Add rsqrt lowering from standard to NVVM

[pifon2a]

Thank you!

mlir/lib/Conversion/GPUToNVVM/LowerGpuOpsToNVVMOps.cpp

void mlir::populateGpuToNVVMConversionPatterns(
   patterns.insert<OpToFuncCallLowering<FloorFOp>>(converter, "__nv_floorf",
                                                   "__nv_floor");
   patterns.insert<OpToFuncCallLowering<LogOp>>(converter, "__nv_logf",
                                                "__nv_log");
   patterns.insert<OpToFuncCallLowering<Log10Op>>(converter, "__nv_log10f",
                                                  "__nv_log10");
   patterns.insert<OpToFuncCallLowering<Log2Op>>(converter, "__nv_log2f",
                                                 "__nv_log2");
+  patterns.insert<OpToFuncCallLowering<RsqrtOp>>(converter, "__nv_rsqrtf",
+                                                 "__nv_rsqrt");
   patterns.insert<OpToFuncCallLowering<SinOp>>(converter, "__nv_sinf",
                                                "__nv_sin");
   patterns.insert<OpToFuncCallLowering<TanhOp>>(converter, "__nv_tanhf",
                                                 "__nv_tanh");
 }
 
 std::unique_ptr<OperationPass<gpu::GPUModuleOp>>
 mlir::createLowerGpuOpsToNVVMOpsPass(unsigned indexBitwidth) {
   return std::make_unique<LowerGpuOpsToNVVMOpsPass>(indexBitwidth);
 }

mlir/test/Conversion/GPUToNVVM/gpu-to-nvvm.mlir

func @gpu_tanh(%arg_f16 : f16, %arg_f32 : f32, %arg_f64 : f64) -> (f16, f32, f64) {
     %result64 = std.tanh %arg_f64 : f64
     // CHECK: llvm.call @__nv_tanh(%{{.*}}) : (!llvm.double) -> !llvm.double
     std.return %result16, %result32, %result64 : f16, f32, f64
   }
 }
 
 // -----
 
+gpu.module @test_module {
+  // CHECK: llvm.func @__nv_rsqrtf(!llvm.float) -> !llvm.float
+  // CHECK: llvm.func @__nv_rsqrt(!llvm.double) -> !llvm.double
+  // CHECK-LABEL: func @gpu_rsqrt
+  func @gpu_rsqrt(%arg_f16 : f16, %arg_f32 : f32, %arg_f64 : f64)
+      -> (f16, f32, f64) {
+    %result16 = std.rsqrt %arg_f16 : f16
+    // CHECK: llvm.fpext %{{.*}} : !llvm.half to !llvm.float
+    // CHECK-NEXT: llvm.call @__nv_rsqrtf(%{{.*}}) : (!llvm.float) -> !llvm.float
+    // CHECK-NEXT: llvm.fptrunc %{{.*}} : !llvm.float to !llvm.half
+    %result32 = std.rsqrt %arg_f32 : f32
+    // CHECK: llvm.call @__nv_rsqrtf(%{{.*}}) : (!llvm.float) -> !llvm.float
+    %result64 = std.rsqrt %arg_f64 : f64
+    // CHECK: llvm.call @__nv_rsqrt(%{{.*}}) : (!llvm.double) -> !llvm.double
+    std.return %result16, %result32, %result64 : f16, f32, f64
+  }
+}
+
+// -----
+
 // Test that we handled properly operation with SymbolTable other than module op
 gpu.module @test_module {
   "test.symbol_scope"() ({
   // CHECK: test.symbol_scope
   // CHECK: llvm.func @__nv_expf(!llvm.float) -> !llvm.float
   // CHECK: llvm.func @__nv_exp(!llvm.double) -> !llvm.double
   // CHECK-LABEL: func @gpu_exp
     func @gpu_exp(%arg_f32 : f32, %arg_f64 : f64) -> (f32, f64) {