl::
cast_to_f8<3, 4, float, FNUZ /*negative_zero_nan*/, false /*clip*/>(
v, (rm == migraphx::fp8::rounding_mode::stochastic), rng);
#endif // MIGRAPHX_F8_DOWNCAST_CLIPPING
}
else
{
#ifdef MIGRAPHX_F8_DOWNCAST_CLIPPING
data = migraphx::fp8::impl::
cast_to_f8<2, 5, float, FNUZ /*negative_zero_nan*/, true /*clip*/>(
v, (rm == migraphx::fp8::rounding_mode::stochastic), rng);
#else // MIGRAPHX_F8_DOWNCAST_CLIPPING
data = migraphx::fp8::impl::
cast_to_f8<2, 5, float, FNUZ /*negative_zero_nan*/, false /*clip*/>(
v, (rm == migraphx::fp8::rounding_mode::stochastic), rng);
#endif // MIGRAPHX_FP8_DOWNCAST_CLIPPING}
}
}
else
{
// runtime branch, use cast_to_f8_from_f32 if want to avoid it
if(rm == migraphx::fp8::rounding_mode::stochastic)
data = cast_to_f8_from_f32<true>(v, rng);
else
data = cast_to_f8_from_f32<false>(v);
}
}
#else
// DEVICE for non-gfx940 using s/w simulation
explicit constexpr __device__
float8(const float v,
migraphx::fp8::rounding_mode rm = migraphx::fp8::rounding_mode::standard,
uint32_t rng = 0)
{
if constexpr(T == migraphx::fp8::f8_type::fp8)
{
#ifdef MIGRAPHX_F8_DOWNCAST_CLIPPING
data = migraphx::fp8::impl::
cast_to_f8<3, 4, float, FNUZ /*negative_zero_nan*/, true /*clip*/>(
v, (rm == migraphx::fp8::rounding_mode::stochastic), rng);
#else // MIGRAPHX_F8_DOWNCAST_CLIPPING
data = migraphx::fp8::impl::
cast_to_f8<3, 4, float, FNUZ /*negative_zero_nan*/, false /*clip*/>(
v, (rm == migraphx::fp8::rounding_mode::stochastic), rng);
#endif // MIGRAPHX_F8_DOWNCAST_CLIPPING
}
else
{
#ifdef MIGRAPHX_F8_DOWNCAST_CLIPPING
data = migraphx::fp8::impl::
cast_to_f8<2, 5, float, FNUZ /*negative_zero_nan*/, true /*clip*/>(
v, (rm == migraphx::fp8::rounding_mode::stochastic), rng);
#else // MIGRAPHX_F8_DOWNCAST_CLIPPING
data = migraphx::fp8::impl::
cast_to_f8<2, 5, float, FNUZ /*negative_zero_nan*/, false /*clip*/>(
v, (rm == migraphx::fp8::rounding_mode::stochastic), rng);
#endif // MIGRAPHX_FP8_DOWNCAST_CLIPPING}
}
}
#endif // __gfx940___
// Constructor from half
explicit constexpr __device__
float8(const _Float16 v, rounding_mode rm = rounding_mode::standard, uint32_t rng = 0)
: float8(static_cast<float>(v), rm, rng)
{
}
// constructor from int
explicit constexpr __device__
float8(const int v, rounding_mode rm = rounding_mode::standard, uint32_t rng = 0)
: float8(static_cast<float>(v), rm, rng)
{
}
// constructor from uint
explicit constexpr __device__
float8(const uint32_t v, rounding_mode rm = rounding_mode::standard, uint32_t rng = 0)
: float8(static_cast<float>(v), rm, rng)
{
}
// constructor from double
explicit constexpr __device__
float8(const double v, rounding_mode rm = rounding_mode::standard, uint32_t rng = 0)
: float8(static_cast<float>(v), rm, rng)
{
}
// constructor from bool
explicit constexpr __device__
float8(const bool v, rounding_mode rm = rounding_mode::standard, uint32_t rng = 0)
: float8(static_cast<float>(v), rm, rng)
{
}
// convert to float
#if defined(__gfx940__) || defined(__gfx941__) || defined(__gfx942__) // NOLINT
// upcast using device specific intrinsic
inline constexpr __device__ operator float() const
{
if(__builtin_is_constant_evaluated())
{
if constexpr(T == migraphx::fp8::f8_type::fp8)
{
return migraphx::fp8::impl::cast_from_f8<3, 4, float, FNUZ /*negative_zero_nan*/>(
data);
} // else
return migraphx::fp8::impl::cast_from_f8<2, 5, float, FNUZ /*negative_zero_nan*/>(data);
}
else
{
float fval = 0;
uint32_t i32val = static_cast<uint32_t>(data);
// upcast
if constexpr(T == migraphx::fp8::f8_type::fp8)
{
__asm__ volatile("v_cvt_f32_fp8 %0, %1 src0_sel:BYTE_0" : "=v"(fval) : "v"(i32val));
}
else
{
__asm__ volatile("v_cvt_f32_bf8 %0, %1 src0_sel:BYTE_0" : "=v"(fval) : "v"(i32val));
}
return fval;
}
}
#else // non gfx940
inline constexpr __device__ operator float() const
{
if constexpr(T == migraphx::fp8::f8_type::fp8)
{
return migraphx::fp8::impl::cast_from_f8<3, 4, float, FNUZ /*negative_zero_nan*/>(data);
} // else
return migraphx::fp8::impl::cast_from_f8<2, 5, float, FNUZ /*negative_zero_nan*/>(data);
}
#endif
inline constexpr explicit __device__ operator bool() const { return not is_zero(); }
// check for zero
inline __device__ constexpr bool is_zero() const
{
if constexpr(FNUZ)
{
return data == 0x00;
}
else
{
return (data == 0x00) || (data == 0x80);
}
}
// check for nan
inline __device__ constexpr bool is_nan() const
{
if constexpr(FNUZ)
{
return data == 0x80;
}
else
{
if(T == migraphx::fp8::f8_type::bf8)
{
return (data == 0x7D) or (data == 0x7E) or (data == 0x7F) or (data == 0xFD) or
(data == 0xFE) or (data == 0xFF);
}
else
{
return (data == 0x7F) or (data == 0xFF);
}
}
}
// check for inf
inline __device__ constexpr bool is_inf() const
{
if constexpr(FNUZ)
{
return data == 0x80;
}
else
{
if(T == migraphx::fp8::f8_type::bf8)
{
