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Warrior_EA/AI/Impl/NeuronOCLLSTM.mqh

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refactor(ai): extract Layer.mqh and deduplicate AI config - Moves CLayer neuron construction to AI/Impl/Layer.mqh to keep Network.mqh clean - Unifies four previously duplicated architecture initialisation blocks (MLP/CONV/LSTM/HYBRID) into a single shared function - Eliminates risk of behavioural drift where one architecture missed a setter, causing mismatched feature sets or targets
2026-08-01 11:27:28 -04:00
//+------------------------------------------------------------------+
//| NeuronOCLLSTM.mqh |
//| |
//| CNeuronLSTMOCL - the accelerated sequence LSTM (fused kernels, |
//| BPTT caches). |
//| |
//| Included from AI\Network.mqh AFTER every class declaration - |
//| bodies only, no declarations. Relocation is behaviour-neutral by |
//| construction: nothing here is reachable until Network.mqh ends. |
//+------------------------------------------------------------------+
#ifndef WARRIOR_AI_IMPL_NEURONOCLLSTM_MQH
#define WARRIOR_AI_IMPL_NEURONOCLLSTM_MQH
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
CNeuronLSTMOCL::~CNeuronLSTMOCL(void)
{
if(CheckPointer(WeightsLSTM) != POINTER_INVALID)
delete WeightsLSTM;
if(CheckPointer(FirstMomentumLSTM) != POINTER_INVALID)
delete FirstMomentumLSTM;
if(CheckPointer(SecondMomentumLSTM) != POINTER_INVALID)
delete SecondMomentumLSTM;
if(CheckPointer(DeltaWeightsLSTM) != POINTER_INVALID)
delete DeltaWeightsLSTM;
if(CheckPointer(WeightsGradient) != POINTER_INVALID)
delete WeightsGradient;
if(CheckPointer(Concatenated) != POINTER_INVALID)
delete Concatenated;
if(CheckPointer(ConcatenatedGradient) != POINTER_INVALID)
delete ConcatenatedGradient;
if(CheckPointer(Memory) != POINTER_INVALID)
delete Memory;
if(CheckPointer(HiddenCache) != POINTER_INVALID)
delete HiddenCache;
if(CheckPointer(CacheGates) != POINTER_INVALID)
delete CacheGates;
if(CheckPointer(CacheCell) != POINTER_INVALID)
delete CacheCell;
if(CheckPointer(CacheHidden) != POINTER_INVALID)
delete CacheHidden;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool CNeuronLSTMOCL::Init(uint numOutputs, uint myIndex, COpenCLMy *open_cl, uint numNeurons, ENUM_OPTIMIZATION optimization_type)
{
if(!CNeuronBaseOCL::Init(numOutputs, myIndex, open_cl, numNeurons, optimization_type))
return false;
uint H = numNeurons;
if(CheckPointer(Memory) == POINTER_INVALID)
{
Memory = new CBufferDouble();
if(CheckPointer(Memory) == POINTER_INVALID)
return false;
}
if(!Memory.BufferInit(2 * H, 0) || !Memory.BufferCreate(OpenCL))
return false;
if(CheckPointer(Concatenated) == POINTER_INVALID)
{
Concatenated = new CBufferDouble();
if(CheckPointer(Concatenated) == POINTER_INVALID)
return false;
}
if(!Concatenated.BufferInit(4 * H, 0) || !Concatenated.BufferCreate(OpenCL))
return false;
if(CheckPointer(ConcatenatedGradient) == POINTER_INVALID)
{
ConcatenatedGradient = new CBufferDouble();
if(CheckPointer(ConcatenatedGradient) == POINTER_INVALID)
return false;
}
if(!ConcatenatedGradient.BufferInit(4 * H, 0) || !ConcatenatedGradient.BufferCreate(OpenCL))
return false;
if(CheckPointer(HiddenCache) == POINTER_INVALID)
{
HiddenCache = new CBufferDouble();
if(CheckPointer(HiddenCache) == POINTER_INVALID)
return false;
}
if(!HiddenCache.BufferInit(H, 0) || !HiddenCache.BufferCreate(OpenCL))
return false;
m_iInputs = -1;
return true;
}
//+------------------------------------------------------------------+
//| DirectML/D3D12 tier equivalent of Init(COpenCLMy*) above. |
//+------------------------------------------------------------------+
bool CNeuronLSTMOCL::Init(uint numOutputs, uint myIndex, CDirectMLMy *direct_ml, uint numNeurons, ENUM_OPTIMIZATION optimization_type)
{
if(!CNeuronBaseOCL::Init(numOutputs, myIndex, direct_ml, numNeurons, optimization_type))
return false;
uint H = numNeurons;
if(CheckPointer(Memory) == POINTER_INVALID)
{
Memory = new CBufferDouble();
if(CheckPointer(Memory) == POINTER_INVALID)
return false;
}
if(!Memory.BufferInit(2 * H, 0) || !Memory.BufferCreate(DirectML))
return false;
if(CheckPointer(Concatenated) == POINTER_INVALID)
{
Concatenated = new CBufferDouble();
if(CheckPointer(Concatenated) == POINTER_INVALID)
return false;
}
if(!Concatenated.BufferInit(4 * H, 0) || !Concatenated.BufferCreate(DirectML))
return false;
if(CheckPointer(ConcatenatedGradient) == POINTER_INVALID)
{
ConcatenatedGradient = new CBufferDouble();
if(CheckPointer(ConcatenatedGradient) == POINTER_INVALID)
return false;
}
if(!ConcatenatedGradient.BufferInit(4 * H, 0) || !ConcatenatedGradient.BufferCreate(DirectML))
return false;
if(CheckPointer(HiddenCache) == POINTER_INVALID)
{
HiddenCache = new CBufferDouble();
if(CheckPointer(HiddenCache) == POINTER_INVALID)
return false;
}
if(!HiddenCache.BufferInit(H, 0) || !HiddenCache.BufferCreate(DirectML))
return false;
m_iInputs = -1;
return true;
}
//+------------------------------------------------------------------+
//| Lazily sized on the first feedForward call, once the previous |
//| layer's neuron count (the input size) is known. |
//+------------------------------------------------------------------+
bool CNeuronLSTMOCL::SetInputs(int count)
{
m_iInputs = count;
uint H = (uint)Neurons();
//--- Resolve the sequence shape BEFORE sizing the weights: in sequence mode the gates see only ONE
//--- timestep, so the weight block is 4H(H + stepInputs + 1), not 4H(H + count + 1). That is the
//--- whole parameter saving of a recurrence - the same weights are reused at every step instead of
//--- one gigantic block reading all T*Iw inputs at once. At H1 defaults on LSTM: 4*16*(16+21+1) =
//--- 2432 weights against the old 4*16*(16+420+1) = 27968.
