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test2/intern/cycles/util/math_float4.h

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/* SPDX-FileCopyrightText: 2011-2013 Intel Corporation
* SPDX-FileCopyrightText: 2011-2022 Blender Foundation
*
* SPDX-License-Identifier: Apache-2.0 */
#ifndef __UTIL_MATH_FLOAT4_H__
#define __UTIL_MATH_FLOAT4_H__
#ifndef __UTIL_MATH_H__
# error "Do not include this file directly, include util/types.h instead."
#endif
CCL_NAMESPACE_BEGIN
ccl_device_inline float4 zero_float4()
{
#ifdef __KERNEL_SSE__
return float4(_mm_setzero_ps());
#else
return make_float4(0.0f, 0.0f, 0.0f, 0.0f);
#endif
}
ccl_device_inline float4 one_float4()
{
return make_float4(1.0f, 1.0f, 1.0f, 1.0f);
}
ccl_device_inline int4 cast(const float4 a)
{
#ifdef __KERNEL_SSE__
return int4(_mm_castps_si128(a));
#else
return make_int4(
__float_as_int(a.x), __float_as_int(a.y), __float_as_int(a.z), __float_as_int(a.w));
#endif
}
#if !defined(__KERNEL_METAL__)
ccl_device_inline float4 operator-(const float4 &a)
{
# ifdef __KERNEL_SSE__
__m128 mask = _mm_castsi128_ps(_mm_set1_epi32(0x80000000));
return float4(_mm_xor_ps(a.m128, mask));
# else
return make_float4(-a.x, -a.y, -a.z, -a.w);
# endif
}
ccl_device_inline float4 operator*(const float4 a, const float4 b)
{
# ifdef __KERNEL_SSE__
return float4(_mm_mul_ps(a.m128, b.m128));
# else
return make_float4(a.x * b.x, a.y * b.y, a.z * b.z, a.w * b.w);
# endif
}
ccl_device_inline float4 operator*(const float4 a, float f)
{
# if defined(__KERNEL_SSE__)
return a * make_float4(f);
# else
return make_float4(a.x * f, a.y * f, a.z * f, a.w * f);
# endif
}
ccl_device_inline float4 operator*(float f, const float4 a)
{
return a * f;
}
ccl_device_inline float4 operator/(const float4 a, float f)
{
return a * (1.0f / f);
}
ccl_device_inline float4 operator/(const float4 a, const float4 b)
{
# ifdef __KERNEL_SSE__
return float4(_mm_div_ps(a.m128, b.m128));
# else
return make_float4(a.x / b.x, a.y / b.y, a.z / b.z, a.w / b.w);
# endif
}
ccl_device_inline float4 operator+(const float4 a, const float4 b)
{
# ifdef __KERNEL_SSE__
return float4(_mm_add_ps(a.m128, b.m128));
# else
return make_float4(a.x + b.x, a.y + b.y, a.z + b.z, a.w + b.w);
# endif
}
ccl_device_inline float4 operator+(const float4 a, const float f)
{
return a + make_float4(f);
}
ccl_device_inline float4 operator-(const float4 a, const float4 b)
{
# ifdef __KERNEL_SSE__
return float4(_mm_sub_ps(a.m128, b.m128));
# else
return make_float4(a.x - b.x, a.y - b.y, a.z - b.z, a.w - b.w);
# endif
}
ccl_device_inline float4 operator-(const float4 a, const float f)
{
return a - make_float4(f);
}
ccl_device_inline float4 operator+=(float4 &a, const float4 b)
{
return a = a + b;
}
ccl_device_inline float4 operator-=(float4 &a, const float4 b)
{
return a = a - b;
}
ccl_device_inline float4 operator*=(float4 &a, const float4 b)
{
return a = a * b;
}
ccl_device_inline float4 operator*=(float4 &a, float f)
{
return a = a * f;
}
ccl_device_inline float4 operator/=(float4 &a, float f)
{
return a = a / f;
}
ccl_device_inline int4 operator<(const float4 a, const float4 b)
{
# ifdef __KERNEL_SSE__
return int4(_mm_castps_si128(_mm_cmplt_ps(a.m128, b.m128)));
# else
return make_int4(a.x < b.x, a.y < b.y, a.z < b.z, a.w < b.w);
# endif
}
ccl_device_inline int4 operator>=(const float4 a, const float4 b)
{
# ifdef __KERNEL_SSE__
return int4(_mm_castps_si128(_mm_cmpge_ps(a.m128, b.m128)));
# else
return make_int4(a.x >= b.x, a.y >= b.y, a.z >= b.z, a.w >= b.w);
# endif
}