return (data == 0x7C) or (data == 0xFC);
}
else
{
// no infinities in e4m3fn, represent them as NaNs
return (data == 0x7F) or (data == 0xFF);
}
}
}
// NOLINTNEXTLINE
#define MIGRAPHX_FP8_SHORT_UNARY_OP(unary_op, binary_op) \
constexpr float8& __device__ operator unary_op(const float8& rhs) \
{ \
const auto tmp = static_cast<float>(*this) binary_op static_cast<float>(rhs); \
*this = static_cast<float8>(tmp); \
return *this; \
} \
constexpr float8& __device__ operator unary_op(const float& rhs) \
{ \
const auto tmp = static_cast<float>(*this) binary_op static_cast<float>(rhs); \
*this = static_cast<float8>(tmp); \
return *this; \
}
MIGRAPHX_FP8_SHORT_UNARY_OP(*=, *)
MIGRAPHX_FP8_SHORT_UNARY_OP(-=, -)
MIGRAPHX_FP8_SHORT_UNARY_OP(+=, +)
MIGRAPHX_FP8_SHORT_UNARY_OP(/=, /)
inline __device__ constexpr float8& operator=(const float8& rhs) = default;
inline __device__ constexpr float8& operator=(float8&& rhs) noexcept = default;
inline __device__ constexpr bool operator<(const float8& rhs) const
{
const auto we = static_cast<float>(*this);
const auto them = static_cast<float>(rhs);
return we < them;
}
inline __device__ constexpr bool operator>(const float8& rhs) const
{
const auto we = static_cast<float>(*this);
const auto them = static_cast<float>(rhs);
return we > them;
}
};
// https://onnx.ai/onnx/technical/float8.html
using fp8e4m3fn = float8<migraphx::fp8::f8_type::fp8, false>;
using fp8e5m2 = float8<migraphx::fp8::f8_type::bf8, false>;
using fp8e4m3fnuz = float8<migraphx::fp8::f8_type::fp8, true>;
using fp8e5m2fnuz = float8<migraphx::fp8::f8_type::bf8, true>;
// NOLINTNEXTLINE
#define MIGRAPHX_FP8_BINARY_OP(binary_op, T, U) \
inline constexpr U __device__ operator binary_op(const T& lhs, const T& rhs) \
{ \
return U(static_cast<float>(lhs) binary_op static_cast<float>(rhs)); \
}
// NOLINTNEXTLINE
#define MIGRAPHX_FP8_OTHER_OPS(T) \
inline constexpr __device__ T fabs(T v) \
{ \
/*NOLINTNEXTLINE*/ \
v.data = v.data & 0x7f; \
return v; \
} \
inline __device__ constexpr bool operator==(const T& lhs, const T& rhs) \
{ \
if(rhs.is_nan() or rhs.is_inf() or lhs.is_nan() or lhs.is_inf()) \
return false; \
else if((rhs.is_zero() and lhs.is_zero()) or (lhs.data == rhs.data)) \
return true; \
return false; \
}
// NOLINTNEXTLINE
#define MIGRAPHX_FP8_GEN_OP_OVERLOADS(T) \
MIGRAPHX_FP8_BINARY_OP(*, T, T) \
MIGRAPHX_FP8_BINARY_OP(-, T, T) \
MIGRAPHX_FP8_BINARY_OP(/, T, T) \
MIGRAPHX_FP8_BINARY_OP(+, T, T) \
MIGRAPHX_FP8_BINARY_OP(>=, T, bool) \
MIGRAPHX_FP8_BINARY_OP(<=, T, bool) \
MIGRAPHX_FP8_BINARY_OP(!=, T, bool) \
MIGRAPHX_FP8_OTHER_OPS(T)
MIGRAPHX_FP8_GEN_OP_OVERLOADS(fp8e5m2)
MIGRAPHX_FP8_GEN_OP_OVERLOADS(fp8e5m2fnuz)
MIGRAPHX_FP8_GEN_OP_OVERLOADS(fp8e4m3fn)
MIGRAPHX_FP8_GEN_OP_OVERLOADS(fp8e4m3fnuz)
template <>
class numeric_limits<fp8e4m3fnuz>
{
public:
static constexpr bool has_infinity = false;
static constexpr __device__ fp8e4m3fnuz epsilon()
{
return fp8e4m3fnuz(0x28, fp8e4m3fnuz::from_bits());
}
// NOLINTNEXTLINE
static constexpr __device__ fp8e4m3fnuz quiet_NaN()
{
return fp8e4m3fnuz(0x80, fp8e4m3fnuz::from_bits());
}
static constexpr __device__ fp8e4m3fnuz max()
{
return fp8e4m3fnuz(0x7F, fp8e4m3fnuz::from_bits());
}
// this is min value that is not DeNormalized(DeNorm). DeNorm min is 0x01
static constexpr __device__ fp8e4m3fnuz min()
{
return fp8e4m3fnuz(0x08, fp8e4m3fnuz::from_bits());
}
static constexpr __device__ fp8e4m3fnuz lowest()
{
return fp8e4m3fnuz(0xFF, fp8e4m3fnuz::from_bits());
}
};
template <>
class numeric_limits<fp8e4m3fn>
{
public:
static constexpr bool has_infinity = false;
static constexpr __device__ fp8e4m3fn epsilon()
{
return fp8e4m3fn(0x20, fp8e4m3fn::from_bits());
}
// NOLINTNEXTLINE
static constexpr __device__ fp8e4m3fn quiet_NaN()
{
return fp8e4m3fn(0x7F, fp8e4m3fn::from_bits());
}
static constexpr __device__ fp8e4m3fn max() { return fp8e4m3fn(0x7E, fp8e4m3fn::from_bits()); }
// this is min value that is not DeNormalized(DeNorm). DeNorm min is 0x01
static constexpr __device__ fp8e4m3fn min() { return fp8e4m3fn(0x08, fp8e4m3fn::from_bits()); }
static constexpr __device__ fp8e4m3fn lowest()
{
return fp8e4m3fn(0xFE, fp8e4m3fn::from_bits());
}
};
template <>
class numeric_limits<fp8e5m2fnuz>
{
public:
static constexpr bool has_infinity = false;
static constexpr __device__ fp8e5m2fnuz epsilon()
{
return fp8e5m2fnuz(0x34, fp8e5m2fnuz::from_bits());
}
static constexpr __device__ fp8e5m2fnuz quiet_NaN() // NOLINT
{
return fp8e5m2fnuz(0x80, fp8e5m2fnuz::from_bits());
}
static constexpr __device__ fp8e5m2fnuz max()
{
return fp8e5m2fnuz(0x7F, fp8e5m2fnuz::from_bits());
}
// this is min value that is not DeNormalized(DeNorm). DeNorm min is 0x01. I am not sure if we
// want to make this distinction. For the floating points we would end up using lowest most of
// the times.
