godot/thirdparty/astcenc/astcenc_weight_align.cpp
K. S. Ernest (iFire) Lee 696346f4cc
Add ASTC compression and decompression with Arm astcenc.
Co-authored-by: Gordon A Macpherson <gordon.a.macpherson@gmail.com>
Co-authored-by: Rémi Verschelde <rverschelde@gmail.com>
2023-01-19 16:27:59 +01:00

480 lines
18 KiB
C++

// SPDX-License-Identifier: Apache-2.0
// ----------------------------------------------------------------------------
// Copyright 2011-2022 Arm Limited
//
// Licensed under the Apache License, Version 2.0 (the "License"); you may not
// use this file except in compliance with the License. You may obtain a copy
// of the License at:
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
// WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
// License for the specific language governing permissions and limitations
// under the License.
// ----------------------------------------------------------------------------
#if !defined(ASTCENC_DECOMPRESS_ONLY)
/**
* @brief Functions for angular-sum algorithm for weight alignment.
*
* This algorithm works as follows:
* - we compute a complex number P as (cos s*i, sin s*i) for each weight,
* where i is the input value and s is a scaling factor based on the spacing between the weights.
* - we then add together complex numbers for all the weights.
* - we then compute the length and angle of the resulting sum.
*
* This should produce the following results:
* - perfect alignment results in a vector whose length is equal to the sum of lengths of all inputs
* - even distribution results in a vector of length 0.
* - all samples identical results in perfect alignment for every scaling.
*
* For each scaling factor within a given set, we compute an alignment factor from 0 to 1. This
* should then result in some scalings standing out as having particularly good alignment factors;
* we can use this to produce a set of candidate scale/shift values for various quantization levels;
* we should then actually try them and see what happens.
*/
#include "astcenc_internal.h"
#include "astcenc_vecmathlib.h"
#include <stdio.h>
#include <cassert>
#include <cstring>
static constexpr unsigned int ANGULAR_STEPS { 32 };
static_assert((ANGULAR_STEPS % ASTCENC_SIMD_WIDTH) == 0,
"ANGULAR_STEPS must be multiple of ASTCENC_SIMD_WIDTH");
static_assert(ANGULAR_STEPS >= 32,
"ANGULAR_STEPS must be at least max(steps_for_quant_level)");
// Store a reduced sin/cos table for 64 possible weight values; this causes
// slight quality loss compared to using sin() and cos() directly. Must be 2^N.
static constexpr unsigned int SINCOS_STEPS { 64 };
static const uint8_t steps_for_quant_level[12] {
2, 3, 4, 5, 6, 8, 10, 12, 16, 20, 24, 32
};
alignas(ASTCENC_VECALIGN) static float sin_table[SINCOS_STEPS][ANGULAR_STEPS];
alignas(ASTCENC_VECALIGN) static float cos_table[SINCOS_STEPS][ANGULAR_STEPS];
#if defined(ASTCENC_DIAGNOSTICS)
static bool print_once { true };
#endif
/* See header for documentation. */
void prepare_angular_tables()
{
for (unsigned int i = 0; i < ANGULAR_STEPS; i++)
{
float angle_step = static_cast<float>(i + 1);
for (unsigned int j = 0; j < SINCOS_STEPS; j++)
{
sin_table[j][i] = static_cast<float>(sinf((2.0f * astc::PI / (SINCOS_STEPS - 1.0f)) * angle_step * static_cast<float>(j)));
cos_table[j][i] = static_cast<float>(cosf((2.0f * astc::PI / (SINCOS_STEPS - 1.0f)) * angle_step * static_cast<float>(j)));
}
}
}
/**
* @brief Compute the angular alignment factors and offsets.
*
* @param weight_count The number of (decimated) weights.
* @param dec_weight_ideal_value The ideal decimated unquantized weight values.
* @param max_angular_steps The maximum number of steps to be tested.
* @param[out] offsets The output angular offsets array.