m_iSteps = -1;
if(m_iStepInputs > 0)
{
if(count % m_iStepInputs != 0)
{
printf("CNeuronLSTMOCL::SetInputs: input width %d is not a whole number of %d-wide timesteps - falling back to single-timestep mode", count, m_iStepInputs);
m_iStepInputs = -1;
}
else
m_iSteps = count / m_iStepInputs;
}
int gateInputs = IsSequenceMode() ? m_iStepInputs : count;
int total = (int)(4 * H * (H + gateInputs + 1));
if(CheckPointer(WeightsLSTM) == POINTER_INVALID)
{
WeightsLSTM = new CBufferDouble();
if(CheckPointer(WeightsLSTM) == POINTER_INVALID)
return false;
}
if(!WeightsLSTM.Reserve(total))
return false;
// Fan-in-scaled (LeCun-uniform) init - see CNeuronBaseOCL::Init's OpenCL overload for the full
// rationale; fan-in here is hidden units + input width (each gate reads both).
double weighScale = 1.0 / MathSqrt((double)(H + gateInputs) + 1.0);
for(int i = 0; i < total; i++)
{
double weigh = ((MathRand() + 1) / 32768.0 - 0.5) * 2.0 * weighScale;
if(weigh == 0)
weigh = 0.001;
if(!WeightsLSTM.Add(weigh))
return false;
}
//--- POSITIVE FORGET-GATE BIAS. The single most important initialization detail in a recurrent net,
//--- and the one that decides whether this layer is a sequence model or an expensive 1-bar model.
//--- With every weight drawn around zero the forget gate starts at sigmoid(0) = 0.5, so the cell
//--- state is HALVED every timestep: c_t = f*c_{t-1} + i*g. Over m_iSteps bars the first bar
//--- survives into the output scaled by ~0.5^T, and the gradient reaches it scaled by the same
//--- factor (dc_prev = dc_total * f). At T=20 that is ~1e-6 - the recurrence exists on paper and
//--- carries nothing. Measured with DirectML\lstm_seq_flowcheck.cpp at the shipped H1 shapes
//--- (H=64, stepInputs=21, T=20), influence of bar 0 on the output relative to bar 19:
//--- bias 0.0 -> 3.0e-05 forward, 3.3e-05 backward (dead: a one-bar model)
//--- bias 1.0 -> 1.2e-02 forward, 1.4e-02 backward
//--- bias 2.0 -> 2.5e-01 forward, 2.7e-01 backward (a genuinely 20-bar-wide receptive field)
//--- This is not a tuning knob discovered by trial: Gers/Schmidhuber/Cummins (2000) introduced the
//--- forget gate with a positive bias, and Jozefowicz/Zaremba/Sutskever (ICML 2015) found "adding a
//--- bias of 1 to the forget gate" closes the LSTM-vs-GRU gap and recommend it as a default (it is
//--- why Keras ships unit_forget_bias=True).
//--- LOWERED 2.0 -> 1.0 on 2026-07-31. Picking 2.0 off the sweep above was a mistake of method: the
//--- sweep measures gradient REACH, and reach is not the objective - it trades directly against
//--- saturation, which the sweep does not measure at all. Over T steps the cell tends to
//--- c* = i*g/(1-sigmoid(b)). At b=2, sigmoid=0.88, so c* ~ 8.3*i*g, |c*| reaches ~4.2 and tanh(c*)
//--- pins at 0.9995 with derivative ~1e-3: h_T = o*tanh(c) becomes near-binary and is set by the gate
//--- biases rather than by the bars. At b=1, sigmoid=0.73, c* ~ 3.7*i*g, |c*| ~ 1.85, tanh ~ 0.95 with
//--- derivative ~0.1 - saturating but alive. That is the difference between a layer that summarises the
//--- window and one that emits a constant, and "outputs do not vary with the input" is precisely the
//--- 2026-07-30 sequence-LSTM symptom (flat IS error, Neutral:100%) that was misread as a gradient fault.
//--- Diagnose with "OOS raw out B:min..max" in the era line: a collapsed span is this, not a dead gradient.
//--- Symptom when this regresses: IS error flat to 2 decimals across many eras with OOS recall
//--- Neutral:100%, because a model that cannot see across bars can only predict the base rate.
//--- Layout: gates are [forget, input, output, candidate]; forget is gate 0, each hidden unit owns a
//--- row of (H + gateInputs + 1) with the bias last. Keep in step with the layout note in feedForward.
int rowWidth = (int)H + gateInputs + 1;
for(uint hid = 0; hid < H; hid++)
if(!WeightsLSTM.Update((int)hid * rowWidth + rowWidth - 1, LSTM_FORGET_BIAS_INIT))
return false;
if(CheckPointer(OpenCL) != POINTER_INVALID ? !WeightsLSTM.BufferCreate(OpenCL) : !WeightsLSTM.BufferCreate(DirectML))
return false;
//---
if(CheckPointer(FirstMomentumLSTM) == POINTER_INVALID)
{
FirstMomentumLSTM = new CBufferDouble();
if(CheckPointer(FirstMomentumLSTM) == POINTER_INVALID)
return false;
}
if(!FirstMomentumLSTM.BufferInit(total, 0))
return false;
if(CheckPointer(OpenCL) != POINTER_INVALID ? !FirstMomentumLSTM.BufferCreate(OpenCL) : !FirstMomentumLSTM.BufferCreate(DirectML))
return false;
//---
if(CheckPointer(SecondMomentumLSTM) == POINTER_INVALID)
{
SecondMomentumLSTM = new CBufferDouble();
if(CheckPointer(SecondMomentumLSTM) == POINTER_INVALID)
return false;
}
if(!SecondMomentumLSTM.BufferInit(total, 0))
return false;
if(CheckPointer(OpenCL) != POINTER_INVALID ? !SecondMomentumLSTM.BufferCreate(OpenCL) : !SecondMomentumLSTM.BufferCreate(DirectML))
return false;
//---
// Momentum accumulator - only meaningfully used when optimization==SGD
// (see updateInputWeights()), but allocated unconditionally regardless of
// the configured optimizer, matching CNeuronBaseOCL::Init's pattern for
// the dense-layer DeltaWeights/FirstMomentum/SecondMomentum trio above.
if(CheckPointer(DeltaWeightsLSTM) == POINTER_INVALID)
{
DeltaWeightsLSTM = new CBufferDouble();
if(CheckPointer(DeltaWeightsLSTM) == POINTER_INVALID)
return false;
}
if(!DeltaWeightsLSTM.BufferInit(total, 0))
return false;
if(CheckPointer(OpenCL) != POINTER_INVALID ? !DeltaWeightsLSTM.BufferCreate(OpenCL) : !DeltaWeightsLSTM.BufferCreate(DirectML))
return false;
//---
if(CheckPointer(WeightsGradient) == POINTER_INVALID)
{
WeightsGradient = new CBufferDouble();
if(CheckPointer(WeightsGradient) == POINTER_INVALID)
return false;
}
if(!WeightsGradient.BufferInit(total, 0))
return false;
if(CheckPointer(OpenCL) != POINTER_INVALID ? !WeightsGradient.BufferCreate(OpenCL) : !WeightsGradient.BufferCreate(DirectML))
return false;
//--- Belt-and-braces on the state buffers. SetInputs() is the LAZY sizing path - it runs on the first
//--- feedForward whenever m_iInputs is still unset, which includes the just-loaded-a-never-run-net case
//--- (see the note in Load()). Every buffer LSTMGates/LSTMState touch must exist by the time this
//--- returns, or the layer fails silently on every pass; do not assume Init() or Load() got here first.
