THE OPTIMIZER ("0.1% an hour per agent", 0 of 39 passes in 78 min,
12 agents): the tester fires OnTimer on SIMULATED time, so the live
chart's 500ms EventSetMillisecondTimer over a 2016-2026 pass is ~600
MILLION OnTimer calls - each walking 4x PollTraining, the vote
readout's string build, the overlay advance and the deployed census.
None of it serves an inference-only pass: training never runs, per-bar
inference is driven by OnTickHandler off the tick stream, the risk
budget re-checks in OnTick, and there is no chart to keep fresh.
StepSetTimer now arms EventSetTimer(3600) in tester/optimizer/forward
(~2,600 calls per pass) and keeps the 500ms timer for live charts.
Plus a TESTER PASS SELF-PROFILE: per-tick buckets (pre / Expert.OnTick
/ journal) and the timer total, printed once at the pass's OnDeinit -
so if a pass is still slow it names its own consumer instead of being
diagnosed from outside.
OFFLOAD (operator: "as much calculation as possible to DLL/OpenCL"):
batch norm was the ONE stage still host-side on the DLL tier - the
device path was OpenCL-only, so every sample crossed the bus twice per
BN layer and normalized in interpreted MQL5 (and every model runs
batchnorm ON). Four new exports mirror AI\Network.cl's BatchNorm*
kernels 1:1 in DOUBLE precision (closer to the host reference than
the float OpenCL kernels): forward with running stats + frozen flag,
hidden gradient with the clamp derivative, gamma/beta accumulate, and
the batch-mean apply (no weight decay, moments-before-skip ordering,
sqrt-stored v). BnDeviceEligible/EnsureBnDeviceBuffers/all four
Dispatch* now route by backend; the EXISTING in-situ self-checks
(host-vs-device on the first real sample, latch-off + host fallback on
mismatch) verify the DLL kernels exactly as they verified OpenCL ones.
batch_accum_check regression: ALL CHECKS PASSED on the rebuilt DLL.
Same deployment coupling as bd46374: the .ex5 imports the new exports
- copy DirectML\WarriorCPU.dll into MQL5\Libraries (terminal closed)
together with the new .ex5, and re-copy it to the tester agents (or
just run DirectML\build_cpu.bat once with everything closed - it
deploys to every discovered Libraries folder).
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
"Hundreds of times slower than a regular EA" decomposed into two
multiplied factors, both measured:
1. THE OPTIMIZER STEP RAN IN INTERPRETED MQL5. The CPU tier shipped
the F4 accumulate exports with deliberately no matching apply
(WarriorCPU.h said so), so on the DLL backend - this box - every
TRAIN_BATCH_SIZE=8 batch fell to the host loop in ApplyAccumToBlock:
a per-weight MQL5 pass through CBufferDouble.At()/Update() plus four
full weight-matrix BufferRead/Write round trips. The 2026-07-26
profile had already shown the per-sample Adam step at 81% of ALL
runtime (feedForward: 8%; feature building: 0.35%) - sqrt+divide
per weight vs one multiply-add; moving it into MQL5 made it worse.
New CPU_ApplyAccumAdam / CPU_ApplyAccumMomentum: one element-wise
ParallelFor takes the batch-mean step and zeroes the accumulator
DLL-side, generic over any flat block (dense/conv/LSTM/batch-norm -
all apply paths funnel through ApplyAccumToBlock, which now tries
the DLL first, with the same one-warning failure latch as the
OpenCL fast path). Math is the shipped step to the last clamp:
sqrt-stored v, ClampDelta, AdamW decay, ClampWeight.
batch_accum_check extended (check 6) and ALL PASS: apply == host
reference at B=8/B=4, accumulator zeroed, and B=1 accumulate+apply
== the unbatched Adam kernel BIT-EXACTLY (kernel-vs-kernel, no
transcription). DLL rebuilt with the shipped /fp:fast recipe.
2. A 24% DUTY CYCLE. Train sliced 120ms per 500ms timer period
(30ms/member x4), leaving the chart thread idle 76% of the time.
Now 300ms total (75ms/member): ~60% duty, ~2.5x, click latency
bounded at ~300ms while training runs - between the fully-reactive
120 and the documented "sticky drag" 480.
DEPLOYMENT COUPLING: the new .ex5 #imports the new exports, so it will
NOT LOAD against the old WarriorCPU.dll ("cannot find function"). Copy
DirectML\WarriorCPU.dll into MQL5\Libraries (terminal closed) in the
same step as deploying the new .ex5.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Three backends left, as the operator specified: OpenCL, the CPU DLL,
and pure MQL5. CDirectMLMy was a two-tier wrapper (GPU via
WarriorDML.dll, CPU via WarriorCPU.dll) whose name only ever named the
tier being removed here; the CPU DLL tier - the one actually used on
the training machine (no OpenCL, no DirectML) - is untouched.
AI/NeuronDirectML.mqh -> AI/ComputeDll.mqh: dropped the DML_* #import
block and COMPUTE_TIER_GPU (checked first that nothing persists the
enum value and only one external site reads .Tier() - safe), collapsed
every tier==CPU?CPU_x():DML_x() ternary to a straight CPU_x() call.
Renamed CDirectMLMy->CComputeDll, InitDirectML()->InitComputeDll(),
member directml/DirectML->computeDll/ComputeDll across every AI/ file
that touched a neuron/net backend plus Topology.mqh/OnlineLearning.mqh.
NetBuild.mqh's InitComputeDll also lost the dead D3D12 error-code
switch and the now-impossible GPU-tier log branch.
Verified via per-file brace-balance diff against HEAD and a whole-repo
grep for every removed symbol (CDirectMLMy/InitDirectML/
COMPUTE_TIER_GPU/DML_*) - the only surviving hit is an intentional
historical-note comment in the new file's header.
DirectML\WarriorDML.cpp/.h and its build scripts are now orphaned C++
source, left in place pending an operator decision. Architecture docs
(AI_NETWORK.md, Warrior_EA_System_Overview.md, etc.) still describe the
4-backend/GPU-tier shape and are not updated in this pass.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
