syknnue5 spec & training

README.md

@@ -92,8 +92,8 @@ Sykora is tested by [CCRL](https://computerchess.org.uk/ccrl/404/). Current entr
 <summary><b>Evaluation</b>: NNUE (default) with classical fallback</summary>
 
 - **NNUE evaluation** (default, embedded in binary):
-  - `SYKNNUE3` and `SYKNNUE4` network loading
-  - Legacy `768 -> Nx2 -> 1` and mirrored king-bucketed sparse-input nets
+  - `SYKNNUE5` material-output-bucket nets, with compatibility for the current embedded net
+  - Mirrored king-bucketed sparse-input nets
   - SCReLU activation with incremental accumulators during search
   - Trained on high-depth self-play data via the Bullet trainer
   - King-bucket training path via `nnue/bullet_repo/examples/sykora_bucketed.rs`
@@ -260,7 +260,7 @@ See `history/README.md` for folder schema and the archived workflow.
 
 ## NNUE
 
-Sykora supports both legacy `768 -> Nx2 -> 1` nets and mirrored king-bucketed nets with dual-perspective accumulator updates and SCReLU activation. The engine can load both `SYKNNUE3` and `SYKNNUE4` files.
+Sykora's current training target is `SYKNNUE5`: mirrored king-bucketed sparse inputs with dual-perspective accumulator updates, SCReLU activation, and material-count output buckets. The engine still keeps loader compatibility for the current embedded net until `src/net.sknnue` is replaced.
 
 ### Runtime
 
@@ -270,7 +270,7 @@ Sykora supports both legacy `768 -> Nx2 -> 1` nets and mirrored king-bucketed ne
 - To use a different net, set `EvalFile` to the path of an external `.sknnue` file.
 - `NnueScale` scales the NNUE score before it is fed into the search.
 
-For exact file-format details, see [specs/syknnue4_spec.md](specs/syknnue4_spec.md) and `src/nnue.zig`.
+For exact file-format details, see [specs/syknnue5_spec.md](specs/syknnue5_spec.md) and `src/nnue.zig`.
 
 ### Training Pipeline
 
@@ -284,8 +284,9 @@ python utils/nnue/bullet/train_cuda_longrun.py \
   --data-format binpack \
   --bullet-repo nnue/bullet_repo \
   --output-root nnue/models/bullet \
-  --network-format syk3 \
-  --hidden 256 --end-superbatch 320 --threads 8
+  --network-format syk5 \
+  --bucket-layout sykora16 \
+  --hidden 512 --end-superbatch 320 --threads 8
 ```
 
 **Using BulletFormat .data files:**
@@ -295,8 +296,9 @@ python utils/nnue/bullet/train_cuda_longrun.py \
   --dataset nnue/data/bullet/train/train_main.data \
   --bullet-repo nnue/bullet_repo \
   --output-root nnue/models/bullet \
-  --network-format syk3 \
-  --hidden 256 --end-superbatch 320 --threads 8
+  --network-format syk5 \
+  --bucket-layout sykora16 \
+  --hidden 512 --end-superbatch 320 --threads 8
 ```
 
 **Multiple datasets** can be passed space-separated:
@@ -319,16 +321,15 @@ python utils/nnue/bullet/train_cuda_longrun.py \
   ...
 ```
 
-**Training a `SYKNNUE4` baseline:**
+**Training a `SYKNNUE5` material-output-bucket net:**
 
 ```bash
 python utils/nnue/bullet/train_cuda_longrun.py \
   --dataset data/training.binpack \
   --data-format binpack \
-  --network-format syk4 \
+  --network-format syk5 \
   --bucket-layout sykora16 \
-  --hidden 1536 \
-  --dense-l1 16 --dense-l2 32 \
+  --hidden 512 \
   --end-superbatch 320 --threads 8
 ```
 
@@ -343,15 +344,15 @@ Sykora can generate its own training data via the `gensfen` command:
 
 ### Exporting a Trained Net
 
-Export a `SYKNNUE4` checkpoint:
+Export a `SYKNNUE5` checkpoint:
 
 ```bash
 python utils/nnue/bullet/checkpoint_raw_to_npz.py \
   --input nnue/models/bullet/<run_id>/checkpoints/<checkpoint> \
-  --output checkpoint_syk4.npz
+  --output checkpoint_syk5.npz
 
-python utils/nnue/bullet/export_npz_to_syk4.py \
-  --input checkpoint_syk4.npz \
+python utils/nnue/bullet/export_npz_to_syk5.py \
+  --input checkpoint_syk5.npz \
   --output-net output.sknnue
 ```
 
@@ -379,7 +380,7 @@ python utils/nnue/bullet/gate_checkpoints.py \
 
 This gate now evaluates recent checkpoints by selfplay only. STS is intentionally not part of the checkpoint promotion path.
 
-SYKNNUE4 design spec: `specs/syknnue4_spec.md`.
+SYKNNUE5 design spec: `specs/syknnue5_spec.md`.
 
 ## Contributing
 

launch_training.ps1

@@ -1,4 +1,4 @@
-# Sykora NNUE V4 Training Launch Script
+# Sykora NNUE V5 Training Launch Script
 # Run from project root: .\launch_training.ps1
 #
 # Dataset: T80-2023 (jun-dec) + T80-2024 (jan-jun) .min-v2.v6 binpacks
@@ -60,19 +60,20 @@ foreach ($bp in $binpacks) {
 }
 
 # --- Training Parameters ---
-# SYKNNUE4 baseline:
-# mirrored king buckets (sykora16) -> FT 768 -> shared linear output
-$networkFormat = "syk4"
+# SYKNNUE5:
+# mirrored king buckets (sykora16) -> FT 512 -> 8 material-count output heads
+$networkFormat = "syk5"
 $bucketLayout = "sykora16"
-$hidden = 768
+$hidden = 512
+$outputBuckets = 8
 $endSuperbatch = 600
 $lrStart = 0.001
 $wdl = 0.25
 $saveRate = 10
 $threads = 8
 
 Write-Host "============================================"
-Write-Host "  Sykora NNUE V4 Training (RTX 4070 Ti SUPER)"
+Write-Host "  Sykora NNUE V5 Training (RTX 4070 Ti SUPER)"
 Write-Host "============================================"
 Write-Host "Data:          T80-2023/2024 filtered set"
 Write-Host "Filtering:     .min-v2.v6 on T80 inputs"
@@ -81,7 +82,8 @@ Write-Host "Format:        binpack (sfbinpack)"
 Write-Host "Net format:    $networkFormat"
 Write-Host "Bucket layout: $bucketLayout"
 Write-Host "FT hidden:     $hidden"
-Write-Host "Dense head:    linear $($hidden * 2) -> 1"
+Write-Host "Output heads:  $outputBuckets material-count buckets"
+Write-Host "Dense head:    bucketed linear $($hidden * 2) -> 1"
 Write-Host "Superbatches:  1 -> $endSuperbatch"
 Write-Host "Save rate:     every $saveRate superbatches"
 Write-Host "Threads:       $threads"
@@ -104,6 +106,7 @@ python "$PSScriptRoot\utils\nnue\bullet\train_cuda_longrun.py" `
     --network-format $networkFormat `
     --bucket-layout $bucketLayout `
     --hidden $hidden `
+    --output-buckets $outputBuckets `
     --end-superbatch $endSuperbatch `
     --save-rate $saveRate `
     --threads $threads `