if(CheckPointer(Memory) == POINTER_INVALID)
{
Memory = new CBufferDouble();
if(CheckPointer(Memory) == POINTER_INVALID)
return false;
}
if(Memory.GetIndex() < 0)
{
if(!Memory.BufferInit(2 * H, 0))
return false;
if(CheckPointer(OpenCL) != POINTER_INVALID ? !Memory.BufferCreate(OpenCL) : !Memory.BufferCreate(DirectML))
return false;
}
if(CheckPointer(Concatenated) == POINTER_INVALID || Concatenated.GetIndex() < 0 ||
CheckPointer(ConcatenatedGradient) == POINTER_INVALID || ConcatenatedGradient.GetIndex() < 0 ||
CheckPointer(HiddenCache) == POINTER_INVALID || HiddenCache.GetIndex() < 0)
{
//--- loud on purpose: the failure this replaces was a silent `return false` out of feedForward,
//--- which looked identical to a healthy net that simply never fires.
printf("CNeuronLSTMOCL::SetInputs: scratch buffers missing (H=%d I=%d) - layer built by neither Init() nor Load()", (int)H, count);
return false;
}
//--- Per-timestep caches. Only sequence mode needs them, and they are pure scratch (recomputed by every
//--- forward pass), so they are never persisted - Load re-creates them from the saved shape.
if(IsSequenceMode() && !AllocateSequenceCaches())
return false;
//---
return true;
}
//+------------------------------------------------------------------+
//| (Re)allocates the per-timestep BPTT caches for the current shape. |
//+------------------------------------------------------------------+
bool CNeuronLSTMOCL::AllocateSequenceCaches(void)
{
if(!IsSequenceMode())
return true;
uint H = (uint)Neurons();
if(CheckPointer(CacheGates) == POINTER_INVALID)
{
CacheGates = new CBufferDouble();
if(CheckPointer(CacheGates) == POINTER_INVALID)
return false;
}
CacheGates.BufferFree();
if(!CacheGates.BufferInit((int)(m_iSteps * 4 * H), 0) || !BackendBufferCreate(CacheGates))
return false;
if(CheckPointer(CacheCell) == POINTER_INVALID)
{
CacheCell = new CBufferDouble();
if(CheckPointer(CacheCell) == POINTER_INVALID)
return false;
}
CacheCell.BufferFree();
if(!CacheCell.BufferInit((int)(m_iSteps * H), 0) || !BackendBufferCreate(CacheCell))
return false;
if(CheckPointer(CacheHidden) == POINTER_INVALID)
{
CacheHidden = new CBufferDouble();
if(CheckPointer(CacheHidden) == POINTER_INVALID)
return false;
}
CacheHidden.BufferFree();
if(!CacheHidden.BufferInit((int)(m_iSteps * H), 0) || !BackendBufferCreate(CacheHidden))
return false;
return true;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool CNeuronLSTMOCL::feedForward(CNeuronBaseOCL *NeuronOCL)
{
if(CheckPointer(NeuronOCL) == POINTER_INVALID)
return false;
if(m_iInputs <= 0)
{
if(!SetInputs(NeuronOCL.Neurons()))
return false;
}
else
if(m_iInputs != NeuronOCL.Neurons())
return false;
int H = Neurons();
int I = m_iInputs;
//--- Sequence mode: one fused call unrolls all T timesteps, sharing the gate weights across them and
//--- caching per-step state for BPTT. h_{-1}/c_{-1} are zero, so nothing leaks between samples.
if(IsSequenceMode())
{
if(CheckPointer(DirectML) != POINTER_INVALID)
{
if(!DirectML.LSTMSeqForward(WeightsLSTM.GetIndex(), NeuronOCL.getOutputIndex(), CacheGates.GetIndex(),
CacheCell.GetIndex(), CacheHidden.GetIndex(), getOutputIndex(),
H, m_iStepInputs, m_iSteps))
{
printf("Error of execution LSTM sequence feedForward (H=%d step=%d steps=%d)", H, m_iStepInputs, m_iSteps);
return false;
}
return Output.BufferRead();
}
if(CheckPointer(OpenCL) == POINTER_INVALID)
return feedForwardCPU(NeuronOCL);
//--- One launch per timestep: each is an implicit global barrier, which is the only ordering
//--- guarantee OpenCL gives across work-groups. See LSTM_SeqStepForward in Network.cl.
uint offSeq[1] = {0};
uint szSeq[1] = {(uint)H};
for(int st = 0; st < m_iSteps; st++)
{
OpenCL.SetArgumentBuffer(def_k_LSTM_SeqStepForward, def_k_lsf_matrix_w, WeightsLSTM.GetIndex());
OpenCL.SetArgumentBuffer(def_k_LSTM_SeqStepForward, def_k_lsf_inputs, NeuronOCL.getOutputIndex());
OpenCL.SetArgumentBuffer(def_k_LSTM_SeqStepForward, def_k_lsf_cache_gates, CacheGates.GetIndex());
OpenCL.SetArgumentBuffer(def_k_LSTM_SeqStepForward, def_k_lsf_cache_cell, CacheCell.GetIndex());
OpenCL.SetArgumentBuffer(def_k_LSTM_SeqStepForward, def_k_lsf_cache_hidden, CacheHidden.GetIndex());
OpenCL.SetArgumentBuffer(def_k_LSTM_SeqStepForward, def_k_lsf_output, getOutputIndex());
OpenCL.SetArgument(def_k_LSTM_SeqStepForward, def_k_lsf_hidden_size, H);
OpenCL.SetArgument(def_k_LSTM_SeqStepForward, def_k_lsf_step_inputs, m_iStepInputs);
OpenCL.SetArgument(def_k_LSTM_SeqStepForward, def_k_lsf_steps, m_iSteps);
OpenCL.SetArgument(def_k_LSTM_SeqStepForward, def_k_lsf_t, st);
if(!OpenCL.Execute(def_k_LSTM_SeqStepForward, 1, offSeq, szSeq))
{
printf("Error of execution kernel LSTM_SeqStepForward (t=%d): %d", st, GetLastError());
return false;
}
}
return Output.BufferRead();
}
if(CheckPointer(DirectML) != POINTER_INVALID)
{
if(!DirectML.LSTMGates(WeightsLSTM.GetIndex(), getOutputIndex(), NeuronOCL.getOutputIndex(), Concatenated.GetIndex(), H, I) ||
!DirectML.LSTMState(Concatenated.GetIndex(), Memory.GetIndex(), getOutputIndex(), HiddenCache.GetIndex(), getOutputIndex(), H))
{
fix: improve error messages and suppress false sharing-violation logs - BufferDouble: replace hardcoded "DirectML/CPU-DLL" with dynamic backend name and add buffer index/element count to all error prints for easier debugging. - NetPersistence: distinguish missing file from transient lock by probing FileIsExist before logging, eliminating false "sharing violation" warnings when no saved model exists on first run.
