add Memory Pool

* Tensor memory pool - initial commit

* Adjusted workspace management to use the memory pool.

* Re-differentiated create and createAsync in Storage

* Set initial memory pool size to 0

* Separated creatAsync and createFromPool

* Removed the redundant WorkspaceHandle

* Adjusted naming

* Set default mempool size to 0 in declaration

* Adjusted format

src/allocator.hpp

@@ -2,24 +2,47 @@
 #define ALLOCATOR_HPP
 
 #include "infinicore_infer.h"
+#include <map>
+#include <memory>
+#include <set>
+#include <unordered_map>
+#include <vector>
 
 class AllocatorBase {
 public:
     virtual void *alloc(size_t size) = 0;
     virtual void release(void *ptr) = 0;
+    virtual ~AllocatorBase() = default;
 };
 
-class WorkspaceAllocator : public AllocatorBase {
-private:
-    void *_memory;
-    size_t _total_size;
-    size_t _align;
-
+class MemoryPool : public AllocatorBase {
 public:
-    WorkspaceAllocator(size_t intial_size, size_t align = 256);
-    ~WorkspaceAllocator();
+    MemoryPool(size_t initialSize = 0);
+    ~MemoryPool();
     void *alloc(size_t size) override;
     void release(void *ptr) override;
+
+private:
+    struct Block {
+        void *base;
+        void *ptr;
+        size_t size;
+        bool is_free;
+        Block(void *b, void *p, size_t s, bool f)
+            : base(b), ptr(p), size(s), is_free(f) {}
+        bool operator<(const Block &other) const {
+            return ptr < other.ptr;
+        }
+    };
+
+    void *allocateNewRegion(size_t size);
+    void insertFreeBlock(Block &&block);
+    void tryCoalesce(const Block &block);
+
+    std::vector<void *> _base_regions;
+    std::set<Block> _all_blocks;
+    std::multimap<size_t, std::set<Block>::iterator> _free_blocks;
+    std::unordered_map<void *, std::set<Block>::iterator> _ptr_to_block;
 };
 
 #endif

src/allocator/memory_allocator.cpp

+#include "../allocator.hpp"
+#include "../utils.hpp"
+#include <algorithm>
+#include <iostream>
+#include <stdexcept>
+
+MemoryPool::MemoryPool(size_t initialSize) {
+    allocateNewRegion(initialSize);
+}
+
+MemoryPool::~MemoryPool() {
+    for (void *region : _base_regions) {
+        RUN_INFINI(infinirtFree(region));
+    }
+}
+
+void *MemoryPool::alloc(size_t size) {
+    auto it = _free_blocks.lower_bound(size);
+    if (it == _free_blocks.end()) {
+        allocateNewRegion(std::max(size, size_t(0)));
+        it = _free_blocks.lower_bound(size);
+        if (it == _free_blocks.end()) {
+            throw std::bad_alloc();
+        }
+    }
+
+    auto block_it = it->second;
+    Block block = *block_it;
+    _free_blocks.erase(it);
+    _all_blocks.erase(block_it);
+
+    if (block.size > size + 256) {
+        // Split
+        void *alloc_ptr = block.ptr;
+        void *rem_ptr = static_cast<char *>(block.ptr) + size;
+        size_t rem_size = block.size - size;
+        Block alloc_block(block.base, alloc_ptr, size, false);
+        Block rem_block(block.base, rem_ptr, rem_size, true);
+        auto alloc_it = _all_blocks.insert(alloc_block).first;
+        auto rem_it = _all_blocks.insert(rem_block).first;
+        _free_blocks.emplace(rem_size, rem_it);
+        _ptr_to_block[alloc_ptr] = alloc_it;
+        return alloc_ptr;
+    } else {
+        // No split
+        block.is_free = false;
+        auto alloc_it = _all_blocks.insert(block).first;
+        _ptr_to_block[block.ptr] = alloc_it;
+        return block.ptr;
+    }
+}
+
+void MemoryPool::release(void *ptr) {
+    auto it = _ptr_to_block.find(ptr);
+    if (it == _ptr_to_block.end()) {
+        throw std::runtime_error("Invalid pointer to free");
+    }
+
+    auto block_it = it->second;
+    Block block = *block_it;
+    _all_blocks.erase(block_it);
+    block.is_free = true;
+    auto new_it = _all_blocks.insert(block).first;
+    _ptr_to_block.erase(ptr);
+    tryCoalesce(*new_it);
+}
+
+void *MemoryPool::allocateNewRegion(size_t size) {
+    void *ptr = nullptr;
+    RUN_INFINI(infinirtMalloc(&ptr, size));
+    _base_regions.push_back(ptr);
+    Block new_block(ptr, ptr, size, true);
+    auto it = _all_blocks.insert(new_block).first;
+    _free_blocks.emplace(size, it);
+    return ptr;
+}
+
+void MemoryPool::tryCoalesce(const Block &block) {
+    auto it = _all_blocks.find(block);
+    if (it == _all_blocks.end()) {
+        return;
+    }
+
+    Block merged = *it;
+    auto next = std::next(it);
+    auto prev = (it == _all_blocks.begin()) ? _all_blocks.end() : std::prev(it);
+
+    _all_blocks.erase(it);
+    _free_blocks.erase(merged.size);
+
+    // Coalesce with next
+    if (next != _all_blocks.end() && next->is_free && static_cast<char *>(merged.ptr) + merged.size == next->ptr) {
+        _free_blocks.erase(next->size);
+        merged.size += next->size;
+        _all_blocks.erase(next);
+    }
+
+    // Coalesce with prev
+    if (prev != _all_blocks.end() && prev->is_free && static_cast<char *>(prev->ptr) + prev->size == merged.ptr) {
+        _free_blocks.erase(prev->size);
+        merged.ptr = prev->ptr;
+        merged.size += prev->size;
+        merged.base = prev->base;
+        _all_blocks.erase(prev);
+    }
+
+    merged.is_free = true;
+    auto new_it = _all_blocks.insert(merged).first;
+    _free_blocks.emplace(merged.size, new_it);
+}

