Support WMMA layout in TritonAMDGPUAccelerateMatmulPass

-Introduce WmmaEncodingAttr for WMMA output
-Introduce BlockedToWMMA rewrite pattern in TritonAMDGPUAccelerateMatmulPass
-Provide a flag tho check if wmma instructions are supported by target

Signed-off-by: joviliast <iveselov.nn@gmail.com>

python/src/triton.cc

@@ -1871,9 +1871,9 @@ void init_triton_ir(py::module &&m) {
                  mlir::createTritonGPUAccelerateMatmulPass(computeCapability));
            })
       .def("add_tritonamdgpu_accelerate_matmul_pass",
-           [](mlir::PassManager &self, int tensorCoreVersion, int instrSize) {
+           [](mlir::PassManager &self, const std::string archGenName, int instrSize) {
              self.addPass(mlir::createTritonAMDGPUAccelerateMatmulPass(
-                 tensorCoreVersion, instrSize));
+                 archGenName, instrSize));
            })
       .def("add_tritongpu_optimize_dot_operands_pass",
            [](mlir::PassManager &self) {

python/triton/compiler/compiler.py

@@ -117,9 +117,9 @@ def optimize_ttgir(mod, num_stages, num_warps, num_ctas, target, cluster_info, e
         pm.add_tritongpu_accelerate_matmul_pass(capability)
     # TODO change interface of accelerate_matmul_pass
     if is_hip():
-        matrix_core_version = target["matrix_core_version"]
+        gfx_arch = target["gfx_arch"]
         matrix_inst_size = matrix_inst_type
-        pm.add_tritonamdgpu_accelerate_matmul_pass(matrix_core_version, matrix_inst_size)
+        pm.add_tritonamdgpu_accelerate_matmul_pass(gfx_arch, matrix_inst_size)
     pm.add_tritongpu_remove_layout_conversions_pass()
     if optimize_epilogue:
         pm.add_tritongpu_optimize_epilogue_pass()