*/
static void compute_angular_offsets(
unsigned int weight_count,
const float* dec_weight_ideal_value,
unsigned int max_angular_steps,
float* offsets
) {
promise(weight_count > 0);
promise(max_angular_steps > 0);
alignas(ASTCENC_VECALIGN) int isamplev[BLOCK_MAX_WEIGHTS];
// Precompute isample; arrays are always allocated 64 elements long
for (unsigned int i = 0; i < weight_count; i += ASTCENC_SIMD_WIDTH)
{
// Add 2^23 and interpreting bits extracts round-to-nearest int
vfloat sample = loada(dec_weight_ideal_value + i) * (SINCOS_STEPS - 1.0f) + vfloat(12582912.0f);
vint isample = float_as_int(sample) & vint((SINCOS_STEPS - 1));
storea(isample, isamplev + i);
}
// Arrays are multiple of SIMD width (ANGULAR_STEPS), safe to overshoot max
vfloat mult = vfloat(1.0f / (2.0f * astc::PI));
for (unsigned int i = 0; i < max_angular_steps; i += ASTCENC_SIMD_WIDTH)
{
vfloat anglesum_x = vfloat::zero();
vfloat anglesum_y = vfloat::zero();
for (unsigned int j = 0; j < weight_count; j++)
{
int isample = isamplev[j];
anglesum_x += loada(cos_table[isample] + i);
anglesum_y += loada(sin_table[isample] + i);
}
vfloat angle = atan2(anglesum_y, anglesum_x);
vfloat ofs = angle * mult;
storea(ofs, offsets + i);
}
}
/**
* @brief For a given step size compute the lowest and highest weight.
*
* Compute the lowest and highest weight that results from quantizing using the given stepsize and
* offset, and then compute the resulting error. The cut errors indicate the error that results from
* forcing samples that should have had one weight value one step up or down.
*
* @param weight_count The number of (decimated) weights.
* @param dec_weight_ideal_value The ideal decimated unquantized weight values.
* @param max_angular_steps The maximum number of steps to be tested.
* @param max_quant_steps The maximum quantization level to be tested.
* @param offsets The angular offsets array.
* @param[out] lowest_weight Per angular step, the lowest weight.
* @param[out] weight_span Per angular step, the span between lowest and highest weight.
* @param[out] error Per angular step, the error.
* @param[out] cut_low_weight_error Per angular step, the low weight cut error.
* @param[out] cut_high_weight_error Per angular step, the high weight cut error.
*/
static void compute_lowest_and_highest_weight(
unsigned int weight_count,
const float* dec_weight_ideal_value,
unsigned int max_angular_steps,
unsigned int max_quant_steps,
const float* offsets,
float* lowest_weight,
int* weight_span,
float* error,
float* cut_low_weight_error,
float* cut_high_weight_error
) {
promise(weight_count > 0);
promise(max_angular_steps > 0);
vfloat rcp_stepsize = vfloat::lane_id() + vfloat(1.0f);
// Arrays are ANGULAR_STEPS long, so always safe to run full vectors
for (unsigned int sp = 0; sp < max_angular_steps; sp += ASTCENC_SIMD_WIDTH)
{
vfloat minidx(128.0f);
vfloat maxidx(-128.0f);
vfloat errval = vfloat::zero();
vfloat cut_low_weight_err = vfloat::zero();
vfloat cut_high_weight_err = vfloat::zero();
vfloat offset = loada(offsets + sp);
for (unsigned int j = 0; j < weight_count; j++)
{
vfloat sval = load1(dec_weight_ideal_value + j) * rcp_stepsize - offset;
vfloat svalrte = round(sval);
vfloat diff = sval - svalrte;
errval += diff * diff;
// Reset tracker on min hit
vmask mask = svalrte < minidx;
minidx = select(minidx, svalrte, mask);
cut_low_weight_err = select(cut_low_weight_err, vfloat::zero(), mask);
// Accumulate on min hit
mask = svalrte == minidx;
vfloat accum = cut_low_weight_err + vfloat(1.0f) - vfloat(2.0f) * diff;
cut_low_weight_err = select(cut_low_weight_err, accum, mask);
// Reset tracker on max hit
mask = svalrte > maxidx;
maxidx = select(maxidx, svalrte, mask);
cut_high_weight_err = select(cut_high_weight_err, vfloat::zero(), mask);
// Accumulate on max hit
mask = svalrte == maxidx;
accum = cut_high_weight_err + vfloat(1.0f) + vfloat(2.0f) * diff;
cut_high_weight_err = select(cut_high_weight_err, accum, mask);
}
// Write out min weight and weight span; clamp span to a usable range
vint span = float_to_int(maxidx - minidx + vfloat(1));
span = min(span, vint(max_quant_steps + 3));
span = max(span, vint(2));
storea(minidx, lowest_weight + sp);
storea(span, weight_span + sp);
// The cut_(lowest/highest)_weight_error indicate the error that results from forcing
// samples that should have had the weight value one step (up/down).
