add test cases

compiler/include/byteir/Conversion/Passes.td

@@ -289,7 +289,7 @@ def MhloToCat : Pass<"mhlo-to-cat", "func::FuncOp"> {
 // HloToTensor
 //===----------------------------------------------------------------------===//
 
-def ConvertHloToTensor : Pass<"hlo-to-tensor", "func::FuncOp"> {
+def ConvertHloToTensor : Pass<"convert-hlo-to-tensor", "func::FuncOp"> {
   let summary = "Convert hlo op to Tensor op.";
   let constructor = "mlir::createConvertHloToTensorPass()";
   let dependentDialects = [

compiler/test/Conversion/HloToTensor/scatter_to_insertslice.mlir

@@ -1,4 +1,4 @@
-// RUN: byteir-opt -hlo-to-tensor --canonicalize --split-input-file %s | FileCheck %s
+// RUN: byteir-opt -convert-hlo-to-tensor --canonicalize --split-input-file %s | FileCheck %s
 
 func.func @forward(%arg0: tensor<6x8x5xf32>, %arg1: tensor<6x1x5xf32>) -> tensor<6x8x5xf32> {
     %0 = mhlo.constant dense<0> : tensor<1x1xi64>

tests/numerical_test/mlir_tests/cpu_ops/scatter_insert_slice.mlir

@@ -0,0 +1,8 @@
+func.func @forward(%arg0: tensor<6x8x5xf32>, %arg1: tensor<6x1x5xf32>) -> tensor<6x8x5xf32> {
+    %c = stablehlo.constant dense<0> : tensor<1x1xi64>
+    %0 = "stablehlo.scatter"(%arg0, %c, %arg1) <{indices_are_sorted = false, scatter_dimension_numbers = #stablehlo.scatter<update_window_dims = [0, 2], inserted_window_dims = [1], scatter_dims_to_operand_dims = [1], index_vector_dim = 1>, unique_indices = false}> ({
+    ^bb0(%arg2: tensor<f32>, %arg3: tensor<f32>):
+      stablehlo.return %arg3 : tensor<f32>
+    }) : (tensor<6x8x5xf32>, tensor<1x1xi64>, tensor<6x1x5xf32>) -> tensor<6x8x5xf32>
+    return %0 : tensor<6x8x5xf32>
+}

tests/numerical_test/mlir_tests/ops/scatter_insert_slice.mlir

@@ -0,0 +1,8 @@
+func.func @forward(%arg0: tensor<6x8x5xf32>, %arg1: tensor<6x1x5xf32>) -> tensor<6x8x5xf32> {
+    %0 = mhlo.constant dense<0> : tensor<1x1xi64>
+    %1 = "mhlo.scatter"(%arg0, %0, %arg1) <{indices_are_sorted = false, scatter_dimension_numbers = #mhlo.scatter<update_window_dims = [0, 2], inserted_window_dims = [1], scatter_dims_to_operand_dims = [1], index_vector_dim = 1>, unique_indices = false}> ({
+    ^bb0(%arg2: tensor<f32>, %arg3: tensor<f32>):
+        mhlo.return %arg3 : tensor<f32>
+    }) : (tensor<6x8x5xf32>, tensor<1x1xi64>, tensor<6x1x5xf32>) -> tensor<6x8x5xf32>
+    return %1 : tensor<6x8x5xf32>
+}

tests/numerical_test/torch_e2e_testing/test_suite/basic.py

@@ -216,3 +216,16 @@ def forward(self, x0):
 @register_test_case(module_factory=lambda: ContiguousSliceModule())
 def ContiguousSliceModule_basic(module, tu: TestUtils):
     module.forward(tu.rand(3, 64))
+
+# ==============================================================================
+
+class InsertSliceScatterModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x):
+        return torch.ops.aten.slice_scatter(x, src, dim=1, start=0, end=1, step=1)
+
+@register_test_case(module_factory=lambda: InsertSliceScatterModule)
+def InsertSliceScatterModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(6, 8, 5), tu.rand(6, 1, 5))