issue/168 InfiniLM接入paged attention

csrc/cache/kv_cache.cpp

 #include "kv_cache.hpp"
 
 #include "../utils.hpp"
-
+#include "infinicore/ops.hpp"
 #include <stdexcept>
 
 namespace infinilm::cache {
@@ -155,6 +155,7 @@ PagedKVCache::PagedKVCache(
     num_blocks_per_layer_ = config.max_kv_memory_bytes()
                           / (k_dim * num_rank_k_heads_ + v_dim * num_rank_v_heads_)
                           / block_size_
+                          / rank_num_layers_
                           / infinicore::dsize(dtype_);
     if (num_blocks_per_layer_ == 0) {
         throw std::runtime_error("Not enough memory for KV cache");
@@ -190,8 +191,11 @@ std::tuple<infinicore::Tensor, infinicore::Tensor> PagedKVCache::update(
     auto k_cache_layer = k_caches_->narrow({{0, layer_idx, 1}})->squeeze(0);
     auto v_cache_layer = v_caches_->narrow({{0, layer_idx, 1}})->squeeze(0);
 
-    /// @todo: implement paged cache update here
-
+    infinicore::op::paged_caching_(k,
+                                   v,
+                                   k_cache_layer,
+                                   v_cache_layer,
+                                   slot_mapping);
     return {k_cache_layer, v_cache_layer};
 }
 } // namespace infinilm::cache

csrc/engine/infer_engine.cpp

@@ -56,8 +56,50 @@ std::vector<std::unordered_map<std::string, infinicore::nn::Parameter>> InferEng
 //------------------------------------------------------
 // forward
 //------------------------------------------------------
-infinilm::InfinilmModel::Input InferEngine::Input::to_model_input() const {
-    return {input_ids, position_ids, cache_lengths, input_lengths, input_offsets, block_tables, slot_mapping};
+infinilm::InfinilmModel::Input InferEngine::Input::to_model_input(infinicore::Device device) const {
+
+    std::optional<infinicore::Tensor> position_ids_on_device;
+    if (position_ids.has_value()) {
+        position_ids_on_device = position_ids.value()->to(device);
+    }
+
+    std::optional<infinicore::Tensor> cache_lengths_on_device;
+    if (cache_lengths.has_value()) {
+        if (block_tables.has_value()) {
+            cache_lengths_on_device = cache_lengths.value()->to(device);
+        } else { // @todo: only paged kv cache support device tensor so far
+            cache_lengths_on_device = cache_lengths.value();
+        }
+    }
+
+    std::optional<infinicore::Tensor> input_lengths_on_device;
+    if (input_lengths.has_value()) {
+        input_lengths_on_device = input_lengths.value()->to(device);
+    }
+
+    std::optional<infinicore::Tensor> input_offsets_on_device;
+    if (input_offsets.has_value()) {
+        input_offsets_on_device = input_offsets.value()->to(device);
+    }
+
+    std::optional<infinicore::Tensor> block_tables_on_device;
+    if (block_tables.has_value()) {
+        block_tables_on_device = block_tables.value()->to(device);
+    }
+
+    std::optional<infinicore::Tensor> slot_mapping_on_device;
+    if (slot_mapping.has_value()) {
+        slot_mapping_on_device = slot_mapping.value()->to(device);
+    }
+
+    return {
+        input_ids, // @todo: on device in the future
+        position_ids_on_device,
+        cache_lengths_on_device,
+        input_lengths_on_device,
+        input_offsets_on_device,
+        block_tables_on_device,
+        slot_mapping_on_device};
 }
 
 InferEngine::Output InferEngine::forward(const InferEngine::Input &input) {

csrc/engine/rank_worker.cpp

@@ -206,7 +206,7 @@ void RankWorker::thread_loop() {
                     local_param_name = pending_param_name_;
                     local_param = pending_param_;
                 } else if (local_cmd == Command::RUN) {
-                    local_args = pending_args_.to_model_input();
+                    local_args = pending_args_.to_model_input(rank_info_.device);
                 } else if (local_cmd == Command::RESET_CACHE) {
                     if (pending_cache_config_ != nullptr) {
                         local_cache_config = pending_cache_config_->unique_copy();
@@ -254,13 +254,18 @@ void RankWorker::thread_loop() {
                             auto random_val{pending_args_.random_val};
 