2026-08-02 01:09:18 -04:00
Print(__FUNCTION__ + ": " + DirectML.BackendName() + " LSTM feedForward failed, error " + IntegerToString(DirectML.LastError()));
refactor(ai): extract Layer.mqh and deduplicate AI config - Moves CLayer neuron construction to AI/Impl/Layer.mqh to keep Network.mqh clean - Unifies four previously duplicated architecture initialisation blocks (MLP/CONV/LSTM/HYBRID) into a single shared function - Eliminates risk of behavioural drift where one architecture missed a setter, causing mismatched feature sets or targets
2026-08-01 11:27:28 -04:00
return false;
}
return Output.BufferRead();
}
if(CheckPointer(OpenCL) == POINTER_INVALID)
return false;
uint offset2[2] = {0, 0};
uint size2[2] = {(uint)H, 4};
OpenCL.SetArgumentBuffer(def_k_LSTM_Gates, def_k_lstmg_matrix_w, WeightsLSTM.GetIndex());
OpenCL.SetArgumentBuffer(def_k_LSTM_Gates, def_k_lstmg_hidden_prev, getOutputIndex());
OpenCL.SetArgumentBuffer(def_k_LSTM_Gates, def_k_lstmg_inputs, NeuronOCL.getOutputIndex());
OpenCL.SetArgumentBuffer(def_k_LSTM_Gates, def_k_lstmg_concatenated, Concatenated.GetIndex());
OpenCL.SetArgument(def_k_LSTM_Gates, def_k_lstmg_hidden_size, H);
OpenCL.SetArgument(def_k_LSTM_Gates, def_k_lstmg_input_size, I);
if(!OpenCL.Execute(def_k_LSTM_Gates, 2, offset2, size2))
{
printf("Error of execution kernel LSTM_Gates: %d", GetLastError());
return false;
}
uint offset1[1] = {0};
uint size1[1] = {(uint)H};
OpenCL.SetArgumentBuffer(def_k_LSTM_State, def_k_lstms_concatenated, Concatenated.GetIndex());
OpenCL.SetArgumentBuffer(def_k_LSTM_State, def_k_lstms_memory, Memory.GetIndex());
OpenCL.SetArgumentBuffer(def_k_LSTM_State, def_k_lstms_hidden_prev, getOutputIndex());
OpenCL.SetArgumentBuffer(def_k_LSTM_State, def_k_lstms_hidden_cache, HiddenCache.GetIndex());
OpenCL.SetArgumentBuffer(def_k_LSTM_State, def_k_lstms_output, getOutputIndex());
OpenCL.SetArgument(def_k_LSTM_State, def_k_lstms_hidden_size, H);
if(!OpenCL.Execute(def_k_LSTM_State, 1, offset1, size1))
{
printf("Error of execution kernel LSTM_State: %d", GetLastError());
return false;
}
//--- Output (== hidden_prev for the next timestep) stays GPU-resident; see the note in
//--- CNeuronBaseOCL::feedForward().
return true;
}
//+------------------------------------------------------------------+
//| Pure-MQL5 double-precision mirror of Network.cl's LSTM_Gates + |
//| LSTM_State kernels, host buffers only (CPU inference). Recurrent |
//| state is carried EXACTLY as the kernels do: hidden_prev is this |
//| neuron's Output, the cell state is Memory[0..H); both persist |
//| across bars. Weight layout: 4 gates (forget,input,output,cand), |
//| each H*(H+I+1), per unit [H recurrent | I input | 1 bias]. |
//| NOTE: like every backend this advances state per call; over a |
//| long backtest fp rounding vs the training backend can drift, but |
//| within the validated per-step tolerance (see ValidateCpuInference)|
//+------------------------------------------------------------------+
bool CNeuronLSTMOCL::feedForwardCPU(CNeuronBaseOCL *NeuronOCL)
{
if(CheckPointer(NeuronOCL) == POINTER_INVALID || CheckPointer(Output) == POINTER_INVALID ||
CheckPointer(WeightsLSTM) == POINTER_INVALID || CheckPointer(Memory) == POINTER_INVALID)
return false;
int H = Neurons();
int I = m_iInputs;
if(I <= 0 || NeuronOCL.Neurons() != I || H <= 0)
return false;
//--- Sequence mode mirror of CPU_LSTMSeqForward (WarriorCPU.cpp) - same gate order, same weight layout,
//--- same zero initial state. Inference only, so no caches are kept: only the final hidden state is
//--- needed. Keep this in step with the kernel; a divergence here shows up as a deployed model that
//--- trades differently from the one that was validated.
if(IsSequenceMode())
{
int Iw = m_iStepInputs, steps = m_iSteps;
int per_gate_s = H * (H + Iw + 1);
if(WeightsLSTM.Total() < 4 * per_gate_s || Output.Total() < H)
return false;
double hPrev[], cPrev[], gates_s[];
if(ArrayResize(hPrev, H) != H || ArrayResize(cPrev, H) != H || ArrayResize(gates_s, 4) != 4)
return false;
ArrayInitialize(hPrev, 0.0);
ArrayInitialize(cPrev, 0.0);
for(int st = 0; st < steps; st++)
{
double hNext[];
if(ArrayResize(hNext, H) != H)
return false;
for(int id = 0; id < H; id++)
{
for(int gate = 0; gate < 4; gate++)
{
int shift = gate * per_gate_s + id * (H + Iw + 1);
double sum = 0.0;
for(int k = 0; k < H; k++)
sum += hPrev[k] * WeightsLSTM.At(shift + k);
for(int k = 0; k < Iw; k++)
sum += NeuronOCL.OutputHost(st * Iw + k) * WeightsLSTM.At(shift + H + k);
sum += WeightsLSTM.At(shift + H + Iw);
gates_s[gate] = (gate < 3) ? (1.0 / (1.0 + exp(-sum))) : tanh(sum);
}
double c_t = gates_s[0] * cPrev[id] + gates_s[1] * gates_s[3];
cPrev[id] = c_t;
hNext[id] = gates_s[2] * tanh(c_t);
}
ArrayCopy(hPrev, hNext);
}
for(int id = 0; id < H; id++)
if(!Output.Update(id, hPrev[id]))
return false;
return true;
}
int per_gate = H * (H + I + 1);
if(WeightsLSTM.Total() < 4 * per_gate || Memory.Total() < 2 * H || Output.Total() < H)
return false;
//--- Gates: read hidden_prev (this Output) and inputs (prev Output) BEFORE Output is overwritten by
//--- the state step below - exactly the two-kernel ordering (Concatenated is a separate buffer there).
double gates[];
if(ArrayResize(gates, 4 * H) != 4 * H)
return false;
for(int gate = 0; gate < 4; gate++)
for(int id = 0; id < H; id++)
{
int shift = gate * per_gate + id * (H + I + 1);
double sum = 0.0;
for(int k = 0; k < H; k++)
sum += Output.At(k) * WeightsLSTM.At(shift + k); // hidden_prev
for(int k = 0; k < I; k++)
sum += NeuronOCL.OutputHost(k) * WeightsLSTM.At(shift + H + k); // inputs
sum += WeightsLSTM.At(shift + H + I); // bias
gates[gate * H + id] = (gate < 3) ? (1.0 / (1.0 + exp(-sum))) : tanh(sum);
}
//--- State: c_t = f*c_prev + i*g ; h = o*tanh(c_t). memory[H+id] keeps c_prev (unused in inference).
