Icemist commented on code in PR #12545:
URL: https://github.com/apache/tvm/pull/12545#discussion_r967756604
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python/tvm/autotvm/tuner/ga_tuner.py:
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@@ -49,41 +48,29 @@ def __init__(self, task, pop_size=100, elite_num=3,
mutation_prob=0.1):
assert elite_num <= pop_size, "The number of elites must be less than
population size"
+ # random initialization
+ self.pop_size = min(self.pop_size, task.config_space.filtered_length)
+ self.elite_num = min(self.pop_size, self.elite_num)
+
# space info
self.space = task.config_space
- self.dim_keys = []
- self.dims = []
- for k, v in self.space.space_map.items():
- self.dim_keys.append(k)
- self.dims.append(len(v))
-
- self.visited = set([])
+ self.visited = set(self.space.sample_ints(self.pop_size))
# current generation
- self.genes = []
+ self.genes = [self.space.point2knob(idx) for idx in self.visited]
self.scores = []
self.elites = []
self.elite_scores = []
self.trial_pt = 0
- # random initialization
- self.pop_size = min(self.pop_size, len(self.space))
- self.elite_num = min(self.pop_size, self.elite_num)
- for _ in range(self.pop_size):
- tmp_gene = point2knob(np.random.randint(len(self.space)),
self.dims)
- while knob2point(tmp_gene, self.dims) in self.visited:
- tmp_gene = point2knob(np.random.randint(len(self.space)),
self.dims)
-
- self.genes.append(tmp_gene)
- self.visited.add(knob2point(tmp_gene, self.dims))
-
def next_batch(self, batch_size):
ret = []
- for _ in range(batch_size):
+ while len(ret) < batch_size and self.trial_pt <
self.space.total_length:
Review Comment:
You're partially right, there should be a filtered_length, under the
condition that it actually gets already filtered points. Right now it is.
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