                             const auto &logits_shape{logits->shape()};
-                            const auto &batch_size{logits_shape[0]};
                             const auto &vocab_size{logits_shape[2]};
+                            const auto &total_len{logits_shape[1]};
+                            const auto &batch_size{logits_shape[0]};
+
+                            auto n_req = pending_args_.input_offsets.value()->size(0);
+                            int64_t *input_lengths = (int64_t *)pending_args_.input_lengths.value()->data();
+                            int64_t *input_offsets = (int64_t *)pending_args_.input_offsets.value()->data();
 
-                            auto output_ids{infinicore::Tensor::empty({batch_size}, infinicore::DataType::I32, rank_info_.device)};
+                            auto output_ids{infinicore::Tensor::empty({n_req}, infinicore::DataType::I64, rank_info_.device)};
 
-                            for (auto i{decltype(batch_size)(0)}; i < batch_size; ++i) {
-                                auto score{logits->narrow({{0, i, 1}})->view({vocab_size})};
+                            for (auto i{decltype(n_req)(0)}; i < n_req; ++i) {
+                                auto score{logits->view({batch_size * total_len, vocab_size})->narrow({{0, size_t(input_offsets[i] + input_lengths[i] - 1), 1}})->view({vocab_size})};
                                 auto out{output_ids->narrow({{0, i, 1}})->view({})};
                                 infinicore::op::random_sample_(
                                     out, score, random_val, top_p, top_k, temperature);

csrc/engine/rank_worker.hpp

@@ -47,7 +47,7 @@ public:
 
         float random_val{0.1};
 
-        infinilm::InfinilmModel::Input to_model_input() const;
+        infinilm::InfinilmModel::Input to_model_input(infinicore::Device device) const;
     };
 
     struct Output {

csrc/models/llama/llama_attention.cpp

 #include "llama_attention.hpp"
 
+#include "../../utils.hpp"
 #include "infinicore/nn/linear.hpp"
 #include "infinicore/nn/rope.hpp"
 #include "infinicore/ops.hpp"
@@ -43,6 +44,7 @@ LlamaAttention::LlamaAttention(const LlamaConfig &config,
     } else {
         throw std::runtime_error("num_attention_heads / tp_size error.");
     }
+    scaling_ = 1.0f / std::sqrt(static_cast<float>(head_dim_));
 
     // Initialize projection layers
     INFINILM_QKV_LINEAR_INIT(qkv_proj, "q_proj", "k_proj", "v_proj", hidden_size_, head_dim_, config.num_attention_heads, config.num_key_value_heads, use_bias_,
@@ -52,17 +54,10 @@ LlamaAttention::LlamaAttention(const LlamaConfig &config,
                               dtype, device, tp_rank, tp_size, rank_info.comm);
 }
 
-infinicore::Tensor LlamaAttention::forward(const infinicore::Tensor &hidden_states,
-                                           const infinicore::Tensor &position_ids,
-                                           std::shared_ptr<cache::Cache> kv_cache,
-                                           std::optional<infinicore::Tensor> cache_lengths,
-                                           std::optional<infinicore::Tensor> input_lengths,
-                                           std::optional<infinicore::Tensor> input_offsets,
-                                           std::optional<infinicore::Tensor> block_tables,
-                                           std::optional<infinicore::Tensor> slot_mapping) const {
-    if (!rotary_emb_) {
-        throw std::runtime_error("LlamaAttention: rotary_emb not configured");
-    }
+infinicore::Tensor LlamaAttention::forward_(const infinicore::Tensor &hidden_states,
+                                            const infinicore::Tensor &position_ids,
+                                            std::shared_ptr<infinilm::cache::Cache> kv_cache,
+                                            std::optional<infinicore::Tensor> cache_lengths) const {
     // Input shape: [batch, seq_len, hidden_size]
     auto hidden_states_mutable = hidden_states;
     auto shape = hidden_states->shape();
@@ -73,7 +68,6 @@ infinicore::Tensor LlamaAttention::forward(const infinicore::Tensor &hidden_stat
     auto [q, k, v] = qkv_proj_->forward_split(hidden_states_mutable);
 