vfloat ssize = 1.0f / rcp_stepsize;
vfloat errscale = ssize * ssize;
storea(errval * errscale, error + sp);
storea(cut_low_weight_err * errscale, cut_low_weight_error + sp);
storea(cut_high_weight_err * errscale, cut_high_weight_error + sp);
rcp_stepsize = rcp_stepsize + vfloat(ASTCENC_SIMD_WIDTH);
}
}
/**
* @brief The main function for the angular algorithm.
*
* @param weight_count The number of (decimated) weights.
* @param dec_weight_ideal_value The ideal decimated unquantized weight values.
* @param max_quant_level The maximum quantization level to be tested.
* @param[out] low_value Per angular step, the lowest weight value.
* @param[out] high_value Per angular step, the highest weight value.
*/
static void compute_angular_endpoints_for_quant_levels(
unsigned int weight_count,
const float* dec_weight_ideal_value,
unsigned int max_quant_level,
float low_value[TUNE_MAX_ANGULAR_QUANT + 1],
float high_value[TUNE_MAX_ANGULAR_QUANT + 1]
) {
unsigned int max_quant_steps = steps_for_quant_level[max_quant_level];
unsigned int max_angular_steps = steps_for_quant_level[max_quant_level];
alignas(ASTCENC_VECALIGN) float angular_offsets[ANGULAR_STEPS];
compute_angular_offsets(weight_count, dec_weight_ideal_value,
max_angular_steps, angular_offsets);
alignas(ASTCENC_VECALIGN) float lowest_weight[ANGULAR_STEPS];
alignas(ASTCENC_VECALIGN) int32_t weight_span[ANGULAR_STEPS];
alignas(ASTCENC_VECALIGN) float error[ANGULAR_STEPS];
alignas(ASTCENC_VECALIGN) float cut_low_weight_error[ANGULAR_STEPS];
alignas(ASTCENC_VECALIGN) float cut_high_weight_error[ANGULAR_STEPS];
compute_lowest_and_highest_weight(weight_count, dec_weight_ideal_value,
max_angular_steps, max_quant_steps,
angular_offsets, lowest_weight, weight_span, error,
cut_low_weight_error, cut_high_weight_error);
// For each quantization level, find the best error terms. Use packed vectors so data-dependent
// branches can become selects. This involves some integer to float casts, but the values are
// small enough so they never round the wrong way.
