File size: 14,299 Bytes
9422383 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 | #include "fixed_memory_plan.h"
#include <algorithm>
#include <array>
#include <cstddef>
#include <limits>
#include "model_contract.h"
namespace yolo26::runtime_internal {
namespace {
constexpr size_t kTuned640ArenaBytes = 7'406'512;
// Boundaries between fixed-schedule operations. A tensor may reuse the storage
// of an input whose final consumer produces that tensor: the old value is dead
// at the boundary where the new value becomes ready. This compact lifetime
// model validates memory; it is not a second executable schedule.
enum class ScheduleBoundary : uint8_t {
kConstantsReady,
kInputConverted,
kStemProjectionComplete,
kP4EntrySplit,
kBackboneP4ToP5Complete,
kP5EntrySplit,
kBackboneAttentionEntryReady,
kBackboneAttentionEntrySplit,
kBackboneQkvReady,
kBackboneQkvSplit,
kBackboneQueryScaled,
kBackboneAttentionInputsCopied,
kBackboneAttentionMatrixReady,
kBackboneAttentionMatrixCopied,
kBackboneComplete,
kP5ProjectionUpsampled,
kP4FusionReady,
kP4FusionSplit,
kNeckP4Ready,
kP4ProjectionUpsampled,
kP3FusionReady,
kP3FusionSplit,
kDetectionP3Ready,
kDetectionP4ProjectionSplit,
kDetectionP4Ready,
kDetectionP5ProjectionSplit,
kDetectionP5QkvReady,
kDetectionP5QkvSplit,
kDetectionP5QueryScaled,
kDetectionP5AttentionInputsCopied,
kDetectionP5AttentionMatrixReady,
kDetectionP5AttentionMatrixCopied,
kDetectionP5AndBoxesReady,
kBoxRegressionSplit,
kDetectionsReady,
kOutputConverted,
};
struct Lifetime {
ScheduleBoundary ready;
ScheduleBoundary released;
};
struct PlannedTensor {
TensorId id;
size_t tuned_640_offset;
Lifetime lifetime;
};
using B = ScheduleBoundary;
// These offsets form the compact activation-memory layout benchmarked for the
// standard 640-pixel, 80-class model on Raspberry Pi 5. They cannot be reused
// at other resolutions or class counts because those settings change tensor
// sizes. For those other shapes, PlanScheduleMemory places tensors sequentially
// with the required alignment; only P3Fusion reuses BackboneP3 storage after
// that input's final use. Overlaps in this compact layout are safe only where
// the recorded lifetimes do not intersect. Changing offsets, lifetimes, or
// declaration order requires the public runtime tests and new Pi performance
// evidence.
constexpr std::array kArenaTensors{
PlannedTensor{TensorId::kAnchors, 7'372'800, {B::kConstantsReady, B::kDetectionsReady}},
PlannedTensor{
TensorId::kBackboneScale, 7'406'432, {B::kConstantsReady, B::kBackboneQueryScaled}},
PlannedTensor{
TensorId::kDetectionScale, 7'406'416, {B::kConstantsReady, B::kDetectionP5QueryScaled}},
PlannedTensor{TensorId::kCenterDivisor, 7'406'400, {B::kConstantsReady, B::kDetectionsReady}},
PlannedTensor{
TensorId::kInputHalf, 4'915'200, {B::kInputConverted, B::kStemProjectionComplete}},
PlannedTensor{
TensorId::kBackboneP3, 4'915'200, {B::kStemProjectionComplete, B::kP3FusionReady}},
PlannedTensor{TensorId::kP4Entry, 0, {B::kStemProjectionComplete, B::kP4EntrySplit}},
PlannedTensor{
TensorId::kP4EntrySkip, 2'099'200, {B::kP4EntrySplit, B::kBackboneP4ToP5Complete}},
PlannedTensor{
