ops: differentiable autograd nodes + per-op grad-check tests

ops.rs wraps each Tensor op as a Var node with its backward closure (forward
caches captured by move). swiglu = mul(silu(gate), up); attention is composed
(matmul+scale+softmax+matmul), no fused kernel. tests/autograd.rs grad-checks
every op via the L=sum(W∘out) template, plus a fan-out grad-accumulation test
(dL/dx=4x) and an end-to-end composed-attention grad-check (dQ/dK/dV). Adds
xtrain-cuda dev-dep for device selection in tests.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

crates/xtrain-autodiff/Cargo.toml

@@ -5,3 +5,7 @@ edition.workspace = true
 
 [dependencies]
 xtrain-tensor = { path = "../xtrain-tensor" }
+
+[dev-dependencies]
+# Acceptance tests need device selection (set_device) to drive the GPU.
+xtrain-cuda = { path = "../xtrain-cuda" }