static constexpr __device__ fp8e5m2fnuz min()
{
return fp8e5m2fnuz(0x4, fp8e5m2fnuz::from_bits());
}
static constexpr __device__ fp8e5m2fnuz lowest()
{
return fp8e5m2fnuz(0xFF, fp8e5m2fnuz::from_bits());
}
};
template <>
class numeric_limits<fp8e5m2>
{
public:
static constexpr bool has_infinity = true;
static constexpr __device__ fp8e5m2 epsilon() { return fp8e5m2(0x34, fp8e5m2::from_bits()); }
// 7D, 7E, 7F are positive NaNs and FD, FE, FF are negative NaNs
static constexpr __device__ fp8e5m2 quiet_NaN() // NOLINT
{
return fp8e5m2(0xFF, fp8e5m2::from_bits());
}
static constexpr __device__ fp8e5m2 max() { return fp8e5m2(0x7B, fp8e5m2::from_bits()); }
// this is min value that is not DeNormalized(DeNorm). DeNorm min is 0x01. I am not sure if we
// want to make this distinction. For the floating points we would end up using lowest most of
// the times.
static constexpr __device__ fp8e5m2 min() { return fp8e5m2(0x4, fp8e5m2::from_bits()); }
static constexpr __device__ fp8e5m2 lowest() { return fp8e5m2(0xFB, fp8e5m2::from_bits()); }
// 7C and FC both are infinity
static constexpr __device__ fp8e5m2 infinity() { return fp8e5m2(0x7C, fp8e5m2::from_bits()); }
};
} // namespace fp8
template <class T,
MIGRAPHX_REQUIRES(is_same<T, fp8::fp8e4m3fnuz>{} or is_same<T, fp8::fp8e5m2fnuz>{} or
is_same<T, fp8::fp8e4m3fn>{} or is_same<T, fp8::fp8e5m2>{})>
constexpr T numeric_max(migraphx::fp8::f8_type unused = migraphx::fp8::f8_type::fp8)
{
// unused parameter is added to make this numeric_max different overload definition
// compared to numeric_max defined in type_traits.hpp
(void)(unused);
return fp8::numeric_limits<T>::max();
}
template <class T,
MIGRAPHX_REQUIRES(is_same<T, fp8::fp8e4m3fnuz>{} or is_same<T, fp8::fp8e5m2fnuz>{} or
is_same<T, fp8::fp8e4m3fn>{} or is_same<T, fp8::fp8e5m2>{})>
constexpr T numeric_lowest(migraphx::fp8::f8_type unused = migraphx::fp8::f8_type::fp8)
{
// unused parameter is added to make this numeric_lowest different overload definition
// compared to numeric_lowest defined in type_traits.hpp
(void)(unused);
return fp8::numeric_limits<T>::lowest();
}
} // namespace migraphx
// =================================================================================================
#if defined(__clang__)
#pragma clang diagnostic pop
#endif // __clang__
#endif // MIGRAPHX_GUARD_KERNELS_FLOAT8_HPP
src/targets/gpu/kernels/include/migraphx/kernels/float8_impl.hpp 0 → 100644
View file @ eafd55de
/* ************************************************************************
* Copyright (C) 2016-2023 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 cop-
* ies 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 IM-
* PLIED, 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 CONNE-
* CTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
*
* ************************************************************************ */
#ifndef MIGRAPHX_GUARD_KERNELS_FP8_IMPL_HPP
#define MIGRAPHX_GUARD_KERNELS_FP8_IMPL_HPP
#include <migraphx/kernels/bit_cast.hpp>
#include <migraphx/kernels/type_traits.hpp>
#if defined(__clang__)
#pragma clang diagnostic push
#pragma clang diagnostic ignored "-Wreserved-identifier"
#endif
namespace migraphx {
namespace fp8 {
namespace impl {
// NOLINTBEGIN
template <int Wm, int We, typename T, bool NegativeZeroNan, bool Clip>
__device__ constexpr uint8_t cast_to_f8(T f_x, bool stoch = false, uint32_t rng = 0)
{
constexpr bool is_float = true;
// half is not supported for now
constexpr bool is_half = false;
static_assert(Wm + We == 7, "Wm+We==7");
static_assert(is_float or is_half, "Only float can be cast to f8");
const uint32_t mfmt = (sizeof(T) == 4) ? 23 : 10;
typename migraphx::conditional_t<sizeof(T) == 2, uint16_t, uint32_t> x;
if constexpr(sizeof(T) == 4)
x = migraphx::bit_cast<uint32_t>(f_x);
else
x = migraphx::bit_cast<uint16_t>(f_x);
uint32_t head = 0;
uint32_t mantissa = 0;
int exponent = 0;
uint32_t bias = 0;
uint32_t sign = 0;
if constexpr(sizeof(T) == 4)
{
head = x & 0xFF800000;
mantissa = x & 0x7FFFFF;
exponent = (head >> 23) & 0xFF;
sign = head >> 31;
bias = 127;
}
else
{
head = x & 0xFC00;
mantissa = x & 0x3FF;
exponent = (head >> 10) & 0x1F;
sign = head >> 15;
bias = 15;
}
uint32_t signed_inf = (sign << 7) + (((1 << We) - 1) << Wm);
uint32_t signed_all_ones = (sign << 7) + ((((1 << We) - 1) << Wm) + ((1 << Wm) - 1));
// Calcualte maximum singed value FLT_MAX, FLT_MIN
uint32_t signed_max = signed_all_ones;
if(not NegativeZeroNan)
signed_max = (Wm == 2) ? (signed_max - 4) : (signed_max - 1);
// Deal with inf and NaNs
if(NegativeZeroNan) // For the FNUZ cases, it is simple just return NaNs
{
if((sizeof(T) == 4 and ((x & 0x7F800000) == 0x7F800000)) or