TensorId::kP4EntryMain, 1'894'400, {B::kP4EntrySplit, B::kBackboneP4ToP5Complete}},
PlannedTensor{
TensorId::kBackboneP4, 1'280'000, {B::kBackboneP4ToP5Complete, B::kP4FusionReady}},
PlannedTensor{TensorId::kP5Entry, 1'689'600, {B::kBackboneP4ToP5Complete, B::kP5EntrySplit}},
PlannedTensor{
TensorId::kP5EntrySkip, 1'996'800, {B::kP5EntrySplit, B::kBackboneAttentionEntryReady}},
PlannedTensor{
TensorId::kP5EntryMain, 1'894'400, {B::kP5EntrySplit, B::kBackboneAttentionEntryReady}},
PlannedTensor{TensorId::kBackboneAttentionEntry,
1'689'600,
{B::kBackboneAttentionEntryReady, B::kBackboneAttentionEntrySplit}},
PlannedTensor{TensorId::kBackboneAttentionBypass,
4'608'000,
{B::kBackboneAttentionEntrySplit, B::kBackboneComplete}},
PlannedTensor{TensorId::kBackboneAttentionResidual,
4'505'600,
{B::kBackboneAttentionEntrySplit, B::kBackboneComplete}},
PlannedTensor{TensorId::kBackboneQkv, 1'689'600, {B::kBackboneQkvReady, B::kBackboneQkvSplit}},
PlannedTensor{
TensorId::kBackboneQuery, 6'553'600, {B::kBackboneQkvSplit, B::kBackboneQueryScaled}},
PlannedTensor{TensorId::kBackboneKey,
4'864'000,
{B::kBackboneQkvSplit, B::kBackboneAttentionInputsCopied}},
PlannedTensor{
TensorId::kBackboneValue, 4'403'200, {B::kBackboneQkvSplit, B::kBackboneComplete}},
PlannedTensor{TensorId::kBackboneScaledQuery,
4'812'800,
{B::kBackboneQueryScaled, B::kBackboneAttentionInputsCopied}},
PlannedTensor{TensorId::kBackboneKeyCopy,
4'761'600,
{B::kBackboneAttentionInputsCopied, B::kBackboneAttentionMatrixReady}},
PlannedTensor{TensorId::kBackbonePositionalInput,
4'300'800,
{B::kBackboneAttentionInputsCopied, B::kBackboneComplete}},
PlannedTensor{TensorId::kBackboneQueryCopy,
4'710'400,
{B::kBackboneAttentionInputsCopied, B::kBackboneAttentionMatrixReady}},
PlannedTensor{TensorId::kBackboneAttentionMatrix,
640'000,
{B::kBackboneAttentionMatrixReady, B::kBackboneAttentionMatrixCopied}},
PlannedTensor{TensorId::kBackboneAttentionMatrixCopy,
0,
{B::kBackboneAttentionMatrixCopied, B::kBackboneComplete}},
PlannedTensor{TensorId::kBackboneAttentionOutput,
4'096'000,
{B::kBackboneComplete, B::kDetectionP4Ready}},
PlannedTensor{
TensorId::kP5Projection, 640'000, {B::kBackboneComplete, B::kP5ProjectionUpsampled}},
PlannedTensor{
TensorId::kP5UpsampledProjection, 0, {B::kP5ProjectionUpsampled, B::kP4FusionReady}},
PlannedTensor{TensorId::kP4Fusion, 819'200, {B::kP4FusionReady, B::kP4FusionSplit}},
PlannedTensor{TensorId::kP4FusionSkip, 3'891'200, {B::kP4FusionSplit, B::kNeckP4Ready}},
PlannedTensor{TensorId::kP4FusionMain, 3'686'400, {B::kP4FusionSplit, B::kNeckP4Ready}},
PlannedTensor{TensorId::kNeckP4, 3'276'800, {B::kNeckP4Ready, B::kDetectionP3Ready}},
PlannedTensor{TensorId::kP4Projection, 0, {B::kNeckP4Ready, B::kP4ProjectionUpsampled}},
PlannedTensor{
TensorId::kP4UpsampledProjection, 204'800, {B::kP4ProjectionUpsampled, B::kP3FusionReady}},
PlannedTensor{TensorId::kP3Fusion, 4'915'200, {B::kP3FusionReady, B::kP3FusionSplit}},
PlannedTensor{TensorId::kP3FusionSkip, 2'867'200, {B::kP3FusionSplit, B::kDetectionP3Ready}},
PlannedTensor{TensorId::kP3FusionMain, 2'457'600, {B::kP3FusionSplit, B::kDetectionP3Ready}},
