add plotting utils

Author: ruipeterpan

plotting/plotting_utils.py

@@ -0,0 +1,76 @@
+import pickle
+import numpy as np
+
+def values_to_cdf(values):
+    cdf_list = []
+    values.sort()
+    count = 0
+    for v in values:
+        count += 1
+        cdf_list.append(count / len(values))
+    return cdf_list
+
+
+def parse_result_file(
+    model: str,
+    dataset: str,
+    slo_multiplier: int,
+    arrival: str,
+    BATCH_DECISION_PATH: str,
+    APPARATE_LATENCY_PATH: str,
+    OPTIMAL_LATENCY_PATH: str,
+):
+    print(f"model {model}, dataset {dataset}")
+    
+    if "slo_multiplier" in BATCH_DECISION_PATH:  # CV workload, with SLO multiplier
+        batch_decision_path = BATCH_DECISION_PATH.format(model=model, 
+                                                        slo_multiplier=slo_multiplier, arrival=arrival)
+        apparate_latency_path = APPARATE_LATENCY_PATH.format(
+            model=model, dataset=dataset, slo_multiplier=slo_multiplier, arrival=arrival
+        )
+        optimal_latency_path = OPTIMAL_LATENCY_PATH.format(
+            model=model, dataset=dataset, slo_multiplier=4, arrival=arrival
+        )
+    else:  # NLP workload, azure arrival traice
+        batch_decision_path = BATCH_DECISION_PATH.format(model=model, arrival=arrival)
+        apparate_latency_path = APPARATE_LATENCY_PATH.format(
+            model=model, dataset=dataset, 
+            arrival=arrival,
+        )
+        optimal_latency_path = OPTIMAL_LATENCY_PATH.format(
+            model=model, dataset=dataset, 
+            arrival=arrival,
+        )
+    
+    with open(batch_decision_path, "rb") as f1, open(apparate_latency_path, "rb") as f2, open(optimal_latency_path, "rb") as f3:
+        batch_decision, apparate_latency, optimal_latency = pickle.load(f1), pickle.load(f2), pickle.load(f3)
+        
+    per_request_stats = batch_decision["per_request_stats"]  # every item: queuing delay, inference time
+    per_request_stats = [x for x in per_request_stats if x is not None]
+    total_num_requests = sum([1 for x in per_request_stats if x is not None])
+    
+    length = min(len(apparate_latency), len(optimal_latency))
+    apparate_latency = apparate_latency[:length]
+    optimal_latency = optimal_latency[:length]
+    num_served_requests = len(apparate_latency)  # NOTE(ruipan): might be smaller than total_num_requests b/c some are dropped
+    print(f"num_served_requests {num_served_requests}")
+    queuing_delays = [s[0] for s in per_request_stats if s is not None]
+    queuing_delays = queuing_delays[:num_served_requests]
+    model_inference_time_vanilla = [s[1] for s in per_request_stats[:num_served_requests]]
+    model_inference_time_ee = apparate_latency
+    model_inference_time_optimal = optimal_latency
+
+    serving_time_vanilla = [sum(x) for x in zip(queuing_delays, model_inference_time_vanilla)]
+    serving_time_ee = [sum(x) for x in zip(queuing_delays, model_inference_time_ee)]
+    serving_time_optimal = [sum(x) for x in zip(queuing_delays, model_inference_time_optimal)]
+
+    apparate_serving_improvement = 100 * (1 - np.median(serving_time_ee) / np.median(serving_time_vanilla))
+    optimal_serving_improvement = 100 * (1 - np.median(serving_time_optimal) / np.median(serving_time_vanilla))
+        
+    return {
+        "apparate_serving_improvement": apparate_serving_improvement,
+        "optimal_serving_improvement": optimal_serving_improvement,
+        "serving_time_vanilla": serving_time_vanilla,
+        "serving_time_ee": serving_time_ee,
+        "serving_time_optimal": serving_time_optimal,
+    }