[Cherry-pick]bmm_transpose to v011dev (#3995)

### What this PR does / why we need it?
Add a custom op to acclerater the deepseek model. The fusion ops combine
the bmm and transpose together, which is applied to mla module.
Cherry-pick from this commtid c68ddc11ce

### Does this PR introduce _any_ user-facing change?
No

---------

Signed-off-by: hust17yixuan <303660421@qq.com>

csrc/batch_matmul_transpose/op_host/tiling/tiling_data.h

@@ -0,0 +1,90 @@
+#ifndef PP_MATMUL_TILING_DATA
+#define PP_MATMUL_TILING_DATA
+#include <cstdint>
+
+namespace pp_matmul {
+struct MatMul {
+    enum class MatMulType : uint32_t {
+        MATMUL_DEFAULT = 0,   // C = op(A) * op(B)
+        MATMUL_DEQUANT,       //
+        MATMUL_ACCUM_ATOMIC,  // C += op(A) * op(B)
+        MATMUL_WITH_BIAS,     // C = op(A) * op(B) + Bias, where Bias is a vector.
+        MATMUL_EIN_SUM
+    };
+    enum class QuantMode : uint32_t { PER_CHANNEL_SYMM = 0, PER_CHANNEL_ASYMM, PER_TOKEN_SYMM };
+};
+
+enum class TensorDType : uint32_t { TENSOR_DTYPE_FLOAT16 = 0, TENSOR_DTYPE_BF16 };
+
+enum class TensorFormat : uint32_t { TENSOR_FORMAT_ND = 0, TENSOR_FORMAT_NZ };
+
+struct MatMulInfo {
+    uint32_t batchSize{0};
+    uint32_t m{0};  // actual input m
+    uint32_t k{0};  // actual input k
+    uint32_t n{0};  // actual input n
+    TensorDType dtypeA{TensorDType::TENSOR_DTYPE_FLOAT16};
+    TensorDType dtypeB{TensorDType::TENSOR_DTYPE_FLOAT16};
+    TensorDType dtypeC{TensorDType::TENSOR_DTYPE_FLOAT16};
+    TensorFormat formatA{TensorFormat::TENSOR_FORMAT_ND};
+    TensorFormat formatB{TensorFormat::TENSOR_FORMAT_ND};
+    TensorFormat formatC{TensorFormat::TENSOR_FORMAT_ND};
+    MatMul::MatMulType mmType{MatMul::MatMulType::MATMUL_DEFAULT};
+    bool transA{0};    // false: 0, true: 1
+    bool transB{0};    // false: 0, true: 1
+    bool biasFlag{0};  // false: 0, true: 1
+    bool isInt8{0};    // false: 0, true: 1
+    float inDtype{0};
+    float outDtype{0};
+    MatMul::QuantMode quantMode{MatMul::QuantMode::PER_CHANNEL_SYMM};
+};
+
+struct OpShape {
+    uint32_t batchSize{0};
+    uint32_t m{0};
+    uint32_t k{0};
+    uint32_t n{0};
+    uint32_t m0{0};
+    uint32_t k0{0};
+    uint32_t n0{0};
+};
+
+struct HardwareInfo {
+    uint32_t coreNum{0};
+    uint32_t l2Size{0};
+    uint32_t l1Size{0};
+    uint32_t l0aSize{0};
+    uint32_t l0bSize{0};
+    uint32_t l0cSize{0};
+    uint32_t hbmBandWidth{0};
+    uint32_t l2BandWidth{0};
+
+    HardwareInfo();
+};
+
+#pragma pack(push, 1)
+struct PpMatmulTilingData {
+    OpShape opShape{};
+    uint32_t mLoop{1};
+    uint32_t kLoop{1};
+    uint32_t nLoop{1};
+    uint32_t coreLoop{1};
+    uint32_t swizzlCount{1};
+    uint32_t tilingKey{0};
+    uint32_t blockDim{1};
+    uint32_t swizzlDirect{0};
+    uint32_t splitk{0};
+    uint32_t enShuffleK{0};
+    uint32_t quantMode{0};
+
+    void SetBaseShape(uint32_t batchSize, uint32_t m, uint32_t k, uint32_t n);
+    void SetBaseOp(uint32_t coreNum, uint32_t mBase, uint32_t nBase, const MatMulInfo &mmInfo);
+    void SetTilingKey(const MatMulInfo &mmInfo, uint32_t swizzleDirect, uint32_t enSplitK);
+    uint32_t End(const MatMulInfo &mmInfo);
+};
+#pragma pack(pop)
+
+void GetPpMatmulTiling(const MatMulInfo &mmInfo, const HardwareInfo &hwInfo, uint32_t &blockDim,
+                       PpMatmulTilingData &tilingData);
+}  // namespace pp_matmul
+#endif