     // 2. Reshape for multi-head attention
-
     // Reshape Q, K, V to include batch dimension
     // Python: query_states = self.q_proj(hidden_states).view(querys_shape)
     // The view operation requires the tensor to be contiguous in the required dimensions
@@ -114,13 +108,6 @@ infinicore::Tensor LlamaAttention::forward(const infinicore::Tensor &hidden_stat
         auto [k_total_tmp, v_total_tmp] = static_kv_cache->update(layer_idx_, k_permuted, v_permuted, cache_lengths.value());
         k_total = k_total_tmp;
         v_total = v_total_tmp;
-    } else if (auto paged_kv_cache = std::dynamic_pointer_cast<cache::PagedKVCache>(kv_cache)) {
-        auto [k_total_tmp, v_total_tmp] = paged_kv_cache->update(layer_idx_, k_permuted, v_permuted, slot_mapping.value());
-        k_total = k_total_tmp;
-        v_total = v_total_tmp;
-
-        /// @todo Implement paged attention here.
-        throw std::runtime_error("LlamaAttention: Paged attention not implemented");
     } else {
         throw std::runtime_error("LlamaAttention: Unsupported kvcache type");
     }
@@ -134,8 +121,7 @@ infinicore::Tensor LlamaAttention::forward(const infinicore::Tensor &hidden_stat
 
     auto K_transposed = K->permute({0, 2, 1}); // [bs * n_kv_head, head_dim, total_seq_len]
 
-    float scaling = 1.0f / std::sqrt(static_cast<float>(head_dim_));
-    auto attn_weight = infinicore::op::matmul(Q, K_transposed, scaling); // [bs * n_kv_head, ng * seq_len, total_seq_len]
+    auto attn_weight = infinicore::op::matmul(Q, K_transposed, scaling_); // [bs * n_kv_head, ng * seq_len, total_seq_len]
 
     auto attn_weight_softmax = attn_weight->view({batch_size * num_attention_heads_, seq_len, total_seq_len});
     infinicore::op::causal_softmax_(attn_weight_softmax, attn_weight_softmax);
@@ -152,6 +138,119 @@ infinicore::Tensor LlamaAttention::forward(const infinicore::Tensor &hidden_stat
     return output;
 }
 