(sizeof(T) == 2 and ((x & 0x7C00) == 0x7C00)))
return 0x80;
}
else
{
// calculate most common NaN mantissa for FP8, which is all Ones in binary
uint32_t nan_mantissa = 1;
for(auto i = 1; i < Wm; ++i)
{
nan_mantissa |= (nan_mantissa << 1);
}
if((sizeof(T) == 4 and ((x & 0x7F800000) == 0x7F800000)) or
(sizeof(T) == 2 and ((x & 0x7C00) == 0x7C00)))
{
// infinity
if(mantissa == 0)
{
if(sign == 0)
return (Wm == 2) ? 0x7B : 0x7E;
else
return (Wm == 2) ? 0xFB : 0xFE;
}
else // NaNs
return signed_inf + nan_mantissa;
}
}
// handle positive zero
if(x == 0)
return 0;
// handle negative zero
else if((sizeof(T) == 4 and x == 0x80000000) or (sizeof(T) == 2 and x == 0x8000))
{
return NegativeZeroNan ? 0 : 0x80; // For FNUZ types neg zero is just positive zero
}
/* First need to check if it is normal or denorm as there is a difference of implict 1
Then need to adjust the exponent to align with the F8 exponent, in the meanwhile, shift
The mantissa. Then for stochastic rounding, add rng to mantissa and truncate. And for
RNE, no need to add rng. Then probably need to check whether there is carry and adjust
exponent and mantissa again*/
// For IEEE bias mode, the bias is 2^(k-1) -1 where k is the width of exponent bits
const int f8_bias = (1 << (We - 1u)) - 1 + (NegativeZeroNan ? 1 : 0);
const int f8_denormal_act_exponent = 1 - f8_bias; // actual exponent of f8 denormal
/* act_exponent is the actual exponent of fp32/fp16 (after subtracting bias)
f8_exponent is the converted f8 exponent with bias encoding
exponent_diff is the diff between fp32/fp16 exponent and f8 exponent,
the difference needs to be adjusted and mantissa shifted*/
int act_exponent = 0;
int f8_exponent = 0;
int exponent_diff = 0;
if(exponent == 0 and mantissa != 0)
{ // fp32/fp16 is in denormal.
/* fp32 denormal is below 2^-127 so it is usually not a concern here, we mostly concern fp16
here. In this case, f8 is usually in denormal. But there could be exceptions. fp16 denormal
has exponent bias 15 while bf8 with FNUZ has exponent bias 16. It means that there are some
numbers in fp16 denormal but they are bf8 (FNUZ) normals - smallest bf8 (FNUZ) normal is
2^-15. fp16 numbers where exponent==0 (actual exponent -14) and highest bit of mantissa is 1
are bf8 (FNUZ) normal. In this case, the fp16 mantissa should be shift left by 1 */
act_exponent = 1 - bias;
exponent_diff = f8_denormal_act_exponent -
act_exponent; // actual exponent is exponent-bias+1 as it is denormal
}
else
{ // fp32/fp16 is normal with implicit 1
act_exponent = exponent - bias;
if(act_exponent <= f8_denormal_act_exponent)
{
/* This is the case where fp32/fp16 is normal but it is in f8 denormal range.
For example fp8 FNUZ mode, denormal exponent is -7, but if the fp32/fp16
actual exponent is -7, it is actually larger due to the implict 1,
Therefore it needs to be adjust to -6 and mantissa shift right by 1.
So for fp32/fp16, exponent -8 is the cut point to convert to fp8 FNUZ */
exponent_diff = f8_denormal_act_exponent - act_exponent;
}
else
{ // both fp32/fp16 and f8 are in normal range
exponent_diff =
0; // exponent_diff=0 does not mean there is no difference for this case,
// act_exponent could be larger. Just that it does not need shift mantissa
}
mantissa += (1 << mfmt); // Add the implicit 1 into mantissa
}
// need to know whether the number is right in the middle of two adjacent fp8 numbers. use max
// value of 31 to avoid undefined behaviour
bool midpoint = (mantissa & ((1u << (mfmt - Wm + exponent_diff)) - 1)) ==
(1u << (mfmt - Wm + exponent_diff - 1));
/* This part is a bit tricky. The judgment of whether it is a tie needs to be done before we
shift right as shift right could rip off some residual part and make something not midpoint look
like midpoint. For example, the fp16 number 0x1002 (0 00100 0000000010), it is larger than
midpoint, but after shift right by 4 bits, it would look like midpoint.
*/
if(exponent_diff > 0)
mantissa >>= exponent_diff;
else if(exponent_diff == -1)
mantissa <<= -exponent_diff;
bool implicit_one = mantissa & (1 << mfmt);
// if there is no implict 1, it means the f8 is denormal and need to adjust to denorm exponent
f8_exponent =
(act_exponent + exponent_diff) /*actual f8 exponent*/ + f8_bias - (implicit_one ? 0 : 1);
// Now we have the exponent and mantissa adjusted
uint32_t drop_mask = (1 << (mfmt - Wm)) - 1;
bool odd =
mantissa & (1 << (mfmt - Wm)); // if the least significant bit that is not truncated is 1
/*
This part is doing rounding by adding mantissa part that is going to get dropped.
e.g. if the dropped part for less than 0.5 than it would round down.
if the dropped part is more than 0.5 then it would round up by rolling carry to LSB of retained
mantissa.
