CANN: Use smart pointers to manage ACL objects (#17238)
* CANN: Use smart pointers to manage ACL objects Previously, ACL objects were managed via manual destruction, which led to multiple memory-leak issues during runtime. This patch replaces manual memory management with smart pointers so that ACL objects are properly released and ownership is clearly defined. Note that the ownership of an ACL object belongs to the function that creates it. Other internal functions should operate on these ACL objects using raw pointers to avoid unintended ownership transfers. Additionally, since aclTensorList automatically frees its contained aclTensor objects, any aclTensor added to a tensor list must release ownership to avoid double free operations. This PR also removes the asynchronous task submission mechanism. Due to changes in recent CANN versions, tiling time has significantly decreased. Even with a dual-thread submission model, the dispatch overhead still falls on the critical path, making async submission less beneficial. Moreover, aclGraph support provides a much better path to reducing operator dispatch latency. * CANN: resolve review comments
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@@ -23,26 +23,26 @@
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#ifndef CANN_COMMON_H
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#define CANN_COMMON_H
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#include <acl/acl.h>
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#include <cstdio>
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#include <iostream>
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#include <map>
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#include <memory>
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#include <string>
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#include <vector>
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#include <atomic>
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#include <condition_variable>
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#include <mutex>
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#include <thread>
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#include <unistd.h>
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#include <functional>
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#include <optional>
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#include <list>
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#include "../ggml-impl.h"
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#include "../include/ggml-cann.h"
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#include "../include/ggml.h"
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#include "../ggml-impl.h"
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#include <acl/acl.h>
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#include <unistd.h>
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#include <atomic>
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#include <condition_variable>
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#include <cstdio>
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#include <functional>
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#include <iostream>
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#include <list>
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#include <map>
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#include <memory>
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#include <mutex>
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#include <optional>
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#include <string>
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#include <thread>
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#include <vector>
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#define MATRIX_ROW_PADDING 512
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#define GGML_CANN_MAX_STREAMS 8
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@@ -214,130 +214,6 @@ struct ggml_cann_pool_alloc {
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ggml_cann_pool_alloc & operator=(ggml_cann_pool_alloc &&) = delete;
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};
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/**
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* @brief Function pointer type for ACLNN operator calls.
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*/
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using aclnn_func_t = aclnnStatus (*)(void *, uint64_t, aclOpExecutor *, aclrtStream);
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/**
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* @brief Base class for all CANN tasks to be submitted to the task queue.
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*
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* Users should override the run_task() method with actual task logic.
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*/
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class cann_task {
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public:
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virtual void run_task() {}
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};
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/**
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* @brief A lock-free ring-buffer based task queue for asynchronously executing cann_task instances.
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*/
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class cann_task_queue {
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public:
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/**
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* @brief Constructs a task queue with a fixed power-of-two capacity for a specific device.
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*
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* @param capacity Queue capacity. Must be a power of 2.
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* @param device Target device ID (used for context setting).
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*/
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explicit cann_task_queue(size_t capacity, int32_t device) :
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buffer_(capacity),
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capacity_(capacity),
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head_(0),
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tail_(0),
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running_(false),
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device_(device) {
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GGML_ASSERT((capacity & (capacity - 1)) == 0 && "capacity must be power of 2");
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mask_ = capacity_ - 1;
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}
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/**
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* @brief Attempts to enqueue a task into the queue.
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*
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* @param item Unique pointer to the task.
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* @return true if the task was successfully enqueued, false if the queue was full.
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*/
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bool enqueue(std::unique_ptr<cann_task> && item) {
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size_t next_tail = (tail_ + 1) & mask_;
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if (next_tail == head_) {
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return false;
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}
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buffer_[tail_] = std::move(item);
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std::atomic_thread_fence(std::memory_order_release);
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tail_ = next_tail;
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return true;
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}
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/**
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* @brief Submits a task to the queue, and starts the worker thread if not already running.
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*
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* @param task Task to be submitted.
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*/
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void submit_task(std::unique_ptr<cann_task> && task) {
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while (!enqueue(std::move(task))) {
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std::this_thread::yield();
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continue;
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}
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if (!running_) {
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running_ = true;
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thread_ = std::thread(&cann_task_queue::execute, this);
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}
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}
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/**
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* @brief Waits until the queue is completely empty and no tasks are being processed.
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*/
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void wait() {
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while (running_ && head_ != tail_) {
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std::this_thread::yield();
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continue;
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}
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}
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/**
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* @brief Stops the task queue and joins the worker thread.
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*/
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void stop() {
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running_ = false;
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if (thread_.joinable()) {
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thread_.join();
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}
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}
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private:
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/**
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* @brief Worker thread function that continuously dequeues and executes tasks.
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*/
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void execute() {
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ggml_cann_set_device(device_);
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while (running_) {
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if (head_ == tail_) {
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std::this_thread::yield();
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continue;
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}
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std::atomic_thread_fence(std::memory_order_acquire);
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buffer_[head_]->run_task();
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buffer_[head_].reset();
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head_ = (head_ + 1) & mask_;
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}
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}
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std::vector<std::unique_ptr<cann_task>> buffer_;
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const size_t capacity_;
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size_t mask_;
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size_t head_;
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size_t tail_;
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bool running_;
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std::thread thread_;
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int32_t device_;
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};
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#ifdef USE_ACL_GRAPH
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struct ggml_graph_node_properties {
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// dst tensor
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@@ -474,7 +350,6 @@ struct ggml_backend_cann_context {
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ggml_cann_graph_lru_cache graph_lru_cache;
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bool acl_graph_mode = true;
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#endif
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cann_task_queue task_queue;
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bool async_mode;
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// Rope Cache
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ggml_cann_rope_cache rope_cache;
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@@ -488,15 +363,10 @@ struct ggml_backend_cann_context {
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* @brief Constructor for initializing the context with a given device.
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* @param device Device ID.
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*/
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explicit ggml_backend_cann_context(int device) :
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device(device),
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name("CANN" + std::to_string(device)),
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task_queue(1024, device) {
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explicit ggml_backend_cann_context(int device) : device(device), name("CANN" + std::to_string(device)) {
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ggml_cann_set_device(device);
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description = aclrtGetSocName();
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async_mode = parse_bool(get_env("GGML_CANN_ASYNC_MODE").value_or(""));
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GGML_LOG_INFO("%s: device %d async operator submission is %s\n", __func__, device, async_mode ? "ON" : "OFF");
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#ifdef USE_ACL_GRAPH
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acl_graph_mode = parse_bool(get_env("GGML_CANN_ACL_GRAPH").value_or("on"));
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GGML_LOG_INFO("%s: device %d execution mode is %s (%s)\n", __func__, device, acl_graph_mode ? "GRAPH" : "EAGER",
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@@ -509,7 +379,6 @@ struct ggml_backend_cann_context {
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*/
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~ggml_backend_cann_context() {
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ggml_cann_set_device(device);
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task_queue.stop();
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if (copy_event != nullptr) {
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ACL_CHECK(aclrtDestroyEvent(copy_event));
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}
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