e37f16739e
Phase 1 group 2 (L27, L26, L28, S21, L13, ST6, S20). - L27: fixed_buffer.hpp + its test + the CMake entry — no consumers. - L26: dislike_multiplier_ and its doubling/halving bookkeeping — upstream InterfaceRL residue that drove nothing. The audit pointed at the wrong test file for the surviving reference; the actual assert was in test_mlp_geo_dislike.cpp:211, removed here. - L28: added copy_weights_to(std::span<float>) to FixedStorage and DynamicStorage and switched feedback.hpp's take_snapshot/push_undo/nudge to it. Drops the permanent whole-net flat_ scratch buffer from FixedStorage and the per-gesture double copy. Behaviour-identical: same source values, same write order, same RNG draw order in nudge(). - S21 + L13: deleted NISPS_AUDIO_MEM / NISPS_APP_SRAM / NISPS_AUDIO_FUNC — zero use sites outside perf.hpp and comments — and rewrote midi_io.hpp's one misshapen NISPS_AUDIO_FUNC use as a plain `inline void`. perf.hpp now documents only the inlining/hotness macros that actually exist, and audio_driver.hpp no longer claims an SRAM discipline the code never had. - ST6: feedback.hpp's header now describes the four current modes and the Geometric default, dropping the retracted "geometric push NOT ported" claim. S20 — OPERATOR DECISION (§7.1): the four legacy feedback behaviours (RandomiseOutputs, RandomiseMlp, AvoidStyle::Diffuse, the RandomiseMlp branch of on_drag) are KEPT, not deleted. They are wanted as building blocks for experimenting with how different instruments feel under different behaviours. Each is now marked at its definition as deliberately-retained research reserve so future audits stop flagging it as dead code. L25 (the 16 KB firmware loss-history buffer) is NOT done here — see the phase report; it turned out to be coupled into the shared mlp.hpp, and its fate belongs with the browser telemetry build (§7.3 / plan §6.5e). Gates: run-all-tests.sh ALL GREEN.
323 lines
15 KiB
C++
323 lines
15 KiB
C++
// nisps/ml/dynamic_storage.hpp — runtime-shaped storage policy for the MLP
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// core. WASM / native-test / VCV targets ONLY.
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//
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// Dimensions are chosen at construction; every buffer lives in ONE arena
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// allocated once in the constructor. There is NO allocation after
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// construction — the algorithm hot paths are as allocation-free as the
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// fixed model.
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//
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// This header is compile-time excluded from embedded (RP2350) builds: the
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// firmware's zero-heap contract is structural, not advisory. lint-cpp.sh
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// additionally allowlists exactly this file for its heap audit — heap use
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// anywhere else under nisps/ml/ still fails the lint.
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//
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// See nisps/ml/storage.hpp for the storage surface contract, and
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// docs/specs/plans/one-core-engine-refactor.md §P2 for the design.
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#pragma once
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#include "../core/perf.hpp"
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#if defined(NISPS_TARGET_EMBEDDED)
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#error "nisps/ml/dynamic_storage.hpp must not be compiled for the RP2350 target (zero-heap contract)"
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#endif
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#include <cstddef>
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#include <cstdint>
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#include <new>
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#include <span>
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#include "storage.hpp" // kMlpNumLayers
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namespace nisps::ml {
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class DynamicStorage {
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public:
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static constexpr std::size_t kNumLayers = kMlpNumLayers;
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// hidden must have exactly 3 entries (the 4-layer topology is fixed;
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// only the dimensions are runtime). All dims must be >= 1.
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DynamicStorage(std::size_t n_in,
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std::span<const std::size_t> hidden,
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std::size_t n_out,
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std::size_t max_examples = 128u,
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std::size_t max_iter_train = 4096u) noexcept {
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if (hidden.size() != 3u || n_in == 0u || n_out == 0u ||
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hidden[0] == 0u || hidden[1] == 0u || hidden[2] == 0u) {
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return; // stays invalid
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}
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dims_[0] = n_in;
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dims_[1] = hidden[0];
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dims_[2] = hidden[1];
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dims_[3] = hidden[2];
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dims_[4] = n_out;
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max_ex_ = max_examples;
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max_iter_ = max_iter_train;
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std::size_t total = 0u;
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auto claim = [&total](std::size_t n) {
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const std::size_t off = total;
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total += n;
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return off;
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};
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for (std::size_t l = 0; l < kNumLayers; ++l) {
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off_w_[l] = claim(fan_in(l) * fan_out(l));
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off_b_[l] = claim(fan_out(l));
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off_pa_[l] = claim(fan_out(l));
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off_a_[l] = claim(fan_out(l));
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off_gw_[l] = claim(fan_in(l) * fan_out(l));
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off_gb_[l] = claim(fan_out(l));
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off_d_[l] = claim(fan_in(l));
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off_e_[l] = claim(fan_out(l));
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}
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off_input_ = claim(dims_[0]);
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off_output_ = claim(dims_[4]);
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off_dsf_ = claim(max_ex_ * dims_[0]);
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off_dsl_ = claim(max_ex_ * dims_[4]);
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off_flat_ = claim(weight_count());
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off_lh_ = claim(max_iter_);
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// The single arena allocation. Value-init zeroes it, matching the
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// zero-initialised std::array members of FixedStorage.
