[NVIDIA XLA] Fix Fp32 biasAdd fusion for FP8 cublasLt matmul
@reedwm  Sorry for informing late. We decided not to cast F32->BF16 directly by XLA since it breaks what we previously did. Instead, our proposal is to only fuse the following pattern:
y_fp32 = matmul(a, b) + broadcast(cast(cast(bias, bf16), fp32))  ---> y_fp32 = cublas-custom-call(a, b, cast(bias, bf16))

 Should I not merge this PR then? Why does casting F32->BF16 break what you previously did? It seems weird to require the user to cast the bias to bf16 then back to f32. Yes, please don't merge this PR. User will not normally do that.  Casting the bias to bf16 then back to f32 is only for FP8 purpose.
Cast Fp32 vector bias to Bf16 to be compatible with  cuBLASLT Fp8 support surface. @reedwm @philipphack 
Once FuseVectorBiasAdd fails, will not try FuseMatrixBiasAdd since matrix bias add logic is implemented in CreateF8CustomCall. And this is only specific to F8 matmul. Open to future changes.