ops: embedding/reshape/transpose/split-merge-heads fwd+bwd

Phase T5 structural ops on top of the T4 set, needed to assemble the
tiny transformer:
- embedding: gather rows by I32 ids (CUDA kernel) / scatter-add backward
  (atomic, so repeated ids accumulate). csrc/ops/model.cu + ffi.
- reshape: contiguous metadata-only view (Tensor::reshape), no kernel.
- transpose_3d01: [a,b,c]->[b,a,c] for the multi-head layout (kernel).
- autograd nodes: embedding/reshape/transpose_3d01/transpose_2d, plus
  split_heads (->Vec<Var>) / merge_heads for per-head attention.
- tape: Var::zero_grad + set_value so a hand-written GD step can update
  params and clear grads between steps.

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

crates/xtrain-autodiff/src/tape.rs

@@ -68,6 +68,19 @@ impl Var {
         self.0.borrow().grad.clone()
     }
 
+    /// Clear the accumulated gradient. Call on every parameter between training
+    /// steps so the next `backward` accumulates from zero (grads SUM otherwise).
+    pub fn zero_grad(&self) {
+        self.0.borrow_mut().grad = None;
+    }
+
+    /// Overwrite this node's value tensor in place. Used by the optimizer to
+    /// apply a parameter update (`p ← p − lr·grad`) while keeping the leaf's
+    /// identity stable across steps.
+    pub fn set_value(&self, value: Tensor) {
+        self.0.borrow_mut().value = value;
+    }
+
     /// Pointer identity, used to dedup nodes during the topological sort.
     fn id(&self) -> *const RefCell<VarNode> {
         Rc::as_ptr(&self.0)