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arena_ = new (std::nothrow) float[total]();
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total_ = (arena_ != nullptr) ? total : 0u;
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}
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~DynamicStorage() { delete[] arena_; }
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DynamicStorage(const DynamicStorage&) = delete;
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DynamicStorage& operator=(const DynamicStorage&) = delete;
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DynamicStorage(DynamicStorage&& o) noexcept { move_from_(o); }
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DynamicStorage& operator=(DynamicStorage&& o) noexcept {
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if (this != &o) {
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delete[] arena_;
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move_from_(o);
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}
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return *this;
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}
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bool valid() const noexcept { return arena_ != nullptr; }
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// ---- dims -----------------------------------------------------------
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std::size_t n_in() const noexcept { return dims_[0]; }
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std::size_t n_out() const noexcept { return dims_[4]; }
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std::size_t max_examples() const noexcept { return max_ex_; }
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std::size_t max_iter_train() const noexcept { return max_iter_; }
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std::size_t fan_in(std::size_t l) const noexcept { return dims_[l]; }
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std::size_t fan_out(std::size_t l) const noexcept { return dims_[l + 1u]; }
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template <std::size_t L> std::size_t fan_in_l() const noexcept { return dims_[L]; }
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template <std::size_t L> std::size_t fan_out_l() const noexcept { return dims_[L + 1u]; }
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std::size_t weight_count() const noexcept {
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return dims_[0] * dims_[1] + dims_[1] * dims_[2] + dims_[2] * dims_[3] +
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dims_[3] * dims_[4] + dims_[1] + dims_[2] + dims_[3] + dims_[4];
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}
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// ---- per-layer buffers ------------------------------------------------
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template <std::size_t L> std::span<float> weights_l() noexcept {
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return {arena_ + off_w_[L], fan_in_l<L>() * fan_out_l<L>()};
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}
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template <std::size_t L> std::span<const float> weights_l() const noexcept {
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return {arena_ + off_w_[L], fan_in_l<L>() * fan_out_l<L>()};
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}
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template <std::size_t L> std::span<float> biases_l() noexcept {
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return {arena_ + off_b_[L], fan_out_l<L>()};
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}
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template <std::size_t L> std::span<const float> biases_l() const noexcept {
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return {arena_ + off_b_[L], fan_out_l<L>()};
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}
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template <std::size_t L> std::span<float> pre_act_l() noexcept {
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return {arena_ + off_pa_[L], fan_out_l<L>()};
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}
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template <std::size_t L> std::span<float> act_l() noexcept {
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return {arena_ + off_a_[L], fan_out_l<L>()};
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}
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template <std::size_t L> std::span<const float> act_l() const noexcept {
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return {arena_ + off_a_[L], fan_out_l<L>()};
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}
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template <std::size_t L> std::span<float> grad_w_l() noexcept {
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return {arena_ + off_gw_[L], fan_in_l<L>() * fan_out_l<L>()};
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}
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template <std::size_t L> std::span<float> grad_b_l() noexcept {
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return {arena_ + off_gb_[L], fan_out_l<L>()};
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}
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template <std::size_t L> std::span<float> delta_l() noexcept {
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return {arena_ + off_d_[L], fan_in_l<L>()};
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}
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template <std::size_t L> std::span<float> eval_act_l() const noexcept {
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return {arena_ + off_e_[L], fan_out_l<L>()};
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}
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// ---- global buffers ---------------------------------------------------
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std::span<float> input_buf() noexcept { return {arena_ + off_input_, dims_[0]}; }
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std::span<const float> input_buf() const noexcept { return {arena_ + off_input_, dims_[0]}; }
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std::span<float> output_buf() noexcept { return {arena_ + off_output_, dims_[4]}; }
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std::span<const float> output_buf() const noexcept { return {arena_ + off_output_, dims_[4]}; }
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std::span<float> ds_features() noexcept { return {arena_ + off_dsf_, max_ex_ * dims_[0]}; }
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std::span<const float> ds_features() const noexcept { return {arena_ + off_dsf_, max_ex_ * dims_[0]}; }
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std::span<float> ds_labels() noexcept { return {arena_ + off_dsl_, max_ex_ * dims_[4]}; }
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std::span<const float> ds_labels() const noexcept { return {arena_ + off_dsl_, max_ex_ * dims_[4]}; }
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std::span<float> flat_buf() noexcept { return {arena_ + off_flat_, weight_count()}; }
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std::span<float> loss_hist_buf() noexcept { return {arena_ + off_lh_, max_iter_}; }
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std::span<const float> loss_hist_buf() const noexcept { return {arena_ + off_lh_, max_iter_}; }
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// Copies the live weights+biases directly into `dst` in the same flat
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// layout as MLPCore::get_weights() (weights layer-major, then biases
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// layer-major) — see FixedStorage::copy_weights_to for the rationale.