For the mid point when bit pattern is like this for Odd: `xy1:10000000` for Odd and
`xy0:10000000` for the Even. where `:` is delimiter for dropped v/s retained part.
For the odd case :
this will add xy1:10000000 + 000:10000000 which would roll over carry to LSB of retained
part making it RNE.
For the even case : this will add xy0:10000000 + 000:01111111 which would
round down and keep number Even
*/
mantissa += (stoch ? rng : (midpoint ? (odd ? mantissa : mantissa - 1) : mantissa)) & drop_mask;
// Now we deal with overflow
if(f8_exponent == 0 and ((1 << mfmt) & mantissa))
{
f8_exponent = 1; // denormal overflow to become normal, promote exponent
}
else if((1 << (mfmt + 1)) & mantissa)
{
mantissa >>= 1;
f8_exponent++;
}
mantissa >>= (mfmt - Wm);
// above range: quantize to maximum possible float of the same sign
// for e5m2 case, max_exp is 14, since exp = 15 is reserved for Infs and Nans
const int max_exp = (1 << We) - ((NegativeZeroNan or Wm == 3) ? 1 : 2);
if(f8_exponent > max_exp)
{
if(Clip)
return signed_max;
else
{
// https://onnx.ai/onnx/technical/float8.html#cast
if(NegativeZeroNan)
return 0x80;
else
return (Wm == 2) ? signed_inf : signed_all_ones;
}
}
if(f8_exponent == 0 and mantissa == 0)
return NegativeZeroNan ? 0 : (sign << 7);
mantissa &= (1 << Wm) - 1;
return (sign << 7) | (f8_exponent << Wm) | mantissa;
}
// NOLINTEND
template <int Wm, int We, typename T, bool NegativeZeroNan>
__device__ constexpr T cast_from_f8(uint8_t x)
{
// half is not supported for now
constexpr bool is_half = false;
constexpr bool is_float = true;
static_assert(is_float or is_half, "Only float are supported");
constexpr int weo = is_half ? 5 : 8;
constexpr int wmo = is_half ? 10 : (is_float ? 23 : 7);
// NOLINTNEXTLINE
T f_inf, f_neg_inf, f_nan, f_neg0;
if constexpr(is_float)
{
const uint32_t if_inf = 0x7F800000;
const uint32_t if_neg_inf = 0xFF800000;
const uint32_t if_nan = 0x7F800001;
const uint32_t if_neg0 = 0x80000000;
f_inf = migraphx::bit_cast<float>(if_inf);
f_neg_inf = migraphx::bit_cast<float>(if_neg_inf);
f_nan = migraphx::bit_cast<float>(if_nan);
f_neg0 = migraphx::bit_cast<float>(if_neg0);
}
if(x == 0)
return 0;
uint32_t sign = x >> 7; // NOLINT
uint32_t mantissa = x & ((1 << Wm) - 1); // NOLINT
int exponent = (x & 0x7F) >> Wm; // NOLINT
if(NegativeZeroNan)
{
if(x == 0x80)
return f_nan;
}
else
{
if(x == 0x80)
return f_neg0;
if(exponent == ((1 << We) - 1) and Wm == 2) // NOLINT
return (mantissa == 0) ? (sign ? f_neg_inf : f_inf) : f_nan;
else if(Wm == 3 and (x == 0x7F or x == 0xFF))
return f_nan;
}
typename migraphx::conditional_t<sizeof(T) == 2, uint16_t, uint32_t> retval;
const int exp_low_cutoff =
(1 << (weo - 1)) - (1 << (We - 1)) + 1 - (NegativeZeroNan ? 1 : 0); // NOLINT
// subnormal input
if(exponent == 0)
{
// guaranteed mantissa!=0 since cases 0x0 and 0x80 are handled above
int sh = 1 + __builtin_clz(mantissa) - (32 - Wm);
mantissa <<= sh; // NOLINT
exponent += 1 - sh;
mantissa &= ((1 << Wm) - 1); // NOLINT
}
exponent += exp_low_cutoff - 1;
mantissa <<= wmo - Wm; // NOLINT
// subnormal output (occurs when T=half, We=5, negative_zero_nan=true)
if(exponent <= 0)
{
mantissa |= 1 << wmo; // NOLINT
mantissa >>= 1 - exponent; // NOLINT
exponent = 0;
}
if(sizeof(T) == 2)
retval = (sign << 15) | (exponent << 10) | mantissa; // NOLINT
else
retval = (sign << 31) | (exponent << 23) | mantissa; // NOLINT
return migraphx::bit_cast<T>(retval);
}
} // namespace impl
} // namespace fp8
} // namespace migraphx
#if defined(__clang__)
#pragma clang diagnostic pop
#endif
#endif // MIGRAPHX_GUARD_KERNELS_FP8_IMPL_HPP
src/targets/gpu/kernels/include/migraphx/kernels/layernorm.hpp
View file @ eafd55de
......@@ -52,22 +52,25 @@ __device__ void generic_binary_layernorm(
block::template run<reduce_output>([&](auto, auto r) {
auto input = r.inner([&](auto x1, auto x2) { return op(x1, x2); })(input1, input2);
using value_type = typename Input1::type;
using vec_value_type = vec_type<value_type>;
constexpr auto relements = r.template elements<Input1>();
constexpr auto relements_r = vec_type<value_type>{1.0 / relements};
constexpr auto relements_r = vec_value_type{1.0 / relements};
auto relements_rsqrt = sqrt(relements_r);
auto means = r.reduce(op::sum{}, make_array<vec_type<value_type>>(0, 0), [&](auto x) {
auto x_out = x * relements_r;
// dividing x by sqrt(relements) before squaring allows computing higher values
// before overflow in low precision
auto x2_sqrt = x * relements_rsqrt;
return make_array(x_out, x2_sqrt * x2_sqrt);
})(input);
auto means = r.reduce(op::sum{},
make_array<vec_value_type>(vec_value_type{0}, vec_value_type{0}),
[&](auto x) {
auto x_out = x * relements_r;
// dividing x by sqrt(relements) before squaring allows computing
// higher values before overflow in low precision
auto x2_sqrt = x * relements_rsqrt;
return make_array(x_out, x2_sqrt * x2_sqrt);
})(input);
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
value_type eps_val = implicit_conversion(eps);
r.inner([&](auto& y, auto x, auto... xs) {
auto m = x - mean_x;
......