vfloat4 best_results[36];
// Initialize the array to some safe defaults
promise(max_quant_steps > 0);
for (unsigned int i = 0; i < (max_quant_steps + 4); i++)
{
// Lane<0> = Best error
// Lane<1> = Best scale; -1 indicates no solution found
// Lane<2> = Cut low weight
best_results[i] = vfloat4(ERROR_CALC_DEFAULT, -1.0f, 0.0f, 0.0f);
}
promise(max_angular_steps > 0);
for (unsigned int i = 0; i < max_angular_steps; i++)
{
float i_flt = static_cast<float>(i);
int idx_span = weight_span[i];
float error_cut_low = error[i] + cut_low_weight_error[i];
float error_cut_high = error[i] + cut_high_weight_error[i];
float error_cut_low_high = error[i] + cut_low_weight_error[i] + cut_high_weight_error[i];
// Check best error against record N
vfloat4 best_result = best_results[idx_span];
vfloat4 new_result = vfloat4(error[i], i_flt, 0.0f, 0.0f);
vmask4 mask = vfloat4(best_result.lane<0>()) > vfloat4(error[i]);
best_results[idx_span] = select(best_result, new_result, mask);
// Check best error against record N-1 with either cut low or cut high
best_result = best_results[idx_span - 1];
new_result = vfloat4(error_cut_low, i_flt, 1.0f, 0.0f);
mask = vfloat4(best_result.lane<0>()) > vfloat4(error_cut_low);
best_result = select(best_result, new_result, mask);
new_result = vfloat4(error_cut_high, i_flt, 0.0f, 0.0f);
mask = vfloat4(best_result.lane<0>()) > vfloat4(error_cut_high);
best_results[idx_span - 1] = select(best_result, new_result, mask);
// Check best error against record N-2 with both cut low and high
best_result = best_results[idx_span - 2];
new_result = vfloat4(error_cut_low_high, i_flt, 1.0f, 0.0f);
mask = vfloat4(best_result.lane<0>()) > vfloat4(error_cut_low_high);
best_results[idx_span - 2] = select(best_result, new_result, mask);
}
for (unsigned int i = 0; i <= max_quant_level; i++)
{
unsigned int q = steps_for_quant_level[i];
int bsi = static_cast<int>(best_results[q].lane<1>());
// Did we find anything?
#if defined(ASTCENC_DIAGNOSTICS)
if ((bsi < 0) && print_once)
{
print_once = false;
printf("INFO: Unable to find full encoding within search error limit.\n\n");
}
#endif
bsi = astc::max(0, bsi);
float lwi = lowest_weight[bsi] + best_results[q].lane<2>();
float hwi = lwi + static_cast<float>(q) - 1.0f;
float stepsize = 1.0f / (1.0f + static_cast<float>(bsi));
low_value[i] = (angular_offsets[bsi] + lwi) * stepsize;
high_value[i] = (angular_offsets[bsi] + hwi) * stepsize;
}
}
/* See header for documentation. */
void compute_angular_endpoints_1plane(
bool only_always,
const block_size_descriptor& bsd,
const float* dec_weight_ideal_value,
unsigned int max_weight_quant,
compression_working_buffers& tmpbuf
) {
float (&low_value)[WEIGHTS_MAX_BLOCK_MODES] = tmpbuf.weight_low_value1;
float (&high_value)[WEIGHTS_MAX_BLOCK_MODES] = tmpbuf.weight_high_value1;
float (&low_values)[WEIGHTS_MAX_DECIMATION_MODES][TUNE_MAX_ANGULAR_QUANT + 1] = tmpbuf.weight_low_values1;
float (&high_values)[WEIGHTS_MAX_DECIMATION_MODES][TUNE_MAX_ANGULAR_QUANT + 1] = tmpbuf.weight_high_values1;
unsigned int max_decimation_modes = only_always ? bsd.decimation_mode_count_always
: bsd.decimation_mode_count_selected;
promise(max_decimation_modes > 0);
for (unsigned int i = 0; i < max_decimation_modes; i++)
{
const decimation_mode& dm = bsd.decimation_modes[i];
if (!dm.is_ref_1_plane(static_cast<quant_method>(max_weight_quant)))
{
continue;
}
unsigned int weight_count = bsd.get_decimation_info(i).weight_count;