+infinicore::Tensor LlamaAttention::forward_paged_(const infinicore::Tensor &hidden_states,
+                                                  const infinicore::Tensor &position_ids,
+                                                  std::shared_ptr<infinilm::cache::PagedKVCache> paged_kv_cache,
+                                                  std::optional<infinicore::Tensor> cache_lengths,
+                                                  std::optional<infinicore::Tensor> input_lengths,
+                                                  std::optional<infinicore::Tensor> input_offsets,
+                                                  std::optional<infinicore::Tensor> block_tables,
+                                                  std::optional<infinicore::Tensor> slot_mapping) const {
+    ASSERT(block_tables.has_value());
+    ASSERT(input_lengths.has_value());
+    ASSERT(slot_mapping.has_value());
+
+    // Input shape: [batch, seq_len, hidden_size]
+    auto hidden_states_mutable = hidden_states;
+    auto shape = hidden_states->shape();
+    size_t batch_size = shape[0];
+    size_t seq_len = shape[1];
+
+    // Only support batchsize==1, all requests should be flattened along seqlen dimension
+    ASSERT_EQ(batch_size, 1);
+    // Decode only if total_len == num_requests
+    bool is_prefill = (seq_len != input_lengths.value()->shape()[0]);
+
+    // 1. Project Q, K, V
+    auto [q, k, v] = qkv_proj_->forward_split(hidden_states_mutable);
+
+    // 2. Reshape for multi-head attention
+
+    // Reshape Q, K, V to include batch dimension
+    // Python: query_states = self.q_proj(hidden_states).view(querys_shape)
+    // The view operation requires the tensor to be contiguous in the required dimensions
+    auto q_reshaped = q->view({seq_len, num_attention_heads_, head_dim_});
+    auto k_reshaped = k->view({seq_len, num_key_value_heads_, head_dim_});
+    auto v_reshaped = v->view({seq_len, num_key_value_heads_, head_dim_});
+
+    // 3. Prepare position_ids for RoPE - align with Python pattern
+    auto pos_shape = position_ids->shape();
+    infinicore::Tensor pos_ids_for_rope = position_ids;
+    if (pos_shape.size() == 2) {
+        auto pos_narrowed = position_ids->narrow({{0, 0, 1}});
+        pos_ids_for_rope = pos_narrowed->view({pos_shape[1]});
+    } else if (pos_shape.size() == 1) {
+        pos_ids_for_rope = position_ids;
+    } else {
+        throw std::runtime_error("Unexpected position_ids shape");
+    }
+
+    // 4. Apply RoPE to Q and K
+    rotary_emb_->forward(q_reshaped, pos_ids_for_rope, true); // [bs, seq_len, n_q_head, head_dim]
+    rotary_emb_->forward(k_reshaped, pos_ids_for_rope, true); // [bs, seq_len, n_kv_head, head_dim]
+
+    //  5. Prepare KV caches
+    //  Ensure contiguous after permute for F16 compatibility with cache operations
+    auto [k_total, v_total] = paged_kv_cache->update(layer_idx_,
+                                                     k_reshaped,
+                                                     v_reshaped,
+                                                     slot_mapping.value());
+
+    // 6. Compute attention
+    infinicore::Tensor attn_output = infinicore::Tensor::empty({seq_len, num_attention_heads_, head_dim_}, q_reshaped->dtype(), q_reshaped->device());
+
+    if (is_prefill) {
+        infinicore::op::paged_attention_prefill_(
+            attn_output,
+            q_reshaped,
+            k_total,
+            v_total,
+            block_tables.value(),
+            cache_lengths.value(),
+            input_lengths.value(),
+            input_offsets.value(),
+            std::nullopt,
+            scaling_);
+
+    } else {
+        infinicore::op::paged_attention_(
+            attn_output,
+            q_reshaped,
+            k_total,
+            v_total,
+            block_tables.value(),
+            cache_lengths.value(),
+            std::nullopt,
+            scaling_);
+    }
+
+    // 7. Project output
+    attn_output = attn_output->view({1, seq_len, num_attention_heads_ * head_dim_});
+    return o_proj_->forward(attn_output);
+}
+
+infinicore::Tensor LlamaAttention::forward(const infinicore::Tensor &hidden_states,
+                                           const infinicore::Tensor &position_ids,
+                                           std::shared_ptr<cache::Cache> kv_cache,
+                                           std::optional<infinicore::Tensor> cache_lengths,
+                                           std::optional<infinicore::Tensor> input_lengths,
+                                           std::optional<infinicore::Tensor> input_offsets,
+                                           std::optional<infinicore::Tensor> block_tables,
+                                           std::optional<infinicore::Tensor> slot_mapping) const {
+    if (!rotary_emb_) {
+        throw std::runtime_error("LlamaAttention: rotary_emb not configured");
+    }
+
+    infinicore::Tensor output;
+    if (auto paged_kv_cache = std::dynamic_pointer_cast<cache::PagedKVCache>(kv_cache)) {
+        output = forward_paged_(hidden_states, position_ids, paged_kv_cache, cache_lengths, input_lengths, input_offsets, block_tables, slot_mapping);
+    } else {
+
+        output = forward_(hidden_states, position_ids, kv_cache, cache_lengths);
+    }
+    return output;
+}
+
 void LlamaAttention::set_rotary_emb(const std::shared_ptr<infinicore::nn::RoPE> &rotary_emb) {
     rotary_emb_ = rotary_emb;
 }