for(int id = 0; id < H; id++)
{
double f = gates[id];
double ii = gates[H + id];
double o = gates[2 * H + id];
double g = gates[3 * H + id];
double c_prev = Memory.At(id);
double c_t = f * c_prev + ii * g;
if(!Memory.Update(H + id, c_prev) || !Memory.Update(id, c_t))
return false;
if(!Output.Update(id, o * tanh(c_t)))
return false;
}
return true;
}
//+------------------------------------------------------------------+
//| Writes the gradient into NeuronOCL (the earlier/input-side layer) |
//| - same inverted-call convention as CNeuronConvOCL::calcInputGradients.|
//+------------------------------------------------------------------+
bool CNeuronLSTMOCL::calcInputGradients(CNeuronBaseOCL *NeuronOCL)
{
if(CheckPointer(NeuronOCL) == POINTER_INVALID || m_iInputs <= 0)
return false;
int H = Neurons();
int I = m_iInputs;
//--- Sequence mode: one fused call walks t = T-1 .. 0, carrying dh and dc back through every step and
//--- accumulating dW across all of them. This is the path that did not exist before - the per-step
//--- kernels below have no way to take dc from the following step, so the recurrent gradient was
//--- simply absent and the layer learned as if each sample were a single timestep.
if(IsSequenceMode())
{
if(CheckPointer(DirectML) != POINTER_INVALID)
{
if(!DirectML.LSTMSeqBackward(WeightsLSTM.GetIndex(), NeuronOCL.getOutputIndex(), CacheGates.GetIndex(),
CacheCell.GetIndex(), CacheHidden.GetIndex(), getGradientIndex(),
WeightsGradient.GetIndex(), NeuronOCL.getGradientIndex(),
H, m_iStepInputs, m_iSteps))
{
printf("Error of execution LSTM sequence calcInputGradients (H=%d step=%d steps=%d)", H, m_iStepInputs, m_iSteps);
return false;
}
double tempSeq[];
return NeuronOCL.getGradient(tempSeq) > 0;
}
if(CheckPointer(OpenCL) == POINTER_INVALID)
return false; // pure-MQL5 tier is inference-only and never backpropagates
//--- BPTT, host-driven so each launch is a global barrier. Scratch reuse: the single-timestep
//--- buffers are idle in sequence mode, so ConcatenatedGradient (4H) carries the gate gradients,
//--- HiddenCache (H) carries dh and Memory (2H, first half) carries dc - no extra allocations.
int seqTotal = 4 * H * (H + m_iStepInputs + 1);
if(!WeightsGradient.BufferInit(seqTotal, 0) || !WeightsGradient.BufferWrite())
return false; // dW ACCUMULATES over the steps below, so it must start at zero
if(!Memory.BufferInit(2 * H, 0) || !Memory.BufferWrite())
return false; // dc_T = 0
uint offG[1] = {0};
uint szH[1] = {(uint)H};
uint szW[1] = {(uint)seqTotal};
uint szIn[1] = {(uint)(m_iStepInputs + H)};
for(int st = m_iSteps - 1; st >= 0; st--)
{
OpenCL.SetArgumentBuffer(def_k_LSTM_SeqStepGateGrad, def_k_lsgg_out_gradient, getGradientIndex());
OpenCL.SetArgumentBuffer(def_k_LSTM_SeqStepGateGrad, def_k_lsgg_dh_buf, HiddenCache.GetIndex());
OpenCL.SetArgumentBuffer(def_k_LSTM_SeqStepGateGrad, def_k_lsgg_dc_buf, Memory.GetIndex());
OpenCL.SetArgumentBuffer(def_k_LSTM_SeqStepGateGrad, def_k_lsgg_cache_gates, CacheGates.GetIndex());
OpenCL.SetArgumentBuffer(def_k_LSTM_SeqStepGateGrad, def_k_lsgg_cache_cell, CacheCell.GetIndex());
OpenCL.SetArgumentBuffer(def_k_LSTM_SeqStepGateGrad, def_k_lsgg_gate_grad, ConcatenatedGradient.GetIndex());
OpenCL.SetArgument(def_k_LSTM_SeqStepGateGrad, def_k_lsgg_hidden_size, H);
OpenCL.SetArgument(def_k_LSTM_SeqStepGateGrad, def_k_lsgg_steps, m_iSteps);
OpenCL.SetArgument(def_k_LSTM_SeqStepGateGrad, def_k_lsgg_t, st);
if(!OpenCL.Execute(def_k_LSTM_SeqStepGateGrad, 1, offG, szH))
{
printf("Error of execution kernel LSTM_SeqStepGateGrad (t=%d): %d", st, GetLastError());
return false;
}
OpenCL.SetArgumentBuffer(def_k_LSTM_SeqStepWeightGrad, def_k_lswg_gate_grad, ConcatenatedGradient.GetIndex());
OpenCL.SetArgumentBuffer(def_k_LSTM_SeqStepWeightGrad, def_k_lswg_cache_hidden, CacheHidden.GetIndex());
OpenCL.SetArgumentBuffer(def_k_LSTM_SeqStepWeightGrad, def_k_lswg_inputs, NeuronOCL.getOutputIndex());
OpenCL.SetArgumentBuffer(def_k_LSTM_SeqStepWeightGrad, def_k_lswg_weights_gradient, WeightsGradient.GetIndex());
OpenCL.SetArgument(def_k_LSTM_SeqStepWeightGrad, def_k_lswg_hidden_size, H);
OpenCL.SetArgument(def_k_LSTM_SeqStepWeightGrad, def_k_lswg_step_inputs, m_iStepInputs);
OpenCL.SetArgument(def_k_LSTM_SeqStepWeightGrad, def_k_lswg_t, st);
if(!OpenCL.Execute(def_k_LSTM_SeqStepWeightGrad, 1, offG, szW))
{
printf("Error of execution kernel LSTM_SeqStepWeightGrad (t=%d): %d", st, GetLastError());
return false;
}
OpenCL.SetArgumentBuffer(def_k_LSTM_SeqStepInputGrad, def_k_lsig_gate_grad, ConcatenatedGradient.GetIndex());
OpenCL.SetArgumentBuffer(def_k_LSTM_SeqStepInputGrad, def_k_lsig_matrix_w, WeightsLSTM.GetIndex());
OpenCL.SetArgumentBuffer(def_k_LSTM_SeqStepInputGrad, def_k_lsig_inputs_gradient, NeuronOCL.getGradientIndex());
OpenCL.SetArgumentBuffer(def_k_LSTM_SeqStepInputGrad, def_k_lsig_dh_buf, HiddenCache.GetIndex());
OpenCL.SetArgument(def_k_LSTM_SeqStepInputGrad, def_k_lsig_hidden_size, H);
OpenCL.SetArgument(def_k_LSTM_SeqStepInputGrad, def_k_lsig_step_inputs, m_iStepInputs);
OpenCL.SetArgument(def_k_LSTM_SeqStepInputGrad, def_k_lsig_t, st);
if(!OpenCL.Execute(def_k_LSTM_SeqStepInputGrad, 1, offG, szIn))
{
printf("Error of execution kernel LSTM_SeqStepInputGrad (t=%d): %d", st, GetLastError());
return false;
}
}
double tempSeqCl[];
return NeuronOCL.getGradient(tempSeqCl) > 0;
}
if(CheckPointer(DirectML) != POINTER_INVALID)
{
if(!DirectML.LSTMGateGradient(getGradientIndex(), Memory.GetIndex(), Concatenated.GetIndex(), ConcatenatedGradient.GetIndex(), H) ||
!DirectML.LSTMWeightsGradient(ConcatenatedGradient.GetIndex(), HiddenCache.GetIndex(), NeuronOCL.getOutputIndex(), WeightsGradient.GetIndex(), H, I) ||
!DirectML.LSTMInputsGradient(ConcatenatedGradient.GetIndex(), WeightsLSTM.GetIndex(), NeuronOCL.getGradientIndex(), H, I))
{
fix: improve error messages and suppress false sharing-violation logs - BufferDouble: replace hardcoded "DirectML/CPU-DLL" with dynamic backend name and add buffer index/element count to all error prints for easier debugging. - NetPersistence: distinguish missing file from transient lock by probing FileIsExist before logging, eliminating false "sharing violation" warnings when no saved model exists on first run.