unsigned int max_precision = dm.maxprec_1plane;
if (max_precision > TUNE_MAX_ANGULAR_QUANT)
{
max_precision = TUNE_MAX_ANGULAR_QUANT;
}
if (max_precision > max_weight_quant)
{
max_precision = max_weight_quant;
}
compute_angular_endpoints_for_quant_levels(
weight_count,
dec_weight_ideal_value + i * BLOCK_MAX_WEIGHTS,
max_precision, low_values[i], high_values[i]);
}
unsigned int max_block_modes = only_always ? bsd.block_mode_count_1plane_always
: bsd.block_mode_count_1plane_selected;
promise(max_block_modes > 0);
for (unsigned int i = 0; i < max_block_modes; i++)
{
const block_mode& bm = bsd.block_modes[i];
assert(!bm.is_dual_plane);
unsigned int quant_mode = bm.quant_mode;
unsigned int decim_mode = bm.decimation_mode;
if (quant_mode <= TUNE_MAX_ANGULAR_QUANT)
{
low_value[i] = low_values[decim_mode][quant_mode];
high_value[i] = high_values[decim_mode][quant_mode];
}
else
{
low_value[i] = 0.0f;
high_value[i] = 1.0f;
}
}
}
/* See header for documentation. */
void compute_angular_endpoints_2planes(
const block_size_descriptor& bsd,
const float* dec_weight_ideal_value,
unsigned int max_weight_quant,
compression_working_buffers& tmpbuf
) {
float (&low_value1)[WEIGHTS_MAX_BLOCK_MODES] = tmpbuf.weight_low_value1;
float (&high_value1)[WEIGHTS_MAX_BLOCK_MODES] = tmpbuf.weight_high_value1;
float (&low_value2)[WEIGHTS_MAX_BLOCK_MODES] = tmpbuf.weight_low_value2;
float (&high_value2)[WEIGHTS_MAX_BLOCK_MODES] = tmpbuf.weight_high_value2;
float (&low_values1)[WEIGHTS_MAX_DECIMATION_MODES][TUNE_MAX_ANGULAR_QUANT + 1] = tmpbuf.weight_low_values1;
float (&high_values1)[WEIGHTS_MAX_DECIMATION_MODES][TUNE_MAX_ANGULAR_QUANT + 1] = tmpbuf.weight_high_values1;
float (&low_values2)[WEIGHTS_MAX_DECIMATION_MODES][TUNE_MAX_ANGULAR_QUANT + 1] = tmpbuf.weight_low_values2;
float (&high_values2)[WEIGHTS_MAX_DECIMATION_MODES][TUNE_MAX_ANGULAR_QUANT + 1] = tmpbuf.weight_high_values2;
promise(bsd.decimation_mode_count_selected > 0);
for (unsigned int i = 0; i < bsd.decimation_mode_count_selected; i++)
{
const decimation_mode& dm = bsd.decimation_modes[i];
if (!dm.is_ref_2_plane(static_cast<quant_method>(max_weight_quant)))
{
continue;
}
unsigned int weight_count = bsd.get_decimation_info(i).weight_count;
unsigned int max_precision = dm.maxprec_2planes;
if (max_precision > TUNE_MAX_ANGULAR_QUANT)
{
max_precision = TUNE_MAX_ANGULAR_QUANT;
}
if (max_precision > max_weight_quant)
{
max_precision = max_weight_quant;
}
compute_angular_endpoints_for_quant_levels(
weight_count,
dec_weight_ideal_value + i * BLOCK_MAX_WEIGHTS,
max_precision, low_values1[i], high_values1[i]);
compute_angular_endpoints_for_quant_levels(
weight_count,
dec_weight_ideal_value + i * BLOCK_MAX_WEIGHTS + WEIGHTS_PLANE2_OFFSET,
max_precision, low_values2[i], high_values2[i]);
}
unsigned int start = bsd.block_mode_count_1plane_selected;
unsigned int end = bsd.block_mode_count_1plane_2plane_selected;
for (unsigned int i = start; i < end; i++)
{
const block_mode& bm = bsd.block_modes[i];
unsigned int quant_mode = bm.quant_mode;
unsigned int decim_mode = bm.decimation_mode;
if (quant_mode <= TUNE_MAX_ANGULAR_QUANT)
{
low_value1[i] = low_values1[decim_mode][quant_mode];
high_value1[i] = high_values1[decim_mode][quant_mode];
low_value2[i] = low_values2[decim_mode][quant_mode];
high_value2[i] = high_values2[decim_mode][quant_mode];
}
else
{
low_value1[i] = 0.0f;
high_value1[i] = 1.0f;
low_value2[i] = 0.0f;
high_value2[i] = 1.0f;
}
}
}
#endif