ccl_device_inline int4 operator<=(const float4 a, const float4 b)
{
# ifdef __KERNEL_SSE__
return int4(_mm_castps_si128(_mm_cmple_ps(a.m128, b.m128)));
# else
return make_int4(a.x <= b.x, a.y <= b.y, a.z <= b.z, a.w <= b.w);
# endif
}
ccl_device_inline bool operator==(const float4 a, const float4 b)
{
# ifdef __KERNEL_SSE__
return (_mm_movemask_ps(_mm_cmpeq_ps(a.m128, b.m128)) & 15) == 15;
# else
return (a.x == b.x && a.y == b.y && a.z == b.z && a.w == b.w);
# endif
}
ccl_device_inline const float4 operator^(const float4 a, const float4 b)
{
# ifdef __KERNEL_SSE__
return float4(_mm_xor_ps(a.m128, b.m128));
# else
return make_float4(__uint_as_float(__float_as_uint(a.x) ^ __float_as_uint(b.x)),
__uint_as_float(__float_as_uint(a.y) ^ __float_as_uint(b.y)),
__uint_as_float(__float_as_uint(a.z) ^ __float_as_uint(b.z)),
__uint_as_float(__float_as_uint(a.w) ^ __float_as_uint(b.w)));
# endif
}
ccl_device_inline float4 min(const float4 a, const float4 b)
{
# ifdef __KERNEL_SSE__
return float4(_mm_min_ps(a.m128, b.m128));
# else
return make_float4(min(a.x, b.x), min(a.y, b.y), min(a.z, b.z), min(a.w, b.w));
# endif
}
ccl_device_inline float4 max(const float4 a, const float4 b)
{
# ifdef __KERNEL_SSE__
return float4(_mm_max_ps(a.m128, b.m128));
# else
return make_float4(max(a.x, b.x), max(a.y, b.y), max(a.z, b.z), max(a.w, b.w));
# endif
}
ccl_device_inline float4 clamp(const float4 a, const float4 mn, const float4 mx)
{
return min(max(a, mn), mx);
}
#endif /* !__KERNEL_METAL__*/
ccl_device_inline const float4 madd(const float4 a, const float4 b, const float4 c)
{
#ifdef __KERNEL_SSE__
# ifdef __KERNEL_NEON__
return float4(vfmaq_f32(c, a, b));
# elif defined(__KERNEL_AVX2__)
return float4(_mm_fmadd_ps(a, b, c));
# else
return a * b + c;
# endif
#else
return a * b + c;
#endif
}
ccl_device_inline float4 msub(const float4 a, const float4 b, const float4 c)
{
#ifdef __KERNEL_SSE__
# ifdef __KERNEL_NEON__
return float4(vfmaq_f32(vnegq_f32(c), a, b));
# elif defined(__KERNEL_AVX2__)
return float4(_mm_fmsub_ps(a, b, c));
# else
return a * b - c;
# endif
#else
return a * b - c;
#endif
}
#ifdef __KERNEL_SSE__
template<size_t i0, size_t i1, size_t i2, size_t i3>
__forceinline const float4 shuffle(const float4 b)
{
# ifdef __KERNEL_NEON__
return float4(shuffle_neon<float32x4_t, i0, i1, i2, i3>(b.m128));
# else
return float4(
_mm_castsi128_ps(_mm_shuffle_epi32(_mm_castps_si128(b), _MM_SHUFFLE(i3, i2, i1, i0))));
# endif
}
template<> __forceinline const float4 shuffle<0, 1, 0, 1>(const float4 a)
{
return float4(_mm_movelh_ps(a, a));
}
template<> __forceinline const float4 shuffle<2, 3, 2, 3>(const float4 a)
{
return float4(_mm_movehl_ps(a, a));
}
# ifdef __KERNEL_SSE3__
template<> __forceinline const float4 shuffle<0, 0, 2, 2>(const float4 b)
{
return float4(_mm_moveldup_ps(b));
}
template<> __forceinline const float4 shuffle<1, 1, 3, 3>(const float4 b)
{
return float4(_mm_movehdup_ps(b));
}
# endif /* __KERNEL_SSE3__ */
template<size_t i0, size_t i1, size_t i2, size_t i3>
__forceinline const float4 shuffle(const float4 a, const float4 b)
{
# ifdef __KERNEL_NEON__
return float4(shuffle_neon<float32x4_t, i0, i1, i2, i3>(a, b));
# else
return float4(_mm_shuffle_ps(a, b, _MM_SHUFFLE(i3, i2, i1, i0)));
# endif
}
template<size_t i0> __forceinline const float4 shuffle(const float4 b)
{
return shuffle<i0, i0, i0, i0>(b);
}
template<size_t i0> __forceinline const float4 shuffle(const float4 a, const float4 b)
{
# ifdef __KERNEL_NEON__
return float4(shuffle_neon<float32x4_t, i0, i0, i0, i0>(a, b));
# else
return float4(_mm_shuffle_ps(a, b, _MM_SHUFFLE(i0, i0, i0, i0)));