src/allocator/workspace_allocator.cpp

-#include "../allocator.hpp"
-
-#include "../utils.hpp"
-
-inline size_t aligned_size(size_t size_, size_t align) {
-    return (size_ + align - 1) & ~(align - 1);
-}
-
-inline void *allocate(size_t size_) {
-    void *ptr;
-    RUN_INFINI(infinirtMalloc(&ptr, size_));
-    return ptr;
-}
-
-WorkspaceAllocator::WorkspaceAllocator(size_t initial_size_, size_t align) {
-    _align = align;
-    _total_size = 0;
-    _memory = nullptr;
-    if (initial_size_ > 0) {
-        _total_size = aligned_size(initial_size_, _align);
-        _memory = allocate(_total_size);
-    }
-}
-
-void *WorkspaceAllocator::alloc(size_t new_size) {
-    if (_total_size < new_size) {
-        if (_total_size != 0) {
-            RUN_INFINI(infinirtDeviceSynchronize());
-            RUN_INFINI(infinirtFree(_memory));
-        }
-        _total_size = aligned_size(new_size, _align);
-        _memory = allocate(_total_size);
-    }
-    return _memory;
-}
-
-void WorkspaceAllocator::release(void *ptr) {
-}
-
-WorkspaceAllocator::~WorkspaceAllocator() {
-    if (_memory != nullptr) {
-        RUN_INFINI(infinirtDeviceSynchronize());
-        RUN_INFINI(infinirtFree(_memory));
-    }
-}
\ No newline at end of file

src/models/jiuge/jiuge.cpp

@@ -41,6 +41,8 @@ void createDeviceResource(DeviceResource *rsrc, const JiugeMeta *meta,
             getFFNDown(meta, weights, layer, idev, ndev));
     }
 
+    auto memory_pool = std::make_shared<MemoryPool>(128 * 1024 * 1024);
+
     *rsrc = DeviceResource{
         device,
         dev_id,
@@ -59,7 +61,7 @@ void createDeviceResource(DeviceResource *rsrc, const JiugeMeta *meta,
         w_ffn_down,
         stream,
         comm,
-        std::make_unique<WorkspaceAllocator>(0),
+        memory_pool,
     };
     RUN_INFINI(infinirtDeviceSynchronize());
 }
@@ -100,7 +102,6 @@ void releaseDeviceResource(DeviceResource &res) {
         t.reset();
     }
     res.w_ffn_down.clear();
-    res.workspace_allocator.reset();
     infiniopDestroyHandle(res.handle);
     res.handle = nullptr;
     infinirtStreamDestroy(res.stream);
@@ -130,13 +131,13 @@ void inferDeviceBatch(const JiugeMeta &meta, DeviceResource &rsrc,
     bool has_qkv_bias = rsrc.b_attn_qkv.size() > 0;
 
