[Misc][Test] add e2e test for apply_top_k_top_p_custom kernel (#6348)

### What this PR does / why we need it?
Add e2e test case for apply_top_k_top_p_custom kernel and eliminate
chinese comments.

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

### How was this patch tested?
pytest passed.

- vLLM version: v0.14.1
- vLLM main:
dc917cceb8

---------

Signed-off-by: linfeng-yuan <1102311262@qq.com>

csrc/apply_top_k_top_p_custom/op_host/aclnn_apply_top_k_top_p_custom.cpp

@@ -38,7 +38,7 @@ static const int64_t EXPECTED_DIM_ONE = 1;
 static const int64_t EXPECTED_DIM_TWO = 2;
 static constexpr size_t DIM_ONE = 1;
 
-// 根据API定义，需要列出所能支持的所有dtype
+// According to the API definition, all supported dtypes must be enumerated.
 static const std::initializer_list<op::DataType> DTYPE_SUPPORT_LIST = {
     op::DataType::DT_FLOAT, op::DataType::DT_FLOAT16, op::DataType::DT_BF16};
 
@@ -57,7 +57,7 @@ static bool CheckNotNull(const aclTensor* logits, const aclTensor* p, const aclT
 
 static bool CheckDtypeValid(const aclTensor* logits, const aclTensor* p, const aclTensor *k, const aclTensor* out)
 {
-  // 检查数据类型是否在支持列表内
+  // Check whether the data type is within the supported list.
   OP_CHECK_DTYPE_NOT_SUPPORT(logits, DTYPE_SUPPORT_LIST, return false);
   if (p != nullptr) {
     OP_CHECK_DTYPE_NOT_SUPPORT(p, DTYPE_SUPPORT_LIST, return false);
@@ -67,7 +67,7 @@ static bool CheckDtypeValid(const aclTensor* logits, const aclTensor* p, const a
   }
   OP_CHECK_DTYPE_NOT_SUPPORT(out, DTYPE_SUPPORT_LIST, return false);
 
-  // 检查数据类型是否相同
+  // Check whether the data types are identical.
   if (p != nullptr) {
     OP_CHECK_DTYPE_NOT_MATCH(p, logits->GetDataType(), return false);
   }
@@ -121,17 +121,17 @@ static bool CheckFormatValid(const aclTensor* logits, const aclTensor* p, const
 
 static aclnnStatus CheckParams(const aclTensor* logits, const aclTensor* p, const aclTensor *k, const aclTensor* out)
 {
-  // 错误码等DFX方案细化后刷新，错误日志在check接口内打印
-  // 1. 检查参数是否为空指针
+  // Refresh after the DFX scheme for error codes, etc. is refined; error logs are printed inside the check interfaces.
+  // 1. Check whether any parameters are null pointers.
   CHECK_RET(CheckNotNull(logits, p, k, out), ACLNN_ERR_PARAM_NULLPTR);
 
-  // 2. 检查输入的数据类型是否在API支持的数据类型范围之内，需要根据api定义校验
+  // 2. Check whether the input data types are within the range supported by the API; validate according to the API definition.
   CHECK_RET(CheckDtypeValid(logits, p, k, out), ACLNN_ERR_PARAM_INVALID);
 
-  // 3. 检查shape是否满足约束
+  // 3. Check whether shapes satisfy the constraints.
   CHECK_RET(CheckShapeValid(logits, p, k, out), ACLNN_ERR_PARAM_INVALID);
 
-  // 4. 检查format是否满足约束
+  // 4. Check whether formats satisfy the constraints.
   CHECK_RET(CheckFormatValid(logits, p, k, out), ACLNN_ERR_PARAM_INVALID);
 
   return ACLNN_SUCCESS;
@@ -144,11 +144,11 @@ aclnnStatus aclnnApplyTopKTopPCustomGetWorkspaceSize(
 {
   OP_CHECK_COMM_INPUT(workspaceSize, executor);
   L2_DFX_PHASE_1(aclnnApplyTopKTopPCustom, DFX_IN(logits, p, k), DFX_OUT(out));
-  // 固定写法，创建OpExecutor
+  // Fixed boilerplate: create OpExecutor.
   auto uniqueExecutor = CREATE_EXECUTOR();
   CHECK_RET(uniqueExecutor.get() != nullptr, ACLNN_ERR_INNER_CREATE_EXECUTOR);
 
-  // 固定写法，参数检查
+  // Fixed boilerplate: parameter validation.
   auto ret = CheckParams(logits, p, k, out);
   CHECK_RET(ret == ACLNN_SUCCESS, ret);
   bool pIsEmpty = false;
@@ -160,12 +160,12 @@ aclnnStatus aclnnApplyTopKTopPCustomGetWorkspaceSize(
     kIsEmpty = k->IsEmpty();
   }
   if (logits->IsEmpty() || pIsEmpty || kIsEmpty) {
-    // 根据实际支持情况补充
+    // Supplement according to actual support status.
     *workspaceSize = 0;
     uniqueExecutor.ReleaseTo(executor);
     return ACLNN_SUCCESS;
   }
-  // 固定写法，将输入selfRef转换成连续的tensor
+  // Fixed boilerplate: convert the input selfRef to a contiguous tensor.
   auto logitsContiguous = l0op::Contiguous(logits, uniqueExecutor.get());
   CHECK_RET(logitsContiguous != nullptr, ACLNN_ERR_INNER_NULLPTR);
   const aclTensor* pContiguous = nullptr;
@@ -190,24 +190,23 @@ aclnnStatus aclnnApplyTopKTopPCustomGetWorkspaceSize(
     CHECK_RET(sortedIndices != nullptr, ACLNN_ERR_INNER_NULLPTR);
     auto res = l0op::ApplyTopKTopPCustom(sortedValue, sortedIndices, pContiguous, kContiguous, uniqueExecutor.get());
     CHECK_RET(res != nullptr, ACLNN_ERR_INNER_NULLPTR);
-    // 固定写法，将计算结果拷贝到输出out上，out可能是非连续的tensor
+    // Fixed boilerplate: copy the computed result to the output 'out'; 'out' may be a non-contiguous tensor.
     viewCopyResult = l0op::ViewCopy(res, out, uniqueExecutor.get());
   }
   CHECK_RET(viewCopyResult != nullptr, ACLNN_ERR_INNER_NULLPTR);
-  // 固定写法，获取计算过程中需要使用的workspace大小
+  // Fixed boilerplate: obtain the workspace size required during computation.
   *workspaceSize = uniqueExecutor->GetWorkspaceSize();
-  uniqueExecutor.ReleaseTo(executor);  // 需要把 uniqueExecutor持有executor转移给executor
+  uniqueExecutor.ReleaseTo(executor);  // Transfer ownership of the executor held by uniqueExecutor to executor.
   return ACLNN_SUCCESS;
 }
 
 aclnnStatus aclnnApplyTopKTopPCustom(void* workspace, uint64_t workspaceSize, aclOpExecutor* executor, aclrtStream stream)
 {
    L2_DFX_PHASE_2(aclnnApplyTopKTopPCustom);
-  // 固定写法，调用框架能力，完成计算
+  // Fixed boilerplate: invoke framework capability to complete the computation.
   return CommonOpExecutorRun(workspace, workspaceSize, executor, stream);
 }
 
 #ifdef __cplusplus
 }
 #endif
-