Add two files to generate and test options of the Genetic algorithm

generate_genetic_options will save a file containg the
best found options by the genetic algorithm.
eval_genetic_options would benchmark these options from the file.

Author: Jules Pondard

python/experimental/options_search/eval_genetic_options.py

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+import tensor_comprehensions as tc
+import tensor_comprehensions.tclib as tclib
+import utils
+
+cache = tc.MappingOptionsCache("genetic_savedopt_conv_default.txt")
+
+exptuner_config = utils.ExpTunerConfig()
+exptuner_config.set_convolution_tc()
+tc_code, tc_name, inp = exptuner_config.tc_code, exptuner_config.tc_name, exptuner_config.inp
+
+print("divs : " + str(utils.getAllDivs(inp)))
+tup = cache.load(tc_code, tc_name, inp, 1)
+if(tup == []):
+    exit()
+best_options, = tup
+best_options = best_options.getDict()
+optsVect = utils.getRawVectorFromTcOpt(best_options)
+opts = utils.optionsFromVector(optsVect)
+print(opts)
+
+time = utils.evalTime(opts, exptuner_config, estimator="median")
+print(time)
+

python/experimental/options_search/generate_genetic_options.py

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+import numpy as np
+import torch
+import tensor_comprehensions as tc
+
+import utils
+
+exptuner_config = utils.ExpTunerConfig()
+exptuner_config.set_convolution_tc()
+tc_code, tc_name, inp = exptuner_config.tc_code, exptuner_config.tc_name, exptuner_config.inp
+#config = tc.autotuner_settings
+#config["pop_size"]=50
+#config["generations"]=1
+opts = tc.MappingOptions("naive")
+print(opts)
+
+tc.autotune(tc_code, tc_name, *inp, starting_options=opts, cache_filename="genetic_savedopt_conv_default.txt", store_to_cache=True)