IQ.Pilot Release Commit @ 0798119

iqpilot/selfdrive/iqmodeld/tests/dmon_lag/repro.cc

@@ -0,0 +1,96 @@
+// Copyright © IQ.Lvbs, apart of Project Teal Lvbs, All Rights Reserved, licensed under https://konn3kt.com/tos/
+// clang++ -O2 repro.cc && ./a.out
+
+#include <sys/types.h>
+#include <unistd.h>
+
+#include <cstdint>
+#include <cstdio>
+#include <cstdlib>
+#include <ctime>
+#include <vector>
+
+namespace {
+
+constexpr int kModelWidth = 320;
+constexpr int kModelHeight = 640;
+constexpr int kRetryWindow = 20;
+constexpr int kSlowThresholdMs = 10;
+
+double millis_since_boot() {
+  timespec stamp{};
+#ifdef CLOCK_BOOTTIME
+  clock_gettime(CLOCK_BOOTTIME, &stamp);
+#else
+  clock_gettime(CLOCK_MONOTONIC, &stamp);
+#endif
+  return stamp.tv_sec * 1000.0 + stamp.tv_nsec * 1e-6;
+}
+
+inline float identity_input(uint8_t value) {
+  return value;
+}
+
+void pack_monitoring_tensor(uint8_t *nv12_frame, float *tensor_out) {
+  const int half_h = kModelHeight / 2;
+  const int half_w = kModelWidth / 2;
+  const int plane_area = half_w * half_h;
+  const int uv_base = kModelWidth * kModelHeight;
+
+  for (int row = 0; row < half_h; ++row) {
+    for (int col = 0; col < half_w; ++col) {
+      const int slot = col * half_h + row;
+      const int y_row = row * 2;
+      const int y_col = col * 2;
+
+      tensor_out[slot] = identity_input(nv12_frame[(y_row * kModelWidth) + y_col]);
+      tensor_out[slot + plane_area] = identity_input(nv12_frame[((y_row + 1) * kModelWidth) + y_col]);
+      tensor_out[slot + (plane_area * 2)] = identity_input(nv12_frame[(y_row * kModelWidth) + y_col + 1]);
+      tensor_out[slot + (plane_area * 3)] = identity_input(nv12_frame[((y_row + 1) * kModelWidth) + y_col + 1]);
+      tensor_out[slot + (plane_area * 4)] = identity_input(nv12_frame[uv_base + (row * half_w) + col]);
+      tensor_out[slot + (plane_area * 5)] = identity_input(nv12_frame[uv_base + plane_area + (row * half_w) + col]);
+    }
+  }
+}
+
+double average_runtime_ms(uint8_t *nv12_frame, float *tensor_out) {
+  double total_ms = 0.0;
+  for (int i = 0; i < kRetryWindow; ++i) {
+    const double start_ms = millis_since_boot();
+    pack_monitoring_tensor(nv12_frame, tensor_out);
+    total_ms += millis_since_boot() - start_ms;
+  }
+  return total_ms / static_cast<double>(kRetryWindow);
+}
+
+void dump_stall_trace(uint8_t *nv12_frame, float *tensor_out) {
+  for (int i = 0; i < 200; ++i) {
+    const double start_ms = millis_since_boot();
+    pack_monitoring_tensor(nv12_frame, tensor_out);
+    printf("%.2f   ", millis_since_boot() - start_ms);
+  }
+  printf("\n");
+}
+
+}  // namespace
+
+int main() {
+  const size_t nv12_bytes = kModelWidth * kModelHeight * 3 / 2;
+  const size_t tensor_floats = (kModelWidth / 2) * (kModelHeight / 2) * 6;
+
+  while (true) {
+    auto *nv12_frame = static_cast<uint8_t *>(malloc(nv12_bytes));
+    auto *tensor_out = static_cast<float *>(malloc(tensor_floats * sizeof(float)));
+    printf("allocate -- %p 0x%zx -- %p 0x%zx\n", nv12_frame, nv12_bytes, tensor_out, tensor_floats * sizeof(float));
+
+    const double mean_ms = average_runtime_ms(nv12_frame, tensor_out);
+    if (mean_ms > kSlowThresholdMs) {
+      printf("HIT %.2f\n", mean_ms);
+      printf("BAD\n");
+      dump_stall_trace(nv12_frame, tensor_out);
+      return 0;
+    }
+
+    printf("got %.2f\n", mean_ms);
+  }
+}