feat: implement chunked prefill and prefix cache for Qwen3 MoE.

Author: yingxudeng

CMakeLists.txt

@@ -32,20 +32,20 @@ if(USE_NPU)
     if(DEVICE_TYPE STREQUAL "USE_A3")
         message("downloading a3 arm xllm kernels")
         file(DOWNLOAD 
-            "https://9n-das-tools.s3.cn-north-1.jdcloud-oss.com/xllm-ai/xllm_kernels/0.7.0/xllm_kernels-1.3.3-Linux.a3.arm.rpm"
+            "https://9n-das-tools.s3.cn-north-1.jdcloud-oss.com/xllm-ai/xllm_kernels/0.7.0/xllm_kernels-1.3.4-Linux.a3.arm.rpm"
             "${CMAKE_BINARY_DIR}/xllm_kernels.rpm"
         )
     else()  
       if(DEVICE_ARCH STREQUAL "ARM")
           message("downloading a2 arm xllm_kernels")
           file(DOWNLOAD 
-              "https://9n-das-tools.s3.cn-north-1.jdcloud-oss.com/xllm-ai/xllm_kernels/0.7.0/xllm_kernels-1.3.3-Linux.a2.arm.rpm"
+              "https://9n-das-tools.s3.cn-north-1.jdcloud-oss.com/xllm-ai/xllm_kernels/0.7.0/xllm_kernels-1.3.4-Linux.a2.arm.rpm"
               "${CMAKE_BINARY_DIR}/xllm_kernels.rpm"
           )
       else()
           message("downloading a2 x86 xllm_kernels")
           file(DOWNLOAD 
-              "https://9n-das-tools.s3.cn-north-1.jdcloud-oss.com/xllm-ai/xllm_kernels/0.7.0/xllm_kernels-1.3.3-Linux.a2.x86.rpm"
+              "https://9n-das-tools.s3.cn-north-1.jdcloud-oss.com/xllm-ai/xllm_kernels/0.7.0/xllm_kernels-1.3.4-Linux.a2.x86.rpm"
               "${CMAKE_BINARY_DIR}/xllm_kernels.rpm"
           )
       endif()

xllm/core/layers/npu/npu_qwen3_moe_decoder_layer_impl.cpp

@@ -317,6 +317,8 @@ void NpuQwen3MoeDecoderLayerImpl::initialize_basic_parameters(
 
   param.mlpLinearTransposeType = {-1, -1, -1, -1};
 
+  param.enableSplitFuse =
+      (FLAGS_enable_chunked_prefill || FLAGS_enable_prefix_cache) && is_prefill;
   if (quantize_type_.empty()) {
     param.moeLinearTransposeType = std::vector<int>{1, 1, -1, 1};
   } else {
@@ -865,7 +867,9 @@ torch::Tensor NpuQwen3MoeDecoderLayerImpl::forward(
     std::atomic<bool>* event_flag,
     int node_id) {
   atb::Status st;
-  if (input_params.global_empty_kv_cache) {
+  bool is_prefill = input_params.decode_seq_range.second !=
+                    input_params.q_seq_lens.size(0) - 1;
+  if (is_prefill) {
     build_node_variant_pack(prefill_node_,
                             x,
                             cos_pos,
@@ -968,6 +972,14 @@ void NpuQwen3MoeDecoderLayerImpl::build_node_variant_pack(
         atb_speed::Utils::AtTensor2Tensor(input_params.new_cache_slots);
   }
 
+  if (is_prefill &&
+      (FLAGS_enable_chunked_prefill || FLAGS_enable_prefix_cache)) {
+    node.variantPack.inTensors.at(WEIGHT_COUNT_PER_LAYER + 16) =
+        atb_speed::Utils::AtTensor2Tensor(input_params.q_seq_lens);
+    node.variantPack.inTensors.at(WEIGHT_COUNT_PER_LAYER + 16).hostData =
+        const_cast<int32_t*>(input_params.q_seq_lens_vec.data());
+  }
+
   for (size_t i = 0; i < WEIGHT_COUNT_PER_LAYER; ++i) {
     CHECK_THROW(node.inTensors.at(i) == nullptr,
                 model_name_ << " inTensor " << i << " is NULL");

xllm/models/llm/qwen3_moe.h

@@ -154,10 +154,9 @@ class Qwen3MoeModelImpl : public torch::nn::Module {
                                        model_args.rope_theta(),
                                        options);
 
-    max_seq_len_ = model_args.max_position_embeddings();
 #if defined(USE_NPU)
     atb_pos_emb_ = layer::PosEmbedding(context);
-    int32_t mask_value = model_args.dtype() == "bfloat16" ? 1 : -9984;
+    int32_t mask_value = FLAGS_enable_chunked_prefill ? -9984 : 1;
     attn_mask_ = layer::AttentionMask(options.device(),
                                       options.dtype().toScalarType(),
                                       /*mask_value=*/mask_value);
@@ -248,11 +247,30 @@ class Qwen3MoeModelImpl : public torch::nn::Module {
     }
 
     torch::Tensor attn_mask;
-    if (num_speculative_tokens_ == 0 || input_params.global_empty_kv_cache) {
-      attn_mask = attn_mask_.get_attn_mask(128, dtype_, device_);
-    } else {
-      attn_mask = attn_mask_.gen_free_mask(
-          num_speculative_tokens_ + 1, dtype_, device_);
+    max_seq_len_ = FLAGS_enable_chunked_prefill
+                       ? std::max(input_params.kv_max_seq_len, max_seq_len_)
+                       : 128;
+    if (FLAGS_enable_chunked_prefill) {
+      attn_mask = attn_mask_.get_attn_mask(
+          max_seq_len_, cos_pos.dtype().toScalarType(), cos_pos.device());
+
+      int batch_size = input_params.q_seq_lens_vec.size();
+      if (batch_size > 0) {
+        std::vector<torch::Tensor> req_mask_vec;
+        req_mask_vec.reserve(batch_size);
+
+        for (int j = 0; j < batch_size; j++) {
+          int start =
+              input_params.kv_seq_lens_vec[j] - input_params.q_seq_lens_vec[j];
+          int end = input_params.kv_seq_lens_vec[j];
+
+          auto req_mask_slice = attn_mask.slice(0, start, end);
+          req_mask_vec.emplace_back(req_mask_slice);
+        }
+        attn_mask = torch::cat(req_mask_vec, 0);
+      }
+    } else if (input_params.global_empty_kv_cache) {
+      attn_mask = attn_mask_.get_attn_mask(max_seq_len_, dtype_, device_);
     }
     auto deep_stacks = input_params.deep_stacks;
     int deep_stack_size = deep_stacks.size();