server: metrics: add llamacpp:prompt_seconds_total and llamacpp:token…

…s_predicted_seconds_total, reset bucket only on /metrics. Fix values cast to int. Add Process-Start-Time-Unix header. (ggerganov#5937)

Closes ggerganov#5850

examples/server/server.cpp

@@ -335,8 +335,12 @@ struct server_slot {
 };
 
 struct server_metrics {
+ const int64_t t_start = ggml_time_us();
+
  uint64_t n_prompt_tokens_processed_total = 0;
+ uint64_t t_prompt_processing_total = 0;
  uint64_t n_tokens_predicted_total = 0;
+ uint64_t t_tokens_generation_total = 0;
 
  uint64_t n_prompt_tokens_processed = 0;
  uint64_t t_prompt_processing = 0;
@@ -348,12 +352,14 @@ struct server_metrics {
  n_prompt_tokens_processed_total += slot.n_prompt_tokens_processed;
  n_prompt_tokens_processed += slot.n_prompt_tokens_processed;
  t_prompt_processing += slot.t_prompt_processing;
+ t_prompt_processing_total += slot.t_prompt_processing;
  }
 
  void on_prediction(const server_slot &slot) {
- n_tokens_predicted_total += slot.n_decoded;
- n_tokens_predicted += slot.n_decoded;
- t_tokens_generation += slot.t_token_generation;
+ n_tokens_predicted_total += slot.n_decoded;
+ n_tokens_predicted += slot.n_decoded;
+ t_tokens_generation += slot.t_token_generation;
+ t_tokens_generation_total += slot.t_token_generation;
  }
 
  void reset_bucket() {
@@ -1502,9 +1508,12 @@ struct server_context {
  { "idle", n_idle_slots },
  { "processing", n_processing_slots },
  { "deferred", queue_tasks.queue_tasks_deferred.size() },
+ { "t_start", metrics.t_start},
 
  { "n_prompt_tokens_processed_total", metrics.n_prompt_tokens_processed_total},
+ { "t_tokens_generation_total", metrics.t_tokens_generation_total},
  { "n_tokens_predicted_total", metrics.n_tokens_predicted_total},
+ { "t_prompt_processing_total", metrics.t_prompt_processing_total},
 
  { "n_prompt_tokens_processed", metrics.n_prompt_tokens_processed},
  { "t_prompt_processing", metrics.t_prompt_processing},
@@ -1517,7 +1526,9 @@ struct server_context {
  { "slots", slots_data },
  };
 
- metrics.reset_bucket();
+ if (json_value(task.data, "reset_bucket", false)) {
+ metrics.reset_bucket();
+ }
  queue_results.send(res);
  } break;
  }
@@ -2709,6 +2720,7 @@ int main(int argc, char ** argv) {
  task.id_multi = -1;
  task.id_target = -1;
  task.type = SERVER_TASK_TYPE_METRICS;
+ task.data.push_back({{"reset_bucket", true}});
 
