feat(cnn_v3): Phase 3 complete — WGSL U-Net inference shaders

5 compute shaders + cnn_v3/common snippet:
  enc0: Conv(20→4,3×3) + FiLM + ReLU             full-res
  enc1: AvgPool + Conv(4→8,3×3) + FiLM + ReLU    half-res
  bottleneck: AvgPool + Conv(8→8,1×1) + ReLU     quarter-res
  dec1: NearestUp + cat(enc1) + Conv(16→4) + FiLM half-res
  dec0: NearestUp + cat(enc0) + Conv(8→4) + FiLM + Sigmoid full-res

Parity rules: zero-pad conv, AvgPool down, NearestUp, FiLM after
conv+bias, skip=concat, OIHW weights+bias layout. Matches PyTorch
train_cnn_v3.py forward() exactly.

Registered in workspaces/main/assets.txt + src/effects/shaders.cc.
Weight layout + Params struct documented in cnn_v3/docs/HOWTO.md §7.

Next: Phase 4 — C++ CNNv3Effect + FiLM uniform upload.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

1 files changed, 57 insertions, 3 deletions

diff --git a/cnn_v3/docs/HOWTO.md b/cnn_v3/docs/HOWTO.md
index 88d4bbc..ad71f1f 100644
--- a/cnn_v3/docs/HOWTO.md
+++ b/cnn_v3/docs/HOWTO.md
@@ -200,13 +200,67 @@ The CNN v3 design requires exact parity between PyTorch, WGSL (HTML), and C++.
 | 1 — G-buffer (raster + pack) | ✅ Done | Integrated, 35/35 tests pass |
 | 1 — G-buffer (SDF + shadow passes) | TODO | Placeholder in place |
 | 2 — Training infrastructure | ✅ Done | blender_export.py, pack_*_sample.py |
-| 3 — WGSL U-Net shaders | TODO | enc/dec/bottleneck/FiLM |
+| 3 — WGSL U-Net shaders | ✅ Done | 5 compute shaders + cnn_v3/common snippet |
 | 4 — C++ CNNv3Effect | TODO | FiLM uniform upload |
 | 5 — Parity validation | TODO | Test vectors, ≤1/255 |
 
 ---
 
-## 7. Quick Troubleshooting
+## 7. CNN v3 Inference Shaders (Phase 3)
+
+Five compute passes, each a standalone WGSL shader using `#include "cnn_v3/common"`.
+The common snippet provides `get_w()` and `unpack_8ch()`.
+
+| Pass | Shader | Input(s) | Output | Dims |
+|------|--------|----------|--------|------|
+| enc0 | `cnn_v3_enc0.wgsl` | feat_tex0+feat_tex1 (20ch) | enc0_tex rgba16float (4ch) | full |
+| enc1 | `cnn_v3_enc1.wgsl` | enc0_tex (AvgPool2×2 inline) | enc1_tex rgba32uint (8ch) | ½ |
+| bottleneck | `cnn_v3_bottleneck.wgsl` | enc1_tex (AvgPool2×2 inline) | bottleneck_tex rgba32uint (8ch) | ¼ |
+| dec1 | `cnn_v3_dec1.wgsl` | bottleneck_tex + enc1_tex (skip) | dec1_tex rgba16float (4ch) | ½ |
+| dec0 | `cnn_v3_dec0.wgsl` | dec1_tex + enc0_tex (skip) | output_tex rgba16float (4ch) | full |
+
+**Parity rules baked into the shaders:**
+- Zero-padding (not clamp) at conv borders
+- AvgPool 2×2 for downsampling (exact, deterministic)
+- Nearest-neighbor for upsampling (integer `coord / 2`)
+- Skip connections: channel concatenation (not add)
+- FiLM applied after conv+bias, before ReLU: `max(0, γ·x + β)`
+- No batch norm at inference
+- Weight layout: OIHW (out × in × kH × kW), biases after conv weights
+
+**Params uniform per shader** (`group 0, binding 3`):
+```
+struct Params {
+    weight_offset: u32,  // f16 index into shared weights buffer
+    _pad: vec3u,
+    gamma: vec4f,        // FiLM γ  (enc1: gamma_lo+gamma_hi for 8ch)
+    beta:  vec4f,        // FiLM β  (enc1: beta_lo+beta_hi for 8ch)
+}
+```
+FiLM γ/β are computed CPU-side by the FiLM MLP (Phase 4) and uploaded each frame.
+
+**Weight offsets** (f16 units, including bias):
+| Layer | Weights | Bias | Total f16 |
+|-------|---------|------|-----------|
+| enc0  | 20×4×9=720 | +4 | 724 |
+| enc1  | 4×8×9=288  | +8 | 296 |
+| bottleneck | 8×8×1=64 | +8 | 72 |
+| dec1  | 16×4×9=576 | +4 | 580 |
+| dec0  | 8×4×9=288  | +4 | 292 |
+| **Total** | | | **2064 f16 = ~4 KB** |
+
+**Asset IDs** (registered in `workspaces/main/assets.txt` + `src/effects/shaders.cc`):
+`SHADER_CNN_V3_COMMON`, `SHADER_CNN_V3_ENC0`, `SHADER_CNN_V3_ENC1`,
+`SHADER_CNN_V3_BOTTLENECK`, `SHADER_CNN_V3_DEC1`, `SHADER_CNN_V3_DEC0`
+
+**C++ usage (Phase 4):**
+```cpp
+auto src = ShaderComposer::Get().Compose({"cnn_v3/common"}, raw_wgsl);
+```
+
+---
+
+## 8. Quick Troubleshooting
 
 **GBufferEffect renders nothing / albedo is black**
 - Check `set_scene()` was called before `render()`
@@ -227,7 +281,7 @@ The CNN v3 design requires exact parity between PyTorch, WGSL (HTML), and C++.
 
 ---
 
-## See Also
+## 9. See Also
 
 - `cnn_v3/docs/CNN_V3.md` — Full architecture design (U-Net, FiLM, feature layout)
 - `doc/EFFECT_WORKFLOW.md` — General effect integration guide