cuda: bf16 cuBLAS GemmEx (16BF in/out, fp32 accum) + cast kernels

Add the bf16 compute primitives for T12 mixed precision:
- DType::BF16 (half::bf16 as TensorDType), 2 bytes.
- cublasGemmEx / cublasGemmStridedBatchedEx FFI + CUDA_R_16BF /
  CUBLAS_COMPUTE_32F constants (values per xserv gemm.rs).
- cublas::gemm_ex / gemm_ex_strided_batched: same row-major⟺col-major
  transpose algebra as sgemm, bf16 in/out, fp32 accumulation.
- csrc/ops/cast.cu: f32<->bf16 cast + bf16 elementwise (add/mul/scale/
  silu(+dx)/add_bias/sum_rows), each load->fp32->compute->store bf16.

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

crates/xtrain-tensor/src/dtype.rs

@@ -1,12 +1,16 @@
 //! Tensor data types.
 //!
-//! T2 only needs `F32`, but the enum + `TensorDType` trait are structured so
-//! half-precision types (F16/BF16) can be added later (T7 mixed precision)
-//! without touching call sites.
+//! T2 only needs `F32`; `BF16` was added in T12 for mixed-precision training
+//! (bf16 linears / activations, fp32 master weights — see
+//! `docs/11-bf16-mixed-precision.md`). The enum + `TensorDType` trait keep call
+//! sites dtype-polymorphic.
 
 #[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
 pub enum DType {
     F32,
+    /// bfloat16: 1 sign / 8 exponent / 7 mantissa. Same exponent range as f32
+    /// (so no loss scaling needed), ~2-3 decimal digits. The T12 AMP compute type.
+    BF16,
     /// 32-bit signed integers. Used for cross-entropy targets (token ids).
     I32,
 }
@@ -15,6 +19,7 @@ impl DType {
     pub fn size_bytes(self) -> usize {
         match self {
             DType::F32 => 4,
+            DType::BF16 => 2,
             DType::I32 => 4,
         }
     }
@@ -22,6 +27,7 @@ impl DType {
     pub fn name(self) -> &'static str {
         match self {
             DType::F32 => "f32",
+            DType::BF16 => "bf16",
             DType::I32 => "i32",
         }
     }
@@ -50,6 +56,16 @@ impl TensorDType for f32 {
     }
 }
 
+impl TensorDType for half::bf16 {
+    const DTYPE: DType = DType::BF16;
+    fn to_f64(self) -> f64 {
+        self.to_f64()
+    }
+    fn from_f64(v: f64) -> Self {
+        half::bf16::from_f64(v)
+    }
+}
+
 impl TensorDType for i32 {
     const DTYPE: DType = DType::I32;
     fn to_f64(self) -> f64 {