src/targets/gpu/kernels/include/migraphx/kernels/math.hpp
View file @ eafd55de
......@@ -29,11 +29,15 @@
#include <migraphx/kernels/functional.hpp>
#include <migraphx/kernels/type_traits.hpp>
#include <migraphx/kernels/hip.hpp>
#include <migraphx/kernels/float8.hpp>
namespace migraphx {
namespace math {
constexpr float as_float(migraphx::half x) { return x; }
constexpr float as_float(migraphx::fp8::fp8e4m3fnuz x) { return x; }
template <class T>
constexpr T as_float(T x)
{
......@@ -57,14 +61,14 @@ constexpr T as_float(T x)
// NOLINTNEXTLINE
#define MIGRAPHX_DEVICE_MATH_FOR(type, name, fname) \
template <class... Ts, MIGRAPHX_REQUIRES(not is_any_vec<Ts...>())> \
auto __device__ name(type x, Ts... xs)->type \
auto __device__ name(type x, Ts... xs) -> type \
{ \
return fname(x, xs...); \
}
// NOLINTNEXTLINE
#define MIGRAPHX_DEVICE_MATH_BINARY_FOR(type, name, fname) \
inline auto __device__ name(type x, type y)->type { return fname(x, y); }
inline auto __device__ name(type x, type y) -> type { return fname(x, y); }
// NOLINTNEXTLINE
#define MIGRAPHX_DEVICE_MATH_HALF(name, fname) \
......@@ -72,6 +76,12 @@ constexpr T as_float(T x)
auto __device__ name(migraphx::half x, Ts... xs) \
MIGRAPHX_RETURNS(fname(math::as_float(x), math::as_float(xs)...))
// NOLINTNEXTLINE
#define MIGRAPHX_DEVICE_MATH_FP8(name, fname) \
template <class... Ts, MIGRAPHX_REQUIRES(not is_any_vec<Ts...>())> \
auto __device__ name(migraphx::fp8::fp8e4m3fnuz x, Ts... xs) MIGRAPHX_RETURNS( \
migraphx::fp8::fp8e4m3fnuz(fname(math::as_float(x), math::as_float(xs)...)))
// Template with two overloads for math functions, one for half2 type and one for more generic
// <half, N> vectorization where N is 4 or another even number.
......@@ -162,6 +172,33 @@ MIGRAPHX_DEVICE_MATH_HALF(tan, ::tan)
MIGRAPHX_DEVICE_MATH_HALF(tanh, ::tanh)
MIGRAPHX_DEVICE_MATH_HALF(fmod, ::fmod)
// use float to compute fp8 overload
MIGRAPHX_DEVICE_MATH_FP8(abs, ::abs)
MIGRAPHX_DEVICE_MATH_FP8(acos, ::acos)
MIGRAPHX_DEVICE_MATH_FP8(acosh, ::acosh)
MIGRAPHX_DEVICE_MATH_FP8(asin, ::asin)
MIGRAPHX_DEVICE_MATH_FP8(asinh, ::asinh)
MIGRAPHX_DEVICE_MATH_FP8(atan, ::atan)
MIGRAPHX_DEVICE_MATH_FP8(atanh, ::atanh)
MIGRAPHX_DEVICE_MATH_FP8(ceil, ::ceil)
MIGRAPHX_DEVICE_MATH_FP8(cos, ::cos)
MIGRAPHX_DEVICE_MATH_FP8(cosh, ::cosh)
MIGRAPHX_DEVICE_MATH_FP8(erf, ::erf)
MIGRAPHX_DEVICE_MATH_FP8(exp, ::exp)
MIGRAPHX_DEVICE_MATH_FP8(floor, ::floor)
MIGRAPHX_DEVICE_MATH_FP8(isnan, ::isnan)
MIGRAPHX_DEVICE_MATH_FP8(log, ::log)
MIGRAPHX_DEVICE_MATH_FP8(pow, ::pow)
MIGRAPHX_DEVICE_MATH_FP8(remainder, ::remainder)
MIGRAPHX_DEVICE_MATH_FP8(round, ::round)
MIGRAPHX_DEVICE_MATH_FP8(rsqrt, ::rsqrt)
MIGRAPHX_DEVICE_MATH_FP8(sin, ::sin)
MIGRAPHX_DEVICE_MATH_FP8(sinh, ::sinh)
MIGRAPHX_DEVICE_MATH_FP8(sqrt, ::sqrt)
MIGRAPHX_DEVICE_MATH_FP8(tan, ::tan)
MIGRAPHX_DEVICE_MATH_FP8(tanh, ::tanh)
MIGRAPHX_DEVICE_MATH_FP8(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
// packed into a 32-bit number. See include/hip/amd_detail/hip_fp16_math_fwd.h for the HIP names
......@@ -253,7 +290,7 @@ MIGRAPHX_DEVICE_MATH_VEC(where)
template <class T, class U>
constexpr auto convert(U v)
{
return vec_transform(v)([](auto x) -> T { return x; });
return vec_transform(v)([](auto x) -> T { return static_cast<T>(x); });
}
} // namespace migraphx
......
src/targets/gpu/kernels/include/migraphx/kernels/pad.hpp
View file @ eafd55de
......@@ -28,6 +28,7 @@
#include <migraphx/kernels/index.hpp>
#include <migraphx/kernels/algorithm.hpp>
#include <migraphx/kernels/ranges.hpp>
#include <migraphx/kernels/vec.hpp>
namespace migraphx {
......@@ -53,9 +54,9 @@ __device__ void pad(const index& idx,
if(any_of(range_multi.begin(), range_multi.end(), [&](auto j) {
return multi[j] < offsets[j] or input_idx[j] >= input_bounds[j];
}))
output[multi] = pad_val;
output[multi] = implicit_conversion(pad_val);
else
output[multi] = input[input_idx];
output[multi] = implicit_conversion(input[input_idx]);
});
}
......