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void copy_weights_to(std::span<float> dst) const noexcept {
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std::size_t k = 0u;
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for (float v : weights_l<0u>()) dst[k++] = v;
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for (float v : weights_l<1u>()) dst[k++] = v;
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for (float v : weights_l<2u>()) dst[k++] = v;
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for (float v : weights_l<3u>()) dst[k++] = v;
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for (float v : biases_l<0u>()) dst[k++] = v;
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for (float v : biases_l<1u>()) dst[k++] = v;
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for (float v : biases_l<2u>()) dst[k++] = v;
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for (float v : biases_l<3u>()) dst[k++] = v;
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}
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private:
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void move_from_(DynamicStorage& o) noexcept {
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for (std::size_t i = 0; i < 5u; ++i) dims_[i] = o.dims_[i];
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max_ex_ = o.max_ex_; max_iter_ = o.max_iter_;
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for (std::size_t l = 0; l < kNumLayers; ++l) {
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off_w_[l] = o.off_w_[l]; off_b_[l] = o.off_b_[l];
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off_pa_[l] = o.off_pa_[l]; off_a_[l] = o.off_a_[l];
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off_gw_[l] = o.off_gw_[l]; off_gb_[l] = o.off_gb_[l];
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off_d_[l] = o.off_d_[l]; off_e_[l] = o.off_e_[l];
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}
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off_input_ = o.off_input_; off_output_ = o.off_output_;
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off_dsf_ = o.off_dsf_; off_dsl_ = o.off_dsl_;
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off_flat_ = o.off_flat_; off_lh_ = o.off_lh_;
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arena_ = o.arena_; total_ = o.total_;
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o.arena_ = nullptr; o.total_ = 0u;
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}
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std::size_t dims_[5] = {0u, 0u, 0u, 0u, 0u};
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std::size_t max_ex_ = 0u;
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std::size_t max_iter_ = 0u;
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std::size_t off_w_[kNumLayers]{}, off_b_[kNumLayers]{}, off_pa_[kNumLayers]{},
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off_a_[kNumLayers]{}, off_gw_[kNumLayers]{}, off_gb_[kNumLayers]{},
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off_d_[kNumLayers]{}, off_e_[kNumLayers]{};
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std::size_t off_input_ = 0u, off_output_ = 0u, off_dsf_ = 0u, off_dsl_ = 0u,
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off_flat_ = 0u, off_lh_ = 0u;
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float* arena_ = nullptr;
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std::size_t total_ = 0u;
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};
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// ---------------------------------------------------------------------------
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// Runtime-sized feedback-controller storage (see nisps/ml/feedback.hpp for
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// the surface contract). One arena allocation at construction; nothing
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// per-call. The focus mask lives in a byte region carved from the same
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// arena (aliased through the float arena's tail, kept byte-aligned by
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// allocating whole floats for it).
