[profiling] Memory Profiling support for iOS (#18516)

Based on Kaushik's work for iOS CPU profiling, I added the memory profiling within the same scheduled task.

The memory profiling methodologies were discussed in the internal doc. Please see go/flutter-ios-memory-profiling.

shell/platform/darwin/ios/framework/Source/profiler_metrics_ios.h

@@ -30,6 +30,8 @@ class ProfilerMetricsIOS {
  private:
   std::optional<CpuUsageInfo> CpuUsage();
 
+  std::optional<MemoryUsageInfo> MemoryUsage();
+
   FML_DISALLOW_COPY_AND_ASSIGN(ProfilerMetricsIOS);
 };
 

shell/platform/darwin/ios/framework/Source/profiler_metrics_ios.mm

@@ -30,7 +30,7 @@ class MachThreads {
 namespace flutter {
 
 ProfileSample ProfilerMetricsIOS::GenerateSample() {
-  return {.cpu_usage = CpuUsage()};
+  return {.cpu_usage = CpuUsage(), .memory_usage = MemoryUsage()};
 }
 
 std::optional<CpuUsageInfo> ProfilerMetricsIOS::CpuUsage() {
@@ -76,4 +76,33 @@ std::optional<CpuUsageInfo> ProfilerMetricsIOS::CpuUsage() {
   return cpu_usage_info;
 }
 
+std::optional<MemoryUsageInfo> ProfilerMetricsIOS::MemoryUsage() {
+  kern_return_t kernel_return_code;
+  task_vm_info_data_t task_memory_info;
+  mach_msg_type_number_t task_memory_info_count = TASK_VM_INFO_COUNT;
+
+  kernel_return_code =
+      task_info(mach_task_self(), TASK_VM_INFO, reinterpret_cast<task_info_t>(&task_memory_info),
+                &task_memory_info_count);
+  if (kernel_return_code != KERN_SUCCESS) {
+    FML_LOG(ERROR) << " Error retrieving task memory information: "
+                   << mach_error_string(kernel_return_code);
+    return std::nullopt;
+  }
+
+  // `phys_footprint` is Apple's recommended way to measure app's memory usage. It provides the
+  // best approximate to xcode memory gauge. According to its source code explanation, the physical
+  // footprint mainly consists of app's internal memory data and IOKit mappings. `resident_size`
+  // is the total physical memory used by the app, so we simply do `resident_size - phys_footprint`
+  // to obtain the shared memory usage.
+  const double dirty_memory_usage =
+      static_cast<double>(task_memory_info.phys_footprint) / 1024.0 / 1024.0;
+  const double owned_shared_memory_usage =
+      static_cast<double>(task_memory_info.resident_size) / 1024.0 / 1024.0 - dirty_memory_usage;
+  flutter::MemoryUsageInfo memory_usage_info = {
+      .dirty_memory_usage = dirty_memory_usage,
+      .owned_shared_memory_usage = owned_shared_memory_usage};
+  return memory_usage_info;
+}
+
 }  // namespace flutter

shell/profiling/sampling_profiler.cc

@@ -32,11 +32,11 @@ void SamplingProfiler::Start() const {
 void SamplingProfiler::SampleRepeatedly(fml::TimeDelta task_delay) const {
   profiler_task_runner_->PostDelayedTask(
       [profiler = this, task_delay = task_delay, sampler = sampler_]() {
+        // TODO(kaushikiska): consider buffering these every n seconds to
+        // avoid spamming the trace buffer.
         const ProfileSample usage = sampler();
         if (usage.cpu_usage) {
           const auto& cpu_usage = usage.cpu_usage;
-          // TODO(kaushikiska): consider buffering these every n seconds to
-          // avoid spamming the trace buffer.
           std::string total_cpu_usage =
               std::to_string(cpu_usage->total_cpu_usage);
           std::string num_threads = std::to_string(cpu_usage->num_threads);
@@ -44,6 +44,16 @@ void SamplingProfiler::SampleRepeatedly(fml::TimeDelta task_delay) const {
                                "total_cpu_usage", total_cpu_usage.c_str(),
                                "num_threads", num_threads.c_str());
         }
+        if (usage.memory_usage) {
+          std::string dirty_memory_usage =
+              std::to_string(usage.memory_usage->dirty_memory_usage);
+          std::string owned_shared_memory_usage =
+              std::to_string(usage.memory_usage->owned_shared_memory_usage);
+          TRACE_EVENT_INSTANT2("flutter::profiling", "MemoryUsage",
+                               "dirty_memory_usage", dirty_memory_usage.c_str(),
+                               "owned_shared_memory_usage",
+                               owned_shared_memory_usage.c_str());
+        }
         profiler->SampleRepeatedly(task_delay);
       },
       task_delay);

shell/profiling/sampling_profiler.h

@@ -29,15 +29,29 @@ struct CpuUsageInfo {
   double total_cpu_usage;
 };
 
+/**
+ * @brief Memory usage stats. `dirty_memory_usage` is the the memory usage (in
+ * MB) such that the app uses its physical memory for dirty memory. Dirty memory
+ * is the memory data that cannot be paged to disk. `owned_shared_memory_usage`
+ * is the memory usage (in MB) such that the app uses its physicaal memory for
+ * shared memory, including loaded frameworks and executables. On iOS, it's
+ * `physical memory - dirty memory`.
+ */
+struct MemoryUsageInfo {
+  double dirty_memory_usage;
+  double owned_shared_memory_usage;
+};
+
 /**
  * @brief Container for the metrics we collect during each run of `Sampler`.
- * This currently holds `CpuUsageInfo` but the intent is to expand it to other
- * metrics.
+ * This currently holds `CpuUsageInfo` and `MemoryUsageInfo` but the intent
+ * is to expand it to other metrics.
  *
  * @see flutter::Sampler
  */
 struct ProfileSample {
   std::optional<CpuUsageInfo> cpu_usage;
+  std::optional<MemoryUsageInfo> memory_usage;
 };
 
 /**