  ctx_server.queue_results.add_waiting_task_id(task.id);
  ctx_server.queue_tasks.post(task);
@@ -2732,36 +2744,44 @@ int main(int argc, char ** argv) {
  {"counter", {{
  {"name", "prompt_tokens_total"},
  {"help", "Number of prompt tokens processed."},
- {"value", data["n_prompt_tokens_processed_total"]}
+ {"value", (uint64_t) data["n_prompt_tokens_processed_total"]}
+ }, {
+ {"name", "prompt_seconds_total"},
+ {"help", "Prompt process time"},
+ {"value", (uint64_t) data["t_prompt_processing_total"] / 1.e3}
  }, {
  {"name", "tokens_predicted_total"},
  {"help", "Number of generation tokens processed."},
- {"value", data["n_tokens_predicted_total"]}
+ {"value", (uint64_t) data["n_tokens_predicted_total"]}
+ }, {
+ {"name", "tokens_predicted_seconds_total"},
+ {"help", "Predict process time"},
+ {"value", (uint64_t) data["t_tokens_generation_total"] / 1.e3}
  }}},
  {"gauge", {{
  {"name", "prompt_tokens_seconds"},
  {"help", "Average prompt throughput in tokens/s."},
- {"value", n_prompt_tokens_processed ? 1e3 / t_prompt_processing * n_prompt_tokens_processed : 0}
+ {"value", n_prompt_tokens_processed ? 1.e3 / t_prompt_processing * n_prompt_tokens_processed : 0.}
  },{
  {"name", "predicted_tokens_seconds"},
  {"help", "Average generation throughput in tokens/s."},
- {"value", n_tokens_predicted ? 1e3 / t_tokens_generation * n_tokens_predicted : 0}
+ {"value", n_tokens_predicted ? 1.e3 / t_tokens_generation * n_tokens_predicted : 0.}
  },{
  {"name", "kv_cache_usage_ratio"},
  {"help", "KV-cache usage. 1 means 100 percent usage."},
  {"value", 1. * kv_cache_used_cells / params.n_ctx}
  },{
  {"name", "kv_cache_tokens"},
  {"help", "KV-cache tokens."},
- {"value", data["kv_cache_tokens_count"]}
+ {"value", (uint64_t) data["kv_cache_tokens_count"]}
  },{
  {"name", "requests_processing"},
  {"help", "Number of request processing."},
- {"value", data["processing"]}
+ {"value", (uint64_t) data["processing"]}
  },{
  {"name", "requests_deferred"},
  {"help", "Number of request deferred."},
- {"value", data["deferred"]}
+ {"value", (uint64_t) data["deferred"]}
  }}}
  };
 
@@ -2775,13 +2795,16 @@ int main(int argc, char ** argv) {
  const std::string name = metric_def["name"];
  const std::string help = metric_def["help"];
 
- auto value = json_value(metric_def, "value", 0);
+ auto value = json_value(metric_def, "value", 0.);
  prometheus << "# HELP llamacpp:" << name << " " << help << "\n"
  << "# TYPE llamacpp:" << name << " " << type << "\n"
  << "llamacpp:" << name << " " << value << "\n";
  }
  }
 
+ const int64_t t_start = data["t_start"];
+ res.set_header("Process-Start-Time-Unix", std::to_string(t_start));
+
  res.set_content(prometheus.str(), "text/plain; version=0.0.4");
  res.status = 200; // HTTP OK
  });

examples/server/tests/features/server.feature

@@ -29,6 +29,7 @@ Feature: llama.cpp server
  And  a completion request with no api error
  Then  <n_predicted> tokens are predicted matching <re_content>
  And  prometheus metrics are exposed
+ And  metric llamacpp:tokens_predicted is <n_predicted>
 
  Examples: Prompts
  | prompt | n_predict | re_content | n_predicted |

examples/server/tests/features/steps/steps.py

@@ -586,14 +586,24 @@ async def step_prometheus_metrics_exported(context):
  metric_exported = False
  if context.debug:
  print(f"/metrics answer:\n{metrics_raw}\n")
+ context.metrics = {}
  for metric in parser.text_string_to_metric_families(metrics_raw):
  match metric.name:
  case "llamacpp:kv_cache_usage_ratio":
  assert len(metric.samples) > 0
  metric_exported = True
+ context.metrics[metric.name] = metric
+ assert int(metrics_response.headers["Process-Start-Time-Unix"]) > 0, "no header process start time"
  assert metric_exported, "No metrics exported"
 
 
+@step(u'metric {metric_name} is {metric_value:d}')
+def step_assert_metric_value(context, metric_name, metric_value):
+ if metric_name not in context.metrics:
+ assert False, f"no metric {metric_name} in {context.metrics.keys()}"
+ assert context.metrics[metric_name].samples[0].value == metric_value, f"metric: {context.metrics[metric_name]}"
+
+
 @step(u'available models')
 def step_available_models(context):
  # openai client always expects an api_key
@@ -879,7 +889,6 @@ def assert_n_tokens_predicted(completion_response, expected_predicted_n=None, re
  f' {n_predicted} <> {expected_predicted_n}')
 
 
-
 async def gather_tasks_results(context):
  n_tasks = len(context.concurrent_tasks)
  if context.debug: