提供支持更完整参数的gemm接口

[guoshengCS]

目前在math_function中gemm的接口如下：

// Support continuous memory now
// If transA = N, and transB = N
// Then matrixA: M * K, matrixB: K * N matrixC : M * N
// For more detailed info, please refer to
// http://www.netlib.org/lapack/explore-html/d4/de2/sgemm_8f.html
template <typename Place, typename T>
void gemm(const CBLAS_TRANSPOSE transA, const CBLAS_TRANSPOSE transB,
          const int M, const int N, const int K, const T alpha, const T* A,
          const T* B, const T beta, T* C, platform::DeviceContext* context);

在将Paddle目前GRU进行OP port过程中，目前有如下部分代码：

      BlasGemm<Device, T>::compute(false,
                                   false,
                                   batchSize,
                                   2 * frameSize,
                                   frameSize,
                                   1,
                                   value.prevOutValue,
                                   frameSize,
                                   value.gateWeight,
                                   frameSize * 2,
                                   1,
                                   value.gateValue,
                                   frameSize * 3);

这里用到了math_function gemm中封装限定了的lda、ldb和ldc这几个参数

template <>
void gemm<platform::CPUPlace, float>(const CBLAS_TRANSPOSE transA,
                                     const CBLAS_TRANSPOSE transB, const int M,
                                     const int N, const int K,
                                     const float alpha, const float* A,
                                     const float* B, const float beta, float* C,
                                     platform::DeviceContext* context) {
  int lda = (transA == CblasNoTrans) ? K : M;
  int ldb = (transB == CblasNoTrans) ? N : K;
  int ldc = N;
  cblas_sgemm(CblasRowMajor, transA, transB, M, N, K, alpha, A, lda, B, ldb,
              beta, C, ldc);
}

能否在math_function中加下gemm的带有完整所有参数的接口。