src/targets/gpu/kernels/include/migraphx/kernels/reduce.hpp
View file @ eafd55de
......@@ -106,7 +106,7 @@ __device__ auto block_reduce(index idx, Op op, T init, Index n, F f)
#endif
using type = decltype(index::invoke_loop(f, 0, _c<0>));
__shared__ type buffer[idx.max_nlocal() / lanes_per_thread];
type x = init;
type x = type(init);
idx.local_stride(n, [&](auto i, auto d) { x = op(x, index::invoke_loop(f, i, d)); });
dpp_reduce(x, op);
......@@ -117,7 +117,7 @@ __device__ auto block_reduce(index idx, Op op, T init, Index n, F f)
}
__syncthreads();
type y = init;
type y = type(init);
for(index_int i = 0; i < idx.nlocal() / lanes_per_thread; i++)
{
y = op(y, buffer[i]);
......@@ -244,9 +244,8 @@ struct reducer_base
{
auto&& derived = static_cast<const Derived&>(*this);
auto t = derived.slice(x);
return make_storage_access<typename decltype(t)::type>([=](auto i, auto...) -> auto& {
return t[i];
});
return make_storage_access<typename decltype(t)::type>(
[=](auto i, auto...) -> auto& { return t[i]; });
}
}
......@@ -393,7 +392,7 @@ struct block
{
using max_iterations = decltype(idx.max_local_stride_iterations(n));
inner_storage<R, max_iterations{}, N> storage;
idx.local_stride(n, [&](auto j, auto d) { storage(j, d) = f(xs(j, d)...); });
idx.local_stride(n, [&](auto j, auto d) { storage(j, d) = R{f(xs(j, d)...)}; });
return storage;
}
};
......@@ -482,7 +481,7 @@ struct lane
__device__ auto reduce_impl(Op op, T init, Read read, N n, U&& x, Us&&... xs) const
{
using type = remove_reference_t<decltype(x(0, _c<0>))>;
type r = init;
type r = type(init);
for(index_int j = 0; j < n; j++)
{
r = op(r, read(x(j, _c<0>), xs(j, _c<0>)...));
......
src/targets/gpu/kernels/include/migraphx/kernels/roialign.hpp
View file @ eafd55de
......@@ -62,7 +62,7 @@ struct avg_pool
template <class T>
MIGRAPHX_DEVICE_CONSTEXPR T final(T x, index_int y)
{
return (y == 0) ? 0.0 : (x / y);
return (y == 0) ? T{0.0} : T{x / y};
}
};
......@@ -76,7 +76,7 @@ MIGRAPHX_DEVICE_CONSTEXPR typename Iterator::value_type bilinear_interpolate(
{
if(xy[ii] < -1.0f or xy[ii] > dims[ii])
{
return 0;
return implicit_conversion(0);
}
xy[ii] = migraphx::max(xy[ii], 0.0f);
......@@ -92,15 +92,16 @@ MIGRAPHX_DEVICE_CONSTEXPR typename Iterator::value_type bilinear_interpolate(
high[0] * dims[1] + low[1],
high[0] * dims[1] + high[1]};
float ly = xy[0] - low[0];
float lx = xy[1] - low[1];
float hy = 1.0f - ly;
float hx = 1.0f - lx;
array<typename Iterator::value_type, 4> ws = {hy * hx, hy * lx, ly * hx, ly * lx};
float ly = xy[0] - low[0];
float lx = xy[1] - low[1];
float hy = 1.0f - ly;
float hx = 1.0f - lx;
// do calculations in floating point and convert final result to required type
array<float, 4> ws = {hy * hx, hy * lx, ly * hx, ly * lx};
auto v01 = pooling(data[locs[0]] * ws[0], data[locs[1]] * ws[1]);
auto v23 = pooling(data[locs[2]] * ws[2], data[locs[3]] * ws[3]);
return pooling(v01, v23);
return implicit_conversion(pooling(v01, v23));
}
template <class Iterator, class Op>
......@@ -113,8 +114,9 @@ MIGRAPHX_DEVICE_CONSTEXPR auto calc_pooling(const Iterator& data,
float roi_offset,
Op op)
{
typename Iterator::value_type output_val = op.init();
const int64_t count = bin_grid_size[0] * bin_grid_size[1];
using in_dtype = typename Iterator::value_type;
in_dtype output_val = in_dtype{op.init()};
const int64_t count = bin_grid_size[0] * bin_grid_size[1];
dfor(bin_grid_size[0], bin_grid_size[1])([&](auto iy, auto ix) {
array<index_int, 2> id = {iy, ix};
array<float, 2> locs =
......@@ -148,7 +150,6 @@ __device__ void roialign(const T& x_t, const U& rois_t, const V& ind_t, W& y_t,
const auto x = x_t.begin();
const auto rois = rois_t.begin();
const auto ind = ind_t.begin();
// input shape
auto x_lens = x_t.get_shape().lens;
auto channel_num = x_lens[1];
......@@ -176,10 +177,12 @@ __device__ void roialign(const T& x_t, const U& rois_t, const V& ind_t, W& y_t,
const auto offset_rois = rois + (n * roi_column_num);
const int batch_ind = ind[n];
array<float, 2> roi_starts = {offset_rois[1] * s.spatial_scale,
offset_rois[0] * s.spatial_scale};
array<float, 2> roi_ends = {offset_rois[3] * s.spatial_scale,
offset_rois[2] * s.spatial_scale};
array<float, 2> roi_starts = {
static_cast<float>(offset_rois[1]) * static_cast<float>(s.spatial_scale),
static_cast<float>(offset_rois[0]) * static_cast<float>(s.spatial_scale)};
array<float, 2> roi_ends = {
static_cast<float>(offset_rois[3]) * static_cast<float>(s.spatial_scale),
static_cast<float>(offset_rois[2]) * static_cast<float>(s.spatial_scale)};
array<float, 2> roi_size{};
array<float, 2> bin_size{};
......