# endif
}
template<> __forceinline const float4 shuffle<0, 1, 0, 1>(const float4 a, const float4 b)
{
return float4(_mm_movelh_ps(a, b));
}
template<> __forceinline const float4 shuffle<2, 3, 2, 3>(const float4 a, const float4 b)
{
return float4(_mm_movehl_ps(b, a));
}
template<size_t i> __forceinline float extract(const float4 a)
{
return _mm_cvtss_f32(shuffle<i, i, i, i>(a));
}
template<> __forceinline float extract<0>(const float4 a)
{
return _mm_cvtss_f32(a);
}
#endif
ccl_device_inline float reduce_add(const float4 a)
{
#if defined(__KERNEL_SSE__)
# if defined(__KERNEL_NEON__)
return vaddvq_f32(a);
# elif defined(__KERNEL_SSE3__)
float4 h(_mm_hadd_ps(a.m128, a.m128));
return _mm_cvtss_f32(_mm_hadd_ps(h.m128, h.m128));
# else
float4 h(shuffle<1, 0, 3, 2>(a) + a);
return _mm_cvtss_f32(shuffle<2, 3, 0, 1>(h) + h);
# endif
#else
return a.x + a.y + a.z + a.w;
#endif
}
ccl_device_inline float reduce_min(const float4 a)
{
#if defined(__KERNEL_SSE__)
# if defined(__KERNEL_NEON__)
return vminvq_f32(a);
# else
float4 h = min(shuffle<1, 0, 3, 2>(a), a);
return _mm_cvtss_f32(min(shuffle<2, 3, 0, 1>(h), h));
# endif
#else
return min(min(a.x, a.y), min(a.z, a.w));
#endif
}
ccl_device_inline float reduce_max(const float4 a)
{
#if defined(__KERNEL_SSE__)
# if defined(__KERNEL_NEON__)
return vmaxvq_f32(a);
# else
float4 h = max(shuffle<1, 0, 3, 2>(a), a);
return _mm_cvtss_f32(max(shuffle<2, 3, 0, 1>(h), h));
# endif
#else
return max(max(a.x, a.y), max(a.z, a.w));
#endif
}
#if !defined(__KERNEL_METAL__)
ccl_device_inline float dot(const float4 a, const float4 b)
{
# if defined(__KERNEL_SSE41__) && defined(__KERNEL_SSE__)
# if defined(__KERNEL_NEON__)
__m128 t = vmulq_f32(a, b);
return vaddvq_f32(t);
# else
return _mm_cvtss_f32(_mm_dp_ps(a, b, 0xFF));
# endif
# else
return (a.x * b.x + a.y * b.y) + (a.z * b.z + a.w * b.w);
# endif
}
#endif /* !defined(__KERNEL_METAL__) */
ccl_device_inline float len(const float4 a)
{
return sqrtf(dot(a, a));
}
ccl_device_inline float len_squared(const float4 a)
{
return dot(a, a);
}
#if !defined(__KERNEL_METAL__)
ccl_device_inline float distance(const float4 a, const float4 b)
{
return len(a - b);
}
ccl_device_inline float4 rcp(const float4 a)
{
# ifdef __KERNEL_SSE__
/* Don't use _mm_rcp_ps due to poor precision. */
return float4(_mm_div_ps(_mm_set_ps1(1.0f), a.m128));
# else
return make_float4(1.0f / a.x, 1.0f / a.y, 1.0f / a.z, 1.0f / a.w);
# endif
}
ccl_device_inline float4 sqrt(const float4 a)
{
# ifdef __KERNEL_SSE__
return float4(_mm_sqrt_ps(a.m128));
# else
return make_float4(sqrtf(a.x), sqrtf(a.y), sqrtf(a.z), sqrtf(a.w));
# endif
}
ccl_device_inline float4 sqr(const float4 a)
{
return a * a;
}
ccl_device_inline float4 cross(const float4 a, const float4 b)
{
# ifdef __KERNEL_SSE__
return (shuffle<1, 2, 0, 0>(a) * shuffle<2, 0, 1, 0>(b)) -
(shuffle<2, 0, 1, 0>(a) * shuffle<1, 2, 0, 0>(b));
# else
return make_float4(a.y * b.z - a.z * b.y, a.z * b.x - a.x * b.z, a.x * b.y - a.y * b.x, 0.0f);
# endif
}
ccl_device_inline bool is_zero(const float4 a)
{
# ifdef __KERNEL_SSE__
return a == zero_float4();
# else
return (a.x == 0.0f && a.y == 0.0f && a.z == 0.0f && a.w == 0.0f);
# endif
}
ccl_device_inline float average(const float4 a)
{
return reduce_add(a) * 0.25f;
}
ccl_device_inline float4 normalize(const float4 a)
{
return a / len(a);
}
ccl_device_inline float4 safe_normalize(const float4 a)
{
float t = len(a);
return (t != 0.0f) ? a / t : a;
}
ccl_device_inline float4 fabs(const float4 a)
{