PlannedTensor{TensorId::kDetectionP3, 0, {B::kDetectionP3Ready, B::kDetectionsReady}},
PlannedTensor{TensorId::kDetectionP4Projection,
4'915'200,
{B::kDetectionP3Ready, B::kDetectionP4ProjectionSplit}},
PlannedTensor{TensorId::kDetectionP4Skip,
2'073'600,
{B::kDetectionP4ProjectionSplit, B::kDetectionP4Ready}},
PlannedTensor{TensorId::kDetectionP4Main,
1'868'800,
{B::kDetectionP4ProjectionSplit, B::kDetectionP4Ready}},
PlannedTensor{TensorId::kDetectionP4, 1'459'200, {B::kDetectionP4Ready, B::kDetectionsReady}},
PlannedTensor{TensorId::kDetectionP5Projection,
4'915'200,
{B::kDetectionP4Ready, B::kDetectionP5ProjectionSplit}},
PlannedTensor{TensorId::kDetectionP5Skip,
2'483'200,
{B::kDetectionP5ProjectionSplit, B::kDetectionP5AndBoxesReady}},
PlannedTensor{TensorId::kDetectionP5Main,
2'380'800,
{B::kDetectionP5ProjectionSplit, B::kDetectionP5AndBoxesReady}},
PlannedTensor{TensorId::kDetectionP5Residual,
2'278'400,
{B::kDetectionP5QkvReady, B::kDetectionP5AndBoxesReady}},
PlannedTensor{
TensorId::kDetectionP5Qkv, 4'915'200, {B::kDetectionP5QkvReady, B::kDetectionP5QkvSplit}},
PlannedTensor{TensorId::kDetectionP5Query,
2'739'200,
{B::kDetectionP5QkvSplit, B::kDetectionP5QueryScaled}},
PlannedTensor{TensorId::kDetectionP5Key,
2'688'000,
{B::kDetectionP5QkvSplit, B::kDetectionP5AttentionInputsCopied}},
PlannedTensor{TensorId::kDetectionP5Value,
2'176'000,
{B::kDetectionP5QkvSplit, B::kDetectionP5AndBoxesReady}},
PlannedTensor{TensorId::kDetectionP5ScaledQuery,
2'636'800,
{B::kDetectionP5QueryScaled, B::kDetectionP5AttentionInputsCopied}},
PlannedTensor{TensorId::kDetectionP5KeyCopy,
2'585'600,
{B::kDetectionP5AttentionInputsCopied, B::kDetectionP5AttentionMatrixReady}},
PlannedTensor{TensorId::kDetectionP5ValueCopy,
2'073'600,
{B::kDetectionP5AttentionInputsCopied, B::kDetectionP5AndBoxesReady}},
PlannedTensor{TensorId::kDetectionP5QueryCopy,
4'915'200,
{B::kDetectionP5AttentionInputsCopied, B::kDetectionP5AttentionMatrixReady}},
PlannedTensor{TensorId::kDetectionP5AttentionMatrix,
819'200,
{B::kDetectionP5AttentionMatrixReady, B::kDetectionP5AttentionMatrixCopied}},
PlannedTensor{TensorId::kDetectionP5AttentionMatrixCopy,
4'915'200,
{B::kDetectionP5AttentionMatrixCopied, B::kDetectionP5AndBoxesReady}},
PlannedTensor{
TensorId::kDetectionP5, 1'868'800, {B::kDetectionP5AndBoxesReady, B::kDetectionsReady}},
PlannedTensor{TensorId::kBoxRegression,
2'585'600,
{B::kDetectionP5AndBoxesReady, B::kBoxRegressionSplit}},
PlannedTensor{TensorId::kBoxLower, 2'686'400, {B::kBoxRegressionSplit, B::kDetectionsReady}},
PlannedTensor{TensorId::kBoxUpper, 2'652'800, {B::kBoxRegressionSplit, B::kDetectionsReady}},
PlannedTensor{TensorId::kDetectionsHalf, 4'915'200, {B::kDetectionsReady, B::kOutputConverted}},
};
constexpr bool ArenaInventoryIsContiguous() {
for (size_t index = 0; index < kArenaTensors.size(); ++index) {
if (TensorIndex(kArenaTensors[index].id) != TensorIndex(TensorId::kAnchors) + index) {
return false;
}
}
return true;
}
static_assert(kArenaTensors.size() ==
TensorIndex(TensorId::kOutput) - TensorIndex(TensorId::kAnchors));
static_assert(ArenaInventoryIsContiguous());
size_t Align(size_t value) {
const size_t alignment = yolo26::kTensorAlignment;