     // Allocate buffers
-    auto logits_in = Tensor::buffer(dt_logits, {ntok, d}, stream);
-    auto logits_out = Tensor::buffer(dt_logits, {ntok, d}, stream);
-    auto qkv_buf = Tensor::buffer(dt_logits, {ntok, (nh + nkvh * 2) * dh}, stream);
-    auto gate_up_buf = Tensor::buffer(dt_logits, {ntok, 2 * di}, stream);
-    auto o_buf = Tensor::buffer(dt_logits, {ntok, nh * dh}, stream);
-    auto prob_buf = Tensor::buffer(dt_logits, {nreq, dvoc}, stream);
-    auto result_buf = Tensor::buffer(INFINI_DTYPE_I64, {nreq}, stream);
+    auto logits_in = Tensor::buffer(dt_logits, {ntok, d}, rsrc.memory_pool);
+    auto logits_out = Tensor::buffer(dt_logits, {ntok, d}, rsrc.memory_pool);
+    auto qkv_buf = Tensor::buffer(dt_logits, {ntok, (nh + nkvh * 2) * dh}, rsrc.memory_pool);
+    auto gate_up_buf = Tensor::buffer(dt_logits, {ntok, 2 * di}, rsrc.memory_pool);
+    auto o_buf = Tensor::buffer(dt_logits, {ntok, nh * dh}, rsrc.memory_pool);
+    auto prob_buf = Tensor::buffer(dt_logits, {nreq, dvoc}, rsrc.memory_pool);
+    auto result_buf = Tensor::buffer(INFINI_DTYPE_I64, {nreq}, rsrc.memory_pool);
     auto result_cpu = std::vector<int64_t>(nreq);
 
     // Prepare inputs
@@ -153,7 +154,7 @@ void inferDeviceBatch(const JiugeMeta &meta, DeviceResource &rsrc,
     if (rsrc.device == INFINI_DEVICE_CPU) {
         pos_ids_buf = Tensor::weight(batch_pos_ids.data(), INFINI_DTYPE_U32, {ntok});
     } else {
-        pos_ids_buf = Tensor::buffer(INFINI_DTYPE_U32, {ntok}, stream);
+        pos_ids_buf = Tensor::buffer(INFINI_DTYPE_U32, {ntok}, rsrc.memory_pool);
         RUN_INFINI(infinirtMemcpyAsync(pos_ids_buf->data(), batch_pos_ids.data(), sizeof(uint32_t) * ntok,
                                        INFINIRT_MEMCPY_H2D, stream));
     }
@@ -164,7 +165,6 @@ void inferDeviceBatch(const JiugeMeta &meta, DeviceResource &rsrc,
     }
 
     // Prepare operators and workspace
-    void *workspace;
     size_t workspace_size = 0, temp_size = 0;
     // attn & mlp rmsnorm
     infiniopRMSNormDescriptor_t desc_norm;
@@ -270,9 +270,9 @@ void inferDeviceBatch(const JiugeMeta &meta, DeviceResource &rsrc,
 
         token_offset += seq_len;
     }
-    auto qk_buf = Tensor::buffer(dt_logits, {nh, max_qk_size}, stream);
-    auto rearrange_q_buf = Tensor::buffer(dt_logits, {nkvh, ngroup * max_seq_len, dh}, stream);
-    auto attn_val_buf = Tensor::buffer(dt_logits, {nh, max_seq_len, dh}, stream);
+    auto qk_buf = Tensor::buffer(dt_logits, {nh, max_qk_size}, rsrc.memory_pool);
+    auto rearrange_q_buf = Tensor::buffer(dt_logits, {nkvh, ngroup * max_seq_len, dh}, rsrc.memory_pool);
+    auto attn_val_buf = Tensor::buffer(dt_logits, {nh, max_seq_len, dh}, rsrc.memory_pool);
 
     // MLP descriptors
     infiniopGemmDescriptor_t desc_ffn_gate_up, desc_ffn_down;
@@ -317,7 +317,8 @@ void inferDeviceBatch(const JiugeMeta &meta, DeviceResource &rsrc,
     RUN_INFINI(infiniopGetRandomSampleWorkspaceSize(desc_sample, &temp_size));
     workspace_size = std::max(workspace_size, temp_size);
     // Allocate workspace
-    workspace = rsrc.workspace_allocator->alloc(workspace_size);
+    std::shared_ptr<Storage> workspace_storage = Storage::createFromPool(workspace_size, rsrc.memory_pool);
+    void *workspace = workspace_storage->memory;
 