# if defined(__KERNEL_SSE__)
# if defined(__KERNEL_NEON__)
return float4(vabsq_f32(a));
# else
return float4(_mm_and_ps(a.m128, _mm_castsi128_ps(_mm_set1_epi32(0x7fffffff))));
# endif
# else
return make_float4(fabsf(a.x), fabsf(a.y), fabsf(a.z), fabsf(a.w));
# endif
}
ccl_device_inline float4 floor(const float4 a)
{
# ifdef __KERNEL_SSE__
# if defined(__KERNEL_NEON__)
return float4(vrndmq_f32(a));
# else
return float4(_mm_floor_ps(a));
# endif
# else
return make_float4(floorf(a.x), floorf(a.y), floorf(a.z), floorf(a.w));
# endif
}
ccl_device_inline float4 floorfrac(const float4 x, ccl_private int4 *i)
{
# ifdef __KERNEL_SSE__
const float4 f = floor(x);
*i = int4(_mm_cvttps_epi32(f.m128));
return x - f;
# else
float4 r;
r.x = floorfrac(x.x, &i->x);
r.y = floorfrac(x.y, &i->y);
r.z = floorfrac(x.z, &i->z);
r.w = floorfrac(x.w, &i->w);
return r;
# endif
}
ccl_device_inline float4 mix(const float4 a, const float4 b, float t)
{
return a + t * (b - a);
}
ccl_device_inline float4 mix(const float4 a, const float4 b, const float4 t)
{
return a + t * (b - a);
}
ccl_device_inline float4 saturate(const float4 a)
{
return make_float4(saturatef(a.x), saturatef(a.y), saturatef(a.z), saturatef(a.w));
}
ccl_device_inline float4 exp(float4 v)
{
return make_float4(expf(v.x), expf(v.y), expf(v.z), expf(v.z));
}
ccl_device_inline float4 log(float4 v)
{
return make_float4(logf(v.x), logf(v.y), logf(v.z), logf(v.z));
}
#endif /* !__KERNEL_METAL__*/
ccl_device_inline bool isequal(const float4 a, const float4 b)
{
#if defined(__KERNEL_METAL__)
return all(a == b);
#else
return a == b;
#endif
}
#ifndef __KERNEL_GPU__
ccl_device_inline float4 select(const int4 mask, const float4 a, const float4 b)
{
# ifdef __KERNEL_SSE__
# ifdef __KERNEL_SSE41__
return float4(_mm_blendv_ps(b.m128, a.m128, _mm_castsi128_ps(mask.m128)));
# else
return float4(
_mm_or_ps(_mm_and_ps(_mm_castsi128_ps(mask), a), _mm_andnot_ps(_mm_castsi128_ps(mask), b)));
# endif
# else
return make_float4(
(mask.x) ? a.x : b.x, (mask.y) ? a.y : b.y, (mask.z) ? a.z : b.z, (mask.w) ? a.w : b.w);
# endif
}
ccl_device_inline float4 mask(const int4 mask, const float4 a)
{
/* Replace elements of x with zero where mask isn't set. */
return select(mask, a, zero_float4());
}
Cycles: Kernel address space changes for MSL This is the first of a sequence of changes to support compiling Cycles kernels as MSL (Metal Shading Language) in preparation for a Metal GPU device implementation. MSL requires that all pointer types be declared with explicit address space attributes (device, thread, etc...). There is already precedent for this with Cycles' address space macros (ccl_global, ccl_private, etc...), therefore the first step of MSL-enablement is to apply these consistently. Line-for-line this represents the largest change required to enable MSL. Applying this change first will simplify future patches as well as offering the emergent benefit of enhanced descriptiveness. The vast majority of deltas in this patch fall into one of two cases: - Ensuring ccl_private is specified for thread-local pointer types - Ensuring ccl_global is specified for device-wide pointer types Additionally, the ccl_addr_space qualifier can be removed. Prior to Cycles X, ccl_addr_space was used as a context-dependent address space qualifier, but now it is either redundant (e.g. in struct typedefs), or can be replaced by ccl_global in the case of pointer types. Associated function variants (e.g. lcg_step_float_addrspace) are also redundant. In cases where address space qualifiers are chained with "const", this patch places the address space qualifier first. The rationale for this is that the choice of address space is likely to have the greater impact on runtime performance and overall architecture. The final part of this patch is the addition of a metal/compat.h header. This is partially complete and will be extended in future patches, paving the way for the full Metal implementation. Ref T92212 Reviewed By: brecht Maniphest Tasks: T92212 Differential Revision: https://developer.blender.org/D12864