return ((value + alignment - 1) / alignment) * alignment;
}
bool ByteRangesOverlap(size_t first_offset, size_t first_size, size_t second_offset,
size_t second_size) {
return first_offset < second_offset + second_size && second_offset < first_offset + first_size;
}
bool LifetimesOverlap(Lifetime first, Lifetime second) {
return first.ready < second.released && second.ready < first.released;
}
bool PlacementIsValid(const ScheduleState& state) {
for (size_t first = 0; first < kArenaTensors.size(); ++first) {
const size_t first_index = TensorIndex(kArenaTensors[first].id);
const size_t first_offset = state.offsets[first_index];
const size_t first_size = state.sizes[first_index];
if (first_size == 0 || first_offset > state.arena_bytes ||
first_size > state.arena_bytes - first_offset) {
return false;
}
for (size_t second = first + 1; second < kArenaTensors.size(); ++second) {
const size_t second_index = TensorIndex(kArenaTensors[second].id);
if (LifetimesOverlap(kArenaTensors[first].lifetime, kArenaTensors[second].lifetime) &&
ByteRangesOverlap(first_offset, first_size, state.offsets[second_index],
state.sizes[second_index])) {
return false;
}
}
}
return true;
}
} // namespace
bool PlanScheduleMemory(ScheduleState* state) {
if (!state) return false;
// Preserve the compact, benchmarked layout for the standard model. Other
// supported shapes receive a conservative sequential layout calculated from
// their tensor sizes below.
const bool use_tuned_640_offsets = state->resolution == 640 && state->class_count == 80;
size_t cursor = 0;
size_t high_water = 0;
for (const PlannedTensor& tensor : kArenaTensors) {
const size_t index = TensorIndex(tensor.id);
const size_t bytes = state->sizes[index];
if (bytes == 0 || bytes > std::numeric_limits<size_t>::max() - yolo26::kTensorAlignment) {
return false;
}
if (use_tuned_640_offsets) {
state->offsets[index] = tensor.tuned_640_offset;
} else if (tensor.id == TensorId::kP3Fusion) {
// The fusion graph consumes BackboneP3 before publishing P3Fusion, so the
// output takes over that input's arena range at the same boundary.
state->offsets[index] = state->offsets[TensorIndex(TensorId::kBackboneP3)];
} else {
state->offsets[index] = Align(cursor);
if (state->offsets[index] >
std::numeric_limits<size_t>::max() - bytes - yolo26::kTensorAlignment) {
return false;
}
cursor = state->offsets[index] + bytes + yolo26::kTensorAlignment;
}
if (state->offsets[index] > std::numeric_limits<size_t>::max() - bytes) return false;
high_water = std::max(high_water, state->offsets[index] + bytes);
}
if (use_tuned_640_offsets) {
state->arena_bytes = kTuned640ArenaBytes;
} else {
if (high_water > std::numeric_limits<size_t>::max() - yolo26::kTensorAlignment) return false;
state->arena_bytes = Align(high_water + yolo26::kTensorAlignment);
}
const size_t first_pixels = size_t{state->resolution / 4} * (state->resolution / 4);
state->project_bytes = 4 * first_pixels * 16;
return ValidateScheduleMemory(*state);
}
bool ValidateScheduleMemory(const ScheduleState& state) { return PlacementIsValid(state); }
} // namespace yolo26::runtime_internal
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