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// ---------------------------------------------------------------------------
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class DynamicFeedbackStorage {
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public:
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DynamicFeedbackStorage(std::size_t n_out,
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std::size_t n_weights,
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std::size_t undo_depth = 4u,
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std::size_t n_in = 2u,
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std::size_t replay_cap = 64u) noexcept
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: n_out_(n_out), n_weights_(n_weights), undo_cap_(undo_depth),
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n_in_(n_in), replay_cap_(replay_cap) {
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if (n_out == 0u || n_weights == 0u || undo_depth == 0u ||
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n_in == 0u || replay_cap == 0u) {
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return;
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}
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// float regions: static_out, placed_out, snapshot, scratch, undo ring,
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// replay (inputs/actions/rewards), centroid + target scratch
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// byte region: focus mask (n_out bytes, rounded up to whole floats)
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const std::size_t focus_floats = (n_out + sizeof(float) - 1u) / sizeof(float);
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const std::size_t total = n_out * 2u // static_out + placed_out
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+ n_weights * 2u // snapshot + scratch
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+ n_weights * undo_depth // undo ring
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+ replay_cap * n_in // replay inputs
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+ replay_cap * n_out // replay actions
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+ replay_cap // replay rewards
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+ n_out * 2u // centroid + target
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+ focus_floats;
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arena_ = new (std::nothrow) float[total]();
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if (!arena_) return;
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off_placed_ = n_out_;
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off_snap_ = off_placed_ + n_out_;
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off_scratch_ = off_snap_ + n_weights_;
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off_undo_ = off_scratch_ + n_weights_;
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off_replay_in_ = off_undo_ + n_weights_ * undo_cap_;
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off_replay_a_ = off_replay_in_ + replay_cap_ * n_in_;
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off_replay_r_ = off_replay_a_ + replay_cap_ * n_out_;
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off_centroid_ = off_replay_r_ + replay_cap_;
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off_target_ = off_centroid_ + n_out_;
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off_focus_ = off_target_ + n_out_;
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}
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~DynamicFeedbackStorage() { delete[] arena_; }
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DynamicFeedbackStorage(const DynamicFeedbackStorage&) = delete;
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DynamicFeedbackStorage& operator=(const DynamicFeedbackStorage&) = delete;
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DynamicFeedbackStorage(DynamicFeedbackStorage&& o) noexcept { move_from_(o); }
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DynamicFeedbackStorage& operator=(DynamicFeedbackStorage&& o) noexcept {
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if (this != &o) {
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delete[] arena_;
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move_from_(o);
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}
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return *this;
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}
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bool valid() const noexcept { return arena_ != nullptr; }
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std::size_t n_out() const noexcept { return n_out_; }
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std::size_t n_weights() const noexcept { return n_weights_; }
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std::size_t undo_cap() const noexcept { return undo_cap_; }
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std::size_t n_in() const noexcept { return n_in_; }
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std::size_t replay_cap() const noexcept { return replay_cap_; }
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std::span<float> static_out() noexcept { return {arena_, n_out_}; }
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std::span<const float> static_out() const noexcept { return {arena_, n_out_}; }
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std::span<float> placed_out() noexcept { return {arena_ + off_placed_, n_out_}; }
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std::span<const float> placed_out() const noexcept { return {arena_ + off_placed_, n_out_}; }
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std::span<float> snapshot() noexcept { return {arena_ + off_snap_, n_weights_}; }
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std::span<const float> snapshot() const noexcept { return {arena_ + off_snap_, n_weights_}; }
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std::span<float> scratch_buf() noexcept { return {arena_ + off_scratch_, n_weights_}; }
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std::span<float> undo_slot(std::size_t i) noexcept {
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return {arena_ + off_undo_ + i * n_weights_, n_weights_};
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}
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std::span<const float> undo_slot(std::size_t i) const noexcept {
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return {arena_ + off_undo_ + i * n_weights_, n_weights_};
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}
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std::span<float> replay_inputs() noexcept { return {arena_ + off_replay_in_, replay_cap_ * n_in_}; }
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std::span<float> replay_actions() noexcept { return {arena_ + off_replay_a_, replay_cap_ * n_out_}; }
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std::span<float> replay_rewards() noexcept { return {arena_ + off_replay_r_, replay_cap_}; }
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std::span<float> centroid_buf() noexcept { return {arena_ + off_centroid_, n_out_}; }
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std::span<float> target_buf() noexcept { return {arena_ + off_target_, n_out_}; }
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std::span<std::uint8_t> focus() noexcept {
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return {reinterpret_cast<std::uint8_t*>(arena_ + off_focus_), n_out_};
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}
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std::span<const std::uint8_t> focus() const noexcept {
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return {reinterpret_cast<const std::uint8_t*>(arena_ + off_focus_), n_out_};
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}
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private:
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void move_from_(DynamicFeedbackStorage& o) noexcept {
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n_out_ = o.n_out_; n_weights_ = o.n_weights_; undo_cap_ = o.undo_cap_;
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n_in_ = o.n_in_; replay_cap_ = o.replay_cap_;
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off_placed_ = o.off_placed_; off_snap_ = o.off_snap_;
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off_scratch_ = o.off_scratch_; off_undo_ = o.off_undo_; off_focus_ = o.off_focus_;
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off_replay_in_ = o.off_replay_in_; off_replay_a_ = o.off_replay_a_;
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off_replay_r_ = o.off_replay_r_; off_centroid_ = o.off_centroid_;
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off_target_ = o.off_target_;
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arena_ = o.arena_;
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o.arena_ = nullptr;
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}
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std::size_t n_out_ = 0u, n_weights_ = 0u, undo_cap_ = 0u, n_in_ = 0u, replay_cap_ = 0u;
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std::size_t off_placed_ = 0u, off_snap_ = 0u, off_scratch_ = 0u,
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off_undo_ = 0u, off_focus_ = 0u, off_replay_in_ = 0u,
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off_replay_a_ = 0u, off_replay_r_ = 0u, off_centroid_ = 0u,
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off_target_ = 0u;
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float* arena_ = nullptr;
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};
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} // namespace nisps::ml
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