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feat: implement chunked prefill and prefix cache for Qwen3 MoE. #455

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feat: implement chunked prefill and prefix cache for Qwen3 MoE. #455

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fb410ee
feat: implement chunked prefill and prefix cache for Qwen3 MoE.
yingxudeng Nov 25, 2025
35234cc
refactor: optimize phase detection logic in qwen3 moe forward pass.
yingxudeng Dec 2, 2025
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6 changes: 3 additions & 3 deletions CMakeLists.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -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()
Expand Down
12 changes: 11 additions & 1 deletion xllm/core/layers/npu/npu_qwen3_moe_decoder_layer_impl.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -321,6 +321,8 @@ 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 {
Expand Down Expand Up @@ -894,7 +896,7 @@ torch::Tensor NpuQwen3MoeDecoderLayerImpl::forward(
std::atomic<bool>* event_flag,
int node_id) {
atb::Status st;
if (input_params.global_empty_kv_cache) {
if (!input_params.batch_forward_type.is_decode()) {
build_node_variant_pack(prefill_node_,
x,
cos_pos,
Expand Down Expand Up @@ -997,6 +999,14 @@ 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");
Expand Down
32 changes: 25 additions & 7 deletions xllm/models/llm/qwen3_moe.h
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Original file line number Diff line number Diff line change
Expand Up @@ -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);
Expand Down Expand Up @@ -251,11 +250,30 @@ 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();
Expand Down