csrc/models/llama/llama_attention.hpp

@@ -55,7 +55,7 @@ public:
                                std::optional<infinicore::Tensor> input_lengths,
                                std::optional<infinicore::Tensor> input_offsets,
                                std::optional<infinicore::Tensor> block_tables,
-                               std::optional<infinicore::Tensor> slot_mappin) const;
+                               std::optional<infinicore::Tensor> slot_mapping) const;
 
     /**
      * @brief Get the layer index
@@ -73,6 +73,21 @@ public:
     size_t head_dim() const { return head_dim_; }
     size_t hidden_size() const { return hidden_size_; }
 
+private:
+    infinicore::Tensor forward_(const infinicore::Tensor &hidden_states,
+                                const infinicore::Tensor &position_ids,
+                                std::shared_ptr<infinilm::cache::Cache> kv_cache,
+                                std::optional<infinicore::Tensor> cache_lengths) const;
+
+    infinicore::Tensor forward_paged_(const infinicore::Tensor &hidden_states,
+                                      const infinicore::Tensor &position_ids,
+                                      std::shared_ptr<infinilm::cache::PagedKVCache> kv_cache,
+                                      std::optional<infinicore::Tensor> cache_lengths,
+                                      std::optional<infinicore::Tensor> input_lengths,
+                                      std::optional<infinicore::Tensor> input_offsets,
+                                      std::optional<infinicore::Tensor> block_tables,
+                                      std::optional<infinicore::Tensor> slot_mapping) const;
+
 protected:
     // Projection layers
     INFINICORE_NN_MODULE(infinilm::layers::QKVParallelLinear, qkv_proj);
@@ -93,6 +108,8 @@ private:
     bool use_bias_;                  // Bias for Q/K/V projections
     bool use_output_bias_;           // Bias for output projection (o_proj)
     size_t max_position_embeddings_; // For cache initialization (deprecated, kept for compatibility)
+
+    float scaling_;
 };
 
 } // namespace infinilm::models::llama

csrc/models/llama/llama_for_causal_lm.cpp

@@ -35,8 +35,7 @@ LlamaForCausalLM::Output LlamaForCausalLM::forward(const Input &input) const {
     auto slot_mapping = input.slot_mapping;
 
     // 1. Forward through base model to get hidden states
-    auto position_ids_device = position_ids->to(device_);
-    auto hidden_states = model_->forward(input_ids, position_ids_device, cache_lengths, input_lengths, input_offsets, block_tables, slot_mapping);
+    auto hidden_states = model_->forward(input_ids, position_ids, cache_lengths, input_lengths, input_offsets, block_tables, slot_mapping);
 
     // 2. Apply language modeling head to get logits
     auto logits = lm_head_->forward(hidden_states);

csrc/models/llama/llama_model.cpp

@@ -59,15 +59,7 @@ infinicore::Tensor LlamaModel::forward(const infinicore::Tensor &input_ids,
         hidden_states = layers_.at(i)->forward(hidden_states, position_ids, kv_cache_, cache_lengths, input_lengths, input_offsets, block_tables, slot_mapping);
     }
 