2026-08-02 01:09:18 -04:00
Print(__FUNCTION__ + ": " + DirectML.BackendName() + " LSTM calcInputGradients failed, error " + IntegerToString(DirectML.LastError()));
refactor(ai): extract Layer.mqh and deduplicate AI config - Moves CLayer neuron construction to AI/Impl/Layer.mqh to keep Network.mqh clean - Unifies four previously duplicated architecture initialisation blocks (MLP/CONV/LSTM/HYBRID) into a single shared function - Eliminates risk of behavioural drift where one architecture missed a setter, causing mismatched feature sets or targets
2026-08-01 11:27:28 -04:00
return false;
}
double temp[];
return NeuronOCL.getGradient(temp) > 0;
}
if(CheckPointer(OpenCL) == POINTER_INVALID)
return false;
uint offset1[1] = {0};
uint size1[1] = {(uint)H};
OpenCL.SetArgumentBuffer(def_k_LSTM_GateGradient, def_k_lstmgg_gradient, getGradientIndex());
OpenCL.SetArgumentBuffer(def_k_LSTM_GateGradient, def_k_lstmgg_memory, Memory.GetIndex());
OpenCL.SetArgumentBuffer(def_k_LSTM_GateGradient, def_k_lstmgg_concatenated, Concatenated.GetIndex());
OpenCL.SetArgumentBuffer(def_k_LSTM_GateGradient, def_k_lstmgg_concatenated_gradient, ConcatenatedGradient.GetIndex());
OpenCL.SetArgument(def_k_LSTM_GateGradient, def_k_lstmgg_hidden_size, H);
if(!OpenCL.Execute(def_k_LSTM_GateGradient, 1, offset1, size1))
{
printf("Error of execution kernel LSTM_GateGradient: %d", GetLastError());
return false;
}
uint sizeW[1] = {(uint)WeightsLSTM.Total()};
OpenCL.SetArgumentBuffer(def_k_LSTM_WeightsGradient, def_k_lstmwg_concatenated_gradient, ConcatenatedGradient.GetIndex());
OpenCL.SetArgumentBuffer(def_k_LSTM_WeightsGradient, def_k_lstmwg_hidden_cache, HiddenCache.GetIndex());
OpenCL.SetArgumentBuffer(def_k_LSTM_WeightsGradient, def_k_lstmwg_inputs, NeuronOCL.getOutputIndex());
OpenCL.SetArgumentBuffer(def_k_LSTM_WeightsGradient, def_k_lstmwg_weights_gradient, WeightsGradient.GetIndex());
OpenCL.SetArgument(def_k_LSTM_WeightsGradient, def_k_lstmwg_hidden_size, H);
OpenCL.SetArgument(def_k_LSTM_WeightsGradient, def_k_lstmwg_input_size, I);
if(!OpenCL.Execute(def_k_LSTM_WeightsGradient, 1, offset1, sizeW))
{
printf("Error of execution kernel LSTM_WeightsGradient: %d", GetLastError());
return false;
}
uint sizeI[1] = {(uint)I};
OpenCL.SetArgumentBuffer(def_k_LSTM_InputsGradient, def_k_lstmig_concatenated_gradient, ConcatenatedGradient.GetIndex());
OpenCL.SetArgumentBuffer(def_k_LSTM_InputsGradient, def_k_lstmig_matrix_w, WeightsLSTM.GetIndex());
OpenCL.SetArgumentBuffer(def_k_LSTM_InputsGradient, def_k_lstmig_inputs_gradient, NeuronOCL.getGradientIndex());
OpenCL.SetArgument(def_k_LSTM_InputsGradient, def_k_lstmig_hidden_size, H);
OpenCL.SetArgument(def_k_LSTM_InputsGradient, def_k_lstmig_input_size, I);
if(!OpenCL.Execute(def_k_LSTM_InputsGradient, 1, offset1, sizeI))
{
printf("Error of execution kernel LSTM_InputsGradient: %d", GetLastError());
return false;
}
//--- NeuronOCL's Gradient stays GPU-resident; see the note in
//--- CNeuronConvOCL::calcInputGradients().
return true;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool CNeuronLSTMOCL::updateInputWeights(CNeuronBaseOCL *NeuronOCL)
{
if(CheckPointer(WeightsLSTM) == POINTER_INVALID)
return false;
int total = WeightsLSTM.Total();
if(CheckPointer(DirectML) != POINTER_INVALID)
{
if(optimization == SGD)
{
if(!DirectML.LSTMUpdateWeightsMomentum(WeightsLSTM.GetIndex(), WeightsGradient.GetIndex(), DeltaWeightsLSTM.GetIndex(), eta, alpha, total,
0))
{
fix: improve error messages and suppress false sharing-violation logs - BufferDouble: replace hardcoded "DirectML/CPU-DLL" with dynamic backend name and add buffer index/element count to all error prints for easier debugging. - NetPersistence: distinguish missing file from transient lock by probing FileIsExist before logging, eliminating false "sharing violation" warnings when no saved model exists on first run.
2026-08-02 01:09:18 -04:00
Print(__FUNCTION__ + ": " + DirectML.BackendName() + " LSTM_UpdateWeightsMomentum failed, error " + IntegerToString(DirectML.LastError()));
refactor(ai): extract Layer.mqh and deduplicate AI config - Moves CLayer neuron construction to AI/Impl/Layer.mqh to keep Network.mqh clean - Unifies four previously duplicated architecture initialisation blocks (MLP/CONV/LSTM/HYBRID) into a single shared function - Eliminates risk of behavioural drift where one architecture missed a setter, causing mismatched feature sets or targets
2026-08-01 11:27:28 -04:00
return false;
}
}
else
{
double lt = eta * sqrt(1 - pow(b2, t)) / (1 - pow(b1, t));
if(!DirectML.LSTMUpdateWeightsAdam(WeightsLSTM.GetIndex(), WeightsGradient.GetIndex(), FirstMomentumLSTM.GetIndex(), SecondMomentumLSTM.GetIndex(), lt, b1, b2, total))
{
fix: improve error messages and suppress false sharing-violation logs - BufferDouble: replace hardcoded "DirectML/CPU-DLL" with dynamic backend name and add buffer index/element count to all error prints for easier debugging. - NetPersistence: distinguish missing file from transient lock by probing FileIsExist before logging, eliminating false "sharing violation" warnings when no saved model exists on first run.