     // Compute
     for (uint32_t layer = 0; layer < nlayer; layer++) {

src/models/jiuge/jiuge_impl.hpp

@@ -27,7 +27,7 @@ struct DeviceResource {
     // Communicator
     infinicclComm_t comm;
 
-    std::unique_ptr<WorkspaceAllocator> workspace_allocator;
+    std::shared_ptr<MemoryPool> memory_pool;
 };
 
 struct InferState {

src/tensor.hpp

 #ifndef INFER_TENSOR_H
 #define INFER_TENSOR_H
 
+#include "allocator.hpp"
 #include "infinicore_infer.h"
 #include "utils.hpp"
 #include <memory>
 #include <string>
 #include <vector>
 
-struct Storage {
+class Storage {
+public:
     void *memory;
     size_t size;
     infiniDevice_t device_type;
     int device_id;
+    std::shared_ptr<MemoryPool> memory_pool;
 
     static std::shared_ptr<Storage> create(size_t size);
     static std::shared_ptr<Storage> createAsync(size_t size, infinirtStream_t stream = nullptr);
+    static std::shared_ptr<Storage> createFromPool(size_t size, std::shared_ptr<MemoryPool> pool = nullptr);
     static std::shared_ptr<Storage> createHost(size_t size);
     ~Storage();
 };
@@ -68,7 +72,7 @@ private:
 public:
     static std::shared_ptr<Tensor> buffer(infiniDtype_t dtype,
                                           const std::vector<size_t> &shape,
-                                          infinirtStream_t stream = nullptr);
+                                          std::shared_ptr<MemoryPool> pool = nullptr);
     static std::shared_ptr<Tensor> weight(void *host_data,
                                           infiniDtype_t dtype,
                                           const std::vector<size_t> &shape);

src/tensor/strorage.cpp

+#include "../allocator.hpp"
 #include "../tensor.hpp"
 
 std::shared_ptr<Storage> Storage::create(size_t size) {
@@ -16,19 +17,37 @@ std::shared_ptr<Storage> Storage::createAsync(size_t size, infinirtStream_t stre
     return storage;
 }
 
+std::shared_ptr<Storage> Storage::createFromPool(size_t size, std::shared_ptr<MemoryPool> pool) {
+    auto storage = std::make_shared<Storage>();
+    storage->memory_pool = pool;
+    if (pool) {
+        storage->memory = pool->alloc(size);
+    } else {
+        RUN_INFINI(infinirtMalloc(&storage->memory, size));
+    }
+    storage->size = size;
+    RUN_INFINI(infinirtGetDevice(&storage->device_type, &storage->device_id));
+    return storage;
+}
+
 std::shared_ptr<Storage> Storage::createHost(size_t size) {
     auto storage = std::make_shared<Storage>();
     RUN_INFINI(infinirtMallocHost(&storage->memory, size));
     storage->size = size;
     storage->device_type = INFINI_DEVICE_CPU;
     storage->device_id = 0;
+    storage->memory_pool = nullptr; // No pool for host memory
     return storage;
 }
 
 Storage::~Storage() {
     if (device_type == INFINI_DEVICE_CPU) {
         RUN_INFINI(infinirtFreeHost(memory));
+    } else {
+        if (memory_pool) {
+            memory_pool->release(memory);
         } else {
             RUN_INFINI(infinirtFree(memory));
         }
+    }
 }

src/tensor/tensor.cpp

@@ -68,7 +68,7 @@ std::shared_ptr<TensorDesc> Tensor::desc() const { return TensorDesc::create(thi
 
 std::shared_ptr<Tensor> Tensor::buffer(infiniDtype_t dtype,
                                        const std::vector<size_t> &shape,
-                                       infinirtStream_t stream) {
+                                       std::shared_ptr<MemoryPool> pool) {
     std::shared_ptr<Tensor> tensor = std::make_shared<Tensor>();
     tensor->_dtype = dtype;
     auto ndim = shape.size();
@@ -83,7 +83,7 @@ std::shared_ptr<Tensor> Tensor::buffer(infiniDtype_t dtype,
         }
     }
     tensor->_strides = strides;
-    tensor->_storage = Storage::createAsync(size, stream);
+    tensor->_storage = Storage::createFromPool(size, pool);
     tensor->_data = tensor->_storage->memory;
     infiniopCreateTensorDescriptor(&tensor->_desc, ndim, tensor->_shape.data(),
                                    strides.data(), dtype);