-    // 3. Apply final layer normalization to last token only (aligns with transformers)
-    // Narrow to last token: [batch, seq_len, hidden_size] -> [batch, 1, hidden_size]
-    auto shape = hidden_states->shape();
-    size_t seq_len = shape[1];
-    auto last_token = hidden_states->narrow({{1, seq_len - 1, 1}});
-
-    auto normalized_last_token = norm_->forward(last_token);
-
-    return normalized_last_token;
+    return norm_->forward(hidden_states);
 }
 
 void LlamaModel::reset_cache(const cache::CacheConfig *cache_config) {

csrc/pybind11/engine/engine.hpp

@@ -90,6 +90,8 @@ inline void bind_infer_engine(py::module &m) {
                     std::move(input_ids),
                     std::move(position_ids),
                     std::move(cache_lengths),
+                    std::move(input_lengths),
+                    std::move(input_offsets),
                     std::move(block_tables),
                     std::move(slot_mapping)}};
 

examples/jiuge.py

@@ -9,6 +9,7 @@ import sys
 import time
 import os
 import numpy as np
+from infinilm.cache import StaticKVCacheConfig, PagedKVCacheConfig
 
 sys.path.insert(0, os.path.join(os.path.dirname(__file__), "../python"))
 
@@ -82,6 +83,18 @@ def get_args():
         default=1,
         help="total rank for tensor parallel",
     )
+    parser.add_argument(
+        "--enable-paged-attn",
+        action="store_true",
+        help="use paged cache",
+    )
+
+    parser.add_argument(
+        "--max-kvcache-size",
+        type=int,
+        default=8 * 1024 * 1024 * 1024,
+        help="max size (in bytes) allocated to paged kv cache",
+    )
 
     return parser.parse_args()
 
@@ -92,6 +105,7 @@ def test(
     max_new_tokens=100,
     infini_device=infinicore.device("cpu", 0),
     tp=1,
+    enable_paged_attn=False,
 ):
     model_path = os.path.expanduser(model_path)
     # ---------------------------------------------------------------------------- #
@@ -150,11 +164,21 @@ def test(
         "input_ids"
     ]  # List: [[1, 1128, 526, 366, 29892]]
 
-    # 根据输入长度和最长输出长度创建KVCache
-    model.reset_cache(
-        1 if prompts is str else len(prompts),
-        max_new_tokens + len(input_ids_list[0]),
-    )
+    # ---------------------------------------------------------------------------- #
+    #                        创建KVCache
+    # ---------------------------------------------------------------------------- #
+    if enable_paged_attn:
+        cache_config = PagedKVCacheConfig(
+            max_kv_memory_bytes=args.max_kvcache_size, block_size=16
+        )
+    else:
+        batch_size = 1 if prompts is str else len(prompts)
+        initial_capacity = max_new_tokens + len(input_ids_list[0])
+        cache_config = StaticKVCacheConfig(
+            max_batch_size=batch_size, max_cache_len=initial_capacity
+        )
+
+    model.reset_cache(cache_config)
 
     # ---------------------------------------------------------------------------- #
     #                        自回归生成
@@ -211,7 +235,7 @@ if __name__ == "__main__":
     max_new_tokens = args.max_new_tokens
     backend = args.backend
     tp = args.tp
-
+    enable_paged_attn = args.enable_paged_attn
     if backend != "cpp":
         raise ValueError(f"Unsupported backend: {backend}.")
 
@@ -223,4 +247,5 @@ if __name__ == "__main__":
         max_new_tokens,
         infini_device=infini_device,
         tp=tp,
+        enable_paged_attn=enable_paged_attn,
     )

python/infinilm/auto_config.py

@@ -21,5 +21,7 @@ class AutoConfig:
 
         if config_dict["model_type"] == "llama":
             return LlamaConfig(**config_dict)
+        elif config_dict["model_type"] == "qwen2":
+            return LlamaConfig(**config_dict)
 
         raise ValueError(f"Unsupported model type `{config_dict['model_type']}`.")

python/infinilm/cache/__init__.py

-from .cache import CacheConfig, StaticKVCacheConfig
+from .cache import CacheConfig, StaticKVCacheConfig,PagedKVCacheConfig
 