src/targets/gpu/kernels/include/migraphx/kernels/softmax.hpp
View file @ eafd55de
......@@ -43,7 +43,7 @@ __device__ void softmax(Input input1, Output output)
auto exp_in = r.inner([&](auto x) { return migraphx::exp(x - c); })(input);
auto batch_sum =
r.reduce(op::sum{}, 0, [](auto x) { return migraphx::convert<float>(x); })(exp_in);
r.inner([&](auto& y, auto x) { y = x / batch_sum; })(output, exp_in);
r.inner([&](auto& y, auto x) { y = implicit_conversion(x / batch_sum); })(output, exp_in);
});
}
......
src/targets/gpu/kernels/include/migraphx/kernels/tensor_view.hpp
View file @ eafd55de
......@@ -27,6 +27,7 @@
#include <migraphx/kernels/shape.hpp>
#include <migraphx/kernels/debug.hpp>
#include <migraphx/kernels/iota_iterator.hpp>
#include <migraphx/kernels/float8.hpp>
namespace migraphx {
......
src/targets/gpu/kernels/include/migraphx/kernels/type_traits.hpp
View file @ eafd55de
......@@ -251,7 +251,7 @@ constexpr T numeric_max()
}
template <class T>
constexpr T numeric_lowest()
constexpr auto numeric_lowest() -> decltype(numeric_max<T>())
{
if constexpr(is_integral<T>{})
{
......
src/targets/gpu/kernels/include/migraphx/kernels/vec.hpp
View file @ eafd55de
......@@ -207,7 +207,7 @@ struct implicit_conversion_op
template <class U>
constexpr operator U() const
{
return x;
return static_cast<U>(x);
}
};
......
src/targets/gpu/target.cpp
View file @ eafd55de
......@@ -98,6 +98,7 @@ std::vector<pass> target::get_passes(migraphx::context& gctx, const compile_opti
ctx.set_exhaustive_tune_flag(options.exhaustive_tune);
std::set<shape::type_t> unsupported_types(shape::types().begin(), shape::types().end());
unsupported_types.erase(shape::type_t::float_type);
unsupported_types.erase(shape::type_t::fp8e4m3fnuz_type);
unsupported_types.erase(shape::type_t::half_type);
unsupported_types.erase(shape::type_t::bool_type);
unsupported_types.erase(shape::type_t::int8_type);
......
test/gpu/jit.cpp
View file @ eafd55de
......@@ -350,18 +350,19 @@ TEST_CASE(compile_math)
auto vec_sizes = {2, 4, 6};
for(auto&& t : migraphx::shape::types())
{
if(contains({migraphx::shape::bool_type,
migraphx::shape::fp8e4m3fnuz_type,
migraphx::shape::tuple_type},
t))
if(contains({migraphx::shape::bool_type, migraphx::shape::tuple_type}, t))
continue;
auto name = migraphx::shape::cpp_type(t);
if(t == migraphx::shape::half_type)
name.insert(0, "migraphx::");
data_types.push_back(name);
migraphx::transform(vec_sizes, std::back_inserter(data_types), [&](auto i) {
return "migraphx::vec<" + name + ", " + std::to_string(i) + ">";
});
// fp8 doesn't have vectorization support yet, therefore skip it for now.
if(t != migraphx::shape::fp8e4m3fnuz_type)
{
migraphx::transform(vec_sizes, std::back_inserter(data_types), [&](auto i) {
return "migraphx::vec<" + name + ", " + std::to_string(i) + ">";
});
}
}
migraphx::shape input{migraphx::shape::float_type, {5, 2}};
migraphx::gpu::hip_compile_options options;
......@@ -431,7 +432,6 @@ TEST_CASE(assert_type_min_max)
min = std::to_string(as.min());
max = std::to_string(as.max());
}
auto src = migraphx::interpolate_string(assert_template,
{{"type", name}, {"max", max}, {"min", min}});
migraphx::shape input{migraphx::shape::float_type, {5, 2}};
......
test/simplify_algebra_test.cpp
View file @ eafd55de
......@@ -1017,6 +1017,40 @@ TEST_CASE(simplify_concat_add_relu_broadcast_same_axis)
EXPECT(m1 == m2);
}
TEST_CASE(concat_convert_fusion)
{
auto s = migraphx::shape{migraphx::shape::float_type, {64}};
migraphx::module m1;
{
auto x = m1.add_parameter("x", s);
auto y = m1.add_parameter("y", s);
auto xh = m1.add_instruction(
migraphx::make_op("convert",
{{"target_type", migraphx::to_value(migraphx::shape::half_type)}}),
x);
auto yh = m1.add_instruction(
migraphx::make_op("convert",
{{"target_type", migraphx::to_value(migraphx::shape::half_type)}}),
y);
auto concat = m1.add_instruction(migraphx::make_op("concat", {{"axis", 0}}), xh, yh);
m1.add_instruction(pass_op{}, concat);
}
run_pass(m1);
migraphx::module m2;
{
auto x = m2.add_parameter("x", s);
auto y = m2.add_parameter("y", s);
auto concat = m2.add_instruction(migraphx::make_op("concat", {{"axis", 0}}), x, y);
auto concath = m2.add_instruction(
migraphx::make_op("convert",
{{"target_type", migraphx::to_value(migraphx::shape::half_type)}}),
concat);
m2.add_instruction(pass_op{}, concath);
}
EXPECT(m1 == m2);
}
TEST_CASE(simplify_div_const)
{
migraphx::module m1;
......
test/verify/test_abs.cpp
View file @ eafd55de
......@@ -27,14 +27,19 @@
#include <migraphx/generate.hpp>
#include <migraphx/make_op.hpp>
struct test_abs : verify_program<test_abs>
template <migraphx::shape::type_t DType>
struct test_abs : verify_program<test_abs<DType>>
{
migraphx::program create_program() const
{
migraphx::program p;
auto* mm = p.get_main_module();
auto x = mm->add_parameter("x", migraphx::shape{migraphx::shape::float_type, {4, 3, 3, 3}});
auto x = mm->add_parameter("x", migraphx::shape{DType, {4, 3, 3, 3}});
mm->add_instruction(migraphx::make_op("abs"), x);
return p;
}
};
template struct test_abs<migraphx::shape::fp8e4m3fnuz_type>;
template struct test_abs<migraphx::shape::half_type>;
template struct test_abs<migraphx::shape::float_type>;
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment