under test, not sure no errors

ex_engine/xllm_kernels/npu/npu_causal_conv1d.cpp

@@ -0,0 +1,59 @@
+/* Copyright 2026 The xLLM Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    https://github.com/jd-opensource/xllm/blob/main/LICENSE
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "core/kernels/npu/aclnn/pytorch_npu_helper.hpp"
+#include "core/kernels/npu/utils.h"
+#include "core/kernels/npu/xllm_ops/xllm_ops_api.h"
+
+namespace xllm::kernel::npu {
+
+torch::Tensor causal_conv1d(const torch::Tensor& x,
+                            const torch::Tensor& weight,
+                            const torch::Tensor& conv_state,
+                            const std::optional<torch::Tensor>& bias_opt,
+                            const torch::IntArrayRef query_start_loc_opt,
+                            const torch::IntArrayRef cache_indices_opt,
+                            const torch::IntArrayRef initial_state_mode_opt,
+                            const torch::IntArrayRef num_accepted_tokens_opt,
+                            int64_t activation_mode,
+                            int64_t pad_slot_id,
+                            int64_t run_mode) {
+  check_tensor(x, "x", "causal_conv1d");
+  check_tensor(weight, "weight", "causal_conv1d");
+  check_tensor(conv_state, "conv_state", "causal_conv1d");
+
+  c10::optional<torch::Tensor> bias_tensor = c10::nullopt;
+  if (bias_opt.has_value() && bias_opt.value().defined()) {
+    bias_tensor = bias_opt.value();
+  }
+
+  torch::Tensor output = torch::empty(x.sizes(), x.options());
+  EXEC_NPU_CMD(aclnnCausalConv1d,
+               x,
+               weight,
+               bias_tensor,
+               conv_state,
+               query_start_loc_opt,
+               cache_indices_opt,
+               initial_state_mode_opt,
+               num_accepted_tokens_opt,
+               activation_mode,
+               pad_slot_id,
+               run_mode,
+               output);
+  return output;
+}
+
+}  // namespace xllm::kernel::npu

ex_engine/xllm_kernels/npu/npu_recurrent_gated_delta_rule.cpp

@@ -0,0 +1,83 @@
+/* Copyright 2026 The xLLM Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    https://github.com/jd-opensource/xllm/blob/main/LICENSE
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include <glog/logging.h>
+
+#include "core/kernels/npu/aclnn/pytorch_npu_helper.hpp"
+#include "core/kernels/npu/npu_ops_api.h"
+#include "core/kernels/npu/utils.h"
+
+namespace {
+
+c10::optional<torch::Tensor> to_c10_optional_tensor(
+    const std::optional<torch::Tensor>& tensor_opt) {
+  if (tensor_opt.has_value() && tensor_opt.value().defined()) {
+    return tensor_opt.value();
+  }
+  return c10::nullopt;
+}
+
+}  // namespace
+
+namespace xllm::kernel::npu {
+
+torch::Tensor npu_recurrent_gated_delta_rule(
+    const torch::Tensor& query,
+    const torch::Tensor& key,
+    const torch::Tensor& value,
+    torch::Tensor& state,
+    const std::optional<torch::Tensor>& beta,
+    const std::optional<double> scale,
+    const std::optional<torch::Tensor>& actual_seq_lengths,
+    const std::optional<torch::Tensor>& ssm_state_indices,
+    const std::optional<torch::Tensor>& num_accepted_tokens,
+    const std::optional<torch::Tensor>& g,
+    const std::optional<torch::Tensor>& gk) {
+  check_tensor(query, "query", "recurrent_gated_delta_rule");
+  check_tensor(key, "key", "recurrent_gated_delta_rule");
+  check_tensor(value, "value", "recurrent_gated_delta_rule");
+  check_tensor(state, "state", "recurrent_gated_delta_rule");
+  CHECK(scale.has_value())
+      << "recurrent_gated_delta_rule requires a valid scale value";
+
+  c10::optional<torch::Tensor> beta_tensor = to_c10_optional_tensor(beta);
+  c10::optional<torch::Tensor> actual_seq_lengths_tensor =
+      to_c10_optional_tensor(actual_seq_lengths);
+  c10::optional<torch::Tensor> ssm_state_indices_tensor =
+      to_c10_optional_tensor(ssm_state_indices);
+  c10::optional<torch::Tensor> num_accepted_tokens_tensor =
+      to_c10_optional_tensor(num_accepted_tokens);
+  c10::optional<torch::Tensor> g_tensor = to_c10_optional_tensor(g);
+  c10::optional<torch::Tensor> gk_tensor = to_c10_optional_tensor(gk);
+  float scale_value = static_cast<float>(scale.value());
+  torch::Tensor output = torch::empty_like(value);
+
+  EXEC_NPU_CMD(aclnnRecurrentGatedDeltaRule,
+               query,
+               key,
+               value,
+               beta_tensor,
+               state,
+               actual_seq_lengths_tensor,
+               ssm_state_indices_tensor,
+               g_tensor,
+               gk_tensor,
+               num_accepted_tokens_tensor,
+               scale_value,
+               output);
+  return output;
+}
+
+}  // namespace xllm::kernel::npu