-__all__ = ["CacheConfig", "StaticKVCacheConfig"]
+__all__ = ["CacheConfig", "StaticKVCacheConfig", "PagedKVCacheConfig"]

python/infinilm/generation/utils.py

@@ -13,6 +13,8 @@ def infini_to_ctype_dtype(infini_dtype):
         return ctypes.c_int32
     elif infini_dtype == infinicore.float32:
         return ctypes.c_float
+    elif infini_dtype == infinicore.int64:
+        return ctypes.c_int64
     else:
         raise ValueError(f"Unsupported py_dtype: {infini_dtype}")
 

python/infinilm/infer_engine.py

@@ -4,7 +4,7 @@ from dataclasses import dataclass
 import infinicore
 
 from infinilm.auto_config import AutoConfig
-from infinilm.cache import StaticKVCacheConfig
+from infinilm.cache import StaticKVCacheConfig, PagedKVCacheConfig
 from infinilm.distributed import DistConfig
 from infinilm.lib import _infinilm
 
@@ -18,6 +18,7 @@ class GenerationConfig:
     top_p: float = 1.0
 
     eos_token_id: list[int] | None = None
+    stop_on_eos: bool = True
 
 
 class InferEngine(_infinilm.InferEngine):
@@ -42,6 +43,8 @@ class InferEngine(_infinilm.InferEngine):
 
         self.use_cache = False
 
+        self.enable_paged_attn = isinstance(cache_config, PagedKVCacheConfig)
+
     def __call__(self, *args, **kwargs):
         return self.forward(*args, **kwargs)
 
@@ -93,15 +96,11 @@ class InferEngine(_infinilm.InferEngine):
         else:
             eos_token_id = generation_config.eos_token_id
 
-        # TODO: Remove the `to_numpy` calls and simplify the corresponding code.
-        batch_size, seq_len = input_ids.shape[:2]
-
-        position_ids = infinicore.from_list(
-            [list(range(0, seq_len)) for _ in range(batch_size)], dtype=infinicore.int64
-        )
-        cache_lengths = infinicore.from_list([0], dtype=infinicore.int64)
-
+        past_seq_len = 0
         output_ids = []
+        initial_batch_size, initial_seqlen = input_ids.shape[:2]
+        seq_len = initial_seqlen
+        batch_size = initial_batch_size
 
         if batch_size != 1 and generation_config.max_new_tokens is None:
             raise ValueError(
@@ -111,14 +110,76 @@ class InferEngine(_infinilm.InferEngine):
         if _measure_and_log_time:
             time_measurements = []
 
-        for _ in range(0, generation_config.max_new_tokens):
+        for iter in range(0, generation_config.max_new_tokens):
             if _measure_and_log_time:
                 start_time = time.perf_counter()
 
+            batch_size, seq_len = input_ids.shape[:2]
+
+            if self.enable_paged_attn:
+                input_ids = input_ids.view([1, batch_size * seq_len])
+                position_ids = infinicore.from_list(
+                    list(range(past_seq_len, past_seq_len + seq_len)) * batch_size,
+                    dtype=infinicore.int64,
+                )
+                cache_lengths = infinicore.from_list(
+                    [past_seq_len] * batch_size, dtype=infinicore.int64
+                )
+                input_lengths = infinicore.from_list(
+                    [seq_len] * batch_size, dtype=infinicore.int64
+                )
+
+                input_offsets = infinicore.from_list(
+                    [seq_len * i for i in range(batch_size)], dtype=infinicore.int64
+                )
+                block_tables = infinicore.from_list(
+                    [
+                        [
+                            i * batch_size + b
+                            for i in range((past_seq_len + seq_len + 15) // 16)
+                        ]
+                        for b in range(batch_size)
+                    ],
+                    dtype=infinicore.int64,
+                )
+                slot_mapping = infinicore.from_list(
+                    [
+                        ((past_seq_len + i + 15) // 16) * batch_size
+                        + b
+                        + (past_seq_len + i + 15) % 16
+                        for i in range(seq_len)
+                        for b in range(batch_size)
+                    ],
+                    dtype=infinicore.int64,
+                )
+            else:
+                position_ids = infinicore.from_list(
+                    [
+                        list(range(past_seq_len, past_seq_len + seq_len))
+                        for _ in range(batch_size)
+                    ],
+                    dtype=infinicore.int64,
+                )
+                cache_lengths = infinicore.from_list(
+                    [past_seq_len], dtype=infinicore.int64
+                )
+                input_lengths = infinicore.from_list(
+                    [seq_len] * batch_size, dtype=infinicore.int64
+                )
+                input_offsets = infinicore.from_list(
+                    [seq_len * i for i in range(batch_size)], dtype=infinicore.int64
+                )
+                block_tables = None
+                slot_mapping = None
+
             output_id = self(
-                input_ids,
+                input_ids=input_ids,
                 position_ids=position_ids,
                 cache_lengths=cache_lengths,
+                input_lengths=input_lengths,
+                input_offsets=input_offsets,
+                block_tables=block_tables,
+                slot_mapping=slot_mapping,
                 temperature=generation_config.temperature,
                 top_k=generation_config.top_k,
                 top_p=generation_config.top_p,
@@ -127,24 +188,16 @@ class InferEngine(_infinilm.InferEngine):
             output_ids.append(output_id)
 
             if (
-                generation_config.max_new_tokens is not None
+                generation_config.stop_on_eos
+                and generation_config.max_new_tokens is not None
                 and output_id.to_numpy()[0] in eos_token_id
             ):
                 break
 
-            seq_len = position_ids.shape[-1]
-
             input_ids = infinicore.from_list(
                 [[output_id] for output_id in output_id.to_numpy().tolist()]
             )
-            position_ids = infinicore.from_list(
-                [1 for _ in range(batch_size)],
-                dtype=position_ids.dtype,
-                device=position_ids.device,
-            ).view((batch_size, 1)) + position_ids.narrow(1, seq_len - 1, 1)
-            cache_lengths += infinicore.from_list(
-                [seq_len], dtype=cache_lengths.dtype, device=cache_lengths.device
-            )
+            past_seq_len = past_seq_len + seq_len
 
             if _measure_and_log_time:
                 end_time = time.perf_counter()
@@ -156,23 +209,21 @@ class InferEngine(_infinilm.InferEngine):
                 f"\n\n\n Generation completed in {round(sum(time_measurements) * 1000, 2)} ms"
             )
             print(
-                f" Batchsize={batch_size}  Per_Batch_Input_Len={seq_len}  Per_Batch_New_Tokens={len(time_measurements)}\n"
+                f" Batchsize={initial_batch_size}  Per_Batch_Input_Len={initial_seqlen}  Per_Batch_New_Tokens={len(time_measurements)}\n"
             )
             print(
-                f" Prefill TTFT: {round(time_measurements[0], 2)}ms  Throughput: {round((batch_size * seq_len) / time_measurements[0], 2)}tok/s\n",
+                f" Prefill TTFT: {round(time_measurements[0], 2)}ms  Throughput: {round((initial_batch_size * initial_seqlen) / time_measurements[0], 2)}tok/s\n",
             )
             if len(time_measurements) > 1:
                 print(
-                    f" Decode  Avg ITL: {round(sum(time_measurements[1:]) * 1000 / (len(time_measurements) - 1), 2)}ms   Throughput: {round((batch_size * (len(time_measurements) - 1)) / sum(time_measurements[1:]), 2)}tok/s\n",
+                    f" Decode  Avg ITL: {round(sum(time_measurements[1:]) * 1000 / (len(time_measurements) - 1), 2)}ms   Throughput: {round((initial_batch_size * (len(time_measurements) - 1)) / sum(time_measurements[1:]), 2)}tok/s\n",
                 )
 
         return output_ids
 
-    def reset_cache(self, batch_size: int, initial_capacity: int = 1024):
+    def reset_cache(self, cache_config):
         infinicore.sync_device()
-
-        cache_config = StaticKVCacheConfig(batch_size, initial_capacity)
-
+        self.enable_paged_attn = isinstance(cache_config, PagedKVCacheConfig)
         super().reset_cache(cache_config)
 
     def state_dict_keyname(self):