2026-08-02 01:09:18 -04:00
Print(__FUNCTION__ + ": " + DirectML.BackendName() + " LSTM_UpdateWeightsAdam failed, error " + IntegerToString(DirectML.LastError()));
refactor(ai): extract Layer.mqh and deduplicate AI config - Moves CLayer neuron construction to AI/Impl/Layer.mqh to keep Network.mqh clean - Unifies four previously duplicated architecture initialisation blocks (MLP/CONV/LSTM/HYBRID) into a single shared function - Eliminates risk of behavioural drift where one architecture missed a setter, causing mismatched feature sets or targets
2026-08-01 11:27:28 -04:00
return false;
}
t++;
}
//--- WeightsLSTM stays DLL-resident; feedForward reads it via GetIndex() (same as the OpenCL
//--- branch below). Save()/BlendWeightsFrom() BufferRead() on demand.
return true;
}
if(CheckPointer(OpenCL) == POINTER_INVALID)
return false;
uint offset1[1] = {0};
uint size1[1] = {(uint)total};
if(optimization == SGD)
{
OpenCL.SetArgumentBuffer(def_k_LSTM_UpdateWeightsMomentum, def_k_lstmuwm_matrix_w, WeightsLSTM.GetIndex());
OpenCL.SetArgumentBuffer(def_k_LSTM_UpdateWeightsMomentum, def_k_lstmuwm_weights_gradient, WeightsGradient.GetIndex());
OpenCL.SetArgumentBuffer(def_k_LSTM_UpdateWeightsMomentum, def_k_lstmuwm_matrix_dw, DeltaWeightsLSTM.GetIndex());
OpenCL.SetArgument(def_k_LSTM_UpdateWeightsMomentum, def_k_lstmuwm_learning_rates, (float)eta);
OpenCL.SetArgument(def_k_LSTM_UpdateWeightsMomentum, def_k_lstmuwm_momentum, (float)alpha);
OpenCL.SetArgument(def_k_LSTM_UpdateWeightsMomentum, def_k_lstmuwm_optimizer, 0);
ResetLastError();
if(!OpenCL.Execute(def_k_LSTM_UpdateWeightsMomentum, 1, offset1, size1))
{
printf("Error of execution kernel LSTM_UpdateWeightsMomentum: %d", GetLastError());
return false;
}
}
else
{
double lt = eta * sqrt(1 - pow(b2, t)) / (1 - pow(b1, t));
OpenCL.SetArgumentBuffer(def_k_LSTM_UpdateWeightsAdam, def_k_lstmuwa_matrix_w, WeightsLSTM.GetIndex());
OpenCL.SetArgumentBuffer(def_k_LSTM_UpdateWeightsAdam, def_k_lstmuwa_weights_gradient, WeightsGradient.GetIndex());
OpenCL.SetArgumentBuffer(def_k_LSTM_UpdateWeightsAdam, def_k_lstmuwa_matrix_m, FirstMomentumLSTM.GetIndex());
OpenCL.SetArgumentBuffer(def_k_LSTM_UpdateWeightsAdam, def_k_lstmuwa_matrix_v, SecondMomentumLSTM.GetIndex());
OpenCL.SetArgument(def_k_LSTM_UpdateWeightsAdam, def_k_lstmuwa_l, (float)lt);
OpenCL.SetArgument(def_k_LSTM_UpdateWeightsAdam, def_k_lstmuwa_b1, (float)b1);
OpenCL.SetArgument(def_k_LSTM_UpdateWeightsAdam, def_k_lstmuwa_b2, (float)b2);
ResetLastError();
if(!OpenCL.Execute(def_k_LSTM_UpdateWeightsAdam, 1, offset1, size1))
{
printf("Error of execution kernel LSTM_UpdateWeightsAdam: %d", GetLastError());
return false;
}
t++;
}
//--- WeightsLSTM stays GPU-resident; see the note in CNeuronBaseOCL::updateInputWeights().
return true;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool CNeuronLSTMOCL::Save(const int file_handle)
{
if(!CNeuronBaseOCL::Save(file_handle))
return false;
//--- Format tag FIRST. The pre-sequence format opened with m_iInputs here, and its weight block is a
//--- different SHAPE - 4H(H+totalInputs+1) against the sequence layer's 4H(H+stepInputs+1) - so a
//--- silent misread would not just be wrong, it would be wrong by a factor of ~13 in element count
//--- and corrupt everything after it in the file. A distinctive value no legitimate old m_iInputs
//--- could take lets Load() reject those files cleanly instead. See LSTM_SEQ_SAVE_TAG.
if(FileWriteInteger(file_handle, LSTM_SEQ_SAVE_TAG, INT_VALUE) < INT_VALUE)
return false;
if(FileWriteInteger(file_handle, m_iInputs, INT_VALUE) < INT_VALUE)
return false;
if(FileWriteInteger(file_handle, m_iStepInputs, INT_VALUE) < INT_VALUE)
return false;
if(m_iInputs <= 0)
return true;
if(CheckPointer(WeightsLSTM) == POINTER_INVALID || !WeightsLSTM.BufferRead() || !WeightsLSTM.Save(file_handle))
return false;
if(CheckPointer(FirstMomentumLSTM) == POINTER_INVALID || !FirstMomentumLSTM.BufferRead() || !FirstMomentumLSTM.Save(file_handle))
return false;
if(CheckPointer(SecondMomentumLSTM) == POINTER_INVALID || !SecondMomentumLSTM.BufferRead() || !SecondMomentumLSTM.Save(file_handle))
return false;
if(CheckPointer(DeltaWeightsLSTM) == POINTER_INVALID || !DeltaWeightsLSTM.BufferRead() || !DeltaWeightsLSTM.Save(file_handle))
return false;
if(CheckPointer(Memory) == POINTER_INVALID || !Memory.BufferRead() || !Memory.Save(file_handle))
return false;
//---
return true;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool CNeuronLSTMOCL::Load(const int file_handle)
{
if(!CNeuronBaseOCL::Load(file_handle))
return false;
int savedTag = FileReadInteger(file_handle, INT_VALUE);
if(savedTag != LSTM_SEQ_SAVE_TAG)
{
//--- Pre-sequence .nnw. Its LSTM weight block is shaped for the whole flattened input as one
//--- timestep and cannot be reinterpreted; refuse so CNet::Load fails cleanly and the caller
//--- rebuilds a fresh topology, rather than reading a differently-shaped buffer and every
//--- subsequent layer at the wrong offset.