2021-10-14 13:53:40 +01:00
ccl_device_inline float4 load_float4(ccl_private const float *v)
{
# ifdef __KERNEL_SSE__
return float4(_mm_loadu_ps(v));
# else
return make_float4(v[0], v[1], v[2], v[3]);
# endif
}
#endif /* !__KERNEL_GPU__ */
ccl_device_inline float4 safe_divide(const float4 a, const float b)
{
return (b != 0.0f) ? a / b : zero_float4();
}
ccl_device_inline float4 safe_divide(const float4 a, const float4 b)
{
return make_float4((b.x != 0.0f) ? a.x / b.x : 0.0f,
(b.y != 0.0f) ? a.y / b.y : 0.0f,
(b.z != 0.0f) ? a.z / b.z : 0.0f,
(b.w != 0.0f) ? a.w / b.w : 0.0f);
}
ccl_device_inline bool isfinite_safe(float4 v)
{
return isfinite_safe(v.x) && isfinite_safe(v.y) && isfinite_safe(v.z) && isfinite_safe(v.w);
}
ccl_device_inline float4 ensure_finite(float4 v)
{
if (!isfinite_safe(v.x))
v.x = 0.0f;
if (!isfinite_safe(v.y))
v.y = 0.0f;
if (!isfinite_safe(v.z))
v.z = 0.0f;
if (!isfinite_safe(v.w))
v.w = 0.0f;
return v;
}
Nodes: add Fractal Voronoi Noise Fractal noise is the idea of evaluating the same noise function multiple times with different input parameters on each layer and then mixing the results. The individual layers are usually called octaves. The number of layers is controlled with a "Detail" slider. The "Lacunarity" input controls a factor by which each successive layer gets scaled. The existing Noise node already supports fractal noise. Now the Voronoi Noise node supports it as well. The node also has a new "Normalize" property that ensures that the output values stay in a [0.0, 1.0] range. That is except for the F2 feature where in rare cases the output may be outside that range even with "Normalize" turned on. How the individual octaves are mixed depends on the feature and output socket: - F1/Smooth F1/F2: - Distance/Color output: The individual Distance/Color octaves are first multiplied by a factor of `Roughness ^ (#layers - 1.0)` then added together to create the final output. - Position output: Each Position octave gets linearly interpolated with the combined output of the previous octaves. The Roughness input serves as an interpolation factor with 0.0 resutling in only using the combined output of the previous octaves and 1.0 resulting in only using the current highest octave. - Distance to Edge: - Distance output: The Distance octaves are mixed exactly like the Position octaves for F1/Smooth F1/F2. It should be noted that Voronoi Noise is a relatively slow noise function, especially at higher dimensions. Increasing the "Detail" makes it even slower. Therefore, when optimizing a scene one should consider trying to use simpler noise functions instead of Voronoi if the final result is close enough. Pull Request: https://projects.blender.org/blender/blender/pulls/106827
2023-06-13 09:18:12 +02:00
/* Consistent name for this would be pow, but HIP compiler crashes in name mangling. */
ccl_device_inline float4 power(float4 v, float e)
{
return make_float4(powf(v.x, e), powf(v.y, e), powf(v.z, e), powf(v.w, e));
}
CCL_NAMESPACE_END
#endif /* __UTIL_MATH_FLOAT4_H__ */