From 830e83d01354ad52f01a1514d73cafadaf95bf80 Mon Sep 17 00:00:00 2001
From: Hashim Sharif <hsharif3@tyler.cs.illinois.edu>
Date: Sat, 12 Oct 2019 18:27:30 -0500
Subject: [PATCH] Using 2 runs in the tuning phase and min_accuracy as
threshold
---
.../autotuner/measure_confidence2.py | 18 ++++++++++++-
.../opentuner/autotuner/pareto_curve.py | 19 +++++++++-----
.../opentuner/autotuner/promise_tuner3.py | 26 +++++++++++++------
3 files changed, 47 insertions(+), 16 deletions(-)
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/measure_confidence2.py b/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/measure_confidence2.py
index b38efa9c82..9ff74128f4 100644
--- a/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/measure_confidence2.py
+++ b/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/measure_confidence2.py
@@ -83,7 +83,23 @@ def getConfidence(accuracy_outfile, acc_threshold):
return conf, avg_acc
-
+
+
+def getMinAccuracy(accuracy_outfile):
+
+ f = open(accuracy_outfile, "r")
+
+ total_acc = 0.0
+ failed = 0
+ it = 0
+
+ acc_list = []
+ for x in f:
+ acc = float(x.strip())
+ acc_list.append(acc)
+
+ return min(acc_list)
+
# NOTE: invokes the binary with the number of runs
def do_multiple_runs2(binary_name, accuracy_threshold, confidence_threshold):
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/pareto_curve.py b/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/pareto_curve.py
index 0fda8f742c..db8233994b 100644
--- a/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/pareto_curve.py
+++ b/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/pareto_curve.py
@@ -5,6 +5,9 @@ import shutil
from measure_confidence2 import getConfigCost
+AL_THRESHOLD = 0.1
+
+
class Config:
def __init__(self):
self.avg_accuracy = 0
@@ -69,10 +72,6 @@ def loadConfigData(result_dir, layer_costs, baseline_accuracy):
-AL_THRESHOLD = 0.1
-SPEEDUP_BAND_SIZE = 0.3
-ENERGY_BAND_SIZE = 10
-
class Configuration:
def __init__(self, name, speedup, energy, accuracy, accuracy_loss):
@@ -223,11 +222,17 @@ def findParetoConfigs(base_dir, layer_costs, accuracy):
config = Configuration(config.fname , config.speedup, 100, config.avg_accuracy, config.avg_loss)
config_list.append(config)
+
+ SPEEDUP_BAND_SIZE = 1.0
+ ENERGY_BAND_SIZE = 10
- if len(config_list) < 30:
- SPEEDUP_BAND_SIZE = 1.2
-
+ # No Pareto Selection if list is < 50 configurations
+ if len(config_list) < 50:
+ SPEEDUP_BAND_SIZE = 100 # Include all in Pareto Frontier
+
+ print ("*SPEEDUP_BAND_SIZE = ", SPEEDUP_BAND_SIZE)
+
ASC, AEC = compute_pareto_points_with_margin(config_list, SPEEDUP_BAND_SIZE, ENERGY_BAND_SIZE)
diff --git a/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/promise_tuner3.py b/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/promise_tuner3.py
index 87ed35bbc4..04ce0d6158 100644
--- a/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/promise_tuner3.py
+++ b/llvm/projects/hpvm-tensor-rt/opentuner/autotuner/promise_tuner3.py
@@ -22,6 +22,7 @@ import threading
import psutil
from measure_confidence2 import dump_promise_confidence_files3
+from measure_confidence2 import getConfidence, getMinAccuracy
from select_top_results import select_top_results
from time import sleep
from pareto_curve import findParetoConfigs
@@ -169,25 +170,36 @@ class ClangFlagsTuner(MeasurementInterface):
createFlagsFile("promise_flags", cfg)
run_cmd = binary_name
- print "binary_name = ", run_cmd
+ print "\nbinary_name = ", run_cmd
#run_result_call_program = self.call_program(run_cmd)
- #print "returned \n\n"
+
+ total_runs = 2
FNULL = open(os.devnull, 'wb')
- p = subprocess.Popen(run_cmd, stdout = FNULL)
+ #p = subprocess.Popen(run_cmd, stdout = FNULL)
+ p = subprocess.Popen([run_cmd, str(total_runs)], stdout = FNULL)
p.wait()
accuracy = getAccuracy("final_accuracy")
+
+ # Get Confidence for multiple runs
+ conf, avg_acc = getConfidence("run_accuracies.txt", accuracy_threshold)
+
# getConfigCost returns the cost associated with the selected configuration
total_comps = getConfigCost(cfg)
Result = opentuner.resultsdb.models.Result()
Result.time = total_comps
- Result.accuracy = accuracy
-
- if accuracy > accuracy_threshold:
+ #Result.accuracy = accuracy
+ min_accuracy = getMinAccuracy("run_accuracies.txt")
+ print ("min_accuracy = ", min_accuracy)
+ Result.accuracy = min_accuracy
+
+ # Only pass conf if conf == 100
+ if min_accuracy > accuracy_threshold and conf == 100:
+ print ("conf = ", conf, " avg_acc = ", avg_acc)
#if accuracy not in evaluated_configs:
config_tuple = (total_comps, accuracy, cfg)
self.configs_list.append(config_tuple)
@@ -199,8 +211,6 @@ class ClangFlagsTuner(MeasurementInterface):
f_acc.close()
- print "done with one run"
-
test_id += 1
return Result
--
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