printf("CNeuronLSTMOCL::Load: this model predates the sequence-LSTM rewrite (found %d, expected %d) - it must be retrained. Delete its .nnw (and _shadow.nnw) to start clean.", savedTag, LSTM_SEQ_SAVE_TAG);
return false;
}
m_iInputs = FileReadInteger(file_handle, INT_VALUE);
m_iStepInputs = FileReadInteger(file_handle, INT_VALUE);
m_iSteps = (m_iStepInputs > 0 && m_iInputs > 0 && (m_iInputs % m_iStepInputs) == 0)
? m_iInputs / m_iStepInputs : -1;
uint H = (uint)Neurons();
//--- scratch buffers (not persisted) were sized for the placeholder Init() unit
//--- count; resize them now that the real neuron count is known.
if(CheckPointer(Concatenated) == POINTER_INVALID)
{
Concatenated = new CBufferDouble();
if(CheckPointer(Concatenated) == POINTER_INVALID)
return false;
}
Concatenated.BufferFree();
if(!Concatenated.BufferInit(4 * H, 0))
return false;
if(!BackendBufferCreate(Concatenated))
return false;
if(CheckPointer(ConcatenatedGradient) == POINTER_INVALID)
{
ConcatenatedGradient = new CBufferDouble();
if(CheckPointer(ConcatenatedGradient) == POINTER_INVALID)
return false;
}
ConcatenatedGradient.BufferFree();
if(!ConcatenatedGradient.BufferInit(4 * H, 0))
return false;
if(!BackendBufferCreate(ConcatenatedGradient))
return false;
if(CheckPointer(HiddenCache) == POINTER_INVALID)
{
HiddenCache = new CBufferDouble();
if(CheckPointer(HiddenCache) == POINTER_INVALID)
return false;
}
HiddenCache.BufferFree();
if(!HiddenCache.BufferInit(H, 0))
return false;
if(!BackendBufferCreate(HiddenCache))
return false;
//--- Memory (the c_prev cell state) is allocated HERE, before the m_iInputs<=0 early return below,
//--- and NOT only alongside the weight buffers further down. Both Init() overloads allocate it
//--- unconditionally; Load() used to allocate it only on the m_iInputs>0 path and SetInputs() never
//--- allocates it at all. A net saved before its first feedForward (Save writes m_iInputs=-1 and
//--- omits every LSTM buffer - e.g. weights-reset then detach, which is exactly what a "reset and
//--- restart" click produces) therefore came back from Load with Memory==NULL. The lazy SetInputs()
//--- on the next feedForward rebuilt the weights but not Memory, so LSTMState() got a dead buffer and
//--- every forward AND backward pass failed - the layer computed nothing, the head sat at a constant
//--- 1.0 for all three classes, and the model was pinned to Neutral forever while the journal filled
fix: improve error messages and suppress false sharing-violation logs - BufferDouble: replace hardcoded "DirectML/CPU-DLL" with dynamic backend name and add buffer index/element count to all error prints for easier debugging. - NetPersistence: distinguish missing file from transient lock by probing FileIsExist before logging, eliminating false "sharing violation" warnings when no saved model exists on first run.
2026-08-02 01:09:18 -04:00
//--- with "... LSTM feedForward failed" (that line read "Error of execution DirectML LSTM feedForward"
//--- until 2026-08-02, when it started naming the backend that actually failed rather than always
//--- saying DirectML). If m_iInputs>0 the Memory.Load() further
refactor(ai): extract Layer.mqh and deduplicate AI config - Moves CLayer neuron construction to AI/Impl/Layer.mqh to keep Network.mqh clean - Unifies four previously duplicated architecture initialisation blocks (MLP/CONV/LSTM/HYBRID) into a single shared function - Eliminates risk of behavioural drift where one architecture missed a setter, causing mismatched feature sets or targets
2026-08-01 11:27:28 -04:00
//--- down simply overwrites what we allocate here.
if(CheckPointer(Memory) == POINTER_INVALID)
{
Memory = new CBufferDouble();
if(CheckPointer(Memory) == POINTER_INVALID)
return false;
}
Memory.BufferFree();
if(!Memory.BufferInit(2 * H, 0))
return false;
if(!BackendBufferCreate(Memory))
return false;
//---
if(m_iInputs <= 0)
return true;
//--- In sequence mode the gates read ONE timestep, so the weight block is sized on the per-step width.
int total = (int)(4 * H * (H + (IsSequenceMode() ? m_iStepInputs : m_iInputs) + 1));
//---
if(CheckPointer(WeightsLSTM) == POINTER_INVALID)
{
WeightsLSTM = new CBufferDouble();
if(CheckPointer(WeightsLSTM) == POINTER_INVALID)
return false;
}
if(WeightsLSTM.GetIndex() >= 0)
WeightsLSTM.BufferFree();
if(!WeightsLSTM.Load(file_handle))
return false;
if(!BackendBufferCreate(WeightsLSTM))
return false;
//---
if(CheckPointer(FirstMomentumLSTM) == POINTER_INVALID)
{
FirstMomentumLSTM = new CBufferDouble();
if(CheckPointer(FirstMomentumLSTM) == POINTER_INVALID)
return false;
}
if(FirstMomentumLSTM.GetIndex() >= 0)
FirstMomentumLSTM.BufferFree();
if(!FirstMomentumLSTM.Load(file_handle))
return false;
if(!BackendBufferCreate(FirstMomentumLSTM))
return false;
//---
if(CheckPointer(SecondMomentumLSTM) == POINTER_INVALID)
{
SecondMomentumLSTM = new CBufferDouble();
if(CheckPointer(SecondMomentumLSTM) == POINTER_INVALID)
return false;
}
if(SecondMomentumLSTM.GetIndex() >= 0)
SecondMomentumLSTM.BufferFree();
if(!SecondMomentumLSTM.Load(file_handle))
return false;
if(!BackendBufferCreate(SecondMomentumLSTM))
return false;
//---
if(CheckPointer(DeltaWeightsLSTM) == POINTER_INVALID)
{
DeltaWeightsLSTM = new CBufferDouble();
if(CheckPointer(DeltaWeightsLSTM) == POINTER_INVALID)
return false;
}
if(DeltaWeightsLSTM.GetIndex() >= 0)
DeltaWeightsLSTM.BufferFree();
if(!DeltaWeightsLSTM.Load(file_handle))
return false;
if(!BackendBufferCreate(DeltaWeightsLSTM))
return false;
//---
if(CheckPointer(Memory) == POINTER_INVALID)
{
Memory = new CBufferDouble();
if(CheckPointer(Memory) == POINTER_INVALID)
return false;
}
if(Memory.GetIndex() >= 0)
Memory.BufferFree();
if(!Memory.Load(file_handle))
return false;
if(!BackendBufferCreate(Memory))
return false;
//---
if(CheckPointer(WeightsGradient) == POINTER_INVALID)
{
WeightsGradient = new CBufferDouble();
if(CheckPointer(WeightsGradient) == POINTER_INVALID)
return false;
}
if(!WeightsGradient.BufferInit(total, 0))
return false;
if(!BackendBufferCreate(WeightsGradient))
return false;
//--- Per-timestep BPTT caches are scratch and never persisted - rebuild them from the loaded shape.
if(!AllocateSequenceCaches())
return false;
//---
return true;
}
#endif
Reference in a new issue Copy permalink