Implement the GPU backend the substrate was built for: Platform.detect() ->
cuda resolves real torch adapters + torch solver ops instead of the numpy
rungs, with the existing loops/policies/scheduler/training unchanged.
WRITTEN-NOT-RUN: this environment has no GPU/torch, so the torch code is
grounded in the verbatim real fastvideo APIs (DiT forward signature confirmed
from source) but cannot be executed/verified here. It is gated available=False
(CPU mini stays green; importing the backends never imports torch), with every
on-box confirm point marked `# BRINGUP` and an ordered checklist in
platform/backends/GPU_BRINGUP.md.
- torch_adapters.py: TorchWanDiT / TorchWanVAE / TorchT5Encoder wrap the real
module named by each card's load_id and bridge it to the mini's duck-typed
surface (numpy<->torch at the boundary; loop math stays numpy fp32). DiT
weight-surface (copy_from/blend_from/clone) for serving sync; mse_grad_step
raises (GPU training is a separate workstream).
- torch_kernels.py: flow_match_step / flow_sde_step as plain torch elementwise.
Grounded conclusion from the kernel audit: fastvideo-kernel ships NO fused
solver kernel (only attention/norm/quant primitives), so the cuda solver is
torch, registered at arch generic with an honest source string.
- torch_cuda.py: rewritten as lazy trampolines (torch imported only inside
builder/kernel bodies). Adds the missing vae + text_encoder cuda components
(they'd otherwise silently fall back to the toy) and corrects the dishonest
"fastvideo-kernel:flow_*" labels.
- ComponentSpec.checkpoint: the weights source for the torch adapter (risk A;
the one field the cards didn't carry). Empty on the CPU toys.
- 7 CPU-verifiable wiring tests: honest registration/sources, torch-free import,
cuda-resolves-real-cells-not-toy, build-fails-loudly-without-torch.
204 tests pass (CPU). The torch path needs a GPU box to verify (GPU_BRINGUP.md).
SolitaryThinker
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